A NEW ECOLOGY
This page intentionally left blank
A NEW ECOLOGY
Systems Perspective
SECOND EDITION
SØREN NORS NIELSEN
Section for Sustainable Biotechnology
Department of Chemistry and Bioscience
Aalborg University
Copenhagen SV, Denmark
BRIAN D. FATH
Department of Biological Sciences, Towson University
Towson, MD, United States;
Advanced Systems Analysis Program
International Institute for Applied Systems Analysis
Laxenburg, Austria
SIMONE BASTIANONI
Department of Earth, Environmental and Physical Sciences
University of Siena, Siena, Italy
JOA˜ O CARLOS MARQUES
MARE e Marine and Environmental Sciences Centre
DCV e Faculty of Sciences and Technology
University of Coimbra, Coimbra, Portugal
FELIX MU¨ LLER
Institute for Natural Resource Conservation
University of Kiel, Kiel, Germany
BERNARD C. PATTEN
Odum School of Ecology
University of Georgia
Athens, GA, United States
ROBERT E. ULANOWICZ
Chesapeake Biological Laboratory
University of Maryland Center for Environmental Science
Solomons, MD, United States;
Department of Biology
University of Florida
Gainesville, FL, United States
SVEN E. JØRGENSEN
y
Environmental Chemistry Section
Royal Danish School of Pharmacy
Copenhagen, Denmark
(y
Deceased)
ENZO TIEZZI
y
Department of Chemical and Biosystems Sciences
University of Siena, Siena, Italy
(y
Deceased)
Elsevier
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Dedication
Starting around the ﬁrst Ecological Summit back in 1996 in Copenhagen, a small group of researchers initiated a project to
understand nature’s functional thermodynamic and information-based principles aimed to improve environmental
management. This project has pulsed along during the ensuing decades, on the way adding new members, but also losing
others. This volume is dedicated to those we have lost in gratitude for the scientiﬁc inheritance we received from them.
Although missing in body, their spirit is still with us in the ideas they expressed and in the many discussions, papers,
and books they contributed. For this we are very grateful, and have continued the work in memory of Giuseppe Bendoricchio,
Sven Erik Jørgensen, James J. Kay, Ramon Margalef, Howard T. Odum, Milan Straskraba, and Yuri Svirezhev.
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Contents
Preface to the Second Edition ix
1. Introduction: A New Ecology Is Needed
1.1 Environmental Management has Changed 1
1.2 Ecology Is Changing 3
1.3 A New Ecology 3
1.4 Book Outline 3
2. Ecosystems Have Thermodynamic
Openness
2.1 Why Must Ecosystems Be Open? 7
2.2 An Isolated System Would Die (Maximum
Entropy) 8
2.3 Physical Openness 11
2.4 The Second Law of Thermodynamics
Interpreted for Open Systems 15
2.5 Dissipative Structure 16
2.6 Quantiﬁcation of Openness and Allometric
Principles 17
2.7 The Cell 22
2.8 What About the Environment? 24
2.9 Conclusion 24
3. Ecosystems Have Ontic Openness
3.1 Introduction 27
3.2 Why Is Ontic Openness so Obscure? 27
3.3 Ontic Openness and the Physical World 30
3.4 What Really Differs Between Physics and
Biology: Four Principles of Elsasser 32
3.5 Ontic Openness and Relative Stability 36
3.6 The Macroscopic OpennessdConnections to
Thermodynamics 37
3.7 Ontic Openness and Emergence 39
3.8 Ontic Openness and Hierarchies 40
3.9 Messages from Ontic Openness to Ecology
and Ecologists/Managers 41
3.10 Consequences of Ontic Openness:
a Tentative Conclusion 43
4. Ecosystems Have Connectivity
4.1 Introduction 45
4.2 Ecosystems as Networks 46
4.3 Food Webs 47
4.4 Systems Analysis 49
4.5 Ecosystem Connectivity and Ecological
Network Analysis 51
4.6 Network Environ Analysis Primer 52
4.7 The Cardinal Hypotheses of Network Environ
Analysis 60
4.8 Conclusions 82
5. Ecosystems as Self-organizing Hierarchies
5.1 History of Hierarchy Concepts in Ecology 85
5.2 Hierarchies Inherent in Biology 85
5.3 Classical Hierarchies in Time and Space 87
5.4 Hierarchical Features: Boundaries, Gradients,
and Constraints 89
5.5 Understanding Hierarchical Function 91
5.6 Managing Ecosystems as Hierarchies 94
6. Ecosystems Have Directionality
6.1 Since the Beginnings of Ecology 97
6.2 The Challenge from Thermodynamics 97
6.3 Deconstructing Directionality? 99
6.4 Agencies Imparting Directionality 100
6.5 Origins of Evolutionary Drive 102
6.6 Quantifying Directionality in Ecosystems 103
6.7 Demystifying Darwin 108
6.8 Directionality in Evolution? 109
6.9 Summary 110
7. Ecosystems Have Complex Dynamicsd
Growth and Development
Preamble 111
7.1 Variability in Life Conditions 111
7.2 Ecosystem Development 113
7.3 Orientors and Succession Theories 117
7.4 The Maximum Power Principle 118
7.5 Exergy, Ascendency, Gradients, and Ecosystem
Development 122
7.6 Support for the Presented Hypotheses 126
7.7 Toward a Consistent Ecosystem Theory 132
7.8 Summary and Conclusions 137
vii
8. Ecosystems Have Complex Dynamicsd
Disturbance and Decay
8.1 The Normality of Disturbance 139
8.2 The Risk of Orientor Optimization 145
8.3 The Characteristics of Disturbance 145
8.4 Adaptability as a Key Function of Ecosystem
Dynamics 149
8.5 Adaptive Cycles on Multiple Scales 152
8.6 A Case Study: Human Disturbance and
Retrogressive Dynamics 155
8.7 Summary and Conclusions 157
9. Ecosystem Principles Have Broad
Explanatory Power in Ecology
9.1 Introduction 159
9.2 Do Ecological Principles Encompass Other
Proposed Ecological Theories? 160
9.3 Evolutionary Theory in the Light of Ecosystem
Principles 164
9.4 Latitudinal Gradients in Biodiversity in the
Light of Ecosystem Principles 170
9.5 The Keystone Species Hypothesis in the Light
of Ecosystem Principles 172
9.6 Liebig’s Law of the Minimum 177
9.7 The River Continuum Theory in the Light of
Ecosystem Principles 180
9.8 Conclusions 181
10. Ecosystem Principles Have Ecological
Applications
10.1 Introduction 183
10.2 Entropy Production as an Indicator of
Ecosystem Trophic State 183
10.3 The Use of Ecological Network Analysis for
the Simulation of the Interactions
Between American Black Bear and Its
Environment 189
10.4 Applications of Network Analysis
and Ascendency to South Florida
Ecosystems 191
10.5 The Application of Eco-exergy as
Ecological Indicator for Assessment of
Ecosystem Health 197
10.6 Emergy as Ecological Indicator to Assess
Ecosystem Health 200
10.7 The Eco-exergy to Empower Ratio and the
Efﬁciency of Ecosystems 205
10.8 Application of Eco-exergy and
Ascendency as Ecological Indicator
to the Mondego Estuary (Portugal) 207
10.9 Conclusions 214
11. Ecosystems Carry Important Messages
to Managers and Policy Makers
11.1 Ecosystems in A Sustainability
Perspective 215
11.2 Management with Nature 216
11.3 Indicating Management Success 219
11.4 Conclusions 234
12. Conclusions and Final Remarks
12.1 Are Fundamental Ecological
Properties Needed to Explain
Our Observations? 235
12.2 Previous Attempts to Present an Ecosystem
Theory 235
12.3 Recapitulation of the Phenomenological
Ecosystem Theory 236
12.4 Are There Basic Ecosystem Principles? 237
12.5 Conclusion 238
References 241
Index 257
CONTENTSviii
Preface to the Second Edition
The ﬁrst edition of A New Ecology emanated out of a brainstorming workshop that Sven Jørgensen organized in
June 2005 on the Danish Island, Møn. Out of those sessions emerged the ideas that shaped that book which was
published in 2007. The ﬁeld of ecology is rapidly changing, and we notice there is movement toward systems ideas
and holistic thinking. Perhaps there is some impact of the ideas in A New Ecology, but there is much more to do to
mainstream this approach and witness its penetration into standard ecology and biology textbooks. A stronger
integration of physical sciences (physics, chemistry, engineering, etc.) with the biological sciences would help realize a
holistic science as envisioned here. Even further work is needed then to incorporate the social sciences and policy
choices that are needed to ﬁnd pathways that promote sustainable development.
During the ensuing period between editions, we unfortunately lost two critical members of our research group,
Tiezzi (2010) and Jørgensen (2016). In fact, it was Sven’s initiative to have a second edition. He made the arrangements
and initial outlines for the new book. The book has the same main elements of the ﬁrst edition but with the addition of
one chapter on hierarchy (Chapter 5) and another on the application of the principles to policy and management
(Chapter 11). Other chapters have been updated and expanded. Due to a close collaboration of the authors, the book is
truly a team effort in which all authors are free to comment and contribute to each chapter. In preparing the second
edition, the “team” was able to meet on two occasions for brainstorming, ﬁrst in Montpelier, France, during the
EcoSummit 2016 (Fig. 1). The second meeting occurred at the International Institute for Applied Systems Analysis in
Laxenburg, Austria, in January 2017 (Fig. 2). In addition to the meetings, numerous emails, “ping-pongs” as Sven
FIGURE 1 Breakout meeting during lunch of the Ecosummit 2016 in Montpelier, France (Simone, Joa˜o, Søren, Larry, Felix, and Brian).
ix
referred to them, have produced this volume. The book also would not have been possible without the patient
persistence of the Elsevier publisher, Emily Thomson. We hope that the reader has as much fun reading the book as
we did in discussing, learning, and constructing the ideas that have shaped our research agenda in systems ecology.
Søren Nors Nielsen
Copenhagen, June 2019
Brian D. Fath
Laxenburg, June 2019
FIGURE 2 Selﬁe taken in January 2017 in the Elisabeth Room of Schloss Laxenburg, current home of the International Institute for Applied
Systems Analysis, Laxenburg, Austria (Joa˜o, Brian, Simone, Søren, and Larry).
PREFACE TO THE SECOND EDITIONx
C H A P T E R
1
Introduction: A New Ecology Is Needed
1.1 ENVIRONMENTAL MANAGEMENT HAS CHANGED
The political agenda imposed on ecologists and environmental managers has changed since the early 1990s. Since
the Rio Declaration and Agenda 21 in 1992, the focus has been on sustainability, which inevitably has made
ecosystem functioning a core issue. Sustainable Development is, according to the Rio Declaration, deﬁned as follows:
“development that meets the needs of the present without compromising the ability of future generations to meet
their own needs.” And, the contrasting parties are invited to “act in a way that is economically proﬁtable, socially
acceptable, and environmentally compatible.” Already the Rio Declaration emphasized the importance of ecosystems
in Principle 7: States shall cooperate in a spirit of global partnership to conserve, protect and restore the
health and integrity of the Earth’s ecosystems.
In view of the different contributions to global environmental degradation, states have common but differentiated
responsibilities. The developed countries acknowledge the responsibility that they bear in the international pursuit
of sustainable development in view of the pressures their societies place on the global environment and of the technologies
and ﬁnancial resources they command.
The changing climate represents one of the largest threats to ecosystems of the Earth, which poses a threat to stable
life conditions of all species including humans all over the world. Considering globalization, there is no region
where human population can ignore this and consider themselves not to be affected by any of the scenarios presented
in an increasing number of reports. We are already seeing the impacts today in increased hurricane strength
and activity, heat waves, ﬂooding, and temperature anomalies that are beyond the “normal” historical trends. Since
1995, the United Nations has been responsible for a series of conferences on climate change known as Conference of
Parties (COPs), of which the third led to an important milestone with the Kyoto Protocol in 1997, COP 3, which was
ratiﬁed by 191 states. Unfortunately, the strategies proposed were not sufﬁcient to mitigate the emission of greenhouse
gases. However, it did demonstrate the need to come together as a global community to address the topic
and change the dialogue toward emission reduction pathways. Already, before it ended, there was discussion of
post-Kyoto (it sunsetted in 2012) and a new agreement that would more aggressively address the issue. The ﬁnancial
crisis of the late 2000s made countries less collaborative; even though the economic collapse resulted in lowering
emissions, the efforts of most countries was to turn that around as soon as possible, without new regulations that
the business community felt would be hamstringing. After some gaps and compromise, a more bottom-up approach
(nationally determined contributions (NDCs)) emerged at the COP21 meeting in Paris in 2015. The Paris Agreement
came into force in early November 2016. The adopting countries have agreed to implement reduction measures from
2020. A recent, fall 2018, IPPC report indicates that warming will reach a critical 1.5C threshold by 2030 unless substantial
and urgent actions are taken in the near term. While the science is clear, are humans capable of managing
such a massive, international, multidimensional issue? Can we use our knowledge of ecological systems and
practicesdhow they balance biogeochemical cycles? Can a new ecology help point the way?
Another major international effort at cooperation for the environment was the Convention on Biological Diversity
(CBD), adopted in 2000. The CBD, with 12 principles, explicitly called for an Ecosystem Approachdthat placed the
ecosystem concept centrally into environmental management considerations. It is particularly clear from the last 10
of the 12 principles:
1) The objectives of management of land, water, and living resources are a matter of societal choice.
2) Management should be decentralized to the lowest appropriate level.
1
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00001-2
3) Ecosystem managers should consider the effects (actual or potential) of their activities on adjacent and other
ecosystems.
4) Recognizing potential gains from management, there is usually a need to understand and manage the ecosystem
in an economic context. Any such ecosystem management program should:
a. reduce those market distortions that adversely affect biological diversity;
b. align incentives to promote biodiversity conservation and sustainable use;
c. internalize costs and beneﬁts in the given ecosystem to the extent feasible.
5) Conservation of ecosystem structure and functioning, in order to maintain ecosystem services, should be a
priority target of the ecosystem approach.
6) Ecosystems must be managed within the limits of their functioning.
7) The ecosystem approach should be undertaken at the appropriate spatial and temporal scales.
8) Recognizing the varying temporal scales and lag effects that characterize ecosystem processes, objectives for
ecosystem management should be set for the long term.
9) Management must recognize that change is inevitable.
10) The ecosystem approach should seek the appropriate balance between, and integration of, conservation and use
of biological diversity.
11) The ecosystem approach should consider all forms of relevant information, including scientiﬁc and indigenous
and local knowledge, innovations and practices.
12) The ecosystem approach should involve all relevant sectors of society and scientiﬁc disciplines.
In addition, in the book Ecosystems and Human Well-being, a Report of the Conceptual Framework Working Group of the
Millennium Ecosystem Assessment from 2003, ecosystems are the core topic. In Chapter 2 of the book, it is emphasized
that an assessment of the ecosystem condition, the provision of services, and their relation to human well-being requires
an integrated approach. This enables a decision process to determine which service or set of services is valued
most highly and how to develop approaches to maintain services by managing the system sustainably. Ecosystem
services are the beneﬁts people obtain from nature. These include provisioning services such as food and water;
regulating services such as ﬂood and disease control; cultural services such as spiritual, recreational, and educational
beneﬁts; and supporting services such as nutrient cycling that maintain the conditions for life on Earth.
Today, environmental managers have realized that maintenance of ecosystem structure and functioning (see Principle
5 above) by an integrated approach is a prerequisite for a successful environmental management strategy,
which is able to optimize the ecosystem services for the beneﬁt of humans and nature. Another question is whether
we have sufﬁcient knowledge in ecology and systems ecology to give adequate and appropriate information about
ecosystem structure, function, and response to disturbance to pursue the presented environmental management
strategy and ecosystem sustainability with a scientiﬁc basis. In any way, the political demands provide a daunting
challenge for ecosystem ecology.
This development in turn has been accentuated by the adoption of the Sustainable Development Goals (SDGs)
by 194 countries. The SDGs describe 17 issues that need to be addressed and considered for humanity to achieve a
sustainable state of our societies and eventually the whole Earth. Although, only three of the goals (13, 14, 15) are
directly or very closely linked to the environment, clearly our societies are embedded in and therefore dependent
on the state of the ecosystems adjacent to us in our everyday life. The goals are addressed in 169 targets believed to
assist in reaching the goals, the SDGs. The targets share some concerns with the previously mentioned report from
the Rio Summit in 1992, which also included some indication of possible actions to be taken. Actions and targets
are not enough if they do not clearly indicate what priorities to give or in which direction to go. This book
carries the idea that such lessons may be learned from nature and that true sustainability may only be
achieved from increasing our understanding of nature’s function and learning to work with rather than at odds
with nature.
Recently, a new important player has entered the scenedthe Catholic churchdwith the Vatican’s release of the
Papal ecclesial named “Laudate Si” (2015), which clearly addresses the connection between poverty and environmental
quality and the fact that there is a strong bias between developed and developing countries. Developed countries’
industries are continuously searching for and exploiting resources from the rest of the world, with increasing
impact on the viability of local populations but also their activities play an important role in the decrease of global
diversity. Sustainable development is a social justice issue as well. All the more, a reason to take the courage and
action to address the issue.
1. INTRODUCTION: A NEW ECOLOGY IS NEEDED2
1.2 ECOLOGY IS CHANGING
As a consequence of the changing paradigm direction of environmental management, we need to focus on
ecosystem ecology. An ecosystem according to the Millennium Report (2003) is deﬁned as “a dynamic complex
of plants, animals, and microorganism communities and the nonliving environment, interacting as a functional
unit. Humans are an integral part of ecosystems.”
A well-deﬁned ecosystem has strong interactions among its components and weak interactions across its boundaries.
A useful ecosystem boundary is the place where a number of discontinuities coincide, for instance, in the distribution
of organism, soil type, drainage basin, or depth in a water body. At a larger scale, regional and even
globally distributed ecosystems can be evaluated based on a commonality of basic structural units. Three questions
are fundamental to pursue for ecosystem-based environmental management:
I: What are the underlying ecosystem properties that can explain their response to perturbations and human
interventions?
II: Are we able to formulate at least building blocks of an ecosystem theory in the form of useful propositions
about processes and properties? We prefer the word “propositions” and not laws because ecosystem dynamics
are so complex that universal laws give way to contextual propensities. The propositions capture these general
tendencies of ecosystem properties and processes that can be applied to understand the very nature of
ecosystems, including their response to human impacts.
III: Is the ecosystem theory sufﬁciently developed to be able to explain ecological observations with practical
application for environmental management?
The scope of this book is an attempt to answer these questions to the extent that is currently possible. The authors
of this book have realized that an ecosystem theory is a prerequisite for wider application of ecological sciences in
environmental management because theory provides a strong guide for environmental management and resource
conservation.
1.3 A NEW ECOLOGY
Over the years, the authors of this book along with other colleagues, collaborators, and researchers have proposed
new ways of looking at ecology primarily from principles of thermodynamics, self-organization, complexity,
dynamics (evolution), information, and interrelations (networks), among other foundational aspects. In particular,
Jorgensen and Fath (2004b) presented 10 thermodynamic principles in ecology, and Jorgensen et al. (2015) expanded
that list to 14 properties of ecosystems. Upon further reﬂection, reﬁnement, and consideration, our team took the task
to revisit the principles, remove redundancies, and derivative concepts (e.g., saying ecosystems are open implies that
they need continued energy to survive). The result, although likely not the ﬁnal word as science is always learning
and changing based on new evidence, is what we believe to be a tight set of nine core ecosystem principles (Table 1.1).
These are subdivided into three categories, the material constraints, the ontological properties, and phenomenological
properties.
The material constraints refer to the laws of thermodynamics and the periodic table (how chemicals react and
interact). The ontological properties consider both the fact that ecosystems are self-organizing systems in response
to physical ﬂows of energydthis is the physically driven biological aspect, and ecosystems are dynamic and changing
due to evolutionary processes and pressuredthis is the biologically driven biological aspect. Lastly, the phenomenological
properties include observed features such as diversity, hierarchies, networks, and information. These
principles are described in detail in the book in a series of statements about ecosystems and how those apply to
both the broader ﬁeld of ecology and to environmental management.
1.4 BOOK OUTLINE
Chapters 2e8 present the fundamental properties that explain typical ecosystem processes under “normal”
growth and development and their responses to disturbance. These are followed by 3 chapters of that show their
1.4 BOOK OUTLINE 3
explanatory power in ecology and application to ecology and environmental management. The book is laid out as
follows:
1) Chapter 1 is the introduction you are reading now.
2) Ecosystems are open systemsdopen to energy, mass, and information. Openness is an absolute necessity because
the maintenance of ecosystems far from thermodynamic equilibrium requires an input of energy (Chapter 2).
3) Ecosystems are ontically open, meaning thatdin addition to the physical openness of Chapter 2ddue to their
enormous complexity, it is impossible to predict accurately all possible outcomes in advance regarding
ecosystem behavior. The implications are that it is more appropriate to discuss the propensity of ecosystems to
show a certain pattern or to discuss the direction of responses (Chapter 3).
4) Ecosystems have network connectivity, which gives them new and emergent properties. Ecosystem networks have
synergistic properties, which are able to explain the cooperative integration of ecosystem components, which
can at least sometimes yield unexpected system relations (Chapter 4).
5) Ecosystems are organized hierarchically in the sense that we can understand one level only by understanding
interactions with the levels below and above the scale of focus. This property gives an interplay of top-down and
bottom-up control within the system (Chapter 5).
6) Ecosystems have directed development, meaning they change progressively to increase, in particular feedback and
autocatalysis (Chapter 6).
7) Ecosystems grow and develop; they gain biomass and structure, enlarge their networks, and increase their
information content. We can follow this growth and development using holistic metrics such as power and ecoexergy,
respectively (Chapter 7).
8) Ecosystems have complex response to disturbance and decay, but when we understand properties of ecosystems such
as adaptation, biodiversity, resistance, and resilience, to mention a few of the most important properties covered
in the book, we can explain and sometimes predict the responses of ecosystems to disturbances (Chapter 8).
9) Ecosystem principles have broad explanatory power in ecology as we show that the principles provide explanations for
many textbook concepts in ecology (Chapter 9).
10) Ecosystem principles have ecological applications (Chapter 10).
11) Ecosystem principles have environmental management and policy applications and need therefore in the future to be
taken much more into account when taking management initiatives, be it prevention/mitigation or remediation
of existing and recognized environmental problems (Chapter 11).
12) Conclusions (Chapter 12).
Chapters 2e8 are directed to answer the ﬁrst question. The second question is addressed in Chapter 9 and summarized
in Chapter 12. The last question regarding the applicability of the presented theory to explain ecological
TABLE 1.1 Ecosystem Principles.
MATERIAL CONSTRAINTS
1) Ecosystems conserve matter and energydﬁrst law
2) All processes are dissipativedsecond law
3) All life uses largely the same biochemical constituents and
processes
ONTOLOGICAL PROPERTIES
4) An ecosystem uses surplus energy to move further away from
thermodynamic equilibrium (physically driven biological
aspect)dcentripetality
5) Ecosystems coevolve and adapt to prevailing conditions
(biologically driven biological aspect)
PHENOMENOLOGICAL PROPERTIES
6) Ecosystems have diversity of structure and function
7) Ecosystems are emergent hierarchically
8) Ecosystems work together in networks that improve the resource
ﬂow utilization
9) Ecosystems have an enormous amount of genetic, biochemical, and
process information
1. INTRODUCTION: A NEW ECOLOGY IS NEEDED4
observations and to be applied in environmental management is addressed in Chapters 10 and 11. The application of
the theory in environmental management has been mostly limited to the use of ecological indicators for ecosystem
health assessment as described in Chapter 9. The theory has much wider applicability, but the use of ecological indicators
has a direct link to ecosystem theory that facilitates testing the theory. Tests of the theory according to its
applicability in practical environmental management and to explain ecological observations is crucial for the general
acceptance of the ecosystem theory, but it does not exclude that it cannot be improved signiﬁcantly. On the contrary,
it is expected that the theory will be considerably improved by persistent and ongoing application because the weaknesses
in the present theory will inevitably be uncovered as the number of case studies increases. Discovery of theoretical
weaknesses will inspire improvements. Therefore, it is less important that the theory has ﬂaws and lacks
important elements than that it is sufﬁciently developed to be directly applied. We, the authors, are of the opinion
that we have an ecosystem theory that is ready to be applied but which also inevitably will be developed signiﬁcantly
during the next one to two decades due to (hopefully) its wider application. In fact, this second edition exempliﬁes
the learning that has taken since the ﬁrst edition was released a decade ago.
An ecosystem theory as the one presented in this book may be compared with geographical maps. Basics maps
were available already 2000 years ago that could provide an overview of where you would ﬁnd towns, mountains,
forests, etc. These maps were considerably improved over time as technologies improved, and the geographical
maps used in the 17th and 18th centuries were much more accurate and detailed, which themselves are not comparable
with the satellite-based and interactive maps of today. Our ecosystem theory as presented here may be comparable
with the geographical maps of the 18th century. They are very useful, but they can be improved considerably
when new methods, information, and observations are available. It may take 20 or 50 years before we have the
quality of an ecosystem theory comparable with today’s geographical maps, but the present level of our ecosystem
theory is nevertheless suitable for immediate application. Only through this application will we discover new
methods and demand for improvements, both theoretical and practical for science and management, ultimately
leading to a more complete and accurate ecosystem theory.
1.4 BOOK OUTLINE 5
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C H A P T E R
2
Ecosystems Have Thermodynamic Openness
Without the Sun, everything on Earth dies! From the plaintive Ukrainian folksong, “6 bayjc >l c{tfr .”.
2.1 WHY MUST ECOSYSTEMS BE OPEN?
The many 1 m trees that we planted more than 30 years ago in our gardens, which were open ﬁelds at the time, are
more than 30 m tall today. The trees have increased their biomass in the form of trunks, stems, leaves, and roots. The
structures of the gardens have also changed. Today, biodiversity is much higherdnot so much due to different
plants, but the tall trees and the voluminous bushes with berries attract many insects and birds. The garden today
is a much more complex ecosystem. The biomass has increased, the biodiversity has increased, and the number of
ecological interactions among the abundant species has increased.
When you follow the development of an ecosystem over a longer period or even during a couple of spring
months, you are witness to one of the many wonders in nature: an inconceivably complex system is developing
in front of you. What makes this development of complex (and beautiful) systems in nature possible?
In accordance with classic thermodynamics, all isolated systems will move toward thermodynamic equilibrium, a
state of equal distribution of components and maximum probability. This means that all the gradients have been
eliminated, i.e., there are no differences in any energy potentials such as concentration of chemicals or temperature
differences, and structures in the system will have ceased to exist: a homogenous dead system will be the result. This
is expressed thermodynamically as follows: entropy will always increase in an isolated system. As work capacity is a
result of gradients in certain intensive variables such as temperature, pressure, and chemical potential, etc. (see
Table 2.1), a system at thermodynamic equilibrium can do no work. But our gardens are moving away from thermodynamic
equilibrium with a seemingly faster and faster rate every year, at least in the early stages of growth and
development. This means that our gardens cannot be isolated. They must be at least nonisolated (in established terminology
this usually refers to the system being either closed, i.e., may exchange only energy with the surroundings,
or open, which may exchange both matter and energy with the surroundings); but birds, insects, squirrels, and an
occasional fox enter from outside the gardendfrom the environment of the garden, maybe from a forest 1000 m
away. In fact, the garden as all other ecosystems must be open (see also Table 2.2, where the thermodynamic
deﬁnitions of isolated, closed, and open systems are presented). Gardens are open to energy inputs from the solar
radiation, which is absolutely necessary to avoid the system moving toward thermodynamic equilibrium. Without
solar radiation the system would die. The energy contained in the solar radiation provides the energy needed for
maintenance of the plants and animals, measured by the respiration. When the demand for maintenance energy
is covered, additional energy is used to move the system further away from thermodynamic equilibrium. The thermodynamic
openness of ecosystems explains why ecosystems are able to move away from thermodynamic equilibrium:
to grow and to build structures and gradients. It should be noted that the use of the term “thermodynamic
equilibrium” in the above context does not refer to true thermodynamic equilibrium, i.e., a system with absolutely
no gradients, a disintegrated system at 0 K. Rather, it refers to a system that has no gradients with respect to its environment
that may be at other conditions of equilibrium like the biosphere, an Oparinian sea, and similar
environments.
7
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00002-4
Ecosystem openness is, in most cases, only a necessary condition. For example, a balanced aquarium and also
our planet are more nonisolated than open; openness is only incidental. One wonders what would be the elements
of sufﬁcient conditions to create an ecosystem from solar radiation? Openness is obviously not a sufﬁcient condition
for ecosystems because all open systems are not ecosystems. If a necessary condition is removed, however, the
process or system in question cannot proceed. So, openness (or nonisolation) as a necessary condition makes this a
pivotal property of ecosystems, one to examine very closely for far-reaching consequences. And, if these are to be
expressed in thermodynamic terms, ecologists need to be aware that aspects of thermodynamicsdparticularly entropy
and the second lawdhave for several decades been under some serious challenges in physics, and no longer
enjoy the solid standing in science they once held (Capek and Sheehan, 2005). Like a garden, science is open tood
ever exploring, changing, and improving. In this chapter, we will not take these modern challenges too into
account.
2.2 AN ISOLATED SYSTEM WOULD DIE (MAXIMUM ENTROPY)
The spontaneous tendency of energy to degrade and be dissipated in the environment is evident in the phenomena
of everyday life. A ball bouncing tends to make smaller and smaller bounces and dissipation of heat. A jug that
falls to the ground breaks (dissipation) into many pieces and the inverse process, which could be seen running a ﬁlm
of the fall backwards, never happens in nature. Except, of course, the jug did come into existence by the same kind of
nonspontaneous processes that make the garden grow. It is instructive to ponder how openness or nonisolation
operates here, as necessary conditions. Perfume leaves a bottle and dissipates into the room; we never see an empty
bottle spontaneously ﬁll, although the laws of probability do allow for this possibility. There is thus a tendency for
energy to disperse and turn into the heat formda process called dissipation. The process of dissipation also relates to
the irreversible spread of matter illustrated in the above examples. The thermodynamic function known as entropy
(S) is the extensive variable for heat and measures the extent to which work has been degraded to heat. Strictly
speaking, the entropy concept only applies to isolated systems close to equilibrium, but it is often used in a metaphorical
sense in connection with everyday far-from-equilibrium systems. We will follow this practice here as a useful
way to consider ecosystems; revisions can come later when thermodynamic ecology is much better understood
TABLE 2.2 Deﬁnitions of Various Thermodynamic Systems.
System Type Deﬁnition
Isolated No exchange of energy, mass, and information with the environment
Nonisolated Exchange of energy and information, but no mass with the environment
Closed Exchange of energy and information, but no mass with the environment
Open Exchange of energy, mass, and information with the environment
TABLE 2.1 Different Forms of Energy and Their Intensive and Extensive Variables.
Energy Form Extensive Variable Intensive Variable
Heat Entropy (J/K) Temperature (K)
Expansion Volume (m3
) Pressure (Pa ¼ kg/s2
m)
Chemical Moles (M) Chemical potential (J/moles)
Electrical Charge (Ampere sec) Voltage (Volt)
Potential Mass (kg) (Gravity) (height) (m2
/s2
)
Kinetic Mass (kg) 0.5(Velocity)2
(m2
/s2
)
Potential and kinetic energy is denoted mechanical energy.
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS8
from theory and greater rigor is possible. Transformations tend to occur spontaneously in the direction of increasing
entropy or maximum dissipation. The idea of the passage of time, of the direction of the transformation, is inherent
in the concept of entropy. The term was coined by Clausius from sroph (transformation) and εnsroph (evolution,
mutation, or even confusion).
Clausius used the concept of entropy and reworded the ﬁrst and second thermodynamic laws in 1865 (Clausius,
1865) in a wider and more universal framework: Die Energie der Welt ist Konstant (the energy of the world is constant)
and Die Entropy der Welt strebt einem Maximum zu (the entropy of the world tends toward a maximum).
Maximum entropy, which corresponds to the equilibrium state of a system, is a state in which the energy is
completely degraded and can no longer produce work. Well, maybe not literally “completely degraded” but rather,
let us say, only “degradiented,” meaning brought to a point of equilibrium where there is no gradient with its surroundings,
therefore no possibility to do work. Energy at 300 K at the Earth’s surface is unusable but can perform
work after it passes to outer space where the temperature is 3 K and a thermal gradient is reestablished. Again, it is
common practice to use the term “degraded” in the sense we have, and “completely” for emphasis; for continuity in
communication these practices will be followed here.
Entropy is, therefore, a concept that shows us the direction of events. “Time’s Arrow,” as Harold Blum (1951) has
called it. Barry Commoner (1971) notes that sandcastles (order) do not appear spontaneously but can only disappear
(disorder); a wooden hut in time becomes a pile of beams and boards: the inverse processes do not occur. The
spontaneous direction of an isolated system is thus from order to disorder and entropy, as metaphor, indicates
this inexorable process, the process which has the maximum probability of occurring. In this way, the concepts of
disorder and probability are linked in the concept of entropy. Entropy is in fact a measure of disorder and probability
even though for systems like a garden it cannot be measured. Entropy generation can be calculated approximately,
however, for reasonably complex systems, and for this one should consult the publications of Aoki (1987, 1988, 1989).
War is a disordering activity, but from such can often arise other levels and kinds of order. For example, a South
Seas chieftain once warred on his neighbors and collected their ornately carved wooden thrones as part of the spoils
and symbols of their defeat; they came to signify his superiority over his enemies and this enabled him to govern for
many years as leader of a well-organized society. This social order, of course, came out of the original disordering
activity of warfare, and it was sustained. The captured thrones were stored in a grand thatched building for display
on special holidays, a shrine that came to symbolize the chieftain’s power and authority over his subjects. One year, a
typhoon hit the island and swept the structure and its thrones away in the night. The disordering of the storm went
far beyond the scattering of matter, for the social order that had emerged from disorder quickly unraveled also and
was swept away with the storm. The remnant society was forced in its recovery to face a hard lesson of the regiond
“People who live in grass houses shouldn’t stow thrones!” In order to understand this orderedisorder relationship
better, it is useful to describe a model experiment: the mixing of gases.
Suppose we have two gases, one red and one yellow, in two containers separated by a wall. If we remove the wall,
then we see that the two gases mix until there is a uniform distribution: an orange mixture. Well, a uniformly mixed
distribution, anyway; in a statistical sense the distribution is actually random. If they were originally mixed, then
they would not be expected to spontaneously separate into red and yellow. The “orange” state is that of maximum
disorder, the situation of greatest entropy because it was reached spontaneously from a situation of initial orderdthe
maximum of which, by the way, is the uniform distribution. Random, uniform; one must take care in choice of
wording. Entropy is a measure of the degree of disorder of the system (notice that the scientiﬁc literature presents
several deﬁnitions of the concept of entropy). The disordered state occurred because it had the highest statistical
probability. The law of increasing entropy expresses therefore also a law of probability, of statistical tendency toward
disorder. The most likely state is realized, namely the state of greatest entropy or disorder. When the gases mix, the
most probable phenomenon occurs: degeneration into disorderdrandomness. Nobel Laureate in Physics, Richard
Feynman (1994), comments that irreversibility is caused by the general accidents of life. It is not against the laws
of physics that the red and yellow gases could separate; it is simply improbable and would not happen in a million
years. Things are irreversible only in the sense that going toward randomness is probable whereas going toward
order, while it is possible and in agreement with the laws of physics, would almost never happen in the case of simple
particulars such as gases. Yet, we see the complex, ordered garden grow before us.
So, it is also in the case of our South Sea islanders. Two populations kept separate by distance over evolutionary
time could be expected to develop different traits. Let one such set be considered “red” traits, and the other “yellow.”
Over time, without mixing, the red traits would get redder and the yellow traits yellowerdthe populations would
diverge. If a disordering event like a storm or war caused the islanders to disperse and eventually encounter one
another and mix reproductively, then their distinctive traits would, over a long period of time, merge and converge
2.2 AN ISOLATED SYSTEM WOULD DIE (MAXIMUM ENTROPY) 9
toward “orange.” A chieftain governing such a population would not be able to muster the power to reverse the
trend by spontaneous means. A tyrant might resort to genocide to develop a genetically pure race of people. Without
entropy such an extreme measure, which has over human history caused much misery, would never be needed.
Spontaneous dehomogenization could occur, reestablishing the kind of thermodynamic gradient (red vs. yellow)
that would again make possible the further ordering work of disordering war. No entropy, no work or ward
necessary or sufﬁcient condition?
The principle of increasing entropy is now clearer in orange molecules and people: high-entropy states are
favored because they are more probable, and this fact can be expressed by a particular relation as shown by Boltzmann
(1905): S ¼ Àk log p, where S is entropy, k Boltzmann’s constant, and p the probability of an event occurring.
The logarithmic dependence makes the probability of zero entropy equal to one. The universality of the law of entropy
increase (we speak metaphorically) was stressed by Clausius in the sense that energy is degraded
(“degradiented”) from one end of the universe to the other and that it becomes less and less available in time, until
“Wa¨rmetode”, or the “thermal death” of the universe. Evolution toward this thermal death is the subject of much
discussion. Jørgensen et al. (1995) showed that the expansion of the universe implies that the thermodynamic equilibrium
is moving farther and farther away. In order to extend the theory from the planetary to the cosmic context it
is necessary to introduce unknown effects such as gravitation. Current astrophysics suggests an expanding universe
that originated in a great primordial explosion (big bang) from a low-entropy state, but the limits of theoretical thermodynamic
models do not allow conﬁrmation or provide evidence.
The study of entropy continues: this fundamental concept has been applied to diverse ﬁelds such as linguistics,
the codiﬁcation of language, and to music and information theory, unfortunately leading to a confusing use
of concepts which must be taken into account when reading literature in the area. Thermodynamics has taught
us many fascinating lessons, particularly that (I) energy cannot be created or destroyed but is conserved and (II)
entropy of isolated systems is always increasing, striking the hours of the cosmic clock, and reminding us that
both for humans and for energy matter, time exists and the future is distinct from the past by virtue of a higher
value of S.
The second law of thermodynamics, still upheld as one of nature’s fundamental laws, addresses the pathways we
should avoid in order to keep life on Earth. It shows the universal, inescapable tendency toward disorder (in
thermodynamics, the general trend toward an entropy maximum), which is also, again metaphorically, a loss of
information and of usable or available energy. This tendency to the Clausius’ “thermal death” speaks to the thermodynamic
equilibrium, namely the death of biological systems and ecosystems, through the destruction of diversity
and hence ﬁnally the removal of gradients. There are two ways to achieve such a condition when:
(a) through energy exchanges as heat ﬂuxes, there are no more differences in temperature and nothing more can be
done because no exchange of usable energy is allowed;
(b) a system, becoming isolated, consumes its resources, reaching a great increase in its internal entropy and, at the
end, to self-destruction.
For this reason, living systems cannot exist at conditions near or at thermodynamic equilibrium, but keep themselves
as far as possible from that state, self-organizing due to material and energetic ﬂuxes, received from outside
and from systems with different conditions of temperature and energy.
To live and reproduce, plants and animals need a continuous ﬂow of energy. This is an obvious and commonly
believed truism, but in fact organisms will also readily accept a discontinuous energy inﬂow, as life in a biosphere,
driven by pulsed energy inputs that the periodic motions of the planet provide, demonstrates. The energy of the
biosphere that originates in the discontinuous luminous energy of the sun is captured by plants and passes from
one living form to another along the food chain. This radiant pathway that provides us with great quantities of
food, ﬁbers, and energydall of solar origindhas existed for over four billion years, a long time if we think that hominids
appeared on the Earth only circa six million years ago and that known history covers only 10,000 years. The
ancestors of today’s plants were the blue-green algae, or cyanobacteria, that began to practice photosynthesis,
assuming a fundamental role in biological evolution.
All vegetation, whether natural or cultivated, has been capturing solar energy for millennia, yet the vast majority
of the energy received by the Earth’s surface from the sun is not part of the photosynthetic energy chain. Rather, most
of the solar energy is dispersed: it is reﬂected, stored in the soil and water, used in the evaporation of water, and so
forth. Approximately 1% of the solar energy that falls on fertile land and water is ﬁxed through photosynthesis by
primary producers in the form of high-energy organic molecules: solar energy stored in chemical bonds available for
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS10
later use. Through additional biochemical processes (respiration) the plants transform this energy into other organic
compounds and work.
The food chain considered in terms of energy ﬂows has a logic of its own: the energy degrades progressively in the
different phases of the chain (primary producers and secondary consumers including decomposers), giving back the
elementary substances necessary to build again the molecules of living cells with the help of solar energy.
The organization of living beings in mature ecosystems slows the dispersal of energy ﬁxed by plants to a minimum,
using it completely for its complex mechanisms of regulation. This is made possible by large “reservoirs”
of energy (biomass) and by the diversiﬁcation of living species. The stability of natural ecosystems, however,
means that the ﬁnal energy yield is zero, except for a relatively small quantity of biomass that is buried underground
to form fossils. Relatively small, true, but in absolute terms fuel enough to power modern civilization for
centuries.
Photosynthesis counteracts entropic degradation insofar as it orders disordered matter: the plant takes up
disordered material (low-energy molecules of water and carbon dioxide in disorderly agitation) and puts it in
order using solar energy. It organizes the material by building it into complex structures. Photosynthesis is, therefore,
the process that by capturing solar energy and decreasing the entropy of the planet paved the way for
evolution. Photosynthesis is the green talisman of life, the bioenergetic equivalent of Maxwell’s demon that decreases
the entropy of the biosphere. On the Earth, living systems need a continuous or discontinuous ﬂow of
“negative entropy” (actually, energy from the outside which is used to bring the bounded system to a lower
entropy state) and this ﬂow consists of the very solar energy captured by photosynthesis. This input of solar energy
is what fuels the carbon cycle. The history of life on the Earth can be viewed as the history of chemotropic life,
followed by the photosynthesis and the history of evolution, as the history of a planet that learned to capture solar
energy and feed on the negative entropy of the universe for the creation of complex self-perpetuating structures
(living organisms).
Compared to us or our society, the sun is an enormous engine that produces energy and offers the Earth the possibility
of receiving large quantities of the abovementioned “negative entropy” (organization, life), allowing a global
balance that does not contradict the second law of thermodynamics. Every year, the sun sends the Earth
5.6310 Â 1024
J of energy, over 10,000 times more energy than humans consume in a year. Wee should not consider
this solar bounty, ours alone, though, as we are only one among millions of species relying on this ﬂow.
2.3 PHYSICAL OPENNESS
An energy balance equation for ecosystems might be written as follows in accordance with the principle of energy
conservation:
Ecap ¼ Qevap þ Qresp þ . þ DEbio (2.1)
Here, Ecap is the external energy captured per unit of time. A part of the incoming energy, solar radiation being the
main source for the ecosystems on Earth, is captured and a part is reﬂected unused, determining the albedo of the
globe. The more biological structure an ecosystem possesses, the more of the incoming energy it is able to capture,
i.e., the lower the albedo. The structure acts as an umbrella or rather a satellite dish, capturing the incoming solar
radiation.
In an ecosystem at steady state, the formation of biological compounds (anabolism) is in approximate balance
with their decomposition (catabolism). That is, in energy terms:
DEbio z 0 and EcapzQevap þ Qresp (2.2)
The energy captured by a system can in principle be any form of energy (electromagnetic, chemical, kinetic, etc.),
but for the ecosystems on Earth the short-wave energy of solar radiation (electromagnetic energy) plays the major
role. The energy captured per unit of time is, however, according to Eq. (2.2) used to pay the maintenance cost per
unit of time including evapotranspiration and respiration. The overall result of these processes requires that Ecap to
be greater than 0, which entails openness (or at least nonisolation).
The following reaction chain summarizes the consequences of energy openness (Jørgensen et al., 1999): source:
solar radiation / anabolism (charge phase): incorporation of high-quality energy, with entrained work capacity
(and information), into complex biomolecular structures, entailing anti-entropic system movement away from
2.3 PHYSICAL OPENNESS 11
equilibrium / catabolism (discharge phase): deterioration of structure involving release of chemical bond energy
and its degradation to lower states of usefulness for work (heat) / sink: dissipation of degraded (low work
capacity and high entropy) energy as heat to the environment (and, from Earth, to deep space), involving entropy
generation and return toward thermodynamic equilibrium. This is how the energy cascade of the planet is usually
described. Another way might be to express it in terms of gradient creation and destruction. The high-quality
entering energy creates a gradient with baseline background energy. This enables work to be done in which the energy
is degradiented and dissipated to space. On arrival there (at approximately 280 K) it locally “regradients” this
new environment (at 3 K) but then rapidly disperses into the vacuum of the cosmos at large.
This same chain can also be expressed in terms of matter: source: geochemical substrates relatively close to thermodynamic
equilibrium / anabolism: inorganic chemicals are molded into complex organic molecules (with low
probability, meaning that the equilibrium constant for the formation process is very low, low entropy, and high distance
from thermodynamic equilibrium) / catabolism: synthesized organic matter is ultimately decomposed into
simple inorganic molecules again; the distance from thermodynamic equilibrium decreases, and entropy increases
/ cycling: the inorganic molecules, returned to near-equilibrium states, become available in the nearly closed
material ecosphere of Earth for repetition of the matter chargeedischarge cycle.
Input environments of ecosystems serve as sources of high-quality energy whose high contents of work and
information and low entropy form of energy serve to raise the organizational states of matter far from equilibrium.
Output environments, in contrast, are sinks for energy and matter lower in work capacity, higher in entropy,
and closer to equilibrium. This is one possibility. On the other hand, since output environments also
contain equilibrium-avoiding entities (organisms), their energy quality on a local basis might be just as great
as that of organisms in input environments. Since, output environments feedback to become portions of input
environments living systems operating in the ecosphere, which is energetically nonisolated but materially nearly
closed, must seek an adaptive balance between these two aspects of their environmental relations in order to sustain
their continued existence. That is, the chargeedischarge cycle of the planet wraps output environments
around to input environments, which homogenizes gradients and forces gradient-building (anabolic) biological
activity.
The expression high-quality energy is used above to indicate that energy can either be applied to do work or it is
what is sometimes called “anergy,” i.e., energy that cannot do work. The ability to do work can be expressed by:
Work ¼ an extensive variables Â a difference in intensive variables
For instance,
Work ¼ mgðh1À h2Þ (2.3)
where m is the mass, g the gravity, h the height, and (h1 e h2) the difference in height (see Table 2.1).
The concept exergy was introduced by Rant (1955, 1956) to express the work capacity of a system relative to its
environment (see details presented in Wall, 1977; Szargut et al., 1988). It was particularly useful when the efﬁciencies
of a power plant or the energy transfer should be expressed. We have therefore:
Energy ¼ exergy þ anergy (2.4)
Qevap and Qresp in Eqs. (2.1) and (2.2) may be considered as anergy because it represents the energy lost and is heat
at the temperature of the environment, i.e., no gradient. The temperature of the ecosystem would rise, if the
ecosystem was not open at both ends, so to say. The heat is exported to the environment. The openness, or actually
nonisolation (closed condition), of ecosystems makes it possible for the systems to capture energy for photosynthesis
and also to export generated heat to maintain an acceptable temperature for life processes.
Exergy, as it is deﬁned technologically, cannot be used to express the work capacity of an ecosystem because the
reference (the environment) is the adjacent ecosystem. The eco-exergy expresses, therefore, the work capacity of an
ecosystem compared with the same system as a dead and completely homogeneous system without gradients. See
Box 2.1 for deﬁnition and documentation of “eco-exergy.”
Eco-exergy expresses the development of an ecosystem by its work capacity (see Box 2.1). While we can measure
the concentrations in the ecosystem proper, the concentrations in the reference state (thermodynamic equilibrium;
see Box 2.1) must be estimated. For instance, such a theoretical value can be based on the usual use of chemical equilibrium
constants. If we have the process:
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS12
Component A4inorganic decomposition products; (2.6)
then it has a chemical equilibrium constant, K:
K ¼ ½inorganic decomposition products=½Component A (2.7)
The concentration of component A at thermodynamic equilibrium is difﬁcult to ﬁnd (see the discussion in
Chapter 7), but we can, based on the composition of A, ﬁnd the concentration of component A at thermodynamic
equilibrium from the probability of forming A from the inorganic components.
Eco-exergy is a function of the reference state, often taken as the prevailing environment of the ecosystem which
may vary and thus be different from ecosystem to ecosystem. Eco-exergy expresses, therefore, the work capacity
relative to the same system but at (thermo-)dynamic equilibrium with the surroundings. Eco-exergy can furthermore,
with the deﬁnition given, be applied far from thermodynamic equilibrium. It should be mentioned that
eco-exergy cannot be measured, as the total internal energy content of a body or system cannot be measured.
Even a small ecosystem contains many microorganisms and it is, therefore, hardly possible by determination of
the weight of all components of an ecosystem to assess the eco-exergy of an ecosystem. The eco-exergy of a model
of an ecosystem can, however, be calculated as it will be demonstrated in Chapter 7.
Using these calculations, we ﬁnd the exergy of the system compared with the same system at the same temperature
and pressure but in form of an inorganic soup without any life, biological structure, information, or organic
molecules. As (mc) can be found from the deﬁnition of the chemical potential replacing activities by concentrations,
we get the following expressions for eco-exergy:
Ex ¼ RT
Xn
i¼0
Ci ln

Ci

Ci;o

(2.8)
where R is the gas constant (8.317 J/K moles ¼ 0.08,207 L atm KÀ1
moles), T the temperature of the environment
(and the system; see Fig. 2.1), while Ci is the concentration of the ith component expressed in a suitable unit, e.g.,
for phytoplankton in a lake Ci could be expressed as mg/L or as mg/L of a speciﬁc nutrient under consideration.
Ci,o is the concentration of the ith component at thermodynamic equilibrium and n is the number of components.
Ci,o is a very small concentration (except for I ¼ 0, which is considered to cover the inorganic compounds), it is therefore
possible to use the probability (pi,0) (see Chapter 7):
Ex
V
¼ RT
Xn
i¼0
pi ln
pi
pi;o
!
(2.9)
BOX 2.1BOX 2.1
E C O - E X E R G Y , D E F I N I T I O N
Eco-exergy was introduced in the 1970s (Jørgensen and
Mejer, 1977, 1979, Mejer, 1979 and Jørgensen, 1982) to
express the development of ecosystems through an increase
of the work capacity. We presume a reference environment
that represents the system (ecosystem) at
thermodynamic equilibrium, which means that all the
components are inorganic at the highest possible oxidation
state: if sufﬁcient oxygen is present (as much free energy
as possible is utilized to do work) and
homogeneously distributed at random in the system (no
gradients), then the situation illustrated in Fig. 2.1 is valid.
As the chemical energy embodied in the organic components
and the biological structure contributes far most to
the exergy content of the system, there seems to be no
reason to assume a (minor) temperature and pressure difference
between the system and the reference
environment. Under these circumstances we can calculate
the exergy content of the system as coming entirely from
the chemical energy:
X
ðmc À mcoÞNi (2.5)
where, mc and mco are the chemical potentials and N in the
number of chemical compounds.
This represents the nonﬂow chemical exergy. It is determined
by the difference in chemical potential (mcemco)
between the ecosystem and the same system at thermodynamic
equilibrium. This difference is determined by the
concentrations of the considered components in the
system and in the reference state (thermodynamic equilibrium),
as it is the case for all chemical processes.
2.3 PHYSICAL OPENNESS 13
By using this particular eco-exergy based on the same system at thermodynamic equilibrium as a reference, the
exergy becomes dependent only on the chemical potential of the numerous biochemical components that are
characteristic for life. This is consistent with Boltzmann’s statement that life is a struggle for free energy, which is the
work capacity in classic thermodynamics.
As observed above, the total eco-exergy of an ecosystem cannot be calculated exactly, as we cannot measure the
concentrations of all the components or determine all possible contributions to eco-exergy in an ecosystem. Nor does
it include the information of interactions. If we calculate the exergy of a fox, for instance, then the above shown
calculations will only give the contributions coming from the biomass and the information embodied in the genes,
but what is the contribution from the blood pressure, the sexual hormones, and so on? These properties are at least
partially covered by the genes but is that the entire story? We can calculate the contributions from the dominant biological
components in an ecosystem, for instance, using a model or measurements, that covers the most essential
components for a focal problem. The difference in exergy by comparing two different possible structures (species
composition) is here decisive. Moreover, exergy computations always give only relative values, as the exergy is calculated
relative to the reference system. These problems will be treated in further details in Chapter 7. For now, it is
important to realize that it is the metaphorical quality of the exergy concept, and not its measurability that is
most useful to ecologists. Entropy and exergy can both not be measured for ecosystems. It is not always necessary
in science to be able to make exact measurements. Ecologists rarely do this anyway. Approximations can yield an
approximate science, and that is what ecology is. Modeling approximates reality, not duplicates it, or reproduces
it exactly because it is impossible due to high ecosystem complexity (see also next chapter). Approximate
ecologydwhich can be quite useful and interestingdcan be used to quantify (approximately), e.g., the inﬂuence
of anthropogenic impacts on ecosystems. Often concepts and theories, not only measurements, make science interesting.
With all the short-comings presented above, eco-exergy gives an approximate, relative measure of how far an
ecosystem is from thermodynamic equilibrium and thereby how developed it is. Such assessment of important holistic
ecosystem properties is important in systems ecology as well as in environmental management. This explains
how eco-exergy has been applied several times successfully to explain ecological observations (see Jørgensen et al.,
2002 and Chapter 9) and as indicator for ecosystem health (see Jørgensen et al., 2004 and Chapter 10).
Ecosystem at temperature T
and pressure p
Reference system: the same
system at the same temperature
and pressure but at thermodymic
equilibrium
WORK CAPACITY = ECO-EXERGY =
i=n
Σmi (µi - µio)
i=0
where mi is the amount of component
i and µi is the chemical potential
of component i in the ecosystem
µio is the corresponding chemical
potential at thermodynamic equili-
brium
FIGURE 2.1 The exergy content of the system is calculated in the text for the system relative to a reference environment of the same system at
the same temperature and pressure at thermodynamic equilibrium, it means as an inorganic soup with no life, biological structure, information,
gradients, and organic molecules.¤
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS14
2.4 THE SECOND LAW OF THERMODYNAMICS INTERPRETED
FOR OPEN SYSTEMS
If ecosystems were isolated, then no energy or matter could be exchanged across their boundaries. The systems
would spontaneously degrade their initially contained exergy and increase their entropy, corresponding
to a loss of order and organization, and increase in the randomness of their constituents and microstates. This
dissipation process would cease at equilibrium, where no further motion or change would be possible. The physical
manifestation would ultimately be a meltdown to the proverbial “inorganic soup” containing degradation
products dispersed equiprobably throughout the entire volume of the system. All gradients of all kinds would
be eliminated, and the system would be frozen in time in a stable, ﬁxed conﬁguration. The high-energy chemical
compounds of biological systems, faced suddenly with isolation, would decompose spontaneously (but not
necessarily instantaneously) to compounds with high-entropy contents. The process would be progressive to
higher and higher entropy states, and would, in the presence of oxygen, end with a mixture of inorganic
residuesdcarbon dioxide, water, nitrates, phosphates, and sulfates, etc. These simpler compounds could never
be reconﬁgured into the complex molecules necessary to carry on life processes without the input of new lowentropy
energy to be employed in biosynthesis. An isolated ecosystem could, therefore, in the best case sustain
life for only a limited period of time, less than that required from the onset of isolation to reach thermodynamic
equilibrium. Observations of properties could not be made, only inferred, because observation requires some
kind of exchanges between the system and an observer. There would be no internal processes because no gradients
would exist to enable them. There would only be uninterrupted and uninterruptible stillness and sameness
which would never change. The system would be completely static at thermodynamic equilibrium. Thus, in
a peculiar way, isolated systems can only be pure abstractions in reality, submitting neither to time passage,
change, nor actual observation. They are the ﬁrst “black holes” of physics, and the antithesis of our systems
plus their environments which are the core model for systems ecology. No ecosystem could ever exist and be
known to us as an isolated system.
The second law of thermodynamics, though open to question, still retains its status as one of the most fundamental
laws of nature. The law has been expressed in many ways. As indicated above, entropy will always increase
and exergy will always decrease for an isolated system. Time has one direction. Tiezzi (2003b) concludes that entropy
applied to far from thermodynamic equilibrium systems is not a state function since it has intrinsic evolutionary
properties, strikingly at variance with classical thermodynamics. Work capacity is constantly lost as heat at the
temperature of the environment that cannot do work. This implies that all processes are irreversible. The total reversibility
of Newton’s Universe (and even of the relativity theories) is no longer valid (Tiezzi, 2003a,b; 2005). The introduction
of irreversibility has, however, opened for new emergent possibilities. Without irreversibility there would
have been no evolution (Tiezzi, 2005), which is one of the clearest examples of a totally irreversible process. The
directionality of ecosystems that will be discussed in Chapter 6 is also a result of the second law of thermodynamics.
The second law of thermodynamics and the irreversibility of all processes have given the world new, rich, and beautiful
possibilities that a reversible world not could offer.
That is the current dogma, at least, and it is probably true. However, it is useful to at least brieﬂy consider the
attributes of a reversible world. Time travel would be possible; this has been amply fantasized in literature. There
would be no “evolution” in the sense we understand and no path dependency; returning to former states could
be seen as quite interesting and refreshing, especially if those states were more desirable, let us say further from equilibrium,
than their current alternatives. Beauty and rich possibilitiesdwhat could be more enriching and beautiful
than restoration of former systems, and lives, after wars or other privations, have driven them nearer to equilibrium.
Reversibility could produce quite an interesting world, from many perspectives, replacing the humdrum grinding
reality of movement toward equilibrium following exergy seeding.
The decrease in entropy or the increase in the eco-exergy in the biosphere depends on its capacity to capture
energy from the sun and to retransmit it to space in the form of infrared radiation (positive entropy). If retransmission
is prevented, in other wordseif the planet were shrouded in an adiabatic membrane (greenhouse effect)ethen
all living processes would cease very quickly and the system would decay toward the equilibrium state, i.e., toward
thermal death. A sink is just as necessary for life as a source to ensure the temperature that is required for carbonbased
life.
Morowitz (1968) continues that all biological processes depend on the absorption of solar photons and the transfer
of heat to the celestial sinks. The sun would not be an exergy source if there were not a sink for the ﬂow of thermal
2.4 THE SECOND LAW OF THERMODYNAMICS INTERPRETED FOR OPEN SYSTEMS 15
energy. The surface of the Earth is at a constant total energy, reemitting as much energy as it absorbs. The subtle difference
is that it is not energy per se that makes life continue but the ﬂow of energy through the system. The global ecological
system or biosphere can be deﬁned as the part of the Earth’s surface that is ordered by the ﬂow of energy primarily
through the process of photosynthesis.
The physical chemistry mechanism was elegantly described by Nobel Prize winner Albert Szent-Gyo¨rgy as the
common knowledge that the ultimate source of all our energy and negative entropy is the sun. When a photon
interacts with a particle of matter on our globe, it raises an electron or a pair of electrons to a higher energy level.
This excited state usually has a brief life and the electron falls back to its basic level in 10À7
to 10À8
s, giving up
its energy in one way or another. Life has learned to capture the electron in the excited state, to uncouple it from
its partner and to let it decay to its fundamental level through biological processes, using the extra energy for vital
processes.
All biological processes, therefore, take place because they are utilizing an energy source. With exception of the
chemotrophic systems at submarine vents, the ultimate energy source is the solar radiation. Morowitz (1968) notes
that it is this tension between photosynthetic construction and thermal degradation that sustains the global
operation of the biosphere and the great ecological cycles. This entropic behavior marks the difference between
living systems and dead things.
2.5 DISSIPATIVE STRUCTURE
The change in entropy for an open system, dSsystem, consists of an external, exogenous contribution from the environment,
deS ¼ SineSout, and an internal, endogenous contribution due to system state, diS, which must always be
positive by the second law of thermodynamics (Prigogine, 1947, 1955, 1962, 1988, 1997, Prigogine and Stengers, 1979,
1984). Prigogine uses the concept of entropy and the 2nd law far from thermodynamic equilibrium, which is outside
the framework of classical thermodynamics, but he uses the concepts only locally and still under conditions much
closer to equilibrium than presented by biological or ecological systems.
There are three possibilities for the entropy balance:
dSsystem=dt ¼ deS=dt þ diS=dt > 0: (2.10)
dSsystem=dt ¼ deS=dt þ diS=dt < 0; (2.11)
dSsystem=dt ¼ deS=dt þ diS=dt ¼ 0: (2.12)
The system loses order in the ﬁrst case, as is typically understood by the 2nd law. Gaining order (case 2), is only
possible if edeS > diS > 0. Creation of order in a system must be associated with a greater ﬂux of entropy out of the
system than into the system. This implies that the system must be open or at least nonisolated.
Case 3, Eq. (2.11), corresponds to a stationary situation, for which Ebeling et al. (1990) used the following two
equations for the energy (U) balance and the entropy (S) balance:
dU = dt ¼ 0 or deU=dt ¼ ÀdiU=dt ¼ 0: (2.13)
and
dSsystem = dt ¼ 0 or deS=dt ¼ ÀdiS=dt ¼ 0: (2.14)
Usually the thermodynamic processes are isothermal and isobaric. This implies that we can interpret the third
case (Eqs. 2.11e2.13) using free energy:
deG = dt ¼ T diS=dt > 0 (2.15)
This means that a “status quo” situation for an ecosystem requires an input of free energy or exergy to compensate
for the loss of free energy and corresponding formation of heat due to maintenance processes, i.e., respiration and
evapotranspiration. If the system does not receive a sufﬁcient amount of free energy, then entropy will increase. If the
entropy of the system will continue to increase, then the system will die; see Section 2.2. This is in accordance with
Ostwald (1931): life without the input of free energy is not possible.
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS16
An average energy ﬂow of approximately 1017
W from solar radiation ensures the maintenance of life on Earth.
The surface temperature of the sun is 5800 K and of the Earth on average approximately 280 K. This implies that the
following export of entropy per unit of time takes place from the Earth to the open space:
1017
Wð1=5800KÀ 1=280KÞz4:1014
W=K (2.16)
corresponding to 1 W mÀ2
K.
Prigogine uses the term dissipative structure to denote self-organizing systems which tend to move toward a state
of minimum entropy production, thereby indicating that such systems dissipate energy (produce entropy) for the
maintenance of their organization (order). The following conclusions are appropriate:
All living systems, because they are subject to the second law of thermodynamics, are inherently dissipative structures.
The anabolism combats and compensates for the catabolic deterioration of structure; the two processes operate
against one another. Note that the equilibrium “attractor” represents a resting or refractory state, one that is
passively devolved to if system openness or nonisolation are compromised (Jørgensen et al., 1999). The term is
also commonly used to express the situation when a system is actively pushed or “forced” toward a steady state.
Though widespread, we do not subscribe to this usage and make a distinction between steady states and equilibria
for two reasons:
(1) The state-space system theory we outlined in the conservation chapter of Ecosystems Emerging (Patten et al., 1997)
precludes anything in system dynamics but a unique inputestateeoutput relationship. Therefore, given an
initial state, state-space theory asserts that there exists one and only one sequence of inputs that will put an open
system in a given state at a speciﬁed ﬁnal time. For this terminal state to be an “attractor,” many input sequences
would have to be able to place the system in it, and from many initial statesdthe attractor would be hard to
avoid. This is inconsistent with dynamical state theory.
(2) As observed above, a steady state is a forced (nonzero input) condition; there is nothing “attractive” about it.
Without a proper forcing function, it will never be reached or maintained. A steady state that is constant may
appear at equilibrium, but it is really far from equilibrium and maintained by a steady input of energy or
matter. We regard equilibrium as a zero-input or resting condition. What are often recognized as local
attractors in mathematical models really have no counterparts in nature. Steady states are forced conditions,
not to be confused with unforced equilibria which represent states to which systems settle when they are
devoid of inputs. The only true natural attractor in reality, and it is global, is the unforced thermodynamic
equilibrium.
As an ecosystem is nonisolated, the entropy changes during a time interval, dt, can be decomposed into the entropy
ﬂux due to exchanges with the environment, and the entropy production due to the irreversible processes inside the
system such as diffusion, heat conduction, and chemical reactions. This can also be expressed using exergy:
Ex = dt ¼ deEx=dt þ diEx=dt; (2.17)
where deEx/dt represents the exergy input to the system and diEx/dt is the exergy consumed (is negative) by the
system for maintenance, etc. Eq. (2.16) dan exergy version of Eqs. (2.9) and (2.10) dshows among other things that
systems can only maintain a nonequilibrium steady state by compensating the internal exergy consumption with a
positive exergy inﬂux (deEx/dt > 0). Such an inﬂux induces order into the system. In ecosystems, the ultimate
exergy inﬂux comes from solar radiation, and the order induced is, e.g., biochemical molecular order. If
deEx > ediEx (the exergy consumption in the system), the system has surplus exergy input, which may be utilized
to construct further order in the system, or as Prigogine (1980) calls it: dissipative structure. The system will thereby
move further away from thermodynamic equilibrium. Evolution shows that this situation has been valid for the
ecosphere on a long-term basis. In spring and summer, ecosystems are in the typical situation that deEx
exceeds ediEx. If deEx < ediEx, the system cannot maintain the order already achieved, but will move closer to
the thermodynamic equilibrium, i.e., it will lose order. This may be the situation for ecosystems during fall and
winter or due to environmental disturbances.
2.6 QUANTIFICATION OF OPENNESS AND ALLOMETRIC PRINCIPLES
All process rates are in physics described as proportional to a gradient, a conductivity or inverse resistance and to
the openness, compare for instance with Fick’s laws of diffusion and Ohm’s law. The import and export from and to
2.6 QUANTIFICATION OF OPENNESS AND ALLOMETRIC PRINCIPLES 17
an ecosystem is, therefore, dependent on the differences between the ecosystem and the environment, as well as of
openness. For instance, the rate of the reaeration process of a water stream can be expressed by the following
equation:
Ra ¼ V dC=dt ¼ KaðTÞAðCsÀ CÞ (2.18)
or
dC = dt ¼ KaðTÞðCsÀ CÞ=d (2.19)
where Ra is the rate of reaeration, Ka a temperature constant for a given stream, A the area ¼ V/d, V the volume, d the
depth, Cs the oxygen concentration at saturation, and C the actual oxygen concentration. Ka is here the “conductivity”
or inverse resistance. The faster the water ﬂow in the stream, the higher is Ka. (Cs e C) is the gradient and A, the
area, is the openness. Numerous expressions for rates in nature follow approximately the same linear equation.
The surface area of the species is a fundamental property. The surface area indicates quantitatively the size of the
boundary to the environment. Flow rates are often formulated in physics and chemistry as area times a gradient,
which can be utilized to set up useful relationships between size and rate coefﬁcients in ecology. For instance,
loss of heat to the environment must be proportional to the surface area and to the temperature difference, according
to the law of heat transfer. The rate of digestion, the lungs, hunting ground, etc. are, on the one hand, determinants
for a number of parameters (representing the properties of the species), and on the other hand, they are all dependent
on the size of the organism. It is, therefore, not surprising that many rate parameters for plants and animals are
highly related to the size, which implies that it is possible to get very good ﬁrst estimates for most parameters based
only on the size. Naturally, the parameters are also dependent on several characteristic features of the species, but
their inﬂuence is often minor compared with the size, and good estimates are valuable in many ecological models, at
least as a starting value in the calibration phase. It is possible, however, to take these variations into account using a
form factor ¼ surface/volume. This form factor may vary considerably among species.
The conclusion of these considerations must, therefore, be that there should be many parameters that might be
related to simple properties, such as size of the organisms, and that such relationships are based on fundamental
biochemistry and thermodynamics (Figs. 2.2e2.6).
Generation Time
100 m
10 m
1m
10 cm
1 cm
1 mm
100 µm
10 µm
1 µm
a
b
c
d
f
e
g
h
i
j
k
l
m
Length
1 hour 1 day 1 week 1 month 1 year 10 years 100 years
FIGURE 2.2 Length and generation time plotted on logelog scale: (a) Pseudomonas, (b) Daphnia, (c) bee, (d) houseﬂy, (e) snail, (f) mouse, (g) rat,
(h) fox, (i) elk, (j) rhino, (k) whale, (l) birch, and (m) ﬁr (Peters, 1983). Reproduced from Jørgensen, S.E., 2000a. Principles of Pollution Abatement.
Elsevier, Oxford, UK, 520 pp.
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS18
Above all there is a strong positive correlation between size and generation time, Tg, ranging from bacteria to the
biggest mammals and trees (Bonner, 1965). This relationship can be explained using the relationship between size
(surface) and total metabolic action per unit of body weight mentioned above. This implies that the smaller the organism
the greater the metabolic activity. The per capita rate of increase, r, deﬁned by the exponential or logistic
growth equations is again inversely proportional to the generation time:
dN = dt ¼ rN (2.20)
dN = dt ¼ rNð1À N=KÞ (2.21)
3
2
1
0
-1
-2
-3
Log W (g)
Unicellular
Heterotherms
Homoetherms
Logrday-1
-16 -14 -12 -10 -8 -6 -4 -2 0 4 6 8
FIGURE 2.3 Intrinsic rate of natural increase against weight for various animals. After Fenchel (1974). Source: Fundamentals of Ecological
Modeling by Jørgensen and Bendoricchio.
+ phytoplankton
+ clams
+ oysters
+ dogs
+ mice
+ homo sapiens
101
10-1
10-2
10-3
10-4
1
1µm 100µm 1cm 10cm 1m
Excretionrate(1/24h)
FIGURE 2.4 Excretion of Cd (24 h)À1
plotted against the length of various animals: (1) Homo sapiens, (2) mice, (3) dogs, (4) oysters, (5) clams,
and (6) phytoplankton (Jørgensen, 1984).
2.6 QUANTIFICATION OF OPENNESS AND ALLOMETRIC PRINCIPLES 19
where N is the population size, r the intrinsic rate of growth, and K the environmental carrying capacity. While r is
related to the size of the organism, Fenchel (1974), showed that it falls into three groups: unicellular, heterotherms,
and homeotherms (see Fig. 2.3).
The same allometric principles are expressed in the following equations, giving the respiration, food consumption,
and ammonia excretion for ﬁsh when the weight, W, is known:
Respiration ¼ constant Â W0:80
(2.22)
100000
10000
1 µm 10 µm 100 µm 1 mm 1 cm 10 cm 100 cm
1000
100
10
CF
5
4
3
2
1
Length
FIGURE 2.6 Biological concentration factor (BCF) denoted CF for Cd versus length: (1) goldﬁsh, (2) mussels, (3) shrimps, (4) zooplankton, (5)
algae (brown-green). After Jørgensen (1984). Source: Fundamentals of Ecological Modeling by Jørgensen and Bendoricchio.
10000
1000
100
10
1
+ phytoplankton
+ clams
+ oysters
Length
Uptakerate
1µm 100µm 1cm 10cm
FIGURE 2.5 Uptake rate (mg gÀ1
(24 h)À1
) plotted against the length of various animals (Cd): (1) phytoplankton, (2) clams, (3) oysters. After
Jørgensen (1984). Source: Fundamentals of Ecological Modeling by Jørgensen and Bendoricchio.
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS20
Feed Consumption ¼ constant Â W0:65
(2.23)
Ammonia Excretion ¼ constant Â W0:72
(2.24)
It is also expressed in the general equation (Odum, 1959, p. 56):
m ¼ kWÀ1=3
(2.25)
where k is roughly a constant for all species, equal to approximately 5.6 kJ/g2/3
day, and m the metabolic rate per
unit weight W.
Similar relationships exist for other animals. The constants in these equations might be slightly different due to
differences in shape, but the equations are otherwise the same. All these examples illustrate the fundamental relationship
in organisms between size (surface) and biochemical activity. The surface determines the contact with the
environment quantitatively, and by that the possibility of taking up food and excreting waste substances.
The same relationships are shown in Figs. 2.4e2.6, where biochemical processes involving toxic substances are
applied as illustrations. The excretion rate and uptake rate (for aquatic organisms) follow the same trends as the
metabolic rate. This is of course not surprising, as excretion is strongly dependent on metabolism and the direct
uptake dependent on the surface.
These considerations are based on allometric principles (see Peters, 1983; Straskraba et al., 1999), which in other
words can be used to assess the relationship between the size of the units in the various hierarchical levels and the
process rates, determining the need for the rate of energy supply. All levels in the entire hierarchy of an ecosystem
are, therefore, due to the hierarchical organization, characterized by a rate which is ultimately constrained by their
size.
Openness is proportional to the area available for exchange of energy and matter, relative to the volume ¼ the
inverse space scale (LÀ1
). This may also be expressed as the supply rate¼k*gradient*area relative to the rate of needs,
which is proportional to the volume or mass. An ecosystem must, as previously mentioned, be open or at least nonisolated
to be able to import the energy needed for its maintenance. Table 2.3 illustrates the relationship between
hierarchical level, openness, and the four-scale hierarchical properties presented in Simon (1973). The openness is
expressed here as the ratio of area to volume.
For the higher levels in the hierarchy, approximate values are used. As we move upwards in the hierarchy, the
exchange of energy (and matter) becomes increasingly more difﬁcult due to a decreasing openness. It becomes
increasingly more difﬁcult to cover needs. This explains why energy density, time scale, and dynamics decrease
according to the inverse space scale or openness, which are expressed differently as the rates are adjusted to
make the possible supply of energy sufﬁcient (Fig. 2.7). These considerations are consistent with the relationship
between size and time scale of levels in the hierarchy, as presented by O’Neill et al. (1986) and Shugart and West
(1981).
TABLE 2.3 Relationship Between Hierarchical Level, Openness (Area/Volume Ratio), and Approximate Values of the Simon’s (1973)
Four Scale-Hierarchical Properties: Energy/Volume, Space Scale, Time Scale, and Behavioral Frequency.
Hierarchical Level Openness1,3
(A/V, mL1
) Energy2
(kJ mL3
) Space Scale1
(m) Time Scale1
(sec) Dynamics3
(g mL3
sec)
Molecules 109
109
10À9
<10À3
104
e106
Cells 105
105
10À5
10e103
1e102
Organs 102
102
10À2
104
e106
10À3
e0.1
Organisms 1 1 1 106
e108
10À5
e10À3
Populations 10À2
10À2
102
108
e1010
10À7
e10À5
Ecosystems 10À4
10À4
104
1010
e12
10À9
e10À7
1
Openness, spatial scale, and time scale are inverse to hierarchical scale.
2
Energy and matter exchange at each level depend on openness, measured as available exchange area relative to volume. Electromagnetic energy as solar photons comes in small
packages (quanta, hn, where h is Planck’s constant and n is frequency), which makes only utilization at the molecular level possible. However, cross-scale interactive coupling
makes energy usable at all hierarchical levels.
3
Openness correlates with (and determines) the behavioral frequencies of hierarchical levels.
2.6 QUANTIFICATION OF OPENNESS AND ALLOMETRIC PRINCIPLES 21
Exchange of matter and information with the environment of open systems is in principle not absolutely necessary,
thermodynamically, as energy input (nonisolation) is sufﬁcient (the system is nonisolated) to ensure maintenance
far from equilibrium. However, it often gives the ecosystem some additional advantages, for instance,
through the input of chemical compounds needed for certain biological processes or by immigration of species offering
new possibilities for a better ordered structure of the system. All ecosystems are open to exchange of energy,
matter, and information with their environment.
The importance of the openness to matter and information is clearly illustrated in the general relationship
between number of species, SD (species diversity), of ecosystems on islands and the area of the islands, A:
SD ¼ C Â Az
ðnumberÞ; (2.26)
where C and z are constants. The perimeter relative to the area of an island determines how “open” the island is to
immigration or dissipative emigration from or to other islands or the adjacent continent. The unit (LÀ1
) is the same as
the above used area to volume ratio as a measure of openness.
Different species have very different types of energy use to maintain their biomass. For example, the blue whale
uses most of the energy available (97%) for increasing biomass and only 3% for reproduction. Whales are what we
call K-strategists, deﬁned as species having a stable habitat with a very small ratio between generation time and the
length of time the habitat remains favorable. This means that they will evolve toward maintaining their population
close to the carrying capacity. K-strategists are in contrast to r-strategists which are strongly inﬂuenced by any environmental
factor. Due to their high growth rate they can, however, utilize suddenly emergent favorable conditions
and increase the population rapidly. Many ﬁshes, insects, and other invertebrates are r-strategists. The proportion of
energy going into reproduction can be over 50%.
2.7 THE CELL
The cell is the basic biological unit, as the elementary particles and the elements are the basic units of chemistry. In
spite of the enormous variation in the structure and function of different organisms, the fundamental unit, the cell, is
with some variations basically the same. Why is the cellular structure the same? First, early in evolution the cell
demonstrated its functionality. But the use of structural units of small size has also ensured effective transportation
Holon
Level
(-1)
Holon Level (+1)
Holon Level (0)
Spatial Extent: Small
Frequencies: High
Constraints
Spatial Extent: High
Frequencies: Low
Minor Interactions
Multiple Interactions
Filter
FIGURE 2.7 A schematic representation of interacting hierarchical levels.
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS22
by diffusion. Most cells have a diameter between 1 and 20 mm (Table 2.4). Cells have, therefore, a relatively high
openness (see Table 2.3), which is necessary for the biochemistry of organisms to work. The hierarchical structure,
which was presented in Fig. 2.7 and will be further discussed in Chapters 5 and 8, is a precondition for the needed
openness for each level in the hierarchy.
Let us, however, demonstrate the importance of openness by focusing on the cell. The problem for the cells is to
have an openness that would match the need for diffusive transportation for the matter needed for the biochemical
syntheses that take place in the cells, ﬁrst of all for the synthesis of proteins.
Protein synthesis takes place in about 10 steps from primary gene expression in DNA inside the nucleus to ﬁnal
production of the mature protein at its ﬁnal destination outside the nucleus but within the plasma membrane. First
there is transcription in which the DNA region encoding the gene is transcribed into a complementary messenger
RNA (mRNA). Next, in eukaryotes, initial pre-mRNA is spliced and processed to mature mRNA. This is exported
across the nuclear envelope into the cytosol. There, codons in ribosomes progressively translate the genetic code into
a mature cytosolic protein. This is followed by several steps of sorting and modiﬁcation involving cytoplasmic
ultrastructures such as the endoplasmic reticulum and Golgi apparatus. All the genes of an organism make up its
genome. Of these, only certain ones will be expressed at a given time or for a speciﬁc cell type. Some genes which
perform basic functions are always required; these are constitutive or housekeeping genes. Others are expressed
only under certain conditions (Klipp et al., 2005, pp. 45e47).
Openness in the scenario just given is particularly pronounced at the nuclear and cytoplasmic boundaries, but in
fact is expressed all along the way as intracellular structures receive, process, and pass along the various intermediary
products in protein synthesis. Is the openness sufﬁcient to ensure uptake of oxygen and nutrients needed for
protein synthesis? Matter needed for the biochemistry is proportional to the volume (we presume that the cell is a
sphere where d is cell diameter):
Volume ¼ pd3

6 (2.27)
The transport from the surface to the cell takes place by a fast, active transport and the concentration at the surface
is, therefore, 0. The area of the sphere is pd2
. The ﬂux of matter toward the cell is considered constant, which implies
that the concentration gradient will decrease with the distance from the cell in the exponent 2:
dC = dr ¼ k rÀ2
(2.28)
where r is the distance from the center of the cell (radius). The concentration is 0 at the surface of the cell, i.e., r ¼ d/2.
The concentration at the distance r from the center of the cell Cr can be found after differentiation of Eq. (2.28) to be:
Cr ¼ Cð1À d=2rÞ. (2.29)
TABLE 2.4 Some Differences Between Prokaryotic and Eukaryotic Cells.
Prokaryotes Eukaryotes
Size 1e10 mm 10e100 mm
Nucleus None. The chromosomal region is called nucleolus Nucleus separated from cytoplasm by nuclear
envelope
Intracellular organization Normally, no membrane-separated compartments and
no supportive intracellular framework
Distinct compartments, e.g., nucleus, cytosol with
cytoskeleton, mitochondria, endoplasmic reticulum,
Golgi complex, lysosomes, plastids
Gene structure No introns; some polycistronic genes Introns and exons
Cell division Simple Mitosis or meiosis
Ribosome Large 50S subunit and small 30S subunit Large 60S subunit and small 40S subunit
Reproduction Parasexual recombination Sexual recombination
Organization Mostly single-cellular Mostly multicellular, with cell differentiation
After Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H., 2005. Systems Biology in Practice. Concepts, Implementation and Application. Wiley-VCH Verlag GmbH,
Weinheim, Germany, 465 pp.
2.7 THE CELL 23
The diffusion rate, corresponding to the uptake rate is a diffusion coefﬁcient (D) times the concentration gradient
(dC/dr ¼ Cd/2r or at the surface ¼ 2C/d) times the openness ¼ area ¼ pd2
, or therefore 2pdDC, where D is the diffusion
coefﬁcient and C the concentration in the environment. The uptake rate relative to the need, denoted UR/N, is
found as:
UR = N ¼ 12DC
À
fd2
Á
(2.30)
where f is the need per unit of time and volume. The relative uptake rate will be four times smaller, if the diameter is
doubled. Relatively small cell sizes are necessary to obtain a sufﬁcient relative uptake rate. This equation demonstrates
the importance of the cell size and explains, therefore, indirectly the hierarchical structure because small cells
are the prerequisite for a sufﬁcient supply of nutrients, although there are many additional explanations.
2.8 WHAT ABOUT THE ENVIRONMENT?
Openness is a requisite for moving substance across boundaries, and boundaries imply an insideeoutside dichotomy.
That is, in departure from thermodynamic equilibrium energy and matter move from outside to inside and
dissipation signiﬁes movement in the reverse direction, from interiors to exteriors.
The term “environment” has appeared 46 times previously in this chapter, in a book on ecology, the biological
science of environment, and yet we have not once anywhere done anything explicitly with this concept except
take it for granted as a reference source and sink from which some older more or less accepted thermodynamics,
without its modern challenges, proceeds to operate in the organization of ecosystems. We use the concept of environment,
but have not attempted to deﬁne it scientiﬁcally or explore it in any deep way. There is little in theoretical
ecology that elaborates it in substantive scientiﬁc terms. It is just a convenient category of “surroundings” that openness
requiresdsome place to derive inputs and exhaust outputs.
Particular scales aside, it is relevant in the context of openness to ask the hard questiond “What is environment?”
We look around the room or outside the window and see what everyone agrees is “environment.” Seeing is only part
of it, however; there is also touching and smelling, etc. In other words, there are sensory stimuli involved. What
about these? Our household pets and the plants in the garden that began this chapter have considerably different
sensory apparatus from us. Does that mean environment is relative, something that can only be deﬁned by perception?
Or are certain aspects of it accessed differently by different open systems? It is clear from the perspective of
reality as a collection of physics’ particles, and from masseenergy conservation, that what comes to me at a given
moment as visual, auditory, tactile, etc. stimuli cannot also come to you or your dog or plant. At this level, it must be
acknowledged that there is a certain uniqueness that attaches to the “environments” of particular open-system receivers
of sense-data. Not only is this true for sensory stimuli, but also for the masses of matter that enter our bodies
as food and exit as biodegradable products useful as food for other organisms. So, environment, it would seem,
courses in and out of open systems, and the ultimate uniqueness of the substance and signals both confer a central
place on the open system as the focal arbiter of environment. Afferent input environments coming from the past are
originated the moment a unit of high-quality energy or matter crosses the boundary of a receiving open system. This
increases the exergy and lowers the entropy of the receiving system. Reciprocally, efferent environments that unfold
with the future are founded the moment a unit of energy or matter exits the said open system. This dualistic concept
of environment is operationalized in the mathematical theory of environs (Patten, 1978; 1982), about which now
numerous papers have been published describing the properties of such structures. The dualism is central, and
so is the unity of the focal entityeenviron triad. Environments and the things with which they are associated can
never be separated in environ theorydthey are a unit of nature, however intractable, but sometimes with surprising
holistic properties which are explored in detail in Chapter 4.
2.9 CONCLUSION
Openness of systems to exchange of energy and matter implies certain foundational properties in natural
organization:
• It is a necessary but not sufﬁcient property for ecosystems.
• It presumes boundaries;
• Boundaries imply bounded entitiesdsomething kept in;
2. ECOSYSTEMS HAVE THERMODYNAMIC OPENNESS24
• Entities imply an elemental discreteness in natural organization (note Table 2.3);
• Therefore, human sensory apparatus operates correctly in perceiving an essentially particulate world.
Or does it? . since it is also true that:
• Boundaries imply environmentsdsomething kept out;
• Environments are unbounded, thus inherently nondiscrete;
• Therefore, human perception is mismatched to environmental reality.
This may be why we know more about disconnected objects than we do about objecteenvironment relationships,
and why science is more reductive than holistic. It appears that the property of openness, the subject of this chapter,
returns us to the same kind of enigma as the waveeparticle duality, giving ecology a deep challenge for its future to
unravel.
2.9 CONCLUSION 25
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C H A P T E R
3
Ecosystems Have Ontic Openness
.next to music and art, science is the greatest, most beautiful and most enlightening achievement of the human spirit Popper (1990)
3.1 INTRODUCTION
This chapter’s title may mean little to many persons, yet the essence may be understood fairly easily on an intuitive
basis. The adjective “ontic,” which hardly appears in any dictionary, clearly relates to the term ontology, which
is used in philosophy in its widest sense to designate “the way we view the world and how it is composed.” Ontic
bears the slight difference that it refers to intrinsic properties of the world as we construct it and its behavior, such
that it addresses phenomenology as well. Therefore, this chapter complements the concepts of thermodynamic
openness addressed in the previous chapter, by including the physical openness available to ecosystem
development.
In fact, everybody knows something about openness. We know how it is to be open to another person’s opinions,
to be open minded, or open to new experiences. We enjoy that surprising things may happen on our (ﬁeld) trips and
journeys (in nature). In fact, any person who has tried to plan exact details for a trip into the wilderness will know
how difﬁcult this is. First, we may address the aspect of realizing such a trip and stress that this also implies the
acceptance of the fact that unexpected things may or rather will occur. But, second, we have also to address the
fact that once an event occurs, it is an outcome of many unexpected events. It is impossible to predict which one
and how often such events actually occur. We may expect to bring extra dry socks to use after one incident, an unexpected
event. How many persons will be able to foresee exactly how many pairs to bring? Or in other cases, we
may return with unused socks but found that we needed extra shirts instead. Any of us will know that it is eventually
not possible to make such a detailed plan.
In fact, one could have chosen another title to the present chapter: “anything maydbut does notdhappen.” Of
which the ﬁrst part deals with, as we shall see in the following, the enormous number of possibilities that exist in
general and also in biological systems. The second part indicates that all possibilities have not been realized, partly
because it is not physically possible, and partly due to constraints that are described in other chapters of this book.
This chapter is about the ontic openness of ecosystems. It relates directly to the theme of this book and the systemness
of ecosystems because ontic openness results, in part, due to the complex web of life constantly combining,
interacting, and rearranging in the natural world to form novel patterns. Furthermore, ontic openness is at least a
partial cause of indeterminacy and uncertainty in ecology and thus the reason that we are not able to make exact
predictions or measurements with such a high accuracy as, for instance, in physical experiments. Therefore,
when understanding ecosystems from a systems perspective, one cannot overlook the importance of physical
openness.
3.2 WHY IS ONTIC OPENNESS SO OBSCURE?
In referring to the above title of this chapter we have already mentioned that it likely will pose a question to the
vast majority of readers, not only the ecologically oriented ones: what is the meaning of the title of this chapter? We
have tried to foresee this question already by giving a ﬁrst vague and intuitive explanation. We guess it is likely that
27
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00003-6
only a few readers have met this “phenomenon” before as far as the term ontic openness is concerned. We also expect
that very few, if any, of the readers are familiar with texts that deal with the role of ontic openness in an ecological
context.
To our knowledge, no such thorough treatment of this topic exists. Rather a number of treatments of more or less
philosophical character existdall of which may be taken into accountdand which all together may add up to a composite
understanding of what ontic openness may mean and what its importance and consequences to ecological
science may be.
Should we attempt to further explain ontic openness very brieﬂy (which is impossible) we would start with openness,
and turn the attention to another related word like open-minded. We normally use this word to designate a
person who is willing to try out new things, accept novel ideas, maybe a visionary person able to think that the world
could be different, that matters may be interdependent in other ways than in which we normally think. Many scientists
make their breakthrough, thanks to such mental openness. Discoveries are often unexpected or unplannedd
a phenomenon known in the philosophy of science as serendipities. Kuhn also addresses this issue of the scientiﬁc
procedure when he stresses that paradigm shifts in the evolution of science involves that scientists come to look
at the same object from a different angle or in a different manner.
We now would like, if possible, to remove the psychology element. If we remove the role of subjectivity, i.e., that
openness relies on one or more person’s ability or willingness to see that the surrounding world may be different or
could have other possibilities realized than hitherto, then we are really on the right track.
We are now left with an objective part of openness. If we can now accept the physical existence of this and that it is
a property that penetrates everything, then we are really getting there. The openness is an objectively existing feature
not only of the world surrounding us but also ourselves and our physical lives (e.g., biochemical individuality introduced
by Williams (1998)). This is the ontic part of the openness.
Another reason for ontic openness not to be so commonly known among biologist and ecologist is the fact that
the progenitors of this concept were dominantly physicists and in particular those in the areas of quantum mechanics,
particle physics, and relativity theory. Furthermore, we typically do not view these areas as being
directly relevant to biology or ecology. Also, these theories are not easy to communicate to “outsiders,” so
even if ecology is considered to be a highly trans-, inter-, and multidisciplinary science, it is perfectly understandable
that no one has thought that these hard-core subdisciplines of physics today could possibly have a message
for ecology.
Luckily, one might say, some of the physicists from these areas turned their attention in other directions
and started speculating about the consequences of their ﬁndings to other areas of natural science such as
biology. On several occasions we have found physicists wondering about the distinction between the physical
systems and living systems, such as Schro¨dinger’s What is life. Living systems are composed of basically
the same units, atoms, and molecules, and yet they are so different. One physicist, Walter Elsasser, will receive
an extra attention in this chapter. Studying his works, in particular from the later part of his productive career,
may turn out to be a gold mine of revelations to any person interested in how biology differs from physics and
about life itself.
Still have not understood or got the idea of what ontic openness is about? Do not worrydyou most probably have
experienced it and its consequences already. Let us investigate some well-known examples.
Most Ecologists Have Experienced Ontic Openness Already!
Most ecologists will have met ontic openness alreadydsomewhat in disguisedas often our background comes
from the gathering of empirical knowledge, an experience we may have achieved through hard ﬁeldwork.
To start, let us consider a hypothetical “test ecologist.” Given the information about latitude and a rough characteristic
ecosystem typedterrestrial or aquaticdshe will be able to decide whether she is expert “enough” in the area
to forecast the system state or if she prefers to enlist aid from a person considered to be more knowledgeable in the
area. If deciding to be an “expert,” then she will for sure be able to tell at least something about the basic properties of
the ecosystem, such as a rough estimate of the number and type of species to be expected. Given more details, such
as exact geographical position, we may now narrow in on ideas considering our background knowledge. There will
be a huge difference in organisms, species composition, production, if we are in the arctic or in the tropics. Likewise,
being, for instance, in the tropics there will be a huge difference between a coral reef in the Paciﬁc Ocean or in a
mangrove swamp in the Ruﬁji River Delta. We will be able to begin to form images of the ecosystem in our minds,
conceptual models of trophic interactions, community linkages, and functional behavior. Meanwhile, we know very
3. ECOSYSTEMS HAVE ONTIC OPENNESS28
well that to get closer in details with our description we will need additional knowledge, for instance, about ecological
drivers, such as hydrodynamics, depth, and other external inﬂuences, such as human impacts from ﬁsheries,
loadings of both organic and inorganic in type, etc.
Nevertheless, given as much information as we possibly can get and, for instance, focusing in on a particular
geographic position, such as the Mondego River Estuary in Portugal, we will not be able to answer simple questions
accurately such as Which plant species are present at a certain locality, how are they distributed, or what are their
biomass and production? We will more likely be able to give an answer something like “that under the given conditions
we would consider it to be most likely that some rooted macrophyte will be present and that it would probably
be of a type that do not break easily, probably with band-shaped leaves, probably some species of Zostera, etc.
We will be able, based on experience and knowledge, to give only an estimate in terms ofdwhat we shall later call
the propensitydthe system to be of a certain ‘kind’.” BUT we will never be completely sure. This is due to ontic
openness.
Examples from the World of Music
Sometimes, when introducing new concepts, it is useful to make an entrance from an unexpected and totally
different angle. In this case, we will consider the world of musicda world with which most people are familiar
and have speciﬁc preferences. We only know very few people to whom music does not say anything and literally
does not “ring a bell.”
We considerdin a Gedanken Experimentdthe situation of an artist set to begin a new composition. To illustrate
the universality of the approach we may illustrate the situation by the possible choices in two situationsda small
etude for piano or a whole symphony. We shall start by looking at both the situations from a statistical and probabilistic
angle. The two situations may look quite different from a macroscopic point of view, but in fact they are
not.
In the case of a short piece for piano, a normal house piano has a span of approximately 7 (or 7¼) octaves of
12 notes each giving 84 (or 88) keys in all. If an average chord on the piano has ﬁve notes in it, then it is theoretically
possible to construct 3,704,641,920 or approx 3.7 billion chords on it (4.7 billion in the case of 88 keys).
(Note that we already here deal with a subset of the 84! ¼ 3.3 Â 10126
possibilities). Meanwhile, if the assumption
that a chord consists of ﬁve notes on average is valid, then it does not take long to reach almost the same level of
complexity sensu lato. Putting a small piece of music together, assuming that we work in a simple 4/4 and change
chords for each quarter, after 16 notes or 4 bars have reached a level 126 Â 10153
of possible ways to construct the
music. Many of these possible combinations of notes and chords would not sound as music at all, and luckily we
are faced with constraints. A physical constraint, such as the human physiology, will serve to limit the number of
notes than can be accessed in a single chord (a good piano player will be able to span maybe over one octave per
hand, thereby lowering the number of possible variations considerably). Psychological constraints of various
kinds do also exist depending on the decisions of the composer or our personal tastedwe do want the music to
sound “nice.”
The situation does not change a lot considering a symphony orchestra although complexity really rises much
faster. Considering a relatively small symphony orchestra of say 50 musiciansdeach having a span of approximately
3 octaves or (36 notes)deven before starting we have 3650
or 6.5 Â 1077
possibilities of how the ﬁrst chord may
sound. By the second note we have already exceeded any of the above numbers.
Almost no physical constraints exist in this case. The task of the composer is very simple, picking a style of music
like the choices between classic or 12-tone music, between piano concerto, opera, or string quartets. The point is now
that for each note, for each chord, there are many possibilities of what the composer could write on the sheet, but in
fact only one ends up being chosen, one “solution” out of an enormous number of possibilities. As we shall see later,
the number of possibilities to choose from is so large (immense) that it makes no physical sense. Therefore, in the end
the choice of the composer is unique. The fact that we anyway will be able to determine and talk about such a thing
like style is that the composers have had a tendency (see propensities later) to choose certain combinations out the
possible.
Let us end this section with a situation most people will know. Considering yourself a skilled person, familiar
with the many styles of music, you listen to an unknown piece of music in a radio broadcast. It is a very melodic
piece of music in a kind of style you really like and with which you are familiar. You, even without knowing the
music, start to hum along with some success, but eventually you will not succeed to be totally right throughout
the whole piece. Do not worry, it is not you that is wrong, neither is the musicdyou are just experiencing the ontic
openness of someone else, in this case the composer.
3.2 WHY IS ONTIC OPENNESS SO OBSCURE? 29
3.3 ONTIC OPENNESS AND THE PHYSICAL WORLD
As mentioned above, a number of treatments of this topic exist that all add up to our possible understanding of
the importance of ontic openness and what it means in the context of our everyday life. Putting them together and
taking the statements to a level where we really see them as ontological features, i.e., as ontic, we will be, on one hand
forced to reconsider what we are doing, on the other hand, we can look upon the world, and in particular the uncertainties,
the emergent properties that we meet, in a much more relaxed manner.
Unfortunately, to ecology and the ecologists, as previously mentioned, the statements that have already been
made on openness almost all originate from physicists. In fact, seen from a philosophy of science point of view,
this means that the statements are often dominated by arguments deeply rooted in reductionist science, often literally
close to an atomistic view. Interesting things happen when the arguments are taken out of the reductionist realm
to other levels of hierarchy, i.e., the arguments are taken out of their physical context and extended to biology and
eventuallydfollowing our purpose of the present bookdinto ecology.
The basic contributions we think of here may be represented by a number of scientists. A sketch of a few essential
ideas that it may be possible to relate to the issue of ontic openness as well as the originators is given in Table 3.1.
In the following, we will take a more detailed look at a few of these perspectives. From the table it is evident that
we deal with quite recent contributions and some noteworthy overlaps in time. It would, of course, be interesting to
know if and how these persons have inﬂuenced each other, a thing which may become clear only from close, intensive
studies of the time development of their works and biographies. Meanwhile, this would be a tedious task and
the possible mutual inﬂuence has not been considered in this paper.
It Is Not Possible to Measure Everything
In the world of physics, the importance of uncertainty and our interference with systems through experiments
has been recognized for less than a century. The introduction of concepts such as complementarity and
TABLE 3.1 A Non-exhaustive List of Various Authors Who Have Addressed the Issues of Ontic Openness of Natural, Physical, and
Biological Systems.
Originator Era Idea Remark
N. Bohr 1885e1962 Complementaritydthe idea that the more descriptions
are needed
Derived from the waveeparticle duality
E. Schro¨dinger 1887e1961 Order from disorder and order from order Relates to Elsasser’s immense numbers and
historical aspects
W. Heisenberg 1901e1976 The principle of uncertainty or indeterminacy, e.g., the
simultaneous determination of position and
momentum of an electron is not possible
Argued to be valid also for ecosystems by
Jørgensen
K.R. Popper 1902e1994 a) End of ﬁxed probabilitiesd we need to work with
propensities
b) The open universe
Basic assumption behind Ulanowicz’s concept
of ascendency
W.M. Elsasser 1904e1991 Biological systems are heterogeneous and therefore
possess immense possibilities which are coped with by
agency and history
The combinatorial explosions shaping this
phase-space occurs at almost any level of
hierarchy
I.A. Prigogine 1917e2003 The understanding of biological systems as dissipative
structures and far from equilibrium systems
Assumes that the “Onsager relation” may be
extended to the conditions of life (Chapter 6)
C.S. Holling 1930e The idea that evolution happens through breakdowns
that opens up to new possibilities through an ordered/
cycling process
See creative destruction (Chapter 7). Similar to
H.T. Odum pulsing paradigm
S.E. Jørgensen 1934e2016 The Heisenberg uncertainty principle extended to
ecosystem measurements
S.A. Kauffman 1939e The continuous evolution of biological systems toward
the edge of chaos
At ﬁrst, the ideas may appear disparate, but in fact all illustrate the necessity to view systems as ontically open.
3. ECOSYSTEMS HAVE ONTIC OPENNESS30
irreversibility has offered solutions to many problems but has simultaneously involved the recognition of limits to
the Newtonian paradigm. Below, we deal with some important ﬁndings in physics from the 20th century such as
the Heisenberg uncertainty principle, the Compton effects, and the relaxation of systems that may have future parallels
in ecology.
The Heisenberg Principle
The Heisenberg uncertainty relation tells that we cannot know exactly both the position and the velocity
of an atom at the same time. At the instant when position is determined, the electron undergoes a discontinuous
change in momentum. The smaller the wavelength of the light employed, the greater the change.
Thus, the more precise the position is determined, the less precise the momentum is known, and vice versa
(Box 3.1).
The Compton Effect
The Compton effect deals with the change in wavelength of light when scattered by electrons. According to the
elementary laws of the Compton effect, p1 and l1 stand in the relation:
p1 Â Dl1yh (3.2)
DE1 Â DT1yh (3.3)
where p1 is the momentum of the electron, Dl1 is the wavelength increase due to the collision, E1 is the energy, and T1
is the time.
Eq. (3.2) corresponds to Eq. (3.3) and shows how a precise determination of energy can only be obtained at the cost
of a corresponding uncertainty in the time (Box 3.2).
Spin Relaxation
Spin relaxation is possible because the spin system is coupled to the thermal motions of the “lattice,” be it
gas, liquid, or solid. The fundamental point is that the lattice is at thermal equilibrium; this means that the
probabilities of spontaneous spin transitions up and down are not equal, as they were for rf-induced transitions
(Box 3.3).
Given the remarks made at the start of this section, one may indeed start to wonder and speculate about the relations
of these physical systems that obey universal laws when involved at the level of chemistry and biology and
how or if these affect living systems at all. This is exactly what the physicist Walter M. Elsasser did, and it may be
worthwhile to spend a few moments studying his work and conclusions.
BOX 3.1
T H E H E I S E N B E R G U N C E R T A I N T Y P R I N C I P L E O R P R I N C I P L E O F
BOX 3.1
T H E H E I S E N B E R G U N C E R T A I N T Y P R I N C I P L E O R P R I N C I P L E O F
BOX 3.1
T H E H E I S E N B E R G U N C E R T A I N T Y P R I N C I P L E O R P R I N C I P L E O F
BOX 3.1
T H E H E I S E N B E R G U N C E R T A I N T Y P R I N C I P L E O R P R I N C I P L E O F
I N D E T E R M I N A C Y
The basic proof shows that the product of position and
momentum will always be larger than the Planck’s constant.
This is given explicitly by the following mathematical
terms:
Ds Â Dp !
1
2
Z ¼
h
4p
(3.1)
where s refers to space, p is the momentum, and h is the
Planck’s constant (6.626 Â 10À34
J s).
3.3 ONTIC OPENNESS AND THE PHYSICAL WORLD 31
3.4 WHAT REALLY DIFFERS BETWEEN PHYSICS AND BIOLOGY:
FOUR PRINCIPLES OF ELSASSER
The one contributor from Table 3.1 that literally takes the step from physics into biology was Walter M. Elsasser
whose “roaming” life is quite impressive. The details of his life are described in a biography1
by Rubin (1995), who
was acquainted with Elsasser the last 10 years of his life. Most of the information on Elsasser mentioned below is
based on this biography and Elsasser’s own autobiography (Elsasser, 1978). From these works, one cannot help sense
that Elsasser’s contributions were sparked by ontic openness on his own “body and soul” throughout his career.
Rubin (1995) summarized Elsasser’s (1987) four basic principles of organisms: (1) ordered heterogeneity, (2) creative
selection, (3) holistic memory, and (4) operative symbolism. The ﬁrst principle is the key reference to ontic openness,
while the other points address how this order arises in this “messy” world of immense numbers. In other words, the
latter three seem more to be ad hoc inventions necessary to elaborate and explain the ﬁrst.
Background
According to Rubin, Theophile Khan inﬂuenced Elsasser’s understanding of the overwhelming complexity dominating
biological systems as compared with the relative simplicity of physics. Probably, he was also inﬂuenced by
Wigner from whom he is likely to have picked up group or set theory.
BOX 3.2BOX 3.2BOX 3.2BOX 3.2
T H E C O M P T O N E F F E C T A N D D I R E C T I O N A L I T Y
From the uncertainty relation between position and
momentum, another relation may be derived. Let v and
E be the velocity and energy corresponding to momentum
px, respectively, then:
vDpx
Dx
v
! h
DE Â Dt ! h
where DE is the uncertainty of energy corresponding to the
uncertainty of momentum Dpx and Dt is the uncertainty in
time within which the particle (or the wave packet) passes
over a ﬁxed point on the x-axis (Fong, 1962). Thus, irreversibility
of time is not taken into account since in the quantum
mechanics paradigm time is assumed to be reversible.
We want to point out that if we take as an axiom the
irreversibility of time, it is an error to calculate the limit:
lim
Dt/0
Ds
Dt
because this means that
cε > 0; dd > 0 :



Dt



<d0




Ds
Dt



 < ε
where
jDtj < d5jt1 À t0j < d5 À d < t1 À t0 < d5t0 À d < t1
< t0 þ d
Simply speaking, it is not possible to think t1 as approximating
t0 from right, in fact, the state S(t0) that the function
S reaches when t1 becomes t0 from right cannot be
the same state S(t0) that the function assumes as t1 reaches
t0 from left.
It is well known that if the left and right limits of a function
are not identical, then the limit does not exist. Hence,
we must redeﬁne the time derivative of a function as the
left limit, if it exists
lim
Dt/0
Ds
Dt
This translates in practice to the statement that in the
Cartesian graph, it is impossible to cover the t-axis in
both sense from left to right and right to left, but in the ﬁrst
manner only.
1
This excellent biography is available on the Internet in several forms. Philosophy of science students will be provided with a deep insight in
how production of a scientist may not necessarily depend on skill or education, but may rather be determined by political and sociological
regimens throughout his life.
3. ECOSYSTEMS HAVE ONTIC OPENNESS32
These studies, together with periodical inﬂuence from von Neumann, caused him to realize a fundamental difference
between physical systems on the one side and living systems on the other. Due to his early life education
in atomic physics, he considered physical systems as homogenous setsdall atoms and molecules of a kind basically
posses the same properties and behavior. At this level, and always near to equilibrium conditions, the world is deterministic
and reversible processes dominate.
As opposed to this view, he considered living systems to differ in this fundamental aspect of the homogenous sets.
Living systems, he argued, are highly heterogeneous and far more complex than physical systems. Their behavior as
opposed to physical systems is nondeterministic and irreversible. This is what we today would designate as far from
equilibrium systems or dissipative structures.
The views of Elsasser are at this point derived from studies and knowledge about biological systems at cellular
and subcellular level, i.e., the boarder between the “dead” physicochemical systems and the living systems. The
“distinction” falls somewhere between the pure chemical oscillations, such as in the BeluzoveZhabotinsky reaction
and the establishing of biochemical cycles (autocatalytic cycles or hypercycles of Eigen and Schuster) together with
chirality and the coupling to asymmetries introduced by separation of elements and processes by membranes. Part
of the living systems indeterminacy is caused by an intrinsic and fundamental (ontic) property of the systemsd
(ontic) openness.
Ordered Heterogeneity
Around the late 1960s, Elsasser directed his attention to the question of what possibly could have happened
since the beginning of the universe, i.e., since the Big Bangdthe thinking is much along the same line as Jørgensen
BOX 3.3BOX 3.3BOX 3.3BOX 3.3
R E L A X A T I O N O F S Y S T E M S
Denoting the upward and downward relaxation probabilities
by Wab and Wba (with Wab s Wba), the rate of
change of Na is given by
dNa
dt
¼ NbWba À NaWab
At thermal equilibrium dNa/dt ¼ 0, and denoting the
equilibrium population by N0a and N0b we see that
N0b
N0a
¼
Wab
Wba
The populations follow from Boltzmann’s law, and so
the ratio of the two transition probabilities must also be
equal to exp(ÀDE/kT). Expressing Na and Nb in terms of
N and n (n ¼ Na À Nb), we obtain
dn
dt
¼ À n
À
Wbaþ Wab
Á
þ N
À
WbaÀ Wab
Á
This may be rewritten as
dn
dt
¼ À
ðn À n0Þ
T1
in which n0, the population difference at thermal equilibrium,
is equal to
n0 ¼ N
Wba À Wab
Wba þ Wab
!
and 1/T1 is expressed by
1
T1
¼ Wab þ Wba
T1 thus has the dimensions of time and is called the
“spin-lattice relaxation time.” It is a measure of the
time taken for energy to be transferred to other degrees
of freedom, that is, for the spin system to approach thermal
equilibrium: Large values of T1 (minutes or even
hours for some nuclei) indicate very slow relaxation
(Carrington and McLachlan. Introduction to Magnetic
resonance).
It is now possible to say something about the width and
shape of the resonance absorption line, which certainly
cannot be represented by a Dirac d function.
First, it is clear that, because of the spin relaxation,
the spin states have a ﬁnite lifetime. The resulting
line broadening can be estimated from the uncertainty
relation:
Dv Dtz1
and thus we ﬁnd that the line width due to spinelattice
relaxation will be in the order of 1/T1.
3.4 WHAT REALLY DIFFERS BETWEEN PHYSICS AND BIOLOGY: FOUR PRINCIPLES OF ELSASSER 33
formulated some decades later where Heisenberg’s uncertainty relation is transferred2
to ecosystems (see
Section 3.2).
Elsasser’s starting point was to calculate, roughly at least, how many quantum-level events could have
taken place since the Big Bang. Since events at quantum level happens within one billionth of a second, he
calculates a number to be in the order of 1025
. Then considering that the number of particles in the form of simple
protons that may have been involved in these events to be approximately 1085
, he calculates the number of
possible events to be 10110
. Any number beyond this “simply loses its meaning with respect to physical reality”
(Ulanowicz subm). Elsasser puts a limit at around 10100
(a number known as Googol). Any number beyond this is
referred to as an immense number. In Elsasser’s terminology, an immense number is a number whose logarithm
itself is large. Such numbers make no sense we claim. And yet, as we saw with the examples from music, any
simple everyday event, such as a piece of music, breaks this limit of physical events easilydalmost before it is
started.
But where does the relevance to ecosystems come in one may ask? Good questiondand for onceda very simple
answer. The point is that any ecosystem easily goes to a level of complexity where the number of possible events that
may occur reaches or exceeds immense numbers. Again, Ulanowicz points out that “One doesn’t need Avogadro’s
number of particles (1023
) to produce combinations in excess of 10110
, a system with merely 80 or so distinguishable
components will sufﬁce” (Ulanowicz, subm.) as 80! is in the order of 7 Â 10118
.
Now, as the vast majority of ecosystems, if not all, exceed this number of components it means that far more
possibilities could have been realized, so that out of the phase space of possibilities only a few combinations
have been realized. Complicating things further is that events entail path dependency; thus, some states that
did not occur the ﬁrst time even lose the possibility to occur in the future. Any state that has occurred is also likely
to occur only oncedand is picked out of superastronomical number of possibilities. The other side of the story, as
the title indicates, is that we are also left with a large number of possibilities that have never been and are never
going to be realized. In other words, almost all events we may observe around us are literally unique. There are
simple, repeatable events in nature within the domain classical probability, but they are sets of a measure zero
in comparison to unique events.
Meanwhile, we cannot foretell the possibilities of the next upcoming events. If we consider any particular situation,
then we face a world of unpredictabilityda world that is totally ontic open. In fact, taken together, the above
means that we should forget about making predictions about ecosystem development or even trying to do this.
Luckily, as we shall see later, Karl Popper (1990) advocated a “milder” version of ontic openness.
Although we up to now have dealt with heterogeneity at the level of probabilities, the following points from
Elsasser try to explain how nature copes with this situation.
Creative Selection
This point addresses the problems that arise from the immense heterogeneity. How do living systems
“decide” among the extraordinary large number of possibilities that exist? Elsasser was precisely aware that
living systems were nondeterministic, nonmechanist systems, whereas opposed to the physical systems that
are always identical. As Rubin (1995) states, they “repeat themselves over and over again . but each organism
is unique.”
Thus, Elsasser gives agency to the organisms, although judging from this point alone it is not very easy to see
where or how the “creativity” arises. Therefore, this point cannot be viewed as isolated from the two additional
points below. Selection mechanisms are not ignored in this view that just stresses the intrinsic causes of evolution.
Holistic Memory
With memory Elsasser addresses part of what is missing from agency. Again, according to Rubin, the criterion for
living system to choose is information stability. Some memory system has to be introduced, as the living systems
have to ensure the stability. This point, in addition to agency, also involves history and the ability to convey this history,
i.e., heredity to living, organic systems. Although again a part misses on how this information is physically
going to be stored, preserved, and conveyed.
2
This transfer would in the context of philosophy of science be designated as a theoretical reductiondindeed with large epistemic
consequences. This is opposed to Elsasser’s approach that we here consider within the normal paradigm of physics.
3. ECOSYSTEMS HAVE ONTIC OPENNESS34
Operative Symbolism
Last, symbolism provides the mechanism for storing this information by introducing DNA as “material carrier of
this information.” This cannot be seen as isolated from the history of science in the area of genetics. Much of the
Elsasser’s philosophical work has been written when the material structure and organization of our hereditary material,
the chromosomes, was revealed.
The above arguments could be taken as if Elsasser was still basically a true reductionist as we have now gotten
everything reduced into “simple” mechanisms for the conveyance of history. Elsasser was indeed aware of this point
and saw the process in a dualistic (not to say dialectic) manner as he stated this mechanism to be holistic in the sense
that it had to “involve the entire cell or organism” (see Section 3.6).
Ecology and Heisenberg
According to Jørgensen (1995), “some of the principles of quantum mechanics are (silently and slowly) introduced
in ecology” during the last 15 years (this was probably written signiﬁcantly earlier than 1995!). This is stated to be
valid in particular to the area of modeling with the following remarks: “An ecosystem is too complex to allow us to
make the number of observations needed to set up a very detailed modeldeven if we still consider models with a
complexity far from that of nature. The number of components (state variables) in an ecosystem is enormous.” Taking
this argument there is a clear correlation to the ontic openness of Elsasser, for instance, through the presentation
by Ulanowicz quoted above. Again, the number of components in an ecosystem alone is enough to form a system
that is ontic open.
To the empiricist, this means that we have to use our limited resources in time and in particular money in
the best possible manner. Who wants to spend unnecessary efforts? Who does not want to be as economically
efﬁcient as possible given that research money is always a limiting constraint? Meanwhile, the calculations
made by Jørgensen imply a theorem of intrinsic empirical incompleteness. The argument goes as follows
(Box 3.4).
According to Jørgensen, the Heisenberg Uncertainty Principle may now be reformulated, so that it refers to two
other measures: uncertainty in time and energy (note the product of the two is consistent with Planck’s constant,
namely energy times time). The analogous formula reduces to
Dt Â DE !
1
2
Z (3.7)
BOX 3.4
S A M P L I N G U N C E R T A I N T I E S
Given that the amount resources that can be spent on
examining an ecosystem is limited to a ﬁnite amount of
measurement. For this calculation, a limit is set to 108
,
an arbitrarily chosen number, which on one hand seems
to be very high in terms of ﬁeldwork, but may be rather
realistic when processes such as data logging is involved.
Considering the number of dependent variables in the
system (n) we need to determine the full “phase space” we
need make at least m, measurements, where
m ¼ 3nÀ1
(3.4)
This assumes that our knowledge about a given system
is so little determined that we have no a priori knowledge
about the interrelations in the ecosystem, i.e., the physical
ﬂows or the regulatory feedbacks in the system. Therefore,
we have to assume the worst casedthat everything is literally
linked to everything. In this case, Jørgensen calculates
that with the limits of 108
number of measurements we
can only deal with a system with fewer than 18 components
(as 318
¼ 387.420.489).
Assuming that our sample is taken from a statistical
population with a normal distribution and the standard
deviation (s) of the sample mean (x streg) is given by:
S.D. ¼
s
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
#samples
p (3.5)
Eq. (3.5) may be reorganized into
s
S.D. $
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
#samples
p ¼ 1 (3.6)
3.4 WHAT REALLY DIFFERS BETWEEN PHYSICS AND BIOLOGY: FOUR PRINCIPLES OF ELSASSER 35
After all, in the end, the amount of empirical work we can do is dependent on the energy available (not only our
own energy) and the time used per measurement.
First, we may now calculate the cumulative amount of energy received by the Earth since its creation and the
number of measurements that could hypothetically have been made since this creation. If we consider the amount
of energy we could have spent in measuring to be equivalent to the amount of energy received for the past 4.5 billion
years, and using 1.731 Â 1017
J sÀ1
as the value for incoming radiation, then this gives a total value of
DE ¼ #years Â #days Â #hours Â #seconds Â energy sÀ1
À
¼ 4:5Â 109
Â 365:3Â 24Â 3600Â 1:73Â 1017
Á
¼ 2:5 Â 1034
J
(3.8)
Inserting the value of Planck’s constant and solving Eq. (3.7), we maydagain hypotheticallydcalculate the time
necessary for every measurement which will now be
Dt ¼
h=4p
DE
¼
À
6:626 Â 10À34
Á
4p
2:5 Â 1034
¼ 10À67
s (3.9)
Thus, we could possibly make a measurement or sample in 10À67
of a second.
If we could have exercised this practice ever since the creation of the Earth, we could have made 4.7 Â 1084
measurements.
Returning to Eq. (3.5), this means that we will have a standard deviation, S.D. (accuracy) of
S.D. ¼
10À17
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
4:7 Â 1084
p z10À59
or in referring to Eq. (3.4), that we may never succeed in measuring systems with more than n ¼ 237! (here, the exclamation
is an exclamation, not factorial symbol).
To make an intermediate summary, there are many ways to express ontic openness. At the same time, it has consequences
to many relevant aspects of ecology such as the time we use for empirical work as well as the expectations
we may have to issues such as accuracy and predictability.
3.5 ONTIC OPENNESS AND RELATIVE STABILITY
After introducing Elsasser’s immense numbers and applying Heisenberg’s principle to ecology, we end up with a
rather pessimistic message to ecologists. In order not to fall totally in despair, let us turn to Popper. Although seemingly
agreeing mostly with Elsasser, he does present a modiﬁed interpretation of the classical probability concept
that at the same time offers us a somewhat more optimistic view of what can be done.
Popper, although also a physicist, is best known for his philosophy of science work and the problem of the logics
connected to the epistemic of carrying out research studies like “Logik der Forschung,” etc. He is considered to be
the father of the research strategy known as falsiﬁcation.
Popper (1990dreprinted from his lectures in 1930s), in a minor publication “A world of Propensities,” states that
he established a common research agenda with Carnap based on “Logik der Forschung.” In this agenda, they agreed
to distinguish sharply between, on the one hand, probability as it is used in the probabilistic hypotheses of physics,
especially of quantum theory, which satisﬁes the mathematical “calculus of probabilities,” and, on the other hand,
the so-called probability of hypotheses, or their degree of conﬁrmation (Popper, 1990, p. 5, see also Ulanowicz, 1996).
In fact, Popper himself, by addressing our failure to prove anything with a 100% certainty, i.e., the total dominance
of uncertainty and the higher likelihood of falsiﬁcation of experiments rather than the opposite, is addressing an
openness that is part of the everyday life of all scientists. But again, if this is a property inherent in the systems
we work, then indeed we will be forced to return to the pessimist view presented above. If a true, real feature of
the world, then why do science at all? Popper refers to the ﬁndings of Heisenberg as “objective indeterminacy,”
but argues against the solution of translating everything into probabilistic terms. Popper claims that most scientists
picking up the probabilities turned it into a question of “lack of knowledge” (the information as entropy approach
that is strongly connected to Shannon and von Neumanndour comment) leading to what he calls a subjectivist theory
of probability (Popper, 1990, p.8).
3. ECOSYSTEMS HAVE ONTIC OPENNESS36
After working with probability theory for more than 35 years, he claims to have come up with “satisfactory and
very simple solutions.” One of which he refers to as “the propensity interpretation of probability,” a concept that
originated back in 1956. Ulanowicz later used this interpretational framework in the development of his ascendency
concept (Box 4.1) proposed to be an indicator of ecosystem development (Ulanowicz, 1986, 1997).
In his explanation of the propensity interpretation, Popper began with an example of tossing a coin or throwing
dice, in which we deal with known equiprobable outcomesdprobability of ½ or 1/6 of any of the possible outcomes,
respectively. Most of us will be familiar with these examples and consider them rather trivial, but what happens in
the case when either the coin or the die is manipulated, i.e., loaded.
First of all, it is clear that in this case our assumption of equiprobable outcomes ends. One may introduce a very
simple solution to this situation and just continue to work with the new weighted possibilities. We could hope that it
would be as simple as that. But the consequence of such a situation on our work is much greater than we may
imagine.
At least two major problems originate from the character of the situation: (1) How are the weights determined?
(2) What is the consequence to our ability to forecast such a system? In determining the weights, a feasible method
may easily be found. We may just continue “normal” coin tossing or dice throwing a considerable number of times,
registering the outcome of each event. The point is now that this procedure will eventually take more time (more
tosses or throws) in order to reach a reliable result and yet the determined weights will still be connected to a relatively
high uncertainty. Popper stated, “instead of speaking of the possibility of an event occurring, we might
speak, more precisely, of an inherent propensity to produce, upon repetition, a certain statistical average” (Popper,
1990, p. 11). Each event will happen with a more or less certain probability, a tendencydor as we now know itda
propensity. The immediate effect will be that our chances of successfully predicting a number of sequences will be
very small.
We may now consider that the evolving world around us is a composite of events that all have nonﬁxed probabilities.
Assigning weights is further complicated if the weights are not ﬁxed, but rather varying, say on the external
conditions in which the event is cast. In fact, adaptation is an inherent property of biological systems, thus, we must
consider that the propensities themselves may change with time. This should lead to the understanding that propensities
are entailed in the situation not the object. Our ability to predict, or our hopes to do this, will vanish within a
short time, just as our abilities to predict the development of music is disappearing after just a few bars of playing as
described earlier.
3.6 THE MACROSCOPIC OPENNESSdCONNECTIONS TO THERMODYNAMICS
Although there is possibly a connection between thermodynamic (Chapter 2) and ontic openness, the relation between
the two is deﬁnitely nontrivial and attempts to distinguish the two will therefore not be included here.
An energy ﬂow can lead to organization (decrease in entropy, e.g., photosynthesis) or destruction (increase in entropy,
e.g., a cannon ball). The same quantity of energy can destroy a wall or kill a man; obviously the loss of information
and negentropy is much greater in the second case. Energy and information are never equivalent as
demonstrated, for instance, through Brillouin’s refusal of Maxwell’s Demon.
The classical example of the mixing of gases in an isolated system shows us that there can be an increase in entropy
without energy input from outside. The point is that energy (E) and entropy (S) are both state functions in classical
thermodynamics, but energy is intrinsically reversible, whereas entropy is not. Entropy has the broken time
symmetry (Blum, 1951). In other words, entropy has an energy term plus a time term that energy does not have.
Herein lies the physical connection to the concept of exergy dealt with in Chapters 2 and 6.
Energy and mass are conservative quantities; thus, it follows that total energy and mass cannot change with time.
They may transform to other types of energy and mass, but the overall quantities remain the same: they are reversible.
Entropy has an intrinsic temporal parameter. Energy obeys spatial and material constraints; entropy obeys
spatial, material, and temporal constraints.
If history and the succession of events are of scientiﬁc relevance, then the concept of a state function should be
revised at a higher level of complexity. The singularity of an event also becomes of particular importance: if a certain
quantity of energy is spent to kill a caterpillar, at the same time we lose the information embodied in the caterpillar.
But were this the last caterpillar, we would lose the direct evolutionary line to replicate that information. Nowadays,
its unique genetic code may have been preserved through sequencing, but that is not the same as the species in its
ecological role. The last caterpillar is different from the nth caterpillar.
3.6 THE MACROSCOPIC OPENNESSdCONNECTIONS TO THERMODYNAMICS 37
The Entropy Paradox
Stories take place in a setting, the details of which are not irrelevant to the story. What happens in the biosphere,
the story of life, depends on the biosphere constraints. Hence it is important to have global models of the biosphere
in terms of space, time, matter, energy, entropy, information, and their respective relations.
If we consider the evolutionary transition from anaerobic to aerobic living systems, then the ratio of energy to
stored information is clearly different. The information that led to evolution and the organization of the two types
of system is not proportional to the ﬂow of energy, due to dissipative losses that also introduce irreversibility.
Thus, entropy breaks the symmetry of time and can change irrespective of changes of energydenergy being a
conservative and reversible property, whereas entropy is evolutionary and irreversible per se. The ﬂow of a nonconservative
quantity, negentropy, makes life go and the occurrence of a negentropic production term is just the point
that differs from analysis based on merely conservative terms (energy and matter).
The situation is explained in Fig. 3.1 “The death of the deer”: mass and energy do not change, whereas entropy
does. There is an “entropic watershed,” a gradient, between far from equilibrium (living) systems and classical systems
(the dead deer or any inorganic nonliving system). The essence of the living organism resides in its being a
“conﬁguration of processes.”
We may conclude that in far from thermodynamic equilibrium systems (biological and ecological), entropy is not
a state function, since it has intrinsic evolutionary properties, strikingly at variance with classical thermodynamics.
It is important to study energy and matter ﬂows, quantities that are intrinsically conserved; it is also important to
study entropy ﬂows, an intrinsically evolutionary and nonconserved quantity. But if energy and mass are intrinsically
conserved and entropy is intrinsically evolutionary, how can entropy be calculated on the basis of energy and
mass quantities (entropy paradox)? This question is still unanswered and all we can do is to note that the ecodynamic
viewpoint is different from that of classical physics and classical ecology.
The Probability Paradox
The following illustrates thatdfor even simple far from equilibrium systemsdunforeseen consequences to predictability
may arise from various aspects of heterogeneity. An event occurs in a stochastic manner because others
precede it. Evolutionary events proceed in a manner that depends on time: they show a direction of time; they are
irreversible. History determines the environmental and genetic constraints making the future largely unpredictable,
as demonstrated several times above. Stochastic or probabilistic elements are unavoidable (although compare the
views of Elsasser, Popper, etc.).
Novelty abounds in biological and ecological systems. Ontic openness allows for the emergence of new form and
patterns. Previously unobserved events cannot be predictable, while rare and extreme events may or will completely
change the dynamics of complex systems.
Fig. 3.2 shows the emergence of a probability paradox in the presence of events:
a) Suppose that an oxidation (chemical event), unknown to the observer, arises in the classic “white and black
spheres” game: the probability white/black is no more ﬁfty-ﬁfty (only if the oxidation is changing the white
sphere, e.g., to gray, may we know what happened);
m
E
S
information
Far from
thermodynamic
equilibrium
Towards
thermodynamic
equilibrium
-ΔS
m
E
Information lost
+ΔS
-ΔSlost
ENTROPICWATERSHED
FIGURE 3.1 The death of the deer, an example showing the difference between a living, far from equilibrium system compared with the
situation after its death where irreversible changes becomes dominant. Courtesy of WIT Press from Tiezzi E : Steps Towards an Evolutionary Physics,
2006, 157 p.
3. ECOSYSTEMS HAVE ONTIC OPENNESS38
b) Suppose that an evolutionary event also occurs, related to the “chameleon” effect (sensible to the environment):
again, the probability is no more ﬁfty-ﬁfty; moreover, the event’s interval depends on the “chameleon”;
c) Suppose an oscillating event occurs, similar to the BeluzoveZhabotinsky reaction: the situation is more complex
and depends on many parameters. Again, the observer has no possibility to predict which sphere will be picked
up from the container.
It is possible to conclude that in the far from equilibrium framework a classical probability approach does not
apply, and new models have to be developed for the Boltzmann’s relation S ¼ k ln W.
3.7 ONTIC OPENNESS AND EMERGENCE
At all levels of nature, we see the emergence of “narrative elements.” We are reminded of Scheherazade who interrupts
her beautiful story to start another one, even more beautiful. In nature, we have the cosmological history
that includes the history of matter, of life, of humans, and so on till we come to our individual history associated
to our consciousness. At all levels, we observe events associated with the emergence of novelties, which we may
associate with the creative power of nature.
These narrative historical aspects are part of complexity. Complex systems share the feature to exhibit a great variety
of behaviors. Take an example from chemistry: the BelousoveZhabotinsky reaction mentioned above. The details
are irrelevant here. Let us suppose that there are two species of molecules: “red” ones and “blue” ones;
moreover, they transform one into the other. The behavior of the system depends on the external constraints. Close
to equilibrium the collisions are random. There may only appear short living local ﬂashes of color. But far from equilibrium
the behavior of this system changes radically. It becomes in succession red then blue then again red. This
periodicity indicates the existence of long-range correlations due to the nonequilibrium conditions. “At equilibrium
matter is blind, far from equilibrium it begins to see” (Ilya Prigogine3
).
The fascination of these physical experiments lies in the fact that small variations in a tiny building block of matter
manifest themselves as large changes in biological processes. The paradox of modern scientiﬁc research in this ﬁeld
lies in the fact that the greater the detail in which we seek “pure” mechanisms or given subparticles, the more conﬁrmation
we have of the validity of quantum mechanics and the more important information we have on the structure
of matter. On the other hand, starting from elementary particles, the more we study interactions with biological systems
and ecosystems, the more we discover the complexity, irreversibility, and intrinsic aleatory character of nature.
FIGURE 3.2 Unexpected events that may occur in living systems: (A) oxidation; (B) chameleon effect; (C) oscillating reaction. Courtesy of WIT
Press from Tiezzi E : Steps Towards an Evolutionary Physics, 2006, 157 p.
3
From the foreword to E. Tiezzi, The Essence of Time, WIT Press, Southampton, 2003.
3.7 ONTIC OPENNESS AND EMERGENCE 39
In chaos, we rediscover the spontaneity of evolutionary history: a universe in which God plays dice, to invert Einstein’s
phrase.4
God was the supreme guarantee of physical determinism. For Einstein, protagonist of the ﬁrst “heroic” phase of
quantum physics, physical determinism applied to any process. However, Max Born5
once told Einstein that a deterministic
universe was innately anathema to him. Born admitted that Einstein might be right, but added that determinism
did not seem to hold in physics, much less in other ﬁelds. Born criticized Einstein’s comment that God plays
dice,6
observing that Einstein’s deterministic world needed chance. Born’s wife, Hedwig, had previously written to
their “dear friend Albert” that she could not admit a universal law according to which everything was predetermined,
including whether or not she vaccinated her child against diphtheria.7
Both uncertainty equations are related to the complex relation between the observer and the experiment. The ﬁrst
one deals with position and momentum, the second one deals with energy and relaxation time. Both equations assume
time reversibility and are valid in a given instant: the momentum is related to the derivative of space with
respect to time and the relaxation time is related to the lifetime of the elementary particle in the excited state.
Both equations are valid in the quantum physics paradigm and deal with conservative quantities (mass, energy),
but not with living systems or evolutionary quantities.
Space and time are categories belonging to different logical types, which should not be confused. By nature, time
is evolutionary and irreversible, whereas the space is conservative and reversible. A reversible quantity cannot be
differentiated with respect to an irreversible one. It is not possible to compare evolving quantities, such as the life
span of the Einstein’s twins, in the framework of reversible mechanics. If we deal with evolutionary (living) systems,
then we may introduce a third concept: Thermodynamic uncertainty related to the intrinsic irreversible character of
time (Tiezzi, 2006a,b).
Let us say that a thermodynamic uncertainty arises from the experimental existence of the arrow of time and from
the experimental evidence that, during the measurements, time goes by. Since during the interval of the experiment
(measurement) time ﬂows, also the conservative quantities (energy or position) may change leading to a further
uncertainty.
Recently, astrophysics discovered that the mass of a star is related to the life span of the star itself. The larger is the
mass, the less is the life span. This ﬁnding may also be related to the uncertainty principle. It seems that there is a sort
of uncertainty relation between space and time, where space is related to mass, energy, and the conservative
quantities.
3.8 ONTIC OPENNESS AND HIERARCHIES
The above section shows the necessity of an extended view of biological systems focusing on the property of heterogeneity
and order at the same time. The pertinent mechanisms are encompassed within Elsasser’s four principles,
but already here the pitfall of a return to reductionism was pointed out. Rather, let us begin with an assumption that
includes as given the genetic-level apparatus.
4
On December 4, 1926, Einstein wrote to Max Born that although quantum mechanics was worthy of respect, an inner voice told him that it was
not yet the right solution because it did not enable us to penetrate the secret of the Great Old Man, who he was sure did not play dice with the
world (Science and Life, Letters, 1916e1955, letter no. 52 in A. Einstein, H. and M. Born). Max Born considered that there was a profound
divergence of viewpoint between Einstein and the following generation, to which Born regarded himself as belonging, though only a few years
younger than Einstein. In a previous letter (April 29, 1924, no. 48 of the above collection), Einstein observed that the ideas of Niels Bohr on
radiation were interesting but he himself did not wish to be led away from rigorous causality. He added that he could not tolerate the idea that an
electron exposed to radiation could freely choose when and in which direction to jump. Were this so, he said he would prefer to be a shoemaker or
a gambler rather than a physicist. In the introduction to this collection of letters, Werner Heisenberg comments that Einstein agreed with Born on
the fact that the mathematical formalism of quantum mechanics, which originated in Go¨ttingen and was subsequently elaborated at Cambridge
and Copenhagen, correctly represented the phenomena occurring inside the atom, but that he did not recognize quantum mechanics as a
deﬁnitive or even exhaustive representation of these phenomena. The theme that God does not play dice recurs elsewhere in the BorneEinstein
correspondence (e.g., Einstein’s letters of September 7, 1944, and October 12, 1953, nos. 81 and 103, respectively).
5
October 10, 1944 (letter no. 84 to Science and Life).
6
The expression “God plays dice” obviously had an irrational overtone for Einstein, but, as we shall see, not for us.
7
October 9, 1944 (letter no. 82 in Science and Life).
3. ECOSYSTEMS HAVE ONTIC OPENNESS40
It is easy to see from the composition of nucleic acids or triplet codes that the genome combinatorics will exhibit
immense numbers. These are also the numbers reached in calculations by Jørgensen et al. (1995) and the many attempts
that have followed to calculate an exergy index for ecosystems based on the information content of the
genome. Ontic openness is deﬁnitely a reality at this level.
Patten later suggested another hierarchical level in addition to the genotype and phenotype levels, namely and
the exosomatic envirotype reﬂecting that an organism’s genetic template and physiological manifestation is only
realized with respect to its ultimate surroundings and the ecosystems, respectively. Recently, Nielsen (in print)
has extended this view by adding a semiotic level above (Fig. 3.3). This layer includes all kinds of communicative
and cognitive process, i.e., semiotics in a wide sense. This represents the ultimate layer of realizing ecosystem
openness.
Thus, at each layer of the biological hierarchy we meet a new side of ontic openness. Interactions between hierarchical
levels may, as indicated, take place in both upward and downward directions. The traditional view is that as
we move up the hierarchy we are narrowing the number of possibilities. Therefore, as O’Neill et al. (1986) state, hierarchies
are systems of constraints, which only are able to provide system regulations at steady-state conditions.
Whenever rare events or system transformations occur, the hierarchies are broken, and uncertainty takes place in
a broad extent. Emergence due to ontic openness always exists but is just realized in other ways that are not covered
by the reductionist view.
3.9 MESSAGES FROM ONTIC OPENNESS TO ECOLOGY AND ECOLOGISTS/MANAGERS
That the concept of ontic openness appears to complicate ecology ramiﬁes as well to ecosystem management (see
also Chapters 5 and 7). This complexity arises both from structural systems components (ontology) and from their
interrelationships and resulting behaviors (phenomenology). Ontic openness implies that we should simply not
expect description to be easydneither in terms of composition nor in their behaviors. Such expectation would be
fatuous!
Because ecosystems are causally open, we should not expect regular systematic behavior to be the rule. We do see
some trends, however, as formulated by early ecologists, such as Warming and Odum and by several contributors to
this volume. Our aim is to synthetize the knowledge we have gained up to now into several simpliﬁed take-home
messages, all based upon the fact that ecosystems and their components are ontologically open. The arguments that
follow are taken mainly from Ulanowicz (2009, 2014, 2016), Nielsen (2009), Nielsen and Ulanowicz (2011), Nielsen
and Emmeche (2013).
Sense & action
communication
Selection, variation
adaptation
Functionality
Diversity
Up/down causation
Envirotype
Phenotype
Genotype
Degrees of freedom
Semiotype?
FIGURE 3.3 A biological hierarchy suggesting that interactions with the environment and ﬁnally the semiotics determine the development of
the ecosystem. From Nielsen, 2007, with permission from Elsevier.
3.9 MESSAGES FROM ONTIC OPENNESS TO ECOLOGY AND ECOLOGISTS/MANAGERS 41
Variation
Ontic openness guarantees that variation will be part of any system so designated. Variation, for example, is an
essential presumption that undergirds (Neo-)Darwinian evolution in its widest sense, and, more speciﬁcally, adaptation
and selection. For some odd reason, such a variation has always been taken for granted, and the reason behind
its existence has been ignored. It has simply been assumed to be there. That the numbers of combinations in genetic
systems are immense lies at the crux of ontic openness and provides us with an explanation for the emergence of
variation.
Uncertainty
At the same time, this ever-persistent variation imparts a high degree of uncertainty about any type of biological
system. About the only thing one can be certain is that a given situation will never be repeated. A state once realized
will never again appear precisely as it was. One is reminded of Heraclitus’ River; change is the only constant. Ontic
openness imparts, therefore, innumerable deviations and nonrepetitiveness to our observations and studies. That
we ourselves are ontically open systems who interfere with our study objects (or rather subjects) sensu Bohr does
not diminish uncertainty. To the contrary, it enlarges and exposes it.
Indeterminacy
Systems that are ontically open not only generate uncertainty but are themselves indeterminatedin fact in two
senses of the word, each of which generates its separate consequences: First, we will never be able to make exact
forecasts about such systems; they are simply unpredictable. Second, we are left with an inability to identify or
specify exact system conditionsdneither in terms of states nor ﬂows. We simply do not have the time or energy
to elaborate the precise situation. Attempts to describe the system in terms of ﬁxed probabilities are also not possible,
i.e., we are left in a world of Popperian propensities.
Nondirectionality
It is also important to note that the variations arising from ontic openness may carry with them no priorities or
directionalities. That is, whatever happens may occur as a result of total and true randomness. Such variation then
acts in a neutral manner among the ontological components and assumes no value. Introduction of direction and
evaluation of something in either a positive or negative manner involve, on one hand, a governing mechanism
(such as autocatalysis) and an evaluative function that makes it possible to judge if a given direction is good or
bad. Directionality and normative judgment may arise in either an objective or a subjective manner (see later in
this section).
The ubiquity of ontic openness at all levels also means that in order for a system not to end up as an indeterminate
“chaotic” system (one characterized by noise only) or in a situation where all emergent states will be considered
equally valid and good in quality, something elsedanother kind of processdmust take action in or on the system.
Voidness
First of all, not all possible states can be realized. They are too enormous in number. Part of system phase spaced
in fact most of itdwill never be ﬁlled. Whether such lacunae represent lost opportunities or simply unrealistic solutions
is not relevant to this discussion. Still, mere intuition dictates that out of all the realized possibilities, some
must be working better than others. Some constellations of processes will arisedbe they good or baddbut not all
will be realized (hence the void space). The quality of their functions can be evaluated based on objective functions
required, for example, by thermodynamic laws (at least at the lower levels of cells and organisms). That such constraints
may be extended to work at higher level systems is one of the goals of this book.
Patterns and Constraints
Because both directionality and evaluation (interpretation/cognition?) do occur in ontic open systems, one must
conclude that agencies and constraints exist that serve to exclude some possibilities and favor others. Normally, such
constraint is the result of objective criteria that promote functionality or optimality in a biophysical sense. More
3. ECOSYSTEMS HAVE ONTIC OPENNESS42
subjectively, we tend to overlay these constraints with an anthropocentric viewpoint. For example, when considering
the relationships between genomes and physiology, we usually view changes that lead to improvements of the system
as healthy and those that lower functionality as malign (e.g., genetically induced diseases or syndromes).
Hierarchy and Causality
It is also important to note that ontic openness emerges at all levels of the biological hierarchy because an immense
number of combinations among ontological units is usually a characteristic of each focal level under consideration.
To demonstrate a direct linkage between outcomes at a speciﬁc level and causality elsewhere in the hierarchy will be
difﬁcult to trace, predict, or identify. Tentatively, we may expect to ﬁnd materials and efﬁcient causes at work in
lower levels, whereas formal and ﬁnal causes should be operative at higher levels.
Ontic Openness Stresses the Precautionary Principle
These perspectives centered around ontic openness, and, in particular, those addressing uncertainty, unpredictability,
and indeterminacy, point to the need for extra caution when dealing with such systems. Should we choose to
remain with the traditional view of biological systems (or even ecosystems) as Newtonian systems that are determinate
and predictable, not only would we be grievously in error philosophically and scientiﬁcally, but we would also
offer advice that is guaranteed to result in bad management. Now, fully aware that ecosystems are ontically open at
all hierarchical levels, we appreciate how the precautionary principle must be taken more seriously than ever.
That ecosystems seem to “have a life on their own” that we hardly understand makes it even more important that
we act with respect for naturedfor hard-core scientiﬁc reasons as well as ethical considerations.
3.10 CONSEQUENCES OF ONTIC OPENNESS: A TENTATIVE CONCLUSION
Here we summarize the consequences of ontic openness that will have a deep impact on ecology:
1) Immense numbers are easily reached
2) Possible development and uncertainty
3) Uniqueness of the ecosystem
4) Agencydhow is this uniqueness chosen
5) Emergent properties are common
In the following section, we will attempt to address the above points in context of applying a systems perspective
to ecology and ecological theory.
Immense Numbers Are Easily Reached
Much of the material given above clearly demonstrates that achieving numbers of interacting elements in ecological
systems that are above Googol (10100
), and thereby do not in themselves carry any physical meaning, is fairly
common if not ubiquitous. A combinatorial view on any level of hierarchy of biological systems is not sufﬁcient
in explaining “the meaning of life”din fact, 42 makes more sense and is a better estimate.8
Possible Development and Uncertainty
As pointed out by several calculations above, ecosystems have too many distinguishable parts for classical understanding.
Even if middle-number systems, they possess enough components to exceed the limits we may have
considering our capabilities of doing experiments and sampling. In order to accept this, we need to recognize
that we do have to live a high uncertainty, e.g., often expressed in the fact that our standard deviations on any measurement
that we make are far beyond the levels accepted by our “colleagues” from physics and chemistry.
8
Meaning of Life given in Douglas Adams’, Hitchhikers Guide to the Galaxy.
3.10 CONSEQUENCES OF ONTIC OPENNESS: A TENTATIVE CONCLUSION 43
The Uniqueness of Ecosystems
This issue could be seen as rather trivial. At each state in its evolution, the ecosystem transitions to a new state.
The one thing we now can be sure of is that the next state will be just as unique as the previous one, the system will
never repeat itself exactly. An event may happen once and never again.
In fact, we did implicitly address this point indirectly in the introduction, without putting much attention to it,
when we described a situation familiar to most of us: our inability to describe precisely a system without measurements.
Meanwhile, not to fall in despair, we may ﬁnd some satisfaction in the world of propensities. We may not
know exactly what happens, but approximately what happens.
Agency of Ecosystems
This topic is probably the most problematic. In fact, many of us probably would like the idea that “nature has a life
on its own,” and this may also correspond nicely with what we observe or have observed. But how to give agency to
ecosystems without being accused of romanticism, teleology, etc. or alternatively getting involved in a debate about
intelligent design?
Uniqueness as Emergence
Given the sum of the possible conclusions from the abovedunexpected things are bound to happen in ecosystems
and likewise in ecological research. In fact, when Odum (1969) made his proposal to follow the study of emergent
properties of ecosystems as a research strategy he was “only” introducing a suggestion of studying the impact of
ontic openness to ecosystems (QED).
The Messages of Ontic Openness to Ecology
Aview of our world as possessing an essential property such as being ontically open does carry several important
messages to ecologists. The ubiquity of emergent properties or unexpected, rare events should, as such, be no surprise
to us any longer. Meanwhile, we should not fall in despair; some predictability is still possible, although we
should expect accuracy to be small and uncertainty to be high. Probably, understanding the world as propensities
rather than ﬁxed possibilities is the way out of this dilemma. The biological world as we see it around us now, its
(bio)diversity consists of the part of the openness that was actually realized. It is, together with its individual components,
unique, and is “locked-in” from many path-dependent evolutionary events. It will never emerge again, and
as such it should be appreciated a lot more than seems to be the case in the moment.
3. ECOSYSTEMS HAVE ONTIC OPENNESS44
C H A P T E R
4
Ecosystems Have Connectivity
Life did not take over the globe by combat, but by networking. Margulis and Sagan. Microcosmos
4.1 INTRODUCTION
The web of life is an appropriate metaphor for living systems, whether they are ecological, anthropological,
sociological, or some integrated combinationdas most on Earth now are. This concept immediately conjures up
the image of interactions and connectedness both proximate and distal: A complex network of interacting parts,
all playing off one another, providing constraints and opportunities for future behavior, wherein the whole is greater
than the sum of the parts. The term “network” has received much attention recently in human affairs, especially with
the Internet, cell phones, etc. In ecology it has a long history, dating back at least to Darwin’s “entangled bank”, and
with the 20th-century rise of systems ecology and ecological modeling, and burgeoning current interest in problems
of biodiversity, stability, sustainability, and global change, etc. networks and how they reﬂect an interconnected
biosphere are hard to avoid as central research topics for ecology. Adopting them as research areas for study immediately
opens a box of technical scientiﬁc tools to be applieddgraph theory, matrix algebra, differential equations
and simulation modeling, and growing methodologies for network analysis.
Networks, as used in Ecological Network Analysis (ENA) and Network Environ Analysis (NEA), are in the ﬁrst
instance transactional. That is, they consist of sets of energyematter storage compartments coupled together by adjacent,
pairwise intercompartmental interchange transactions. Energy and matter are conserved, hence the interchanges
are zero-sum. These two properties anchor ENA and NEA to basic physics. Subsequent catenated
exchanges from points of introduction (boundary inputs) give rise to nonconservative, nonzero-sum relations. In
these, ecology begins to leave the realm of physics and empirical science and enters a zone where theory must be
employed to compensate an inability to directly observe and measure. Relations like competition and mutualism
are virtual, hence empirically intractable. The predation relation is, by comparison, real and concrete. Because networks
are nothing if not virtual vehicles for transporting causality away from its origins, they serve also as vehicles
for transporting ecology away from what is directly and empirically observable and measurable. Reductive science
leaves off in this process, and holistic science must take over in its place, but this is widely, not only in ecology but
everywhere in science, only in its nascent stages. Holistic ecologyd“holoecology”dis still ahead to be created as
part of the New Ecology conception of this book.
Although transactional exchange involves discrete substance transfer, transactions taken in total directly and indirectly
link elements together in interconnected webs, giving rise to network structure. This structure, and the relationships
it carries, both concrete and virtual, can outlast the component parts that do the work, providing
habituated patterns of history and context for life’s continuity. The web of life, all life, has important impacts on
both the objects within the networks and science’s attempts to understand the wholeepart relationships. If the
immanent web goes unseen, and unconnected organisms or their tangible groupings are the only categories favored
by scientiﬁc attention, then system-level effects of wider interconnection, one pole of bipolar determination, will go
unseen also. For example, in a holistic investigation of the Florida Everglades, Bondavalli and Ulanowicz (1999)
showed that the American alligator (Alligator mississippiensis) has a mutualistic relationship with several of its
prey, such that inﬂuence of the network trumps the direct, observable act of predation, which actually initiates
network propagations leading to the ultimate mutualisms. The connected web makes this so because each isolated
act of predation concatenates, bifurcates, converges, and cycles to complexly link together the entire system. In this,
45
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00004-8
indirect effects mediated by higher order relations propagated over network distances by pathways that extend
through many other objects can dictate overall relations (see sections Grounding Hypotheses and CH-2: Network
Nonlocality). While this might seem irrelevant to the nourished alligator and the hapless organisms that end their
lives in its gut, as a whole the prey population beneﬁts from the alligator’s presence in the web since it also feeds on
other organisms that in turn are predators on or competitors with the prey. In NEA, this process of emergent positive
relations deriving from zero-sum adjacent transactions is referred to as “network synergism” and “network mutualism”
(see sections Relational Hypotheses, CH-13: Network Synergism and CH-15: Network Mutualism).
Such discoveries as these, involving virtual elements, are not possible without viewing the ecosystem as a connected
network, and as these are immanent and virtual structures one can readily appreciate the problems this raises
for empirical research, which effectively cannot take inquiry where it needs to go for understanding and prediction.
This chapter deals with ecological connectivity essential for understanding and prediction. It provides an overview
of systems approaches, introduces quantitative ENA/NEA methods to investigate connectivity, and concludes with
a set of hypotheses formulated from viewing ecosystems as networks. Insights from networks often appear at ﬁrst
glance to be esoteric, surprising, and unintuitive, but once understood through examination by network methods
they usually fall into place in the growing body of systems-ecological knowledge now under construction from
holism, rather than from the atomistic reductionism that has dominated past ecology. The new problems of the
day, from regional to global scales, are complex systems problems, and they do require the paradigm, not shift,
but evolution, now underway. We hope this chapter’s treatment of nature’s connectivity will give further impetus
to ecology’s adopting the systems perspective promoted throughout this book.
4.2 ECOSYSTEMS AS NETWORKS
Ecosystems are conceptual and functional units of study that entail the ecological community together with its
abiotic environment. Implicit in the concept of any system, such as an ecosystem, is that of a system boundary
that demarcates objects and processes occurring within the system from those occurring outside the system. This
insideeoutside perspective gives rise to a ﬁrst dualism in the concept of environment, one environment existing
outside the focal system and another inside. The latter is explicit in the state variables of mathematical descriptions,
but the external environment is relegated to having presence only in afferent and efferent boundary-crossing phenomena
(inputs and outputs). Mathematizing intraboundary inﬂuences gives rise to a second dualism, which sees a
system’s internal environment as the external environments of component parts. These were named environs by
Patten (1978), and there are two of them, one incoming, the other outgoing. We typically are not concerned with
events beyond the system boundary that start and end there without entering the system by crossing its boundary.
Moreover, as open systems, ﬂuxes do occur across boundaries, and these provide needed energyematter inputs as
well as outputs to sinks for waste heat or unusable organic compounds. In addition to continuous energy input and
output, allochthonous inputs of dead organic matter are important in some ecosystems like streams and deltas, and
biotic dispersal can also play important boundary-crossing roles.
The spatial extent of ecosystems varies greatly and depends often on the functional processes inside boundaries.
O’Neill et al. (1986) deﬁned an ecosystem as the smallest unit that can persist in isolation with only its abiotic environment,
but this does not give an indication about the necessary area required. Cousins (1990) proposed the home
range or foraging range of locally dominant top predators as an arbiter of ecosystem size, referred to as an ecosystem
trophic module, or “ecotrophic module.” Similar to the watershed approach in hydrology, Power and Rainey (2000)
proposed a “resource shed” to delineate the spatial extent of an ecosystem. In the extreme, one could eliminate the
environment altogether by expanding boundaries indeﬁnitely outward to subsume all boundary ﬂows, thus making
the very concept of environment a paradox (Gallopin, 1981). The idea is not to make the “resource shed” so vast as to
include everything inside the system boundary, but rather to establish a demarcation line based on gradients of interior
and exterior activities. In fact, as open systems an external reference state is a necessary condition for framing the
ecosystem of interest (Patten, 1978). We give the last word to Post et al. (2005), who stated that different organisms
within ecosystems based on their resource needs and mobility will operate at different temporal and spatial scales,
typically leaving the scale context-speciﬁc for research questions at hand.
Deﬁnitional difﬁculties aside, following O’Neill’s prescription of identifying the smallest unit that could sustain
life, a minimum set of functional categories might be said to be autotrophs alone. However, on a ﬁnite planet with
ﬁnite resources, unprocessed metabolic residues would accumulate and ultimately choke off any possibilities for
autonomous continuance. Adding decomposers seems necessary, therefore autotrophy and saprotrophy together
might be taken as minimum process constituents for a sustainable functioning ecosystem. But ecosystems also
4. ECOSYSTEMS HAVE CONNECTIVITY46
universally possess heterotrophic consumers. Why and how did these emerge if not needed? Returning to the saprovores,
one could argue that evolution produced them not because they were needed (though they were) but
because the prior established autotrophs represented an open niched“opportunity” in economy-of-nature terms,
and “nature abhors a vacuum.” Such opportunistic emergence of a new trophic category without regard for its necessity
for indeﬁnite sustainability could also then apply to (unneeded) consumers. So, by the simple logic of opportunity,
or if preferred goal orientation (see sections Ampliﬁcation Hypotheses, CH-4: Network Aggradation, CH-5:
Network Throughﬂow Maximization, CH-6: Network Storage Maximization, Integrative Hypotheses and CH-8:
Network Interior Ampliﬁcation), we arrive rather quickly at the observable trophic constituents of virtually all ecosystems,
classiﬁed in accordance with their network-relational properties:
1. Reﬂexive autotrophic primary producers that draw in and organically ﬁx external energy and matter;
2. Symmetric saprotrophic decomposers that ingest dead organic matter and close cycles of substance ﬂow to add
network pathways; autotrophs and saprotrophs together are a sufﬁcient set to enable indeﬁnitely sustainable
ecosystems; and
3. Transitive heterotrophic consumers of live organisms, forming trophic networks that build upward in trophicdynamics
extended from primary ﬁxed energy and matter; recent research (Patten, 2019; Section 4, TSUNAMI
Effectdsee later, CH-8: Network Interior Ampliﬁcation) shows transitive (þ,e) agonism (consumption) or its
sign-opposite (e,þ) altruism (saprovory) to be necessary processes for maximizing gains in the energyematter
economy of nature.
Biotic communities made up of these relational categories entail networks of relationships in ecosystems built on
interactions between individuals in populations, individuals of multiple species, and active and passive interactions
of individuals with their environment.
In ecosystem studies two approaches are employed. The ﬁrst, a “black-box” approach, concerns exosystem inputs
and outputs without elucidating the processes that generate these (Likens et al., 1977). The second, generally termed
ENA, gives a detailed accounting of energy and material ﬂows within ecosystems. In these studies, the focus is usually
at the scale of species or “trophospecies” (functional feeding groups) and how these interact, rather than on interactions
between individuals of the same species, although these are considered in individual-based studies and models.
ENA could even be called “reductionistic holism” because it takes ﬁne-scale details of ecosystem constituents and their
interconnections and uses these to reveal or fashion global patterns that shape system structure and function.
Although interaction networks are ubiquitous, observing them is difﬁcult (their virtuality), and this has led to
slow recognition of their importance. For example, empirical observations reveal direct transactions between individuals,
but do not immediately reveal the contextual network in which they play out. Sitting in a forest, one does
not see networks, but rather an occasional act of grazing, predation, or death. While watching wolves hunt deer, it is
not apparent what shrubs the deer browsed on, now assimilated by the deer and soon the wolf, not to mention
energy tracebacks to solar radiation or nutrients in soil pore water. Since food web components form a connected
reticulum, it is necessary to study and understand them in relation to the latter, not in isolation or as a limited subset
of the whole. Networks are abstract quantities, and paradoxically empirical science must admit abstraction also in
order to inform about “real” reality.
Every component, in fact, must be connected to others through both its input and output transactions. There are
no trivial, isolated components in an ecosystem, not in the sense of being needed, but in the sense of contributing to
overall function when present. Pulling out one species is like pulling one intersection of a spider’s web, such that
although that one particular crosshair is brought closer for inspection, the entire web becomes distorted by the
disturbance. Those sections of the web more closely and strongly connected to any selected node are more affected,
but the entire system is warped as each node is embedded within the whole network of webbed interactions. The
indicator species approach works because it focuses on those organisms that are deeply embedded in the web
(Patten, 2006) and thereby produce large systemic deformation. The food web is, therefore, in fact, more than just
a metaphor; it acknowledges the inherent connectivity of ecosystem interactions.
4.3 FOOD WEBS
Food web ecology has been a driving force in studying the interconnections among species (e.g., MacArthur, 1955;
Paine, 1980; Cohen et al., 1990; Polis, 1991). In fact, we typically think of the abundance and distribution of species in
an ecological community as being heavily inﬂuenced by interactions with other species (Andrewartha and Birch,
1984). But a species is more than just a locus in an envirogram; it is its interactions with other species and with
4.3 FOOD WEBS 47
the environment that provide the connectivity that constructs the ecosystem. The diversity, stability, and behavior of
the whole complex are governed by such interactions. Wholeness is of the essence. In this section we introduce the
standard food web treatment in ecosystem ecology, discuss some of its weaknesses and suggest improvements, and
end with an overview of general insights gained from understanding ecosystem connectivity as revealed by ENA.
Food webs are typically depicted as directed graphs (“digraphs”) representing “who eats whom.” Species and
other trophic categories are graph nodes and the arcs denote transactional ﬂows of energy or matter in response
to the binary relation E ¼ “eats”, E0 its negation. E has the following relational properties:
➢ Irreﬂexive: a E0 adself-consumption is atypical, cannibalism an exception;
➢ Asymmetric: a E b L b E adagain, exceptions occur in carnivorous plants, and also species with mutually
predatory life-cycle stages, such as frogs eating dragonﬂies, and dragonﬂy larvae eating tadpoles;
➢ Intransitive: a E b and b E c L a E c. However, similar relations like N ¼ “nourishes” and T ¼ “transfers energy to”
are transitive: a (N n T) b and b (N n T) c 0 a (N n T) c.
These simple distinctions show how aphysical relations arise from physical energyematter transactions in networks.
For example, let C ¼ “competes with” be another relation. Then a E b and c E b 0 a C c. The relation C is
irreﬂexive, symmetric, and intransitive. In ecological networks, relational transformations frequently occur. For
example, a E b and b E c 0 a H c, where H ¼ “helps” is a new relation, generally reﬂexive, typically asymmetric,
and always transitive.
Fig. 4.1 is a food web diagram typifying what one would ﬁnd in an introductory biology textbook. Energy enters
the primary producer compartment (Phytoplankton) and is transferred “up” the trophic chains by feeding (E) interactions,
ﬁrst grazing and then several levels of predation, losing energy (not shown) along each step, where after a
few steps it has reached a terminal node called Top Predator. The reader can demonstrate how relational intricacy
emerges in networks by attempting to superimpose the above simple relations E, C, and H (and also any made-up
ones) onto the trophic diagram, even with its deﬁciencies. The latter would come to light if one wanted to understand
the entire connectedness as established by the mattereenergy ﬂow pattern of the ecosystem; here are some
of them:
• First, the diagram excludes any representation of decomposers, identiﬁed above as a more fundamental element
of ecosystems than more familiar consumer groups like herbivores, carnivores, and omnivores. While
decomposers have been an integral part of some ecological research (e.g., microbial ecology, eutrophication
models, network analysis, etc.), their role in community food web ecology is just now gaining stature. Prejudices
Phytoplankton
Secondary
Consumer 2
Primary
Consumer 1
Zooplankton 2
Top Predator
Zooplankton 1 Zooplankton 3
Primary
Consumer 2
Primary
Consumer 3
Primary
Consumer 4
Secondary
Consumer 1
Secondary
Consumer 3
FIGURE 4.1 Typical textbook depiction of an aquatic food web. Models of this type are simplistic as revealed in ecological network analyses,
but they still provide baseline thinking for much of mainstream food-web theory. A short list of omitted universals enjoyed by all actual food webs
is: no system boundary, therefore no external inputs or outputs; no microbes or other saprovores. Therefore no energy or matter cycling; the ﬁve
trophic levels are discrete, whereas in reality they are always distributed; the pathways from bottom to top compartments are few and short, in
reality they are many (astronomical) and long; and no other transfer processes than feeding are shown, whereas in nature trophic and nontropic
processes are always commingles and must be unravelled in any realistic food-web analysis. This is a 19th Century diagram appropriate for 21st
Century use only at the most elementary levels of primary education, and then only if erroneous imprinting subject to later correction is to be
allowed.
4. ECOSYSTEMS HAVE CONNECTIVITY48
and biases often work to shape science; for example, few food web ecologists would be likely to classify our
species (Homo sapiens) as saprovorous (detritus-feeding) based on our diet of predominantly dead or not freshly
killed organisms (living microbes, parasites, and inquilants in our food aside).
• Second, the diagram shows the top predators as dead ends for resource ﬂows; if that were the case, there would be
a continual accumulation of top predator carcasses throughout the millennia that biological entities called “top
predators” have existed. Nature would be littered with residues of lions, hawks, owls, cougars, wolves, and other
such animals, even the ﬁercest of the ﬁerce like Tyrannosaurus rex (not to mention other nongrazed or directly
eaten materials such as tree trunks, excreta, etc.). Ours would be a different organic world than it is. Obviously,
this is not the case because in reality there is no “top” as far as food resource and energy ﬂow are concerned. The
bulk of the energy from “top-predator” organisms, like all others, is consumed by other organisms, although
perhaps not as visibly or dramatically as in active predation. Although there have been periods in which
accumulation rates exceed decomposition rates, resulting in among other things the formation of fossil fuels and
limestone deposits, most organic matter most of the time is oxidized to carbon dioxide. Considering the network
organization of ecosystems, the relevance of ﬂows from top predators, in fact all life forms, to detritus is that they
provide additional intrasystem connectivity.
• Third, when decomposers are included in ecosystem models, as is happening more and more, they are treated as
source compartments only. Resources ﬂow out of compartments to exploiting organisms, but are not returned as
the products and residues of such exploitation. For example, in a commonly studied data set of 17 ecological food
webs (Dunne et al., 2002), 10 included detrital compartments, but all of these had node in-degrees equal to zero,
meaning they received no inputs from other compartments. In reality, all organic compartments in food webs are
sources for dead organic material (Lindeman, 1942; Fath and Halnes, 2007). It is easy to correct these ﬂow
structures by allowing material from each compartment to ﬂow into detritus compartments. However, this
introduces cycling into the network and gives a signiﬁcantly different picture of connectance patterns and
resulting system dynamics.
The point is that while food webs have been one way to examine feeding relations in ecology, they are just a starting
point for investigating the full connectivity of ecosystems. Other, more complete, descriptive, and analytical
methodologies are needed. On the last point, it is noteworthy that a substantial ENA/NEA-based literature on
“network trophic dynamics” has been produced in systems ecology that (1) introduces the mathematics of true
Lindeman (1942) food cycles (Higashi et al., 1989, Patten et al., 1990); (2) enables the description, by a “network
unfolding” procedure, of the real nondiscrete, distributed nature of trophic pyramids wherein each trophic level
is made up of elements from many cross-feeding (omnivorous) compartments, and each compartment is made
up of elements in multiple trophic levels (Burns, et al., 1991; Higashi et al., 1991; 1993a,b); and (3) discriminates trophic
and nontrophic processes that combine to produce the true network trophic dynamics of realistic food webs
(Whipple and Patten, 1993). This failure over a quarter century to incorporate new technical developments into
mainstream studies has multiple causes:
• First, food webs as complexly reticulated networks are difﬁcult to describe and quantify empirically. Multiple
scientiﬁc specialtiesddifferent for terrestrial, marine, and freshwater environmentsdspanning biogeochemistry,
and microbial-to-predatoreprey biology and ecology involving a broad phylogenetic spectrum of organisms, are
hard to assemble, coordinate, support, and sustain.
• Second, the visionary, conceptual, mathematical, institutional, and other technical and human management
requirements of true complex-systems study are daunting and hard to acquire and maintain in today’s world
where applied agenda science is favored in the competition for scientiﬁc resources.
• Third, imprinting to existing scientiﬁc culture, social forces for group membership, conformity, and career
advancement, together with limiting material and human resources combine to make reactionary avoidance and
retreat into simplicity an easy practical path. Old ecology has strong forces to keep it in place. New ecology, as
espoused in this book, will accordingly be slow in coming to our rapidly changing world now being shaped by
three accelerationsdin technology, economic globalization, and global change (Friedman, 2016).
4.4 SYSTEMS ANALYSIS
If the environment is organized and can be viewed as networks of ordered and functioning systems, then it is
necessary to have analysis tools and investigative methodologies that capture this wholeness. Just as one cannot
see statistical relationships by visually observing an ecosystem or a mesocosm experiment, one must collect data
on the local interactions that can be estimated or measured, then analyze the connectivity and properties that arise
4.4 SYSTEMS ANALYSIS 49
from this. In that sense, systems analysis is a tool, similar to statistical analysis, but one that allows the identiﬁcation
of holistic, global properties of organization.
Historically, there are several approaches employed to do just that. One of the earliest was Forrester’s (1971) boxand-arrow,
feedback-dynamics diagrams. Building on this approach, Meadows et al. (1972) showed the system inﬂuence
primarily of human population on environmental resource use and degradation. The Forrester approach
also later formed the basis for Barry Richmond’s STELLA modeling software ﬁrst developed in 1985, a widely
used simulation modeling package. This type of modeling is based on a simple, yet powerful, “4 Cs” of modeling
approachdCompartments, Currencies, Connections, Controls. One of Richmond’s main aims with this software
was to provide a tool to promote systems thinking. The ﬁrst chapter of the user manual is an appeal for increased
systems thinking (Richmond, 2001). In order to reach an even wider audience, he developed a “Story of the Month”
feature which applied systems thinking to everyday situations such as terrorism, climate change, and gun violence.
In such scenarios, the key linkage is often not the direct one. System behavior frequently arises out of indirect interactions
(held in NEA to be dominant in causation: CH-2: Network Nonlocality) that are difﬁcult to incorporate into
connected mental models. Many societal problems, be they environmental, economic, or political, stem from the lack
of a systems perspective that goes to remote distributed causes rather than stopping at proximate derivative ones.
Many systems analysis approaches are based on state-space theory (Zadeh and Desoer, 1963), which provides a
conceptual and mathematical foundation to understand how input-state-output sequences work to generate system
dynamics. Linking multiple states together creates networks of causation (Patten et al., 1976), such that input and
output orientation and embeddedness of objects inﬂuence the overall behavior.
Box 4.1 from course material of Patten (pers. comm.) describes a progression from a simple causal sequence in
which one object, through simple connectance, exerts inﬂuence over another. Causal chains and networks exhibit
indirect causation, followed by a degree of self-control in which feedback ensures that an object’s output environ
wraps around to its input environ. Lastly, with holistic causation, systems inﬂuence systems. Using network analysis
several holistic control parameters have been developed (Patten and Auble, 1981; Fath, 2004; Schramski et al., 2005).
Further testing is necessary but these approaches are promising for understanding the overall inﬂuence each species
(or other compartment) has in the system.
Another approach to systems analysis is HT Odum’s use of energy ﬂow diagrams, which spawned the entire subﬁeld
of emergy (embodied energy) ﬂow analysis for ecosystems, industrial systems, and urban systems (e.g., Odum,
1996; Bastianoni and Marchettini, 1997; Wang et al., 2005; Huang, and Chen, 2005; Tilley and Brown, 2006).
The systems analysis approach is also an organizing principle for much of the work at the International Institute
of Applied Systems Analysis (IIASA) in Laxenburg, Austria. This institute was established during the height of the
Cold War as a meeting ground for East and West scientists and found common ground in the systems approach
(www.iiasa.ac.at). Although its focus is not ecology, IIASA has produced several large-scale interdisciplinary environmental
models such as Greenhouse Gas e Air Pollution INteractions and Synergies (GAINS), GLObal BIOsphere
Management Model (GLOBIOM), and the long-term energy planning model MESSAGE.
Another systems approach, Food Web Analysis, is the main one from ecology, but as stated earlier has limited
perspective by including only the feeding relations of organisms easily observed and measured, largely ignoring
decomposition processes and abiotic resources, and operating with a limited analysis toolbox. For example, without
a basis in ﬁrst principles of thermodynamics or graph theory (which are now starting to be incorporated) the
discipline has been trapped in several “debates” such as “top-down” versus “bottom-up” control, and
BOX 4.1BOX 4.1
D I S T R I B U T E D C A U S A T I O N I N N E T W O R K S
1. The causal connective: B / C
There is only a direct effect of B on C
2. The causal chain: A / B(A) / C
B affects C directly, but A inﬂuences C indirectly
through B, and C has no knowledge of A
3. The causal network:{A} / B({A}) / C
{A} is a system, with a full interaction network giving
potential for holistic determination
4. Self-inﬂuence:{A(C)} / B({A(C)}) / C
C is in network {A} and exerts indirect causality on
itself
5. Holistic inﬂuence:{A(B,C)} / B({A(B,C)}) / C
B is also in {A} so that B, C and all else in {A} inﬂuence
C indirectly.
4. ECOSYSTEMS HAVE CONNECTIVITY50
interaction-strength determination, which have ready alternatives in ENA methodologies. Speciﬁcally, regarding
top-down versus bottom-upcontrol, Patten and Odum (1981), Fath (2004), and Schramski et al. (2006) all used
network analysis to demonstrate and try to quantify the cybernetic and distributed nature of ecosystems. ENA arose
speciﬁcally to address issues of wholeness and connectivity. It has two major directions, Ascendency Theory (Ulanowicz,
1986a; 1997), which is concerned with ecosystem growth and development, and Environ Theory or NEA
(Patten, 1978), an environmental system theory. Ascendency Theory is summarized elsewhere in this volume (see
Box 6.1). After some general remarks on ENA, the remainder of this chapter will sketch connectivity perspectives
from “20 Cardinal Hypotheses” (CHs) developed in Environ Theory.
4.5 ECOSYSTEM CONNECTIVITY AND ECOLOGICAL NETWORK ANALYSIS
The exploration of network connectivity has led to the identiﬁcation of many interesting, important, and nonintuitive
properties. Reiterating previously made, and important, distinctions in this chapter, ENA starts with the
assumption that a system can be represented as a network of nodes (vertices, compartments, components, etc.)
and the connections between them. When there is an exchange (ﬂow) of matter or energy between any node pair,
we say there is a “direct” transfer, a transaction, between them. This empirical transfer is, however, typically
made up of multiple indirect transfer threads originating at many sources. Empirical directness is better referred
to as adjacent directness, meaning macroscopically direct between two nodes. Adjacency matrices, A ¼ (aij), in
NEA mark the presence (aij ¼ 1) or absence (aij ¼ 0) of such binary links between all (i, j) node pairs in a system.
The binary transactions that convey adjacent causation are conservative and zero-sum. They give rise to both adjacent
and nonadjacent, meaning empirically and macroscopically indirect, relations, which are nonconservative and
nonzero-sum. Adjacency matrices are the basis for pathway analysis in ENA. There is an important distinction to be
made in transactional ﬂows between analytical (microscopic) directness and empirical (macroscopic) directness, both of
which occur in adjacent links, aij ¼ 1, between (i, j) compartment pairs. This is illustrated in Fig. 4.2. Basically, empirically
direct refers to a bulk transfer of substance from j to i, whereas analytically direct refers to the ﬁrst transfer of
substance from j to i after its boundary introduction at compartment j.
Nobel Prizeewinning economist Wassily Leontief ﬁrst developed a form of network analysis called InputeOutput
Analysis (Leontief, 1936, 1951, 1966). Based on system connectivity, this has been applied to many ﬁelds. For example,
there is a large body of research in the area of social network analysis, which uses the inputeoutput methodology to
investigate how individual lives are affected by their web of social connections (Wellman, 1983; Wasserman and
Faust, 1994; Trotter, 2000). Inputeoutput analysis has also successfully been applied to study the ﬂow of energy
or nutrients in ecosystem models (e.g., Wulff et al., 1989; Higashi and Burns, 1991).
Bruce Hannon (1973) is credited with ﬁrst applying economic inputeoutput analysis techniques to ecosystems.
He pursued this research primarily to determine the interdependence of organisms in an ecosystem based on their
direct and indirect energy ﬂows. Others quickly picked up on this powerful new application and further reﬁned and
extended the methodology. Some of the earlier research studies in this ﬁeld include Finn (1976, 1980), Patten et al.
(1976), Levine (1977, 1980, 1988), Barber (1978a, b), Patten (1978, 1981, 1982, 1985, 1992), Matis and Patten (1981),
Higashi and Patten (1986, 1989), Ulanowicz (1980, 1983, 1986, 1997), Ulanowicz and Kemp (1979), Szyrmer and Ulanowicz
(1987), and Herendeen (1981, 1989). Both Environ Analysis and Ascendancy Theory rely on the Inpute
Output Analysis basis of ENA.
1 32
FIGURE 4.2 Distinction between adjacent, nonadjacent, and direct and indirect transactional ﬂows. The link ﬂow from compartment 1 to 2 is
both adjacent (next to) and analytically direct (ﬁrst transfer after boundary input). The link ﬂow from compartment 2 to 3 is adjacent (empirically
or macroscopically direct) but with analytically direct (white) and indirect or nonadjacent (black) components. This is a composite ﬂow, not a
direct ﬂow in the analytical (microscopic) sense even though empirically it appears “direct.” Most empirical ﬂows in ecological networks,
including adjacent link ﬂows, have many sources and thus are actually composite ﬂows with a high proportion of (analytically) indirect elements.
4.5 ECOSYSTEM CONNECTIVITY AND ECOLOGICAL NETWORK ANALYSIS 51
Network-related software developments such as ECOPATH (Christensen and Pauly, 1992), EcoNetwork
(Ulanowicz, 1999), WAND (Allesina and Bondavalli, 2004), NEA.m (Fath and Borrett, 2006), EcoNet (Kazanci,
2007), and ena.R (Borrett and Lau, 2014; version 3.0, 2017) are available to perform computations and will ease
the dissemination of these techniques.
4.6 NETWORK ENVIRON ANALYSIS PRIMER
Details of NEA have been presented elsewhere (e.g., Patten, 1978, 1981, 1982, 1985, 1991, 1992), so here we will
provide only a general overview. This will give background for this chapter’s concluding section describing
NEA’s “CHs.” Ecosystem connections, such as ﬂow of energy or nutrients, provide the conceptual framework.
The directed connections between ecosystem compartments provide necessary and sufﬁcient information to
construct a network diagram (digraph) and its alternative representation as an adjacency matrixdas explained
above, an n Â n matrix with 1’s or 0’s in each element depending on whether or not the corresponding compartment
pairs are adjacently connected.
Using this information, structural analysis is employed to identify the number of indirect pathways and the rate at
which these increase (as they do, exponentially) with increasing path length. With quantitative information
regarding the storages and ﬂows (internal and boundary) of the system compartments, functional analyses become
possibledfor ﬂows, storages, and utilities (Box 4.2). Each of these analyses relies on a base matrix, the “ABCDs” of
NEA as described below. Structural analysis proceeds from the direct adjacency matrix, A ¼ (aij). Functional
analyses follow:
• for ﬂows, from intensive ﬂow matrix, B ¼ (bij) (or with diagonal elements zeroed, in probabilistic form, G ¼ (gij));
• for storages, from a partial turnover rate matrix C ¼ (cij), nondimensionalized to a probabilistic form, P ¼ (pij);
• and for utilities, D ¼ (dij).
The network parameters, gij, pij, and dij, are all dimensionless, enabling them to be manipulated algebraically at
will without carrying along any complicating physical units. Contributions along pathways of lengths m ¼ 0, 1, 2, .
are revealed through powers of the direct matrices, G, P, and D. For example, G represents analytically direct ﬂow
intensities; G2
gives second-order indirect ﬂow contributions that have traveled over 2-step pathways from boundary
introduction, represented by the zero’th-order term, G0
; G3
, third-order indirect over 3-step pathways; and .
Gm
, mth order over m-step pathways. Given the series constraints provided by boundary outputs > 0 (thermodynamic
dissipation, a universal property of ecological systems), higher order terms approach zero as the series
BOX 4.2
O V E R V I E W O F N E T W O R K E N V I R O N A N A L Y S I S
Pathway (aij) -
enumerates
number of
pathways in a
network
Flow Analysis (gij = fij/Tj) –
identifies flow intensities along
indirect pathways
Network Environ Analysis
Storage Analysis (cij = fij/xj) –
identifies storage intensities along
indirect pathways
Utility Analysis (dij = (fij f– ji)/Ti) –
identifies utility intensities along
indirect pathways
4. ECOSYSTEMS HAVE CONNECTIVITY52
converges; that is, Gm
/ 0 as m / N. This makes it possible to sum the direct (m ¼ 1) and all indirect contributions
(m ¼ 2 to N) to obtain an integral, or holistic, system evaluation (see Box 4.3, and later, Fig. 4.4). In the functional
analyses, integral ﬂow, storage, or utility values are the summation of the direct plus all indirect contributions (in
Box 4.3, N, Q, and U, respectively). In this manner it is possible to quantify the total indirect contribution and
compare it to the direct ﬂows. The result in well-conditioned realistic models is almost always direct < indirect,
and most often direct << indirect, prompting the conclusion (see section CH-2: Network Nonlocality) that indirect
effects are dominant in nature (Higashi and Patten, 1989), and holism therefore reigns supreme, leading to the need
for holistic methodologies that account for and quantify wholeness and indirectness.
Although nuanced conceptually, NEA analysis is not very demanding computationally, though it does require
some familiarity with matrix algebra and graph theory concepts. The notations and methodology of the two
main ENA approaches, ascendency and environ analyses, differ slightly and have been developed in detail in the
above-cited references, among others. Box 4.2 lists the main lines of NEA development, in chronological order
from top to bottom at the right, and also shows how all of these are anchored in pathway analysis. Box 4.3 summarizes
the principal notations, and Fig. 4.4 illustrates these for a simple compartment model (upper left) and also how
they are related to the deﬁning system equations (upper right), in both time-forward and time-backward directions.
The ﬁrst is input (z)-driven and generates output environs to boundary outputs (y); the second is output (y)-referenced
and back-traces input environs to boundary inputs (z). Note that the F matrix used in the deﬁning equations
has negative throughﬂows (T) on the principal diagonal, and is the form used to calculate the B and B0 matrices of
throughﬂow analysis. When the diagonals of F are zeroed, F0, this is the form used to compute the G matrices. Note
that the throughﬂow and storage analyses have completely parallel development. In fact, all four analyses of NEA
(Box 4.2) have parallel treatments throughout the theory, including the generation (Fig. 4.4) of direct and indirect
relationships of all orders (m ¼ 0, 1, ., N) by power series, Mm
, comprising the core “ABCDs” of environ analysis.
M ¼ {A, B, C, D} is the set of base matrices for, respectively, pathway (A), throughﬂow (B), storage (C), and utility
(D) analyses. These are the primary methodologies for investigating system structure, function, and organization
employing NEA.
Fig. 4.5 illustrates the breakout of three output and three input environs from the three-compartment system previously
employed to illustrate discussions in connection with Figs. 4.3 and (later) 4.10. The environs are virtual, but
the compartments that form as their cumulative aggregates are concrete, and just so, the physical biota and abiota of
nature are made up from such threads of substance transformation and passage from histories to futures through the
tangible objects in the systems they collectively become, which in their turn also move and change through time. It is
a complex conception, unregistered in human cognitiondinvisible threads of energyematter ﬂow and ﬂow delay
BOX 4.3BOX 4.3BOX 4.3
B A S I C N O T A T I O N S F O R N E T W O R K E N V I R O N A N A L Y S I S
Flows: fij ¼ within system ﬂow directed from j to i,
comprise a set of transactive ﬂows.
Boundary transfers: zj ¼ input to j, yi ¼ output from i.
Storages: xj represent n storage compartments (nodes).
Throughﬂow: T
ðinÞ
i ¼ zi þ
Pi¼1
n
fij
T
ðoutÞ
i ¼
Xi¼1
n
fij þ yi
At steady state: T
ðinÞ
i ¼ T
ðoutÞ
i hTi
Nondimensional, intercompartmental ﬂow intensities
are given by gij ¼ fij/Tj
Nondimensional, intercompartmental, intensive
utilities are given by, dij ¼ (fijÀ fji)/Ti.
Nondimensional, storage-speciﬁc, intercompartmental
ﬂows are given by pij ¼ cijDt, where, for i s j, cij ¼ fij/xj,
and for i ¼ j, pii ¼ 1 þ ciiDt, where cii ¼ eTi/xi.
Nondimensional integral ﬂow, storage, and utility intensity
matrices, N, Q, and U, respectively, can be
computed as the convergent power series:
N ¼ G0
þ G1
þ G2
þ G3
þ . þ Gm
þ . ¼ ðIeGÞe1
Q ¼ P0
þ P1
þ P2
þ P3
þ . þ Pm
þ . ¼ ðIePÞe1
U ¼ D0
þ D1
þ D2
þ D3
þ . þ Dm
þ . ¼ ðIeDÞe1
The mth order terms, m ¼ 1,2, ., account for interﬂows
over all pathways of lengths m in the system. The terms
m ¼ 0 denote identity matrices I; these allow cross-boundary
ﬂows to enter the system from its surrounding
environment.
4.6 NETWORK ENVIRON ANALYSIS PRIMER 53
(storage) that comprise these environs that are, in effect, extended building blocks of systems’ component parts
deﬁned at the system boundaries.
The succeeding sections provide two numerical examples to illustrate typical results generated by NEA. Then, the
chapter concludes with a summary of NEA’s “20 CHs” to show the span of new ecological insights emergent from
the study of network organization. There is unarguably very “New Ecology” in this, not established, certainly, but
hypothesized from formal theory, and it remains for the mainstream ﬁeld to connect to these new perspectives and
begin to evaluate them for permanent standing or not in the ediﬁce of ecological knowledge.
Network Example 1dAggradation
Using NEA, it is possible to demonstrate how increasing connections in networks are beneﬁcial for both system
components and the whole. Fig. 4.6 presents a very simple example, presuming steady-state (input ¼ output for
compartments and the system) and ﬁrst-order donor-determined ﬂows. The latter are often used in ecological
modeling to cut through to and reﬂect the fundamental (thermodynamic) gradient dynamics (Mu¨ller, 1998) that
FIGURE 4.3 Summary of basic matrices and formulas for steady state throughﬂow (T) and storage (x) environ analyses (NEA). All matrices
are oriented from column (j) to rows (i). The upper left panel displays a three-compartment digraph as an example. The lower left panel shows
matrices and vectors associated with this digraph. A0 and A1 are adjacency matrices, respectively, without (diagonal elements ¼ 0) and with
(diagonal elements ¼ 1) self-loops used to represent storage; F0 (zero diagonals) and F (negative throughﬂow diagonal elements) are ﬂow
matrices; 1 ¼ vector of ones, z ¼ input vector, x ¼ state (storage) vector, T ¼ throughﬂow vector, and y ¼ output vector. The middle and right-hand
panels display the time-forward (upper left) and time-backward (upper right) deﬁning ﬂow equations for, respectively, input-driven output
environ analysis, and output-referenced input environ analysis. These are followed by equation forms derived from them for throughﬂow (middle
panel) and storage (right panel) analyses, then the coefﬁcient matrices for these equations: B, B0 and G, G0 for throughﬂow generation, and C, C0, Q,
Q0, and S, S0 for storage generation. Lastly, the steady-state mappings of boundary inputs (z) and outputs (y) into the throughﬂows and storages
are shown. The mapping matrices are convergent sums of inﬁnite series (Fig. 4.4) representing mth
order transfers (m ¼ 0, 1, ., N) of substances
over all pathways of all lengths m leading from points of entrance to points of exit in the system. Source: Patten (2016, Fig. 3.1, p. 67).
4. ECOSYSTEMS HAVE CONNECTIVITY54
are in fact the ultimate causes behind whatever other kinds of coupling functions modelers with a more mechanistic,
proximate-causal view prefer. Fig. 4.6A shows the throughﬂow and energy (as exergy) storage (based on a retention
time of ﬁve time units) in the two components with no coupling, i.e., no network interconnections. Making a connection
between the two compartments links them physically, and not only changes their individualistic behavior but
also alters the overall system performance. In this case, the throughﬂow and exergy storage both increase because
the part of the ﬂow that previously exited the system is now used by the second compartment. Total system throughﬂow
(TST; “function”as ﬂow), exergy stored (“function”as storage), and average path length (“structure”) all increase.
The advantages of extending structure and function in integrated systems is well known from industrial
ecology, in which, as in saprotrophy, waste from one industry is used as raw material for another industry (e.g., Gradel
and Allenby, 1995; McDonough and Braungart, 2002; Jørgensen, 2006).
Network Example 2dCone Spring Ecosystem
For the second example, we look at the same Cone Spring ecosystem model from the previous chapter, where it
was used to demonstrate Ascendency Analysis calculations. This will also serve to show some similarities and differences
between ascendency and environ analyses. Figure B3.1 (in Box 4 of Chapter 3) shows only the system ﬂows
as modeled in the original ecosystem study. Since standing stocks (storages) were omitted there (they are shown in
Fig. 4.7), we limit ourselves here to NEA’s ﬂow and utility analyses. First, referring to Fig. 4.7, the left panel contains
code for online simulation software, EcoNet (Kazanci, 2009), to simulate and analyze the Cone Spring model for
NEA’s and some other measures. The reader is encouraged to open the EcoNet link (http://eco.engr.uga.edu/),
copy and paste the Fig. 4.7 program into it, and run the model. This will generate the digraph shown in the upper
right panel, and also the selected results (and quite a few more) shown in the lower right-hand panel. EcoNet documentation
will also serve as an instructional resource for interested readers.
boundary
(m = 0)
boundary
(m = 0)
adjacent
(m =1)
adjacent
(m =1)
indirect
(m >1)
indirect
(m >1)
dx/dt = F1 + z = FT1 + y =dx/dt = F1 + z = FT1 + y =
AT: I + (AT) + (AT)2 + (AT)3 + … + (AT)m + …AT: I + (AT) + (AT)2 + (AT)3 + … + (AT)m + …
A : I + (A) + (A)2 + (A)3 + … + (A)m + …A : I + (A) + (A)2 + (A)3 + … + (A)m + …
D = B'– B; I + (D) + (D)2 + (D)3 + . . . = (I – D)–1 = UD = B'– B; I + (D) + (D)2 + (D)3 + . . . = (I – D)–1 = U
output referencedoutput referencedinput driveninput driven
B': I + (I+B') + (I+B')2 + (I+B')3 + … + (I+B)m + … = –B'–1B': I + (I+B') + (I+B')2 + (I+B')3 + … + (I+B)m + … = –B'–1
B : I + (I+B) + (I+B)2 + (I+B)3 + … + (I+B)m + … = –B–1B : I + (I+B) + (I+B)2 + (I+B)3 + … + (I+B)m + … = –B–1
C: I + (I+CΔt) + (I+CΔt)2 + (I+CΔt)3 + … + (I+CΔt)m + … = –C–1C: I + (I+CΔt) + (I+CΔt)2 + (I+CΔt)3 + … + (I+CΔt)m + … = –C–1
C': I + (I+C'Δt) + (I+C'Δt)2 + (I+C'Δt)3 + … + (I+C'Δt)m + … = –C'–1C': I + (I+C'Δt) + (I+C'Δt)2 + (I+C'Δt)3 + … + (I+C'Δt)m + … = –C'–1
boundary
(m = 0)
adjacent
(m =1)
indirect
(m >1)
dx/dt = F1 + z = FT1 + y =
AT: I + (AT) + (AT)2 + (AT)3 + … + (AT)m + …
A : I + (A) + (A)2 + (A)3 + … + (A)m + …
D = B'– B; I + (D) + (D)2 + (D)3 + . . . = (I – D)–1 = U
output referencedinput driven
B': I + (I+B') + (I+B')2 + (I+B')3 + … + (I+B)m + … = –B'–1
B : I + (I+B) + (I+B)2 + (I+B)3 + … + (I+B)m + … = –B–1
C: I + (I+CΔt) + (I+CΔt)2 + (I+CΔt)3 + … + (I+CΔt)m + … = –C–1
C': I + (I+C'Δt) + (I+C'Δt)2 + (I+C'Δt)3 + … + (I+C'Δt)m + … = –C'–1
FIGURE 4.4 The “ABCDs” of steady-state network environ analysis. Matrix pairs are for forward (from inputs) and reverse (from outputs)
directions. A, AT
(transpose) are adjacency matrices used in pathway analyses. B, B0 are throughﬂow-speciﬁc ﬂows used in throughﬂow analyses. C
and C0 are storage-speciﬁc ﬂows used in storage analyses. D ¼ B0 À BT
¼ C0 À CT
(representing throughﬂow- and storage-speciﬁc net ﬂows,
respectively) is the generating matrix for environ utility analyses which provides the metrics for network synergism, the ultimate goal function in
the Janus Hypothesis (see section CH-16: Network Janus Enigma Hypothesis). Note that D brings both reverse (B0, C0) and forward environ
orientations into this equivalently ﬂow- or storage-based value oriented analysis, the key to which is sum(D) ¼ 0 (zero-sumness) whereas sum(U)
>>0 (nonzero-sumness).
4.6 NETWORK ENVIRON ANALYSIS PRIMER 55
From Fig. 4.7 (and also the EcoNet run) we obtain the following information (recall the book’s convention that
ﬂows are oriented from columns to rows; also, the matrices A and F here correspond to A0 and F0 of Fig. 4.3):
A ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
4
0 0 0 0 0
1 0 1 1 1
0 1 0 0 0
0 1 1 0 0
0 0 1 0 0
3
7
7
7
7
7
7
7
7
7
7
7
7
7
5
F ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
4
0 0 0 0 0
8881 0 1600 200 167
0 5205 0 0 0
0 2309 75 0 0
0 0 370 0 0
3
7
7
7
7
7
7
7
7
7
7
7
7
7
5
z ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
4
11184
635
0
0
0
3
7
7
7
7
7
7
7
7
7
7
7
7
7
5
y ¼ ½ 2303 3969 3530 1814 203 
E2
z2
f21|2
f13|2
f31|2
f32|2
x1|2 x2|2
x3|2
E3
z3
f21|3
f13|3
f31|3
f32|3
x1|3 x2|3
x3|3
y2
y3
dx1/dt = z1 + f31 – (y1 + f21 + f31)
dx2/dt = z2 + f21 – (y2 + f32)
dx3/dt = z3 + f31 + f32 – (y1 + f21 + f13)
y3
T2T3
T1
y1 y2
z1 z2
z3
x2
x3
f21
f32f31
f13
x1
E1
f21|1
f13|1
f31|1
f32|1
z1
x1|1 x2|1
x3|1
f21|2'
f13|2'
f31|2'
f32|2'
E2'
x3|2'
x1|2' x2|2'
E1'
y1
f21|1'
f13|1'
f31|1'
f32|1'
x2|1'
x3|1'
x1|1'
f21|3'
f13|3'
f31|3'
f32|3'
x2|1'
x3|1'
E3'
x1|1'
(A)
(B)
(C)
FIGURE 4.5 Depiction of the 2n environs of an n ¼ 3 compartment model. (A) Composed model showing compartment stocks (xi), boundary
inputs (zi), outputs (yi), interior ﬂows (fij), throughﬂows (Ti), and state-transition equations. (B) Partition of model (A) into its three output environs
Ek¼1,2,3 encompassing all the stock (xijk) and ﬂow (fijjk) elements generated by each labeled input (zk), k ¼ 1, 2, 3. (C) Partition of model (A) into its
three input environs Ek
0
¼1,2,3 encompassing all the stock (xijk
0) and ﬂow (fijjk
0) elements reachable against the direction of the arrows from each
labeled output (yk). Each of the six environs shown has unique structural and dynamic characteristics that sum to the whole system (A) expressed
as composite stocks and ﬂows with empirically measurable aggregate properties. Source: Modiﬁed after Patten (2015, Fig. 2, p. 49).
4. ECOSYSTEMS HAVE CONNECTIVITY56
Compartmental throughﬂow is the sum of either entering or exiting ﬂows to or from the compartments because at
steady state these are equal; therefore, Ti ¼ zi þ
P
j
fij ¼
P
j
fji þ yi. TST is the sum of all compartmental throughﬂows:
TST ¼
P
i
Ti; i ¼ 1, ., n compartments, which here where n ¼ 5 is 30,626 kcal mÀ2
yÀ1
. (Note, in Ascendency
Analysis, that compartmental throughput. TPi, is a concept that includes both inputs (zi) and outputs (yi): TPi ¼ zi þ
Pj¼1
n
fji þ yi). Continuing on with the NEA we present (refer to Box 4.3 and Fig. 4.3) the nondimensional ﬂow and utility
matrices, G ¼ I þ B and D ¼ G0 À GT (transposed), respectively:
G ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
0 0 0 0 0
0:794 0 0:308 0:084 0:451
0 0:453 0 0 0
0 0:201 0:014 0 0
0 0 0 0:155 0
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
D ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
0 À0:794 0 0 0
0:773 0 À0:314 À0:184 0:015
0 0:693 0 À0:014 0
0 0:885 0:0315 0 À0:155
0 À0:451 0 1:00 0
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
A
50 exergy
units
B
50 exergy
units
10
1010
10
10 10
10 10
5
5
15
7
5
2
13
No coupling between A and B. The throughflow is 20
and the exergy storage is 100 exergy units
A coupling from A to B . The throughflow is now 25
and the exergy storage is 125 exergy units.
A B
A C
(A)
(B)
(C)
50 exergy
units
60 exergy
units
75 exergy
units
75 exergy
units
A coupling from A to B and a coupling from B to A.The
throughflow is now 27 and the exergy storage is 135 exergy units
FIGURE 4.6 Two-compartment system illustrating
network aggradation as increased total system throughﬂow
and, let us say, usable energy (exergy) storage relative
to boundary inputs resulting from internal transactional
coupling. (A) No coupling; (B) single-link coupling;
(C) cyclic coupling.
4.6 NETWORK ENVIRON ANALYSIS PRIMER 57
Running the analysis gives the integral ﬂow intensity matrix:
N ¼
2
6
6
6
6
6
6
6
6
6
6
4
1:000 0 0 0 0
0:958 1:207 0:374 0:186 0:545
0:434 0:547 1:169 0:084 0:247
0:199 0:251 0:092 1:039 0:113
0:031 0:039 0:014 0:161 1:018
3
7
7
7
7
7
7
7
7
7
7
5
along with the following information (which appears also in Fig. 4.7):
• the Finn Cycling Index is 0.0919, meaning about 9% of ﬂow is cycled ﬂow;
• the ratio of indirect to direct ﬂow is 0.9126, meaning an almost equal amount (0.91:1.00) of TST travels over
indirect versus direct paths. This is actually a rare exception to the rule as in most models indirect ﬂow
contributions usually exceed direct ones; and
# CONE SPRING ENERGY MODEL
# Flow units kcal/m^2*y^-1
# M Williams & D Crouthamel
# In BC PaƩen (Ed) 1971 SimulaƟon & Systems
Analysis in Ecology, VI
# Academic Press, NY 607 pp
# Data from PaƩen 1982 Elementary parƟcles for
ecology.
# Am. Nat. 119: 179-219
#
# INPUTS
* -> x1_Plants c=11184.0
* -> x2_Detritus c=635.0
#
# INTERFLOWS
x1_Plants -> x2_Detritus c=31.1649123
x2_Detritus -> x3_Bacteria c=1.4539588
x2_Detritus -> x4_DetriƟvores c=0.6450522
x3_Bacteria -> x4_DetriƟvores c=0.6440823
x3_Bacteria -> x2_Detritus c=13.7238422
x4_DetriƟvores -> x2_Detritus c=3.3333333
x4_DetriƟvores -> x5_Carnivores c=6.1666667
x5_Carnivores -> x2_Detritus c=9.8235294
#
# OUTPUTS
x1_Plants -> * c=8.0771930
x2_Detritus -> * c=1.1094689
x3_Bacteria -> * c=30.2675815
x4_DetriƟvores -> * c=30.2333333
x5_Carnivores -> * c=11.9411765
#
# INITIAL CONDITIONS
x1_Plants = 285.0
x2_Detritus = 3579.4
x3_Bacteria = 116.6
x4_DetriƟvores = 60.0
x5_Carnivores = 17.0
Link Density 1.6
Connectance 0.32
Total System Throughﬂow 30626.1
Finn's Cycling Index 0.0919357
Indirect Eﬀects Index 0.912593
Ascendency 56076.8
Development Capacity 130264
AggradaƟon Index 2.59126
Synergism Index 7.11204
Mutualism Index 2.125
HomogenizaƟon Index 1.87497
hƩp://eco.engr.uga.edu
FIGURE 4.7 EcoNet code and selected results for the Cone Spring energy-ﬂow model. EcoNet (Kazanci, 2009) is an online modeling,
simulation, and analysis tool for transactional network models. Directions for its use together with examples are included, enabling convenient
self-instruction.
4. ECOSYSTEMS HAVE CONNECTIVITY58
• the network evenness measure (homogenization) is 1.8750, meaning values in the integral ﬂow matrix, N, are
nearly twice as evenly distributed as values in the direct ﬂow matrix, Gdwhich is obvious just from inspecting
the two matrices. Another analysis possible with the ﬂow data, but not displayed here, is calculation of the actual
unit environs, i.e., ﬂow decompositions showing the amount of ﬂow within each system environ (Fig. 4.5)
generated by one unit of input or needed to generate one unit of output at each compartment. The jth columns of
N give this for each jth output environ, and the ith rows of N0 give it for the ith input environs. Regarding utility,
this particular example is one in which the powers of D do not enjoy guaranteed convergence (the maximum
eigenvalue of D is greater than 1), which was originally thought to be require to compute valid network synergism
metrics. However, Kazanci and Adams (2017) have recently shown that convergence may not be a needed
property in order to interpret valid utility information from the inverse integral utility matrix, U ¼ (I À D)À1
. This
is another area of NEA that calls to attention how unexpected and unintuitive results frequently come to be
generated in network analyses. Although this is an area of ongoing research, we can still speculate about the
proximate and ultimate ecological relations in such cases by looking at paired signs across the principal diagonals
of, respectively, the proximate (D) and ultimate (U) utility matrices:
sgnðDÞ ¼
2
6
6
6
6
6
6
6
6
6
6
4
0 À 0 0 0
þ 0 À À þ
0 þ 0 À 0
0 þ þ 0 À
0 À 0 þ 0
3
7
7
7
7
7
7
7
7
7
7
5
sgnðUÞ ¼
2
6
6
6
6
6
6
6
6
6
6
4
þ À þ þ À
þ þ À À þ
þ þ þ À þ
þ þ À þ À
þ þ À þ þ
3
7
7
7
7
7
7
7
7
7
7
5
The direct utility matrix is zero-sum in that for every donor there is a receiver of the ﬂow. In ecological terms we
think of a (þ, À) relationship as consumption (predation) or exploitation, but more generally it represents zero-sum
conservative transfer from one compartment to another. Matching compartments pairwise across the main diagonal
gives the relationship type as shown in Table 4.1.
Notice ﬁrst, that all the compartments are interrelated ultimatelydthere are no zero elements in the U matrix.
Next, notice that while two of the neutral (0, 0) direct relations in D become either (þ, À) exploitation or (À, þ)
altruism, two others become (þ, þ) mutualisms. Also, the proximate (À, þ) relation in D directed from compartment
2 to 5 becomes (þ, þ) mutualism in the holistic U-matrix evaluation. In this, the presence of each compartment of a
pair beneﬁts the other. Lastly, note that overall the integral matrix has twice as many positive signs (12) as the proximate
matrix (6), where each added ultimate þ replaces either a proximate 0 or À; this illustrates the holistic
TABLE 4.1 Direct and Integral Relations for Cone Spring Ecosystem.
Direct Integral
(sd21, sd12) ¼ (þ, À) 0 agonism (su21, su12) ¼ (þ, À) 0 agonism
(su31, su13) ¼ (0, 0) 0 neutralism (su31, su13) ¼ (þ, þ) 0 mutualism
(su41, su14) ¼ (0, 0) 0 neutralism (su41, su14) ¼ (þ, þ) 0 mutualism
(su51, su15) ¼ (0, 0) 0 neutralism (su51, su15) ¼ (þ, À) 0 agonism
(sd32, sd23) ¼ (þ, À) 0 agonism (su32, su23) ¼ (þ, À) 0 agonism
(sd42, sd24) ¼ (þ, À) 0 agonism (su42, su24) ¼ (þ, À) 0 agonism
(sd52, sd25) ¼ (À, þ) 0 altruism (su52, su25) ¼ (þ, þ) 0 mutualism
(sd43, sd34) ¼ (þ, À) 0 agonism (su43, su34) ¼ (À, À) 0 competition
(su53, su35) ¼ (0, 0) 0 neutralism (su53, su35) ¼ (À, þ) 0 altruism
(sd54, sd45) ¼ (þ, À) 0 agonism (su54, su45) ¼ (þ, À) 0 agonism
4.6 NETWORK ENVIRON ANALYSIS PRIMER 59
emergence of network mutualism in proximate to ultimate utility transitions, which is one of the “CHs” described in
the next section. The reader is referred to other literature (Patten, 1991, 1992; Fath and Patten, 1998; Fath, 2006, Patten
and Whipple, 2007; Tuomonen et al., 2014) for other examples of utility analysis calculations and interpretations.
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS
We conclude this chapter with a review of some ecological “Principles in waiting” derived from quantitative
network properties inherent in NEA mathematics. In this, we will in effect be pursuing an exercise that is discovering
ecology in mathematical models. There are 20 “CHs” (see Patten, 2016a for a more extended presentation
than here) that arose from over 40 years of NEA development at the University of Georgia, USA. These hypotheses
might well be said to circumscribe a general holistic theory of ecology whose baseline orientation is to energye
matter transactional ﬂow and storage networks.
Grounding Hypotheses
The ﬁrst two CHs are anchoring properties of network structure (pathways) and function (ﬂows and storages).
They are foundational to the entire hypothesis set and all other aspects of network organization.
CH-1: Network Pathway Proliferation
After conservative substance enters a system through its boundary, it is transacted, that is, conservatively transferred,
between the living and nonliving compartments within the system, being variously transformed and reconﬁgured
by work performed along the way. The substance that enters as input to a particular compartment always
while in the system remains within the output environ of that, and only that, compartment. Thus, ﬂows within
output environs as within-system partition units (Patten, 1978) deﬁned by different inputs become entangled within
and between the tangible (empirical) components of the system (this is network enfolding, CH-9, another of the
properties discussed later). Reciprocally, these same empirical ﬂows running in output-referenced input environs
become progressively untangled to appear as boundary outﬂows comprising the said system outputs.
In accordance with second-law requirements, a part (and eventually all) of this substance is continually dissipated
back to the environment by the entropy-generating processes that do work and make the system function. At any
point in time subsequent to initial introduction, remaining substance continues to be transported around the system,
and as it does so it traces out virtual pathways that extend in length by one unit at each transfer step. Pathway
numbers increase exponentially with this increasing pathway length, with the result that the interior of the system
becomes a complex interconnected network, albeit virtual, in which all components communicate, indirectly if not
directly, with all or almost all (depending on the connectivity structure) the others. This pathway proliferation is one
of the sources of the essential holism that environ theory impresses onto the interiors of systems. Without the openness
of semipermeable boundaries, pathways would neither begin nor end (except at the Big Bang and Big Crunch),
and the interior networks initiating output environs and terminating input environs at boundary points of entry and
exodus would never exist.
Ecological systems at all scales are, by their higher order, indirect (m ! 2) pathways, more highly interconnected
and interdependent than denoted by adjacent, ﬁrst-order (m ¼ 1) linkages alone. In ecosystems, in particular, the
pathways of food chains hidden in food webs are much longer, energy cycling is the rule, and food web constituents
are much more interrelated (network homogenization, CH-3) than usually described in empirical studies. These
properties, demonstrated decades ago in NEA, still run generally counter to currently accepted ecological thought.
Such is the cultural schism still in force between conventional ecology and the systems ecology espoused in this
book.
CH-2: Network Nonlocality
As pathways extend, the amount of substance carried along at any given step is less than in the previous step due
to dissipation. Therefore, pathways eventually end as they run out of originally introduced material. The rate of
decay can be expressed as an exponential function, just as is the rate of pathway proliferation. Dissipation and
pathway extension and growth in numbers are in conﬂict, but early in the transactional sequence following introduction
the rate of the former exceeds that of the latter such that the total substance transferred between compartment
pairs over the aggregate of pathways of a given length interconnecting them exceeds that of direct
4. ECOSYSTEMS HAVE CONNECTIVITY60
intercompartmental transfers. In other words, indirect pathways (those of lengths ! 2) deliver more substance from
any compartment to any other than a direct empirical (adjacent) link between them. The inﬂuence carried by this
transferred substance follows the substance itself in its being associated with pathways of particular lengths, and
thus the conservative as well as nonconservative causes in the system can be said to be nonlocal. Indirect effects
are dominant in realistically conﬁgured network systems, and this is especially true for complex systems, like ecosystems.
The limit process that carries introduced conservative substance throughout the system to ultimate dissipation
ensures that direct energyematter links are quantitatively insigniﬁcant in comparison to the total. These
links, provided by direct interactions such as feeding, serve only to structure the network; they make little contribution
to intrasystem determination once this structuring is established (see network interior ampliﬁcation, CH-8).
Dominant indirect effects in nature is a very different proposition from that espoused in mainstream ecology at
the present time. It is only an hypothesis, but robust in the mathematics of steady-state environ analysis. Each
extended pathway that collectively provides its basis begins with openness at the boundarydeither reception of
input followed by forward passage of material in output environs to ultimate dissipation or exhaustion from outputs
preceded by the traceback of substance in input environs to its boundary points of original introduction. Network
nonlocality underscores the essential holism of all reasonably well-connected transactional systems. Every component
in ecosystems, large and sparse in adjacent linkages though these may be, are richly interconnected to a multitude
of others by interactions at a network distance, only few of which have direct or adjacent (Fig. 4.2) links to the
one in question. Indirect effects virtually expressed in concrete empirical reality are the glue of holistic network organization,
and this is universal in the organization of nature.
Growth and Development Hypotheses
Four CHs describe aspects of network processes expressed in systemic growth and development. Growth is increase
in system size and activity, development the increase in organization (Ulanowicz, 1995, p. 650). To organize is
to move away from thermodynamic equilibrium. The ﬁrst growth and development hypothesis (CH-3) concerns the
closing of network organization by the elongating pathways of CH-1 to produce the indirect effects and holism of
CH-2. The second (CH-4), a corollary of the ﬁrst, places network growth and development within the ascendency
framework of movement away from thermodynamic equilibrium. The last two (CH-5 and CH-6) are goal functions
that contribute to nonlocality (CH-2) and also serve as generating properties for the utility hypotheses, network synergism
(CH-13) and mutualism (CH-5), which ﬁnd expression in the Janus Enigma Hypothesis (CH-16).
CH-3: Network Homogenization
One consequence of network nonlocality is the tendency for intermediate sources and sinks within systems to
become blurred. That is, in the limit process that takes introduced energy and matter to boundary dissipation there
is so much transactional intercompartmental mixing around over the long and high-number pathways that causality
tends to become evenly spread over entire reachable networks. The result is all compartments in well-connected systems
are about equally signiﬁcant in generating and receiving inﬂuences to and from others. As a consequence, holism
takes on the character of becoming more or less uniformly distributed in its universal expression. Beginning and
ending at the open-system boundaries, the web of life based on local transactions of energy and matter tends to
become quite homogeneous in its unseen, ultimate, intercomponent relationships (Patten et al., 1990; Fath and
Patten, 1999).
Thus, by the matrix algebra behind this hypothesis, connected systems may tend to operate as much more closed
networks of more evenly distributed ﬂow-storage relationships than is generally appreciated from empirical observations.
However, applied to real ecological systems, there are scale considerations to be taken into account. In very
large and presumably sparse networks of actual ecosystems, a mole of nodes, say, with only 10À6
fractional connectivity,
would produce a lot of indirect relationships but the network as a whole would still be a rariﬁed universe, the
majority of its bodies neither proximately nor remotely related. In such systems, there is the real question whether or
not an introduced amount of propagating substance remains long enough in system storage and circulation to ﬁll in
all the blanks, even over evolutionary time, in correspondingly large and sparse adjacency and ﬂow intensity
matrices. In addition, nonuniform input patterns will tend to cause lopsided departures from any homogenization
ideal. Obviously, many points of clariﬁcation remain to be realized.
This CH is a place, then, where the unreality of small-scale models may mislead about what is empirically achievable
or expressed in ecosystems. Present models are stark ideals against the scales involved. They help formulate
concepts and see relationships, but they do need to be tempered against what is empirically realizable. That is
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 61
why all the CHs are considered hypotheses, not principles. There is little in observational experience to suggest that
the elements making up ecological ﬂows and stocks actually are uniformly distributed. Therefore, network homogenization
might best be interpreted as a tendency expressed in graph algebra, but perhaps is not much realized in
reality. Diversity, heterogeneity, and differentiation are the attributes of observable order, not uniformity, homogeneity,
and sameness. The signiﬁcance of homogenization as a mathematical property is therefore very much in
need of further assessment as a real-world property.
CH-4: Network Aggradation
When energy or matter enters a system across its boundary, the system is moved further from thermodynamic
equilibrium and to that extent can be said to aggrade, the opposite of dissipation (boundary exit) and degradation
(energy quality reduction). Aggradation is negentropic, although entropy is still generated (e.g., as heat) and
boundary-dissipated by interior aggrading processes. Environ theory appears to inform Schro¨dinger’s What-isLife?
riddle (1944) of how negentropic development can proceed against the gradient of second-law degradation
and dissipation. It shows a necessary condition for aggradation to be one single interior transaction within the system
networkdsimple adjacent electromagnetic connection! Fig. 4.6 shows this in simple terms, letting increase in the
TST (the sum of ﬂows into or out of all compartments) relative to total boundary input or output (they both equal
10) be the aggradation measure. The linkage of f21 ¼ 5 ﬂow units from compartment one to two in Fig. 4.6B induces a
throughﬂow of T2 ¼ 15 at compartment 2. The boundary contributions remain unchanged in both diagrams at total
inputs and outputs equal 20, with the result that the TST/total cross-boundary input (
P
z) or output (
P
y) equals 1.0
for Fig. 4.6A but 1.25 for Fig. 4.6B. This is network aggradation, interpretable as further departure from equilibrium
in Fig. 4.6B compared to Fig. 4.6A and resulting in a more complex system.
Given openness and sustained boundary input and output, there would appear to be no upper bound on this interior
aggradation process. Thus, everything in nature that concerns differentiation and diversiﬁcation of living and
nonliving structures and processes, and transactional interactions between these both within and across scales, can
be seen as incrementally contributing to network aggradationdmovement away from equilibrium. Realizing that
solar photons come in small quanta that can only power processes at similarly small scales, and the fact that scales
increase bottom-up through interactive coupling, network aggradation would appear to provide, perhaps, an
electromagnetic-coupling answer to Scho¨dinger’s durable “What is life?” question, Unbounded energy- and
matter-based linkage following on boundary openness would be an elegant basis indeed for life in its thermodynamic
dimensionsdsimple, and ubiquitous. Is a photon-by-photon accounting possible?
CH-5: Network Throughﬂow Maximization
Network aggradation processes generate maximum intrasystem throughﬂows at steady state. This can be taken
as one of the growth-and-development “objectives” of self-organization. Throughﬂow reﬂects kinetic “activity” and
performance of work, and these are the antithesis of the randomness and stasis associated with the maximum entropy
condition. Patten (2016b) showed, employing a structured sequence of ecosystem models to simulate “building
a biosphere”, that networks of increasing connectivity contribute to maximum throughﬂow generation. When
energy is the transfer currency, maximum throughﬂow is maximum power. Considering the degradation of order
by entropy generation to be “time’s arrow” (Blum, 1951), maximum power becomes “time’s speed regulator”
(Odum and Pinkerton, 1955), the basis for maximum network synergism in the Janus Hypothesis (CH-16). This confers
combined organismeenvironment ﬁtness in the place of traditional biological ﬁtness, which generally entails
environmental degradation in its acquisition. The interconnection of components by substance ﬂows increases
and becomes more complex as systems mature, and this becomes a determinant of mutually beneﬁcial organisme
environment relationships.
CH-6: Network Storage Maximization
Masseenergy storage as standing stocks is (compared to ﬂows, which are virtual) the tangible expression of thermodynamic
aggradation in systems growth and development. Aggradation processes generate maximum intrasystem
storages at steady state, and this can be taken as another growth-and-development “objective” of aggradative
self-organization. Storage reﬂects potential “activity” and performance of work, again the antithesis of randomness
and stasis in the maximum entropy condition. Another element in network aggradation (CH-4) is therefore the maximization
of standing stocks at steady state. If throughﬂow reﬂects kinetic antientropic activity, then storages represent
the accumulated potential of natural capital, which also stands far from thermodynamic equilibrium. When
energy is the transactional currency, the quantity maximized is energy density. This CH was championed by S. E.
Jørgensen as a maximum exergy storage principle in numerous publications since its original introduction (Jørgensen
4. ECOSYSTEMS HAVE CONNECTIVITY62
and Mejer, 1979. It also serves (Patten, 2016a; see Fig. 8) as a storage alternative to maximum throughﬂow generation
(CH-5) in the Janus Hypothesis (CH-16). In a recent CH (CH-17), Patten (2016a) showed how storage self-transfer
subsequences along network pathways contribute most to the quantiﬁcation of pathway transfer rates. Thus, paradoxically,
at steady state the maximization of storages (non-transfers) also serves to maximize transfers (nonstorages)danother
of nature’s network mysteries that become so obvious after the fact. We will look at this again
further below in the description of CH-17.
Ampliﬁcation Hypotheses
Like successful investments in economics, energyematter received at system boundaries grows in signiﬁcance in
passing through the system toward its ultimate dissipation. More activity (throughﬂow) is supported and more natural
capital (storage) produced than the face value of boundary inputs would seemingly allow. A simple illustration
of the “networking” processes involved was brieﬂy discussed earlier in connection with Fig. 4.6. These processes
entail both boundary and interior contributions.
CH-7: Network Boundary Ampliﬁcation
When a compartment within a system brings substance into the system from outside, the importing compartment
is favored in development over others that do not do this. The reason is a technical property of both the throughﬂowand
storage-generating matrices of environ analysis known as diagonal dominance. The throughﬂow case is easiest to
explain. Its generating matrix (N) multiplies the system input vector (z) to produce a throughﬂow vector (T) (Fig. 4.3,
bottom middle panel) Elements (nij) of the generating matrix represent the number of times substance introduced at
one compartment (j) will appear in another (i). First introduction by boundary input constitutes a ﬁrst “hit” to the
importing compartment. Non-importing (off-diagonal) compartments do not receive such ﬁrst hits. In matrix multiplication
of the generating matrix and input vector, importing compartments line up with their corresponding inputs
such that ﬁrst hits are recorded in diagonal positions; that is, input zi to compartment i appears in the iith
position of the generating matrix. This alignment produces the diagonal dominance. Off-diagonal elements represent
contributions to i (in row i) from the other interior compartments (j s i), not across the boundary. These do
not receive their ﬁrst-hit from boundary input, but from other interior compartments, and so are correspondingly
smaller in numerical value. Storage generation is similar. Elements (sij) of storage-generating matrices denote residence
times in each ith compartment of substance derived from other jth compartments. Diagonal dominance in
these generating matrices also associates longer residence times with boundary versus non-boundary inputs due
to the ﬁrst-hit phenomenon of the throughﬂow model, and longer residence times result in greater standing stocks.
Boundary ampliﬁcation may offer explanations for many phenomena in ecosystemsdedge effects, zonation, ecotones,
invasive species, trophic levels, etc. Consider the latter as an example. The transfer levels of network unfolding
(see section CH-10: Network Unfolding) are non-discrete due to the mixing around (CH-3) of energy and matter
in the complex network of indeﬁnitely extending pathways. This negates the Lindeman (1942) conception of discrete
trophic levels.
Boundary ampliﬁcation is a relatively new property in environ theory. It has the potential to explain the emergence
of discrete trophic levels within complex reticular networks, and of course the more prior property behind
this is system openness.
CH-8: Network Interior Ampliﬁcation
It is sometimes observed in the environ mathematics of particular networks that substance introduced into one
compartment at the boundary will appear more than once at another compartment, despite boundary dissipation
in the interim. This is due to recycling, and it is easily seen how progressively diminishing fractions of a unit of introduced
substance can cumulatively produce a sum over time in a limit process that exceeds the original amount. The
second law cannot be defeated by this means, but energy cycling (Patten, 1985) following from open boundaries can
compensate it and make it appear at least challenged in network organization. This is but one of numerous unexpected
properties of networks contributed by cyclic interconnection and system openness.
The interior ampliﬁcation property extends beyond cycling, however. The fact is that transactional digraphs and
their isomorphic representations as transition matrices can be relationally partitioned into reﬂexive, symmetric, and
transitive elements. Figs. 4.8A and B show this partitioning for, respectively, digraph and transition matrix depictions
of three ecological compartment models. Let i, j, k ¼ 1, ., n compartments in a system, and let T ¼ “transfers
(directly or indirectly) energy-matter to” be the canonical binary linkage relation of all transactional networks. Then:
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 63
• the reﬂexive elements (j T j) are the digraph nodes, Fig. 4.8A, and the matrix diagonal entries, Fig. 4.8B;
• the left(L)-transitive elements (j T k and k T i 0 j T i) are the blue downward-directed ﬂow arrows pointing away
from (i.e., leaving(L)) sources in Fig. 4.8A, and the blue-colored lower triangle matrix entries in Fig. 4.8B;
• the right(R)-transitive elements are the red upward directed ﬂow arrows pointing back (i.e., returning (R)) toward
sources in Fig. 4.8A, and the red-colored upper triangle matrix entries in Fig. 4.8B; and
• the symmetric (cycle closing) elements (j T i 0 i T j) are identiﬁed as magenta-circled arc pairs in Fig. 4.8A and
paired magenta ovals in Fig. 4.7B.
In NEA throughﬂow analysis, distribution matrices (N) map boundary input vectors (z) into throughﬂow vectors
(T) (Fig. 4.3, bottom middle panel). TST is a kinetic measure of antientropic activity, hence network aggradation
(CH-4). In NEA storage analysis, residence time matrices (S) map boundary input vectors (z) into standing stock
vectors (x) (Fig. 4.3, bottom right panel). Diagonalizing z produces storage matrices (X ¼ S$diag(z)) whose entries
give the storage (xij) at each compartment i generated by each boundary input (zj) at j. Fig. 4.8C shows, for the three
models of Figs. 4.8A and B, the sums of values in the S and X matrices (labeled “sum_S_totl” and “sum_X_totl”,
respectively) and, in each case, their three partition submatrices. Ampliﬁcation factors are of the form sum_S_totl/sum_S_reﬂ
and sum_X_totl/sum_X_reﬂ. These ampliﬁcation factors tell the story. Referring back to Section
4.2, where the trophic categories autotrophs (reﬂexive), consumers (transitive), and decomposers (symmetric) were
aligned with the relational properties of networks, the following conclusions can be drawn from the Fig. 4.8C results:
TSS = 1,230 kcal m–2
TST = 141 kcal m–2 d–1
CON = 0.234, FCI = 0.080
IE = 1.290, NS = 3.868
TSS = 5,709 mol N m–2
TST = 41,314 mol N m–2 season–1
CON = 0.449, FCI = 0.886
IE = 51.295, NS = 4.778
Oyster Biocoenosis
Energy Flow
Dame & Pa en (1981)
Mar. Ecol. Progr. Ser. 5:
115-124
TSS = 3,086 kcal m–2
TST = 30,302 kcal m–2 y–1
CON = 0.333, FCI = 0.111
IE = 1.535, NS= 7.714
μ
μ
(A)
x1_Fliter_Feeders
x5_Deposit_Feeders
x6_Predators
x2_Detritus
x4_Meiofauna
x3_Microbiota
Sapelo I. Salt Marsh
Energy Flow
Teal (1962)
Ecol. 43: 614-624
x2_Algae x7_Spiders
x4_Insects
x1_Spar na
x3_Detritus
x5_Bacteria
x6_Nematodes
x8_Mudcrabs
Neuse River
Nitrogen Flow
Chris an & Thomas (2003)
Estuaries 6: 815-826x1_PN_Phyto
x2_PN_Hetero
x6_NH4
x3_N_Sed
x7_PN_Abio cx4_DON
x5_NOx
FIGURE 4.8A Relational partitioning of digraphs for three ecological compartment models, two for energy, the third for nitrogen. The nodes
are the reﬂexive elements. Arcs marked with blue arrows (leaving, L) or directed downward away from sources are elements in L-transitivity. Arcs
marked with red arrows (returning, R) directed upward toward sources are elements in R-transitivity. Arc pairs identiﬁed by magenta circles express
symmetric relations. Total system storages (TSS), throughﬂows (TST), connectances (CON), Finn cycling indexes (0 FCI 1), indirect effects
indexes (IE), and network synergism indexes (NS) are shown at the bottom of each diagram.
4. ECOSYSTEMS HAVE CONNECTIVITY64
• Reﬂexive compartments alone, acting in isolation from others, can generate considerable network aggradation.
Ecologically, these can only be autotrophs.
• Transitive consumers, interchanging only between themselves without access to node storages, as reﬂected in
NEA storage-free ﬂow analysis of acyclic networks, have little capacity for sustained antientropic activity.
• Positive ampliﬁcation factors, showing sum_S_totl > sum_S_reﬂ and sum_X_totl > sum_X_reﬂ (and in one
model, >>), indicate not only the power of interactions to move and keep transactional systems far from
equilibrium but also that this is the fundamental role of interactions (trophic and nontrophic) in bioenergetic
phenomenology since, in the above results, interactions not involving standing stocks evidence little capacity on
their own to generate antientropic development.
Several points of ecological interest follow from these results:
➢ First, reﬂexivity is the only obligatory (necessary) relational property to be associated with ecological
compartments. Transitive and symmetric linkages enhance network aggradation, to be sure, so capacities to form
these in adaptive radiation will be favored, but still they are facultative in evolutionary development.
Compartmental reﬂexivity translates into universal self-interested behavior and irrepressible growth of biotic
categories, and is also expressed in individualistic dynamics and high holding capacities (nontransfer
probabilities) of both living and nonliving substance storage compartments (cells, organisms, sequestering
deposits, etc.). The more kinds (n) of compartments there are in a system, the greater will tend to be, by reﬂexive
processes alone (X_reﬂ ¼ S_reﬂ$diag(z)), the system’s contribution (X_reﬂ) to network aggradation.
State transition Equation
derived from dx/dt = Cx + z
where P = I + C t
x/ t = (P–I)/ t•x +z
x = (x ), i = 1, …, n, state vector
z = input vector
t = discrete time interval
P = Prefl+PL-tran+PR-tran
x1 x2 x4 x5 x6 x7x3 x8
x6
x5
x4
x3
x8
x7
x2
P (Salt Marsh Model, t = 5 e–5 d)
x1 x2 x4 x5 x6 x7x3
x2
x6
x5
x4
x3
x1
x7
P (Neuse River N Model, t = 8.5 e–4 seasons)
Key:
reflexive
symmetric
(node pairs)
L-transitive
R-transitive
0.9909 0 0 0 0 0 0 0
0 0.9930 0 0 0 0 0 0
0.0015 0.0010 0.9980 0.0100 0.0250 0.0300 0.0050 0.0050
0.0001 0 0 0.9825 0 0 0 0
0 0 0.0005 0 0.9545 0 0 0
0 0.0010 0.0005 0 0.0005 0.9595 0 0
0 0 0 0.0025 0 0 0.9925 0
0 0 0 0 0 0.0005 0 0.9925
P (Oyster Model, t = 5 e–5 y)
x2
x6
x5
x4
x3
x1
x2 x3 x4 x5 x6x1
0.9996 0 0 0 0 0
0.0001 0.9996 0 0.0032 0.0021 0.0001
0 0.0001 0.9382 0 0 0
0 0.0001 0.0091 0.9936 0 0
0 0.00001 0.0091 0.0005 0.9972 0
0 0 0 0 0.0002 0.9998
0.0032
0.0001
0.00001
0.0021
x1
(B)
Δ
Δ
Δ Δ
Δ
Δ
Δ
Δ
FIGURE 4.8B Relational partitioning of transition probability matrices (P) corresponding to each of the three Fig. 4.8A models. The diagonal
entries are the reﬂexive elements (Preﬂ), the lower triangles contain left-(or leaving-) transitivity elements (PL-tran), and the upper triangles contain
right-(or returning-) transitivity elements (PR-tran). The paired ovals denote symmetric elements (Psymm).
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 65
➢ Second, the more compartments (n) in a system, the more opportunities (n2
À n) there will be for pairwise
transitive and symmetric linkages to form. Thus, in effect, all three relational categories call for increasing the
number of nodes in transactional networks. Ecologically, increasing biodiversity is the major way to do this
because biota are plastic whereas abiotic categories are more or less ﬁxed. It is biota that ever expand to ﬁll,
change, and create new niches (opportunities) for life in the biosphere. Development begets development. Life,
then, is the true engine of aggradative activity on the planetary surface, and biodiversity manifests the radiative
tendency to generate and meet opportunity more than any other property. Conventional ecology holds that
biodiversity is important and a good thing to promote and protect, but it has been unable to put forward any real
scientiﬁc argument or evidence for this. In this present network property, interior ampliﬁcation, there may lie the
beginnings of a general ecological answer to the “why biodiversity” question.
Integrative Hypotheses
Stocks and ﬂows are composite quantities in networks. Multiple inputs generate a like number of output
environs, and all are mutually entwined to form the aggregate throughﬂows and storages that make up compartmental
systems. Similarly, multiple outputs are generated from stocks and ﬂows that contribute to a
NEUSE RIVER ESTUARY
(Pmoles N m–2)
sum_S_totl = 51.6770
sum_S_reﬂ = 4.2971
AmpliﬁcaƟon = 12.03×
sum_S_L-tran = 0.0051
sum_S_R-tran = 0.0051
sum_X_totl = 5.7087e+03
sum_X_reﬂ = 151.2276
AmpliﬁcaƟon = 37.75×
sum_X_L-tran = 0.5599
sum_X_R-tran = 0.5768
OYSTER COMMUNITY
(kcal m–2)
sum_S_totl = 1.0849
sum_S_reﬂ = 0.5600
AmpliﬁcaƟon = 1.94 ×
sum_S_L-tran = 3.0096e-04
sum_S_R-tran = 3.0027e-04
sum_X_totl = 3.0856e+03
sum_X_reﬂ = 2.0229e+03
AmpliﬁcaƟon = 1.53 ×
sum_X_L-tran = 0.7569
sum_X_R-tran = 0.7568
SAPELO ISLAND MARSH
(kcal m–2)
sum_S_totl = 212.2812
sum_S_reﬂ = 56.1583
AmpliﬁcaƟon = 3.78 ×
sum_S_L-tran = 0.4004
sum_S_R-tran = 0.4038
sum_X_totl = 1.2283e+03
sum_X_reﬂ = 555.4779
AmpliﬁcaƟon = 2.21 ×
sum_X_L-tran = 4.9916
sum_X_R-tran = 4.9835
= 1.0849
= 0.5600
on
= 3.0856e+03
= 2.0229e+03
on
= 212.2812
= 56.1583
on
= 1.2283e+03
= 555.4779
on
= 51.6770
= 4.2971
on =
= 5.7087e+03
= 151.2276
on =
TSUNAMIMI
==== 1.94 ×
==== 1.53 ×
==== 3.78 ×
==== 2.21 ×
==== 112.03××
=== 3= 37.75×
aa
aaa
n = 0.7569
an = 0.7568
nnn
nn
nn = 3.0096e-04
nn = 3.0027e-04
nnn
n
nn = 4.9916
nn = 4.9835
nnn
n
nn = 0.4004
nn = 0.4038
an
aa
nn = 0.5599
n = 0.5768
nnn
aa
nn = 0.0051
nn = 0.0051
(C)
FIGURE 4.8C Network interior ampliﬁcation, reﬂected in summations of elements in intensive storage, or residence time, matrices (S), and
extensive storage matrices (X), as generated by the entire systems (S_totl, X_totl) and their partition subsystems (S_reﬂ, X_reﬂ; S_L-tran, X_L-tran;
and S_R-tran, X_R-tran). Ampliﬁcation factors in all cases are referenced to S_totl and X_totl.
4. ECOSYSTEMS HAVE CONNECTIVITY66
corresponding number of input environs. NEA unravels these environs from the compositions that form the
wholes that are physically constituted systems. There is no mystery, only realization, in this as all systems are compositions
formed from their components. What is different here is that what is being mixed together are virtual
quantitiesdtransactional networks representing tangled energyematter webs. The four hypotheses of this section
concern various aspects of composition, the synthesis of parts into wholes, and decomposition, the reduction of
wholes into parts.
CH-9: Network Enfolding
This property refers to the incorporation of indirect energy and matter ﬂows and storages into empirically
observed and measured ﬂows and storages. It is another property of coupled systems elucidated by environ mathematics,
and it potentially touches many areas of ecology such as chemical stoichiometry, embodied energy
(“emergy”), and ecological indicators. Ecologists observe and measure, for example, the chemical composition of
organisms or bulk samples. For an entity to have a “composition” means it is a compositedmade up of materials
brought to it from wherever its incoming network reaches in the containing systemddirectly and indirectly. This
has consequences for even a seemingly straightforward concept like a “direct” ﬂow where, it turns out, “macroscopically”
or “empirically direct” must be distinguished from “microscopically” or “analytically direct.” To illustrate,
referring back to Fig. 4.2, the ﬂow f21 from compartment 1 to 2 is unambiguously direct. Macroscopically, the link
and the process responsible for it (like eating) are direct, and microscopically so are the molecules (food) transferred
because it is derived directly from the boundary input z1. This latter directness is due to the fact that ﬂow f21 represents
the ﬁrst transfer of boundary input from the compartment that received it to another; it is uncommingled with
substance from other sources because, in this case, input z2 cannot reach compartment 1. The situation is different in
Fig. 4.3. The ﬂow f21 in this model is still macroscopically adjacent to compartments 1 and 2 (i.e., empirically
“direct”), but now it contains composited ﬂow derived from all three inputs. These inputs, the throughﬂows and
storages (not shown) they generate, and also the other three adjacent ﬂows are all complexly enfolded into f21.
The enfolding is mutual, and in this case it is universal because all interior network elements are reachable from
all the others. The entire system of Fig. 4.3 is thus (at steady state) a composite of itself, which is the ultimate expression
of holism. Moreover, this composition property, network enfolding, is true for complex systems generally. If one
can imagine empirically sampling this system, then f21 (and the other interior ﬂows as well) strike the senses as
direct. However, they are not, when referenced to origins, since they contain indirect ﬂows from the other sources
a few to (due to cycling) many times removed, and so are better considered as “adjacent”, or perhaps just
“observed.” In environ mathematics, this embedding or entrainment of newly received inputs into the established
ﬂow stream of the system is reﬂected in inﬁnite series, as previously illustrated in Fig. 4.4.
Let f21/T1 ¼ g21 deﬁne a throughﬂow-speciﬁc dimensionless ﬂow intensity. The coefﬁcient g21 is a probability,
therefore its powers form a convergent inﬁnite series: (1 þ g21 þ g21
(2)
þ . þ g21
(m)
þ .). The parenthesized superscripts
denote coefﬁcients derived from matrix, not scalar, multiplication. This power series maps the boundary
input z1 into the portion of throughﬂow at 2 contributed by this source: T21 ¼ (1 þ g21 þ g21
(2)
þ . þ g21
(m)
þ .)z1.
The ﬁrst term of the series brings the input into the system: 1$z1. The second term represents the “direct” ﬂow
over the link of length 1: g21z1. All other terms represent indirect ﬂows associated with pathways of all lengths 2,
3, ., m, . as m / N. The throughﬂow component T21 accordingly contains a plethora of indirect ﬂows:
(g21
(2)
þ . þ g21
(m)
þ .)z1. This is one of three elements in the throughﬂow at compartment 2: T2 ¼ T21 þ T22 þ T23.
At compartment 1 the throughﬂow is similarly decomposable: T1 ¼ T11 þ T12 þ T13, and at compartment 3:
T3 ¼ T31 þ T32 þ T33. Each term in these sums has a similar inﬁnite series decomposition to that just given for T21.
With this, one can now appreciate there is more than meets the eye in the Fig. 4.3 network. The focal ﬂow f21 in question
has a decomposition into enfolded elements as follows:
f21 ¼ g21T1 ¼ g21ðT11þ T12þ T13Þ
¼ g21
h
1þ g11þ g
ð2Þþ.þ
11 g
ðmÞþ.
11

z1þ

1þ g12þ g
ð2Þþ.þ
12 g
ðmÞþ.
12

z2þ

1þ g13þ g
ð2Þþ.þ
13 g
ðmÞþ.
13

z3
i
.
This is what an ecologist measuring f21 would measure empirically and consider a “direct” ﬂow. One can see,
however, that the entire system is embodied in this measurement. This is network enfolding. It gives a strong message
about the inherent holism one can expect to be expressed in natural systems and, as stated above, its broad realization
is likely eventually to inﬂuence many areas of ecology.
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 67
CH-10: Network Unfolding
Ever since Raymond Lindeman (1942) pursued Charles Elton’s original food cycles, but they came out unintendedly
as sequential food chains instead, to which ecologists could better relate, mainstream empirical ecology (for
example, food web theory, biogeochemical cycling) has had a difﬁcult time returning to meaningful analysis of the
concept of cycling. The preoccupation with chains prompted Higashi and Burns (Higashi et al., 1989) to develop a
methodology for unfolding an arbitrary network into corresponding isomorphic “macrochains.” Emanating from
boundary points of input and arrayed pyramidally, these resemble the food pyramids of popular textbook depictions.
Because the networks are cyclic, however, the macrochains differ from normal acyclic food chains in being indeﬁnite
in extent. Network unfolding refers to the indeﬁnite proliferation of substance-transfer levels in ecosystems. The
terminology “transfer pathways” and “transfer levels” is preferred to “food chains” and “trophic levels” because nontrophic
as well as trophic processes are involved in any realistic ecosystem. Examples of nontrophic processes include
import and export, anabolism and catabolism, egestion and excretion, diffusion and convection, sequestering, immobilization,
and so on. Whipple subsequently modiﬁed the original unfolding methodology to discriminate the various
trophic and nontrophic processes involved (Whipple and Patten, 1993; Whipple, 1998). The transfer levels so discriminated
are nondiscrete in containing contributions from most, if not all, the compartments in a system, and also they
continue to increase in accordance with continuation of the limit process that ultimately dissipates all the introduced
substance from the system. Exchange across open borders is at the heart of network unfolding.
Fig. 4.9 illustrates how networks of arbitrary topology are recomposed to form pyramidal macrochains by the
network unfolding algorithms. Labeling and vertical positioning of the stocks, ﬂows, and outputs at each level (I,
II, III, IV, .) denote that all these categories (except transfer level I in Fig. 4.9B) are composites. In reasonably
well-connected networks, as in the real world, compartments (Fig. 4.9A) are made up of multiple, not single, transfer
levels, and reciprocally, transfer levels are made up of multiple, not single, compartmental categories. Level structuring
by diminishing quantities of undissipated substance as material moves down pathways in networks forms
ecological pyramids (“macrochains”) with distributed (in Fig. 4.9, multicolored) compartmental compositions at
each level. This is shown to the right in Fig. 4.9B and C. The latter shows the information in Fig. 4.9B, obtained
by Higashi et al. (1989) unfolding, with trophic (solid arrows) and nontrophic (broken arrows) transfers as discriminated
by the Whipple (1998) algorithm. The level structuring in this case gives true trophic levels.
CH-11: Network Centrifugality and Centripetality
These two properties follow from the expression of network enfolding (CH-9) and unfolding (CH-10) in the divergent
and convergent subsystems that constitute output and input environs, respectively. Ulanowicz (1986; see also
this book, Chapter 6) called attention to centripetality in the ascendency framework of autocatalytic nodes in networks
pulling resources from near and far corners of systems into themselves, not unlike vacuum cleaners sweeping
particles into their storage containers and holding them for later disposition. In environ theory, network centripetality
is manifested as afferent ﬂows in input environs that are convergent on focal compartments and ultimately comprise
the outputs from those compartments. That is, each input environ circumscribes the set of all incoming (centripetal)
ﬂows to each compartment that converge to become that compartment’s boundary output. The dual, network
centrifugality, is manifested as efferent ﬂows in output environs that are divergent from focal compartments and
are constituted from the inputs to those compartments. Each output environ encompasses the collection of outgoing
(centrifugal) ﬂows from each compartment that propagate and diverge from that compartment’s boundary input.
How network centrifugality comes to be expressed through individual particles passing though constituted systems
is illustrated in Patten (2016a, ﬁg. 11.1).
Centrifugality and centripetality are the searching and reaching, then combining and closing phenomena that
make physical concreteness, and the next conjecture prescribing it, entirely possible.
CH-12: Network Topogenesis
This CH proposes that in transactional networks qualitative digraph topology on a global level is sufﬁcient to
quantify both system stocks and ﬂows at the local level. If true, such a proposition would considerably alter the
human worldview by showing how system-wide connectivity holistically determines from all over connected networks
the quantitative properties of entities and processes in the small. Cross-scale bipolarity of causal determinism
would ﬁnd expression in the mutual reﬂection of small in large, and large in small. Right now, this hypothesis is far
from proved, but it has promising directions.
Two methodologies designed to illustrate the reasoning behind it are, for ﬂows Link Tracking (LkT), and for standing
stocks Loop Tracking (LpT). Hill (1981), in his PhD dissertation, proposed in a concept he called “inﬂuence”, that
4. ECOSYSTEMS HAVE CONNECTIVITY68
digraph structure contains all the information needed to determine stocks and ﬂows of conserved quantities. He did
not show this, but the idea has endured through years of NEA development. One paper on link-tracking ﬂow
generation has been published (Patten, 2015a). This method is brieﬂy described in Patten (2016a, ﬁgs. 11.1e11.3).
Storage generation by LpT still eludes development as an operational methodology. This is described at length as
a new property in Patten (2016a; ﬁgs. 12.1e12.5). LpT is especially difﬁcult computationally, and may actually be
NP-complete (http://en.wikipedia.org/wiki/NP-complete) as expressed in networks of realistic scale and
complexity. Computational intractability notwithstanding, both methodologies provide important insights into
how digraph structure, a holistic property, determines or at least constrains, energyematter movements in networks.
If mechanistic reductionism explains network causality from below, then topogenic holism explains it from above.
The two together comprise a dualistic explanatory system of bidirectional, bottom-up/top-down, centrifugal/centripetal
(CH-11), within- and across-scale hierarchical organization.
LkT takes the nodeelink adjacency structure of qualitative digraphs to be like a road system in providing channels
of constraint for trafﬁc ﬂow. The difference is one is physical infrastructure and the other virtual. Just as road systems
evolve to accommodate (and stimulate) more and more trafﬁc ﬂow, reﬂecting the growth-and-development imperatives
of CH-4 and CH-5, such goal functions may be “topogenetically” imprinted in evolved pathways etched into
landscapes by history (see section CH-17: Network Clockwork Stockworks). That is, they may constitute epigenetic
y3
y1 y2
z1 x2
f21
f32f31
f13
x1
x3
TransferLevels
Compartments
x1 x2 x3
IV
III
II
I
y1(I)
y1(III)
y1(IV) y2(IV) y3(IV)
y3(III)
y2(II
)
y3(II)
z1
TrophicLevels
Compartments
x1 x2 x3
IV
III
II
I
y1(I)
y1(III)
y1(IV) y2(IV)
y3(III)
y3(II)y2(II)
y3(II)
z1 z1
(A) (B)
(C)
f21(I) f31(I)
x1(I)
x2(II)
x2(IV)
x3(II)
x3(III)
x3(IV)
f32(II)
f31(III)x1(III)
f13(III)
f21(III)
x1(IV) f32(IV)
f21(IV)
f31(IV)
f13(IV)
y1(II)
y2(III)
z1
f21(I)
f21(II)
f21(III)
f31(II)
f32(II)
f31(I)
f32(III)
f13(II)
f13(III)
f13(IV)
f32(IV)
f31(III)
f31(IV)
f31(II)
x1(II) x2(II) x3(II)
x1(I) x3(I)
FIGURE 4.9 Illustration of network unfolding for the model (A) of Figs. 4.3 and 4.5 with only one input, z1. For a food web orientation, the
compartments can be interpreted as x1 ¼ Autotrophs and Mixotrophs, x2 ¼ Secondary Consumers, and x3 ¼ Detritus and Decomposers. (B)
Graphic depiction of how network unfolding maps (left panel) the digraph in (A) into a pyramid of transfer levels (right panel). (C) Same as (B) but
discriminating (left panel) trophic and nontrophic transfers to generate (right panel) a pyramid of true trophic levels. Multiple nontrophic processes
can be so discriminated. In the left panels of (B) and (C), solid arrows denote trophic ﬂows and broken arrows nontrophic. In the right panel
pyramid diagrams, the arrows are composites of ﬂows from/to corresponding levels in the left panels. Further description in text. Sources:
Redrawn and modiﬁed after Higashi et al. (1989, ﬁg. 4, p. 250), and Whipple, S.J. 1998, ﬁg. 2, p. 267).
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 69
inheritance systems at landscape scales. From the air, avenues, trails, and other pathway scars left from former generations
of activity are clearly visible, and it is hard not to conclude they reﬂect (and this continues downscale) an
inheritance system expressed in the present in ongoing landscape evolution. It is such history that current states
encapsulate and leave behind in state-space formulations of system dynamics. CH-20 at the end of this section deals
broadly with extragenetic inheritance at ecological scales.
LpT is a corresponding prospective methodology to LkT for storage generation. Just as LkT formulates a matrix,
NLkT ¼
P
m¼0,N (GLkT)m
that maps an input set into throughﬂows, NLkT $ z ¼ TLkT (Fig. 4.3, Storage Analysis column),
the goal of LpT is to ﬁnd another matrix, SLpT ¼
P
m¼0,N (PLpT)m
Dt, that can similarly map an input set into calculated
node storages, SLpT $ z ¼ xLpT (Fig. 4.3, Storage Analysis column). This has been achieved in principle in Patten
(2016a), but for realistic cases computational intractability impedes development of an operational methodology.
From the rationale and results described below, it can be concluded with some certainty that storage information
is in fact enfolded (CH-9) within qualitative nodeelink digraphs used to depict the structure of transactional networks.
Numbered steps in Patten (2016a) explain the loop-tracking rationale, illustrated by the salt marsh energy
model of Teal (1962; see Fig. 4.8), parameterized using a unit time step, Dt ¼ 1.0 y.
From the still very tentative developments that are LkT and LpT at the present time, the way does seem clear to
begin seriously considering the Hill (1981) conjecture that qualitative digraphs are sufﬁcient to determine the quantitative
ﬂow and storage properties of transactional networks. Clearly, more research of a fairly advanced mathematical
nature is needed to deﬁnitively resolve this topogenic issue.
Relational Hypotheses
Energyematter storage-and-ﬂow networks are transactional, meaning the quantities they transfer are conservative
and zero-sum. The elementary particles involved are countable and ﬁnite and cannot be in more than one place at a
time. This means if one ﬂow or storage element in a system gains or possesses (þ) a particular particle at a moment in
time, another ﬂow or compartment must simultaneously lose or not contain (À) that particle. Adjacent transactions
between compartment pairs are accordingly zero-sum. Binary relations are nonconservative interactions that form
secondary relational networks as a direct consequence of transactional activity. For example, in Fig. 4.1 each ﬂow link
shown represents a primary transaction that entails a secondary trophic relation, which is consumptive (þ, À) as
deﬁned by zero-sum passage of energy-matter particles to each consumer (þ) from each consumed (À) compartment.
The three zooplankton compartments are all competitors (À, À) in that each vies with the others for the ﬁnite
conservative particles that comprise the phytoplankton compartment. The competition relation (À, À) is secondarily
derived from the transaction-based consumptive relations (þ, À) directed from phytoplankton to the zooplankton
groups 1, 2, and 3. Considering the three signs þ, À, 0, there are nine combinations of these taken two at a time
(Patten and Whipple, 2007; Fath, 2007). Some of these were expressed earlier in Table 4.1 of the Cone Spring model
example, Section “Network Example 2 e Cone Spring Ecosystem”.
Three CHs will be discussed below under the relational heading; others also have relational characteristics superimposed
on their primary transactions, and could very well also have been included here.
CH-13: Network Synergism
The quantitative methodology of environ theory lends itself to development of certain qualitative aspects of the
environmental relation with organisms. Energy and matter are objective quantities, but when cast as resources they
produce subjective consequences of having or not having them. A concept introduced in game theory (von Neumann
and Morgenstern, 1944, 1947) to describe the usefulness of outcomes or payoffs in games is utility. Environ
mathematics implements this concept to bridge the gap between objective energy and matter and their subjective
value as resources. Utility measures the relative value of absolute quantities; it is subjective information extracted
from and added onto objective facts (Patten, 1991, 1992). A zero-sum game is one in which a winner gains exactly
what the loser loses. As stated earlier, each conservative transaction in ecosystems is zero-sum, but its relative beneﬁt
to the gainer and loss to the loser may be different.
Network synergism is the emergence by network propagation of boundary inputs to interior throughﬂows and storages,
of positive > negative utilities (beneﬁts > costs) in system organization. Fath (1998b; also in Fath and Patten,
1998b; appendix) proved this property is always expressed in transactional networks due to the diagonally dominant,
reﬂexive, positive self-mutualism terms in the integral utility matrix, U. Network synergism concerns how
nonzero-sum interactions arise in conservative ﬂowestorage networks whose proximate transactional linkages
are zero-sum (Fath and Patten, 1998). Nonzero-sum interactions tend to be positive such that beneﬁt/cost ratios,
4. ECOSYSTEMS HAVE CONNECTIVITY70
which equal one in direct transactions, tend in absolute value to exceed one when nonlocal indirect effects are taken
into account. Such network synergism involves huge numbers of pathways (CH-1), dominant indirect effects (CH-2),
and an indeﬁnite transfer-level structure that unfolds as a limit process (CH-10)dall features of utility generation
(Fig. 4.4) that reﬂect holistic organization in ecosystems and the ecosphere. Once again, without open boundaries
there would be no interior networks, no transactional or relational interactions, and thus no nonzero-sum beneﬁts
to components. Life in networks is worth living, it can be said, because the key property of openness as a necessary
condition has made all subsequent properties derived from it possible.
If network nonlocality (dominant indirect effects, CH-2) is the ﬁrst principle of environ system theory, then network
synergism is a good choice for second because this converts the Darwinian struggle-for-existence world of conﬂict,
competition, and strife that has dominated ecological thought for over a century into a more benign place of
habitation for living organisms. The zero-sum struggles of local levels of organization are still there, but in nature’s
hidden extended networks these are transformed to form a more accommodating world of positive, nonzero-sum,
system-wide beneﬁts for all. Network synergism is the endpoint of an ecological Janus Enigma Hypothesis, CH-16
(Patten, 2016b), which starts by conferring proximate ﬁtness to organisms at the expense of degraded environments,
but then goes on to fashion out of network indirect effects ultimate, cost-exceeding ﬁtnesses for both. Further observations
about the ecological signiﬁcance of network synergism will be deferred until after the description later in this
section of its corollary hypothesis, network mutualism.
CH-14: Network Interaction Typing
If causal determination in nature is bipolar, then it is reasonable to consider how top-down holism might
contribute to the determination of ecological interaction types such as competition, predation, mutualism, etc.,
which are usually considered locally and mechanistically deﬁned. Patten and Whipple (2007) and Fath (2007)
investigated this problem and showed there were two ways interaction types could be determined by networks.
In structural determination the magnitudes of network ﬂows have no inﬂuence. Interaction types are determined
strictly by digraph structure. This kind of determination is limited to simple and elementary graph types. With
increasing complexity, network ﬂows become determining. Thus, quantitative information is needed, and this
was referred to by these authors as parametric determination. Patten (2016b) showed that integral utility matrices
(U) on which these determination categories are based contain throughﬂows arrayed in various (complex) algebraic
conﬁgurations, with or without additional single ﬂows not contained in throughﬂows. Tuominan et al. (2014)
showed that those networks without leftover single ﬂows are structurally determined while those with such ﬂows
are parametrically determined. This and other developments discussed at length in Patten (2016a; Section 3.5.3)
demonstrate there is much still to be learned about the role of digraph structure and function in the determination
of ecological interaction types, both directly and also indirectly at near and far network distances. Understanding
holistic determinism requires that such studies be undertaken in the near or far future.
CH-15: Network Mutualism
Under Darwinism, the ecological worldview is one of organisms gnashing and clawing their way to the top of the
“struggle-for-existence” heap. Yet nature observed is a pretty serene and settled place where life for the most part
seems supported and good for most organisms over most of their existence times. The struggle is there, to be
sure, but seems softened by more success than failure much of the time. Life starts and ends in moments, but
goes on agreeably for longer periods in between.
Network mutualism is a qualitative extension of CH-13 (Patten, 1991, 1992) that speaks to “agreeably.” Every pair
of compartments in a transactional network experiences positive (þ), negative (À), or neutral (0) relations derived
from the transactions that directly and indirectly connect them. Ordered pairs of these three signs are nine in number
and each pair reﬂects a qualitative interaction type. For example, the most common types of ecological interactions
are (þ, À) ¼ predation, (À, À) ¼ competition, (þ, þ) ¼ mutualism, and (0, 0) ¼ neutralism. Since the signs of beneﬁts
and costs in network synergism are þ and À, respectively, the shift to jbeneﬁt/costj ratios > 1 in CH-13 carries with it
a shift to positive interaction types. This is network mutualism, and it indicates the beneﬁts that automatically accrue
to living organisms by their being coupled into transactive networks. Network synergism and mutualism together
make nature a beneﬁcial place conducive to agreeable life. This is quite different from seeing life only as a “struggle
for existence”; this is locally and episodically true, but not globally. The built-in positive properties of networks
expressed in network synergism and mutualism operate to reduce the struggle.
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 71
CH-16: Network Janus Enigma Hypothesis
Fig. 4.10 diagrams the Janus hypothesis, which will be abbreviated JH/e in the subsequent discussion. The upper
section broadly reﬂects local and mechanistic relationships (the Janus “ﬁrst look”). The lower section represents
more global and systemic relationships (“second look”). The upper section concludes with enhanced biological
ﬁtness (Fitness-I), but at the cost of a degraded environment from which the resources to do the thermodynamic
work to produce desired departure-from-equilibrium outcomes were drawn. Biota and the biosphere aggrade
(CH-4), and environmental life support degrades, in the dialectical resolution of antientropic versus entropic forces.
The lower section realizes both biological and environmental ﬁtness (Fitness-II). Biota and environment both
aggrade, and the processes by which this occurs are inherent in the mathematics and logic behind transactional
network organization. There is a long way to go to full understanding.
Let us take a closer lookdwe will follow the diagram (Fig. 4.10). The background second law is on the left, pulling
everything down to randomized, gradientless, static equilibrium. Everything to the right ﬁghts against this. The
ABCs in the Aggradation Nexus are some of the processes involved, all emergent from and functioning within
the graph structure that, in CH-12, is held to sufﬁciently determine them. The energyematter outcome of all the processing
is a dual maximizationdof kinetic activity on the one hand, registered in TST (CH-5), and of kinetic potential
on the other, registered as the natural capital of total system storage (TSS; CH-6). The upper, ﬁrst-look line is manifested
by binary transactions between adjacent standing-stock compartment pairs, exchanges that are conservative
and zero-sum, and transmit temporally and spatially immediate cause and effect. The lower, second-look line
Network
Synergism
(health)
Biological
Fitness-I
Adjacent
(“Direct”)
Effects
systemic
enhancement
systemic
degradation
Network
Nonlocality
Non-Adjacent
("Indirect")
Effects
Connectivity
Centrifugality
Centripetality
Network
Aggradation
Nexus
Autonomy
Autocatalysis
Autopoesis
2nd
Law
Maximum
Throughflow (TST)
& Storage (TSS)
(wealth)
Network
Topo-
synergetics
Biological
&
Environmental
Fitness-II
(wellbeing)
Biodiversity
Biome
Biosphere
Conventional
Empirical
Biogeoecology
Holistic – Theoretical / synergistic (HT/s) Line
Non-Adjacent Relations
ultimate
non-conservative
nonzero-sum
win–win
Adjacent Transactions
immediate
conservative
zero-sum
win–lose
Atomistic – Empirical / agonistic (AE/a) Line
FIGURE 4.10 The Janus Enigma Hypothesisdmaximizing ﬁtness for life in the geobiosphere. This diagram interrelates all the elements of the
Janus Hypothesis. The upper section represents proximate, generally observable and unmeasurable, empirical processes of mechanistic causality
that lead to biological ﬁtness, but at a cost of degraded life support. The lower section represents higher level processes, generally carried out by
largely inaccessible, and therefore generally (because distributed) unobservable and unmeasurable empirical processes of holistic causation. The
antientropic Network Aggradation Nexus contains three core processes (“ABCs”), and several related subsidiary processes, implicated in
movement away from thermodynamic equilibrium. Further discussion in text. Source: Modiﬁed after Patten (2016), ﬁg. 1, p. 103.
4. ECOSYSTEMS HAVE CONNECTIVITY72
involves reticulated, relational sequences of transactions transmitted directly and indirectly between compartment
pairs. These relations are nonconservative and nonzero-sum and transmit ultimate causes and effects. The key
property that makes the lower line dominant over the upper is network nonlocalityddominance of indirect effects
(CH-2). Three biologically and ecologically signiﬁcant goal functions follow on this last one logically and sequentially
from one another in the JH/e: max(indirect > direct effects; CH-2) 0 max(TST, TSS; CH-5, -6) 0 max(network
synergism; CH-13) 0 max(Fitness-I and -II; CH-20). To use a common and somewhat overworked
metaphor of the present period, in this Janus Hypothes is a perfect storm of individually improbable relationships
comes together automatically as a built-in property of transactional network organization, and this makes salubrious
life possible and ubiquitous throughout the planetary biosphere.
The JH/e logic is summarized for the throughﬂow-based formulation in Fig. 4.11. Let D and U ¼ (I À D)À1
be
intensive (dimensionless), direct (D), and integral (U) utility matrices, respectively (ref. Fig. 4.4; also Section
“Network Example 2 e Cone Spring Ecosystem”). Let Y ¼ U$T be extensive (throughﬂow-weighted) utility.
Then, a useful measure of extensive network synergism is NSext ¼
P
(U$T). With these notations, the symbols in
Fig. 4.11 can be interpreted. For example, the Janus Theorem states that maximizing TST (or TSS for the storagebased
formulation) is sufﬁcient (s) to maximize NSext, and the latter is necessary (n) to maximize the former. The
Theorem and Contrapositive are logical equivalents, denoted by the large diagonally oriented equal sign (\\), and similarly,
the Converse and Inverse are logical equivalents (//). As summarized in the upper right box, Patten (2016b)
gave three lines of supporting evidence for the JH/e: (1) TST and NSext both increased proportionally in a sequence
of simple compartment models with equal boundary inputs and progressively more complex connectivity; (2) U
FIGURE 4.11 Logic-of-propositions structuring (if LHS then RHS) of the throughﬂow-based Janus Hypothesis. Each proposition has a truthvalue,
either true or false. The left-hand (if) sides state sufﬁcient conditions (s) for the right (then) sides to follow. The right-hand sides are necessary
conditions (n) for the left sides. The theorem and contrapositive are logical equivalents (denoted by the two diagonal equals (¼) signs); prove one
and the other is proved also. The converse and inverse are also logically equivalent. The symbol (w) denotes negation; wLHS 0 wRHS means
“not LHS implies not RHS.” If both LHS 0 RHS and RHS 0 LHS, this may be written LHS 5 RHS, and stated LHS iff RHS, where iff means “if
and only if.” The storage-based formulation has the same logical structure.
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 73
matrices were shown to contain algebraic arrays of throughﬂows that, when increased, would also increase NSext;
and (3) positive correspondences of TSS and NSext pairings along an increasing sequence of values were demonstrated
for 31 pastoral food webs in Ukraine. Since these results were published, mathematician M. Adams (2017;
see Patten, 2019) has obtained an algebraic proof of the JH/e converse. A similar proof of the primal theorem is still
being sought; whether the outcome is true or false, the ecological implications will be interesting.
The enigma problem in the JH/e lies in the converse, or more speciﬁcally the inverse which is logically equivalent
and thus also implied in M. Adams’ proof. The inverse states that if TST (also TSS, in a parallel storage formulation of
network synergism)dFig. 4.10) is not maximized in network organization, then neither is NSext. This means, as
stated in the converse, that maximizing TST (or also presumably TSS, though this proof is still pending) is necessary
to maximize NSext, hence systemic Fitness-II. In evolutionary selection, networks that fail to meet this prescription
will tend to be reshaped in ecosystemic and biospheric dynamics until the condition is satisﬁed. Thus, a dilemma
and possible danger confronts humanity. Nature is objective and unforgiving. If one conﬁguration of organisms
can achieve max(NSext) better than another in a landscape or biome setting, evolutionary selection will favor the
former, and if Homo sapiens is, by its own actions or not, in the latter group, it will not fare well. All organisms,
humans included, are inherently local problem-solving entities. The sensory-motor apparatus of biota shaped in
evolutionary dynamics is geared to the identiﬁcation and solution of local problems associated with the immediate
needs of making a livingdobtaining food, shelter, mates, etc.; protecting against disasters, pathogens, and other
threats. Distal conditioning phenomena in time and space may be known and understood, but in competition
with immediate needs, they lack sufﬁcient urgency to prompt the evolution of a cognitive sensitivity to indirectness
(CH-2). Such adaptation may be underway now in humanity, however, as the globe shrinks and the spheres of
technology-driven human activity expand.
Autonomy and Self-Organization Hypotheses
CH-17: Network Clockwork Stockworks
This hypothesis seems as strange and counterintuitive as its alliterative name when ﬁrst confronted, but when
understood it makes good ecological sense. Referring to Figs. 4.8AeC, the “standing stockworks’ of CH-8 manifesting
compartment reﬂexivity alone were shown to possess considerable capacity to produce standing stocks. The
transitive (and also symmetric) links that language usually associates with the “net” in networks have little such
stock-generating ability on their own. When “stockworks” (reﬂexive) and “networks” (transitive, symmetric)
combine, however, Ch-8’s ampliﬁcation of stock-generation over reﬂexive generation by itself occurs. The reason
is all about how standing stocks contribute to pathway proliferation, and to the pathways generated. This is developed
and exempliﬁed at length in Patten (2016a, ﬁgs. 12.2e12.6).
Standing stocks determine the dynamics and other properties of conservative substance movements in ecosystems
and the biosphere. Reﬂexively based “clockwork standing stockworks”, to which transitive and symmetric
“networks” of interconnection add profoundly (CH-8) but do not anchor, are the order of the day in living concrete
nature. Empirical ecology has a key part of the truth completely rightdnature, as perceived, is all compartmental
standing stocks. But it misses the vital virtual part: nature’s invisible networks are all about standing stocks tood
their extensions into the world around them, even as their substance marks time in the clockwork stockworks of
place.
CH-18: Network Environ Autonomy
Fig. 4.5 illustrated the partition property of both output and input environs for a simple compartmental system.
Autonomy goes with thisdeach of the six environs enjoys this property by the fact that every conservative element
within it is completely conﬁned to or circumscribed by it, and the environ whole is generated by or referenced to a
unique input or output relationship at the system-level boundary. One of the richer NEA consequences of boundary
openness, therefore, is the extension of reﬂexive, symmetric, centripetal “selves” that are compartments into the
broader transitive and centrifugal surrounds that environ theory seeks to identify, quantify, and subject to comparative
analysis. Input and output environs converge inward and diverge outward from their deﬁning entities, and in
a sense reﬂect and project, respectively, the unique individuality of the latter in and into the world at large. Ownership
of this “projection” by the deﬁning compartments is never released, however. They and their paired environs
retain a unity that can never be disassembled, but can be decomposed by mathematical analysis. Moreover, not only
are the compartments themselves, particularly the living ones, unique, but so also are the environs they generate and
encompass. A careful reading of the consequences of organisms as open systems having environments that uniquely
4. ECOSYSTEMS HAVE CONNECTIVITY74
attach to them is that not only are the organisms autonomous in their innate reﬂexivity in standing stockworks, but
so also are their environs in their innate transitivity and symmetricity as extended networks, although of necessity
more diffusely so.
It was Estonian physiologist Jacob von Uexku¨ll (1926) who ﬁrst put forward a view of the organismeenvironment
relationship that is not very far from the one environ theory affords. Uexku¨ll’s organisms had an incoming “worldas-sensed”
and an outgoing “world-of-action”, corresponding to input and output environs, respectively. He held
that the world-of-action wrapped around to the world-as-sensed via “function-circles” of the organisms to produce
an organismeenvironment complex that was a continuous whole, the true functional unit of nature that decidedly
was not the organism alone in isolation. However, Uexku¨ll acknowledged the impossibility of tracing pathways of
inﬂuence through the general environment from outputs back to inputs, and to that extent the theoretical autonomy
he held for his organismeenvironment complexes was operationally, though not conceptually, compromised.
The stocks and ﬂows of each output or input environ within a system have their own unique structural and dynamic
characteristics from those deﬁned by the same substance running in different environs. Every environ is
unique. For those of the Sapelo Island salt-marsh energy model (Fig. 4.6A), this uniqueness is registered structurally
in the enumerated pathways of Fig. 4.12A, generated by the mth path-length (and for storage analysis, time-passage
mDt) powers of the adjacency matrices, A(0) and A(1). Functional uniqueness is generated in pathway progressions
of link transfer probabilities, expressed by mth powers of corresponding matrices G, G0 converging to N, N0 for
throughﬂows, and P, P0 leading to S, S0 for storages.
Environs are boundary deﬁned, and the ﬁrst reason for their differences is that different energyematter entry or
exit points, respectively, for output and input environs, deﬁne different pathway sets (structure) over which introduced
substance travels. These necessarily impose differing link-transfer probability sequences along the pathways,
and this generates different dynamics (function). Referring to Fig. 4.12A, each jth column in the matrices shows the
numbers of pathways of lengths m ¼ 1, 2, ., 5 and 100 propagated in output environs Ej to all eight compartments
by inputs (zj) to compartments j. Columnwise comparisons in each mth matrix show clearly that different input entry
points generate different numbers of pathways (and, by inference, corresponding ﬂows) of each given length.
Similarly, each ith row in the matrices shows the numbers of pathways of lengths m ¼ 1, 2, ., 5 and 100 that
lead back through input environs Ei
0 to all j ¼ 1, 2, ., 8 compartments contributing to outputs (yi). The differing
numbers of environ pathways determine different pathway identities, each involving a unique link-transfer
sequence and mth-order probabilities associated with these.
Ignoring input environs for present purposes (the same considerations apply), Figs. 4.12B and C show ﬁrst-order
P(0)m
and P(1)m
matrices corresponding to A(0)m
and A(1)m
, m ¼ 1, 2, ., 5, in Fig. 4.12A. Each jth column in the P(•)m
matrices shows transition probabilities associated with corresponding numbers of pathways, A(•)m
, of lengths
m ¼ 1, 2, ., 5 in each jth output environ, Ej. Each ith row in the P(•)m
matrices shows transition probabilities generated
by the numbers of length m pathways (of lengths m ¼ 1, 2, ., 5 and 100) that lead back through output environs
Ei
0 to all j ¼ 1, 2, ., 8 compartments from outputs (yi).
Fig. 4.12D shows how structure and function combine to produce composite ﬂows to throughﬂows and storages
in transactional networks. Pathways are of two types. Those without self-loops (j / k / . / i) are termed paths;
they carry ﬂows to throughﬂows in throughﬂow analysis. Pathways with self-loops (j / . / {k / k / .
k} / . / i) are called walks; they carry ﬂows to storages in storage analysis. Paths of lengths m ¼ 0, 1, 2, . are
enumerated by matrices A(0)m
, and they generate corresponding ﬂow transfer probabilities P(0)m
. Walks of lengths
m ¼ 0, 1, 2, . are enumerated by matrices A(1)m
, and they generate corresponding storage transfer probabilities
P(1)m
. Fig. 4.12D details four examples, the color-highlighted pathways and probabilities in Figs. 4.12AeC, of
how composite mth-order transfer probabilities are generated over each set of length m paths and walks. Paths
are subsets of walks, and therefore are included in the walk counts and probability values. In each section of
Fig. 4.12D, the boxed and color-coded sums of matrix powers on the left, P(0)ij
(m)
and P(1)ij
(m)
, equal the sums of products
of link probabilities, P(0)ij and P(1)ij, shown parenthetically beneath the links j / i that comprise each pathway.
The main points illustrated are enumerated in the ﬁgure legend; they bear repeating here: (1) Each path and walk is
unique. (2) Each such pathway is uniquely quantiﬁed by products of their individual link probabilities, which may
be transfer probabilities, pij (j s i), associated with transfer links, j / i, or self-looping transfer delay probabilities,
pjj, associated with storage links, j / j. (3) Storage, by introducing transport delay, materially increases the number
of transfer pathways between each (i, j) compartment pair. (4) The storage self-looping (j / j) link contributions, pjj,
to the products of aggregate link transfer probabilities along each walk are always greater than link transfer (j / i)
contributions, pij (j s i); storage is the dominant component of transactional transfers in nature’s interconnected networks,
justifying the term “clockwork stockworks” used in CH-17 to refer to transactional networks. And ﬁnally, (5)
the identiﬁed relationships are valid for all pathways of all lengths m that indeﬁnitely extend to complete dissipation
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 75
Maximum
Eigenvalues:
For A(0) = 1.8393
For A(1) = 2.8393
A(1)5 =
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
113 74 74 62 74 62 40 40
5 0 0 1 0 0 0 0
62 39 40 34 40 34 22 22
91 62 62 51 62 52 34 34
10 0 0 5 0 0 1 0
41 34 34 23 34 28 17 18
A(0)4 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
7 4 5 4 4 4 2 2
0 0 0 0 0 0 0 0
4 3 2 2 3 2 2 2
6 4 4 3 4 3 2 2
0 0 0 0 0 0 0 0
3 2 2 2 2 2 1 1
A(0)3 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
4 3 2 2 3 2 2 2
0 0 0 0 0 0 0 0
2 1 2 1 1 1 0 0
3 2 2 2 2 2 1 1
0 0 0 0 0 0 0 0
2 1 1 1 1 1 1 1
A(0)2 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
2 1 2 1 1 1 0 0
0 0 0 0 0 0 0 0
1 1 0 1 1 1 1 1
2 1 1 1 1 1 1 1
1 0 0 0 0 0 0 0
0 1 1 0 1 0 0 0
A(0)1 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 1 0 1 1 1 1 1
1 0 0 0 0 0 0 0
1 0 1 0 0 0 0 0
0 1 1 0 1 0 0 0
0 0 0 1 0 0 0 0
0 0 0 0 0 1 0 0
A(0)5 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
13 9 8 7 9 7 5 5
0 0 0 0 0 0 0 0
7 4 5 4 4 4 2 2
11 7 7 6 7 6 4 4
0 0 0 0 0 0 0 0
6 4 4 3 4 3 2 2
A(1)4 =
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
39 26 26 22 26 22 14 14
4 0 0 1 0 0 0 0
22 13 14 12 14 12 8 8
30 22 22 17 22 18 12 12
6 0 0 4 0 0 1 0
11 12 12 6 12 10 5 6
A(1)3 =
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
13 9 9 8 9 8 5 5
3 0 0 1 0 0 0 0
8 4 5 4 5 4 3 3
9 8 8 5 8 6 4 4
3 0 0 3 0 0 1 0
2 4 4 1 4 4 1 2
A(1)2 =
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
4 3 3 3 3 3 2 2
2 0 0 1 0 0 0 0
3 1 2 1 2 1 1 1
2 3 3 1 3 2 1 1
1 0 0 2 0 0 1 0
0 1 1 0 1 2 0 1
A(1)1 =
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
1 1 1 1 1 1 1 1
1 0 0 1 0 0 0 0
1 0 1 0 1 0 0 0
0 1 1 0 1 1 0 0
0 0 0 1 0 0 1 0
0 0 0 0 0 1 0 1
xtotal = Qz t = Sz =
520.2636
35.2143
652.0651
2.9729
7.2227
9.0088
0.9910
0.6006
Total = 1.2283e+03
x100 = ∑P1-100 z t =
520.2636
35.2143
600.9328
2.9729
6.6440
8.3489
0.9910
0.5472
Total = 1.1759e+03
A(0)100 = 1.0e+26 *
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1.8040 1.1686 1.1686 0.9808 1.1686 0.9808 0.6354 0.6354
0 0 0 0 0 0 0 0
0.9808 0.635 4 0.6354 0.5332 0.6354 0.5332 0.3454 0.3454
1.5140 0.9808 0.9808 0.8232 0.9808 0.8232 0.5332 0.5332
0 0 0 0 0 0 0 0
0.8232 0.5332 0.5332 0.4475 0.5332 0.4475 0.2899 0.2899
A(1)100 = 1.0e+45 *
0.0000 0 0 0 0 0 0 0
0 0.0000 0 0 0 0 0 0
1.2948 0.8388 0.8388 0.7040 0.8388 0.7040 0.4560 0.4560
0.0000 0 0 0 0 0 0 0
0.7040 0.4560 0.4560 0.3827 0.4560 0.3827 0.2479 0.2479
1.0867 0.7040 0.7040 0.5908 0.7040 0.5908 0.3827 0.3827
0.0000 0 0 0 0 0 0 0
0.5908 0.3827 0.3827 0.3212 0.3827 0.3212 0.2081 0.2081
Δ Δ
FIGURE 4.12A Powers, m ¼ 1, ., 5, and 100, of structure matrices for the Teal (1962) salt marsh energy model of Fig. 4.8A. The digraph is shown again at the lower left for convenient
reference. Adjacency matrices without storage self-loops have zero entries on the principal diagonals, and are denoted A(0); those with self-loops have unit diagonal entries and are denoted
A(1). Pathways lacking self-loop link sequences are called paths, and those with such sequences are walks. Paths of lengths m are enumerated by powers A(0)m
, and walks of lengths m by
A(1)m
. Maximum eigenvalues of the A(0) and A(1) matrices deﬁne the rates of pathway growth in numbers with length; these are shown adjacent to the digraph at the lower left. The circled
entries identify selected pairs of entries (same colors) for comparison without, in A(0)m
, and with, in A(1)m
, self-looping storage subsequences along the pathways. Diagonal elements are
circumscribed to assist orientation. Note that despite astronomical numbers of walks in A(1)100
, total storage is not yet fully accounted for by all walks of lengths m 100. This is shown by
xtotal > x100 in the green boxes to the lower right. Network nonlocality (CH-2) and its concomitant holistic causality, are enormous realities in the natural world, and they do not even begin to
be recognized, much less accounted for, in contemporary ecology. Source: Patten (2016a; ﬁg. A4).
4.ECOSYSTEMSHAVECONNECTIVITY76
as m / N in the process of interconnecting (i, j) compartment pairs in accordance with the given constraints of
digraph structure. The general conclusion to be drawn from these observations is that each output and input environ
in a system is structurally (pathways) and functionally (transfer probabilities) unique, and environ autonomy (CH-
18) follows from this in at least two ways.
First, the status of environs as boundary-deﬁned interior partition elements of stocks and ﬂows in compartmental
systems confers autonomy by deﬁnition because no substance running in any one environ of a given orientation also
runs in another. There is no energyematter mixing between environs. They are space-time extensions of their concrete
deﬁning nodes, distributed and virtual in their fundamental openness such as to make the said nodes more like
puppets dancing to nature’s tune on its hidden strings, and less like punctate beings, sessile or motile, divorced from
the greater stocks and ﬂows of planetary movement.
Second, there is the structural and functional autonomy described in earlier paragraphs above that reﬂects where
(for output environs) substance enters and (for input environs) exits a system. These structural and functional differences
confer behavioral autonomy, and this extends to substances of each particular kind in one environ versus
that of the same kind in other environs. Energy and matter of different kinds behave differently in each unique environ,
caused by a substance’s intrinsic properties and propensities for interaction expressed from different points of
entry or exit into or from a system. There is more. Boundaries are not permanent barriers to substance ﬂow; they vary
both objectively and also by the purposes and choices of observers. Environs of ﬁxed-boundary systems are unique
and autonomous, but against variable boundaries they become themselves puppetlike in responding to the dynamics
of boundary establishment and disestablishment. Add to this the ﬁnal perspective of scaledboundaries circumscribing
levels of organizationdand the full scope and multiscalar nature of nested environs coming into and
out of existence by virtue of boundary dynamics in hierarchical organization begins to be realized.
P0
2 = P(0)2 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0.0005 0.0120 0.0110 0.0050 0.0060 0.0010 0 0
0 0 0 0 0 0 0 0
0.0003 0.0002 0 0.0020 0.0050 0.0060 0.0010 0.0010
0.0003 0.0002 0.0001 0.0020 0.0050 0.0060 0.0010 0.0010
0.0001 0 0 0 0 0 0 0
0 0.0002 0.0001 0 0.0001 0 0 0
P0
4 = P(0)4 = 1.0e–03 *
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0.0071 0.1334 0.1211 0.0690 0.1010 0.0530 0.0070 0.0070
0 0 0 0 0 0 0 0
0.0034 0.0024 0.0007 0.0220 0.0551 0.0660 0.0110 0.0110
0.0035 0.0036 0.0018 0.0225 0.0557 0.0661 0.0110 0.0110
0 0 0 0 0 0 0 0
0.0001 0.0012 0.0011 0.0007 0.0011 0.0007 0.0001 0.0001
P0
5 = P(0)5 = 1.0e–04 *
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0.0378 0.0348 0.0154 0.2457 0.6108 0.7273 0.1211 0.1211
0 0 0 0 0 0 0 0
0.0007 0.0133 0.0121 0.0069 0.0101 0.0053 0.0007 0.0007
0.0010 0.0136 0.0122 0.0091 0.0156 0.0119 0.0018 0.0018
0 0 0 0 0 0 0 0
0.0003 0.0004 0.0002 0.0023 0.0056 0.0066 0.0011 0.0011
P0 = P(0)
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0.0300 0.0200 0 0.2000 0.5000 0.6000 0.1000 0.1000
0.0020 0 0 0 0 0 0 0
0.0001 0 0.0100 0 0 0 0 0
0 0.0200 0.0100 0 0.0100 0 0 0
0 0 0 0.0500 0 0 0 0
0 0 0 0 0 0.0100 0 0
P0
3 = P(0)3 =
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0.0003 0.0002 0.0001 0.0022 0.0055 0.0066 0.0011 0.0011
0 0 0 0 0 0 0 0
0 0.0001 0.0001 0.0001 0.0001 0 0 0
0 0.0001 0.0001 0.0001 0.0001 0.0001 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0.0001 0.0001 0 0
0.0007
0.0003
0.0005 0.00220000
FIGURE 4.12B Powers, P0
m
¼ P(0)m
(m ¼ 2, ., 5), of the ﬁrst order (m ¼ 1) link-transfer probability matrix P0 ¼ P(0), where the zeros denote
zero diagonal elements. Selected entries in each matrix are identiﬁed by boxes color coded to correspond to entries in the Fig. 4.12A adjacency
matrices, A(0)m
. Diagonal elements are circumscribed for convenient inspection. Note the quick appearance in the power sequence of nonzero
diagonal entries. These are not self-loops (m ¼ 1), denoting storage, but rather cycles of higher order (m > 1). Source: Patten (2015c; ﬁg. A7).
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 77
Here is a questiondif an established boundary disappears, and the environs it allows deﬁnition fade with it,
does the implicit environ order remain to be expressed again should the boundary reappear? In other words,
do systems and their pathways once established persist aphysically through time, available to be reinvented physically
whenever the circumstances that allow this come together? A question like this has metaphysical overtones
that take inquiry to a new level. Environs are reminiscent of quantum physicist David Bohm’s (1980) concept of
implicate order, which holds that order, once established in the ﬂow-stream that is reality, becomes permanently
imprinted in that ﬂow-stream. Reality has memorydeven though state-space theory teaches that unique histories
are carried into present states, which then combine with inputs to uniquely generate futures, the pasts in the present
states are implicitly carried forward into these futures. That metaphysical considerations can enter ecology in
the latter’s theoretical pursuit of environment as a concept is surprising. It does, perhaps, help explain why mainline
ecological science resists the call to holism lest it be drawn into realms of thought and empirical intractability
that cultural wisdom senses will not contribute to hard ecological understanding, the kind that can be “taken to the
ﬁeld.”
The picture that emerges overall, then, is that environsdpartition elements in aggregate networksdhave integrity
as constituted though diffuse units, and are indeed by the partition and dynamic-difference properties fully
autonomous yet node-contingent within the systems they occupy. No substance is ever exchanged between any
two environs. Also, all measures of them that environ theory has so far allowed have never revealed two environs
the same in any of their characteristics. Every environ is unique. A given compartmental element in one environ will
have, for a ﬁxed unit of boundary input or output, different ﬂow, throughﬂow, storage, turnover, and output attributes
from the same element in other environs. Substance ﬂow and storage packets associated with the same
P1
4 = P(1)4 =
0.4475 0 0 0 0 0 0 0
0 0.5470 0 0 0 0 0 0
0.0875 0.0957 0.8834 0.4591 0.5076 0.6786 0.3008 0.3008
0.0032 0 0 0.1785 0 0 0 0
0.0008 0.0008 0.0100 0.0044 0.0058 0.0076 0.0026 0.0026
0.0008 0.0171 0.0114 0.0048 0.0066 0.0098 0.0029 0.0029
0.0004 0 0 0.0859 0 0 0.5220 0
0.0000 0.0005 0.0003 0.0001 0.0002 0.0080 0.0000 0.5220
P1
5 = P(1)5 =
0.3660 0 0 0 0 0 0 0
0 0.4704 0 0 0 0 0 0
0.0991 0.1136 0.8599 0.4901 0.4941 0.6620 0.3440 0.3440
0.0030 0 0 0.1160 0 0 0 0
0.0010 0.0010 0.0097 0.0050 0.0056 0.0075 0.0032 0.0032
0.0010 0.0152 0.0111 0.0055 0.0064 0.0087 0.0036 0.0036
0.0005 0 0 0.0819 0 0 0.4437 0
0.0000 0.0006 0.0004 0.0001 0.0002 0.0069 0.0001 0.44380.0001
0.0005
P1
2 = P(1)2 =
0.6690 0 0 0 0 0 0 0
0 0.7396 0 0 0 0 0 0
0.0538 0.0484 0.9326 0.3270 0.5310 0.6910 0.1810 0.1810
0.0029 0 0 0.4225 0 0 0 0
0.0004 0.0002 0.0105 0.0020 0.0131 0.0060 0.0010 0.0010
0.0003 0.0212 0.0116 0.0020 0.0078 0.0421 0.0010 0.0010
0.0001 0 0 0.0750 0 0 0.7225 0
0 0.0002 0.0001 0 0.0001 0.0104 0 0.7225
P1
3 = P(1)3 =
0.5471 0 0 0 0 0 0 0
0 0.6361 0 0 0 0 0 0
0.0727 0.0741 0.9075 0.4081 0.5210 0.6927 0.2471 0.2471
0.0032 0 0 0.2746 0 0 0 0
0.0006 0.0005 0.0103 0.0034 0.0065 0.0074 0.0019 0.0019
0.0006 0.0193 0.0116 0.0037 0.0069 0.0150 0.0020 0.0020
0.0002 0 0 0.0849 0 0 0.6141 0
0.0000 0.0004 0.0002 0.0000 0.0002 0.0093 0.0000 0.6141
0.0538
0 0029
0.4081
P1 = P(1)
0.8179 0 0 0 0 0 0 0
0 0.8600 0 0 0 0 0 0
0.0300 0.0200 0.9600 0.2000 0.5000 0.6000 0.1000 0.1000
0.0020 0 0 0.6500 0 0 0 0
0.0001 0 0.0100 0 0.0900 0 0 0
0 0.0200 0.0100 0 0.0100 0.1900 0 0
0 0 0 0.0500 0 0 0.8500 0
0 0 0 0 0 0.0100 0 0.8500
FIGURE 4.12C Powers, P1
m
¼ P(1)m
(m ¼ 2, ., 5), of the ﬁrst order (m ¼ 1) link-transfer probability matrix P1 ¼ P(1), where the ones denote
unit diagonal elements in the corresponding adjacency matrices, A(1)m
, in Fig. 4.12A. Selected entries in each matrix are identiﬁed by boxes color
coded to correspond to entries in Fig. 4.12A adjacency matrices, A(1)m
. Diagonal elements are circumscribed for convenient inspection. Source:
Patten (2015c; ﬁg. A6).
4. ECOSYSTEMS HAVE CONNECTIVITY78
compartments in different environs are different. Uexku¨ll may have been more correct than he realized when he
wrapped his worlds-of-action around to his worlds-as-sensed via his functions-circles of the organism, and said
that the entire existence of the organism is imperilled should these function circles (implicate order?) be interrupted.
Concrete organisms and their virtual environ pairs have a continuity of being that is undeniable, and from the considerations
outlined above it is concluded the environs are in several ways autonomous network elements in the
economy of nature.
Holism Hypotheses
All the CHs of this chapter are in some sense or other expressions of holism. NEA is fundamentally an approach to
holistic systems analysis that occupies places in inquiry where reductionistic empiricism cannot go. The remaining
two hypotheses have system-wide reaches and implications that qualify them as expressly reﬂecting systemic
holism, perhaps somewhat more overtly than some of the others.
pLpT(1)71
(5)
= (10/10)/5 = 0.2000
= (10/10)e–5 = 0.0067
pLpT(1)84
(4) = (1/6)/4= 0.0417
= (1/6)*e–4 = 0.0031
pLpT(1)34
(3) = (4/8)/3 = 0.1667
= (4/8)*e–3 = 0.0249
p (1) = (1/4)/2 = 0.1250
= (1/4)*e = 0.0338
8 walks, 2 paths
from node 4 to 3
in 3 steps
10 walks, 0 paths
from node 1 to 7
in 5 steps
6 walks, 2 paths
from node 4 to 8
in 4 steps
4 3 5 6 8
(0.2000) (0.0100) (0.0100) (0.0100) = 2.0000e-07
4 7 3 6 8
(0.0500) (0.1000) (0.0100) (0.0100) = 5.0000e-07
[4 4] 3 6 8
(0.6500) (0.2000) (0.0100) (0.0100) = 1.3000e-05
4 [3 3] 6 8
(0.2000) (0.9600) (0.0100) (0.0100) = 1.9200e-05
a(0)84
(4) = 2
p(0)84
(4) = 7.0000e-07
a(1)84
(4) = 6
p(1)84
(4) = 0.0001
4 3 [6 6] 8
(0.2000) (0.0100) (0.1900) (0.0100) = 3.8000e-06
4 3 6 [8 8]
(0.2000) (0.0100) (0.0100) (0.8500) = 1.7000e-05
+
+
+
+
5.3700e-05
+
(A)
(C) (D)
(B)4 walks, 2 paths
from node 1 to 3
in 2 steps
1 4 3
(0.0020) (0.2000) = 4.0000e-04
1 5 3
(0.0001) (0.5000) = 5.0000e-05
[1 1] 3
(0.8179)(0.0300) = 0.0245
1 [3 3]
(0.0300)(0.9600) = 0.0288
+
a(0)31
(2) = 2
p(0)31
(2) = 0.0005
a(1)31
(2) = 4
p(1)31
(2) = 0.0538
+
0.0537
4.5000e-04
+
4 3 5 3
(0.2000) (0.0100) (0.5000) = 0.0010
4 3 6 3
(0.2000) (0.0100) (0.6000) = 0.0012
[4 4 4] 3
(0.6500) (0.6500) (0.2000) = 0.0845
[4 4] [3 3]
(0.6500) (0.2000) (0.9600) = 0.1248
+
a(0)34
(3) = 2
p(0)34
(3) = 0.0022
a(1)34
(3) = 8
p(1)34
(3) = 0.4081
4 [3 3 3]
(0.2000) (0.9600) (0.9600) = 0.1843
[4 4] 7 3
(0.6500) (0.0500) (0.1000) = 0.0033
4 7 [3 3]
(0.0500) (0.1000) (0.9600) = 0.0048
4 [7 7] 3
(0.0500) (0.8500) (0.1000) = 0.0043
+
+
+
+
+
0.4082
0.0022
[1 1 1 1] 4 7
( (0.8179)3 (0.0020) (0.0005) = 5.4714e-05
[1 1 1] [4 4] 7
(0.8179)2 (0.0020) (0.6500) (0.0500) = 4.3482e-05
[1 1] [4 4 4] 7
(0.8179) (0.0020) (0.6500)2 (0.0500 ) = 3.4556e-05
[1 1 1] 4 [7 7]
(0.9982)2 (0.0020) (0.0500) (0.8500) = 5.6862e-05
+
a(1)71
(5) = 10
p(1)71
(5) = 0.0005
[1 1] [4 4] [7 7]
0.8179•0.0020•0.6500•0.0500•0.8500 = 4.5189e-05
[1 1] 4 [7 7 7]
(0.8179) (0.0020) (0.0500) (0.8500)2 = 5.9093e-05
1 [4 4 4 4] 7
(0.0020) (0.6500)3 (0.0500) = 2.7463e-05
1 [4 4 4] [7 7]
(0.0020) (0.6500)2 (0.0500) (0.8500) = 3.5913e-05
+
+
1 [4 4] [7 7 7]
(0.0020) (0.6500) (0.0500) (0.8500)2 = 4.6963e-05
1 4 [7 7 7 7]
(0.0020) (0.0005) (0.8500)3 = 6.1413e-05
+
+
+
+
+
+
4.6565e-04
+
+
7.0000e-07
FIGURE 4.12D Details of the circled paths, A(0)m
, and walks, A(1)m
, of lengths m ¼ 2, 3, 4, and 5 as color-highlighted in Figs. 2.12AeC. Paths
are subsets of walks, and so are included in the walk counts and probability values. In each section, the boxed and color coded sums of matrix
powers on the left, P(0)ij
(m)
and P(1)ij
(m)
, equal the sums of products of link probabilities, P(0)ij and P(1)ij, shown parenthetically beneath the links
j / i that comprise each pathway (path or walk). The main points illustrated are: (1) Each path and walk is unique. (2) Each such pathway is
uniquely quantiﬁed by products of their individual link probabilities, which may be transfer probabilities, pij (j s i), associated with transfer links,
j / i, or self-looping transfer delay probabilities, pjj, associated with storage links, j / j. (3) Storage, by introducing transport delay, materially
increases the number of transfer pathways between each (i, j) compartment pair. (4) The storage self-looping (j / j) link contributions, pjj, to the
products of aggregate link transfer probabilities along each walk are always greater than link transfer (j / i) contributions, pij (j s i); storage is the
dominant component of transactional transfers in nature’s interconnected networks, justifying the term “clockwork stockworks” used in CH-17 to
refer to transactional networks. Finally, (5) the identiﬁed relationships are valid for all pathways of all lengths m that indeﬁnitely extend to
complete dissipation as m / N in the process of interconnecting (i, j) compartment pairs in accordance with the given constraints of digraph
structure. Source: Patten (2016a; ﬁg. 12.5, p. 75).
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 79
CH-19: Network Distributed Control
Ecologists from the beginnings of the subject have always been concerned with issues of controldallogenic or
autogenicdat physiological, population, community, and ecosystem levels of organization. The subject permeates
the discipline in many forms. There are no obvious discrete controllers in ecosystems, though there are concepts
like “key industry organisms” and “keystone species” that are suggestive of such possibilities. In general, in view
of the nonlocality property (CH-2), control in ecosystems would have to be considered as realized by dominantly
indirect means. This is the postulate of distributed control, and as with indirectness itself it has origins in boundary
openness (Schramski et al., 2006).
Distributed control means decentralized regulation of system components by direct and dominant indirect (CH-2)
effects carried by conservative substance transfers propagated and progressively dissipated over network distances.
The numbers of pathways manifesting this control are enormous, e.g., the paths (A(0)100
z 1026
) and walks
(A(1)100
z 1045
) of length m ¼ 100 in the Teal (1962) salt marsh model (Fig. 4.12A). The transferred substances
may represent resources that facilitate the processes of recipients, or toxins that limit said processes. Component j
in a system is deﬁned as controlling another, i, if j facilitates or inhibits i more than i facilitates or inhibits j. This
CH holds that (1) distributed, diffuse, indirect, decentralized, remote, etc. control is the dominant regulatory force
operating throughout nature’s living networks; (2) this control is manifested by long pathways (CH-1) and dominant
network indirect effects (CH-2); and (3) the resultant combination of forces is what keeps ecosystems functioning
coherently, near-linearly (Patten, 1975, 1983, 1997, 2013, 2014), and stably, resisting and recovering from perturbations,
and tracking and adapting to planetary changedat times biotically induceddthrough bio-geo-physicochemical
evolutionary time.
Such concepts, with their implied linearity, quasi-linearity, and linearization around operating planetary trajectories,
lend themselves to investigation by the methodologies of linear algebraic environ analyses. In the throughﬂow
analysis of environ theory (Fig. 4.3), compartment j controls i if the throughﬂow at i generated by j is greater
than the reverse, that is nijzj > njizi, or in matrix format, Nz > (N0)T
y. Similarly, in the storage methodology
(Fig. 4.3), compartment j controls i if the standing stock at i generated by j is greater than the opposite, that is
sijzj > sjizi, or in matrix terms, Sz > (S0)T
y. Schramski (2006) extensively developed the Patten (1978b) distributed control
concept by elaborating and applying what he called control difference (CD) and control ratio (CR) measures.
The need for cross-scale consistency in bipolar causality would have it that lower levels of organization must
participate as equally as upper scales in distributed control relationships. Mechanistic lower scales must mesh
consistently with upper-scale holism. Therefore, distributed control must be manifested through an incredibly complex
scale-spanning network of transactional interchange. Darwin (1859, pp. 122, 123), for his time, had uncanny
naturalistic insight into the nature of the systemic processes involved. Speaking of evolutionary improvement
through natural selection, he gave hint to an understanding of distributed control: “This improvement inevitably
leads to the gradual advancement of organisation of the greater number of living beings throughout the world.
But . if all organic beings thus tend to rise in the scale, how is it that throughout the world a multitude of the lowest
forms still exist . ? Why have not the more highly developed forms everywhere supplanted and exterminated the
lower?” He gave a systems answer (p. 125), fashioned at a time that knew little about microbiological saprovory and
its ecological need in biogeochemical nutrient regeneration: “ . under very simple conditions of life a high organisation
would be of no service . .” In the economy of nature, in any age, the same basic functions of organizing energy
and material resources exist to be performed. Any ecosystem order, simple or complex, must produce
protoplasm and degrade it. Commitment to tracking a particular planetary order at a particular time means that
a given set of organisms must be systemically coevolved to mutually consistently perform these tasks. If an
ecosystem order is simple, then control requirements will be simple and so also the organisms required to carry
it out. Darwin understood the unity of the entire organization, but not in supporting network terms, and certainly
not including implicate order whose preservation through time (if true) would have precluded the following hypothetical
experiment: “ . if under nearly similar climate the eocene inhabitants of the world could be put into competition
with the existing inhabitants [they couldn’t, because their world that gave rise to them could not be duplicated
in present time], the former would be beaten and exterminated by the latter ..” The animals in any zoo could never
be a match for their wild counterparts coadapted and coevolved with the distributed-controlling environments that
produced them.
CH-20: Network Ecogenetic Coevolution
The “Modern Synthesis” of biological evolution describes the process in terms of two fundamental phenomenad
transgenerational descent, and its modiﬁcation over time. It recognizes one kind of descent, genetic, and one
4. ECOSYSTEMS HAVE CONNECTIVITY80
modifying process, natural selection. Ecologically, selection steers genetic descent nonrandomly toward adaptive,
planet-tracking, ﬁtness-maximizing biotic conﬁgurations in the biosphere. Other modifying processes, such as genetic
drift and mutation, act at random. Thus, genetic ﬁtness becomes a matter of genes contributed by ancestral organisms
to descendants via germ-line inheritance expressed exclusively in “vertical gene transfer.” The dogma
separates this “germ track” from a corresponding body or “soma track” of supposedly nonheritable, mortal phenotypes
by an impenetrable “Weismann barrier” (Weismann, 1885). In the post-WatsoneCrick era of the second half of
the 20th century, this barrier came to mean unidirectional DNA / RNA / protein coding. It ensures that “nothing
that happens to the soma can be communicated to the germ cells and their nuclei” (Mayr, 1982, p. 700). This is genetic
determinismdthe doctrine that the structure and function of organisms are exclusively determined by genes protected
from outside inﬂuences.
The doctrine has always been questioned, but it is now under serious revision involving a greater role for environment.
Numerous mechanisms for environmental control of gene expression have long been recognized (e.g.,
Klipp et al., 2005). Recently, gene-sequencing technology (genomics) is increasingly revealing the introduction of
environmentally selected genetic material into the soma and genomes of macrobiota by commensal and parasitic
microorganismsdviruses, archaea, bacteria, and fungi. Internet sources estimate that 8% of the human genome
has viral origins. The number of bacterial species in our bodies is more than ten thousand, and by most estimates
they well outnumber and comprise several percent at least of the mass of human cells. Moreover, the microbes
are arrayed in microbiomes, endogenous communities of ultimately exogenous origins with differentiated functions
complementing host-organism functions. “Ultimately exogenous” means they got there by, not vertical, but “horizontal
gene transfer” (e.g., Quammen, 2018) through the open door of the Weismann barrier their breaching renders
obsolete. The breakdown of verticality opens another door to a serious consideration of “environmental inheritance”
which, in a wider view of planetary biogeoecology, is the only kind that makes sense in a biosphere that must track
and adapt to environmental change as its most basic task in enduring the exigencies and ensuring the contingencies
of sustainable life.
Environment is underplayed in conventional evolutionary theory, where it appears only as a general agent in natural
selection. The environment of environ theory is two-sided, and both sides can be seen to possess potentially
heritable elements, enough to support the hypothesis that environment and genomes both code for phenotypes,
one from inside, the other from outside the organism. The never-published term envirotype was coined more than
25 years ago to convey this idea (Patten, Holoecology, in prep.), and so “holoevolution” (CH-20) came to postulate
joint and balanced contributions to mortal phenotypes from two evolutionary, potentially immortal, lines of inheritance.
These are the conventional genotype, engaged in bottom-up coding within the cell, and a corresponding
external envirotype, effectively manifesting in natural selection top-down coding from without. The envirotype is
a third element lurking, but never identiﬁed, in geneticist R. C. Lewontin’s (2000) “triple-helix” account of evolution
which, without that author’s actually saying so, distinctly explored the possibility that biogenic elements of environment
constituted a bona ﬁde biological inheritance system in its own right. Both input and output environs
contribute to the heritable qualities of these envirotypes, as follows:
• Input-environ-based inheritance. We can begin with the cell, and then mentally extrapolate outward through higher
levels of organization to the organism and beyond. Each level, including that of the whole ecosystem, can be
understood to have its own mechanisms of receiving environmental information, generating responses to this,
and retaining (inheriting) through natural selection the ability to continue responses that prove beneﬁcial to
survival. Consider a cell receiving an energy- or matter-based signal from a near or distant source in its input
environ. Biologist B. Lipton (www.brucelipton.com/newbiology.php) presents a scenario that effectively
breaches the Weismann barrier and allows transmission of environmental data directly to the genome.
Openness is at the heart of this process because the “cellular brain,” as Lipton refers to it, is not located deep
inside the cytoplasm or nucleus, but at the cell boundary. It is the cell membrane, or plasmalemma, a
crystalline bilayer of proteins and phospholipids that includes a set of “integral membrane proteins” (IMPs)
that serve as receptors and effectors. Receptor proteins respond to incoming molecules, or equally
electromagnetic energy ﬁelds, by changing shape. This enables them to bond with speciﬁc effector proteins
(enzymes, cytoskeletal elements, or transporters of electrons, protons, ions, or other chemical categories) that
carry out behavior. If the requisite effector proteins are not already present in the cytoplasm, the IMP
perception units activate expression of appropriate genes in the nucleus to produce new ones. Such genes
introduced into the DNA / RNA / protein sequence in the process remain behind to be copied, enabling the
response to be repeated if adaptive, or ultimately fall obsolete and become consigned to the genomic set of
inactive “junk” genes. Correct activations lead to life-enhancing behaviors, incorrect ones to maladaptation
4.7 THE CARDINAL HYPOTHESES OF NETWORK ENVIRON ANALYSIS 81
and death. Cellular adaptability thus becomes encoded in response to environmental inputs into new genes that
encode new proteins, enabling survival in changing, but history-laden, environments. From the environ
perspective, receptor molecules respond to signals transmitted in input environs, and effector molecules
transmit the consequences to output environs. This initiates the second phase of environmental inheritance.
• Output-environ-based inheritance. When cells or other living entities act on their environments the latter are
changed as a result. This is “niche construction” (Odling-Smee et al., 1996). Its essence is that it alters the
machinery of natural selection because selection is, in the ﬁrst instance, a manifestation of input environs. To
the extent, however, that output environs generated by responses of their deﬁning entities wrap around and
become elements in those entities’ input environs, the process becomes heritable, and epiphenomena such as
autoevolution (Lima de Faria, 1988) emerge as distinct possibilities. Metazoan organization as “symbiogenic”
aggregates of protozoan antecedents (Margulis, 1981, 1991) is an example. This is based on wrap-around
feedback in which unicellular input- and output-environ overlaps are established in multicellular organization
and achieve integration and identity. Organized cell communities possess self-similar IMP receptors
responsive to the signal content of hormones and other intercellular regulatory macromolecules. This requires
that output-environ elements become input-environ elements. Membrane proteins convert adjacent
environmental signals into cellular “awareness,” expressed as changes in protein conﬁgurations. The
movements occasioned by these changes represent useful kinetic energy that does the work of achieving
further departure from thermodynamic equilibrium, which multicellular organization represents compared to
unicells. This is the essence of all antientropic growth and development extending to ecosystems and the
ecosphere. Each level has mechanisms peculiar to it for implementing environment-based inheritance and
perpetuating through time all forms of life that are operational genotypeephenotypeeenvirotype complexes.
Organisms and their cells below, and communities and ecosystems above, can be said to inherit both their
contained genes and attached environments from ancestral forms, and to the extent that these environments
manifest holism, the great panoply of life spread over the globe at all levels of organization can be seen as
evolving jointly, altogether, in the ecosphered“holoevolution.”
4.8 CONCLUSIONS
As evident throughout this chapter, ecosystems are networks of interacting biota and abiota. Rigorous methodological
tools such an ENA and NEA have been developed to deal with this. Reactionary clinging to a culture of
old science will not change the realities to which the new science of this book is trying to open some doors, at least
ever so slightly. As more and more applications of systems and network analyses arise, it is important to remember
the common methodological roots of the approaches. In fact, because of its basic assumption about objects connected
together as part of a larger system, which is used in several disciplines, the most promising application of network
analysis may be as a platform for integrated environmental assessment models to address sustainability issues of
combined human and natural systems.
4. ECOSYSTEMS HAVE CONNECTIVITY82
C H A P T E R
5
Ecosystems as Self-organizing Hierarchies
All sciences are now under the obligation to prepare the ground for the future task of the philosopher, which is to solve the problem of
value, to determine the true hierarchy of values. Friedrich Nietzsche.
Ecologists often seem to take the existence of a hierarchical organization of nature for granted, and the reasons to
this judgment are many (Rowe, 1961). First of all, looking at reality through a sequence of subordinate levels matches
with our normal ways of doing science within the realms of biology, in a more or less reductionist manner, seeking
explanations and identifying causes of phenomena predominantly at lower levels. With respect to this reductionistic
viewpoint, the purpose of applying hierarchy theory is to move us closer to nature and not the opposite as feared by
some authors (e.g., Guidetti et al., 2014). Such an open, comprehensive, and improved understanding of nature can
be easily based on the linkage of some simple terms, elements, and concepts. In short, a hierarchy can be characterized
as a partly ordered set with asymmetric relationships inside the whole entity (Allen and Starr, 1982; O’Neill
et al., 1986, Allen and Hoekstra, 1992; Wu, 2013; Allen, 2001,1
Eldredge et al., 2016). These interactions connect
different levels of organization, which can be distinguished by their spatial and temporal characteristics. Due to
those basic differences of typical extents and frequencies, hierarchies are developing as emergent properties of
self-organized ecosystem dynamics (Nielsen and Mu¨ller, 2000). Within such development, the overall system is
enfolding hierarchical constraints, limiting the degrees of freedom of lower hierarchical levels and potentials,
derived from the lower levels’ constitutions.
As a ﬁrst example for such a well-known hierarchical distinction, we may view the existence of an organism as a
result of its ability to ﬁnd food, its ingestion, metabolism, growth, reproduction, and further factors which may be
reduced to physiology, later to biochemistry, and ultimately to mere physicochemical reactions. Such an intuitive
view may for sure be advantageous in reducing and organizing complexity and analyzing the particular set of
hierarchies that we later will identify as having physical embeddedness, i.e., that the lower levels are successively
surrounded by upper levels, as a central core feature. This represents a more holistic view by making use of at least
a conceptual modeling approach for the embeddedness where it may also be a virtual construct, e.g., where the
individual members of a population are not strictly conﬁned by a distinct physical boundary and that allows for
the analysis of ecosystems. It is also inclusive as it recognizes the relative success of reductionistic strategies and
allows for the use of data from all levels. Thus, hierarchical viewpoints are suitable for unifying holistic and reductionistic
approaches (Jørgensen, 2012). Meanwhile, when it comes to current discussions on environmental issues,
we are moving toward the upper end of the hierarchy where the systems are composed of many organisms of many
kinds, eventually belonging to any possible functional group of species, being it evolutionary primitive or a highly
advanced species; monocellular or multicellular; bacteria, algae, or cormophytes; protozoans, invertebrates, and
vertebrates; and maybe even humanoids. Systems come to include many of these organism types, with highly varying
temporal and spatial scales, and as a consequence, the distinction into clear physical boundaries becomes
obscure or is totally lost. One solution will be to deﬁne and design what we are going to work with so our conceptualization
is ﬁtting the actual speciﬁc conditions of the ecosystem in the best possible manner. In other words, our
system analysis is case speciﬁc; and thus, our resulting ecosystem may take almost any form that relates to the real
system under consideration as it mostly depends on a problem-oriented perspective. The systems as well as their
boundaries are dependent on the problem to be solved; they are deﬁned by the observer with respect to the questions
on hand.
1
http://web.archive.org/web/20011218001638/http://isss.org/hierarchy.htm.
83
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00005-X
Taking the whole together, we observe (at least) two distinct types of hierarchies, the series of embedded systems
(also often referred to as nested systems) together with a series of nonembedded systems that must be constructed
from relevant elements (Fig. 5.1).
To make things simple and considering the knowledge we have gained about ecosystems over the recent decades,
the latter type seems to be the more relevant one for the analysis of ecological problems and ecosystems. As seen
from the ﬁgure above, the obvious embeddedness reaches up to the organism level and therefore its elucidation
is mainly concerned with the subdisciplines of autecology or ecophysiology. Both of them deal with mostly internal
aspects of the organisms and the close relations to their respective environments, at most with the predatory processes
of either “to eat” or “being eaten.” That is, most often papers deal with energetics of the life cycles of representatives
of a given speciesdtaking the surroundings, the complex environment in which they are truly embedded,
as a “black box” for granted and representing relatively stable conditions (continuous repetition of seasonal cycles as
a Lagrangian stability).
The other type of the disciplinary hierarchy deals exactly with attempts of describing and characterizing the surroundings
(Umwelt) in which the organisms are embedded because organisms or collections of species are never on
their own. They mostly enter in herbivorous or preyepredator relations leading to sets of LotkaeVolterra relationships
that may or may not be linked in a (Lindeman) trophic chain. But, at present we know that attempting to understand
the behavior of natural systems from fundamental views of two-species preyepredator relationship or
linear food chains is far too simple to make the full picture. Species interactions take the full set of species involved
to capture at least a part of the prevailing complexity. Furthermore, the species and their functional groups are organized
as networks where they may interact with several levels at the same time in space, and these networks may not
FIGURE 5.1 Examples of the two principal types of hierarchy found in the literaturedthe one of embeddednessdthe upper part being real,
physical embeddedness where an organism is having organs, tissues, and cells as continuously internalized levels, whereas organisms make up
the population which in turn make up the ecosystem with no distinct physical bound, and the second representing a mere ranking of levels and
processes.
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES84
necessarily be constant in time. These circumstances are clearly demonstrated in the previous chapter dealing with
ecosystems as connected, complex networks. Also, in addition to the feeding interactions, the organism is dependent
on and connected with its environment for all other aspects that allow it to continue as a living, self-organizing,
aggrading, and dissipating system: the air, the soil, the water, the temperature, the chemical gradients, etc. The environment
provides the conditions that sustain life (which the organism itself is part of, provides feedback to). We have
found it useful to distinguish between discrete life (an organism) and sustained life (the ecosystemic processes)
(Fiscus and Fath, 2019).
5.1 HISTORY OF HIERARCHY CONCEPTS IN ECOLOGY
Applying those demanded holistic attitudes, hierarchy theory as such was formulated during the late 20th century
presumingly as part of the appearance of the general systems theory of von Bertalanffy (1968). Authors like
Koestler (1967), Weiss (1971), Pattee (1973), and Simon (1962) can be addressed as pioneers of the hierarchical systems
view. Although the view has become somewhat inherent in biological thinking, a real breakthrough in ecology
seems to have come in the late 1970s where the ﬁrst papers dealing with hierarchies in ecology emerged following
the statistics of the Web of Science (WOS). Since then, the number of papers published in the area has grown markedly.
Using WOS for a quick investigation in the area reveals that around 350 and 650 papers per year (2017 data)
were published in the area when a combined search of the term hierarchy is coupled to ecosystems or ecology,
respectively.
The stronger representation of papers containing the term hierarchy during recent decades is likely to be tightly
coupled with the recognition of the extremely high level of complexity exhibited by ecosystems. This has led to an
increased attention of the problems that arise both when doing research and management on these systems. Ecosystems
and their counterparts, socioecological systems, have been widely recognized as belonging to a special class of
middle numbered systems (Allen and Starr, 1982), self-organizing holarchic open (SOHO) systems (Kay et al., 1999),
or complex adaptive systems (CAS) (Holland, 1992). To these features, the property of hierarchical (self-) organization,
and emergent properties should deﬁnitely be added (Mu¨ller and Nielsen, 2000; Nielsen and Mu¨ller, 2000).
Hierarchical thinking therefore represents a core issue in interpreting and understanding biological systems in a
holistic manner (Farnsworth et al., 2017; Wu, 2013).
5.2 HIERARCHIES INHERENT IN BIOLOGY
Hierarchical concepts have been penetrating scientiﬁc thinking ever since the ancient Greeks and are often taken
as a tacit background to the things we are doing as ecologist, the way we practice our research (Bizzarri et al., 2013).
We often forget that the very way we see species organizeddin a ranking in accordance to evolutionary
systematicsdimplicitly contains an inherent idea that something, here typically a certain species or a functional
group, is somehow more important, more advanced, more complex, more organized, or more central than others;
and therefore, valued differently.
Traditionally, outcomes of these concepts are phylogenetic or evolutionary trees, which place primitive organisms
at the base of the scheme and when climbing up it splits into branches of species being more and more advanced,
mostly with increasing complexity (see Fig. 5.2). We demonstrate such a view in our practices as humans, ecologist,
and managers. We tend to prioritize and protect species closer to ourselves or at least animals we believe possess the
ability to present feelings, thinking, or any type of cognition in a mirror of ourselves.
Another core point implicit in the above reﬂection is the idea that some organisms or species are more sophisticated
or complex than others. This idea also lies behind the launch of the concept of exergy and emergy in the late
1970s. As an extension, in exergy analyses the level of complexity was hypothesized to be reﬂected in the size of the
genome (Jørgensen et al., 1995) or subsets hereof (Fonseca et al., 2000) for a relative weighting of the complexity level.
Clearly, organisms such as chordates are more complex than protozoans, and such a view is even reﬂected in our
ideas about the food chain where higher level (advanced) organisms are usually feeding on lower levels, simple
organisms. Meanwhile, many conceptualizations of ecosystem food chains are often neglecting the simplest forms,
bacteria that really make up a fundamental and essential part of the ecosystem function by ensuring the circulation
of nutrients within the system and from a functional perspective share a role of consumers (Steffan et al., 2015).
Meanwhile, the recent way of presenting the organisms in the cladistic trees presents the organisms in a manner
5.2 HIERARCHIES INHERENT IN BIOLOGY 85
that is more “fair” as it places all species or phylogenetic groups at the top. This ought to remind us that when
viewed from a functional angle all representatives in the ecosystems count equal as each performs an essential function
in the system, i.e., makes the system exist.
O’Neill et al. (1986) make this mix of observations the starting point of one of the earliest books dedicated to
hierarchical understanding of ecosystems. This treatment begins by pointing out the apparent gap in our understanding
of systems of seemingly either staticdfocusing on elements such as species or populationsdor dynamic,
which covers a more process-oriented entrance to the analysis. As in many cases of complex systems, for such an
analysis to be successful it is not a question of “either or” but rather “both and,” i.e., we need a more holistic
approach and the authors point at the hierarchical approach to be the optimal concept needed in the future to increment
or complete our understanding of biological systems. Such ideas actually represent a predecessor of postmodern
science (Funtowicz and Ravetz, 1993) in ecology. This implementation of the hierarchy theory in ecology
has had a tremendous impact and has been expanded on by many other contributors like Allen and Hoekstra
(1992), Mu¨ller (1992), Hari and Mu¨ller (2000), Gardner et al. (2001), or Salthe (1985). Meanwhile, it can be argued
that hierarchical thinking was present in systems theory much earlier (Gayer, 1969; Drack and Pouvreau, 2015).
As with the phylogenetic example, many texts on reductionism and holism in biology take a very distinct hierarchical
approach to understand such systems. We know traditionally from the ﬁrst scholarly studies in biology
that organisms are composed of organs consisting of tissues, cells organelles, macromolecules, etc.dall of which
are getting consistently smaller and smallerdthe smaller, being embedded in the above/previous level. Such an
illustration is shown in Fig. 5.1 and Table 5.1. What is most characteristic is that within the sequence of levels
each level is typically separated by physical boundaries that are most often of a membrane-like character. The
cell membrane separates the cell from its environment or adjacent cells, other membranes are separating the organelles
from the cell sap and so on. Moving in the other direction, tissues and organs are most often delineated by
another set of membranes until the ﬁnal boundary with yet another cell layer, in the human/mammalian case
our skin.
Whereas, the evolutionary tree certainly expresses a ranking of the organisms more or less in accordance with the
time it has taken to evolve (see Jacob, 1977) that very organism type, another question that appears is how to describe
FIGURE 5.2 Phylogenetic tree where the branches terminate at various levels believed to indicate a hierarchical position in accordance to a
hypothetical and constructed level of evolutionary level of complexity.
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES86
such a hierarchy? We may talk about a scalar hierarchy to a certain extent as organisms tend to be larger and take up
more space the higher they are placed in the hierarchy. When organizing the ranking in another way, for instance,
expressed in the food chain or other ecophysiological process bundles, we may even talk about a control hierarchy.
At least, we frequently did so earlier when ecosystems were considered to be regulated by predation and the whole
series of levels was considered to be what is usually referred to as “top-down regulated.” Meanwhile, it has been
shown on several occasions that the lower levels also play very important roles in shaping the function and ﬂow
of the ecosystem. For instance, the capture of solar radiation by primary producers has a prominent role and is
responsible for the whole energetic input into the system from which all other activities are made possible. Further
recycling and retention of matter, such as nutrients, is carried out by bacteria or very simple organisms mostly in
sediments or soils. Both the latter are placed at the lowest levels of the hierarchy, but nevertheless lead to the fact
that bottom-up control must be applied in order to get a full view of the regulatory mechanisms. When dealing
with embedded systems we may correspondingly talk about outsideeinward regulations versus an inwardeoutside
perspective. For further discussions on this, the reader may refer to Nielsen and Ulanowicz (2000), Nielsen (2007,
2016), and Salthe (1985).
5.3 CLASSICAL HIERARCHIES IN TIME AND SPACE
While the above hierarchies in biological thinking remained relatively unarticulated, it became more and more
apparent that systems thinking from physics penetrated into the biological sciences, most likely with the introduction
of a more holistic view as applied, for instance, through the works of E.P. and H.T. Odum, making it easier to
observe the importance of spatial and temporal patterns.
A central role in this evolution has been played by Arthur Koestler who introduced the concept of the holons,
units that were continuously embedded in each other: every level consists of a set of smaller parts, which in turn
consisted of even smaller parts, and so on. Each level thus contains a “Janus-faced,” dualistdnot to say dialectic
property of having relations with other levels in both upward and downward directions. The relationships to ecology
became apparent when B.C. Patten declared ecosystems to be understood as holonsdor environs as they were
nameddin what must be seen as a seminal paper in this connection (Patten, 1978).
Following the view of Koestler, hierarchies are normally observed in a vertical manner (cf. above) as illustrated in
Fig. 5.3. The view ﬁts well with the physically embedded system. Meanwhile, this seems not to play as large a role as
one might expect although all boundaries are more or less virtual or mental having been constructed by human
thinking. This is due to the fact that we most often tend to construct these types of hierarchies when used for either
research or management, regulation purposes in a manner where the upper layer tends to be inclusive, i.e., including
lower level elements.
One major observation was that time and space scales are often linked so that larger systems are coupled with a
longer time scale and at the same time are embedding or including smaller and smaller systems with successively
smaller and smaller time scales. The various time scales inﬂuence the way the systems communicate. Communication
between levels takes place so that the upper level communicates with signals of lower frequency than the lower
level and that the lower level communicates upwards with signals of a higher frequency. Signaling here can take
place by the exchange of energy, matter, or information. The upper levels tend to dampen and level out or ﬁlter
TABLE 5.1 Examples of the Distinction Between Scalar, Control, and Nonnested Hierarchies as Opposed to an Understanding Which
Involves a Physical Embedding of the (Sub-)systems.
Nonnested Hierarchies Nested Hierarchies
Examples: The military command hierarchy; food webs Examples: The army consisting of soldiers of all ranks; taxonomic
systems
Not suitable for exploration Suitable for exploration
Same criteria (or measurement units) pressing across all levels Different criteria (or measurement units) at different levels
Comparison between hierarchies is more feasible Comparison between hierarchies is less feasible
System-level understanding cannot be obtained by knowledge of
parts
System-level understanding can be obtained by knowledge of parts
After Wu (2013)
5.3 CLASSICAL HIERARCHIES IN TIME AND SPACE 87
the signals from the lower levels. They do not necessarily react on them and are thus acting as constraints on the
lower levels because these cannot ﬁlter the signals from above. Interactions may also take place between components
at the same leveldin a horizontal signal transfer at the same spatiotemporal leveldwhere frequencies also may
differ and constrain each other. Following the ideas of ontic openness (Chapter 3), we have a guarantee that internal
competition and adaptation between the holons at each level will take place in this manner.
The consequences of the hierarchical structure from Fig. 5.3 may be illustrated by Fig. 5.4, which shows some
hypothetical relations between different functional holons: due to broad extent processes the geological and geomorphological
conditions of an ecosystem have been set by extremely long-term processes. For instance, the Holocene
glaciation dynamics have been determining the actual shapes of several landscape types. The subsequent geomorphological
processes have been operating since 10,000 years and are producing slow changes for very large regional
FIGURE 5.4 Exemplary consequences of spatiotemporal hierarchies for ecosystem structures and functions.
Spatial Extent: High
Frequencies: Low
Holon Level (+1)
Holon Level (0)
Holon
Level
(-1)
Spatial Extent: Small
Frequencies: High
Spatial and temporal scales are related
Minor Interactions
Multiple Interactions
Filter
Constraints
FIGURE 5.3 Basic features of ecosystem hierarchies, following Mu¨ller (1992).
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES88
entities. Due to these (slow and large) features, many soil characteristics are determined, only certain soil development
processes can become active, and therefore the geological structure is constraining the soil development,
reducing the degrees of freedom of soil evolution. Due to the resulting pedological structures, only certain vegetation
will have the potential to grow, and due to this speciﬁc plant cover, the developing freedom of the fauna is also
reduced. Finally, the faunistic and ﬂoristic conditions determine the potentials of the microﬂora. The different holons
are in fact reducing the degrees of freedom in a top-down hierarchy. In the other direction, the direct effects are low.
For instance, soils may only impact the extremely long-term changing geological features. Thus, the bottom-up
direction is characterized by biotic or abiotic potentials. Their changes are mostly ﬁltered or dampened by the upper
level as long as the system is operating under steady-state conditions. In situations of instabilities, this hierarchy is
broken and the future development can be determined by the very actual lower level situations as well.
Evolution at a larger scale is possible when mismatches between lower levels and upper levels become too large
(O’Neill et al., 1986). In such a case, e.g., when the prevailing biotic potential cannot follow the dynamics of climatic
constraints, the scaling mismatch will eventually lead to a breakdown of the system, and a new constellation or
conﬁguration will emerge either by bifurcation or jump-wise in a form following the pattern of punctuated
equilibria. Such a behavior is most likely linked with the fact that all holon levels are in fact ontically open (see
also Chapter 8 on disturbance and decay).
5.4 HIERARCHICAL FEATURES: BOUNDARIES, GRADIENTS, AND CONSTRAINTS
One fundamental property essential to form a hierarchyda necessary conditiondis the establishment of a physical
or conceptual boundary. Boundaries make it possible to distinguish holons and the holons’ levels from one or
another. The levels do not perform the same functions and they allow for strong differences between themselves. The
importance of such boundaries and their recognition, is e.g., discussed by Yarrow and Salthe (2008).
Within the boundaries, the differences are in fact the gradients that make the whole series of systems and subsystems
tick, or in other words the gradients make things move between holons, they create potentials, make the system
function, make it alive. Also, these gradients can be assigned to different hierarchical levels. A simple example is the
sequence of carbon gradients in an ecosystems, reaching from extremely slowly developing soil carbon fractions
over long-living tree biomasses, bushes to perennial or annual herbs, animals with different life spans and microbes
which have a generation time of hours. These temporal characteristics of the carbon pools can be correlated with
their spatial extents and thus be documented in a hierarchy similar to the one shown in Fig. 5.4. The accounted pools
provide the potentials for ﬂows between them; thus, gradients can be understood as the bases for all ecosystem processes.
They can easily follow the gradient proﬁles from places with high concentrations to regions with low
amounts of matter, energy, information, or organisms. Thereby certain resistances are regulating the speed of the
reactions. On the other hand, they can also work with slow frequencies, accumulating the pools as a long-term process.
Between both directions of gradient creation and degradation typical equilibria exist in different ecosystem
types (see Mu¨ller, 1998).
Consequently, the relations between the various levels, their very functions, become constrainers of at least the
adjacent levels. In fact, it is not too hard to imagine the relations constraining the whole system when considering
the complexity of the ecosystem network, e.g., thermodynamic constrains imposed on upper levels (Nielsen, 2009).
A process such as photosynthesis even when normally considered to be placed at lower levels of the hierarchy is a
major regulator to the upper levels by determining what is fed upwards into the systems functioning as a signiﬁcant
element of the system’s net primary production and its overall biological potential. The herbivores clearly are
limiting biomass but also acting as stimulators of primary production. Likewise, in a situation of nutrient limitation,
the role of dead organic material and recirculation becomes evident as a constraint.
Boundary Issues
In an evolutionary perspective, the separation of systems from their respective surroundings represents an essential
event that allows many features of the system to emerge and must be seen as a necessary condition. This is maybe
not valid for the self-organizational process alone as self-organization may appear without boundaries such as autocatalytic
cycling or hypercyclic organization. But, when a separation occurs it allows for the growth of the system,
although this property can really not be separated by the existence of growth and dissipation.
In the case of physical boundaries, a clear material and energetic relationship exists as matter or energy has to
arrive from somewhere and dissipation needs to end up somewhere outside the system. Usually, for matter or
5.4 HIERARCHICAL FEATURES: BOUNDARIES, GRADIENTS, AND CONSTRAINTS 89
energy, this is an outsideeinward relation, whereas in the traditional views, information ﬂows are supplied from the
inside, i.e., the genetic apparatus. The new science of biosemiotics is putting a new perspective on this (Emmeche
and Hoffmeyer, 1991, Nielsen, 2007, 2016).
Adding the perspective that systems may interact via hierarchies which are not necessarily physically embedded
complicates our understanding of the systems. We need to understand the processes at new levels and new manners
as physical embeddedness stops with organisms. The inclusion of more species in a population or in communities
makes it possible at ﬁrst to understand higher systems’ performances as collective behavior, i.e., what happens is
only a result of actions and reactions at lower levels, e.g., at the individual level, such as is known from the Gleasonian
individualistic perspective on ecology/ecosystems. Many examples on emergent properties of ecosystems
serve to illustrate that this is deﬁnitely not the full story.
Meanwhile, adding the hierarchical perspective to parts of the ecological systems that are not necessarily
embedded is of high value to ecosystem studies. For instance, Kolasa (2014) recommends a hierarchical entrance
to boundary studies to be essential for understanding ecosystems. In particular, it may assist in “(1) categorizing
types, (2) deciding what is and what is not a boundary, (3) fruitful resolution, (4) approaching, complex, nested systems,
and (5) deciding criteria to use about a particular boundary type.” Eventually, this approach includes boundaries
of both embedded (nested) and virtually embedded systems. Finally, when discussing delineations, besides
easily visible physical borders as skin or cuticula, virtual or case-speciﬁc boundaries can exist. That is especially
the case when we move to scales situated above the organism level. Here, home ranges of organisms can play a
role, watersheds as containments of certain hydrological ﬂow directions, or ecosystem complexes that can be characterized
by soft borders like ecotones. At these scales, the selection and determination of borders is a task of the
observer with respect to his very speciﬁc ﬁeld of problems.
Thermodynamic Gradients
As remarked above, living systems in general are existing and carrying out their activities due to gradients, differences
in concentrations over their boundaries, made possible exactly due to the establishment of that very boundary.
To fulﬁll the thermodynamic demands, a gradient is not enough since the system also needs to be
thermodynamically open, i.e., allowing the passage of both energy and matter as well as information over its boundaries.
Of course, the respective ﬂows can also be understood as transfers within a spatiotemporal hierarchy.
The two ecosystem indicators derived from thermodynamicsdemergy and exergydrepresent measures that are
clearly based on a hierarchical interpretation of the systems. Emergy expresses the hierarchical ranking of the levels
by their respective transformations of energy levels (such as trophic levels) that originated with solar radiation,
whereas exergy correspondingly is using the relative information content of the genome to do this, leading to the
eco-exergy as described in Chapter 2.
Constraint
The outside of an ecosystemdbeing it a physical surrounding in case of embedded, nested systems, or neighboring
systemsdacts as a constrainer to the development of the system. We may in many cases relate these constraints
to what is imported or imposed as forcing functions on the system mostly of either matter or
energydbut also the possibility to export may, in some cases, be quite important. Hence, it is closely related to
the gradient relationships between the systems.
At the same time, the system may be constrained from the inside, for instance, by limitations in genetic or physiological
capabilities. In this context, it is important to note that the system of constraints is a self-organized consequence
of successional development (see Mu¨ller, 1998; Nielsen and Mu¨ller, 2000). If this can be carried out in an
undisturbed manner, then the developing ecosystem creates its internal hierarchy on its own without artiﬁcial pressures
from the outside. The hierarchy emerges as a consequence of complexiﬁcation. Furthermore, the strength of the
hierarchical constraintsdthe functional top-down limitations of developmental degrees of freedomdis a basic
precondition for the metastability of the ecological systems (see Chapter 8).
The constraining process gets an extra dimension through the hierarchical view of higher levels normally having
not only larger spatial scales but also longer temporal scales symbolized by the fact that signaling in the system is
taking place with longer wavelengths. It is believed that the difference in time scale and lower frequency acts to
dampen and level out the signals from lower levels and that this dampening acts as a constraint but also as a means
for stability on the system.
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES90
Emergence
The three factors mentioned above, (1) the boundaries separating the systems from the environment, (2) the gradients
between the systems, and (3) the constraints, shape properties that are not necessarily predictable from the
basic system knowledge. The respective phenomenon is often referred to as emergence, and the respective systems
possess emergent properties. In fact, emergence is said to imply a hierarchy (Gignoux et al., 2017), or hierarchy is a
precondition for emergence. Nielsen and Mu¨ller (2000) have deﬁned emergence as “the consequence of interactions
between the parts of a system, which can create new properties of the whole. These new properties cannot be foreseen
from the knowledge about the parts alone.” This means that in self-organized dynamics, each hierarchical level
has at its disposal certain features which cannot be found nor foreseen at the lower levels. These features arise from
the interactions of the parts. Therefore, self-organization automatically leads to hierarchical structures, and one quality
of the respective interactions is the constraints that appear to be self-imposed by the entity.
The lack of predictability caused by the phenomenon of emergence is truly annoying to our chances of doing
proper management but seems to be penetrating the whole world of biological hierarchies (Ponge, 2005). If properties
are truly emergent but not recognized as such or not well understood, then how do we ensure that we preserve
them? In all cases, the uncertainties induced by such phenomena are really pointing back to the precautionary principle.
We really have to understand emergent properties much better in order to manage the environment. We must
learn how to work with them and induce them in ways that are consistent to the beneﬁts of ecosystems and humans.
Odum’s classic 1977 article suggested using emergent properties as a research target of ecosystem development
(Odum, 1977). This idea has been realized, for instance, within some indicator systems that use ecological orientors
(see Chapter 10).
5.5 UNDERSTANDING HIERARCHICAL FUNCTION
The usage of hierarchical understanding and interpretation of the various holon levels may be exercised from two
quite different entrance points. The ﬁrst one is primarily used for structuring our knowledge and organizing our
work on ecosystem performance in an attempt to deal with focal units of the system (ontological perspective).
The second approach is directed toward the question how do the units behave on their own or as interacting
with members of the same or other holon levels (phenomenological perspective).
Hierarchical Interpretations Above the Organism level
As mentioned above, the organization of organisms is at ﬁrst represented by the individuals combined into collections
called populations with no actual physical boundary and where a dominant part of the function is made up
by the sum of individual organisms. Here, no major temporal or spatial scale variation exists within the population.
Another hierarchical interpretation commonly used in ecology is that of the scalar hierarchic organization into the
trophic hierarchy, food chains, and trophic networks. The scales differ with types of organismsdas larger animals
generally eat smaller ones (with the exception of big herbivorous mammals)dand hence the body size often is
related to the trophic function and position. In principle, this should lead to the possibility of using an allometric
approach to interpretation, description, and modelingdan obvious idea but it maybe not as trivial as it sounds
(Ritterskamp et al., 2016). Additionally, besides body size, life span, and trophic positions, the typical expansion
of the home range of organisms linked with the duration of community processes can be considered as a means
of scale distinction. And, of course several abiotic holons can be deﬁned by the observer, which have certain spatial
and temporal specialities.
As an example, hierarchical responses in lakes have been hypothesized to be composed of immediate, short-term
responses on fast reacting, lower levels of the system, while long-term reactions are exhibited by the upper levels of
the system (Wagner and Adrian, 2009). Carpenter (1989) has used a hierarchy approach to distinguish different
levels of eutrophication processes (Fig. 5.5, right side). The author distinguishes four groups at different spatial
and temporal scales, whereby the higher levels limit the degrees of freedom of the lower ones. On the highest spatiotemporal
level, we can ﬁnd the edaphic potential. It is as a property of the corresponding watershed, characterized
by the ecophysiological potentials of the terrestrial ecosystems, the long-term land-use structure and the agricultural
intensity. These are factors that change slowly. At this scale, the nutrient input into the lake is regulated. In the
aquatic system, the nutrient inputs can be ampliﬁed by mobilization from the sediment. Relatively fast changing
factors such as temperature, light, and oxygen concentrations are forcing functions for the colonization of the water
5.5 UNDERSTANDING HIERARCHICAL FUNCTION 91
column by phytoplankton. The next level of analysis is the community structure of the epilimnion. In very short
process sequences, blue-green algae reach dominance, regulated by the N/P ratio, the pH, and the herbivorous
structure. Finally, blooms that take place in phases with low mixing are inﬂuenced by light conditions and available
CO2 concentrations with the highest frequencies of change. Thus, the described sequence of processes demonstrates
hierarchical determinations in systems beyond the organism level.
A structuralehierarchical viewpoint concerning aquatic ecosystems has been discussed by Steele (1989): On the
left-hand side of Fig. 5.5, correlations between the size of nekton, herbivorous zooplankton, and phytoplankton in
pelagic marine environments, and the lifetimes of these groups are illustrated. The size of the organisms’ home
ranges and the degrees of dispersion are related by this correlation, and consequently nutrition can be regarded
as a scale transition.
PopulationdCommunity Level
A population may be described as a collection of individuals which all belong to the same species. In ecology, it is
considered to have its own subdiscipline of population ecology. Likely to be one of the oldest ecological disciplines, it
deals with a wide range of subtopics related to the survival of populations, demographics, growth and survival, age
distributions, intra- and interspeciﬁc competition, dispersion of organisms, migratory behavior, and how all of it relates
to environmental issues such as conservation and biodiversity.
In a hierarchical context, this means that the time scale of the dominant population processes is more or less the
same for all participants. Thus, we deal with relations within horizontal holon levels. This is particularly accentuated
for systems where several populations interact as they compete for the same resources as, for instance, in the case of
phytoplankton populations. Samples rarely consist of one species only and eventually represent a mix of what must
be seen as a set of competing holons each waiting to take over as a consequence of interactions between internal and
external constraints.
The interaction would be much the same for all sublevels composing the structure of the several to many communities
of the ecosystem.
FIGURE 5.5 Two examples of structural and functional hierarchies referring to aquatic ecosystems. After Steele (1989), Carpenter (1989), and
Mu¨ller, E., 1992. Hierarchical approaches to ecosystem theory. Ecol. Model. 63, 215e242.
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES92
EcosystemsdPatches and Mosaics
Populations, communities, and societies come to interact through their respective trophic interactions and relations
as described in the previous sections. As a result of the interacting processes, in many cases, assemblages
are observed to take on complex patterns expressed as mixes of uniform distributions of populations or even communities.
The phenomenon of spatial distribution is known as patch, mosaics, or biocoenotic relations. To each of
these, adjacent science branches exist that are dealing with the changes and dynamics of such distributions. In a hierarchical
context the relationships may still be described as horizontal but at another level of hierarchy.
We are now reaching a level of complexity where our present conceptualization and our modeling efforts are challenged
and found insufﬁcient in fully explaining empirical observations made, i.e., the actual phenomenology of
ecosystems most likely to be due to emergent properties (e.g., Fox, 2006). Much of this partly seems to conﬁrm or
explain the ﬁndings of B.C. Patten et al. through their theoretical works on ecosystem networks coming up with
new phenomena such as network synergism, ampliﬁcation, and aggradation (see Chapters 4).
Hierarchical interpretations seem to be particularly abundant in a few areas such as riverine or stream ecosystems
(Thorp et al., 2006; Cisielka and Bailey, 2007) or terrestrial systems (Bardgett et al., 2005).
One major importance is that while an ecosystem might be found constant at a larger scale, the patches and resulting
mosaics may in itself be dynamic. Thus, the ecosystem is still in balance, but a dynamic one, where changes are
exhibited in components that are horizontally combined at a lower level. This has been suggested to represent a new
paradigm of patch dynamics, mosaic cycles, and stratiﬁed stabilities in ecology (Wu and Loucks, 1995).
Landscapes Sensu Lato
The hierarchical approach to ecosystems seems to have gained much popularity in investigations of larger systems
that are actually more likely to represent several ecosystems and deﬁnitely more than one population or trophic
level.
In fact, we may identify different landscapes and regions which are more uniﬁed by physico-geographical conditions
rather than belonging to the same ecosystem types. These areas may be deﬁned by belonging to a type of
shedsdfor instance, a water- or sea-shed, where linkage is made up by physical ﬂow patterns of water, or airsheds
where major inputs may be driven by atmospheric relationships.
Landscapes in general, probably due to the large space and time scale perspective, have many possibilities to
include many hierarchical views, such as those of subsystems being embedded or enclosed in, sharing a space
and making up horizontal hierarchies, and exhibiting the importance of constraints at or between any of the levels
chosen (Szabo and Meszena, 2006). Consequently, in landscape ecology, a broad ﬁeld of literature has been developed
around the problem of scales and scaling (e.g., Levin, 1992; Blo¨schl and Sivapalan, 1995; O’Neill, 1996, Wu
and Li, 2006).
Thus, in particular watershedsdsystems of larger rivers and smaller streams have received much attention and
have been found to exhibit patterns that demonstrate the value of the hierarchical approach for analyzing size dependencies,
trophic relations, or even emergent properties, and issues such as top-down versus bottom-up regulation
as well (Lowe et al., 2006; Parsons and Thoms, 2007).
Oceanic ecosystems may be analyzed for their spatial patterns over regional scalesdcorresponding to
landscapesdalthough they may be characterized better as being seascapes (Kavanaugh et al., 2014). The authors
applied a “hierarchical patch mosaic paradigm” in order to compare the pelagic systems of the North Paciﬁc and
their result may be a bit surprising showing that seasonality is playing a minor importance in determining function
at all levels. Similar studies have been carried for marine coastal areas and related to their wetland environments
(Schaeffer-Novelli et al., 2005).
Regional Scales
The community structure of rocky intertidal communities has been analyzed with the aim to reveal whether real
macroscale processes would dominate the development of this type of ecosystem or if it would be dominated by
factors that originate from meso- or local scale (Menge et al., 2015). Over a wide spatial scaledfrom 100 km to
100 m they came up with the conclusion that while environmental factors dominated at meta-ecosystem level where
the “normal” ecological processes would play a more important role at the lower levels.
As we are widening the scope in this analysis, ﬁnally several attempts should be mentioned to assign any ecological
structures and processes to spatial and temporal scales (e.g., Di Castri and Hadley, 1988; Gunderson and Holling,
2002; Gunderson et al., 2017; Lampitt et al., 2010). One example is depicted in Fig. 5.6. These hierarchical schemes can
be found with reference to vegetation dynamics, climate change, landscape processes, or resilience assessments.
5.5 UNDERSTANDING HIERARCHICAL FUNCTION 93
They are also often used to assign different environmental decision-making processes to different levels of the
administrative hierarchy, thus they demonstrate one path of linkage between ecological and human systems.
5.6 MANAGING ECOSYSTEMS AS HIERARCHIES
The messages of hierarchy theory to the management of ecosystems are clear although not always pleasant. On a
scientiﬁc level, it often requires additional views and work as it requires data that are not always existing. Second, it
requires one to focus not only at the focal level but also on at least the two adjacent levels to both sidesdupward and
downwarddof the hierarchy.
This means that ecosystem studies potentially must take place over a wide range of hierarchies and therefore get
increasingly complex. In addition, it also has the consequence that ecosystem studies are inherently multidisciplinary
as it takes “local” experts at each level to analyze what exactly is going on and what might be the causal
mechanism(s) leading to observed ecosystem behavior.
In short, scientists must be strongly focused, but so must managers and policy makers. The overall goal of a
responsible and adequate ecosystem management must include almost all levels, which in the end is not an easy
task both due to temporal and economical conditions. Meanwhile, an approach founded in hierarchy theory might
be able to assist in pointing out proper and relevant target areas both for the management and the production of the
underlying understanding of such management as well.
One consequence of the theory is to realize the signiﬁcant role of constraints: if there is a problem appearing at the
focal level (n), then the reasons might be originating in level (n þ 1) as well as in (n À 1); thus, both have to be
analyzed. Concerning the respective measure for improvement, also both directions might be possible. While a
modiﬁcation in (n À 1) is related to changes in the system’s potentials, a measure at the level (n þ 1) is directed toward
the system of constraints. Therefore, if management is based on strengthening the ecosystem’s selforganization
processes, a modiﬁcation of the constraints at (n þ 1) gives the focal and lower levels the chances to
develop in an orientor-based direction, capable of optimizing several emergent ecosystem properties.
Furthermore, it is essential to realize the correct working scale in management. Fig. 5.7 tries to demonstrate these
conditions using eutrophication processes as examples for a hierarchical distinction of the indication and management
process. The object of this case study is a nested hierarchy, consisting of an ecosystem, which is part of a landscape,
which is part of a watershed, which is part of a whole region. The ﬁgures consider some elements of the DPSIR
Second Minute Day
Temporal scale (Year)
100 yr 102 yr 104 yr 106 yr 109 yr
SpaƟalscale(km2)
Local
Global
104
103
102
101
100
Global
weather
systems
Carbon dioxide
variaƟons
Atmospheric
composiƟon
Origin of
earth and life
Climate Plate tectonics
Mantle convecƟon
Mountain building
ExƟncƟon events
Metallogenesis
El Nino
Glacial
periods
Ocean circulaƟon
Soil
development
Uppeer
ocean
mixing
SynopƟc
weather
systems
Soil
moisture
variaƟons
Soil
erosion
Seasonal
vegetaƟon
cycles
Earthquake
cycles
Nutrient
cycles
Volcanic erupƟons
Atmospheric
convecƟons
Atmospheric
turbulence
FIGURE 5.6 Spatiotemporal scales of ecological processes within a spaceetime correspondence arrangement. From Wu (2013)
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES94
management approach (see Burkhard and Mu¨ller, 2008). Starting with part (B), we see the pressure (developments in
release of substances, physical and biological agents, the use of resources, and the use of land by human activities) of
our series, that is nitrogen fertilization, which takes place at the ecosystem level, e.g., of an arable ﬁeld. The high local
nutrient load leads to high nitrogen concentrations in the groundwater (landscape scale), in the river water (catchment
scale), and the danger of NO3 accumulations in the drinking water at the regional scale. This pressure provokes
changes in the ecosystem state (changes in quantiﬁable and qualitative physical, biological, and chemical conditions
in a deﬁned area). In the next step (C) certain impacts become visible, which modify the systems’ capacities to provide
ecosystem services and which therefore will have economic impacts (D). Also, these processes are different at
different scales. When these disturbances are realized by society, there will be a responding decision process
(A) which will include a change of the drivers (A), the social, demographic, and economic developments in the
society and corresponding changes in lifestyles, overall levels of consumption and production patterns as well as
motivations for speciﬁc land-use strategies. These dynamics can be accompanied by political measures, which
also differ from scale to scale. The example shows that implementation of hierarchical views is important when managing
for ecosystem conservation in what is considered a “regional and historical context” (Rodzilksy et al., 2001).
Furthermore, it becomes obvious that in fact the speciﬁc measures are only suitable at different scales. Therefore, the
manager has to be aware not only of her or his own situation but also of the detailed scale of the management object.
In order to manage at the scales beyond the ecosystem level, awareness is needed of properties such as
self-organization and emergent properties that act together and affecting the properties of the system’s resilience
(Peterson, 2000). These ecosystem characteristics are strongly dependent on the hierarchical functioning of systems
(see Chapter 8), creating a feedback of hierarchy that begets more hierarchy. Several authors ascribe the metastability
of ecosystems to the functioning of their internal system of constraints (see e.g., Mu¨ller, 1992; Gundersson and
Holling, 2002). This means that a strong determination by activities at higher levels will make the system more
FIGURE 5.7 Hierarchical example of eutrophication processes regarding the DPSIR approach.
5.6 MANAGING ECOSYSTEMS AS HIERARCHIES 95
robust for changes and will increase the system’s overall ability for ﬁltering and dampening external disturbances.
Therefore, the strength of hierarchical constraints is a basic indicator for stability. Whenever bigger changes appear,
the prevailing hierarchy is broken and the system will move to a new scheme of self-organized constraints.
The concept of panarchy may offer an opportunity to deal with the plethora of scales ever present in ecosystem
functioning (Gunderson et al., 2017). This point additionally stresses the importance of the precautionary principle
to be considered and respected even more when doing planning and management in future. The panarchy
approach, occurring in both natural and social systems, has been applied to management of government agencies
in Auad et al. (2018).
An hierarchical understanding has been implemented in an attempt to carry out a more holistic way of introducing
mitigation and the resilience concept as strategies in the “Canadian boreal biome” (Fenton, 2016). According
to this author, management can be done in a mitigation hierarchy in which “impacts on ecosystems are avoided,
mitigated, restored, or compensated.” Meanwhile, the strategies still need to be fully integrated and merged properly
with the hierarchical concept. Another attempt to apply hierarchy, and particularly panarchy, to management
was introduced by Auad et al. 2018 when looking at the United States Department of Energy, Bureau of Energy
Ocean Management.
Fishery management may be one area where an extended and hierarchical understanding of the ecosystem is
highly needed in order to improve ecosystem health in the future. Usually ﬁshery management is carried out at organism,
species, or population level (e.g., Apollonio, 2015) that is managing for the regulation and protection at only
one level. One thing might be that the exact trophic position of a particular species might not be exactly knowndbut
another is that the ﬁsh species in general belong to quite different levels in the trophic chain or rather network
(Zacharias and Roff, 2000). Similar investigations and ﬁndings have been done on (semi-)terrestrial systems (Palik
et al, 2000). Therefore, it is necessary to consider the entire ecosystem of interactions when managing for sustainable
ﬁsheries.
All in all, our management efforts for preserving biodiversity in general must be done with a hierarchical perspective
in mind. Careful planning that considers both the structure of the food web together with its function is of
utmost importance in preserving stable ecosystems (Thompson et al., 2012; Borelli et al., 2015). A management
approach that only considers preservation of a single species, even rare, sensitive or keystone ones, may lead to total
failure not considering the qualitative perspectives ensured by applying a hierarchic approach. The US Environmental
Protection Agency’s Endangered Species Act (1973), set a strong precedent for an ecosystem approach in
the legal language:
The purposes of this Act are to provide a means whereby the ecosystems upon which endangered species and threatened species
depend may be conserved, to provide a program for the conservation of such endangered species and threatened species. ESA;
16 U S C. x 1531 et seq.
However, implementation of the ecosystem approach takes time, money, patience, and regulatory means that are
rarely aligned in the species’ favor. Only 54 of the 1661 species listed have recovered to the point of being delisted. It
is also important to keep in mind that during this time population growth, and subsequently habitat loss, has
continued putting pressure on the health of the species and their ecosystems.
Finally, we might make the remark that recently the traditional features of hierarchical functioning have been
broken concerning humaneenvironmental systems. The previously observed strong relation between space and
time, events concerning big spatial areas usually are rare and related to slow developments, has been shown in
several examples in most ecological systems. But, when we turn to humaneenvironmental systems, it becomes
more and more obvious that traditional spaceetime correlations have been broken. Humans can produce changes
so fast that natural systems hardly can follow. The fast traveling behavior, the speed of technical innovations, or the
rapid information exchange over large distances via the Internet is forming new conditions for hierarchical structurations.
Distances are decreasing and frequencies are increasing in the modern human comprehension of the world;
and therefore, new circumstances for hierarchical ordinations must be found. The respective activities must be
guided by the perception that we humans are depending on nature and ecosystems, their services, and their integrity.
Thus, if we look for Nietzsche’s hierarchy of values (see initial quotation of the chapter), then a high valuation of
hierarchical performances in ecosystems will be a good basis for argumentation.
5. ECOSYSTEMS AS SELF-ORGANIZING HIERARCHIES96
C H A P T E R
6
Ecosystems Have Directionality
From the way the grass bends, one can know the direction of the wind. Chinese Quotation
All nature is but art unknown to thee;
All chance, direction which thou canst not see;
All discord, harmony not understood;
All partial evil, universal good;
And, spite of pride, in erring reasons spite,
One truth is clear, Whatever IS, is RIGHT. Alexander Pope, 1773
6.1 SINCE THE BEGINNINGS OF ECOLOGY
Ecosystems have directionality! This is an extraordinary statement, although the reader might at ﬁrst wonder
why. After all, one observes directional behavior everywhere: A billiard ball, when struck by another ball, will
take off in a prescribed direction. Sunﬂowers turn their heads to face the sun. Copepods migrate up and down in
the water column on a diurnal basis. Yet, despite these obvious examples, scientists have increasingly been trained
to regard instances of directionality in nature as having no real basisdepiphenomenal illusions that distract one
from an underlying static, isotropic reality.
Before embarking on how ecological direction differs from directionality observed elsewhere, it is worthwhile
describing the ecological notion of succession (Odum and Odum, 1959). The classical example in American ecology
pertains to successive vegetational communities (Cowles, 1899) and their associated heterotrophs (Shelford, 1913)d
research conducted on the shores of Lake Michigan. Both Cowles and Shelford had built upon the work of the
Danish botanist, Warming (1909). Prevailing winds blowing against a shore will deposit sand in wavelike fashion.
The most recent dunes have emerged closest to the lake itself, while progressively older and higher dunes occur as
one proceeds inland. The assumption here, much like the famed ergodic assumption in thermodynamics, is that this
spatial series of biotic communities represents the temporal evolution of a single ecosystem. The younger, presumably
less mature community consisted of beach grasses and cottonwood. This “sere” was followed by a jack pine
forest, a xeric black oak forest, an Oak and hickory moist forest, and the entire progression was thought to “climax”
as a Beech-maple forest. You cannot have one without the others. The invertebrate and vertebrate communities were
observed to segregate more or less among the vegetational zones, although there was more overlap among the mobile
heterotrophs than among the sessile vegetation.
Other examples of succession involve new islands that emerge from the sea, usually as the result of volcanic
activity. One particular ecosystem that was followed in detail is the sudden emergence in 1963 of the approximately
2.8 km2
island, Surtsey, some 33 km south of the large island of Iceland in the North Atlantic. Fig. 6.1 depicts
the rise in the number of plant species found on the island. (Other measures of succession on Surtsey will be
given below.)
6.2 THE CHALLENGE FROM THERMODYNAMICS
Now one might well ask how the directionality of these ecosystems differs in any qualitative way from, say the
billiard ball mentioned in the opening paragraph of this chapter? For one, the direction of the billiard ball is a
97
A New Ecology, Second Edition
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consequence of the collision with the other ball, the Newtonian law of momentum and the Newtonian-like law of
elasticity. The ball itself remains essentially unchanged after the encounter. Furthermore, if the ball is highly
elastic, the encounter is considered reversible. That is, if one takes a motion picture of the colliding balls and
the movie is shown to a subject with the projector operating in both the forward and reverse modes, the subject
is incapable of distinguishing the original take from its reverse. Reversibility is a key attribute of all Newtonian
systems, and until the mid-1960s all Newtonian laws were considered strictly reversible. Early in the 20th century,
Noether (1918) demonstrated how the property of reversibility was fully equivalent to that of conservation, i.e., all
reversible systems are conservative. There is no fundamental change in them, either before or after the event in
question.
This pair of fundamental assumptions about how objects behaved set the stage for the ﬁrst challenge to the Newtonian
worldview. In 1820, Carnot (1824) had been observing the performance of early steam engines in pumping
water out of mines. He observed how the energy content (caloric) of the steam used to run the engines could never
be fully converted into work. Some of it was always lost forever. This meant that the process in question was irreversible.
One could not reverse the process, bringing together the work done by the engine with the dispersed heat
and create steam of the quality originally used to run the engine. See also the discussion of the second law of thermodynamics
in Chapter 2.
But the steam, the engine, and the water were all material things, made up of very small particles, according
to the atomic hypothesis that had recently been formulated. Elementary particles should obey Newton’s laws,
which always gave rise to reversible behaviors. Whence, then, the irreversibility? This was a conundrum that
for a while placed the atomic hypothesis in jeopardy. The enigma occupied the best minds in physics over the
next half century. How it was “resolved” demonstrates volumes about common attitudes toward scientiﬁc
belief.
von Boltzmann (1872) considered the elements of what was called an “ideal gas” (i.e., a gas made up of point
masses that did not interact with each other) to obey Newton’s laws of motion. He then assumed that the distribution
of the momenta of the atoms was normally random. This meant that nearby to any conﬁguration of atoms there were
always more equivalent distributions (having same mass and momentum) that were more evenly distributed than
there were conﬁgurations that were less evenly distributed. Any random walk through the distributions would,
-5
0
5
10
15
20
25
30
35
40
45
50
Numberofplantspecies
60 65 70 75 80 85 90 95 100
year
Line Chart
FIGURE 6.1 Increase over time in the number of plant species found on the newly created island of Surtsey.
6. ECOSYSTEMS HAVE DIRECTIONALITY98
therefore, be biased in the direction of the most probable distribution (the maximum of the normal distribution).
Ergo, without violating conservation of mass or momentum at the microlevel, the system at the macrolevel was
biased to move in the direction of the most even distribution.
This was a most elegant model, later improved upon by Gibbs (1902). It is worth noting, however, that the resolution
was a model that was applicable to nature under an exceedingly narrow set of conditions. Nevertheless, it
was accepted as validation of the atomic hypothesis and Newtonian reversibility everywhere, and it put an end to
the controversy. This rush to consensus was, of course, the very antithesis of what later would be exposited as
logical positivismdthe notion that laws cannot be veriﬁed, only falsiﬁed. Laws should be the subject of constant
and continual scrutiny; and scientists should always strive to falsify existing laws. But when conservation, reversibility,
and atomism were being challenged, the response of the community of scholars was precisely the opposited
discussion was terminated on the basis of a single model that pertained to conditions that, in relation to the full set
of conditions in the universe, amounted to “a set of measure zero”!
Such inconsistencies notwithstanding, the second law does indeed provide a direction for time and introduces
history into science. The second law serves as a very signiﬁcant constraint on the activities of living systems and
imparts an undeniable directionality to biology (Schneider and Sagan, 2005).
6.3 DECONSTRUCTING DIRECTIONALITY?
Events in biology have been somewhat the reverse of those in physics. Whereas, physics began with directionless
laws and was confronted with exceptions, biologists had originally thought that phylogeny took a progressive direction
over the eons, culminating in the appearance of humankind at the apex of the natural orderdthe socalled
“natural chain of being.” Evolutionary biologists, however, have sought to disabuse other biologists of
such directional notions (Gould, 1994). At each turn in its history, a biotic system is subject to random, isotropic inﬂuences.
What looks in retrospect like a progression has been merely the accumulation of the results of chance inﬂuences.
Complexity simply accrues until such time as a chance catastrophe prunes the collection back to a
drastically simpler composition.
We thus encounter a strong bias at work within the community of scientists to deny the existence of bias in nature
(a statement which makes sense only because humanity has been postulated to remain outside the realm of the natural).
Physicists and (perhaps by virtue of “physics envy”) evolutionary theorists appear keen to deny the existence
of direction anywhere in the universe, preferring instead a changeless Eleatic worldview. It is against this background
that the notion of direction in ecology takes on such importance.
Directionality, in the form of ecological succession, has been a key phenomenon in ecology from its inception
(Clements, 1916). By ecological succession is meant “the orderly process of community change” (Odum and
Odum, 1959) whereby communities replace one another in a given area. Odum and Odum (1959) do not equivocate
in saying, “The remarkable thing about ecological succession is that it is directional.” In those situations where the
process is well known, the community at any given time may be recognized and future changes predicted. That is,
succession as a phenomenon appears to be reproducible to a degree.
Of course, it was not long after the ideas of community succession came into play that the opinion arose that
its purported direction was illusory. Gleason (1917) portrayed succession in plant communities as random associations
of whatever plant species happened to immigrate into the area. Others have pointed out that
“seres” of ecological communities almost always differ in terms of the species observed (Cowles, 1899). The
ecosystem ecologist takes refuge in the idea that the functional structure nonetheless remains predictable
(Sheley, 2002).
The question thus arises as to whether ecological succession is orderly in any sense of the word, and, if so, what
are the agencies behind such order? We begin by noting that the directionality of ecosystems is of a different ilk
from those mentioned in the opening of this chapter. With regard to all three of those examples, the direction of
the system in question was determined by sources exterior to the systemdby the colliding billiard ball in the ﬁrst
instance, and by the sun as perceived by the sunﬂower and copepod. It will be argued below, however, that the
directionality of an ecosystem derives from an agency active within the system itself. Surely, external events do
impact the system direction by providing constraints, but any one event is usually incremental in effect. On
rare occasions an external event can radically alter the direction and the constitution of the system itself (Prigogine,
1978, Tiezzi , 2006), but this change is every bit as much a consequence of the system conﬁguration as it is of the
external event (Ulanowicz, 2009). The direction an ecosystem takes is both internal and constitutional. Most
change seen elsewhere is neither.
6.3 DECONSTRUCTING DIRECTIONALITY? 99
6.4 AGENCIES IMPARTING DIRECTIONALITY
It remains to identify the agency behind any directionality that ecosystems might exhibit. Our natural inclination
is such a search would be to look for agencies that conform to our notions of “lawful” behaviors. But such a scope
could be too narrow. It would behoove us to broaden our perspective and attempt to generalize the notion of “law”
and consider as well the category of “process.” A process resembles a law in that it consists of rule-like behaviors,
whereas a law always has a determinate outcome, a process is guided more by its interactions with aleatoric events.
The indeterminacy of such action is perhaps well illustrated by the artiﬁcial example of Polya’s Urn (Eggenberger
and Polya, 1923). Polya’s process consists of picking from an urn containing red and blue balls. The process starts
with one red ball and one blue ball. The urn is shaken and a ball is drawn at random. If it is a red ball, then the
ball is returned to the urn with yet another red ball; if a blue ball is picked, then it likewise is returned with another
blue ball. The question then arises whether the ratio of red to blue balls approaches a ﬁxed value. It is rather easy to
demonstrate that the law of large number takes over and that after a sufﬁcient number of draws, the ratio changes
only within bounds that progressively shrink as the process continues. Say the ﬁnal ratio is 0.3879175. The second
question that arises is whether that ratio is unique? If the urn is emptied and the process repeated, then will the ratio
once again converge to 0.3879175? The answer is no. The second time it might converge to 0.81037572. It is rather
easy to show in Monte Carlo fashion that the ﬁnal ratios of many successive runs of Polya’s process are uniformly
distributed over the interval from 0 to 1.
One sees in Polya’s Urn how direction can evolve out of a stochastic background. The key within the process is the
feedback that is occurring between the history of draws and the current one. Hence, in looking for the origins of
directionality in real systems, we turn to consider feedback within living systems. Feedback, after all, has played
a central role in much of what is known as the theory of “self-organization” (e.g., Eigen, 1971; Maturano and Varela,
1980; DeAngelis et al., 1986; Haken, 1988; Kauffman, 1995). Central to control and directionality in cybernetic systems
is the concept of the causal loop. A causal loop, or circuit is any concatenation of causal connections whereby
the last member of the pathway is a partial cause of the ﬁrst. Primarily because of the ubiquity of material recycling
in ecosystems, causal loops have long been recognized by ecologists (Hutchinson, 1948).
It was the late polymath, Bateson (1972) who observed that “a causal circuit will cause a non-random response to a
random event at that position in the circuit at which the random event occurred.” But why is this so? To answer this
last question, let us conﬁne further discussion to a subset of causal circuits that are called autocatalytic1
(Ulanowicz,
1997). Henceforth, autocatalysis will be considered any manifestation of a positive feedback loop whereby the direct
effect of every link on its downstream neighbor is positive. Without loss of generality, let us focus our attention on a
serial, circular conjunction of three processesdA, B, and C (Fig. 6.2). Any increase in A is likely to induce a corresponding
increase in B, which in turn elicits an increase in C, and whence back to A.2
A didactic example of autocatalysis in ecology is the community that builds around the aquatic macrophyte,
Utricularia (Ulanowicz, 1995). All members of the genus Utricularia are carnivorous plants. Scattered along its
feather-like stems and leaves are small bladders, called utricles (Fig. 6.3A). Each utricle has a few hairlike triggers
at its terminal end, which, when touched by a feeding zooplankter, opens the end of the bladder, and the animal is
sucked into the utricle by a negative osmotic pressure that the plant had maintained inside the bladder. In nature the
FIGURE 6.2 Simple autocatalytic conﬁguration of three species.
1
This focus is not made to the exclusion of other forms of positive feedback, nor is it meant to displace the very necessary role of negative
feedback, which play a vital role in the regulation systems processes (Chapter 7).
2
The emphasis in this chapter is upon positive feedback and especially autocatalysis. It should be mentioned in passing that negative feedback
also plays signiﬁcant roles in complex ecosystem dynamics (Chapter 8), especially as an agency of regulation and control.
6. ECOSYSTEMS HAVE DIRECTIONALITY100
surface of Utricularia plants is always host to a ﬁlm of algal growth known as periphyton. This periphyton in turn
serves as food for any number of species of small zooplankton. The autocatalytic cycle is closed when the Utricularia
captures and absorbs many of the zooplankton (Fig. 6.3B).
In chemistry, where reactants are simple and ﬁxed, autocatalysis behaves just like any other mechanism. As soon
as one must contend with organic macromolecules and their ability to undergo small, incremental alterations, however,
the game changes. With ecosystems we are dealing with open systems (see Chapter 2), so that whenever the
action of any catalyst on its downstream member is affected by contingencies (rather than being obligatory), a number
of decidedly nonmechanical behaviors can arise (Ulanowicz, 1997). For the sake of brevity, we discuss only a few:
Perhaps most importantly, autocatalysis is capable of exerting selection pressure upon its own, ever-changing,
malleable constituents. To see this, one considers a small spontaneous change in process B. If that change either
makes B more sensitive to A or a more effective catalyst of C, then the transition will receive enhanced stimulus
from A. In the Utricularia example, diatoms that have a higher P/B ratio and are more palatable to microheterotrophs
would be favored as members of the periphyton community. Conversely, if the change in B makes it either less sensitive
to the effects of A or a weaker catalyst of C, then that perturbation will likely receive diminished support from
A. That is to say, the response of this causal circuit is not entirely symmetric, and out of this asymmetry emerges a
direction. This direction is not imparted or cued by any externality; its action resides wholly internal to the system.
As one might expect from a causal circuit, the result is to a degree tautologousdautocatalytic systems respond to
random events over time in such a way as to increase the degree of autocatalysis. As alluded to above, such asymmetry
has been recognized in physics since the mid-1960s, and it transcends the assumption of reversibility. It should
be emphasized that this directionality, by virtue of its internal and transient nature cannot be considered teleological.
There is no externally determined or preexisting goal toward which the system strives. Direction arises purely out of
immediate response by the internal system to a novel, random event impacting one of the autocatalytic members.
To see how another very important directionality can emerge in living systems, one notes, in particular, that any
change in B is likely to involve a change in the amounts of material and energy that are required to sustain process B.
As a corollary to selection pressure, we immediately recognize the tendency to reward and support any changes that
serve to bring ever more resources into B. Because this circumstance pertains to any and all members of the causal
circuit, any autocatalytic cycle becomes the epicenter of a centripetal ﬂow of resources toward which as many resources
as possible will converge (Fig. 6.4). That is, an autocatalytic loop deﬁnes itself as the focus of centripetal ﬂows.
One sees didactic example of such centripetality in coral reef communities, which by their considerable synergistic
activities draw a richness of nutrients out of a desert-like and relatively inactive surrounding sea. Centripetality is
obviously related to the more commonly recognized attribute of system growth (Chapter 6). A key feature of living
systems is how they selectively uptake and accumulate parts of the environment, thus, driving the biogeochemical
cycles and affecting the global stores of mineral and energy resources. The autocatalysis moves the systems to a new
FIGURE 6.3 The Utricularia system. (A) View of the macrophyte with detail of a utricle. (B) The three-ﬂow autocatalytic conﬁguration of
processes driving the Utricularia system.
6.4 AGENCIES IMPARTING DIRECTIONALITY 101
state that is often novel and more diverse. The system itself gives roles for new entrants into the system (Hordijk and
Steele, Cazzolla-Gatti et al., 2018).
6.5 ORIGINS OF EVOLUTIONARY DRIVE
Evolutionary narratives are replete with explicit or implicit references to such actions as “striving” or “struggling,”
but the origin of such directional behaviors is either not mentioned, or glossed over. Such actions are simply
postulated. But with centripetality, we now encounter the roots of such behavior. Suddenly, the system is no longer
acting at the full behest of externalities, but it is actively drawing ever more resources unto itself. Russell (1960)
called this behavior “chemical imperialism” and identiﬁed it as the very crux of evolutionary drive.
Centripetality further guarantees that whenever two or more autocatalytic loops exist in the same system and
draw from the same pool of ﬁnite resources, competition among the foci will necessarily ensue, so that another postulated
element of Darwinian action ﬁnds its roots in autocatalytic behavior. In particular, whenever two loops share
pathway segments in common, the result of this competition is likely to be the exclusion or radical diminution of one
of the nonoverlapping sections. For example, should a new element D happen to appear and to connect with A and C
in parallel to their connections with B, then if D is more sensitive to A and/or a better catalyst of C, the ensuing dynamics
should favor D over B to the extent that B will either fade into the background or disappear altogether
(Fig. 6.5). That is, the selection pressure and centripetality generated by complex autocatalysis (a macroscopic
ensemble) is capable of inﬂuencing the replacement of its own elements. Perhaps the instances that spring most
quickly to mind here involve the evolution of obligate mutualistic pollinators, such as yuccas (Yucca) and yucca
moths (Tegeticula, Parategeticula) (Riley, 1892), which eventually displace other pollinators.
It is well worth mentioning at this point that the random events with which an autocatalytic circuit can interact are
by no means restricted to garden-variety perturbations. By the latter are meant simple events that are generic and
repeatable. In Chapter 3, it was pointed out how random events can have a complex nature as well and how many
such events can be entirely unique for all time. For example, if a reader were to stand on the balcony overlooking
Grand Central Station in New York City and photograph a 10 Â 10 m space below, she might count some 90 individuals
in the picture. The combinatorics involved guarantee that it is beyond the realm of physical reality that
repeating the action at a subsequent time would capture the same 90 individuals in the framedthe habits and routines
of those concerned notwithstanding (Elsasser, 1969). Nor are such unique events in any way rare. Even the
FIGURE 6.4 The centripetality of an autocatalytic system, drawing progressively more resources unto itself.
FIGURE 6.5 Autocatalytic action causing the replacement of element B by a more effective one, D. The evolution of the network of three
elements A, B, and C towards a new similar network A, B, and D is shown. As D enters the network in completion with compartment B and
eventually replaces it as this autocatalytic cycle turns out to be more efﬁcient than the previous one.
6. ECOSYSTEMS HAVE DIRECTIONALITY102
simplest of ecosystems contains more than 90 distinguishable individual organisms. Unique events are occurring all
the time, everywhere, and at all levels of the scalar hierarchy. Furthermore, the above-cited selection by autocatalytic
circuits is not constrained to act only on simple random events. They can select from among complex, entirely novel
events as well.
This ability of an autocatalytic circuit to sift from among the welter of complex events that can impinge upon it
opens the door fully to emergence. For in a Newtonian system, any chance perturbation would lead to the collapse of
the system. With Darwin systems, causality was opened up to chance occurrences, but that notion failed to take hold
for a long while after Darwin’s time, for his ideas had fallen into the shadows by the end of his century (Depew and
Weber, 1995). It was not until Fisher and Wright during the late 1920s had rehabilitated Darwin through what is
commonly known as “The Grand Synthesis” that evolution began to eclipse the developmentalism that had prevailed
in biology during the previous decades. The Grand Synthesis bore marked resemblance to the reconciliation
effected in the physical sciences by Boltzmann and Gibbs in that Fisher applied almost the identical mathematics that
had been used by Gibbs in describing an ideal gas to the latter’s treatment of noninteracting genetic elements.
Furthermore, the cardinal effect of the synthesis was similar to the success of Gibbsdit reestablished a degree of predictability
under a very narrow set of circumstances.
With the recognition of complex chance events, however, absolute predictability and determinism had to be abandoned.
There is simply no way to quantify the probability of an entirely unique event (Tiezzi In review). Events must
recur at least several times before a probability can be estimated. As compensation for the loss of perfect predictability,
emergence no longer need take on the guise of an enigma. Complex and radically chance events are continuously
impinging upon autocatalytic systems. The overwhelming majority has no effect whatsoever upon the system
(which remains indifferent to them). A small number impacts the system negatively, and the system must reconﬁgure
itself in countering the effect of the disturbances. An extremely small fraction of the radical events may actually resonate
with the autocatalysis and shift it into an entirely new mode of behavior, which can be said to have emerged
spontaneously.3
Forrester (1987), for example, describes major changes in system dynamics as “shifting loop dominance,” by
which he means a sudden shift from control by one feedback loop to dominance by another. The new loop could
have been present in the background prior to the shift, or it could be the result of new elements entering or arising
within the system to complete a new circuit. Often loops can recover from single insults along their circuit, but multiple
impacts to several participants, as might occur with complex chance, are more likely to shift control to some
other pathway.
One concludes that autocatalytic conﬁgurations of ﬂows are not only characteristic of life, they are central to it. As
Popper (1990) once rhapsodically proclaimed, “Heraclitus was right: We are not things, but ﬂames. Or a little more
prosaically, we are, like all cells, processes of metabolism; nets of chemical pathways.” The central agency of networks
of processes is illustrated nicely with Tiezzi’s (2006) comparison of the live and dead deer (just moments after
death). The mass of the deer remains the same, as does its form, chemical constitution, energy, and genomic conﬁguration.
What the live deer had that the dead deer does not possess is its conﬁguration of metabolic and neuronal
processes.
6.6 QUANTIFYING DIRECTIONALITY IN ECOSYSTEMS
It is one thing to describe the workings of autocatalytic selection verbally, but science demands at least an effort at
describing how one might go about quantifying and measuring key concepts. At the outset of such an attempt, we
should emphasize again the nature of the directionality with which we are dealing. The directionality associated
with autocatalysis does not appear in either physical space or, for that matter, in phase space. It is rather more
like the directionality associated with time. There direction, or sense, is indicated by changes in a system-level
indexdthe system’s entropy. Increasing entropy identiﬁes the direction of increasing time.
The hypothesis in question is that augmented autocatalytic selection and centripetality are the agencies behind
increasing self-organization. Here, we note that as autocatalytic conﬁgurations displace more scattered interactions,
material and energy become increasingly constrained to follow only those pathways that result in greater
3
This emergence differs from Prigogine’s “order through ﬂuctuations” scenario in that the system is not constrained to toggle into one of two
predetermined states. Rather, complex chance can carry a system into entirely new modes of behavior (Tiezzi, 2006). The only criterion for
persistence is that the new state be more effective, autocatalytically speaking, than the original.
6.6 QUANTIFYING DIRECTIONALITY IN ECOSYSTEMS 103
autocatalytic activities. This tendency is depicted in cartoon fashion in Fig. 6.6. At the top is an arbitrary system of
four components with an inchoate set of connections between them. In the lower ﬁgure, one particular autocatalytic
feedback loop has come to dominate the system, resulting in fewer effective ﬂows and greater overall activity (as
indicated by the thicker surviving arrows). Thus, we conclude that quantifying the degree of constraint in an
ecosystem must reﬂect these changes in both the magnitude and intensity of autocatalytic activities. Looked at in
obverse fashion, ecosystems with high autocatalytic constraints will offer fewer choices of pathways along which
resources can ﬂow.
The appearance of the word “choice” in the last sentence suggests that information theory might be of some help
in quantifying the results of greater autocatalysis. And, so it is. Box 6.1 details the derivation of a measure called the
System Ascendency, which quantiﬁes both the total activity of the system as well as the degree of overall constraint
extant in the system network. A change in the system pattern as represented in Fig. 6.6 will result in a higher value
of the ascendency.
BOX 6.1
A S C E N D E N C Y , A M E A S U R E O F O R G A N I Z A T I O N
In order to quantify the degree of constraint, we begin
by denoting the transfer of material or energy from prey
(or donor) i to predator (or receptor) j as Tij, where i and j
range over all members of a system with n elements. The
total activity of the system then can be measured simply
as the sum of all system processes, TST ¼
Pnþ2
i;j¼1
Tij, or
what is called the “total system throughput” (TST). With
a greater intensity of autocatalysis, we expect the overall
level of system activity to increase, so that T appears to be
an appropriate measure. For example, growth in economic
communities is reckoned by any increase in gross domestic
product, an index closely related to the TST.
InFig. B6.1 is depicted the energy exchanges (kcal mÀ2
yÀ1
)
among the ﬁve major compartments of the Cone Spring
ecosystem (Tilly, 1968). The TST of Cone Spring is simply
the sum of all the arrows appearing in the diagram.
Systematically, this is calculated as follows:
TST ¼
X
i;j
Tij
¼ T01 þ T02 þ T12 þ T16 þ T17 þ T23 þ T24
þ T26 þ T27 þ T32 þ T34 þ T36 þ T37 þ T42
þ T45 þ T47 þ T52 þ T57
¼ 11184 þ 635 þ 8881 þ 300 þ 2003 þ 5205
þ 2309 þ 860 þ 3109 þ 1600 þ 75 þ 255
þ 3275 þ 200 þ 370 þ 1814 þ 167
þ 203
¼ 42; 445 kcal mÀ2
yÀ1
;
FIGURE 6.6 Cartoon showing the generic effects of autocatalysis. (A) Inchoate system. (B) Same system after autocatalytic loop has developed.
6. ECOSYSTEMS HAVE DIRECTIONALITY104
BOX 6.1 (cont’d)
where the subscript 0 represents the external environment
as a source, 6 denotes the external environment as a
receiver of useful exports, and 7 signiﬁes the external environment
as a sink for dissipation.
Again, the increasing constraints that autocatalysis
imposes on the system channel ﬂows ever more
narrowly along fewer, but more efﬁcient pathwaysd
“efﬁcient” here meaning those pathways that most effectively
contribute to the autocatalytic process. Another
way of looking such “pruning” is to consider that constraints
cause certain ﬂow events to occur more
frequently than others. Following the lead offered by information
theory (Abramson, 1963; Ulanowicz and Norden,
1990), we estimate the joint probability that a
quantum of medium is constrained both to leave i and
enter j by the quotient Tij /T. We then note that the unconstrained
probability that a quantum has left i can be acquired
from the joint probability merely by summing the
joint probability over all possible destinations. The estimator
of this unconstrained probability thus becomes
P
q
Tiq=T: Similarly, the unconstrained probability that a
quantum enters j becomes
P
k
Tkj=T: Finally, we remark
how the probability that the quantum could make its
way by pure chance from i to j, without the action of any
constraint, would vary jointly as the product of the latter
two frequencies, or
P
q
Tiq
P
k
Tkj

T2. This last probability
obviously is not equal to the constrained joint probability,
Tij /T.
Information theory uses as its starting point a
measure of the rareness of an event, ﬁrst deﬁned by Boltzmann
(1872) as (ek log P), where P is the probability
(0 P 1) of the given event happening and k is a scalar
constant that imparts dimensions to the measure. One notices
that for rare events (P z 0), this measure is very large
and for very common events (P z 1), it is diminishingly
small. For example, if P ¼.0137, the rareness would be
6.19 k-bits, whereas if P ¼.9781, it would be only
0.032 k-bits.
Because constraint usually acts to make things happen
more frequently in a particular way (e.g., ﬂow along
certain pathways), one expects that, on average, an unconstrained
probability would be rarer than a corresponding
constrained event. The rarer (unconstrained) circumstance
that a quantum leaves i and accidently makes its
way to j can be quantiﬁed by applying the Boltzmann formula
to the joint probability deﬁned above, i.e.,
Àk log
P
k
Tkj
P
q
Tiq

T2
!
, and the correspondingly less
rare condition that the quantum is constrained both to
leave i and enter j becomes Àk log(Tij/T). Subtracting
the latter from the former and combining the logarithms
yields a measure of the hidden constraints that channel
the ﬂow from i to j as k log TijT
,
P
k
Tkj
P
q
Tiq
!
.
Finally, to estimate the average constraint at work in
the system as a whole, one weights each individual
constraint by the joint probability of constrained ﬂow
Continued
FIGURE B6.1 Schematic of the network of energy exchanges (kcal mÀ2
yÀ1
) in the Cone Spring ecosystem (Tilly, 1968). Arrows not
originating from a box represent inputs from outside the system. Arrows not terminating in a compartment represent exports of useable
energy out of the system. Ground symbols represent dissipations.
6.6 QUANTIFYING DIRECTIONALITY IN ECOSYSTEMS 105
BOX 6.1 (cont’d)
from i to j and sums over all combinations of i and j.
That is,
AMC ¼ k
X
i;j

Tij
T

log
0
B
@
TijT
P
k
Tkj
P
q
Tiq
1
C
A;
where AMC is the “average mutual constraint” known in
information theory as the average mutual information
(Rutledge et al., 1976).
To illustrate how an increase in AMC actually tracks
the “pruning” process, the reader is referred to the three
hypothetical conﬁgurations in Fig. B6.2. In conﬁguration
(a) where medium from any one compartment will next
ﬂow is maximally indeterminate. AMC is identically
zero. The possibilities in network (b) are somewhat more
constrained. Flow exiting any compartment can proceed
to only two other compartments, and the AMC rises
accordingly. Finally, ﬂow in schema (c) is maximally constrained,
and the AMC assumes its maximal value for a
network of dimension 4.
One notes in the formula for AMC that the scalar constant,
k, has been retained. We recall that although autocatalysis
is a unitary process, one can discern two separate
effects: (1) an extensive effect whereby the activity, T, of
the system increases and (2) an intensive aspect whereby
constraint is growing. We can readily unify these two
aspects into one measure simply by making the scalar
constant k represent the level of system activity, T.
That is we set k ¼ T, and we name the resulting product
the system ascendency, A, where
A ¼
X
i;j
Tijlog
0
B
@
TijT
P
k
Tkj
P
q
Tiq
1
C
A.
Referring again to the Cone Spring ecosystem network
in Fig. B6.1, we notice that each ﬂow in the diagram generates
exactly one and only one term in the indicated
sums. Hence, we see that the ascendency consists of the
18 terms:
A ¼ T01 log
0
B
@
T01T
P
k
Tk1
P
q
T0q
1
C
A
þ T02 log
0
B
@
T02T
P
k
Tk2
P
q
T0q
1
C
A þ . þ T57 log
0
B
@
T57T
P
k
Tk7
P
q
T5q
1
C
A.
¼ 20629 À 1481 þ 13796 À 94 À 907 þ 9817 þ 4249
þ 1004 þ 446 þ 295 À 147 þ 142 þ 4454 À 338 þ 1537
þ 2965 þ 123 þ 236
¼ 56; 725 kcal À bits mÀ2
yÀ1
FIGURE B6.2 Three conﬁgurations of processes illustrating how autocatalytic “pruning” serves to increase overall system constraint.
(A) A maximally indeterminate four-component system with 96 units of ﬂow. (B) The system in (A) after constraints have arisen that
channel ﬂow to only two other compartments. (C) The maximally constrained system with each compartment obligated to support only one
other component.
6. ECOSYSTEMS HAVE DIRECTIONALITY106
In his seminal paper, “The strategy of ecosystem development,” Odum (1969) identiﬁed 24 attributes that characterize
more mature ecosystems, i.e., that indicate the direction of ecological succession. These can be grouped into
categories labeled species richness, dietary speciﬁcity, recycling, and containment. All other things being equal, a
rise in any of these four attributes also serves to augment the system ascendency (Ulanowicz, 1986). One proposal
at the time was that “in the absence of major perturbations, ecosystems have a propensity to increase in ascendency.”
While this tendency is true during early stages of growth and development, further research (Ulanowicz, 2009) has
shown that ecosystems have a dual nature exempliﬁed by a trade-off between ascendency and redundancy, as
described further below.
The ecologist reading this book is likely to have a healthy appreciation for those elements in nature that do not
resemble tightly constrained behavior, as one ﬁnds with autocatalysis. In fact, Chapter 3 was devoted in large measure
to describing the existence and role of aleatoric events and ontic openness. Hence, increasing ascendency is only
half of our dynamical story. Ascendency accounts for how efﬁciently and coherently the system processes medium.
Using the same mathematics as employed above, however, it is also shown in Box 6.1 how one can compute as well
an index called the system overhead, F, that is complementary to the ascendency and captures how much ﬂexibility
the system retains (Ulanowicz and Norden, 1990).
The ﬂexibility quantiﬁed by overhead is manifested as the inefﬁciencies, incoherencies, and functional redundancies
present in the system. Although these latter properties may encumber overall system performance at processing
medium, we saw in Chapter 3 how they become absolutely essential to system survival whenever the system
incurs a novel perturbation. At such time, the overhead comes to represent the repertoire of potential tactics from
which the system can draw to adapt to the new circumstances. Without sufﬁcient overhead, a system is unable to
create an effective response to the exigencies of its environment. The conﬁgurations we observe in nature, therefore,
appear to be the results of a dynamical tension between two antagonistic tendencies (ascendency vs. overhead,
Ulanowicz, 2009). The ecosystem needs this tension in order to persist. Should either direction in the transaction
atrophy, the system will become fragile either to external perturbations (low overhead) or internal disorder (low
ascendency). System fragility is discussed further in Chapter 8.
One disadvantage of ascendency as an index of directionality is that its calculation requires a large amount of
data. Currently, there are few examples of networks accompanying a series of ecological stages. One of the earliest
examples for which data are available is a comparison of two tidal marsh communities, one of which was perturbed
BOX 6.1 (cont’d)
While ascendency measures the degree to which the
system possesses inherent constraints, we wish also to
have a measure of the degree of ﬂexibility that remains
in the system. To assess the degrees of freedom, we ﬁrst
deﬁne a measure of the full diversity of ﬂows in the system.
To calculate the full diversity, we apply the Boltzmann
formula to the joint probability of ﬂow from i to j,
Tij/T, and calculate the average value of that logarithm.
The result is the familiar Shannon formula,
H ¼ À
X
i;j

Tij
T

log

Tij
T

;
where H is the diversity of ﬂows. Scaling H in the same
way we scaled A, i.e., multiplying H by T, yields the system
development capacity, C, as
C ¼ À
X
i;j
Tij log

Tij
T

.
Now, it can readily be proved that C ! A ! 0, so that
the residual, (C À A) ! 0, as well. Subtracting A from C
and algebraically reducing the result yields the residual,
F, which we call the systems “overhead” as
F ¼ C À A ¼ À
X
i;j
Tij log
0
B
@
T2
ij
P
k
Tkj
P
q
Tiq
1
C
A.
The overhead gauges the degree of ﬂexibility remaining
in the system.
Just as we substituted the values of the Cone Spring
ﬂows into the equation for ascendency, we may similarly
substitute into this equation for overhead to yield a value
of 79,139 kcal-bits mÀ2
yÀ1
. Similarly, substitution into the
formula for C yields a value of 135,864 kcal-bits mÀ2
yÀ1
,
demonstrating that the ascendency and the overhead
sum exactly to yield the capacity.
6.6 QUANTIFYING DIRECTIONALITY IN ECOSYSTEMS 107
by a 6C rise in temperature caused by thermal efﬂuent from a nearby nuclear power plant, and the other of which
remained unimpacted (Homer et al., 1976). Under the assumption that perturbation regresses an ecosystem to an
earlier stage, one would expect the unimpacted system to be more “mature” and exhibit a higher ascendency
than the heated system.
Homer et al. parsed the marsh gut ecosystem into 17 compartments. They estimated the biomass in each taxon in
mgC mÀ2
and the ﬂows between taxa in mgC mÀ2
dÀ1
. The total system throughput (T) in the control ecosystem was
estimated to be 22,420 mgC mÀ2
dÀ1
, and that in the impacted system as 18,050 mgC mÀ2
dÀ1
(Ulanowicz, 1986).
How much of the decrease could be ascribed to diminution of autocatalytic activities could not be assessed, sufﬁce
it to say that the change was in the expected direction. The ascendency in the heated system fell to 22,433 mgCbits
mÀ2
dÀ1
from a value of 28,337 mgC-bits mÀ2
dÀ1
for the control. The preponderance of the drop could be
ascribed to the fall in T, as the corresponding average mutual constraint (AMC) fell by only 0.3%.
6.7 DEMYSTIFYING DARWIN
One possible way around the copious data required to calculate the ascendency might be to search for an indirect
measure of the effect of autocatalysis. Along those lines, Jørgensen and Mejer (1977) suggested that the directionality
in ecosystem succession might be gauged by the amount of exergy stored among the components of the ecosystem.
Exergy being the net amount of total energy that can be converted directly into work. More to come in Chapter 7. The
working hypothesis is that ecosystems accumulate more stored exergy as they mature. Exergy can be estimated once
one knows the biomass densities of the various species, the chemical potentials of components that make up those
species and the genetic complexity of those species (Jørgensen et al., 2005, see also Chapter 6). In Fig. 6.7, one sees
that the stored ecological exergy among the biota of Surtsey Island began to increase markedly after about 1985.
It is perhaps worthwhile at this juncture to recapitulate what has been done: First, we have shifted our focus in
ecosystem dynamics away from the normal (symmetrical) ﬁeld equations of physics and concentrated instead upon
the origins of asymmetry in any systemdthe boundary constraints. We then noted how biotic entities often serve as
the origins of such constraint upon other biota, so that the kernel of ecodynamics is revealed to be the mutual (selfentailing)
constraints that occur within the ecosystem itself. We then identiﬁed a palpable and measurable entity (the
network of materialeenergy exchanges) upon which this myriad of mostly hidden constraints writes its signature.
Finally, we described a calculus that could be applied to the network to quantify the effects of autocatalytic selection.
Hence, by following changes in the ascendency and overhead of an ecosystem, we are focusing squarely upon that
which makes ecodynamics fundamentally different from classical dynamics (Ulanowicz, 2004a,b).
-1000
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Eco-exergyGJ/ha
60 65 70 75 80 85 90 95 100
year
Line Chart
FIGURE 6.7 Estimated stored exergy among the biota inhabiting Surtsey Island.
6. ECOSYSTEMS HAVE DIRECTIONALITY108
The dynamical roots of much of Darwinian narrative having been demystiﬁed by the directionality inherent in
autocatalysis, it is perhaps a bit anticlimatic to note that several other behaviors observed among developing ecosystems
also can trace their origins to autocatalysis and its attendant centripetality. Jørgensen and Mejer (1977), as
mentioned above, have concluded that ecosystems always develop in the direction of increasing the amount of
exergy stored in the system. Maximal exergy storage has proved a useful tool with which to estimate unknown parameters
and rates (Jørgensen, 1992; see also growth and development forms in Chapter 7). Schneider and Kay (1994)
hypothesize how systems develop so as to degrade available exergy gradients at the fastest rate possible. This is,
however, only correct for the ﬁrst growth form, growth of biomass because more biomass needs more exergy for
respiration to maintain the biomass far from thermodynamic equilibrium. For further details, see Chapter 7. Third,
the inputs of ecosystems engender manifold system circulations among the full communityda process called
network aggradation (Fath and Patten, 2001). All three behaviors can be traced to autocatalysis and its attendant centripetality
(Ulanowicz et al., 2006).
It should be noted in passing how autocatalytic selection pressure is exerted in top-down fashiondcontingent
action by the macroscopic ensemble upon its constituent elements. Furthermore, centripetality is best identiﬁed
as an agency acting at the focal level. Both of these modes of action violate the classical Newtonian stricture called
closure, which permits only mechanical actions at smaller levels to elicit changes at higher scales. As noted above,
complex behaviors, including directionality, can be more than the ramiﬁcation of simple events occurring at smaller
scales.
Finally, it is worthwhile to note how autocatalytic selection can act to stabilize and regularize behaviors across the
hierarchy of scales. Under the Newtonian worldview, all laws are considered to be applicable universally, so that a
chance happening anywhere rarely would ramify up and down the hierarchy without attenuation, causing untold
destruction. Under the countervailing assumption of ontic openness, however, the effects of noise at one level are
usually subject to autocatalytic selection at higher levels and to energetic culling at lower levels. As a result, nature
as a whole takes on habits (Hoffmeyer, 1993) and exhibits regularities; but in place of the universal effectiveness of all
natural laws, we discern instead a granularity inherent in the real world. That is, models of events at any one scale can
explain matters at another scale only in inverse proportion to the remoteness between them. For example, one would
not expect to ﬁnd any connection between quantum phenomena and gravitation, given that the two phenomena are
separated by some 42 orders of magnitude, although physicists have searched ardently, but in vain, to join the two.
Obversely, the domain within which irregularities and perturbations can damage a system is usually circumscribed.
Chance need not unravel a system. One sees demonstrations of systems “healing” in the higher organisms, and even
in large-scale organic systems such as the global ecosystem (Lovelock, 1979).
6.8 DIRECTIONALITY IN EVOLUTION?
With the cybernetic and thermodynamic narrative of ecosystem development (the new ecology) now before us, it
is perhaps useful to revisit the question of whether the process of biotic evolution might exhibit any form of directionality?
Perhaps an unequivocal response is premature, sufﬁce it here to compare the differences in the dynamics
of ontogeny, ecosystem development, and evolution. With ontogenetic development, there is no denying the directionality
evident in the developing organism. Convention holds that such direction is “programmed” in the genomic
material, and no one is going to deny the degree of correspondence between genome and phenome. The question
remains, however, as to where does the agency behind such direction reside? It is awkward, to say the least, to treat
the genome as some sort of homunculus that directs the development process. Genomic material such as DNA is
unlikely to have evolved by random assembly, and outside its network of enzymatic and proteomic reactions it
can do nothing of interest (Kauffman, 1993). Its role in ontogeny is probably best described as that of material cause,
sensu Aristotledit is materially necessary, but passive with respect to more efﬁcient (again, sensu Aristotle) agencies
that actively read and carry out the anabolic processes. As regards those processes, they form a network that indubitably
contains autocatalytic pathways, each with its accompanying directions.
The entire scenario of ontogeny is rather constrained, and noise plays a distinct secondary role. In contrast, the
role of genomes is not as prominent in the development of ecosystems (Stent, 1981). While some hysteresis is
required of the participating species, the central agencies that provide directions (as argued above) are the autocatalytic
loops among the species. The constraints among the species are nowhere near as tight as at the ontogenetic
level, and noise plays a much larger role in the direction that a system takes over time.
Evolutionary patterns are not as stereotypical as those in ecological succession. What happens before some cataclysm
can be very different from what transpires after the disaster. So evolutionary theorists are probably correct in
6.8 DIRECTIONALITY IN EVOLUTION? 109
pointing to random events as playing the larger role over the long run. It appears premature, however, to rule out
directional processes altogether. Many species and their genomes survive catastrophes, as do entire autocatalytic ensembles
of species at the level of the ecosystem. They provide a degree of history that helps to direct the course of
evolution until the next upheaval.
This dynamic is already familiar to us from the workings of Polya’s Urn, which we considered earlier. In fact, a
reasonable simile would be to consider what might happen if Polya’s Urn were upset after some 1000 draws and
only a random subset of say 15 balls could be recovered and put back into the Urn to continue the process. Although
the subsequent evolution of the ratio of red to blue balls might not converge very closely to what it was before the
spill, some remnants of the history would likely keep the ratio from making an extreme jump. Suppose, for example,
that before the spill the ratio had converged rather tightly to 0.739852, and that after the accident 10 red balls and 5
blue balls were recovered. It is exceedingly unlikely that the continuing process would converge to, say 0.25835.
And, so it may be on the evolutionary theater. Not all directions established by ecosystems during one era are
necessarily destroyed by a catastrophe that initiates the next. Surviving directions are key to the evolutionary
play during the next interval. Thermodynamic and other physical directions notwithstanding, anyone who argues
that evolution involves only chance and no directionality is making an ideological statement and not a reasoned
conjecture because ecosystem have directionality.
6.9 SUMMARY
Ecology, from its very inception, has been concerned with temporal direction. Ecological communities are
perforce open systems and thus are subject to the imperatives of the second law, but there is yet another, internal
drive within ecosystems, efforts by evolutionary theorists to deny directionality notwithstanding. Ecosystem dynamics
are rooted in conﬁgurations of autocatalytic processes, which respond to random inputs in a nonrandom
manner. Autocatalytic processes build upon themselves, and in the process give rise to a centripetal pull of energy
and resources into the community. Such centripetality is central to the very notion of life and is more basic than even
competition, upon which conventional evolutionary theory is built. Conﬁgurations of processes can select from
among complex chance events, any of which can exhibit its own, accidental directionality. Ensuing directionality
can be quantiﬁed as an increase in an information theoretic measure called ascendency. This directionality opposes
the tendency of the second law to disorder systems, but healthy ecosystems need a modicum of both trendsd
efﬁciency and redundancydin order to persist. The resulting dynamic resembles that of a natural dialectic. Finally,
although evolution over the longer span might appear adirectional, selection in the nearer ecological time span always
provides the ecosystem with an inherent direction that is an obligate element in a complete description of any
particular evolutionary scenario.
6. ECOSYSTEMS HAVE DIRECTIONALITY110
C H A P T E R
7
Ecosystems Have Complex Dynamicsd
Growth and Development
Newton’s Laws explain how an apple falls to the ground,
Ecological principles explain how an apple got above the ground in the ﬁrst place.
PREAMBLE
The story of Sir Isaac Newton sitting under an apple tree is a well-known story in science. He intuited that the
apple that fell on his head was propelled by the force of gravity, which acting on all objects at all times can also
explain the motion of planetary bodies falling, but never landing, through space and time. This is a story of irreversible
energy degradation, the utilization of a thermodynamic gradient (height above ground) to do work on the surface
through impact. The usefulness of that insight played out over centuries of scientiﬁc advancement. However,
the ﬂipside of the scenario, of how the apple found itself in a position with gravitational potential, was not so thoroughly
investigated or understood. This story is one of gradient formation and how ecological systems are able to
use energy ﬂows to create structure and organization. In this case, the tree, through its own biochemical processes,
worked against the gravitational forces to raise a trunk, and branches, and leaves, and the accompanying ﬂowers
and fruits, several meters above the ground (the local equilibrium), which furthermore was pollinated by ﬂying insects
also using ecological principles to work against gradients. This is one example of why we refer to ecological
systems as operating at “far from equilibrium.” This chapter explores some of those natural and lawful processes
that move the system toward organized complexity and greater dissipative gradients.
7.1 VARIABILITY IN LIFE CONDITIONS
All known life on earth resides in the thin layer enveloping the globe known as the ecosphere. This region extends
from sea level to about 10 kilometers into the ocean depths and approximately the same distance up into the atmosphere.
It is so thin that if an apple were enlarged to the size of the earth, then the ecosphere would be thinner than
the peel. Yet a vast and complex biodiversity has arisen in this region. Furthermore, the ecosphere acts as integrator
of abiotic factors on the planet, accumulating in disproportionate quantities particular elements favored by the
biosphere (Table 7.1). In particular, note that carbon is not readily abundant in the abiotic spheres yet is highly concentrated
in the biosphere, where nitrogen, silicon, and aluminum, while largely available, are mostly unincorporated.
However, even in this limited domain the conditions for living organisms may vary enormously in time and
space.
The climatic conditions are as follows:
1) The temperature can vary from about À70 to about 55 centigrade.
2) The wind speed can vary from 0 km/h to several hundred km/h
3) The humidity may vary from almost 0% to 100%
4) The precipitation from a few mm in average per year to several m/y which may or may not be seasonally aligned
5) Annual variation in day length according to longitude
6) Unpredictable extreme events such as tornadoes, hurricanes, earthquakes, tsunamis, and volcanoes
111
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00007-3
The physicalechemical environmental conditions are as follows:
1) Nutrient concentrations (C, P, N, S, Si, etc.)
2) Salt concentrations (it is important both for terrestrial and aquatic ecosystems)
3) Presence or absence of toxic compounds, whether they are natural or anthropogenic in origin
4) Rate of currents in aquatic ecosystems and hydraulic conductivity for soil
5) Space requirements
6) The solar constant, on average 1388 Watt per square meter
The biological conditions are as follows:
1) The concentrations of food for herbivorous, carnivorous, and omnivorous organisms
2) The density of predators
3) The density of competitors for the resources (food, space, etc.)
4) The concentrations of pollinators, symbionts, and mutualists
5) The density of decomposers
The human impact on natural ecosystems today. The remarkable interplay between biotic features and the environmental
context adds to this complexity.
The list of factors determining the life conditions is much longerdwe have only mentioned the most important
factors. Yet, in spite of this great variety, life has evolved and adapted to occupy almost all reaches of the planet.
In addition to these contextual conditions, the ecosystems have history or path dependency, meaning that the
initial conditions determine the possibilities of development. If we modestly assume that 100 factors are deﬁning
the life conditions and each of these 100 factors may be on 100 different levels, then 10200
different life conditions
are possible, which can be compared with the number of elementary particle in the Universe 1081
(see also Chapter 3).
The conﬂuence of path dependency and an astronomical number of combinations afﬁrms that the ecosphere could
not experience the entire range of possible states, otherwise known as nonergodicity. Furthermore, its irreversibility
ensures that it cannot track back to other possible conﬁgurations. Time’s arrow is unidirectional. In addition to these
combinations, the formation of ecological networks means that the number of indirect effects are magnitudes higher
than the direct ones and they are not negligible; on the contrary, they are often more signiﬁcant than the direct ones,
as discussed in Chapter 4.
What is the result of this enormous variability in the natural life conditions? We have found about 0.5 Â 107
species
on earth and it is presumed that the number of species is double or 107
. They have developed all types of mechanisms
to live under the most varied life conditions including ones at the margin of their physiological limits. They
have developed defense mechanisms. For example, some plants are toxic to avoid grazing, others have thorns, etc.
Animals have developed horns, camouﬂage patterns, well-developed auditory senses, fast escaping rates, etc. They
have furthermore developed integration mechanisms, ﬁtting into their local web of life, often complementing and
creating their environmental niche. The multiplicity of life forms is inconceivable.
The number of species may be 107
, but living organisms are all different. An ecosystem has normally from 1015
to
1020
individual organisms that are all different, which, although is a lot, makes ecosystems middle number systems.
This means that the number of organisms is magnitudes less than the number of atoms in a room, but all the organisms,
opposite the atoms in the rooms, have individual characteristics. Whereas large number systems such as the
number of atoms in a room are amenable to statistical mechanics and small number problems such as planetary systems
to classical mechanics or individual-based modeling, middle number problems contain their own set of challenges.
For one thing, this variation, within and among species, provides diversity through coadaptation and
coevolution, which is central both to Darwinian selection and network aggradation.
TABLE 7.1 Percent Composition Spheres for the First Five Elements.
Lithosphere Atmosphere Hydrosphere Biosphere
Oxygen 62.5 Nitrogen 78.3 Hydrogen 65.4 Hydrogen 49.8
Silicon 21.22 Oxygen 21.0 Oxygen 33.0 Oxygen 24.9
Aluminum 6.47 Argon 0.93 Chloride 0.33 Carbon 24.9
Hydrogen 2.92 Carbon 0.03 Sodium 0.28 Nitrogen 0.27
Sodium 2.64 Neon 0.002 Magnesium 0.03 Calcium 0.073
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT112
The competitive exclusion principle (Gause, 1934) claims that when two or more species are competing for the
same limited resource, only one will survive. The contrast between this principle and the number of species has
for long time been a paradox. The explanation is rooted in the enormous variability in time and space of the conditions
and in the variability of a wide spectrum of species’ properties. A competition model, where three or more resources
are limiting, gives a result very different from the case where one or two resources are limiting. Due to
signiﬁcant ﬂuctuations in the different resources, one species is prevented from becoming dominant and a model
can demonstrate that many species occurring around the same resources can coexist. It is therefore not surprising
that there exists many species in an environment characterized by an enormous variation of abiotic and biotic
factors. The diversity itself contributes to greater diversity; development begets codevelopment. This is a positive
feedback loop pushing the ecosystem toward greater and greater diversity and complexity.
To summarize, the number of different life forms is enormous because there are a great number of both challenges
and opportunities. The complexity of ecosystem dynamics is rooted in these two incomprehensible types of
variability.
7.2 ECOSYSTEM DEVELOPMENT
Ecosystem development in general is a question of the energy, matter, and information ﬂows to and from the ecosystems.
No transfer of energy is possible without matter and information and no matter can be transferred without
energy and information. The higher the levels of information, the higher the utilization of matter and energy for
further development of ecosystems away from the thermodynamic equilibrium; see also Chapters 2 and 6. These
three factors are intimately intertwined in the fundamental nature of complex adaptive systems such as ecosystems
in contrast to physical systems that most often can be described completely by material and energy relations. Life is
therefore both a material and a nonmaterial (informational) phenomenon. The self-organization of life essentially
proceeds by exchange of information.
E.P. Odum has described ecosystem development from the initial stage to the mature stage as a result of continuous
use of the self-design ability (Odum, 1969, 1971); see the signiﬁcant differences between the two types of systems
listed in Table 7.2 and notice that the major differences are on the level of information. Table 7.2 show what we
often call E.P. Odum’s successional attributes, but also a few other concepts such as, for instance, exergy and ecological
networks have been introduced in the table.
The information content increases in the course of ecological development because an ecosystem integrates all the
modiﬁcations that are imposed by the environment. Thus, it is against the background of genetic information that
systems develop which allow interaction of information with the environment. Herein lies the importance in the
feedback organismeenvironment that means that an organism can only evolve in an evolving environment, which
in itself is modifying.
The conservation laws of energy and matter set limits to the further development of “pure” energy and matter,
while information may be ampliﬁed (almost) without limit. Limitation by matter is known from the concept of the
limiting factor: growth continues until the element that is the least abundant relatively to the needs of the organisms
is used up. Very often in developed ecosystems (for instance an old forest) the limiting elements are found entirely in
organic compounds in the living organisms, while there is no or very little inorganic forms left in the abiotic part of
the ecosystem. The energy input to ecosystems is determined by the solar radiation and, as we shall see later in this
chapter, many ecosystems capture about 75%e80% of the solar radiation, which is their upper physical limit.
Unlike energy capture which has an upper limit, the information content may continue to increase in terms of
both structural information (i.e., the network) and also genetic information (see Fig. 7.1).
Information has some properties that are very different from mass and energy.
1) Information, unlike matter and energy, is not conserveddit can disappear without a trace. When a frog dies, the
enormous information content of the living frog may still be there microseconds after the death in form of the
right amino acid sequences but the information is useless and after a few days the organic polymer molecules
have decomposed. In contrast, the living organism is able to multiply information by copying already achieved
successful information. The information is able to provide a pattern of biochemical processes that ensure survival
of the organisms as they interact with the physicalechemical environment and the other organisms present in the
ecosystem. Organisms copy the information embodied in the genomes as they grow and reproduce. Growth and
reproduction require input of energy in terms of environmental food resources. The food has energy content, but
it also has information content, which like the dead frog, no longer has the capacity to act upon. Another way to
7.2 ECOSYSTEM DEVELOPMENT 113
look at this is using the concept of emergy instead of energy. Emergy is deﬁned later in this chapter (Box 7.2). The
emergy of the food would be calculated as the amount of solar energy it takes to provide the food, which would
require multiplication by a weighting factor >> 1.
2) The disappearance and copying of information, that are characteristic processes for living systems, are irreversible processes.
A made copy cannot be taken back and death is an irreversible process. Although information can be expressed as
eco-exergy in energy units, it is not possible to recover chemical energy from information on the molecular level
as known from the genomes. It would require a Maxwell’s Demon that could sort out the molecules and it would
therefore violate the second law of thermodynamics. There are, however, challenges to the second law (e.g.,
Capek and Sheehan, 2005), and this process of copying information could be considered one of them. Note that
since the big bang, enormous amounts of matter have been converted to energy (E ¼ mc2
) in a form that makes it
TABLE 7.2 Differences Between Initial Stage and Mature Stage are Indicated.
Properties Early Stages Late or Mature Stage
(A) ENERGETIC
Production/respiration >>1 Close to 1
Production/biomass High Low
Respiration/biomass High Low
Yield (relative) High Low
Speciﬁc entropy High Low
Entropy production/time Low High
Eco-exergy Low High
Information Low High
(B) STRUCTURE
Total biomass Small Large
Inorganic nutrients Extrabiotic Intrabiotic
Diversity, ecological Low High
Diversity, biological Low High
Patterns Poorly organized Well organized
Niche specialization Broad Narrow
Organism size Small Large
Life cycles Simple Complex
Mineral cycles Open Closed
Nutrient exchange rate Rapid Slow
Life span Short Long
Ecological network Simple Complex
(C) SELECTION AND HOMEOSTASIS
Internal symbiosis Undeveloped Developed
Stability (resistance to external
perturbations)
Poor Good
Ecological buffer capacity Low High
Feedback control Poor Good
Growth form Rapid growth Feedback controlled growth
Types r-strategists K-strategists
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT114
impossible directly to convert the energy again to mass. Similarly, the conversion of energy to information that is
characteristic for many biological processes cannot be reversed directly in most cases. The transformation
matter / energy / molecular information, which can be copied at low cost is possible on earth, but these
transformation processes are irreversible.
3) Information exchange is communication, which brings about the self-organization of life. Life is an immense
communication process that happens over several hierarchical levels (Chapter 5). Information exchange is
possible with a very tiny consumption of energy, while information storage requires that it is linked to material
substances. For instance, genetic information is stored in the genomes and it is transferred to the amino acid
sequence.
A major design principle observed in natural systems is the feedback of energy from storages to stimulate the
inﬂow pathways as a reward from receiver storage to the inﬂow source (Odum, 1971a). See also the “centripetality”
in Chapter 6. By this feature, the ﬂow values developed reinforce the processes that are doing useful work. Feedback
allows the circuit to learn. A wider use of the self-organization ability of ecosystems in environmental or rather
ecological management has been proposed by Odum (1983, 1988).
Researchers have modiﬁed and further developed E.P. Odum’s idea of using attributes to describe the development
and the conditions of an ecosystem during the ensuing years. Here, we assess ecosystem development using
ecological orientors to indicate that the development is not necessarily following all E.P. Odum’s details because ecosystems
are ontically open (Chapter 3). In addition, it is also rare that we can obtain data to demonstrate the validity
of the attributes in complete detail. This recent development is presented in the next section.
The concept of ecological indicators was introduced around 30 years ago. These metrics indicate the ecosystem
condition or the ecosystem health and are widely used to understand ecosystem dynamics in an environmental
management context. E.P. Odum’s attributes could be used as ecological indicators. Another approach is to use speciﬁc
indicator species that show with their presence or absence that the ecosystem is either healthy or not. Speciﬁc
contaminants that indicate a speciﬁc disease are used as indicators. Finally, it should be mentioned that indicators
such as biodiversity or thermodynamic variables are used to indicate a holistic image of the ecosystems’ condition;
for further details see Chapter 10. The relationship between biodiversity and stability was previously widely discussed
(e.g., May, 1973, which showed that there is not a simple relationship between biodiversity and stability
of ecosystems). Tilman and coworkers (Tilman and Downing, 1994) have shown that temperate grassland plots
with more species have a greater resistance or buffer capacity to the effect of drought (a smaller change in biomass
between a drought year and a normal year). However, there is a limitdeach additional plant contributed less; see
Fig. 7.2. Many experiments (Tilman and Downing, 1994) have also shown that higher biodiversity increases the
biomass.
Upper limit determined by limiting element and/or
energy captured.
Information
Present information
level
about 40MJ /g
Upperlimitofinformation
intheorderof10^7GJ/g
Physicalstructureexpressedasenergy/ha
FIGURE 7.1 Life conditions are currently changed and have a high variability in time and space. This creates new challenges (problems) to
survival. Organisms adapt or a shift to other species takes place. This requires an information system that is able to transfer the information about
good solutions to the coming generations of organisms. Consequently, an information system is very beneﬁcial, but it has to be considered as a new
source of constraints that however can open up for new possibilities.
7.2 ECOSYSTEM DEVELOPMENT 115
Box 7.1 gives the deﬁnitions for ecological orientors and ecological indicators. In ecological modeling, goal functions
are used to develop structurally dynamic models. Also, the deﬁnition of this third concept is included in the
box.
It is theoretically possible to divide most of E.P. Odum’s attributes into groups, deﬁning four different growth and
development forms for ecosystems (Jørgensen et al., 2000; Fath et al., 2004):
I. Boundary Growth refers to the capture of energy input by the ecosystem and bringing it in across its system
boundaries. This is the basic prerequisite for all further activities in the ecosystem and maintaining them as open
far-from-equilibrium systems.
II. Biomass Growth refers to the increase of protoplasmdthe biomass substance of the ecosystem. This explains
why P/B and R/B decreases with the development and the nutrients go from extrabiotic to intrabiotic pools.
III. Network Growth and Development refers directly to increased complexity of the ecological network, more
complex life and mineral cycles, a slower nutrient exchange rate, and a more narrow niche specialization. It also
implies a longer retention time in the system for energy and matter. This is not only an increase in the network
connectivity but also the placement or articulation of those links.
IV. Information Growth and Development refers to the evolutionary advances that explain the higher diversity,
larger animals, longer life span, more symbiosis and feedback control, and a shift from r-strategists to
K-strategists.
BOX 7.1
D E F I N I T I O N S O F O R I E N T O R S , I N D I C A T O R S , A N D G O A L
F U N C T I O N S
Ecological Orientors: Ecosystem variables that
describe the range of directions in which ecosystems
have a propensity to develop. The word orientors is used
to indicate that we cannot give complete details about
the development, only the direction.
Ecological Indicators: These indicate the present
ecosystem condition or health. Many different indicators
have been used such as speciﬁc species, speciﬁc contaminants,
indices giving the composition of groups of organisms
(for instance, an algae index), E.P. Odum’s attributes
and holistic indicators included biodiversity and thermodynamic
variables such as entropy or exergy.
Ecological Goal Functions: Ecosystems do not have
deﬁned goals, but their propensity to move in a speciﬁc direction
indicated by ecological orientors can be described
in ecological models by goal functions. Clearly, in a model,
the description of the development of the state variables of
the model has to be rigorously indicated, which implies
that goals are made explicit. The concept should only be
used in ecological modeling context.
Number of species
0 10 20 25
Droughtresistance
-0.5
-1.0
-1.5
FIGURE 7.2 The resistance of a temperate grassland towards drought increases with number of species in an asymptotic manner so that the
relative importance of adding new species decreases.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT116
7.3 ORIENTORS AND SUCCESSION THEORIES
The orientor approach that was brieﬂy introduced above describes ideal-typical trajectories of ecological properties
on an integrated ecosystem level. Therefore, it follows the traditions of various concepts in ecological theory,
which are related to environmental dynamics. A signiﬁcant example is succession theory, describing “directional
processes of colonization and extinction of species in a given site” (Dierssen, 2000). Although there are big intersections,
these conceptual relationships have not become sufﬁciently obvious in the past, due to several reasons, which
are mainly based on methodological problems and critical opinions that have been discussed eagerly after the
release of Odum’s paper on the strategy of ecosystem development (1969). Which were the reasons for these
controversies?
Traditional succession theory is basically oriented toward vegetation dynamics. The pioneers of succession research,
Clements (1916) and Gleason (1917), were focusing mainly on vegetation. Consequently, also the succession deﬁnitions
of Whittacker (1953), Egler (1954), Grime (1979), or Picket et al. (1987) are related to plant communities, while
heterotrophic organisms often are neglected (e.g., Connell and Slayter, 1977, Horn, 1974). Therefore, also the conclusions
of the respective investigations often have to be reduced to the development of vegetation components of ecosystems,
while the orientor approach refers to the whole ensemble of organismic and abiotic subsystems and their
interrelations. These conceptual distinctions for sure are preferable sources for misunderstandings.
A sufﬁcient number of long-term data sets are not available. Therefore, as some authors state throughout the discussions
of Odum’s “strategy” paper (1969), the theoretical predictions of succession theory seem to be “based on untested
assumptions or analogies” (e.g., Connell and Slayter, 1977; Drury and Nisbet, 1973, Horn, 1974), while there is
only small empirical evidence. This situation becomes even more problematic if ecosystem data are necessary to test
the theoretical hypotheses. Consequently, we will also in the future have to cope with this lack of data, but we can
use more and more empirical investigations, referring to the orientor principle, which have been reported in the literature
(e.g., Marques et al., 2003, Mu¨ller et al. i.p.). We can hope for additional results from ecosystem analyses and
Long-Term Ecological Research Programs. Meanwhile validated models can be used as productive tools for the analysis
of ecosystem dynamics.
The conceptual starting points differ enormously. Referring to the general objections against the maturity concept,
Connell and Slayter (1977) funnel their heavy criticism about Odum’s 24 ecosystem features into the questions of
whether mature communities really are “internally controlled” and if “steady states really are maintained by internal
feedback mechanisms.” Having doubts in these facts, they state that therefore no characteristics can be deduced
from this idea. Today, there is no doubt about the existence of self-organizing processes in all ecosystems (e.g., Jørgensen,
2002). Of course, there are external constraints, but within the speciﬁc degrees of freedom, in fact the internal
regulation processes are responsible for the development of ecosystems. Hence, the basic argument against the
maturity concept has lost weight throughout the years.
Comparing successional dynamics, often different spatial and temporal scales are mixed. This point is related to the typical
time scales of ecological investigations. They are most often carried out in a time span of 2e4 years. Of course, it is
very difﬁcult to draw conclusions over centuries from these short-term data sets. Also using paleoecological
methods give rise to broad uncertainties, and when spatial differences are used to represent the steps of temporal
developments, the questions of the site comparability introduces problems which might reduce the evidence of
the ﬁndings enormously. Furthermore, there is the general problem of scale. If we transfer short-term results to
long-term processes, then we cannot be sure to use the right algorithms and to take into account the correct, scale
conform constraints and processes (O’Neill et al., 1986). And, looking at the spatial scale, the shifting mosaic hypothesis
(Remmert, 1991) has shown that there will be huge differences if different spatial extents are taken into account,
and that local instabilities can be leading to regional steady-state situations. What we can see is that there are many
empirical traps we can fall into. Maybe the connection of empirical research and ecological modeling can be helpful
as a “mechanism of self-control” in this context.
Due to the “ontic openness” of ecosystems, predictability in general is rather small but in many cases, exceptions can be
found. The resulting dilemma of a system’s inherent uncertainty can be regarded as a consequence of the internal
complexity of ecosystems, the nonlinear character of the internal interactions, and the often unforeseeable dynamics
of environmental constraints. Early on, succession researchers found the fundamentals of this argument, which are
broadly accepted today. The nondeterministic potential of ecological developments has already been introduced in
Tansley’s polyclimax theory (1935), which is based on the multiple environmental inﬂuences that function as constraints
for the development of an ecosystem. Simberloff (1982) formulates that “the deterministic path of succession,
in the strictest Clementsian mono-climax formulation, is as much an abstraction as the Newtonian particle
7.3 ORIENTORS AND SUCCESSION THEORIES 117
trajectory” and Whittaker (1972) states, “the vegetation on the earth’s surface is in incessant ﬂux.” Stochastic elements,
complex interactions, and spatial heterogeneities take such important inﬂuences that the idea of Odum
(1983) that “community changes . are predictable,” must be considered in relative terms today, if detailed prognoses
(e.g., on the species level) are desired. However, this does not mean that general developmental tendencies can be
avoided, i.e., this fact does not contradict the general sequence of growth and development forms as formulated in
this volume. Quite the opposite: This concept realizes the fact that not all ecosystem features are optimized
throughout the whole sequence, a fact that has been pointed out by Drury and Nisbet (1973) and others.
Disturbances are causes for separating theoretical prognoses from practical observations. One example for these nondeterministic
events is disturbance, which plays a major role in ecosystem development (e.g., Drury and Nisbet, 1973;
Sousa, 1984). Odum (1983) has postulated that succession “culminates in the establishment of as stable an ecosystem
as its biologically possible on the site in question” and he notes that mature communities are able to buffer the physical
environment to a greater extent than the young community. In his view, stability and homoeostasis can be seen as
the result (he even speaks about a purpose) of ecological succession from the evolutionary standpoint. However, the
guiding paradigm has changed: Today Holling’s adaptive cycle model (1986) has become a prominent concept, and
destruction is acknowledged as an important component of the continuous adaptation of ecosystems to changing
environmental constraints. This idea also includes the feature of brittleness in mature states, which can support
the role of disturbance as a setting of new starting points for an oriented development.
Terminology has inhibited the acceptance of acceptable ideas. The utilization of terms like “strategy,” “purpose,” or
“goal” has led to the feeling that holistic attitudes toward ecological successions in general are loaded with a broad
teleological bias. Critical colleagues argued that some of these theories are imputing ecosystems to be “intentionally”
following a certain target or target state. This is not correct: The series of states is a consequence of internal feedback
processes that are inﬂuenced by exterior constraints and impulses. The ﬁnally achieved attractor state thus is a result,
not a cause. These indicators track that development but do not cause the observed changes.
Summarizing, many of the objections against the initial theoretical concepts of ecosystem development and especially
against the stability paradigm have proven to be correct, and they have been modiﬁed in between. Analogies
are not used anymore, and the number of empirical tests is increasing. On the other hand, the theory of selforganization
has clariﬁed many critical objections. Thus, a consensus can be reached if cooperation between theory
and empiricism is enhanced in the future.
7.4 THE MAXIMUM POWER PRINCIPLE
Lotka (1925, 1956) formulated the maximum power principle. He suggested that systems that succeed are those
that develop designs to maximize the ﬂow of useful (for maintenance and growth) energy, and Odum used this principle
to explain much about the structure and processes of ecosystems (Odum and Pinkerton, 1955). Boltzmann
(1905) said that the struggle for existence is a struggle for free energy available for work, which is a deﬁnition
very close to the maximum exergy principle introduced in the next section. Similarly, Schro¨dinger (1944) pointed
out that organization is maintained by extracting order from the environment. These two last principles may be interpreted
as the systems that are able to gain the most free energy under the given conditions, i.e., to move most away
from the thermodynamic equilibrium will succeed. Such systems will gain most biogeochemical energy available for
doing work and therefore have most energy stored to use for maintenance and buffer against perturbations.
Odum (1983) deﬁnes the maximum power principle as a maximization of useful power. It is applied on the
ecosystem level by summing up all the contributions to the total power that are useful. This means that nonuseful
power is not included in the summation. Usually the maximum power is found as the sum of all ﬂows expressed in
energy terms, for instance kJ/24h.
Brown et al. (1993) and Brown (1995) has restated the maximum power principle in more biological terms. According
to the restatement it is the transformation of energy into work (consistent with the term useful power)
that determines success and ﬁtness. Many ecologists have incorrectly assumed that natural selection tends to increase
efﬁciency. If this were true, then endothermy could never have evolved. Endothermic birds and mammals
are extremely inefﬁcient compared with reptiles and amphibians. They expend energy at high rates in order to maintain
a high, constant body temperature, which, however, gives high levels of activities independent of environmental
temperature (Turner, 1970). Brown (1995) deﬁnes ﬁtness as reproductive power, dW/dt, the rate at which energy can
be transformed into work to produce offspring. This interpretation of the maximum power principle is even more
consistent with the maximum exergy principle that is introduced in the next section, than with Lotka’s and Odum’s
original idea.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT118
In the book Maximum Power: The Ideas and Applications of H.T. Odum, Hall (1995) has presented a clear interpretation
of the maximum power principle, as it has been applied in ecology by H.T. Odum. The principle claims that
power or output of useful work is maximized, not the efﬁciency and not the rate, but the trade-off between a
high rate and high efﬁciency yielding most useful energy or useful work (see Fig. 7.3).
Hall is using an interesting seminatural experiment to illustrate the application of the principle in ecology.
Streams were stocked with different levels of predatory cutthroat trout. When predator density was low, there
was considerable invertebrate food per predator, and the ﬁsh used relatively little maintenance energy searching
for food per unit of food obtained. With a higher ﬁsh-stocking rate, food became less available per ﬁsh, and each
ﬁsh had to use more energy searching for it. Maximum production occurred at intermediate ﬁsh-stocking rates,
which means intermediate rates at which the ﬁsh utilized their food.
Hall (1995) mentions another example. Deciduous forests in moist and wet climates tend to have a leaf area index
(LAI) of about 6 m2
/m2
. Such an index is predicted from the maximum power hypothesis applied to the net energy
derived from photosynthesis. Higher LAI values produce more photosynthate, but do so less efﬁciently because of
the metabolic demand of the additional leaf. Lower leaf area indices are more efﬁcient per leaf, but draw less power
than the observed intermediate values of roughly 6.
The same concept applies for regular fossil fuel power generation. The upper limit of efﬁciency for any thermal
machine such as a turbine is determined by the Carnot efﬁciency. A steam turbine could run at 80% efﬁciency, but it
would need to operate at a nearly inﬁnitely slow rate. Obviously, we are not interested in a machine that generates
electricity or revenues inﬁnitely slowly, no matter how efﬁciently. Actual operating efﬁciencies for modern steam
powered generator are therefore closer to 40%, roughly half the Carnot efﬁciency.
These examples show that the maximum power principle is embedded in the irreversibility of the world. The
highest process efﬁciency can be obtained by endoreversible conditions, meaning that all irreversibilities are located
in the coupling of the system to its surroundings, there are no internal irreversibilities. Such systems will, however,
operate too slowly. Power is zero for any endoreversible system. If we want to increase the process rate, then we also
increase the irreversibility and thereby decrease the efﬁciency. The maximum power is the compromise between
endoreversible processes and very fast completely irreversible processes.
The concept of emergy (embodied energy) was introduced by Odum (1983) and attempts to account for the energy
required in the formation of organisms in different trophic levels. The idea is to correct energy ﬂows for their
energy quality. Energies of different types are converted into equivalents of the same type by multiplying by the energy
transformation ratio. For example, ﬁsh, zooplankton, and phytoplankton can be compared by multiplying their
actual energy content by their solar energy transformation ratios. The more transformation steps there are between
two kinds of energy, the greater the quality and the greater the solar energy required to produce a unit of energy (J) of
that type. When one calculates the energy of one type that generates a ﬂow of another, this is sometimes referred to as
Rate
Efficiency
Poweroutput
Low rate, high
efficiency
High rate, low
efficiency
FIGURE 7.3 The power output shows a parabolic relationship as a function of the rate having low outputs if rates are very low or very high.
The efﬁciency being the highest at lowest rates.
7.4 THE MAXIMUM POWER PRINCIPLE 119
the embodied energy of that type. Fig. 7.4 presents the concept of embodied energy in a hierarchical chain of energy
transformation. One of the properties of high-quality energies is their ﬂexibility (which requires information).
Whereas low-quality products tend to be special, requiring special uses, the higher quality part of a web is of a
form that can be fed back as an ampliﬁer to many different web components.
A good down-to-earth example of what emergy is might be the following: in 1 year one human can survive on 500
ﬁsh each of the size of 500 g, that may have consumed 80,000 frogs with the size of 20 g. The frogs may have eaten
18 Â 106
insects of the size of 1 g. The insects have got their food from 200,000 kg dry matter of plants. As the photosynthetic
net production has an efﬁciency of 1%, the plants have required an input of about 3.7 Â 109
J, presuming an
energy content of plant dry matter of 18.7 kJ gÀ1
. To keep one human alive costs therefore 3.7 Â 109
J, although the
energy stock value of a human being is only in the order 3.7 Â 105
J or 10,000 times less. The transformity is therefore
10,000. The ﬂipside of this is that if the humans eat the plant matter directly it could instead feed 1000 people by
foregoing the three additional trophic transfers.
H.T. Odum has revised the maximum power principle by replacing power with emergyepower (empower),
meaning that all the contributions to power are multiplied by a solar equivalent factor that is named transformity
to obtain solar equivalent joules (sej) (see Box 7.2). The difference between embodied energy ﬂows and power,
see Eq. (7.1), simply seems to be a conversion to solar energy equivalents of the free energy.
Embodied energy is, as seen from these deﬁnitions, determined by the biogeochemical energy ﬂow into an
ecosystem component, measured in solar energy equivalents. The stored emergy, Em, per unit of area or volume
to be distinguished from the emergy ﬂows can be found from:
Em ¼
Xn
i¼1
Uici (7.1)
where Ui is the quality factor which is the conversion to solar equivalents, as illustrated in Fig. 7.4, and ci is the concentration
expressed per unit of area or volume.
The calculations reduce the difference between stored emergy (¼ embodied energy) and stored exergy (see next
section), to the energy quality factor. The quality factor for exergy accounts for the information embodied in the
various components in the system (detailed information is given in the next section), while the quality factor for
SUN
1000 100 10 1
kJ / m2 h
Solar Equivalents kJ / m2 h
Energy Transformation Ratios =
Embodied Energy Equivalents.
kJ / m2 h
1000
SUN
1000 1000 1000
1 10 100 1000
SUN
FIGURE 7.4 Energy ﬂow, solar equivalents, and energy transformation ratios ¼ embodied energy equivalents in a food chain (Jørgensen,
2002).
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT120
BOX 7.2
E M E R G Y
“Emergy is the available energy of one kind previously
used up directly and indirectly to make a service or product.
Its unit is the emjoule [(ej)]” and its physical dimensions
are those of energy (Odum, 1996). In general, since
solar energy is the basis for all the energy ﬂows in the
biosphere, we use Solar emergy (measured in sej, solar
emjoules), the solar energy equivalents required (directly
or indirectly) to make a product.
The total emergy ﬂowing through a system over some
unit time, referenced to its boundary source, is its
empower, with units [sej/(time)] (Odum, 1988). If a system,
and in particular an ecosystem, can be considered
in a relatively steady state, the empower (or emergy
ﬂow) can be seen as nature’s “labor” required for maintaining
that state.
The emergy approach starts from Lotka’s maximum
power principle (1922, 1956) and corrects the function,
which is maximized, since not all the energy types have
the same ability of doing actual work. Thus power (ﬂow
of energy) is substituted by empower (ﬂow of emergy),
that is “in the competition among self-organizing processes,
network designs that maximize empower will prevail”
(Odum, 1996).
Transformity is the ratio of emergy necessary for a process
to occur to the exergy output of the process. It is an
intensive function and it is dimensionless, even though
sej/J is used as unit.
Emergy can be written as a function of transformity
and exergy as follows (i identiﬁes the inputs):
Em ¼
X
i
siEXi
While transformity can be written as
sk ¼ Emk=EXk
even though it is often calculated as
sk ¼
Emk=time
EXk=time
By deﬁnition the transformity of sunlight is equal to 1
and this assumption avoids the circularity of these expressions.
All the transformities (except that of solar energy)
are therefore greater than 1.
Transformities are always measured relative to a planetary
solar emergy baseline and care should be taken to
ensure that the transformities used in any particular
analysis are all expressed relative to the same baseline
(Brown et al., 2016). However, all the past baselines can
be easily related through multiplication by an appropriate
factor and the results of an emergy analysis do
not change by shifting the baseline (Odum et al., 2000).
Emergy and transformity are not state functions, i.e.,
they strongly depend on the process that is used to
obtain a certain item. There are transformities that are
calculated from global biosphere data (i.e., rain, wind,
geothermal heat) and others that, being the result of
more complex and variable processes have high variability:
for example, electricity can be generated by
many processes (using wood, water, coal, gas, tide, solar
radiation, etc.) each with a different transformity
(Odum, 1996).
In general, transformity can be seen as a measure of
“quality”: while emergy, following “memorization”
laws, can in general remain constant or grow along transformation
chains, since as energy decreases, transformities
increase. On the other hand, when comparing homologous
products, the lower the transformity, the higher the
efﬁciency in transforming solar emergy into a ﬁnal
product.
Emergy is a donor-referenced concept and a measure of
convergence of energies, space, and time, both from global
environmental work and human services into a product. It
is sometimes referred to as “energy memory” (Scienceman,
1987) and its logic (of “memorization” rather than
“conservation”) is different from other energy-based analyses
as shown by the emergy “algebra.” The rules of
emergy analysis are as follows:
• All source emergy to a process is assigned to the
processes’ output;
• By-products from a process have the total emergy
assigned to each pathway;
• When a pathway splits, the emergy is assigned to each
“leg” of the split based on its percentage of the total
energy ﬂow on the pathway;
• Emergy cannot be counted twice within a system:
(a) emergy in feedbacks cannot be double counted; (b)
by-products, when reunited, cannot be added to equal
a sum greater than the source emergy from which they
were derived.
For in-depth discussion of this issue and the differences
between energy and emergy analysis see Brown and
Herendeen (1996) and Brown et al. (2016).
7.4 THE MAXIMUM POWER PRINCIPLE 121
emergy accounts for the solar energy cost to form the various components. Emergy calculates thereby how much
solar energy (which is our ultimate energy resource) it has cost to obtain 1 unit of biomass of various organisms.
Both concepts attempt to account for the quality of the energy. Emergy by looking into the energy ﬂows in the ecological
network to express the energy costs in solar equivalents. Exergy by considering the amount of biomass and information
that has accumulated in that organism. One is measure of the path that was taken to get to a certain
conﬁguration, the other a measure of the organisms in that conﬁguration.
7.5 EXERGY, ASCENDENCY, GRADIENTS, AND ECOSYSTEM DEVELOPMENT
The second law of thermodynamics dissipation acts to tear down structure and eliminate gradients, but ecosystems
have the ability to move away from thermodynamic equilibrium in spite of the second law dissipation due to
an inﬂow of energy from solar radiationdsee the story in the preamble to this chapter. Physical systems can also
create structured gradients such as a hurricane, tornado, or the red spot on Jupiter. A Bernard cell is an example
of a simple physical system using an inﬂow of energy to move away from thermodynamic equilibrium. A Bernard
cell consists of two plates that are horizontally placed in water a few cm from each other. The lower plate has higher
temperature than the upper plate. Consequently, energy is ﬂowing from the lower to the upper plate. When the temperature
difference is low, the motion of the molecules is random. When the temperature exceeds a critical value, the
water molecules are organized in a convection pattern, series of rolls or hexagons. The energy ﬂow increases due to
the convection. The greater the ﬂow of energy the steeper the temperature gradient (remember that work capacity ¼
entropy times temperature gradient) and the more complex the resulting structure. Therefore, greater exergy ﬂow
moves the system further away from thermodynamic equilibriumdhigher temperature gradient and more ordered
structure containing information corresponding to the order. The origin of ordered structures is therefore openness
and a ﬂow of energy (see Chapter 2). Openness and a ﬂow of energy are both necessary conditions (because it will
always cost energy to maintain an ordered structure) and sufﬁcient (as illustrated with the Bernard cell). Morowitz
(1968, 1992) has shown that an inﬂow of energy always will create one cycle of matter, which is an ordered structure.
Openness and a ﬂow of energy is, however, not a sufﬁcient condition for ecosystems (see Chapter 2), as additional
conditions are required to ensure that the ordered structure is an ecosystem.
Biological systems, especially, have many possibilities for moving away from thermodynamic equilibrium, and it
is important to know along which pathways among the possible ones a system will develop. This leads to the
following hypothesis (Jørgensen and Mejer, 1977, 1979; Jørgensen, 1982, 2001, 2002; Jørgensen et al., 2000): If a system
receives an input of useable energy (exergy), then it will utilize this energy to perform work in a hierarchy of steps.
First, work performed is used to maintain the system (far) away from thermodynamic equilibrium; exergy is lost
during the transformation into heat at the temperature of the environment. Second, if more exergy is available,
then the system moves further away from thermodynamic equilibrium, reﬂected in growth of gradients. Third, if
there is more than one pathway to depart from equilibrium, then the one yielding the highest exergy storage
(denoted Ex) will tend to be selected (in ecological examples, eco-exergy is used rather than engineering exergydsee
Box 7.3). Another way to express this last point is: Among the many ways for ecosystems to move away from thermodynamic
equilibrium, the one maximizing dEx/dt under the prevailing conditions will have a propensity to be
selected.
de Wit (2005) has expressed preference for a formulation where the ﬂow of exergy is replaced by a ﬂow of free
energy, which of course is fully acceptable and makes the formulation closer to classic thermodynamics. However,
eco-exergy storage can hardly be replaced by free energy because it is a free energy difference between the system and
the same system at thermodynamic equilibrium. The reference state is therefore different from ecosystem to ecosystems,
which is considered in the deﬁnition of eco-exergy. In addition, free energy is not a state function far from thermodynamic
equilibriumdjust consider the immediate loss of eco-exergy when an organism dies. Before death, the
organism has high eco-exergy because it can utilize the enormous information that is embodied in the organism, but
at death the organism loses immediately the ability to use this information that becomes therefore worthless. Moreover,
the information part of the eco-exergy cannot be utilized directly as work; see the properties of information
presented in Section 7.2.
Just as it is not possible to prove the three laws of thermodynamics by deductive methods, so can the above hypothesis
only be “proved” inductively. A number of concrete cases which contribute generally to the support of the
hypothesis will be presented below and in Chapters 9e11. Models are often used in this context to test the hypothesis.
The exergy can be approximated using the calculation methods in Box 7.3. Strictly speaking, exergy is a measure
of the useful work that can be performed by the system. Conceptually, this obviously includes the energetic content
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT122
BOX 7.3
C A L C U L A T I O N O F E C O - E X E R G Y
It is possible to distinguish between the exergy of
information and of biomass (Svirezhev, 1998). pi deﬁned
as ci/B, where
B ¼
Xn
i¼1
ci
is the total amount of matter in the system, is introduced
as new variable in Eq. (2.8):
Ex ¼ B RT
Xn
i¼1
pi ln
pi
pi;o
þ B ln
B
Bo
As the biomass is the same for the system and the reference
system, B z Bo exergy becomes a product of the total
biomass B (multiplied by RT) and Kullback measure:
K ¼
Xn
i¼1
pi ln
pi
pi;o
where pi and pi,o are probability distributions, a posteriori
and a priori to an observation of the molecular detail of the
system. It means that K expresses the amount of information
that is gained as a result of the observations. If we
observe a system that consists of two connected chambers,
then we expect the molecules to be equally distributed in
the two chambers, i.e., p1 ¼ p2 is equal to 1/2. If we, on the
other hand, observe that all the molecules are in one chamber,
then we get p1 ¼ 1 and p2 ¼ 0.
Speciﬁc exergy is exergy relatively to the biomass and
for the ith component: Sp. ex.i ¼ Exi/ci. It implies that
the total speciﬁc exergy per unit of area or per unit of volume
of the ecosystem is equal to RTK.
For the components of the ecosystem, 1 (covers
detritus), 2, 3, 4 . N, the probability, pi,o, is the probability
of producing the organic matter (detritus), i.e., p1,o, and the
probability, pi,a, to ﬁnd the correct composition of the enzymes
determining the biochemical processes in the organisms.
Living organisms use 20 different amino acids
and each gene determines on average a sequence of about
700 amino acids (Li and Grauer, 1991). pi,a, can be found
from the number of permutations among which the characteristic
amino acid sequence for the considered organism
has been selected.
By summing up the contributions originating from
all components, we ﬁnd the total eco-exergy. The contribution
by inorganic matter can be neglected as the contributions
by detritus and even to a higher extent from the
biological components are much higher due to an
extremely low probability of these components in the
reference system. Roughly, the more complex (developed)
the organism is the more enzymes with the right amino
acid sequence are needed to control the life processes,
and therefore the lower is the probability pi,a, The probability.
pi,a, for various organisms has been found on basis
of our knowledge about the genes that determine the
amino acid sequence. As the concentrations are multiplied
by RT and ln (pi/pi,o), denoted bi a table with the b-values
for different organisms have been prepared; see Table 7.2.
The contribution by detritus, dead organic matter, is in
average 18.7 kJ/g times the concentration (in g/unit of
volume). The eco-exergy can now be calculated by the
following equation:
Eco À Exergy total ¼
Xn
i¼1
biciðas detritus equivalentÞ
The b-values are found from Table 7.3 and the concentration
from modeling or observations. Multiplying by
18.7 converts the eco-exergy into kJ.
Continued
TABLE 7.3 b-Values ¼ Exergy Content Relatively to the Exergy of Detritus (Jørgensen et al., 2005).
Early Organisms Plants Animals b-Values
Detritus 1.00
Virus 1.01
Minimal cell 5.8
Bacteria 8.5
Archaea 13.8
Yeast 17.8
Protists Algae 20
Continued
7.5 EXERGY, ASCENDENCY, GRADIENTS, AND ECOSYSTEM DEVELOPMENT 123
BOX 7.3 (cont’d)
TABLE 7.3 b-Values ¼ Exergy Content Relatively to the Exergy of Detritus (Jørgensen et al., 2005).dcont’d
Early Organisms Plants Animals b-Values
Mesozoa, Placozoa 33
Protozoa, amoeba 39
Phasmida (stick insects) 43
Fungi, molds 61
Nemertini 76
Cnidaria (corals, sea anemones, jelly ﬁsh) 91
Rhodophyta 92
Gastroticha 97
Prolifera, sponges 98
Brachiopoda 109
Platyhelminthes (ﬂatworms) 120
Nematoda (round worms) 133
Annelida (leeches) 133
Gnathostomulida 143
Mustard weed 143
Kinorhyncha 165
Seedless vascular plants 158
Rotifera (wheel animals) 163
Entoprocta 164
Moss 174
Insecta (beetles, ﬂies, bees, wasps, bugs, ants) 167
Coleodiea (sea squirt) 191
Lipidoptera (buffer ﬂies) 221
Crustaceans 232
Chordata 246
Rice 275
Gymnosperms (inl. Pinus) 314
Mollusca, bivalvia, gastropoda 310
Mosquito 322
Flowering plants 393
Fish 499
Amphibia 688
Reptilia 833
Aves (birds) 980
Mammalia 2127
Monkeys 2138
Anthropoid apes 2145
Homo sapiens 2173
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT124
of the material, i.e., biomass, but also the state of organization of the material. One way to measure the organization
is the information content of the material, which could be the complexity at the genetic, organismal, or ecosystem
levels. Currently, we express the organizational aspect of exergy as Kullbach’s measure of information based on
the genetic complexity of the organism:
Ex ¼ B RT K (7.2)
where B is the biomass, R the gas constant, T the Kelvin temperature, and K Kullbach’s measure of information
(further details see Box 7.3). In this manner, the information that the organism carries is the basis for the organism’s
eco-exergy:
Exi ¼ bici (7.3)
where Exi is the exergy of the ith species, bi is a weighting factor that consider the information the ith species is carrying
in ci (Table 7.2). Jørgensen et al. (2005) show the b-values for different organisms. A high uncertainty is, however,
associated with the assessment of the b-values, which implies that the exergy calculations have a corresponding
high uncertainty. In addition, the eco-exergy is calculated based upon models that are simpliﬁcations of the real ecosystems.
Therefore, the eco-exergy calculations should only be used relatively and considered an index and not a real
absolute exergy value.
Consistency of the eco-exergy storage hypothesis, as we may call it, with other theories (goal functions, orientors;
see Sections 7.2 and 7.3) describing ecosystem development will be demonstrated as a pattern in a later section of this
chapter. It should, however, in this context be mentioned that eco-exergy storage in the abovementioned main hypothesis
can be replaced by maximum power. Eco-exergy focuses on the storage of biomass (energy) and information,
while power considers the energy ﬂows resulting from the storages.
Ascendency (Box 6.1) is a measure of the information and ﬂows embodied in the ecological network. At the crux
of ascendency lies the action of autocatalysis (Chapter 6). One of the chief attributes of autocatalysis is what Ulanowicz
(1997) calls “centripetality” or the tendency to draw increasing amounts of matter and energy into the orbit of
the participating members. This tendency inﬂates ascendency both in the quantitative sense of increasing total system
activity and qualitatively by accentuating the connections in the loop, above and beyond, pathways connecting
nonparticipating members. At the same time, increasing storage of exergy is a particular manifestation of the centripetal
tendency, and the dissipation of external exergy gradients to feed system autocatalysis describes centripetality
in almost tautological fashion.
In retrospect, the elucidation of the connections among ascendency, eco-exergy, and aggradation (Ulanowicz
et al., 2006) has been affected by stages that are typical of theory-driven research. First, it was noted in phenomenological
fashion how quantitative observations of the properties were strongly correlated; the correlation coefﬁcient,
r2, was found for a number of models to be 0.99 (Jørgensen, 1995). Thereafter, formal deﬁnitions were used to forge
theoretical ties among the separate measures. Finally, the perspective offered by these new theoretical connections
facilitated a verbal description of the common unitary agency that gave rise to the independent trends that had been
formalized as separate principles. Eco-exergy and ascendency represent two sides of the same coin or two different
angles in the description of ecosystem growth and development. A simple physical phenomenon as light requires
both a description as waves and as particle to be fully understood. It is therefore understandable that ecosystem
growth and development, which is much more complicated than light, requires multiple descriptions. Eco-exergy
covers the storage, maximum power the ﬂows, and ascendency the ecological network and all three concepts
contribute to the overall aggradation, moving away from thermodynamic equilibrium. All three concepts have
BOX 7.3 (cont’d)
Notice that
Exbio ¼
Xn
i¼1
ci ðas detritus equivalentÞ
while
Exinfo ¼
Xn
i¼1
ðbiÀ 1Þci ðas detritus equivalentÞ
7.5 EXERGY, ASCENDENCY, GRADIENTS, AND ECOSYSTEM DEVELOPMENT 125
well-structured roots in the theoretical soil. Their shortcomings are, however, that calculations of eco-exergy,
maximum power, and ascendency always will be incomplete due to the enormous complexity of ecosystems (see
Section 7.1).
Ecosystems can also be understood as a high number of interacting and interdependent gradients, which are
formed by self-organizing processes (Mueller and Leupelt, 1998). Gradient formation and maintenance costs exergy
that is transformed by decomposition processes to heat at the temperature of the environment, i.e., the exergy is lost.
The gradients can be classiﬁed in various ways. Here, we distinguish three types of gradients corresponding to the
four growth and development forms (see Section 7.2): (1) gradients due to organisms in the ecosystems (trees are
good illustrations); (2) gradients due to formation of a more complex network (for instance, the spatial distribution
of more or fewer niches); and (3) gradients due to information (the level of information could be used directly as
illustration). The ﬁrst mentioned class of gradients requires the most exergy for maintenance, while information gradients
require very little or no exergy for maintenance. Gradients summation is captured in the exergy measure since
work capacity is an extensive variable times a gradient (see Chapter 2).
Eco-exergy storage is the simplest of the three concepts to calculate; but clearly, the assessment of the b-values has
some shortcomings. The latest list is more differentiated than the previous ones (Jørgensen et al., 1995; Fonseca et al.,
2000) and is based on the results of the entire genome analyses for 11 species plus a series of complexity measures for
a number of species, families, orders or classes. The list will likely be improved as experiments reveal more information
about the genomes and proteomes of more species. The total information of an ecosystem should furthermore
include the information of the network. All ecological models that are used as basis for the eco-exergy
calculations are much simpler than the real network and the information contained in the network of the model
become negligible compared to the eco-exergy in the compartments. A calculation method to assess the information
of the real ecological network is needed to account for the contribution to the total ecosystem eco-exergy.
Measuring power is more difﬁcult because it is related to the amount of ﬂow. Most ecological observations are
based on concentrations and not on ﬂows, making it harder to validate the ﬂow values resulting from ecological
models. In addition, the number of ﬂows in the real ecological network is magnitudes higher than the few ﬂows
that can be included in our primitive calculations. Calculations of ascendency have the same shortcomings as calculations
of power.
The three concepts may all have a solid theoretical basis but their applications in practice still have deﬁnite weaknesses
that are rooted in the complexity of real ecosystems. Integrating the three concepts, we are able to expand on
the earlier hypothesis based on eco-exergy alone and let it comprise ascendency and power in addition.
7.6 SUPPORT FOR THE PRESENTED HYPOTHESES
Below we present a few case studies from Jørgensen (2002) and Jørgensen et al. (2000) supporting the eco-exergy
storage hypothesis, noting that maximum power or ascendency could also have been applied as discussed in Section
7.5. Additional examples can be found in these references and in Chapter 9.
Genome Size
In general, biological evolution has been toward organisms with an increasing number of genes and diversity of
cell types (Futuyma, 1986). If a direct correspondence between free energy and genome size is assumed, then this can
reasonably be taken to reﬂect increasing eco-exergy storage accompanying the increased information content and
processing of “higher” organisms.
Le Chatelier’s Principle
The eco-exergy storage hypothesis might be taken as a generalized version of “Le Chatelier’s Principle.” Expressing
biomass synthesis as a chemical reaction, we get:
energy þ nutrients ¼ molecules with more free energy ðexergyÞ and organization þ dissipated energy. (7.4)
According to Le Chatelier’s Principle, as we add energy into a reaction system at equilibrium, then the system will
shift its equilibrium composition in a way to counteract the change. This means that more molecules with more free
energy and organization will be formed. If more pathways are offered, then those giving the most relief from the
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT126
disturbance (displacement from equilibrium) by using the most energy, and forming the most molecules with the
most free energy, will be the ones followed in restoring equilibrium.
The Sequence of Organic Matter Oxidation
The sequence of biological organic matter oxidation (e.g., Schlesinger, 1997) takes place in the following order: by
oxygen, by nitrate, by manganese dioxide, by iron (III), by sulfate, and by carbon dioxide. This means that oxygen, if
present, will always out compete nitrate, which will out compete manganese dioxide, and so on. The amount of ecoexergy
stored as a result of an oxidation process is measured by the available kJ/mol of electrons which determines
the number of adenosine triphosphate molecules (ATPs) formed. ATP represents an exergy storage of 42 kJ/mol.
Usable energy as exergy in ATPs decreases in the same sequence as indicated above. This is as expected if the
eco-exergy storage hypothesis were valid (Table 7.4). If more oxidizing agents are offered to a system, then the
one giving the highest storage of free energy will be selected.
In Table 7.3, the ﬁrst (aerobic) reaction will always out compete the others because it gives the highest yield of
stored exergy. The last (anaerobic) reaction produces methane; this is a less complete oxidation than the ﬁrst because
methane has a greater exergy content than water.
Formation of Organic Matter in the Primeval Atmosphere
Numerous experiments have been performed to imitate the formation of organic matter in the primeval atmosphere
on earth four billion years ago (Morowitz, 1968). Energy from various sources were sent through a gas
mixture of carbon dioxide, ammonia, and methane. There are obviously many pathways to utilize the energy
sent through simple gas mixtures, but mainly those forming compounds with rather large free energies (amino acids
and RNA-like molecules with high exergy storage, decomposed when the compounds are oxidized again to carbon
dioxide, ammonia and methane) will form an appreciable part of the mixture (according to Morowitz, 1968).
Photosynthesis
There are three biochemical pathways for photosynthesis: (1) the C3 or CalvineBenson cycle, (2) the C4 pathway,
and (3) the crassulacean acid metabolism (CAM) pathway. The latter is least efﬁcient in terms of the amount of plant
biomass formed per unit of energy received. Plants using the CAM pathway are, however, able to survive in harsh,
arid environments that would be inhospitable to C3 and C4 plants. CAM photosynthesis will generally switch to C3
as soon as sufﬁcient water becomes available (Shugart, 1998). The CAM pathways yield the highest biomass production,
reﬂecting eco-exergy storage, under arid conditions, while the other two give highest net production (ecoexergy
storage) under other conditions. While it is true that a gram of plant biomass produced by the three pathways
has different free energies in each case, in a general way, improved biomass production by any of the pathways is
consistent, under the conditions, with the eco-exergy storage hypothesis.
TABLE 7.4 Yields of kJ and ATP’s/mol of Electrons, Corresponding to 0.25 mol of CH2O Oxidized
(Carbohydrates).
Reaction kJ/mol ee
ATP’s/mol ee
CH2O þ O2 ¼ CO2 þ H2O 125 2.98
CH2O þ 0.8 NOÀ
3 þ 0.8 Hþ
¼ CO2 þ 0.4 N2 þ 1.4 H2 119 2.83
CH2O þ 2 MnO2 þ Hþ
¼ CO2 þ 2 Mn2
þ
þ 3 H2O 85 2.02
CH2O þ 4 FeOOH þ8 Hþ
¼ CO2 þ 7 H2O þ Fe2þ
27 0.64
CH2O þ 0.5 SO2À
4 þ 0.5 Hþ
¼ CO2 þ 0.5 HSe
þ H2O 26 0.62
CH2O þ 0.5 CO2 ¼ CO2 þ 0.5 CH4 23 0.55
The released energy is available to build ATP for various oxidation processes of organic matter at pH ¼ 7.0 and 25C.
7.6 SUPPORT FOR THE PRESENTED HYPOTHESES 127
Leaf Size
Givnish and Vermelj (1976) observed that leaves optimize their size (thus mass) for the speciﬁc conditions. This
may be interpreted to mean that they maximize their free energy content. Larger leaves have higher respiration and
evapotranspiration, but also can capture more solar radiation. Deciduous forests in moist climates have a LAI of
about 6 m2
/m2
(see also Section 2.4). Such an index can be predicted from the hypothesis of highest possible leaf
size, resulting from the trade-off between the beneﬁts (sunlight capture) and costs (maintenance) (Givnish and Vermelj,
1976), which is even noticeable on leaves from different parts of the on the same plant such as sun and shade
conditions. Leaf size in a given environment depends on the solar radiation and humidity regime, and in a general
way, leaf size and LAI relationships are consistent with the hypothesis of maximum eco-exergy storage.
Biomass Packing
The general relationship between animal body weight, W, and population density, D, is D ¼ A/W, where A is a
constant (Peters, 1983). Highest packing of biomass depends only on the aggregate mass, not the size of individual
organisms. This means that it is biomass rather than population size that is maximized in an ecosystem, as density
(number per unit area) is inversely proportional to the weight of the organisms. Of course, the relationship is complex.
A given mass of mice would not contain the same eco-exergy or number of individuals as an equivalent weight
of elephants. Also, genome differences (Example 1) and other factors would ﬁgure in. Later we will discuss exergy
dissipation as an alternative objective function proposed for thermodynamic systems. If this were maximized rather
than storage, then biomass packing would follow the relationship D ¼ A/W0.65e0.75
(Peters, 1983). As this is not the
case, biomass packing and the free energy associated with this lend general support for the eco-exergy storage
hypothesis.
Cycling
If a resource (for instance, a limiting nutrient for plant growth) is abundant, then it will typically cycle faster. This
is a little strange because cycling is not needed when a resource is nonlimiting. A modeling study (Jørgensen, 2002)
indicated that free energy storage increases when an abundant resource cycles faster. Fig. 7.5 shows such results for a
lake eutrophication model. The ratio of nitrogen (N) to phosphorus (P) cycling that gives the highest exergy is
0.5 1.0 1.5 2.0
Log (N / P)
R
0.5
2.5
+
+
1.0
2.0
+
+
+
+
FIGURE 7.5 Logelog plot of the ratio of nitrogen to phosphorus turnover rates, R, at maximum exergy versus the logarithm of the nitrogen/
phosphorus ratio, log N/P. The plot is consistent with Vollenweider (1975).
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT128
plotted in a logarithmic scale versus log(N/P) (Fig. 5.5). This plot is also consistent with empirical results
(Vollenweider, 1975). Of course, one cannot “inductively test” anything with a model, but the indications and
correspondence with data do tend to support in a general way the eco-exergy storage hypothesis. The cycling ratio
giving the highest ascendency is also correlated similarly to the N/P ratio (Ulanowicz pers. comm.). In the light of
the close relationship between eco-exergy and ascendency this result is not surprising (see above, Jørgensen, 1995;
Ulanowicz et al., 2006).
Structurally Dynamic Modeling
Dynamic models whose structure changes over time are based on nonstationary or time-varying differential or
difference equations. We will refer to these as structurally dynamic models. A number of such models, mainly of
aquatic systems (Jørgensen, 1986, 1988, 1990; Nielsen, 1992a,b; Jørgensen and Padisak, 1996; Coffaro et al., 1997;
Jørgensen and de Bernardi, 1997, 1998), but also as population dynamic models (Jørgensen, 2002) and terrestrial systems
(Jørgensen and Fath, 2004a) have been investigated to see how structural changes are reﬂected in eco-exergy
changes. Changes in parameters, and thus system structure, not only reﬂect changes in external boundary conditions
but also mean that such changes are necessary for the ongoing maximization of exergy. For all models investigated
along these lines, the changes obtained were in accordance with actual observations (see references). These studies
therefore afﬁrm, in a general way, that systems adapt their parameter values to maximize their content of eco-exergy.
The shortcomings of assessing the eco-exergy content of an ecosystem were discussed above. At least in models, the
applicability of eco-exergy calculations have shown their more practical use, which can be explained by robust
model calculations.
It is noteworthy that Coffaro et al. (1997), in his structural-dynamic model of the Lagoon of Venice, did not calibrate
the model describing the spatial pattern of various macrophyte species such as Ulva and Zostera, but used ecoexergy-index
optimization to estimate parameters determining the spatial distribution of these species. He found
good accordance between observations and model, which was able to explain more than 90% of the observed spatial
distribution of various species of Zostera and Ulva (Box 7.4).
Seasonal Changes
In natural history, it is often observed, particularly at latitudes where there are winters, that taxonomically more
primitive forms tend to pass through their nondormant phenological states earlier in growing seasons and more
advanced forms later. It is as though ecosystems must be rebuilt after the “creative destruction” of winter, and until
they are reconstituted, the active life-history stages of more complex forms of life cannot be supported. Does the
maximum eco-exergy storage principle comply with the annual activity cycles of species and communities? Phenological
ﬂuctuations of biota, in fact the growth of individual organisms themselves are generally parallel to the four
stages of succession, and also the four ecosystem growth and development forms (Jørgensen et al., 2000). This is true
for the progression of individual species and their assemblages and is best seen at mid to high latitudes. Toward the
tropics, a great variety of the life history stages of the rich assortment of species is expressed at any given time. At
higher latitudes, phenological cycles are more obviously entrained to seasonal ﬂuctuations. Focusing at midlatitudes,
and letting “time” be relative to the unit in question (i.e., biological time, whether for a species or whole
ecosystem), “winter” represents the initial condition (Stage 0). During “spring”,” the growth and development
forms unfold in quick succession. Form I dominates early (Stage I), Form II later (Stage II), and Form III in “summer”,”
which advances toward seasonal maturity (Stage III). Ephemeral species pass quickly through their own
Stage III to seed set, dispersal, senescence (Stage IV), and often, disappearance. Permanent species remain more
or less in Stage III until near the end of the growing season when they or their parts pass into quasi-senescent states
(Stage IV), as in leaf fall and hibernation.
Eco-exergy storage and utilization patterns may be intuited from the principles laid down for succession (Figs. 7.7
and 7.8 related text) to follow these seasonal trends also, in mass, throughﬂow, and informational characteristics. In
winter, biomass and information content are at seasonal lows. The observations of the seasonal changes may be
considered an indirect support for the hypothesis. In spring, the ﬂush of new growth (dominantly Form I) produces
rather quickly a signiﬁcant biomass component of exergy (Fig. 7.8), but the information component remains low due
to the fact that most active ﬂora, fauna, and microbiota of this nascent period tend to be lower phylogenetic forms.
These lower forms rapidly develop biomass but make relatively low informational contributions to the stored ecoexergy.
As the growing season advances, in summer, Growth and Development Forms II and III become successively
7.6 SUPPORT FOR THE PRESENTED HYPOTHESES 129
BOX 7.4
I L L U S T R A T I O N O F S T R U C T U R A L L Y D Y N A M I C M O D E L I N G
Jørgensen and Fath (2004b) developed a structurally
dynamic model of Darwin’s ﬁnches reﬂecting the comprehensive
available knowledge. The main feature of the
model was the validation of the changes in the beak size
as a result of climatic changes, which led to variation in
the amount, availability, and quality of the seeds that
compromise the ﬁnches’ food. The medium ground
ﬁnches, Geospiza fortis, on the island Daphne Major were
selected due to very detailed information found in Grant
(1986). The model has three state variables: seed, Darwin’s
Finches adult, and Darwin’s ﬁnches juvenile. The juvenile
ﬁnches are promoted to adult ﬁnches 120 days after birth.
The mortality of the adult ﬁnches is expressed as a normal
mortality rate (Grant, 1986) þ an additional mortality rate
due to food shortage and an additional mortality rate
caused by a disagreement between bill depth and the
size and hardness of seeds.
Thebeakdepthcanvarybetween3.5 and10.3 cm (Grant,
1986), the beak size ¼
ﬃﬃﬃﬃﬃﬃﬃﬃ
DH
p
, where D is the seed size and
H the seed hardness which are both dependent on the precipitation,
particularly during JanuaryeApril(Grant, 1986).
Grant (1986) determines a handling time for the ﬁnches for
a given
ﬃﬃﬃﬃﬃﬃﬃﬃ
DH
p
as function of the bill depth, which explains
that the accordance between
ﬃﬃﬃﬃﬃﬃﬃﬃ
DH
p
and the beak depth becomes
an important survival factor. The relationship is
used in the model to ﬁnd a function called “diet” which
is compared with
ﬃﬃﬃﬃﬃﬃﬃﬃ
DH
p
to ﬁnd how well the bill depth
ﬁts to the
ﬃﬃﬃﬃﬃﬃﬃﬃ
DH
p
of the seed. This ﬁtness function is based
on information given by Grant (1986) about the handling
time. It inﬂuences, as mentioned above, the mortality of
adult ﬁnches, but has also impacts the number of eggs
laid and the mortality of the juvenile ﬁnches. The growth
rate and mortality of seeds are dependent on the precipitation
which is a forcing function know
73 74 75 76 77 78 79 80 81 82 83
Year
100
300
500
700
900
1100
1300
1500
1700
NumberG.fortis
•
•
•
•
•
• 0 • 0
• •
•
•
•
•
+
•+
+
0
•
0 0
0 0
0
0
0
0
•
Calibration
period
+
FIGURE 7.6 The observed number of ﬁnches (•) from 1973 to 1983, compared with the simulated result (0). 75 and 76 were used for
calibration and 77/78 for the validation referred to in Box 6.5.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT130
dominant. Following the expansion of system organization that this represents, involving proliferation of food webs
and interactive networks of all kinds, and all that this implies, waves of progressively more advanced taxonomic
forms can now be supported to pass through their life cycles. Albedo and reﬂection are reduced, dissipation increases
to seasonal maxima following developing biomass, and as seasonal maxima are reached, further increments
BOX 7.4 (cont’d)
as function of time (Grant, 1986). A function called food
shortage is calculated from the known food requirements
(Grant, 1986), and from the food available (the seed state
variable). Food shortage inﬂuences the mortality of juvenile
ﬁnches and adult ﬁnches; and, the seed biomass and
number of G. fortis as function of time from 1975 to 1982
are known (Grant, 1986). The values from 1975 to 1976
were used to calibrate the following parameters:
i) the inﬂuence of the ﬁtness function on (1) adult
ﬁnches mortality, (2) juvenile ﬁnches mortality, and (3)
the number of eggs laid;
ii) the inﬂuence of food shortage on the mortality of
adult and juvenile ﬁnches is known (Grant, 1986). The
inﬂuence is therefore calibrated within a narrow
range of values;
iii) the inﬂuence of precipitation on the seed biomass
(growth and mortality).
All other parameters are known from the literature.
The eco-exergy density is calculated (estimated) as
275 3 the concentration of seed D 980 3 the concentration
of Darwin’s ﬁnches (see Table 7.2). The model algorithm
searches every 15 days if a feasible change in the
beak size, given the generation time and the variations
in the beak size into consideration, will give a higher
eco-exergy. If it is the case, then the beak size is changed
accordingly. The modeled changes in the beak size were
conﬁrmed by the observations. The model results of the
number of Darwin’s ﬁnches are compared with the observations
(Grant, 1986) in Fig. 7.6. The standard deviation
between modeled and observed values was 11.6% and
the correlation coefﬁcient, r2
, for modeled versus observed
values is 0.977. The results of a nonstructural dynamic
model would not be able to predict the changes in the
beak size and would therefore give too low values for
the number of Darwin’s ﬁnches because their beak would
not adapt to the lower precipitation yielding harder and
bigger seeds.
Time
Exergy/area*time(MJ/ym2
)
Upper limit corresonding to solar radiation
Captured exergy
Applied for
maintenanceAdded to the
exergy storage
FIGURE 7.7 Exergy utilization of an ecosystem under development is shown versus time. Notice that the consequence of the growth in exergy
is increased utilization of exergy for maintenance.
7.6 SUPPORT FOR THE PRESENTED HYPOTHESES 131
taper to negligible amounts (Fig. 7.8). The biotic production of advancing summer reﬂects more and more advanced
systemic organization, manifested as increasing accumulations of both biomass and information to the exergy stores.
In autumn, the whole system begins to unravel and shut down in pre-adaptation to winter, the phenological equivalent
of senescence. Networks shrink, and with this all attributes of eco-exergy storage, throughﬂow, and information
transfer decline as the system slowly degrades to its winter condition. Biological activity of the ecosystem
returns to more “primitive” states of eco-exergy organization required for adaptation to winter. The suggestion
from phenology is that the exergetic principles of organization apply also to the seasonal dynamics of ecosystems.
7.7 TOWARD A CONSISTENT ECOSYSTEM THEORY
Ecosystem properties can only be revealed by a plurality of views. It is therefore not surprising that there are
many different ecosystem theories published in the scientiﬁc literature. It is also important to try to understand
the theories in relation to each other and examine if they are contradictory or form a pattern that can give a better
understanding of the nature of ecosystems and to solve the global environmental problems. The goal is to give a
common framework of reference for further development of a more profound and comprehensive ecosystem theory
than the one we are able to present today. The next stage will be more holistic, comprehensive, and empirical, but in
the meantime the framework proposed here is a good working platform. This pattern should serve as a “conceptual
diagram”,” which can be used as a basis for further discussion of ecosystems. We are still in an early stage of an
ecosystem-theoretical development. Modeling has taught us that it is better to consolidate one’s thoughts in a conceptual
diagram at an early stage and then be ready to make changes than to let all modeling efforts wait until all
details are known, as this will never be the case due to the immense complexity of nature (Jørgensen, 2002). Moreover,
recent development in ecosystem theory has made it possible to conclude that the theories presented here are
indeed consistent and complimentary (Fath et al., 2001). The special issue in Ecological Modeling 158.3 (2002) has
demonstrated that the theory can be applied to explain ecological observations, although the ecosystem theory presented
here does not contain laws in the classical physical sense that we can make exact predictions. The theory is
rather closer to quantum mechanics (we have to accept an uncertainty), chaos theory (sometimes predictions of complex
systems are impossible), and the Prigogine thermodynamics (all processes are irreversible). Given the limitations
in our theory, that ecosystems are enormously complex and we can therefore not know all details and that
ecosystems have an ontic openness (see Chapter 3), it is still possible to apply the theory in ecology and environmental
management.
Biomass (Vegetation)
Incoming solar radiation
Time
21. Dec 22, March 23. of July 22. of Sep. 21.Dec
FIGURE 7.8 The seasonal changes in incoming solar radiation and biomass (vegetation) are shown for a typical temperate ecosystem. The
slope of the curve for biomass indicates the increase in exergy due to Growth Form I. The Growth Form I can continue as long as the captured solar
radiation is larger than the exergy applied for maintenance. Therefore the biomass has its maximum at about August 1. The biomass is at minimum
about the February 1 because at that time is the captured exergy and the exergy applied for maintenance in balance.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT132
The core pattern concerns the systemness of life, and how these interactions lead to complex organization and
dynamics. Understanding, measuring, and tracking these patterns is of paramount importance and the various holistic
indicators have been developed to do so. Taken together, we can use this systems-oriented thermodynamic
approach to formulate a limited number of propositions or hypotheses to explain a very large number of ecological
observations. These recent developments in systems ecology represent a profound paradigm shift. The paradigm
that is now receding has dominated our culture for several hundred years. It views the universe as a mechanical
system composed of elementary building blocks. The new paradigm is based on a holistic worldview. The world
is seen as an integrated whole and recognizes the fundamental interdependence of all phenomena.
In the paper by Jørgensen et al. (2000), Fig. 7.9 illustrated the concomitant development of ecosystems, exergy
captured (most of that being degraded), and eco-exergy stored (biomass, structure, information). Data points correspond
to different ecosystems (see Table 7.5, which shows the values). Debaljak (2001) showed the same shape of the
curve when determining exergy captured and eco-exergy stored in managed forest and virgin forest on different
stages of development (Fig. 7.10). The exergy captured was determined as in Table 7.5 by measuring the temperature
of the infrared radiation, while the coexergy storage was determined by a randomized measurement of the size of all
trees and plants. The stages are indicated on the ﬁgure, where also pasture is included for comparison. Catastrophic
events as storm or ﬁre may cause destructive regeneration, which is described below.
Holling (1986), see Fig. 7.11 has suggested how ecosystems progress through the sequential phases of renewal
(mainly Growth and Development Form I), exploitation (mainly Growth and Development Form II), conservation
(dominant Growth and Development Form III), and creative destruction. The latter phase ﬁts also into the four
growth and development forms, but will require a further explanation. The creative destruction phase is either a
result of external or internal factors. In the ﬁrst case (for instance, hurricanes and volcanic activity), further explanation
is not needed as an ecosystem has to use the growth and development forms under the prevailing conditions,
which are determined by the external factors. If the destructive phase is a result of internal factors, then the question
is “why would a system be self-destructive?”
A possible explanation is that the onset of some senescence that weakens the robustness and buffer capacity of the
ecosystem as it ages. For example, once in the conservation phase, almost all nutrients will be contained in organisms,
leaving little or no nutrients available to test new and possibly better solutions to move further away from
Exergy storage MJ / m2
Exergyutilisation%ofsolarradiation
0
20
40
60
80
90
..
.
.
. . . .
0 20 40 60 70
FIGURE 7.9 The exergy captured (Kay and Schneider, 1992, % of solar radiation) is plotted versus the exergy stored (unit J/m2
or J/m3
),
calculated from characteristic compositions of the focal eight ecosystems. The numbers from Table 6.5 are applied to construct this plot. Notice that
exergy utilization is parallel (proportional) to energy absorbed.
7.7 TOWARD A CONSISTENT ECOSYSTEM THEORY 133
thermodynamic equilibrium. Therefore, the next stage, collapse, can open up new opportunities and new beginnings
(Fath et al., 2015). Holling also recognizes this by calling the release phase as “creative destruction.”
In the long run, it is beneﬁcial for the ecosystem to decompose the organic nutrients into inorganic components
that can be utilized to test the new solutions. The creative destruction phase can be considered a method to utilize the
three other phases and the four growth and development forms more effectively (Fath et al., 2004). This is indicated
on the ﬁgure as “trend of each further cycle” and it is shown that the ecosystem is moving toward a higher speciﬁc
exergy (and biomass if possible, as growth of biomass is dependent on the available amount of the limiting element),
if the inorganic components are available to form more biomass for each cycle (Fig. 7.12).
Five of the presented hypotheses to describe ecosystem growth and development are examined with respect to
four growth and development forms, excluding the boundary growth:
A. Entropy production tends to be minimum (proposed by Prigogine (1947, 1955, 1980) for linear systems at steady
nonequilibrium state, not for far from equilibrium systems). Mauersberger (1981, 1983, 1995) applied this to
TABLE 7.5 Exergy Utilization and Storage in a Comparative Set of Ecosystems.
Ecosystem Exergy Utilization, % Eco-exergy Storage, MJ/m2
Quarry 6 0
Desert 2 0.073
Clear-cut forest 49 0.594
Grassland 59 0.940
Fir plantation 70 12.70
Natural forest 71 26.00
Old-growth deciduous forest 72 38.00
Tropical rain forest 70 64.00
Exergy stored (GJ / ha)
Exergydestruction,relativenumbers
120
140
160
180
0 5 10 15 20 25 30 35 40
.
. .
. .
Gap
. Pasture
Juvenile
Regeneration
Optimum
Mixed
FIGURE 7.10 The plot shows the result by Debeljak (2002). He examined managed a virgin forest in different stages. Gap has no trees, while
the virgin forest changes from optimum to mix to regeneration and back to optimum, although the virgin forest can be destroyed by catastrophic
events as ﬁre or storms. The juvenile stage is a development between the gap and the optimum. Pasture is included for comparison.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT134
Specific exergy = exergy / biomass
Biomass
I. RENEWAL
II EXPLOITATION
III. CONSERVATION
IV CREATIVE DESTRUCTION
Trend of each
further cycle
FIGURE 7.11 Holling’s four phases of ecosystems, described terms of biomass versus speciﬁc exergy. The presentation is inspired by
Ulanowicz (1997).
Eff-Ex
Eff-rad
0 1.0
K1
K2
K3
FIGURE 7.12 hEx is plotted versus hrad for three information level K3 > K2 > K1. Increasing Kullback’s measure of information implies that the
ecosystem will generate more information up to a higher hrad.
7.7 TOWARD A CONSISTENT ECOSYSTEM THEORY 135
derive expressions for bioprocesses at a stable stationary state (see also Chapter 2). Reduction of the entropy
production means that the energy utilization is increased, which is obtained by an increased cycling of the energy
and reduced loss of energy to the environment, or expressed differently: the retention time of a given portion of
energy in the system is increased.
B. Natural selection tends to maximize the energy ﬂux through the system, so far as compatible with the constraints
to which the system is subject (Odum, 1983). This is the maximum power principle; see Section 7.4.
C. Ecosystems organize to maximize exergy degradation (Kay, 1984). Living systems transform more exergy to heat
at the temperature of the environment, or said differently, produce more entropy, than their nonliving
complements. Living systems therefore increases the destruction of exergy but at the same time living systems
increase the order and organization.
D. A system that receives a throughﬂow of exergy will move away from thermodynamic equilibrium, and the
system has the propensity to select the organization that gives the system as much stored eco-exergy as possible,
see Section 7.5, Jørgensen and Mejer (1977, 1979), Jørgensen (1982, 2002), Mejer and Jørgensen (1979).
E. Ecosystem have a propensity to develop toward an optimization of robustness, which is a measure of the tradeoff
between the articulation (ascendency) and redundancy (Ulanowicz, 2018); see Section 7.5.
The usual description of ecosystem development illustrated, for instance, by the recovery of Yellowstone Park after
ﬁre, an island formed after a volcanic eruption, reclaimed land, etc. is well covered by Odum (1969): at ﬁrst the
biomass increases rapidly which implies that the percentage of captured incoming solar radiation increases but also
the energy needed for the maintenance. Growth and development Form I is dominant in this ﬁrst phase, where ecoexergy
stored increases (more biomass, more physical structure to capture more solar radiation), but also the
throughﬂow (of useful energy), exergy dissipation, and the entropy production increases due to increased need
of energy for maintenance. Living systems are effectively capturing (lower the albedo) and utilizing energy (exergy)
(both the exergy decomposed due to respiration and the exergy stored in the living matter are increased) from the
ambient physical systems.
Growth and Development Forms II and III become, in most cases, dominant later, although an overlap of the three
forms takes place. A smaller ecosystem will have a high relative openness (see Chapter 2), which allows Forms II and
III to operate more quickly as refuge species recolonize the space. Patrı´cio et al. (2006) examined the recovery of small
intertidal rocky communities ﬁnding that biodiversity and speciﬁc eco-exergy (¼ eco-exergy/biomass) recover
much faster than biomass and eco-exergy, i.e., that the Forms II and III are dominant in the initial phase of recovery.
When the percentage of solar radiation captured reaches about 80%, it is not possible to increase the amount of
captured solar radiation further (due in principle to the second law of thermodynamics). Further growth of the physical
structure (biomass) does therefore not improve the exergy balance of the ecosystem. In addition, all or almost all
the essential elements are in the form of dead or living organic matter and not as inorganic compounds ready to be
used for growth. Therefore, Form I will and cannot proceed, but Growth and Development Forms II and III can still
operate. The ecosystem can still improve the ecological network and can still evolve novel, more complex organisms
and environments. One tendency is to increase the occurrence of larger, longer-lived organisms (Cope’s law: the later
descendant may be increasingly larger than their ancestors; for instance, the horse today is much bigger than the
horse fossils from 20 to 30 million years ago). Growth and Development Forms II and III are efﬁcient energetically,
and do not require more exergy for maintenance. Exergy degradation is therefore not increasing but is maintained at
a constant level (see Figs. 7.9 and 7.10). The accordance with the ﬁve descriptors þ speciﬁc entropy production and
the four growth and development forms based on this description of ecosystem development is shown in Table 7.6
(see also Fath et al., 2001).
I. Biomass Growth is the increase in physical structure of the ecosystem, which occurs primarily by the capture
and conversion of incoming solar radiation into organic compounds. This ﬁrst stage implies that more exergy is
degraded due to an increased demand constructing and maintaining biomass. The most dynamic indicator of
this form is the exergy degradation, although the eco-exergy storage, throughﬂow, and ascendency also will
increase. While there is an upper limit to the amount of solar radiation available, and to the amount that can be
harvested by ecosystems, system development continues through Forms II and III.
II. Network Growth and Development entails a richer, more complex interaction structure, which through
increased cycling offers better utilization of the available energy. In turn, both throughﬂow and exergy storage
increase without an increase in exergy degradation. It means that speciﬁc exergy degradation and speciﬁc
entropy production are decreasing during this stage. Throughﬂow, eco-exergy, speciﬁc, and eco-exergy can all
be used as dynamic indicators for this growth form.
7. ECOSYSTEMS HAVE COMPLEX DYNAMICSdGROWTH AND DEVELOPMENT136
III. Information Growth and Development represents an increase in the genetic and organizational diversity in the
ecosystem. One way this occurs is through an increase in the number of species with longer and more complex
life histories, larger body size, and complex physiologies. This implies that, similar to Form II, throughﬂow and
eco-exergy storage increase while speciﬁc exergy degradation and entropy production decrease. Eco-exergy and
speciﬁc eco-exergy can all be used as indicators for this growth and development form, with speciﬁc eco-exergy
capturing the genetic information.
7.8 SUMMARY AND CONCLUSIONS
Ecosystems have a very complex dynamics. It is rooted in the enormous number of different life forms that is the
result of the enormous variability in the interplay of life and environment. Ecosystem development can be described
by a wide spectrum of ecological indicators and orientors from single species and concentrations of speciﬁc chemical
pollutants to holistic indicators such as biodiversity and thermodynamic functions. Physicalechemical systems can
usually be described by matter and energy relations, while biological systems require a description that encompasses
matter, energy, and information.
Due to the high complexity of ecosystems, it is, however, not possible to apply these different descriptors of
ecosystem dynamics for the entire ecosystems with all its detailed information, but only for models of ecosystems.
Dependent on the knowledge about the ecosystem, it can be advantageous to apply eco-exergy (when the concentrations
or biomasses of the focal species are known), ascendency (when a good model of the ecological network is
known) or power (when the ﬂows are known). Eco-exergy can be found as the sum of the concentrations and weighting
factors considering the information that the various organisms are carrying. Eco-exergy indicates the distance
from thermodynamic equilibrium according to the deﬁnition, while emergy indicates the cost in terms of solar radiation.
The ratio between the two thermodynamic concepts, exergy/emergy, indicates the efﬁciency of the systemd
more efﬁciency if more exergy is obtained relatively to the solar radiation. In Chapter 10, it is shown how this ratio
can be applied as a powerful indicator for an ecosystem.
Ecosystems have four growth and development forms or methods to move away from thermodynamic equilibrium:
boundary, biomass, network, or information. When an ecosystem has attained a certain level of energy capture,
it will be able to continue to generate organismal and structural information such that eco-exergy,
throughﬂow, and ascendency increase throughout development without necessarily an increase in the physical
biomass. The abovementioned three descriptors, eco-exergy, power, and ascendancy, will all increase with the
four growth and development forms, while exergy destruction and entropy production only increases with the ﬁrst
growth form and speciﬁc entropy only decreases with the second and third growth form.
TABLE 7.6 Growth and Development (G&D) Forms and the Proposed Descriptors. Note, in addition to the three mentioned in the text
this includes Form 0 which is boundary growth when the ecosystem receives a greater amount of energy across its boundary.
Hypothesis
G&D G&D G&D
Form 0 Form I Form II Form III
Eco-exergy storage Up Up Up Up
Power/throughﬂow Up Up Up Up
Exergy dissipation Up Up Equal Equal
Retention time Equal Equal Up Up
Entropy production Up Up Equal Equal
Exergy/Biomass ¼ speciﬁc exergy Equal Equal Up Up
Entropy/biomass ¼ spec. entropy prod. Equal Equal Down Down
Ratio indirect/Direct effects Equal Equal Up Up
Retention time Equal Equal Up Up
7.8 SUMMARY AND CONCLUSIONS 137
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C H A P T E R
8
Ecosystems Have Complex Dynamicsd
Disturbance and Decay
Du siehst, wohin du siehst nur Eitelkeit auf Erden.
Was dieser heute baut, reißt jener morgen ein:
Wo itzund Sta¨dte stehn, wird eine Wiese sein
Auf der ein Scha¨ferskind wird spielen mit den Herden:
Was itzund pra¨chtig blu¨ht, soll bald zertreten werden.
Was itzt so pocht und trotzt ist morgen Asch und Bein
Nichts ist, das ewig sei, kein Erz, kein Marmorstein.
Itzt lacht das Glu¨ck uns an, bald donnern die Beschwerden.
Der hohen Taten Ruhm muß wie ein Traum vergehn.
Soll denn das Spiel der Zeit, der leichte Mensch bestehn?
Ach! was ist alles dies, was wir fu¨r ko¨stlich achten,
Als schlechte Nichtigkeit, als Schatten, Staub und Wind;
Als eine Wiesenblum, die man nicht wiederﬁnd’t.
Noch will, was ewig ist, kein einig Mensch betrachten! Andreas Gryphius, 1616e64: Es ist alles eitel
8.1 THE NORMALITY OF DISTURBANCE
Up to this point, the focus of this book has been on growth and development processes in ecosystems. In fact,
these are most important features of ecosystem dynamics and they provide the origins of various emergent
ecosystem properties. But the picture remains incomplete if disturbance and decay are not taken into account. On
the following pages, we will try to include those “destructive” processes into the “new” ecosystem theory as elaborated
in this book. As a starting point for these discussions, we can refer to common knowledge and emotion, as it is
described in the poem of Andreas Gryphius (see above) who outlines the transience of human and environmental
structures: Nothing lasts forever, towns will turn into meadows, ﬂourishing nature easily can be destroyed, our
luck can turn into misfortune, and in the end, what remains is emptiness, shadow, dust, and wind. Although the
poet seems to be comprehensible concerning the signiﬁcance of decay, we cannot agree with his pessimistic ultimate:
In the end, the inevitable death of organisms and disturbance of ecosystems can be useful elements of the growth,
development, and survival of the whole structure, i.e., if they expire within suitable thresholds and if we observe
their outcomes over multiple scales.
On a small scale, we can notice that the individual living components of ecosystems have limited life spans that
range from minutes to millennia (see Table 8.1). For any species, environmental conditions play a role in the life
expectancy. Average human life expectancy is around 70 years but ranges from 52 years in Afghanistan to 89 years
in Monaco. As life expectancy increases and death rates drop, birth rates need to fall even more to maintain stable
populations, a goal for any species working within the constraints of a carrying capacity (meaning all species,
including humans). Death and decay of organisms and their subsystems are integral elements of natural dynamics.
From a functional viewpoint, these processes are advantageous, to replace highly loaded or exhausted components
(e.g., short life expectancies of some animal cells), or to adjust physiologies to changing environmental conditions
(e.g., leaf litter fall in autumn). As a consequence of these processes, energy and nutrients are provided for the
saprophagous branches of food webs, which in many cases show higher turnover rates than the phytophagous
branches of the energy and nutrient ﬂow networks. In those situations of death, self-organized units give up their
139
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00008-5
autonomy and their ability to capture and actively transform exergy, their structures are subject to dissipation, reactivity,
self-regulation, and the ability for replication and repair are desist, releasing the internal order and constituents
which thus potentially become ingredients of the higher system-level self-organization (see Chapter 3).
Also, populations have limited durations at certain places on earth. Operating in a hierarchy of constraints, populations
break down, e.g., if the exterior conditions are modiﬁed, if imperative resources are depleted, if the living
conditions are modiﬁed by human actions, or if competition processes result in a change of the community assemblage.
Following the thermodynamic argumentation of this book (see Chapters 2 and 7), in these situations a modiﬁed
collection of organisms will take over, being able to increase the internal ﬂows and to reduce the energetic,
material, and structural losses into the environment in a greater quantity than the predecessors. During such processes,
of course, only the very immediate conditions can be inﬂuential: The developmental direction is deﬁned
due to a short-term reaction, which increases orientor values at the moment the decision is made, on the basis of
the disposable elements and the prevailing conditions. Thereafter, the structural fate of the system is deﬁned by
new constraints, an irreversible reaction has taken place, and the sustainability of this pathway will be an object
of the following successional processes.
Of course, such community dynamics have consequences for the environmental processes and structures. There
is an interplay between the life processes and the perceived abiotic processes. In fact, we (Fath and Mu¨ller, 2019)
have proposed a new term, conbiotadwith biota, to capture the deep dependency that environmental features
(such as temperature, humidity, soil chemistry, etc.) have on ecological processes. Therefore, also ecosystems themselves
exist for a limited period of time only. Their typical structural and organizational features are modiﬁed, if
the external conditions change signiﬁcantly, but also if due to internal competition processes certain elements attain
dominance displacing other species. These processes can be observed on many different scales with distinct temporal
characteristics: Slow processes can occur as results of climatic changes (e.g., postglacial successions throughout
the Holocene), shifts of biomes (e.g., Pleistocene dynamics of rain forests), or continuous invasions of new species.
On the contrary, abrupt processes often modify ecosystems very efﬁciently within rather short periods of time.
The most commonly known extreme event has taken place at the end of the Cretaceous age, 65 million years ago,
whendpurportedly due to an asteroid impactdenormous changes of the global community structures took place:
on land no organism bigger than 25 kg survived, planktonic foraminifera went extinct by 83%, the extinctions of ammonites
reached 100%, marine reptiles were affected by 93%, and the nonavian dinosaurs were driven totally extinct.
No doubt, this was a big loss of biodiversity, and many potential evolutionary pathways disappeared; but, as we
know 65 million years later, this event was also a starting shot for new evolutionary traits and for the occupation
of the niches by new species, e.g., for the rapid development of mammals or organisms which are able to read
and write books (see Box 8.1).
TABLE 8.1 Some Data About Life Expectancies of Cells
and Organisms.
Example Average Life Span
Generation time of
Escherichia coli
20 min
Life spans of some human cells
- Small intestine
- White blood cells
- Stomach
- Liver
1e2 days
1e3 days
2e9 days
10e20 days
Life span of some animals
- Water ﬂea
- Mouse
- Nightingale
- Dog
- Horse
- Human
- Giant tortoise
0.2 years
3e4 years
4 years
12e20 years
20e40 years
67 years
177 years
Life span of some plants
- Sunﬂower
- Corylus avellana
- Fagus sylvatica
- Pinus aristata
1 year
4e10 years
200e300 years
4900 years
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY140
BOX 8.1
C R E A T I V I T Y N E E D S D I S T U R B A N C E ( S E J )
Necessity is the Mother of Invention
Constraints mean problems, but problems require solutions,
and (new) solutions require creativity. Let us exemplify
this by two evolutionary processes, the genetic code
and language. The constraints in the chemical beginning
of the evolution were that whenever a primitive but relatively
well-functioning assemblage of organic molecules
was formed, the composition that made the entity successful
was forgotten with its breakdown. The next entity
would have to start from scratch again. If at least the major
part of the well-functioning composition could be remembered,
then the entities would be able to improve their
composition and processes generation by generation.
For organisms the problem is to survive. When new
living conditions are emerging, the accompanying problems
for the phenotypes are solved by new properties of
the genotypes or their interactions in the ecological networks.
Survival based on the two growth and development
forms “biomass growth” and “network growth”
occurs by adaptation to the currently changed prevailing
conditions for life. But information growth is needed,
too, because survival under new emergent conditions requires
a system to transfer information to make sure that
solutions are not lost. The problem on the need for information
transfer has been solved by the development of a
genetic system that again puts new constraints on survival.
Fitting into the environmental structures to survive
is possible due to an adopted genetic system. Furthermore,
genes have created new possibilities because mutations
and, later in the evolution, sexual recombinations create
new possible solutions. Therefore, as shown in Fig. 8.1,
what starts with constraints and new and better properties
of the organisms or their ecological networks ends
up as new possibilities through a coding system that
also may be considered initially as a constraint.
An organism’s biochemistry is determined by the
composition of a series of enzymes that again are determined
by the genes. Successful organisms will be able to
produce more offspring than less successful organisms
and as the gene composition is inherited, the successful
properties will be more and more represented generation
after generation. This explains that evolution has directed
more and more complex organisms that have new and
emerging properties.
The genetic code is a language or an alphabet. It is a
constraint on the living organisms that have to follow
the biochemical code embodied in the genes. The
sequence of three amino bases with four possibilities determines
the sequence of amino acids in the proteins.
There are 4 Â 4 Â 4 ¼ 64 different codings of the three
amino bases; but as there are only 20 amino acids to select
from, the amino base coding contains redundant combinations
in the sense that for some amino acids two or more
combinations of amino bases are valid. As an alphabet is
a constraint for a language (certain combinations are
acceptable and convey meaning and others do not), the genetic
code is a constraint for a living organism. But as the
alphabet gives a writer almost unlimited opportunities
to express thoughts and feelings, so the genetic code has
Continued
Selective Processes
Information System
Provides
Internal Constraints
New
Constraints
Creation of New
Solutions
Adapted System
Composition
Memory of
Optimal Solutions in
an Information System
Survival of an
Optimal Solution
FIGURE 8.1 Life conditions are currently changed and have a high variability in time and space. This creates new challenges
(problems) to survival. Organisms adapt or a shift to other species takes place. This requires an information system that is able to transfer
the information about good solutions to the coming generations of organisms. Consequently, an information system is very beneﬁcial, but
it has to be considered as a new source of constraints that however can open up for new possibilities.
8.1 THE NORMALITY OF DISTURBANCE 141
Table 8.2 shows that there have been several extinction events during the history of the Earth. An interesting hypothesis
concerning global extinction rates was published by Raup and Sepkoski (1986): The authors have observed
the development of families of marine animals during the last 250 million years. Their result, which is still discussed
very critically in paleontology, was that mass extinction events seem to have occurred at rather regular temporal intervals
of approximately 26 million years. Explanations were discussed as astronomic forces that might operate with
rather precise schedules, as well as terrestrial events (e.g., volcanism, glaciation, sea level change). We will have to
wait for further investigations to see whether this hypothesis has been too daring.
Today, we can use these ideas to rank the risk of perturbations in relation to their temporal characteristics: While
mass extinctions seem to be rather rare (Table 8.2), smaller perturbations can appear more frequently (Fig. 8.2). In
hydrology, ﬂoods are distinguished due the temporal probability of their occurrence: 10-, 100-, and 1000-year events
are characterized not only by their typical probabilities (translated into typical frequencies) but also by their extents.
The rarer the event is, the higher is the risk of the provoked damages. A 100-year ﬂood will result in bigger disturbances
than a 10-year event. Also, the effects of other disturbance types can be ordered due to their “typical frequencies”
(Table 8.3). An often discussed example is ﬁre. The longer the period between two events, the higher is
the probability that the amount of fuel (accumulated burnable organic material) has also increased, and therefore
the consequences will be higher if the ﬁre interval has been longer. Similar interrelations can be found concerning
BOX 8.1 (cont’d)
given living organisms opportunity to evolve, becoming
more and more complex, more and more creative, having
more and more connectivity among the components and
becoming more and more adaptive to the constraints that
are steadily varying in time and space. The genetic code,
however, has not only solved the problem associated
with these constraints, but it has also been able to give
the living organisms new emergent properties and has
enhanced the evolution.
When the human language was created a couple of
million years ago, it ﬁrst provided new constraints for
humans. They had to learn the language and use it, but
once they mastered the language it also gave new opportunities
because it made it possible to discuss cooperation
such as a better hunting strategy, which would increase
the possibility for survival. Written language was developed
to solve the problem of making the message transfer
more independent of time and space. To learn to write and
read were new constraints to humans that, however, also
opened many new possibilities of expressing new ideas
and thoughts and thereby moving further away from thermodynamic
equilibrium. Animals also communicate
through sounds for warnings or chemicals, for instance,
by marking of hunting territories with urine. The use of
these signals has most likely been a factor that has reduced
the mortality and increased the chance of survival.
We will use a numeric example to illustrate the enormous
evolutionary power of genes to transfer information
from generation to generation. If a chimpanzee would try
to write this book by randomly using a computer
keyboard, the chimpanzee would not have been able to
write the book even if he started at the big bang 15 billion
years ago, but if we could save the signs that were correct
for the second round and so on, then 1/40 of the volume
would be correct in the ﬁrst round (assuming 40 different
symbols on the keyboard), (39 Â 39)/(40 Â 40) would still
be incorrect after the second round, (39 Â 39 Â 39)/
(40 Â 40 Â 40) after the third round, and so on. After 500
rounds, which may take a few years, there would only
be 5 “printed” errors left, if we presume that this book
contains 500 000 signs. To write one round of the volume
would probably require 500,000 s or about a week. To
make 500 rounds would therefore take about 500 weeks
or about 9 years.
The variation in time and space of the conditions for
living organisms has been an enormous challenge to life
(see Chapter 7) because it has required the development
of a wide range of organisms. Living nature has met the
challenge by creating enormous differentiation. There
are 8.7 million known species on earth with new ones
discovered daily. It is estimated that the earth has about
107
species. We see the same pattern as we have seen for
the genetic constraints: the constraints are a challenge
for nature, but the solution gives new emergent possibilities
with an unexpected creative power.
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY142
TABLE 8.2 Five Signiﬁcant Mass Extinctions.
Geological Period Million years bp Families Lost Potential Reason
Ordovician 440 25% Sudden global cooling
Devonian 370 19% Global climate change
Permian 245 54% Global climate change induced by
a bolide
Triassic 210 23% Under debate, but leading theory
is climate change and sea level
rise
Cretaceous 65 17% Asteroid strike
After Eldredge, N., 1998. Life in the Balance: Humanity and the Biodiversity Crisis. Princeton University Press, Princeton, NJ, 224 pp.
TABLE 8.3 Temporal Characteristics of Some Disturbances.
Example
Typical Temporal Scale (Orders
of Magnitude)
Plate tectonics w 105
years
Climatic cycles w 104
years
Killing frost w 102
years
Drought cycles w 101
years
El Nino w 101
years
Seasonal change 1 year
After Di Castri F, Hadley M., 1988. Enhancing the credibility in ecology: interacting along
and across hierarchical scales. Geo. J. 17, 5e35, Mu¨ller, E., 1992. Hierarchical approaches
to ecosystem theory. Ecol. Model. 63, 215e242, and Gundersson, L.H., Holling, C.S.
(Eds.), 2002a. Panarchy. Washington, DC.
High
Low
Low High
Frequency
Magnitude
Disturbance
FIGURE 8.2 Interrelationship between frequencies and magnitudes of perturbations and disturbances. After White, P.S., Jentsch, A., 2001. The
search for generality in studies of disturbance and ecosystem dynamics. Progress in Botany 62, 2e3. Checkliste fﬁr die Anwendung. In: Frﬁnzle, O., Miiller, F.,
Schro¨der, W. (Eds.), Handbuch der Umweltwissenschaften, Chapter III. Ecomed-Verlag, Landsberg, pp. 2e3.
8.1 THE NORMALITY OF DISTURBANCE 143
the other signiﬁcant sources of “natural” disturbances, such as volcanoes, droughts, soil erosion events, avalanches,
landslides, windstorms, pests, or pathogen outbreaks. The consequences of such rare events can be enormous, and
they can be compounded due to human interventions and management regimes. Further information about the hierarchical
distinction of rare events included the required time for recovery (Box 8.2).
BOX 8.2
H I E R A R C H I C A L D I S T I N C T I O N O F R A R E E V E N T S
In Chapter 5, hierarchy theory has been introduced. A
key message of this concept is that under steady-state conditions
the slow processes with broad spatial extents provide
constraints for the small-scale processes, which
operate with high frequencies, thus demonstrating an
interplay between top-down and bottom-up effects.
When disturbances occur, these hierarchies can be broken
and as a consequence (as demonstrated in Section 8.5)
small-scale processes can determine the developmental
directions of the whole ensemble. An extended theory to
describe this process, called panarchy, which combines
the Greek root, pan meaning all, with archy meaning rules.
So the system behavior is determined by (i.e., ruled by) all
scales.
In Fig. 8.3, disturbance events are arranged hierarchically,
based on quantiﬁcations and literature reviews
from Vitousek (1994) and Di Castri and Hadley (1988).
Here we can also ﬁnd direct interrelations between spatial
and temporal characteristics, i.e., concerning the processes
of natural disasters: The broader the spatial scale of a
disturbance, the longer time is necessary for the recovery
of the system. Furthermore, as shown in Section 8.1, we
can assume that events that provoke long recovery times
occur with smaller frequencies than disturbances with
smaller effects.
Gigon and Grimm (1997) argue that the effect chain of
disturbances can also be comprehended from a hierarchical
viewpoint. The disturbing event occurs with typical
spatiotemporal characteristics, and initially it mainly
hits those ecosystem structures that operate on the
same scales. Thereafter, an indirect effect chain starts
because the internal constraints have changed abruptly.
Thus, in a next step potentially those components
should be affected that operate on a lower scale than
the initially changed component. The realization that
a component is both part and whole has been given the
10-3
10-2
10-1
1 101
102
103
1
10
102
103
104
Recovery
time (years)
Spatial scale (km
2
)
Lightning
strike
10-3
10-2
10-1
1 101
102
103
1
10
102
103
104
Spatial scale (km
2
)
Tree fall
Land slide
Forest fire
Flood
Volcano
Tsunami
Meteor
strike
Rain
storms
Slash and
burn Oil spill
Modern
agriculture
Urbanisation
Pollution
Acid
rain
Salination
Ground water
exploitation
Natural disasters Anthropogenic disasters
FIGURE 8.3 Spatial and temporal characteristics of some natural and anthropogenic disasters. The temporal dimension is being
depicted by the speciﬁc recovery times after the disturbances have taken place. After Vitousek, P.M., 1994. Beyond global warming: ecology
and global change. Ecology 75, 1861e1876 and Di Castri, F., Hadley, M., 1988. Enhancing the credibility in ecology: interacting along and across
hierarchical scales. Geo. J. 17, 5e35.
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY144
8.2 THE RISK OF ORIENTOR OPTIMIZATION
Translating these general points into our ecosystem theory, it is obvious that two general processes are governing the
dynamics of ecosystems: besides growth and development processes, living systems are also susceptible to inﬂuences
that move them back toward thermodynamic equilibrium: On the one hand, there are long phases of complexiﬁcation.
Starting with a pioneer stage, orientor dynamics bring about slow mutual adaptation processes with long
durations, if there is a dominance of biological processes (see Mu¨ller and Fath, 1998; Ulanowicz, 1986).
A system of interacting structural gradients is created, which provokes very intensive internal ﬂows and regulated
exchanges with the environment (Mu¨ller, 1998). The processes are linked hierarchically, and the domain of
the governing attractor (Fig. 8.4) remains rather constant, whereupon optimization reactions provoke a long-term
increase of orientors, efﬁciencies, and information dynamics.
The highest state of internal mutual adaptation is attained at the mature stage of the ecosystem growth and development
(Odum, 1969). But, the further the system has been moved away from thermodynamic equilibrium, the
higher is the risk of getting moved back (Schneider and Kay, 1994) because the forces are proportional to the gradients.
Since there is a temporal scale of disturbance events, the more time that has been used for complexiﬁcation, the
higher is the risk of being seriously hit by disturbance (Table 8.3). Similarly, the longer the elements of the system
have increased their mutual connectedness, the stronger is the mutual interdependency (Chapter 4) and the total
system’s brittleness (Holling, 1986). Table 8.4 combines some features of mature ecosystems and lists some riskrelated
consequences of the orientor dynamics. In general, it can be concluded that the adaptability after changes
of the constraints may be decreased when a high degree of maturity is attained.
8.3 THE CHARACTERISTICS OF DISTURBANCE
In such mature states, if certain thresholds are exceeded, fast dynamics can easily become destructive. If there is a
change of the exterior conditions, or if strong physical processes become predominant, then the inherent brittleness
(Holling, 1986) enhances the risk of gradient degradation, thus the ﬂow schemes are interrupted, and energy,
BOX 8.2 (cont’d)
name holon (a combination of “holistic” and “particle”).
Consequently, the biological potential is modiﬁed, and
then also higher levels of the hierarchy can be affected
in an ongoing and cascading manner.
Another important feature of disturbance is that certain
disturbances are necessary for the long-term development
and stability of the affected system. For instance, forest
ﬁres are events that necessarily belong into the developmental
history of forests. Therefore, the concepts of stratiﬁed
stability or incorporated disturbances have been set
up (e.g., Urban et al., 1987; van der Maarel, 1993). They
can today be used as illustrative examples for the natural
functioning of the adaptive cycle concept. Looking at
Fig. 8.3 there are numerous natural disasters that occur
in the absence of anthropogenic disturbances, which
over the long term of ecological and evolutionary development
have driven planetary changes. The anthropogenic
disturbances have increased considerably in the past
two centuries and moreover are chronic pressures on the
fundamental drivers such as biogeochemical cycles and
land conversion. These inﬂuences seem to be so manifold
and complex that only a minor scale dependency can be
found. Furthermore, the recovery potential may be based
on internal processes and is therefore not dependent on
the quantiﬁcation of openness.
The ﬁgure can also be used to illustrate the quantiﬁcation
of openness as introduced in Section 2.6 (Table 2.3).
The recovery time is approximately proportional to the
periphery of the affected area and can be represented by
the square root of the area. As seen on the ﬁgure for natural
disasters, a meteor strike is affecting an area approximately
six orders of magnitude higher than rainstorms.
The recovery time after the strike should therefore require
three orders of magnitude longer time than after the rainstorm.
This is approximate due to the relationships of the
peripheries, which express the exposure of an area to the
environment.
8.3 THE CHARACTERISTICS OF DISTURBANCE 145
information, and nutrients are lost. Hierarchies break down, the attractors are modiﬁed, and the system experiences
a reset to a new starting point.
Ecologists have studied these events with emphasis on the processes of disturbances. Picket and White (1985)
have used a structural approach to deﬁne these events: “any relatively discrete event in space and time that disrupts
ecosystem, community, or population structure and changes resources, substrates, or the physical environment is called disturbance.”
Certainly, also functional features are exposed to respective changes, ecosystem processes and interactions
are also disrupted. Chronic stress or background environmental variabilities are not included within this deﬁnition,
although these relations can also cause signiﬁcant ecosystem changes. If a disturbance exceeds certain threshold
values, then ﬂips and bifurcations can occur, which provoke irreversible changes of the system’s trajectory. Therefore,
understanding ecosystems requires an understanding of their disturbance history.
A focal problem of any disturbance deﬁnition is the question how to indicate the “normal state” of an ecosystem
(White and Jentsch, 2001) because most biological communities “are always recovering from the last disturbance”
(Reice, 1994). For our orientor-based viewpoint it might be appropriate to distinguish the temporal phases during
which orientor dynamics are executed from phases of decreasing complexiﬁcations caused by exceeding threshold
values.
Some basic terms from disturbance ecology are introduced in Fig. 8.4. Disturbances exhibit certain magnitudes
(sizes, forces, and intensities of the events, as variables of the source components), speciﬁcities (spectrum of disturbed
elements), and severities (the impacts of the events on system properties). They can be characterized by various temporal
indicators, such as their spatiotemporal scales, their duration, abruptness, recurrence interval, frequency, or return
times. In the literature, exogeneous disturbances resulting from processes outside the system are distinguished
from endogeneous disturbances. The latter result from internal ecosystem processes, e.g., as a product of successional
development.
Disturbance can have various effects on structural biodiversity. It is clear that high magnitudes can easily reduce
diversity enormously, while minor inputs might have no effects at all. Connell and Slayter (1977) have found that the
highest species numbers are produced by intermediate disturbances because such situations provide suitable living
conditions for the highest number of species with relation to their tolerance versus the prevailing disturbances
(Sousa, 1984). Furthermore, disturbance is a primary cause of spatial heterogeneity in ecosystems, thus it also determines
the potential for biodiversity (Jentsch et al., 2002). This concept has been widely discussed within the pattern
A
B
C
D
E F
G
Ecosystem
Variable
Disturbances
Time
d1 d2
FIGURE 8.4 Some characteristics of disturbances.The state of the ecosystemis indicated by one ecosystemvariable. Due to the disturbance d2 the
system is shifted from state A to B, the indicator value thus decreases signiﬁcantly. The effective disturbance d2 has a higher abruptness (E), a longer
duration (G), and a higher magnitude (F) than d1 which does not affect the system. Throughout the following development a high impact affects the
trajectory D, which provides a long-term decrease of the ecosystem variable, while a more resilient ecosystem turns back to orientor dynamics (C).
After White, P.S., Jentsch, A., 2001. The search for generality in studies of disturbance and ecosystem dynamics. Progress in Botany 62, 2e3. Checkliste fu¨r die
Anwendung. In: Fra¨nzle, O., Mu¨ller, F., Schro¨der, W. (Eds.), Handbuch der Umweltwissenschaften, Chapter III. Ecomed-Verlag, Landsberg, pp. 2e3.
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY146
process hypotheses of patch dynamics (Remmert, 1991). Other ideas concerning the crucial role of disturbance have
been formulated, for example, by Drury and Nisbet (1973) and Sousa (1984). Natural disturbances are an inherent
part of the internal dynamics of ecosystems (O’Neill et al., 1986) and can set the timing of successional cycles. Natural
disturbances thus seem to be crucial for the long-term ecosystem resilience and integrity.
Taking into account these high dynamic disturbance features, correlating them with the orientor principles (which
also are based on changes), focusing on long-term dynamics and adopting Heraclitus’ knowledge from 500 BC
(“nothing is permanent but change”!), it becomes rather difﬁcult to ﬁnd good arguments for an introduction of
the stability principle. This conception has been the dominant target of environmental management in the last decades
(Svirezhev, 2000), and it was strongly interrelated with the idea of a “balance of nature” or a “natural equilibrium”
(Barkmann et al., 2001).
Stability has been described by several measures and concepts, such as resistance (the system is not affected by a
disturbance), resilience (the systems is able to return to a referential state) or buffer capacity, which measures the
overall sensitivities of system variables related to a certain environmental input. Indicators for the stability of ecosystems
are, for instance, the structural effects of the input (recoverability: to what extentde.g., represented by the
percentage of quantiﬁed structural elementsddo the state variables of a system recover after an input?), the return
times of certain variables (how long does it take until the referential state is reached again?), or the variance of their
time series values after a disturbance (how big are the amplitudes of the indicator variable and how does that size
develop?). All of these measures have to be understood in a multivariate manner; due to indirect effects, disturbances
always affect many different state variables.
TABLE 8.4 Some Characteristics of Mature Ecosystems and Their Potential Consequences for the System’s Adaptability1
.
Orientor Function Risk-Related Consequences
High exergy capture The system operates on the basis of high energetic inputs
/ high vulnerability if the input pathways are reduced
High exergy ﬂow density Many elements of the ﬂow webs have lost parts of their autonomy as they are dependent on inputs
which can be provided only if the functionality of the whole system is guaranteed
/ high risk of losing mutually adapted components
High exergy storage and
residence times
Exergy has been converted into biomass and information
/ high amount of potential fuel and risk of internal eutrophication
High entropy production Most of the captured exergy is used for the maintenance of the mature system
/ minor energetic reserves for structural adaptations
High information High biotic and abiotic diversity
/ risk of accelerated structural breakdown if the elements are correlated
High degree of indirect effects Many interactions between the components
/ increase of mutual dependency and risk of cascading chain effects
High complexity Many components are interacting hierarchically
/ reduced ﬂexibility
High ascendancy and trophic
efﬁciency
Intensive ﬂows and high ﬂow diversities have resulted in a loss reduction referring to all single
energetic transfers
/ changing one focal element can bring about high losses
High degree of symbiosis Symbiosis is linked with dependencies, i.e., if it is inevitable for one or both partners
/ risk of cascading chain effects
High intraorganismic storages Energy and nutrients are processed and stored in the organismic phase
/ no short-term availability for ﬂexible reactions
Long life spans Focal organisms have long life expectancies
/ no ﬂexible reactivity
High niche specialization and K
selection
Organisms are specialized to occupy very speciﬁc niche systems and often have a reduced fecundity
/ reduced ﬂexibility
1
Maturity is attained due to a long-term mutual adaptation process. In the late stages of development, the interrelations between components are extremely strong, sometimes rigid.
Reactivity is reduced. If the constraints change, then this high efﬁcient state runs the risk of being seriously disturbed.
8.3 THE CHARACTERISTICS OF DISTURBANCE 147
Our foregoing theoretical conceptions show both, that (a) the basic feature of natural systems is a thermodynamic
disequilibrium and that (b) ecosystems are following dynamic orientor trajectories for most time of their existence.
Steady state thus is only a short-term interval where the developmental dynamics are artiﬁcially frozen into a
small-scale average value. Therefore, more progressive indicators of ecosystem dynamics should not be reduced
to small temporal resolutions that exclude the long-term development of the system. They should be much more
oriented toward the long-term orientor dynamics of ecosystem variables and try to represent the respective potential
to continue to change instead of evaluating a system due to its potential to return to one deﬁned (nondevelopmental
and perhaps extremely brittle) state. A good potential seems to lie in the concept of resilience, if we deﬁne it as the
capacity of a disturbed system to return to its former complexifying trajectory (not to a certain referential state).
Therefore, the reference situation (or the aspired dynamics of ecosystem management) would not be the static lines
in Fig. 8.5, but the orientor trajectory t. Similar ideas and a distinction of stability features with reference to the systems’
stability are discussed in Box 8.3.
Time
Ecosystem
orientor
A B
C
D
E F
G H I J K
t
s
FIGURE 8.5 Sketch of the dynamics of ecosystem variables on two scales, both variables are inﬂuenced by the disturbances (A and B) with
different magnitudes (C and D) and durations (H and J), and both variables are due to orientor dynamics during the phases G, I, and K. The
development of the fast variable shows a high variance, which can be averaged to the slow dynamics. The long-term effects of the disturbances A
and B can be distinguished on the base of the orientor differences E (reduced resilience and recovery potential) and F (enhanced potential for
resilience and recovery).
BOX 8.3
S T A B I L I T Y I S R E L A T E D T O U N C O R R E L A T E D C O M P L E X I T Y
Summary: The complexity of the pattern of ecosystem
transfers can be gauged by the ShannoneWeaver diversity
measure applied to the various ﬂows. This index, in turn,
can be decomposed into a component that refers to how
the ﬂows are constrained by (correlated with) each other
and another that represents the remaining degrees of
freedom which the system can reconﬁgure into responses
to novel perturbations. It is the latter (uncorrelated)
complexity that supports system stability.
Development: In order to see how system stability is
related only to part of the overall system complexity, it
helps to resolve the complexity of a ﬂow network into
two components, one of which represents coherent
complexity and the other, its incoherent counterpart
(Rutledge et al., 1976.)
Prior to Rutledge et al., complexity in ecosystems had
been reckoned in terms of a single distribution, call it
p(ai). The most common measure used was the Shannon
(1948) “entropy,”
H ¼ À
X
i
pðaiÞlog½pðaiÞ:
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY148
8.4 ADAPTABILITY AS A KEY FUNCTION OF ECOSYSTEM DYNAMICS
Having introduced general aspects of disturbance ecology, we can now start to integrate the complexiﬁcation and
the disturbance-induced dynamics of ecosystems. The respective approach is based upon the concepts of the “Resilience
Alliance” (see e.g., Holling, 1986; Gundersson and Holling, 2002; Elmquist et al., 2003; Holling, 2004; Walker
et al., 2004; Walker and Meyers, 2004), but they have been restricted to ecosystem dynamics and combined with the
sequence of growth and development forms after Jørgensen et al. (2000, see also Fath et al., 2004; Ulanowicz, 1986,
1997). Under these prerequisites, we can distinguish the following principle steps of ecosystem development:
- Start of the successiond(pioneer stage, boundary growth after Jørgensen et al., exploitation function after Holling):
In this initial state, an input of low-entropy material into the system starts the sere. The developmental potential
depends on the genetic information that is available in the seed bank or by lateral inputs. Due to a minor
connectivity between the elements, self-regulation is low, leaky-ness is high, and the sum of potential
developmental opportunities (developmental uncertainty) is high. The system provides a very high adaptability
and ﬂexibility.
BOX 8.3 (cont’d)
Rutledge et al. showed how information theory allows
for the comparison of two different distributions. Suppose
one wishes to choose a “reference” distribution with
which to compare p(ai). Call the reference distribution
p(bj). Now Bayesian probability theory allows one to
deﬁne the joint probability, p(ai,bj), of ai occurring jointly
with bj. Ulanowicz and Norden (1990) suggested applying
the Shannon formula to the joint probability to measure
the full “complexity” of a ﬂow network as,
H ¼ À
X
i;j
pðai; bjÞlog½pðai; bjÞ:
Then, using Rutledge’s formulation, this “capacity”
could be decomposed into two complementary terms as,
H ¼
X
i;j
pðai; bjÞlog
2
4
pðai; bjÞ
pðaiÞpðbjÞ
3
5
À
X
i;j
pðai; bjÞlog
2
6
4
pðai; bjÞ2
pðaiÞpðbjÞ
3
7
5;
where the ﬁrst summation represents the coherence between
the ai and the bj, and the second on the remaining
dissonance between the distributions.
The genius of Rutledge et al. was to identify p(ai) and
p(bj) with the compartmental distributions of inputs and
outputs, respectively. That is, if Tij represents the quantity
of ﬂow from compartment i to j, and T.. represents the sum
of all the ﬂows (a dot in place of a subscript means summation
over that index), then
pðai; bjÞw
Tij
T::
; pðaiÞw
X
j
Tij
T::
; and pðbjÞw
X
i
Tij
T::
:
Substituting these estimates into the decomposition
equation yields,
H ¼
X
i;j
Tij
T::
log

Tij
T::

¼
X
i;j
Tij
T::
log
"
TijT::
Ti:T:j
#
À
X
i;j
Tij
T::
log
"
T2
ij
Ti:T:j
#
;
or H ¼ I þ D;
where I is known as the “average mutual information”
inherent in the ﬂow structure and D is the residual disorder.
In other words, the complexity has been decomposed into a
term that measures how well the ﬂows are constrained (coordinated)
and how much they remain independent (free.)
Rutledge at al. suggested that the ability of the
ecosystem network to respond in new ways to novel
disturbance is related to D, while Ulanowicz (1980) argued
that I quantiﬁes the organization inherent in the ﬂow
network. It is important to notice that I and D are complementary,
which is to say that, other things being equal, any
change in I will be accompanied by a complementary
change in D. The system cannot “have its cake and eat
it, too.” Coherent performance, I, comes at the expense
of reliability, D, and vice versa.
In other words, one should expect system stability to be
more related to the value of the disordered complexity, D,
and less correlated to the overall complexity, H, as the
latter also encompasses the complexity encumbered by
system constraints.
The degree of order can be given as the ratio of order to
the total system capacity, a ¼ I/H and the complementary
degree of freedom (uncorrelation) by w ¼ D/H, so that a
þ w ¼1. The accumulation of data on many diverse, quantiﬁed
trophic networks revealed a cluster of most systems
around a ¼ 0.40, reﬂecting the large investment (w ¼ 0.60)
natural systems must make to persevere in a world of
radical contingency (Ulanowicz, 2009).
8.4 ADAPTABILITY AS A KEY FUNCTION OF ECOSYSTEM DYNAMICS 149
- Fast growthd(pioneer stage, structural growth after Jørgensen et al., (2007) exploitation function after Holling):
Pioneer stages can also be characterized by a high and rapid increase of biomass, correlated with an increase of the
numbers and sizes of the ecosystem components. To provide the growing number of participants, the energy
throughﬂow increases as well as exergy degradation which is necessary for the maintenance of the components.
Connectivity is low, and therefore external inputs can modify the system easily: the adaptability is high.
- Fast developmentd(middle succession, network growth after Jørgensen et al., transition from exploitation to
conservation function after Holling): After a ﬁrst structure has been established, the successful actors start
funneling energy and matter into their own physiology. Due to the mutual adaptation of the winning community,
the connectivity of the system increases by additional structural, energetic, and material interrelations and cycling
mechanisms. The single species become more and more dependent on each other, uncertainty decreases, and the
role of self-regulating processes grows, reinforcing the prevailing structure. Adaptability is reduced.
- Maturity (information growth after Jørgensen et al., conservation function after Holling): In this stage, a qualitative
development in system behavior takes place, changing from exploitative patterns to more conservative patterns
with high efﬁciencies of energy and matter processing. Species that easily adapt to external variability (r-selected
species) have been replaced by the variability controlling K-strategists, the niche structure is enhanced widely, and
loss is reduced. The information content of the system increases continuously. A majority of the captured exergy is
used for the maintenance of the system; thus, there is only a small energetic surplus which can be used for
adaptation processes. Sensitivities versus external perturbations have become high, while the system’s buffer
capacities are much smaller compared with the former stages of the development. These items result in a rise of the
system’s vulnerability and a decrease of resilience (see Table 8.4). Adaptability has reached minimum values.
- Breakdown (release function after Holling, creative destruction after Schumpeter, 1942): Due to the “brittleness” of
the mature stages (Holling, 1986), their structure may break down very rapidly due to minor changes of the exterior
conditions. Accumulated resources are released, internal control and organization mechanisms are broken, and
positive feedbacks provoke the decay of the mature system. Uncertainty rises enormously, hierarchies are broken,
and chaotic behavior can occur (Fig. 8.3). There are only extremely weak interactions between the system
components, nutrients are lost, and cycling webs are disconnected. Adaptability and resilience have been exceeded.
- Reorganization: During this short period the structural and functional resources can be arranged to favor new
directions, new species can occur and become successful, anddin spite of the inherited memory (e.g., seed bank of
the old system and neighboring inﬂuences)dunpredictable developmental traits are possible. There are weak
controls, and innovation, novelty, and change can lead to an optimized adaptation on a higher level.
- Reset: A new ecosystem succession starts.
The described sequence has been illustrated in Fig. 8.6 as a function of the system’s internal connectedness and the
stored exergy. Starting with the exploitation function, there is a slow development. The trajectory demonstrates a
Exergy
stored
Connectedness
Exploitation – pioneer stage
Conservation – mature stage
Release –
creative
destruction
Reorganization
FIGURE 8.6 Ecosystem succession as a function of structural and functional items.
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY150
steady increase in mutual interactions as well as an increase in the stored exergy. As has been described above, this
energetic fraction can be distinguished into a material fraction (e.g., biomass, symbolizing the growth conception of
Ulanowicz, 1986) and the speciﬁc exergy which refers to a complexiﬁcation of the system’s structure (development
after Ulanowicz). In spite of variability (e.g., annual cycles), the long-term development shows a steady increase up
to the mature state. Here, the maximum connectivity can be found, which on the one hand is a product of the system’s
orientation, but which also is correlated with the risk of missing adaptability, which has been nominated as
overconnectedness by some authors. After the fast releasing event, the short-term conditions determine the further
trajectory of the system. It might turn into a similar trajectory or ﬁnd a very different pathway.
Fig. 8.6 looks very similar to the well-known four-box model of the Resilience Alliance, which has been depicted
in Fig. 8.7. The difference between these approaches lies in the behavior of the cycle along the y-axis. While for interdisciplinary
approaches and analyses of humaneenvironmental system the special deﬁnition of “potential” in the
adaptive cycle metaphor seems to be advantageous, from our thermodynamic viewpoint, the key variable is an orientor,
such as total stored exergy, that gives the overall energetics of the system. In either case, potential or energetics,
it is more logically consistent for the collapse to be monotonic, never rising until after creative destruction. The nutrients
as well as the energetic resources do not grow after the release, but get eroded or leached, and the change of
their availability is due to the activities of the organisms which appear right after the reset of the pioneer stage.
Furthermore, the upward trajectory of the exploitation stage explicitly shows the nonsmooth path of progress,
including small-scale setbacks along the way to the conservation stage.
To illustrate the risk discussion from above, Fig. 8.8 shows a correlated trajectory of the developmental potential of
ecosystems during the adaptive dynamics. This point shows another difference with the concept of the Resilience
Alliance, due to another understanding of “potential.” Originating from ecosystem theory, we can use the totality
of potential trajectories (possibilities, developmental directions) of the system during the whole cycle. As has
been described above, there are a high number of developmental possibilities in the beginning during the pioneer
FIGURE 8.7 Adaptive cycle after Holling. From Gundersson, L.H., Holling, C.S. (Eds.), 2002a. Panarchy. Washington, DC. Schoon, M., and van der
Leeuw, S., 2015. The shift toward social-ecological systems perspectives: Insights into the human-nature relationship. In: Natures Sciences Socie´te´s, 23,
166e174 (2015) © NSS-Dialogues, EDP Sciences 2015 https://doi.org/10.1051/nss/2015034.
Develop-
mental
potential
Connectedness
FIGURE 8.8 Developmental opportunities during the successional cycle from Fig. 8.6.
8.4 ADAPTABILITY AS A KEY FUNCTION OF ECOSYSTEM DYNAMICS 151
phase while thereafter the prevailing interactions are limiting the degrees of freedom and the adaptability of the system
continuously. Self-organizing processes have created internal hierarchical constraints, which reduce the ﬂexibility
of the entity. Integrating Figs. 8.6 and 8.8 demonstrates the dilemma of the orientor philosophy: The more
complex and efﬁcient an ecological system performs, the better (and more successful) its “old” adaptation to the
environmental conditions has been, the lower is its adaptability against unknown environmental changes, and
the higher is the system’s vulnerability. Thus, a further adaptation to changing conditions is only possible on the
base of destruction of the old structures.
8.5 ADAPTIVE CYCLES ON MULTIPLE SCALES
With the following argumentation we want to link these concepts with another approach to ecosystem theories:
Ecosystems are organized hierarchically (see Chapter 5). Hereafter, we will assume that throughout complexiﬁcation
periods the focal processes always are inﬂuenced by the lower levels’ dynamics and the higher levels’ development,
forming a system of constraints and dynamics of biological potentials. Thus, there are four general hierarchical determinants
for ecosystem dynamics:
(i) The constraints from higher levels are completely effective for the fate of the focal variable. The constraints
operate in certain temporal features, with speciﬁc regularities and intervals. Some examples for these temporal
characteristics are as follows:
• Dayenight dynamics (e.g., determining ecosystem temperature, light, or humidity)
• Tides (e.g., determining organism locations in the Wadden Sea)
• Moon phases (e.g., determining sexual behavior)
• Annual dynamics (e.g., determining production phases of plants)
• Longer climatic rhythms (e.g., sun spots inﬂuencing production)
• Dynamics of human-induced environmental stress factors
- Typical periodic land use activities (e.g., crop rotation)
- Land use change (structural and functional)
- Emission dynamics and environmental policy (e.g., sulfur emission in Germany and their effects on forests)
- Global change and greenhouse gas emissions (e.g., temperature rise)
- Continuous climate change
• Biome transitions
These constraints are interacting and constantly changing; therefore, the maximum degree of mutual
adaptation is a dynamic variable as well. This is a focal reason why the orientor approach is nominated as a
“very theoretical outline” only: As ecosystems “always are recovering from the last disturbance,” the
orientor dynamics often are practically superseded by the interacting constraints dynamics.
(ii) The dynamics of the focal variables themselves exhibit certain natural frequencies. As in the patch dynamics
concepts, there can be internal change dynamics on the observed level itself. For example, we can observe the
undisturbed succession on the base of biological processesdfrom a lake to a fen: The system changes
enormously due to its internal dynamics. Throughout this process often a limited number of species become
dominant, e.g., stinging nettles in secondary successions on abandoned agricultural systems. This leads to an
interruption of orientor dynamics because the dominant organisms do not allow competitors to rise.
(iii) The biological potential of the lower levels results from mostly ﬁltered, smoothened, and buffered variables with
high frequencies. They can only become effective if the system exceeds certain threshold values. This can
happen if disturbances unfold their indirect effects, as has been described above.
(iv) Disturbances primarily meet elements that operate on similar spatial and temporal scales. Only after these
components have been affected, indirect effects start inﬂuencing the interrelated scales and thus can provoke
far-reaching changes.
Summarizing these points, we can state that ecosystems under steady-state conditions are regulated by a hierarchy
of interacting processes on different scales. The slow processes with large extents build up a system of constraints
for the processes with high dynamics. Thereby limiting their degrees of freedom, steady states can be
characterized by relatively low variability of low-level processes (O’Neill et al., 1986). Furthermore, under steadystate
conditions, these high dynamic processes cannot inﬂuence the system of constraints, resulting in a rather
high resilience. Thus, the question arises, what will happen during disturbances?
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY152
This can be depicted by the concept of stability landscapes (see Walker et al., 2004) or hypothetical potential functions.
In Fig. 8.9, the system state is plotted on the x-axis, the z-axis represents the parameter values (may also be
taken as a temporal development with changing parameter loadings), and the potential function is plotted on the
y-axis. This function can be regarded as the slope of a hill, where the bottom of the valley represents steady-state
conditions. If we throw a marble into this system, then it will ﬁnd its position of rest after a certain period of
time at the deepest point of the curve. If the parameter values change continuously (A / B / C), then a set of local
attractors appears, symbolized by the longitudinal proﬁle of the valley, or the broad-scale bifurcation line (H) at level
I. This manifold sketches a sequence of steady states referring to different parameter values. In Fig. 8.9, the straight
line below on level I may be interpreted as the sequence of a parameter of a high hierarchical level while the oscillating
parameter value line L indicates the states of a lower level holon. The return times of this holon to its different
steady states will be different if the states A, B, and C are compared: The steeper the slope, the more rapidly a local
steady state will be reached, and smaller amplitudes will be measured. When the parameter value is changed continuously
within long-term dynamics we will ﬁnd small variations near state A. As our parameter shifts from A via B
toward C, the potential curve’s slopes decrease, ﬁnding a minimum at B. In this indifferent state the amplitudes of
the low-level holon will be very high (see level I). If there is a further change of the parameter value, then a ﬁrst-order
phase transition takes place. The state can be changed radically passing the bifurcation point B before a more stable
state is achieved again, ﬁnally reaching C. Passing B there are two potential states the system can take, and the direction
our holon takes is determined by all levels of the broken hierarchy, including the high frequent (small scale)
dynamics. This process is accompanied by temporal decouplings, by a predominance of positive feedbacks, and by
autocatalytic cycles. We would be remiss not to mention that the action of the ball rolling across the landscape, itself,
leaves deformation of the landscape in which the ball is rolling. This level of tight coupling, interconnectivity, and
feedback is what makes predicting ecosystem (or any other self-organizing, complex adaptive system) behavior.
This property that balances development and structure makes it possible for ecosystems to operate at the edge of
chaos (Kaufmann, 1993), but frequently avoid chaos and utilize all the available resources at the same time; see also
Box 8.4.
After having elucidated disturbance from the hierarchical viewpoint, one last aspect should be taken into consideration.
As we have mentioned above, the adaptive cycle is a metaphor which can be assigned to a multitude of
interacting scales. There is a high normality in disturbance with adaptability as a key function. If this feature cannot
I.
A
B
C
H
L
FIGURE 8.9 Hypothetical potential function of a hierarchical system.
8.5 ADAPTIVE CYCLES ON MULTIPLE SCALES 153
BOX 8.4
C H A O S I N E C O S Y S T E M M O D E L S
The prevailing conditions including the abundance of
other species determine which growth rate is optimal. If
the growth rate is too high, then the resources (food)
will be depleted and the growth will cease. If the growth
rate is too low, then the species does not utilize the resources
(food) to the extent that it is possible. The optimal
growth rate also yields the highest system exergy. If, in a
well-calibrated and well-validated eutrophication
modeldstate variables include phytoplankton, nitrogen,
phosphorus, zooplankton, ﬁsh, sediment nitrogen, and
sediment phosphorusdwe vary the zooplankton growth
rate, then exergy will show a maximum at a certain
growth rate (which is frequently close to the value found
by the calibration and approved by the validation). At
both lower and higher growth rates, the average exergy is
lower because the available phytoplankton is either not
utilized completely or is overexploited. When overexploitation
occurs the phytoplankton and zooplankton show
violent ﬂuctuations. When the resources are available
the growth rate is very high, but the growth stops and
the mortality increases, as soon as the resources are
depleted, which gives the resources a chance to recover
and so on. At a growth rate slightly higher than the value
giving maximum exergy, the model starts to show deterministic
chaos. Fig. 8.10 illustrates the exergy as function
of the zooplankton growth rate in the model referred
to above, focusing on the time when the model starts to
show deterministic chaos. These results are consistent
with Kauffman’s (1993) statement: biological systems
tend to operate at the edge of chaos to be able to utilize
the resources at the optimum. In response, thermodynamic
constraints move the system as far away from
thermodynamic equilibrium as possible under the prevailing
conditions, such that the system is operating
close to chaos but has a high probability to avoid it.
Considering the enormous complexity of natural ecosystems,
and the many interacting processes, it is surprising
that chaos is not frequently observed in nature, but it can
be explained by an operation at the edge of chaos to
ensure a high utilization of the resourcesdto move as
far away as possible from thermodynamic equilibrium
under the prevailing conditions. Keep in mind the idea
of chaos in nature is a metaphor for abrupt system
change due to bifurcations and other nonlinearities.
However, in the absence of disturbances (and even in
the face of many), the growth and development are
largely linear. How often, when one visits a forest or
meadow after an interval of a year or even many years,
are the general characteristics of the ecosystem largely
familiar, indicating a smooth transition from past to present?
At a recent meeting of the International Society for
Ecological Modeling held at Towson University in May
2016, one of the coauthors here (B. Patten) remarked
in an ornery way, inviting bifurcation proponents to go
Exergy
A
0 0.25 0.5 0.75 0.9
Growth rate of zooplankton (1 / 24h)
FIGURE 8.10 Exergy is plotted versus maximum growth rate for zooplankton in a well-calibrated and well-validated eutrophication
model. The shaded line corresponds to chaotic behavior of the model, i.e., violent ﬂuctuations of the state variables and the exergy. The
shown values of the exergy above a maximum growth rate of about 0.65e0.7 dayÀ1
are therefore average values. By a minor change of the
initial value of phytoplankton or zooplankton in the model, signiﬁcant changes are obtained after 2-month simulations as an indication of
deterministic chaos.
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY154
reach sufﬁcient quantities by low-scale ﬂexibility, then the breakdown on a higher hierarchical level enables the system
to start a reset under the new prevailing conditions. Thus, in the end, disturbance really can be understood as a
part of ecosystem growth and development on a higher scale, as indicated in Fig. 8.11; disturbance may even be
extremely necessary to enable a continuation of the complexifying trajectory of the overall system.
8.6 A CASE STUDY: HUMAN DISTURBANCE AND RETROGRESSIVE DYNAMICS
Up to now, we have focused on “natural dynamics.” Thus, in the end of this chapter, we demonstrate human disturbances
using a wetland case study. In general, human activities inﬂuence disturbance regimes in several mechanisms,
such as:
• the rescaling of natural disturbances,
• the introduction of novel disturbances,
• the modiﬁcation of the reception mechanisms of the disturbed components,
• inﬂuences on disturbance rates and intensities,
• the suppression of natural disturbances to ensure the potential of aspired ecosystem services,
• the change of successional pathways due to irreversible changes.
As an example for human pressures and disturbance dynamics, Fig. 8.12 describes a case study from ecosystem
research in the wetlands of the Bornho¨ved Lakes District in Northern Germany. Here a holistic indicator system,
which has been developed on the base of the orientor theory (Mu¨ller 2005), has been used to demonstrate the steps
of wetland retrogression as provoked by eutrophication and drainage.
Exergy
stored
Connectedness
FIGURE 8.11 Long-term succession of ecosystems, indicated on different scales: small-scale disturbances may support the development of the
overall system.
BOX 8.4 (cont’d)
out into nature with him and show him this edge that
everyone talks about. [It occurred later that one response
could have been to go to the Grand Canyon, where there
is a clear edge, but that is a slow spatial artifact not due
to a fast temporal bifurcation.] Patten’s comment was probably
the most provocative one of the entire conference,
which is exactly what science needs if it is to keep progressing,
challenging, and advancing.
8.6 A CASE STUDY: HUMAN DISTURBANCE AND RETROGRESSIVE DYNAMICS 155
Based on ﬁeld measurement, mappings, and classiﬁcations, different ecosystem types have been analyzed with
the computer-based “digital landscape analysis system” (Reiche, 1996) and the modeling system “Wasmode
Stomod” (Reiche, 1996) which was used to simulate the dynamics of water budgets and nutrient and carbon ﬂuxes
based on a 30-year series of daily data about meteorological and hydrological forcing functions. The model outputs
were validated by measured data in some of the systems (Schrautzer, 2003). The model outputs were extended to
include data sets concerning the ecosystem indicators by the following variables:
• Exergy capture: net primary production (NPP)
• Entropy production: microbial soil respiration
• Storage capacity: nitrogen balance, carbon balance
• Ecosystem efﬁciency: evapotranspiration/transpiration, NPP/soil respiration
• Nutrient loss: N net mineralization, N leaching, denitriﬁcation
• Ecosystem structures: Number of plant species (measured values)
The wet grasslands of the Bornho¨ved Lakes District are managed in a way that includes the following measures:
drainage, fertilization, grazing, and mowing in a steep gradient of ecosystem disturbances. The systems have been
classiﬁed due to these external input regimes, and in Fig. 8.12 the consequences can be seen in a synoptic manner:
While the farmer’s target (improving the production and the yield of the systems), the NPP, is growing by a factor of
10, the structural indicator is decreasing enormously throughout the retrogression. Also, the efﬁciency measures
(NPP/soil respiration) are going down, and the biotic water ﬂows get smaller. On the other hand, the development
of the N and C balances demonstrates that the system is turning from a sink function into a source, the storage capacity
is being reduced, and the loss of carbon and nitrogen compounds (all indicators on the right side of the ﬁgure)
is rising enormously. With these ﬁgures, we can state an enormous decrease of ecosystem health, and as many of the
processes are irreversible, the capacity for future self-organization is reduced up to a very small degree.
0
50
100
150
200
NetPrimary Production
N Net Mineralization
N Leaching
Denitrification
Microbial Soil Respiration
Carbon Balance
Nitrogen Balance
NPP / Soil Respiration
Evapotranspiration /
Transpiration
No. of Plant Species
D: Intensively Drained, Eutrophic
C: Moderately Drained, Eutrophic
B: Weakly Drained, Eutrophic
A: Weakly Drained, Mesotrophic
A
B
C
D
FIGURE 8.12 Retrogressive ecosystem features at different steps of human intervention. The ﬁgure shows a set of 10 holistic indicators which
as a whole represent ecosystem integrity. Starting with the initial state A, drainage and eutrophication of the wet grassland ecosystems affect
irreversible changes up to the degraded state D. During that development ecosystem structures (complexity) are reduced, energy and matter
efﬁciencies decrease, and the originally sink ecosystem turns into a source for nitrogen and carbon compounds. After Mu¨ller et al. (2006).
8. ECOSYSTEMS HAVE COMPLEX DYNAMICSdDISTURBANCE AND DECAY156
8.7 SUMMARY AND CONCLUSIONS
In this chapter we have discussed the role of destructive processes for ecosystem dynamics. After some examples
of destructive events on the organism scale, the population scale, and the ecosystem scale and after a general integration
of the disturbance concept into the orientor model, it is shown that especially mature states can suffer from
the high risk of reduced adaptability. Therefore, breakdown is the consequent reaction if the living conditions of a
community change strongly. Thereafter, new potentials can be realized and the orientor behavior will start again
with renewed site conditions. Adopting this argumentation, natural disturbances seem to be crucial for the longterm
self-organization, for the ecological creativity and for the long-term integrity of ecological entities. Destructive
processes are focal components of the overall ecosystem adaptability, and they can be found on all relevant scales.
If we follow the ecosystem-based argumentation that integrity and health are relevant variables for ecological
evaluation, then the potential for self-organizing processes becomes a key variable in environmental management.
It is strictly related to the long-term ecosystem adaptability and its buffer capacity. Therefore, human disturbances in
fact intervene the natural dynamics: They operate on artiﬁcial spatio1temporal scales, they introduce novel qualities
and quantities of change, they modify the reception mechanisms of the ecosystems, they often reduce ecosystem
adaptability, anddas shown in the case studydthey set new constraints for successional pathways, thus suppressing
the natural dynamics.
8.7 SUMMARY AND CONCLUSIONS 157
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C H A P T E R
9
Ecosystem Principles Have Broad Explanatory
Power in Ecology
The best answer raises most questions
9.1 INTRODUCTION
There is a recurrent criticism that ecology as a whole lacks universal laws and predictive theory (Riera et al., 2018),
and sometimes it is even argued that theoretical ecology related, for instance, with ﬁtness and natural selection is not
truly scientiﬁc (Murray, 2001). We argue in this chapter that the ecosystem principles outlined earlier in this book can
drive the creation, understanding, and testing of ecological concepts.
Scientiﬁc observations carried out in nature usually give origin to possible explanations and, in a further step,
intend to provide tentative generalizations that may comprehend the entire set of accessible information. Generalizations
can be essentially descriptive and inductive, based on the observation of evident characteristics, or become
deeper and convert into the base of deductive theories. In ecology, it must be recognized, there are essentially no
universal laws, maybe because such laws cannot even be formulated in the same sense they are in other sciences,
for instance, in physics. As a consequence, in ecology, most explanations constitute inductive generalizations,
without any deductive theory supporting them. Therefore, we may ﬁnd in ecology a large number of nonuniversal
uncertain generalizations.
Ecology is more complex than physics, and it will therefore be much more difﬁcult to develop an applicable,
predictive ecological theory. The easiest way to test illustrative hypothesis is possibly doing it by veriﬁcation, instead
of doing it by falsiﬁcation. Nevertheless, a more general integrative theory, that may help in explaining empirical
observations and experimental results, is felt as necessary by many ecologists.
More than a few new ideas, hypotheses, and possible approaches emerged in the ﬁeld of system’s ecology in the
last 20 or 30 years, which when analyzed more in detail appear to form a theoretical pattern able to explain the
dynamics of ecosystems (Jørgensen, 1997). Considering the complexity of such dynamics, more than probably
the science of ecology will indeed need a number of different complementary approaches to explain ecosystem
structure and function (Jørgensen, 1994a). Such ecosystem theories were only used in a limited way in ecological
modeling, namely in developing nonstationary models, able to take into account the adaptation of biological components
(Jørgensen and de Bernardi, 1997, 1998; Jørgensen, 1986; 1992, 1994b, 1997). It has been argued that to
improve substantially the predictive power of ecological models it will probably be necessary to apply theoretical
approaches much more widely (Jørgensen and Marques, 2001). Nevertheless, a central question remains to be
answered: Is it possible to develop a theoretical framework able to explain the numerous observations,
rules, and correlations dispersed in the ecological literature during the last few decades (Jørgensen and Marques,
2001)?
There is no comprehensive or conclusive answer to this question, but it may be argued that it would at least be
possible to propose a promising direction for ecological thinking (Jørgensen and Marques, 2001). The objective of
this chapter is precisely to verify the possible compliance of ecosystem principles to evolutionary theory and also
check if several other nonuniversal explanations, proposed by different authors in the ecological literature about
various ecological problems, can be additionally enlightened in accordance with those ecological principles.
159
A New Ecology, Second Edition
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9.2 DO ECOLOGICAL PRINCIPLES ENCOMPASS OTHER PROPOSED
ECOLOGICAL THEORIES?
Evolutionary Theory
Through evolution, natural populations acquire and convey novel characteristics from generation to generation, a
process that was ﬁrst proposed by Darwin and Wallace (1858) and subsequently detailed by Darwin (1859) as the
theory of evolution by natural selection. At ﬁrst gradually accepted, subsequently the theory became deﬁnitively
established within the scientiﬁc community. In the 1930s, the Darwin’s natural selection theory was combined
with the theory of heredity, originally proposed by Gregor Mendel and rediscovered by several geneticists, originating
the modern evolutionary synthesis (Huxley, 1942). In this modern synthesis, the evolutionary process consists
of changes in the frequencies of alleles within the genes pool of consecutive generations, which may be caused by
natural selection, genetic drift, or changes in population structure (gene ﬂow).
(a) Natural selection consists of the rate of success in survival and reproduction in a given environment. Selection
takes place at the individual level, and individuals may exhibit differential mortality, expressed as their survival
rate at the reproductive age, and differential fertility, expressed as their total genetic contribution to the next
generation. Natural selection, due to its central role in evolutionary theory, allows establishing a strong
connection between that and ecology.
We may therefore consider two categories of natural selection:
• Ecological selection takes place when the frequency of genes of organisms able to survive and reproduce
increases in the population gene pool, in comparison with those of individuals that do not survive.
• Sexual selection takes place when the frequency of genes of organisms that are more attractive to the opposite
sex, as a function of their greater reproductive success, increases in the population gene pool, in comparison
with those of individuals with less attractive features.
Additionally, regarding mutations, natural selection also works in several different ways by:
• Eliminating harmful mutations from a population (purifying or background selection).
• Increasing the frequency of advantageous mutations (positive selection).
• Maintaining variation within a population (balancing selection) through mechanisms such as:
Overdominance of heterozygous individuals that occurs when they have advantage in terms of ﬁtness
when compared with either of the homozygous forms (e.g., human sickle cell anemia, which confers
resistance to malaria);
Frequency-dependent selection, which occurs when infrequent genetic variations exhibit a better ﬁtness.
• Stabilizing selection, which favors average characteristics in a population and consequently reduces gene
variation by retaining the mean.
• Directional selection, which favors one extreme of a characteristic, resulting in a shift in the mean in the
direction of the extreme.
• Disruptive selection, which favors the two extremes and originates a bimodal distribution of genes frequency,
while the mean may shift or not.
(b) Genetic drift is the process, originated by sampling variance that describes changes in allele in between
generations. In the progeny, the frequency of a given allele will ﬂuctuate in accordance to a probability
distribution of its frequency in the parental generation.
In many ways, genetic drift depends on how big the population is, which is particularly relevant in small mating
populations because random ﬂuctuations in allele frequency between generations can be large. Actually, such ﬂuctuations
between successive generations may cause the disappearance of some alleles from the population, and
therefore two populations starting with the same allele frequency may diverge through genetic drift, with alleles
being lost in one of them and remaining in the other.
Natural selection and genetic drift determine the destiny of new mutations, but their relative importance in inﬂuencing
the process depends on the size of the population (N) and the force of selection (s), being given by N times s. If
the strength of selection is small, or N times s is small, then genetic drift prevails, while if N times s is large, then
selection dominates. This means that genetic drift is stronger in small populations, whereas natural selection acts
more efﬁciently in large populations. Lastly, the time necessary for all individuals in a population to carry a given
allele (gene ﬁxation) by genetic drift for an allele to become ﬁxed in the population by genetic drift also depends on
the size of populations, being shorter in the smaller ones.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY160
The theory underlying the modern synthesis has three major aspects:
1. The common descent of all organisms from a single ancestor.
2. The manifestation of novel traits in a lineage.
3. The mechanisms that cause some traits to persist while others perish.
The novelty of neo-Darwinism was connecting genes, as evolution units, and natural selection, as mechanism of
evolution, and concurrently contributing to the uniﬁcation of different ﬁelds in biology, such as systematics,
genetics, paleontology, cytology, botany, zoology, etc.
In 1910, Morgan’s work with Drosophila melanogaster, the fruit ﬂy, established a crucial connection between experimental
biology and evolution studies, illustrating the compliance Mendelian genetics, natural selection, and the
chromosome theory of inheritance (Allen, 1978). Succeeding to Morgan’s work, Dobzhansky applied his chromosome
theory to natural populations (e.g., of Drosophila pseudoobscura), combining it with the mathematics of
population genetics. His work Genetics and the Origin of Species, published in 1937, contributed to consolidate neoDarwinism,
being later followed by other authors in different ﬁelds, such as systematics and evolution (Mayr,
1942; Huxley, 1940, 1942), paleontology (Simpson, 1944), botany (Stebbins, 1950), and cytology (Darlington, 1943,
1953).
To summarize, according to neo-Darwinism, evolution is explained by changes in the frequencies of alleles in
between generations, between one generation and another as a function of genetic drift, gene ﬂow, and natural
selection. Genetic variation in populations results from random mutations, caused by errors in DNA replication,
and recombination, consisting of crossing over of homologous chromosomes during meiosis. Speciation takes place
gradually when populations become reproductively isolated by geographic barriers.
The modern evolutionary synthesis suffered many reﬁnements, but the most important paradigm shift occurred
only when Williams (1966) proposed a gene-centric view of evolution, which extended Darwin’s natural selection
theory to include the later knowledge and concepts about DNA and genetics, allowing a more rigorous (e.g., mathematical)
analysis of, for instance, speciation, altruistic behavior, and kin selection.
Examples
Example 1: Industrial Melanism in the Peppered Moth
According to Wallace (1858), we should expect that insects exhibiting colors that look like the trunks of the trees
where they ﬁnd shelter will have longer survival because of the camouﬂage from predators. Rapid changes in the
frequency of the allele responsible for the mutation-based melanism in populations of Biston betularia, the peppered
moth (Fig. 9.1), which occurred simultaneously in Europe and North America, constitute a splendid example of fast
microevolution caused by mutation and natural selection. The more than probable explanation for the observations
for industrial melanism is that in habitats darkened by industrial fumes birds will rather eat noticeable insects
(Majerus, 1998; Cook, 2000; Coyne, 2002; Grant, 2002).
Example 2: Warning Coloration and Mimicry
Warning coloration was one of the most interesting topics analyzed by Wallace (1889), elegantly illustrated by
Lepidoptera. A good example is provided by Opthalmis lincea (Agaristidae) and Euproctis subnobilis Snellen, 1881
(A) (B)
FIGURE 9.1 Industrial melanism in populations of the peppered moth (Biston betularia). Before 1850, white moths peppered with black spots
(typica) dominated in England (A). With the increase of heavy industries fumes, between 1850 and 1920, typica was largely replaced by a black
form (carbonaria) (B), produced by a single allele. Later, between 1950 and 1995, in response to a decrease in smoke emissions, the carbonaria form
became rare, while the typica one became common again. (Adapted from Kettlewell, 1965.)
9.2 DO ECOLOGICAL PRINCIPLES ENCOMPASS OTHER PROPOSED ECOLOGICAL THEORIES? 161
(syn. Artaxa simulans) (Liparidae). O. lincea (Fig. 9.2A) is a brightly colored day-ﬂying moth, very common in the
Eastern tropics, which developed chemical repellents that make them repugnant to predators (Mullerian mimetics).
E. subnobilis (¼ A. simulans) (Fig. 9.2B) mimics O. lincea, being also avoided by predators (Batesian mimetics)
(Kettlewell, 1965).
Example 3: Darwin’s Finches
Darwin’s observations on ﬁnches illustrated how species’ gene pools may adapt via offspring to improve longterm
survival. The Darwin’s ﬁnches diagram below illustrates how each ﬁnch species adapts to optimize feeding
performance in different ecological niches (Fig. 9.3).
Through time, ﬁnches’ beaks suffered evolution to adapt to their feeding function. For instance, ﬁnches eating
grubs have a tinny and long beak to extract it from holes in the ground, while ﬁnches eating buds and fruit have
clawlike beaks that allow grinding down the food, which shows to be advantage when buds represent the only
food resource available.
Example 4: The Role of Size in Horses’ Lineage
One of the best illustrative examples on the role of size is provided by the horses’ lineage, which is very well
known through the fossil record horses’ ancestors from the early Eocene (50e55 million years ago) were mostly
small species around the size of cats, although a few could be bigger, weighing 35 kg. In the Oligocene (30 million
years ago), equine ancestral species were already bigger, maybe weighing up to around 50 kg, and in the middle
Miocene (17e18 million years ago), the size would be often about 100 kg. More recently, horses could easily reach
200 kg (approximately 5 million years ago), and about 500 kg 20,000 years ago. This increase in size obviously offered
a selective advantage. Why?
Fig. 9.4 shows a model in form of a STELLA diagram that has been used to answer this question. The model equations
are shown in Table 9.1.
The model has been used to calculate the efﬁciency for different maximum weights. The heat loss is (Peters, 1983)
proportional to the weight with an exponent 0.75. The growth rate also follows the surface but is proportional to the
FIGURE 9.2 Wallace (1889) described several insect survival mechanisms in detail. Opthalmis lincea (A), which developed chemical repellents
that make them repugnant to predators (Mullerian mimetics), is mimicked by Euproctis subnobilis (¼ Artaxa simulans) (B) that like succeeds being
also avoided by predators.
Sharp beaked ground
Cactus
Large cactus
Small ground
Medium ground
Large ground
Vegetarian
Small tree
Medium tree
Large tree
Mangrove
Woodpecker
Warbler
Cocos
Original finch
cactus ground
finch
Tool Using Finch
small tree
finch
Insects
warbler
finch
Leaves
Large ground
finch
Buds / Fruit
Seeds
Grubs
Vegetarian tree
finch
woodpecker
finch
FIGURE 9.3 Darwin’s ﬁnches’ adaptive radiation diagram.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY162
weight with an exponent 0.67 (see equations in Table 9.1). The results are shown in Table 9.2 and the conclusion is
clear: the bigger the maximum weight, the better the eco-exergy efﬁciency (see Chapter 2 for eco-exergy deﬁnition).
This is not surprising because a larger weight means that the speciﬁc surface that determines the heat loss by respiration
decreases. As the respiration loss is the direct loss of free energy, relatively more heat is lost when the body
weight is smaller. Notice that the maximum size is smaller than the upper maximum size that is a parameter to be
used in the model equations (see also Table 9.2).
org
growth respiration
total f ood
consumption
f ood ef f
Graph 1
Table 1
FIGURE 9.4 Growth and respiration follows allometric principles (Peters, 1983). The growth equation describes logistic growth with a
maximum weight. The food efﬁciency is found as a result of the entire life span, using the b-values for mammals and grass (mostly Gramineae).
The equations are shown in Table 9.1.
TABLE 9.1 Model Equations.
d(org(t))/dt ¼ (growth À respiration)
INIT org ¼ 1 kg
Inﬂows:
Growth ¼ 3*orgˆ(0.67)*(1 À org/upper maximum size)
Outﬂows:
Respiration ¼ 0.5*orgˆ(3/4)
d(total_food(t))/dt ¼ (consumption)
INIT total_food ¼ 0
Inﬂows:
Consumption ¼ growth þ respiration
Food_eff % ¼ 2127*100*org(t)/(200*total_food(t))
See the Conceptual Diagram Fig. 9.4.
TABLE 9.2 Eco-Exergy Efﬁciency for the Life Span for Different Maximum Sizes.
Maximum Size Eco-exergy Efﬁciency % Upper Maximum Size Parameter
35 kg 1.41% 45 kg
50 kg 1.55% 65 kg
100 kg 1.84% 132 kg
200 kg 2.20% 268 kg
500 kg 2.75% 690 kg
2127 is the beta-value for mammals and 200 for grass.
9.2 DO ECOLOGICAL PRINCIPLES ENCOMPASS OTHER PROPOSED ECOLOGICAL THEORIES? 163
9.3 EVOLUTIONARY THEORY IN THE LIGHT OF ECOSYSTEM PRINCIPLES
Although living systems constitute very complex systems, they obviously comply with physical laws (although
they are not entirely determined by them), and therefore ecological theory should conform to basic laws of physics.
One needs to understand the implications of the three generally accepted laws of thermodynamics in terms of
understanding organisms’ behavior and ecosystems’ function. Nevertheless, although the three laws of thermodynamics
are effective in describing system behavior close to thermodynamic equilibrium, in far from equilibrium systems,
such as ecosystems, it has been recognized that although the three basic laws remain valid, there must be some
more basic physics beyond them that is of ultimate importance for ecosystems’ functioning. This is the purpose of
“irreversible thermodynamics” or “nonequilibrium thermodynamics.” A tentative Ecological Law of Thermodynamics
was proposed by Sven Erik Jørgensen (1997) as If a system has a throughﬂow of Exergy, it will attempt to utilize
the ﬂow to increase its Exergy, moving further away from thermodynamic equilibrium; If more combinations and processes are
offered to utilize the Exergy ﬂow, the organization that is able to give the highest Exergy under the prevailing circumstances will
be selected. This hypothesis can be reformulated, as proposed by De Wit (2005) as If a system has a throughﬂow of free
energy, in combination with the evolutionary and historically accumulated information, it will attempt to utilize the ﬂow to
move further away from the thermodynamic equilibrium; if more combinations and processes are offered to utilize the free energy
ﬂow, the organization that is able to give the greatest distance away from thermodynamic equilibrium under the prevailing
circumstances will be selected.
Both formulations mean that to ensure the existence of a given system, a ﬂow of energy, or more precisely exergy,
must pass through it: the system cannot be isolated. Exergy may be seen as energy which can do work (Jørgensen,
1997; Jørgensen and Marques, 2001). A ﬂow of exergy through the system is sufﬁcient to form an ordered structure,
or dissipative structure (Prigogine, 1980). If we accept this, then questions arise: Which ordered structure among the
possible ones will be selected? Which factors inﬂuence how an ecosystem will grow and develop?
Jørgensen (1992, 1997) proposed a hypothesis to interpret this selection, providing an explanation for how growth
of ecosystems is determined, the direction it takes, and its implications for ecosystem properties and development.
Growth may be deﬁned as the increase of a measurable quantity, which in ecological terms is often assumed to be the
biomass. But growth can also be interpreted as an increase in the organization of ordered structure or information.
From another perspective, Ulanowicz (1986) makes a distinction between growth and development, considering
these as the extensive and intensive aspects, respectively, of the same process. He argues that growth implies increase
or expansion, while development involves increase in the amount of organization or information, which
does not depend on the size of the system.
According to the tentative Ecological Law of Thermodynamics, when a system grows it moves away from thermodynamic
equilibrium, dissipating part of the exergy in catabolic processes and storing part of it in its dissipative
structure. We shall refer to this exergy as eco-exergy, in accordance to the deﬁnition provided in Chapter 2, which can
be seen as a measure of the maximum amount of work that the ecosystem can perform when it is brought into
thermodynamic equilibrium with its environment. In other words, if an ecosystem were in equilibrium with the surrounding
environment, its eco-exergy would be zero (no free energy), meaning that it would not be able to produce
any work, and that all gradients would have been eliminated.
Structures and gradients, resulting from growth and developmental processes, will be found everywhere in the
universe. In the case of ecosystems, during ecological succession, eco-exergy is used to build biomass, which is ecoexergy
storage. In other words, in a trophic network, biomass and exergy will ﬂow between ecosystem compartments,
supporting different processes by which exergy is both degraded and stored in different forms of biomass
belonging to different trophic levels.
Biological systems are an excellent example of systems exploring a plethora of possibilities to move away from
thermodynamic equilibrium, and thus it is most important in ecology to understand which pathways among the
possible ones will be selected for ecosystem development. In thermodynamic terms, at the level of the individual
organism, survival and growth imply maintenance and increase of the biomass, respectively.
From the evolutionary point of view, it can be argued that adaptation is a typically self-organizing behavior of
complex systems, which may explain why evolution apparently tends to develop more complex organisms. On
one hand, more complex organisms have more built-in information and are further away from thermodynamic equilibrium
than simpler organisms. In this sense, more complex organisms should also have more stored eco-exergy
(thermodynamic information) in their biomass than the simpler ones. On the other hand, ecological succession
drives from simple to more complex ecosystems, which seem at a given point to reach a sort of balance between
keeping a given structure, emerging for the optimal use of the available resources, and modifying the structure,
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY164
adapting it to a permanently changing environment. Therefore, an ecosystem trophic structure as a whole will be a
continuous evolution of the structure as a function of changes in the prevailing environmental conditions, during
which the combination of the species that contribute the most to retain or even increase eco-exergy storage will
be selected.
This constitutes a translation of Darwin’s theory into thermodynamics because survival implies maintenance of
the biomass, and growth implies increase in biomass. Eco-exergy is necessary to build biomass, and biomass contains
eco-exergy, which may be transferred to support other eco-exergy (energy) processes.
The examples of industrial melanism in the peppered moth and warning coloration and mimicry are compliant
with the Ecological Law of Thermodynamics, illustrating at the individual and population levels how the selected
species are able to improve survival and maintenance or increase in biomass under the prevailing conditions. Also,
the Darwin’s ﬁnches adapt to take advantage of feeding in different ecological niches, which constitute another good
illustration at the individual and population levels. Depending on the food resources available at each niche, the
beaks evolved throughout time to adapt their function under the current conditions, improving survival and
biomass growth capabilities. Finally, the horses’ lineage increase in size illustrates very well how a bigger weight
determines a decrease in body speciﬁc surface and consequently a decrease in the direct loss of free energy (heat
loss by respiration). From the thermodynamic point of view, we may say that the solutions able to give the highest
eco-exergy under the prevailing circumstances were selected, maintaining or increasing gradients and therefore
keeping or increasing the distance to thermodynamic equilibrium.
Island Biogeography
We may easily corroborate that there are, in principle, many more bird species on a large island than on a small
one. A possible explanation is related with the area of the island, since the number of species normally augment as a
function of the available space. Nevertheless, this does not explain why sometimes small island assemblages, having
together approximately the same area of isolated bigger islands, may host many fewer species and, in other cases,
many more species than the last.
MacArthur and Wilson (1967) proposed and elaborated a theory of “island biogeography” in order to elucidate
irregular distributions like the ones referred above. According to the theory, the number of species found on an island
will reﬂect an equilibrium between the rate of colonization by new species and the rate of extinction of species
previously established (Fig. 9.5). For instance, if a new volcanic island forms at a given distance from mainland and
becomes habitable, then some bird species will start to immigrate across that distance and establish on the island. If X
species exist in the adjacent mainland, then the rate of new species establishment in the island will gradually
decrease because for each new species established the pool of possible new invaders diminishes by one. The species
that became resident on the new island are not potential invaders anymore.
On the other hand, the rate of species extinction on the island is related to the number of species that became
residents. At the beginning of the island colonization, the extinction rate is obviously low because there are few species
and therefore only few candidates to become extinct. But as the island resources are limited, the increase in the
number of resident species determines, as counterpart, that their individual populations will tend to be smaller and
have a higher probability to become extinct. So, when the island is still almost unoccupied, new species will establish
resident populations at a higher rate, while the rate of extinction of these populations will be higher as the island
becomes almost fully colonized. It is assumed that there will a point between 0 and X (the number of species existent
seicepsforebmuNseicepsforebmuN
Numberofspecies
goingextinctperyear
Numberofnewspecies
arrivingperyear
EVRUCNOITARGIMMIEVRUCNOITCNITXE
FIGURE 9.5 Extinction and Immigration curves.
9.3 EVOLUTIONARY THEORY IN THE LIGHT OF ECOSYSTEM PRINCIPLES 165
on the mainland) for which the two rates will be equal, balancing the colonization and extinction processes and thus
stabilizing the number of species on the island. That number of species (Fig. 9.6) may remain stable if the factors
controlling the two rates do not suffer alterations, although the composition of the faunal community may vary
continuously as a function of the dynamics of the different species invasions and extinctions.
Example
Krakatau Island
The island of Krakatau, between Java and Sumatra, was devastated by a volcanic explosion in 1883, which caused
the complete annihilation of the fauna and ﬂora. The island remnant and two adjacent islands, also distressed by the
explosion, became a natural laboratory to test the island biogeography theory. Observations carried out after the catastrophe
showed that in 1908, 25 years later, 13 bird species had recolonized what remained from the island, by 1921
28 species were established, and by 1934 this number raised to 29. Actually, from the moment of the explosion up to
1934, the number of species that colonized the island was 34, but 5 became extinct. Subsequently, during the next half
century, by 1952 33 species were recorded, and by 1985 35 species, but again the faunal composition varied dynamically,
as 14 new species established populations, but 8 species became extinct. These observations showed that, in
accordance with theory, the rate of colonization declined as the number of species established on the island
increased. Also, although the number of species more or less stabilized, the faunal composition gradually changed.
Additionally, again in agreement with the theoretical predictions, if all other factors are equal, the rate of immigration
to islands more distant from the mainland was lower than to closer islands, and thus the equilibrium was
reached with a smaller number species established (Fig. 9.7). Finally, larger islands, providing more abundant resources
than the small ones had comparatively lower extinction rates and thus more species established.
The Island Biogeography Theory can also be applicable in other contexts, for instance, to predict faunal changes in
biodiversity as a function of habitat fragmentation. In fact, the remaining patches of previously continuous habitats,
such as large forests, will largely behave like islands. Depending on their dimensions and on the difﬁculties in
crossing the gaps between them, such patches will exhibit variable immigration and extinction rates, and the
approach to predict their biodiversity is comparable to what we could utilize regarding islands with different sizes
and at different distances from the mainland.
Island Biogeography Theory in the Light of Ecosystem Principles
In general terms, the Island Biogeography Theory explains therefore why, if everything else is similar, distant
islands will have lower immigration rates with fewer species than those close to a mainland, while close islands
will have high immigration rates and support more species. It also explains why large islands, presenting lower
extinction rates, will have more species than small ones. This theory has been applied, for instance, in forecasting
the effects of fragmenting previously continuous habitat, considering that fragmentation leads to both lower immigration
rates (gaps between fragments are not crossed easily) and higher extinction rates (less area supports fewer
species).
Number of species
EXT Curve
IMMIG Curve
S
FIGURE 9.6 The equilibrium number of species: For each island, there will be a point where the extinction (EXT curve) and immigration
curves (IMMIG curve) intersect. At this point, the rate of immigration of new species to the island will be equal to the rate at which species are
going extinct.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY166
The Ecological Law of Thermodynamics equally provides a sound explanation for the same observations. Let us
look in the ﬁrst place to the problem of the immigration curves. In all the three examples, the decline in immigration
rates as a function of increasing isolation (distance) is fully covered the concept of openness introduced by Jørgensen
(2000). Once accepted the initial premise that an ecosystem must be open or at least nonisolated to be able to import
the energy needed for its maintenance, islands’ openness will be inversely proportional to its distance to mainland. As
a consequence, more distant islands have lower possibility to exchange energy or matter and decreased chance for
information inputs, expressed in this case as immigration of organisms. The same applies to fragmented habitats, the
smaller the plots of the original ecosystem the bigger the difﬁculty in recovering (or maintaining) the original characteristics.
After a disturbance, the higher the openness, the faster information and network (which may express as
biodiversity) recovery will be.
The fact that large islands present lower extinction rates and more species than small ones, as well as less fragmented
habitats in comparison with more fragmented ones, also complies with the Ecological Law of Thermodynamics.
All three examples can be interpreted in this light. Provided that all the other environmental are similar,
larger islands offer more available resources. Under the prevailing circumstances, solutions able to give the highest
eco-exergy will be selected, increasing the distance to thermodynamic equilibrium not only in terms of biomass but
also in terms of information (i.e., network and biodiversity). Moreover, after a disturbance, such as in the case of
Krakatau Island, the rate of recolonization and ecosystem recovery will be a function of the system’s openness.
Latitudinal Gradients in Biodiversity
Observations have noted that at a global scale species diversity typically declines as a function of increasing latitudes,
from the equator to poles (Rosenzweig, 1995; Stevens and Willig, 2002). Nevertheless, although this pattern of
variation of biodiversity with latitude is more than conspicuous, the dynamics behind it are not well understood.
The observed gradient is generally thought as the result of species in situ origination and extinction, and in this
context the tropics are seen as either generating biodiversity (the tropics-as-a-cradle hypothesis), or accumulating
biodiversity (the tropics-as-museum hypothesis), eventually both.
The possible causes for latitudinal gradients in biodiversity (www.ecology.info/gradients-biodiversity.htm).
The variation of biodiversity according to a gradient from the equator to the poles (Fig. 9.8) is not determined by
the latitude per se, but by the environmental factors correlated with it. Many different mechanisms, actually more
than 25, have been proposed to explain how latitudinal diversity gradients may be generated, but in fact there is no
consensus about it (Gaston, 2000).
The area of the different climatic zones is considered as one of the possible factors responsible for latitudinal
diversity gradients. In fact, the area of land masses with similar climate characteristics, namely small temperature
ﬂuctuations, is larger at the tropics than at higher latitudes (Rosenzweig, 1992). Eventually, this might be connected
to higher levels of speciation and lower levels of extinction in the tropics (Rosenzweig, 1992; Gaston, 2000; Buzas
et al., 2002). Additionally, the present higher diversity found in the tropics may, at least partially, be explained by
seicepSforebmuNseicepSforebmuN
SS
SDNALSITNATSIDEROMSDNALSIRESOLC
evruCTXEevruCTXE
evruCGIMMIevruCGIMMI
FIGURE 9.7 The inﬂuence of distance of an island from the source on the equilibrium number of species. EXTcurve, extinction curve; IMMIG
curve, immigration curve.
9.3 EVOLUTIONARY THEORY IN THE LIGHT OF ECOSYSTEM PRINCIPLES 167
the historical evolutionary process, since the land surface of the Earth was for the most located in tropical or subtropical
zones during a long period in the Cenozoic (Ricklefs, 2004).
More intense solar radiation in the tropics will in principle increase primary productivity, which subsequently
might augment biological diversity. Nevertheless, such relation is not evident, since an increase in primary productivity
may indeed explain a surge of total biomass, but is not a sufﬁcient condition to explain why that biomass
should be allocated into more individuals belonging to more species (Blackburn and Gaston, 1996). In fact, both
population density and individual body sizes tend to be lower in the tropics, which appears correlated to the
occurrence of more species, but the reasons for such interactions are complex (Blackburn and Gaston, 1996).
One explanation is that higher temperatures in the tropics determine shorter generation times and larger mutation
rates, and thus faster speciation (Rohde, 1992). Maybe speciation is also hastened by the tropical higher habitat
complexity, although this explanation cannot really apply to freshwater and marine ecosystems. Maybe higher
speciation rates in the tropics results from a combination of several factors, each factor affecting differently distinct
groups of organisms, areas (e.g., northern vs. southern hemisphere), and ecosystems, creating the observable patterns
diversity.
Examples
Example 1: Latitudinal Distribution of Hyperiid Amphipods Diversity in the Atlantic Ocean
Burridge et al. (2016) published a very interesting paper analyzing the distribution and diversity of hyperiid amphipods
along a latitudinal gradient in the Atlantic Ocean. Although their diversity and biogeographical distribution
is not well known, it is recognized that marine hyperiid amphipods, as commensals or as parasites of gelatinous
plankton, play a very important role in marine pelagic food webs.
Having collected hyperiids from epipelagic and upper mesopelagic depths, these authors could identify a total of
70 species belonging to 36 genera and 17 families. Additionally, they observed the occurrence of maxima in species
and genus richness in the equatorial upwelling region (induced by Coriolis force), with species richness showing a
signiﬁcant positive correlation to sea surface temperature. The analysis allowed identifying different main assemblages
of hyperiid species, respectively, gyral, equatorial, transitional, and sub-Antarctic, actually matching with
the biochemical provinces proposed by Longhurst (2010). Although hyperiids are part of the zooplankton, mechanisms
controlling hyperiids diversity appear to be different from other zooplankton taxa. In fact, while most
zooplankton groups reach maxima in diversity in subtropical waters, hyperiids show a maximum diversity of species
and genus in the equator. This might depend on the distribution and diversity of gelatinous plankton species
that host hyperiids, which are not well characterized at the global ocean scale.
FIGURE 9.8 Latitudinal trends of biodiversity variation, exhibiting a decreasing gradient from the equator to the poles. Reproduced from Roy K,
Jablonski D, Valentine JW, Rosenberg G. 1998. Marine latitudinal diversity gra tests of causal hypothesis. Proc. Natl. Acad. Sci. USA 95, 3699e3702.
Copyright (1998) National Academy of Sciences, U.S.A.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY168
Example 2: Latitudinal Trends in Vertebrate Diversity (http://www.meer.org/chap3.htm)
Amphibians are not present in the arctic regions but, on the other hand, are common in the mid-latitudes
(Fig. 9.9A) (Jenkins et al., 2013). Amphibians are not able to regulate body temperature and therefore ﬁnd their preferential
habitats and reach their highest diversity in the tropics, where the climate is warmer and humid. Actually,
the distribution of one of the three orders of Amphibian, the Gymnophiona or Apoda (represented by less than 200
species), is restricted to the tropics.
Reptiles, for similar reasons, are also represented by many more species in low latitudes. For instance, both lizards
(Fig. 9.9B) and snakes (Fig. 9.9C) peak in diversity in the tropics. Nevertheless, in some cases, in low latitudes
between 15 and 30, where most of the deserts are located, their diversity may decrease (Laudenslayer and Grenfell,
1983). As a pattern, these two major groups of reptiles are represented by far more species in the tropics than in
higher latitudes, which is even more evident for turtles.
Birds also present more species in the tropics. Although homeothermic, the group also presents a high diversity
in temperate latitudes. For example, at least 88 bird species breed on the Labrador Peninsula of northern Canada
(55N.), 176 species breed in Maine (45N.), and more than 300 species can be found in Texas (31N.; Peterson,
1963), and 540 in California, while the total number of species reaches approximate 700 for all of North (Welty,
1975).
Mammalian higher species diversity is also recognizable at low latitudes. This was illustrated by Simpson (1964),
who analyzed the latitudinal trend of mammals’ diversity for continental North American mammals, although he
also identiﬁed a secondary pattern of variation superimposed to the latitudinal trend. Simpson refers the occurrence
of more mammal species in mountainous regions than in lowlands.
Fish also present much higher diversity in tropical water, where most of the marine and freshwater species can be
found. Coral reefs and tropical reefs, for instance, present one of the most amazingly diverse ﬁsh communities,
encompassing something like 30%e40% of all marine ﬁsh species, which is illustrated by the fact that in a single
large reef complex, or in association with it, 2200 species could be found (Moyle and Cech, 1996). Another excellent
example is the Amazon River, 3700 miles long, which drains one quarter of South America. This huge tropical river
presents more than 2400 ﬁsh species, while its tributary, the Rio Negro, has alone more ﬁsh species than all the rivers
of the United States taken together.
Example 3: Trends within Plant Communities and Across Latitude
The way plant species richness (expressed as the number of species) and species-speciﬁc plant density
(expressed as number of individuals per species) vary within community size frequency distributions across latitude
was examined in an interesting work published by Niklas (2003), based on data sets from 226 forested plant
communities from Asia; Africa; Europe; and North, Central, and South America available at the Gentry database.
Species richness and species-speciﬁc plant density exhibited an opposite variation as a function of the latitudinal
gradient. Species richness showed a clear increase toward the tropics, while species-speciﬁc plant density showed
the inverse trend.
FIGURE 9.9 Diversity versus latitude plots for (A) genera of amphibians; (B) genera of lizards; (C) genera of snakes, showing that the highest
number of genera occurs in 5 latitude classes.
9.3 EVOLUTIONARY THEORY IN THE LIGHT OF ECOSYSTEM PRINCIPLES 169
Example 4: Trends within Marine Epifaunal Invertebrate Communities
The inﬂuence of latitude on local and regional pools of marine invertebrate epifauna species richness was
analyzed by Witman et al. (2004), based on data provided by samples carried out in 12 independent biogeographic
regions from 62oS to 63oN. Both regional and local species richness varied as a function of latitude, exhibiting the
highest values at low latitudes and decreasing toward high latitudes (Fig. 9.10).
9.4 LATITUDINAL GRADIENTS IN BIODIVERSITY IN THE LIGHT OF
ECOSYSTEM PRINCIPLES
Latitudinal gradients in biodiversity are easily interpretable in the light of the Ecological Law of Thermodynamics.
Obviously, the higher solar radiation in the tropics increases productivity, which in turn is thought to
increase biological diversity. In fact, Blackburn and Gaston (1996) found that one parameter that correlated signiﬁcantly
with diversity in both oceans was solar energy input, as represented by average sea surface temperature.
Moreover, these authors claim that if that correlation was causal, sea surface temperature is probably linked to
diversity through some aspect of productivity. However, they could not establish the causal nexus, considering
that productivity could only explain why there is more total biomass in the tropics, not why this biomass should
be allocated into more individuals, and these individuals into more species.
This apparent inconsistency can nevertheless be explained within the frame of ecosystem principles. In fact,
Jørgensen et al. (2000), proposed that ecosystems show three growth and development forms:
I. Growth and development of physical structure (biomass), which is able to capture more of the incoming energy
in the form of solar radiation but also requires more energy for maintenance (respiration and evaporation).
II. Growth and development of the network interactions which provides more cycling of energy or matter.
III. Growth and development of information (more developed plants and animals with more genes), from
r-strategists to K-strategists, which waste less energy.
This was experimentally conﬁrmed by Debeljak (2002) examining managed and virgin forest in different development
stages (e.g., pasture, gap, juvenile, optimum forest). Accordingly, these growth and development forms may
be considered an integration of E.P. Odum’s (1969) attributes, which describe changes in ecosystems associated with
successional development from the early stage to the mature stage. Clearly, Blackburn and Gaston (1996) were
considering only growth form I.
Keystone Species Hypothesis
The keystone species concept was introduced by Paine (1969, 1980) and since then has been playing a central position
in conservation biology. According to Paine, keystone species are understood as those whose importance in
their environments with regards to ecosystems function is much greater than their representativeness in terms of
abundance or biomass. In other words, they play, in ecological terms, the same role that a keystone does in the
apex of an arch, by maintaining the structure and integrity of ecological communities.
FIGURE 9.10 variation of marine invertebrate epifauna species richness as a function of latitude. (A) Regional species richness. (B) Local
species richness.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY170
This means that although the collapse of dominant species, in terms of abundance and biomass, will also have
dramatic effects on ecosystems, such effects will be proportional to their importance, and therefore such species
could not be considered as keystone. Nonetheless, the keystone species concept is theoretically young and still under
development, and the term as originally proposed by Paine to predator species has been extended to include prey
species, plants, and even habitat resources. This might be controversial; deﬁnitions of keystone species are many and
eventually not always consensual. But, in any case, keystone refers to species that play critical roles, whose disappearance
will result in dramatic alterations in their communities’ structure and functioning, which has been
observed in many ecosystems and for an extensive variety of organisms.
Independently from controversies, regarding a certain oversimpliﬁcation of complex systems, the keystone species
concept has been largely applied as a model to understand the drivers controlling ecological communities,
allowing an easier communication between scientists and politicians and inﬂuencing managers and decision makers
in establishing priorities in terms of species and habitats conservation.
Examples
Example 1dSea Stars’ Predation on Mussels
Paine’s (1969a) work provides a classical example of a keystone predator species, Pisaster ochraceus, a sea star that
usually preys on intertidal mussels and other bivalves, limpets, barnacles, chitons, etc. (Fig. 9.11), which have no
other natural predators. In case P. ochraceus disappears from the intertidal zone, the mussels’ populations tend to
increase out of control and exclude many other species, damaging the whole community and decreasing
biodiversity.
Example 2dSea Otters’ Predation on Sea Urchins
The same applies to sea otters that predate sea urchins and protect kelp forests. Without such predation, sea urchins’
populations would increase excessively and consume kelp uncontrollably, destroying the habitat that kelp forests
represent for many species, and therefore decreasing the system’s productivity and biodiversity.
Example 3dGray Wolves’ Predation on Elks
Other well-know example is the control exerted by gray wolves (Canis lupus) on elk (Cervus canadensis) populations.
The wolves’ eradication to protect farmers’ livestock (Grooms, 1993; Breck and Meier, 2004; Outland, 2010)
(a classical example of an absurd economic-driven management decision) triggered an uncontrolled growth of
elk populations, since their primary predators disappeared, and a consequent overgrazing of plants, namely in riparian
zones, causing the loss of habitat for many other wild animal species, such as beavers and songbirds, and
inﬂuencing other ecological factors, such as the stability of stream banks, the deposition of organic matter and
ﬁne particles sediments, nutrients cycling, water temperature via changes in shading (Smith and Bangs, 2009).
Example 4dElephants in Savannas
Keystone species are frequently predators because even small populations of these may exert a strong inﬂuence
on the distribution, behavior, and abundance of prey species. Nevertheless, species other than predators, herbivores
for example, may also play the role of keystone species. That is the case of elephants in savannas. By eating trees’
FIGURE 9.11 Food web of species that can be found in intertidal temperate rocky shores ecosystems illustrating the keystone species
hypothesis.
9.4 LATITUDINAL GRADIENTS IN BIODIVERSITY IN THE LIGHT OF ECOSYSTEM PRINCIPLES 171
sprouts, the elephants prevent these habitats from gradually becoming a woodland. Such vegetation management
by elephants ensures grasses’ abundant growth in savannas, which beneﬁts other herbivores populations, such as
small rodents, zebras, gnus, antelopes, etc.
9.5 THE KEYSTONE SPECIES HYPOTHESIS IN THE LIGHT OF
ECOSYSTEM PRINCIPLES
All the examples illustrate that the keystone species hypothesis is clearly within the Ecological Law of Thermodynamics.
In fact, ecosystems constitute dissipative, ordered, open structures, whose change in entropy depends on
an external exogenous contribution from the environment and an internal endogenous contribution due to the system
state (see Chapter 2 of this book). The origin of the ecosystem’s ordered structure is therefore its openness and a
ﬂow of energy, which drives biogeochemical cycles. Biological systems may explore different possibilities to increase
their eco-exergy (or work energy) and move away from thermodynamic equilibrium (Jørgensen, 1997), and therefore
it is necessary to understand through which pathways among the possible ones the system will develop.
In general terms, to move away from thermodynamic equilibrium ecosystems may apply the growth and development
forms listed above, which are utilized to store more work energy, or eco-exergy. In a trophic network,
biomass and exergy will therefore ﬂow between ecosystem compartments, supporting different processes by which
eco-exergy is both degraded and stored in different forms of biomass belonging to different trophic levels. When a
keystone disappears from the ecosystem, for natural or other causes, the pathways of the system’s dissipative structure
change, and biomass growth (form I) becomes predominant in moving away from thermodynamic equilibrium,
while growth and development forms II and III become less important, with a concomitant decrease in the systems’
network complexity and information (biodiversity).
Optimal Foraging Theory
Researchers have pursued theories to explain species’ diversity since Charles Darwin’s publication of The Origin
of Species in 1859. Different approaches often focused on the observation of animals’ feeding behavior, having as ﬁrst
concern quantifying adaptation, ﬁtness, and natural selection. The assumption has been that feeding behaviors
reﬂect these processes, and thus will encompass adaptation mechanisms, which will act in a feedback loop to create
an interactive system between an animal’s phenotypes and their environment.
In this context, the Optimal Foraging Theory was proposed by MacArthur and Pianka (1966), based on the argument
that successful foraging is a crucial requisite to individual’s survival, and therefore the use of decision theory
should allow predicting the individual foraging behavior that would allow maximizing food intake. In their seminal
paper, based on costebeneﬁt analysis, these authors developed a graphical model allowing predictions of animal
feeding activities once a forager’s optimal diet is speciﬁed. The degree to which a predator could be selective in
choosing its preys was dependent on prey abundance and therefore on the time spent to search and capture each
food item eaten. So, diets tend to be broad when prey are scarce (longer search time) and narrow when food items
are abundant (shorter search time), meaning that predators can only afford to disdain mediocre preys if they have a
reasonably high chance to ﬁnd a superior food item in a period equivalent to what it would have taken to consume
the previous one. Likewise, predators having larger available areas can be more specialized than those having
smaller ones because travel time between preferred food items will be lower.
Summarizing, the Optimal Foraging Theory allowed to approach three main concepts: (1) the period that a predator
will forage in a given area, which depends (2) on the inﬂuence of prey density on the time spent by the predator
foraging in the area and (3) on the inﬂuence of the variety of prey on the predator’s choice regarding preys captured.
The predator’s behavior to optimize foraging could be described as a function of its relationships with the prey
consumed, taking into account the time spent to capture and eat them and prey availability, and consequently
encompassing the study of differential land and resources use.
Examples
Example 1: Rufous Hummingbirds
A study on Rufous hummingbirds carried out by Carpenter et al. (1983) allowed testing the Optimal Foraging
Theory. Hummingbirds perform long migrations between the north-western Paciﬁc, where they breed, and southern
Mexico, where they spend the winter. During their migratory stops, they establish feeding territories, which they
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY172
guard very actively, expulsing other hummers, hawk moths, butterﬂies, and even bees competing for the nectar.
Moreover, to compete even more successfully with other nectar consumers, they exhaust the nectar resources in
areas adjacent to their territories as early in the day as they can.
Experimentally, Carpenter and coworkers, covered one-half of the ﬂowers in a territory with a fabric and
observed that, being unable to drain those ﬂowers, the resident bird increased his territory. This simple experiment
illustrated that hummers’ territoriality was connected to the availability of nectar, and also that the birds were able to
appraise the amount of nectar they had under control. In order to calculate the bird’s weight each time it rested, the
usual perch used by the territory holder was replaced by a false one connected to a sensitive scale. Using this technique,
it was possible to conclude that the birds optimized their territory by trial and error, dimensioning for the size
that allowed maximizing their daily weight increase. Regarding Rufous hummingbirds, as a whole, the theory successfully
predicted the birds’ behavior in nature.
Example 2: Optimal Clam Selection by Predator Crows in North-Western Paciﬁc
Another good illustration of the Optimal Foraging Theory is provided by clam selection by predator crows
(Richardson and Verbeek, 1986). The authors observed that crows in the Paciﬁc Northwest frequently did not eat
littleneck clams after having located them. In fact, crows digging the sediments looking for food usually only
care for capturing the larger clams, which they break by dropping them on the rocks before eating it. Crows acceptance
rate increases as a function of the prey size, disdaining the smaller clams and consuming all the larger ones.
Although bigger clams can be broken more easily when they are dropped on the rocks, the authors did not think this
was the reason why crows preferred them. In accordance with the theory, they explained instead that the most proﬁtable
clams were the largest because they contained more calories than smaller ones, providing superior caloric beneﬁts
in exchange of the work necessary for searching, digging up, opening, and feeding on them. A mathematical
model was built based on the assumption that crows would optimize their diet in terms of maximizing caloric ingestion.
This model was able to predict successfully the crows’ behavior in selecting clams as a function of the prey size,
with simulations matching empirical observations carried out in the ﬁeld (Fig. 9.12).
The Optimal Foraging Theory in the Light of Ecosystem Principles
The Optimal Foraging Theory clearly complies with the Ecological Law of Thermodynamics. The fact that prey
abundance inﬂuences consumers’ selectivity and that diets are broad when preys are scarce and narrow if food is
abundant, as a function of search for food time, is clearly translated by .If more combinations and processes are offered
to utilize the exergy ﬂow, the organization that is able to give the highest exergy under the prevailing circumstances will be
selected or by if more combinations and processes are offered to utilize the free energy ﬂow, the organization that is able to
give the greatest distance away from thermodynamic equilibrium under the prevailing circumstances will be selected. Both examples
can therefore be easily explained by the same ecosystem principles.
Niche Theory
Hutchinson’s Fundamental Niche Theory (Hutchinson, 1957, 1965) proposes that the ecological niche of an organism
can be assumed as an abstract multidimensional mapping of population dynamics onto an environmental space, the
axes of which are biotic and abiotic factors inﬂuencing reproduction and survival. In other words, niche can be
FIGURE 9.12 Model based on the Optimal Foraging Theory showing an optimal relationship between clams’ size and their predation rate by
crows, which assumes that birds will tend to maximize the rate of energy gain per unit of time spent foraging for clams. The curve corresponds to
the predicted percentages; Solid circles correspond to empirical observations (Richardson and Verbeek, 1986).
9.5 THE KEYSTONE SPECIES HYPOTHESIS IN THE LIGHT OF ECOSYSTEM PRINCIPLES 173
interpreted as an “n-dimensional hypervolume” enclosing the total range of environmental conditions under which
the individual (or population) can successfully live and replace itself.
The “fundamental niche” concept can be deﬁned as the maximum inhabitable hypervolume where competitors
and predators do not exist (biotic interactions such as competition, predation, and parasitism are absent), i.e., where
the entire set of optimal conditions under which an organism can thrive are present. It can be distinguished from the
concept of “realized niche,” which designates a smaller part of hypervolume occupation by the species which is under
biotic constraints, i.e., restricted due to competition. For single speciﬁc environmental variables, “niche breadth”
can be thought of as the habitable range for an organism, i.e., the extent of the hypervolume projected onto each
individual environment.
After Hutchinson’s distinction, “niche” denotes the requirements of the species and “habitat” in the physical
place in the environment where those requirements can be met. A clear understanding of Hutchinson’s emphasis
on the fundamental importance of competition as a force inﬂuencing the distribution of species in nature is required
when interpreting the distribution of the species A and B in Fig. 9.13.
According to Hutchinson, a species will not use its whole fundamental niche when facing competition. Instead, a
smaller realized niche comprising those portions of the fundamental niche where the species is competitively dominant
will be actually utilized. In this sense, the realized niche is thus smaller than the fundamental niche, meaning
that a species can often be absent from portions of its fundamental niche as a consequence of competitive exclusion.
Competition will obviously be greater with the increase in resemblance of the species niches thus, when two populations
are sharing limited resources.
Niche has been used to describe and analyze different aspects:
1. Forms of species interaction (including competition, resource portioning, exclusion, or coexistence);
2. To explain patterns of species abundance (dominance and rarity);
3. Factors responsible for governing species geographical distribution;
4. Factors determining structure and stability of multispecies communities.
Considering a hypothetical example, corresponding to an extreme situation:
Can two populations occupying the same resource niche coexist in the same environment? (http://courses.
washington.edu/anth457/nichelec.htm).
By deﬁnition, if two populations occupy the same resource niche, it means they utilize the same resources and in
the same way.
Commonly thinking, three possible results could be expected: (1) resources are shared equally (neither population
changes niche); (2) one or both populations may change niche to reduce overlap (niche partitioning); (3) one
population totally fails (competitive exclusion). Which result is most probable to occur?
Answer from niche theory would consider options 2 or 3, but not 1. This somewhat counterintuitive result has
been named as competitive exclusion principle (CEP) (Gause, 1934), a central tenet of modern niche theory. In essence,
this states that two species with identical niches cannot coexist indeﬁnitelydeither the niches will differ or one will
be excluded by the other (with “exclusion” meaning it has been replaced by differential population growth, not
necessarily by ﬁghting or territoriality). A complete niche overlapping (100%) is, however, very unlikely if not
impossible; such an extreme case is unnecessary for competitive exclusion or other forms of niche change.
Predicting what happens when there is niche overlap and competition has been central to ecology in theory development
and in advancing research. Questions that have been addressed, include the following: When does exclusion
result, when coexistence? How much overlap is possible (a question treated by the “theory of limiting similarity”)?
How do environmental ﬂuctuations affect this? Why are some species generalists, and others specialists?
Competitive exclusion and coexistence via reduction in niche overlap are two responses to niche competition which
have often been observed, with their determinants and features being investigated using three main procedures: laboratory
experiments, ﬁeld observations, and mathematical models/simulations. The ﬁrst response is frequently
observed when a species colonizes a habitat and outcompetes indigenous species (probably linked to absence of parasites
and predators adapted to exploit the colonizer) (e.g., introduced placentals vs. indigenous marsupials in
Australia). The second, coexistence through niche partitioning, can hardly be directly observed, but it is often inferred
from traces left by “the ghost of competition”.” It can be achieved through inspection of the two populations that
partially overlap in space, by comparing the niches of each population in the area of overlap and nonoverlap. Most
of the time, in areas where competitors coexist, one or both present narrower niche range (e.g., diet breadth) compared
to areas without competitors; this can be explained if we consider that competition compels each population toward
specialization in resources (or other niche dimensions) which give them competitive advantage, and conversely, to
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY174
abandon those in which the other population surpasses it. Thus, “competitive release” occurs when a certain species
has the ability to use a broader range of resources in the absence of competitors (closer to its fundamental niche), i.e., to
utilize the resource more fully than it could in the presence of the competitor.
The phenomenon termed character displacement implies an evolutionary change in attributes (“characters”) of
competing populations when niches shift. It constitutes the best evidence for the role of competition in shaping
Species BSpecies A
Species B
Divergence
Species A
Species A
Strong competitor
Species B
Weak competitor
Resources gradient
NumberofindividualsNumberofindividualsNumberofindividuals
Resources gradient
Resources gradient
Niche separation
Niche overlap
(A)
(B)
(C)
FIGURE 9.13 If two populations occupy the same resource, they utilize all the same resources, and in the same manner. Theoretically, there are
three possible outcomes to this situation: (A) Share resources more or less equally (neither population changes niche); (B) One or both populations
change niche to reduce overlap (niche partitioning); (C) One will be excluded by the other. Competitive exclusion principle: No two species can
permanently occupy the same niche, and either the niches will differ, or one will be excluded by the other. Reproduced from G. F. Gauze. 1934. The
struggle for Existence. Williams and Wilkins, Baltimore.
9.5 THE KEYSTONE SPECIES HYPOTHESIS IN THE LIGHT OF ECOSYSTEM PRINCIPLES 175
niches as other explanation is unlikely. The change in length or shape of beaks in ecologically similar bird species
that overlap geographically is a typical example of character displacement.
Example 1 of the Competitive Exclusion Principle or Gause’s Principle: two species of Paramecium
Two species of Paramecium were placed into ﬂasks with a bacterial culture used as food source (Gause, 1934). Both
species were thus forced to share the same niche in this microcosm. Through a daily count of the Paramecium number,
a pattern became evident to Gause: a few days after, one species always became extinct, as apparently it was
unable to compete with the other species for the single food resource (Fig. 9.14).
Yet, other results can be observed for two species sharing the same niche, extinction is not the only possible. There
is a chance that when two competing species coexist for a long time, they will evolve differences to minimize competition,
i.e., they can develop different niches.
Example 2 of the Competitive Exclusion Principle or Gause’s Principle: Geospiza spp.
Revisiting Darwin’s ﬁnches of the Galapagos Islands, we can see examples of character displacement. The genus
Geospiza has a widespread distribution among the islands. For instance, Geospiza fortis is found only on Daphne Island
and Geospiza fulginosa on Crossman Island. Both are ground-feeding birds and have similar size. In turn, the
species coexist on Charles and Chatham Islands. Here, G. fortis is similar in size to their relatives from Daphne,
but G. fulginosa is smaller than their relatives from Crossman. This size modiﬁcation allows the G. fulginosa to avoid
competition with the larger G. fortis on Charles and Chatham Islands by feeding on smaller seeds. In this way,
competition is important in shaping ecosystems as suggested in the abovementioned example of character displacement.
Displacement is interpreted as evidence of historical competition.
Example 3 of the Competitive Exclusion Principle or Gause’s Principle: Squirrels in England (www.
saburchill.com/IBbiology/chapters02/035.html)
Due to competitive exclusion, disease, and the disappearance of hazel coppices and mature conifer forests in
lowland Britain, the native population of red squirrels (Sciurus vulgaris) has declined. On the other hand, the gray
squirrel (Sciurus carolinensis) introduced to Britain between 1876 and 1929 in only 30 sites easily adapted to parks
and gardens replacing the red squirrel. The current distribution is shown below in Fig. 9.15.
The Niche Theory in the Light of Ecosystem Principles
In general terms, Hutchinson’s niche theory considers that the fundamental niche (theoretical) of a given species
comprises all the combinations of environmental conditions which permit an individual of that species to survive
and reproduce indeﬁnitely. But from all these possible combinations, only the ones where the species is competitively
dominant will in fact be utilized, constituting the realized niche. There will be of course limits of tolerance
(maximum and minimum) of the organisms regarding each environmental variable, which constitute the niche
breadth.
This formulation, designed to be used at the species and individual level, is clearly compliant with the Ecological
Law of Thermodynamics. In fact, what is said can be translated as under the prevailing circumstances the organisms
will attempt to utilize the ﬂow to increase its Exergy, moving further away from thermodynamic equilibrium (Jørgensen, 1997),
or alternatively in combination with the evolutionary and historically accumulated information, it will attempt to utilize the
ﬂow to move further away from the thermodynamic equilibrium (Wit, 2005).
FIGURE 9.14 Experimental competition in laboratory between two populations of Paramecium having comparable necessities, illustrating the
CEP. Maps prepared by the Biological Records Centre at the Centre for Ecology & Hydrology, from records collated by the Mammal Society and others; drawn
using Dr Alan Morton’s DMAP software.
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY176
In accordance with the CEP (Gause, 1934), if the populations of two species occupy the same niche, then one of the
two will become outcompeted. In trophic terms, in the absence of competitors, a given species will probably
specialize less than it will in competitor’s presence (competitive release). This also clearly complies with the Ecological
Law of Thermodynamics and can be translated as
If more combinations and processes are offered to utilize the Exergy ﬂow, the organization that is able to give the highest
Exergy under the prevailing circumstances will be selected (Jørgensen, 1997) or alternatively as if more combinations and
processes are offered to utilize the free energy ﬂow, the organization that is able to give the greatest distance away from thermodynamic
equilibrium under the prevailing circumstances will be selected (Wit, 2005).
All the three examples illustrating the CEP can be explained in the light of the Ecological Law of
Thermodynamics.
9.6 LIEBIG’S LAW OF THE MINIMUM
Many different environmental factors have the potential to control the growth of a population. These factors
include the abundance of preys or nutrients that the population consumes and also the activities of predators. A
given population will usually interact with a multitude of different prey and predator species, and ecologists
have described these many interactions by drawing food webs. Although a given population may interact with
many different species in a food web and also interact with many different abiotic factors outside the food web,
not all of these interactions are of equal importance in controlling that population’s growth. Experience shows
that “only one or two other species dominate the feedback structure of a population at any one time and place (Berryman,
1993).” The identity of these dominating species may change with time and location, but the number of species
that limits a given population (i.e., actively controls its dynamics) is usually only one or two. Liebig’s Law (Liebig,
1840), in its modern form, expresses this idea. It says that of all the biotic or abiotic factors that control a given population,
one has to be limiting (i.e., active, controlling the dynamics) (Berryman, 1993, 2003). Time delays produced
by this limiting factor are usually one or two generations long (Berryman, 1999). Moreover, Liebig’s Law stresses the
importance of limiting factors in ecology. “A factor is deﬁned as limiting if a change in the factor produces a change
in average or equilibrium density (Krebs, 2001).”
To summarize, “the functioning of an organism is controlled or limited by that essential environmental factor or
combination of factors present in the least favorable amount. The factors may not be continuously effective but only
at some critical period during the year or only during some critical year in a climatic cycle.”
FIGURE 9.15 Present geographic distribution of two squirrel species (Sciurus vulgaris and Sciurus carolinensis) in Britain.
9.6 LIEBIG’S LAW OF THE MINIMUM 177
The Liebig’s Law of the Minimum in the Light of Ecosystem Principles
The Liebig’s Law of the minimum may be seen as a deductive consequence of the principle of increasing ascendency
(Ulanowicz and Baird, 1999). Let us see why.
Increasing ascendency also implies greater exergy storage, as can be demonstrated by two propositions in sequence:
Proposition 1: Longer biomass retention times contribute to increasing ascendency.
Let Bi represent the amount of biomass stored in the ith compartment of the ecosystem. Similarly, let Tij be the
amount of biomass that is transferred from compartment i to compartment j within a unit of time.
Information is now the measure of change in a probability assignment (Tribus and McIrvine, 1971). The two distributions
in question are usually the a priori and a posteriori versions of a given probability, which in the present
case is the probability that a quantum of biomass will ﬂow from i to j. As the a priori estimate that a quantum of
biomass will leave i during a given interval of time, one may use an analogy from the theory of mass action that
the probability can be estimated as (Bi/B.), where B. represents the sum of all the Bi. In a strictly similar manner,
the probability that a quantum enters some other compartment j should be proportional to the quotient (Bj/B.). If
these two probabilities were completely independent, then the joint probability that a quantum ﬂows from i to j
would become proportional to the product (BiBj/B2
).
Of course, the exit and entrance probabilities are usually coupled and not entirely independent. In such case the a
posteriori probability might be measured by empirical means in terms of the Tij. That is the quotient (Tij/T..) would
be an estimate of the a posteriori joint probability that a quantum leaves i and enters j.
Kullback (1959) provides a measure of information that is revealed in passing from the a priori to the a posteriori.
This is called the KullbackeLeibler information measure, which is given by
I ¼
X
i;j
pðai; bjÞlog
0
@
pðai; bjÞ
pðaiÞpðbjÞ
1
A;
where p(ai) and p(bj) are the a priori probabilities of event ai and bj, respectively, and p(ai,bj) is the a posteriori probability
that ai and bj happen jointly. Substituting the probabilities as estimated in the preceding paragraphs, one obtains
the form for the KullbackeLeibler information of biomass ﬂow in a network as
I ¼
X
i;j
Tij
T::
log
TijB2
:
T::BiBj
!
:
Following the lead of Tribus and McIrvine, as in Ulanowicz (1980), one may scale this information measure by the
total activity (T..) to yield the storage-inclusive ascendency (Ulanowicz and Abarca, 1999) as
A ¼
X
i;j
Tijlog
TijB2
:
T::BiBj
!
:
Biomass Storage
Eugene Odum (1969) proposed 24 properties as indicators of maturity in ecosystems. These could be grouped
under increases in species richness, trophic speciﬁcity, cycling, and containment. It happens that, other things being
equal, an increase in any of these attributes will result in an increase of systems ascendency. As a result, Ulanowicz
(1980, 1986) proposed as a phenomenological principle describing ecosystem development that “in the absence of
major perturbations, ecosystems exhibit a tendency to increase in ascendency.” Those factors which lend to an
increasing ascendency, therefore, should be considered as signiﬁcant contributors to ecosystem development.
From a mathematical point of view, one can elucidate how a system gains in magnitude by calculating what contributes
to positive gradients in ascendency. So, for example, if one wishes to know what changes in the Bk foster
increases in ascendency, one would want to study the partial derivatives (vA/vBk). After rather tedious algebraic
manipulation, the results reduce to
vA
vXk
¼ 2

T::
B:
À
1
2
Tk: þ T:k
Bk

:
This formula has a straightforward meaning. The ﬁrst term in parentheses is the overall throughput rate. The second
quotient is the average throughput rate for compartment k. That is, the sensitivity of the biomass-ascendency is
proportional to the amount by which the overall throughput rate exceeds that of the compartment in question. If the
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY178
throughput of compartment is smaller than the overall rate, then ascendency is abetted. In other words, increasing
ascendency is favored by slower passage (longer storage) of biomass through compartment k., i.e., biomass storage
favors increased ascendency.
Proposition 2: When several elements ﬂow through a compartment, that element ﬂowing in the least proportion
(as identiﬁed by Liebig (1840)) is the one with the longest retention time in the compartment.
We begin by letting Tijk be the amount of element k ﬂowing from component i to component j. We then consider
the hypothetical situation of ideally balanced growth (production). In perfectly balanced growth, the elements are
presented to the population in exactly the proportions that are assimilated into the biomass. This can be stated in
quantitative fashion: For any arbitrary combination of foodstuff elements, p and q, used by compartment j,
TÃ
:jp
TÃ
:jq
¼
Bjp
Bjq
where an asterisk is used to indicate a ﬂow associated with balanced growth. Now we suppose that one and only one
element, say p without loss of generality, enters j in excess of the proportion needed. That is, T.jp ¼ T.jp* þ ep where ep
represents the excess amount of p presented to j. Under these conditions we have the inequality
T:jp þ ep
T:jp
>
Bjp
Bjq
Multiplying both sides of this inequality by the ratio T.jq*/Bjp yields
T:jp þ ep
Bjp
>
TÃ
:jq
Bjq
In words, this latter inequality says that the input rate of p into j is greater (faster) than that of any other element by
the “stoichiometric” amount ep/Bjp. Over a long-enough interval, inputs and outputs must balance, and so we can
speak about the input rate and throughput rate as being one and the same. (This does not weaken our argument, as
there is an implied steady-state assumption in the Liebig statement as well.)
Now we suppose that only two of the elements ﬂowing into j are supplied in excess. Again, without loss of generality,
we call the second element q. It is immediately apparent that if ep/Bjp > eq/Bjq, then the throughput rate of p exceeds
that of q, and vice versa. That is, a slower throughput rate indicates that one is closer to stoichiometric proportions. This
last result can be generalized by mathematical induction to conclude that the element having the slowest throughput
rate is being presented in the least stoichiometric proportion, i.e., it is limiting in the sense of Liebig.
The River Continuum Concept
The River Continuum Concept (RCC) was proposed by Vannote et al. (1980) (Fig. 9.16) to describe the variation of
physical conditions and environmental variables within a river ecosystem, according to a continuous gradient from
the source to the mouth. The gradient causes a set of differential responses within the river biological populations
(e.g., benthic communities), reﬂected in a continuum of biotic adjustments and constant patterns of organic matter
loading, transport, utilization, and storage along a river. Starting from the theory on energy equilibrium formulated
by ﬂuvial geomorphologists, Vannote et al. (1980) conjectured that the structural and functional features of stream
communities will be ﬁtted to the most likely position or middling state of the system’s physical component. They
argued that the characteristics of the biological communities (producers, consumers, and decomposers) in a given
river section will establish in compliance with the channel’s dynamic physical conditions, and consequently, along
natural river ecosystems, there will be a consistent temporal continuum of species substitutions.
This incessant replacement of performers works to allocate the consumption of energy inputs through time, with
the biological system moving to an equilibrium between a propensity for using efﬁciently those inputs through
resource partitioning (food, substrate, etc.), and an opposite tendency for an even rate of energy processing
throughout the year. In this context, Vannote et al. (1980) hypothesized that, in natural rivers, biological communities
will optimize their performance by adopting processing strategies involving a minimal loss of energy. For instance,
there are always predictable processing inefﬁciencies in upstream communities, and downstream communities will
also predictably adjust alongside, becoming specialized in using wastes resulting from that inefﬁcient processing. In
general, the RCC offers an elegant framework that allows integrating both predictable and observable biological
characteristics of river ecosystems.
9.6 LIEBIG’S LAW OF THE MINIMUM 179
9.7 THE RIVER CONTINUUM THEORY IN THE LIGHT OF ECOSYSTEM PRINCIPLES
The river continuum theory can almost be seen as a different verbalization of the Ecological Law of Thermodynamics
applied to rivers, since it is fully compliant with it. Along a continuous gradient of changing environmental
conditions, river communities attempt to utilize the ﬂow to increase its Exergy, moving further away from thermodynamic
equilibrium. Changing conditions along the gradient determine different constraints and therefore other processing
strategies because If more combinations and processes are offered to utilize the Exergy ﬂow, the organization that is able to give
the highest Exergy under the prevailing circumstances will be selected (Jørgensen, 1997) or alternatively as if more combinations
and processes are offered to utilize the free energy ﬂow, the organization that is able to give the greatest distance away
from thermodynamic equilibrium under the prevailing circumstances will be selected (Wit, 2005).
Hysteresis in Nature
Numerous examples in nature show that there are combinations of environmental factors (external constraints)
that may give rise to two equally viable community structures, i.e., they may provide the same degree of support
(possibilities to grow and develop) to different sets of internal constraints. In such cases, a hysteresis relationship
exists between the dominant external constraining factors and the community structure (internal constraints
relieving the external constraints). For instance, in freshwater shallow lakes ecosystems there are references to
such type of scenarios:
1) For concentrations between approximately 50 mg P/L and 120e140 mg P/L a plankton community structure
dominated by zooplankton and carnivorous ﬁsh has the same probability to occur than structure dominated by
planktivorous ﬁsh and phytoplankton (de Bernardi and Giussani, 1995);
2) For concentrations between approximately 100 and 250 mg P/L shallow lakes can be dominated either by
submerged vegetation or by phytoplankton (Scheffer, 1998).
Dominant plant
community
Dominant community
Shredders
1
2
3
4
5
6
7
8
9
10
11
12
Shredders
Predators
Predators
Predators
Grazers
Grazers
Microbes
Microbes
Microbes
Collectors
Collectors
Collectors
CPOM
FPOM
Phytoplankton
Zooplankton
Channel
width
FoodplainFoodplain
FPOM
Vascular
water plants
Vascular
water plants
Periphyton
Periphyton
Periphyton
P/R<1
P/R>1
Streamorder
P/R<1
FIGURE 9.16 A possible relationship between the size of a stream and the gradual shift in the structure and functional characteristics of the
dominant plant and benthic communities. Based on the concept proposed by Vannote et al. (1980).
9. ECOSYSTEM PRINCIPLES HAVE BROAD EXPLANATORY POWER IN ECOLOGY180
In both cases, the system history will determine which one of the two possible community structures occurs. Once
the community installed, within the indicated ranges, a shift in its structure will only take place in case the community
(the internal constraints) is changed by external factors (forcing functions). In the ﬁrst case, this might mean that
the planktivorous ﬁsh are physically removed and replaced by more carnivorous ﬁsh, and in the second case that
phytoplankton are removed and submerged vegetation planted. In fact, such interventions are called biomanipulation,
and the experience has shown that it only works in the indicated ranges of nutrient concentrations.
It can furthermore be shown in the two referred cases that the relief (indicated as the growth measured by ecoexergy)
(Jørgensen et al., 2000) is the same for the two possible community structures within the indicated ranges
(Fig. 9.17) (Jørgensen and de Bernardi, 1997, 1998). The occurrence of hysteresis can thus be explained in the light
of the maximum eco-exergy principle.
9.8 CONCLUSIONS
The objective of this chapter was to explore common ecological concepts alongside the principles of the Ecological
Law of Thermodynamics proposed herein. The observed compliance between them shows that the same principles
can provide explanations to different ecological problems usually addressed by different approaches. The Ecological
Law of Thermodynamics is a more general and integrative theory. Does this mean that the other theories have no
explanatory power? Deﬁnitely no. This just means that they are not universal, and therefore they can only be
used to explain a relatively narrow number of observations, being in most cases speciﬁc for a given type of system.
We have demonstrated that the ecosystem principles of a new ecology are fully compliant with evolutionary theory
and can cover some of the most well-known nonuniversal ecological theories.
Nutrient concentration
Phytoplanktonconc.or
primaryproduction
Range where biomanipulation
can be applied
Range where
biomanipulation
cannot be
applied
Faster recovery
obtained by bio-
manipulation
FIGURE 9.17 The hysteresis relation between nutrient level and eutrophication measured by the phytoplankton concentration is shown. The
possible effect of biomanipulation is shown. The biomanipulation gives the expected results in the range where two different structures are
possible. The range for a change from zooplanktonecarnivorous ﬁsh control to planktivorousephytoplankton control is about 50e120/140 mg P/
L and for a change from dominance by submerged vegetationephytoplankton is about 100 mg P/L to about 250 mg P/L.
9.8 CONCLUSIONS 181
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C H A P T E R
10
Ecosystem Principles Have Ecological
Applications
Tempus item per se non est, sed rebus ab ipsis
consequitur sensus, transactum quid sit in aevo,
tumquae res instet, quid porro deinde sequantur.
Time per se does not exist: the sense of what
has been done in the past, what is in the present
and what will be is embodied in things themselves. Lucretius, De Rerum Natura, I, 459e461.
10.1 INTRODUCTION
Orientors, being holistic ecological indicators, can give further information on the state of an ecosystem than can
simply reductionistic indicators. Information coming from systematic or analytical approaches should never be
neglected, but holistic indicators allow us to understand if the system under study is globally following a path
that takes the system to a “better” or to a “worse” state. And, we can also compare macroscopic state of different
systems, which is impossible to do with isolated reductionistic information. So, advantages of holistic indicators
are additional aggregate information without losing information, ability to compare, ability to compare states of
the same system at different times, and possibility of understanding what new data types are needed for this
approach.
With indicator concepts like ecosystem health, ecosystem integrity can ﬁnd operational values, using information
coming from approaches like Network analysis, Eco-exergy, Ascendency, Emergy evaluation, and other related indicators.
Here, we present several examples in which the systems perspective in ecology has been applied. The types
and locations of systems in which they have been applied are very diverse: terrestrial and aquatic ecosystems in
Europe, North and South America, and Asia, as are the goals of the research and management questions involved.
Regardless of the setting or objective, at its core, holistic indicators always give a broader understanding of the amalgamation
of the ecosystem parts into a context of the whole.
10.2 ENTROPY PRODUCTION AS AN INDICATOR OF ECOSYSTEM TROPHIC STATE
References where entropy production has been used as indicator:
Aoki, I., 1987. Entropy balance in lake Biwa. Ecol. Model. 37, 235e248.
Aoki, I., 1995. Entropy production in living systems: from organisms to ecosystems. Thermochim. Acta 250,
359e370.
Aoki, I., 2000. Entropy and Exergy principles in living systems. In: Thermodynamics and Ecological Modeling.
Lewis Publishers, New York, pp. 165e190.
Ludovisi, A., Poletti, A., 2003. Use of thermodynamic indices as ecological indicators of the development state of
lake ecosystems. 1. Entropy production indices. Ecol. Model. 159, 203e222.
183
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00010-3
Entropy ﬂow and entropy production (see Chapter 2) can be quantitatively estimated using physical modeling or calculated
from observed energy ﬂow data of biological systems. Here entropy production in lake ecosystems is examined in detail for three
ecosystems located in Japan, the United States, and Italy.
Case Studies
Lake Biwa is located at 34580e3530 N, 135520e136170 E (near Kyoto, Japan) and consists of a northern basin (the
main part) and a southern basin (the smaller part). The former is oligotrophic and the latter is nearly eutrophic. Only
the northern basin is considered. Data for this study were collected in the 1970s. The annual adsorbed solar energy
was 4153 MJ, while the mean depth of the lake is of 44 m. It is possible to identify two zones in the column water: a
light one (20 m) and a dark one (24 m). The average amount of suspended solid (SS) in the light zone was 1.3 [g mÀ3
]
(National Institute for Research Advancement, 1984), while the average amount of dissolved organic carbon (DOC)
was 1.6 [gC mÀ3
] (Mitamura and Sijo, 1981). The average amount of total plankton plus zoobenthos in the whole
water column was 0.16 [gC mÀ3
] (Sakamoto, 1975).
Lake Mendota is located at 43040 N, 89240 W (near Madison, Wisconsin, USA) and is a eutrophic lake. Its energy
budget was investigated by Dutton and Bryson (1962) and Stewart (1973). The annual adsorbed solar energy was
4494 MJ, while the mean depth of the lake is 12.2 m. Two zones of the water column were identiﬁed: the euphotic
one (until 9 m) and the aphotic one (the lasts 3.2 m). The average amount of SS in the light zone was 1.9 [g mÀ3
] (National
Institute for Research Advancement, 1984), while the average amount of DOC was 3.3 [gC mÀ3
] (Brock, 1985).
The average amount of total plankton plus zoobenthos in the whole water column was 0.62 [gC mÀ3
] (Brock, 1985).
Lake Trasimeno is the largest lake in peninsular Italy (area 124 km2
); it is shallow (mean depth 4.7 m, maximum
6.3 m), and accumulation processes are favored. The water level of the lake showed strong ﬂuctuations with respect
to meteorological conditions; hydrological crises occur after several years with annual rainfall <700 mm. Lake Trasimeno
can be considered homogeneous for chemical and physical parameters (MARU, 1994) and very sensitive to
meteorological variability or human impact. According to the VollenweidereOECD classiﬁcation (Giovanardi et al.,
1995), Lake Trasimeno is mesotrophic, whereas by using the annual phosphorus loading estimation method (MARU,
1994) and the HillbricheIlkowska method (Hamza et al., 1995), the lake is classiﬁed as eutrophic.
Entropy production Indices for Waterbodies
The quantities necessary to estimate entropy production (see Aoki, 1989, 1990) can be obtained from experimentally
observed data. Entropy production plotted against adsorbed solar radiation energy for Lake Biwa and Lake
Mendota are shown in Figs. 10.1 and 10.2, respectively. The monthly entropy production per unit of volume (Sp)
Lake Biwa (northern basin)
0.0
1.0
12
11
10
9
3
4
6 8
7 5
2
1
2.0
0 200 400 600
Absorbed solar energy/[MJ m-2 month-1]
Entropyproduction/[MJm-2month-1
K-1
]
FIGURE 10.1 Monthly entropy production in the northern basin of Lake Biwa per m2
of the lake surface plotted against monthly solar radiation
energy absorbed by 1 m2
of the lake surface. The numbers near the circles are the months.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS184
of the Trasimeno lake was calculated by simple division of Sprod by monthly mean values of water depth; the annual
values were calculated as the sum of monthly values and is given in Table 10.1.
Entropy production is expressed in MJ mÀ2
monthÀ1
KÀ1
, while solar radiation in MJ mÀ2
monthÀ1
. According to
Aoki, entropy production in month j (denoted as (DiS)j) is a linear function of the absorbed solar radiation energy in
month j (denoted as Qj):
ðDiSÞj ¼ a þ bQj (10.1)
According to Ludovisi (2003) the deﬁnition of the b index as a ratio of Sp (in units MJ mÀ3
yearÀ1
KÀ1
) and annual
Qs (MJ mÀ2
yearÀ1
KÀ1
) is not proper because entropy and energy ﬂows do not refer to the same spatial unit. This
fact introduces an artiﬁcial dependence on the water depth. Partially following Aoki’s indices, a set of new ones (c, d,
d0) analogous to the a, b, and b0 were proposed by Ludovisi and Poletti (2003) on the basis of the relationship between
the entropy production per surface units (Sprod) and the solar energy absorbed by the lake surface (Qs). The index
d0 does not demonstrate any signiﬁcant trend during the years 1988e96 (Table 10.1).
A good linear correlation between the monthly entropy production (Sprod) per surface unit of Lake Trasimeno and
the monthly solar energy absorbed by the lake (Qs) has been found on a monthly time scale (Fig. 10.3) and the
Absorbed solar energy [MJ m-2 month-1]
0.0
1.0
2.0
0080040020 600
Entropyproduction[MJm-2
month-1
K-1]
Lake Mendota
12
1
112
3 10
9
4 8
5
6
7
FIGURE 10.2 Monthly entropy production in Lake Mendota per m2
of the lake surface plotted against monthly solar radiation energy
absorbed by 1 m2
of the lake surface. The numbers near the circles are the months.
TABLE 10.1 Annual Values of Sprod (MJ mÀ2
yearÀ1
KÀ1
), Sp (MJ mÀ3
yearÀ1
KÀ1
) and of the Indices b0
(10À4
mÀ1
KÀ1
), d0 (10À4
KÀ1
), Calculated for Lake Trasimeno in the Years 1988e96.
Year Sprod Sp b0 d0
1988 16.02 3.20 6.2 31.0
1989 15.60 3.34 6.4 29.9
1990 15.72 3.65 7.3 31.4
1991 15.57 3.74 7.4 30.8
1992 15.42 3.54 7.1 30.8
1993 15.62 3.68 7.1 30.1
1994 16.40 3.91 7.4 30.8
1995 15.60 3.93 7.6 30.2
1996 15.62 4.17 8.0 29.8
Average 15.73 3.69 7.2 30.6
10.2 ENTROPY PRODUCTION AS AN INDICATOR OF ECOSYSTEM TROPHIC STATE 185
regression coefﬁcients of the curve (c, intercept and d, slope) can be compared with the analogous Aoki’s indices a, b
(Table 10.2).
The comparison of c, d (regression coefﬁcients of the curve Fig. 10.3 intercept and slope), d0 (the ratio between the
annual Sprod and Qs) values (Table 10.2) calculated for Lake Mendota and the northern basin of Lake Biwa, significantly
distinguishes the eutrophic Lake Mendota from the oligotrophic Lake Biwa, and attributes to Lake Trasimeno
higher values of d and d0 than both other lakes.
0 200 400 600 800
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Qs (MJ m-2 month-1)
Sprod(MJm-2
month-1
K-1
)
Sprod = c + d * Qs
R = 0.97
FIGURE 10.3 Linear regression between the monthly entropy production (Sprod) per surface unit of Lake Trasimeno and the monthly solar
energy absorbed by the lake (Qs).
TABLE 10.2 Environmental Parameters, TSI Values and Values of Trophic Indices Proposed by Aoki (a, b, b0) and
Those of the New Set of Indices c, d, d0 for Lake Mendota, Lake Biwa, Lake Trasimeno.
Parameter Lake Biwa Lake Mendota Lake Trasimeno
Mean depth (m) 44 12.2 4.7
Residence time (year) 5.5 3.1e8.8 >20
Transparency (Secchi depth (m)) 5.2 2.9 1.2
Chlorophyll a (mg LÀ1
) 5 32 8
Total phosphorus (mg LÀ1
) 0.01 0.07 0.05
TSI (SD)a
36 45 58
TSI (Chla)a
46 65 51
TSI (TP)a
37 65 59
TSI (average)a
39 58 56
Trophic classiﬁcationb
Oligotrophic Hypereutrophic Eutrophic
a (MJ mÀ3
monthÀ1
KÀ1
) 0.002 0.006
b (10À4
mÀ1
KÀ1
) 0.6 2.3
b0 (10À4
mÀ1
KÀ1
) 0.6 2.4 7.2c
c (MJ mÀ2
per mese KÀ1
) 0.070 0.0070 0.014
d (10À4
KÀ1
) 26.7 27.9 31.0
d0 (10À4
KÀ1
) 26.4 29.3 30.7c
a
Trophic state index calculated by using Carlson (1997) equations.
b
Based on the Kratzer and Brezonik (1981) classiﬁcation system.
c
Average value of the years 1988e96.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS186
Regarding Eq. (10.1), the second term on the right-hand side is the entropy production dependent on solar radiation
energy that is caused by the conversion in heat of the solar energy absorbed by water, by dissolved organic
matter, and by SS (negligible are the contributions from photosynthesis and light respiration of phytoplankton).
The ﬁrst term on the right-hand side of Eq. (10.1) is the entropy production independent of solar radiation energy
and it is caused by respiration of organisms in the lake.
For Lake Biwa and Lake Mendota total and solar energyedependent entropy productions (per year per MJ of
absorbed solar radiation energy per m3
of the lake water) and entropy productions independent of solar radiation
energy (per year per m3
of the lake water) are shown in Table 10.3. The values of entropy production dependent on
solar radiation in the light zone (eutrophic zone) are related to the amount of dissolved organic matter and SS per m3
of lake water in the light zone. The ratio of the amount of SS in Lake Mendota to that in Lake Biwa (1:5) and the ratio
of DOC in Lake Mendota to that in Lake Biwa (2:1) are consistent with the ratio of entropy production dependent on
solar radiation between Lake Mendota and Lake Biwa (Table 10.3). Thus, the greater the amount of SS and DOC, the
more the entropy production is dependent on solar radiation. The entropy production dependent on solar radiation
gives a kind of physical measure for the amount of dissolved organic matter and SS in the lake water by means of
reactions to incident solar radiation.
The entropy production independent of solar radiation energy (Table 10.3) is the measure of activity of respiration
of organisms distributed over the whole water column. The ratio of the amount of plankton plus zoobenthos in Lake
Mendota with respect to Lake Biwa is 3:9 and is consistent with the ratio of entropy production independent of solar
radiation (3:6). The larger the amount of organisms, the more the entropy production is independent of solar radiation.
The entropy productions in eutrophic Lake Mendota are larger than those in oligotrophic Lake Biwa in any of
the categories considered (i.e., due to light absorption, respiration, and total).
Fig. 10.4 reports the linear regression curves between d and TSI, TSI(SD) (Carlson, 1977) and the mean depth
(because of the little data available, the regression curves cannot be considered highly signiﬁcant). As can be
seen, d0 is positively correlated to TSI, although the relation is not very sharp because of the similarity of TSI for
Lake Trasimeno and Lake Mendota. The index d0 shows a good negative linear correlation with the lake’s mean
depth: the intercept value given by the linear regressions (30.9 Â 10À4
KÀ1
) could approach the higher values for
d0 at the limits of existence of an aquatic ecosystem, which is reached at a rate of 0.1 Â 10À4
KÀ1
mÀ1
.
The indices d and d0 could be considered measures of the ability of the ecosystems to dissipate the incoming solar
energy into the system; the positive correlation between these indices and the trophic state of the lakes indicates that
they could account for the inﬂuence of the biological productivity on the whole entropy production of the system. As
high nutrient concentrations increase the whole biological production as well as the energy ﬂow through an
ecosystem, an increase in d and d0 values with eutrophication is expected because of the irreversibility of the biological
processes.
Furthermore, the efﬁciency of the energy transfer between the trophic levels in eutrophic systems was found to be
lower than in oligotrophic systems (Jonasson and Lindegaard, 1988). In ecological terms, this should mean that a
higher nutrient availability in more eutrophic systems induces the achievement of a biological community possessing
a better ability to dissipate energy, following a development strategy based on the maximization of the productivity,
rather than optimization of the energy exploitation.
Conclusions
The entropy production of the three categories (total entropy production, dependent entropy production, and independent
entropy production) can be proposed to be larger in a eutrophic lake than in an oligotrophic lake. Natural
TABLE 10.3 Comparison of Entropy Productions in Lake Biwa and in Lake Mendota.
Lake Total (in Whole Water Column)
Solar Energy Dependent (in
Light Zone)
Solar Energy Independent (in
Whole Water Column)
Lake Biwa 0.07 0.13 19
Lake Mendota 0.24 0.31 69
Lake Mendota/Lake Biwa 3:7 2:3 3:6
Total and solar energy dependent entropy productions (per year per MJ of absorbed solar radiation energy per m3
of the lake water) are shown, respectively, in the ﬁrst
and in the second column, and entropy productions independent of solar radiation energy (per year m3
of the lake water) are in the third column units are
[kJ KÀ1
mÀ3
yearÀ1
]. Ratios of the values for the two takes are shown in the last row.
10.2 ENTROPY PRODUCTION AS AN INDICATOR OF ECOSYSTEM TROPHIC STATE 187
processes tends to proceed with time from oligotrophy to eutrophy in most of present lake ecosystems surrounded
by the environment full of organic matter; the entropy production of the three categories in a lake will increase with
time accompanying the process of eutrophication (Aoki, 1989, 1990).
These entropy production indices can be useful tools for characterizing the trophic status of a water body; however,
their ecological interpretation might need more investigation as they depend on the successional stage (Margalef,
1977; Reynolds, 1984) or on the “prevailing condition” the system is following.
35 40 45 50 55 60 65
26
27
28
29
30
31 Trasimeno
Mendota
Biwa
d' = 20 + 0.2 * TSI(SD)
R = 0.95
d'(10
-4
°K
-1
)
TSI (SD)
0 10 20 30 40 50
26
27
28
29
30
31 Trasimeno
Mendota
Biwa
d' = 30.9 - 0.1 * mean depth
R= -0.99
d'(10
-4
°K
-1
)
Mean depth (m)
35 40 45 50 55 60 65
26
27
28
29
30
31 Trasimeno
Mendota
Biwa
d' = 19.1 + 0.2 * TSI
R = 0.91
d'(10
-4
°K
-1
)
TSI
FIGURE 10.4 Linear regression between the entropy production index d0 and TSI, TSI(SD), the mean water depth for Lake Biwa, Lake
Mendota, and Lake Trasimeno.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS188
10.3 THE USE OF ECOLOGICAL NETWORK ANALYSIS FOR THE SIMULATION OF THE
INTERACTIONS BETWEEN AMERICAN BLACK BEAR AND ITS ENVIRONMENT
Reference where ecological network analysis has been applied to show the importance of indirect effects:
Patten, B.C., 1997. Synthesis of chaos and sustainability in a non stationary linear dynamic model of the American
black bear (Ursus americanus Pallas) in the Adirondack Mountains of New York. Ecol. Model. 100, 11e42.
Here an application of a dynamic model is used to show the importance of indirect effects even within a linear approach.
There are many examples of indirect relationships in natural systems, some of them involving the global onedthe
biosphere. The majority of these relationships remain either overlooked or poorly understood (Krivtsov et al., 2000).
To model such systems requires the use of many integrated submodels due to the complexity of processes involved.
The knowledge that all species in nature are complexly interconnected directly and indirectly to all other biotic
and abiotic component of their ecosystems is slow in being translated into models and even more in management
practice.
An example for such a synthesis is the simulation model of a wildlife population, the American black bear (Ursus
americanus Pallas) on the 6000 ha Huntington Wildlife Forest in the central Adirondack Mountain region of upper
New York State, USA (Costello, 1992). The model was designed to be conceptually complex but mathematically simple
so its behavior would derive more from biology and ecology than from mathematics. The STELLA II (High Performance
Systems, Hanover NH) model of the Adirondack black bear is linear, donor-controlled, nonstationary, and
phenomenological (Patten, 1983).
The model’s purposes are to express black bear biology as a population system inseparable from its ecosystem
and to demonstrate how chaos and sustainability can be realistically incorporated into models, minimizing the
use of inappropriate mathematics that, though traditional or classical, may not be well chosen due to an inadequate
rationale.
If envirograms for all the taxa and signiﬁcant abiotic categories of the Huntington Wildlife Forest could be formed,
then the centrum of each would account for one row and one column of an n Â n interconnection matrix for the
whole ecosystem. The centrum of each black bear envirogram for a life history stage would then represent one
such row and column within the ecosystem matrix and from these indirect connections between bear and ecosystem
compartments could be determined. Of course the forest ecosystem model does not exist, but the rationale for
embedding the bear subsystem within it is clear, and the purpose of the envirograms was to implement this in principle
by way of organizing relevant information for modeling.
A further criterion was that all the direct interactions between the bear compartments and the environment would
be by mass energy transactions, enabling the conservation principle to be used in formulating system equations. The
envirograms prepared for this model are depicted in Simek (1995) and were then used to construct a quantitative
difference equation model employing STELLA 11.
Quantiﬁcation of the model is still approximate, based on general data and knowledge of the bear’s life history,
reproductive behavior, environmental relationships, and seasonal dynamics as known for the Huntington Forest
and the Adirondack region. The equations are all linear and donor-controlled, with details of temporal dynamics
introduced by nonstationary (time-varying) coefﬁcients rather than by nonlinear state variables and constant
coefﬁcients.
The model’s behavior is here described in detail only for the cub compartment and selected associated parameters
(Fig. 10.5). The other compartments behave with similar realism.
A baseline simulation was achieved which generated 33e64 individuals 6000 haÀ1
during a typical model year;
this is consistent with a mean of about 50 animals typically considered to occur on the Huntington property. Yearling
M/F sex ratios generated by the model varied slightly around 0.85, compared to 0.6 observed during 1989e94. Besides
the baseline simulation, model parameters were manipulated to investigate sensitivity relationships. The compartments
were indicated to be more sensitive to inputs and less sensitive to outputs. The sensitivity relationships
described below for cubs generally hold true also for the other age classes in the model.
Conclusion
In descending order, the most sensitive inputs were maternal milk (cubs), fruit production, and plant food availability
(Fig. 10.6); relatively insensitive inputs were immigration, animal food, and recruitment (to yearlings and
adults). Sensitivities to outputs, lower than for inputs, were, in descending order, respiration, egestion, accidental
10.3 THE USE OF ECOLOGICAL NETWORK ANALYSIS FOR THE SIMULATION OF THE INTERACTIONS 189
x2MYrlg
FIGURE 10.5 Submodel layer depiction of the cub compartment of the black bear model.
10.ECOSYSTEMPRINCIPLESHAVEECOLOGICALAPPLICATIONS190
mortality, emigration, parasitic infection, predation (on cubs), harvest, and sickness. Since the model is linear, it can
be considered to represent near steady-state dynamics, but its realism suggests that the neighborhood of applicability
may actually be very broad around steady state.
10.4 APPLICATIONS OF NETWORK ANALYSIS AND ASCENDENCY TO SOUTH
FLORIDA ECOSYSTEMS
Reference where ascendency has shown its practical applicability:
Heymans, J.J., Ulanowicz, R.E., Bondavalli, C., 2002. Network analysis of the South Florida Everglades graminoid
marshes and comparison with nearby cypress ecosystems. Ecol. Model. 149, 5e23.
Ascendency (see Chapter 6) is used to compare a cypress system and a graminoids one and to discern the degree of maturity
shown by the two systems.
2
3
1 1 1
2 2
33
2
1
6.00
12.00
0.00
0.00 48.0036.0024.0012.00
0.00 48.0036.0024.0012.00
6.00
12.00
0.00 1 112 2 2 2
3 3 3 3
Months
1
Input Sensitivity: Cubs to Fruit Food
x91:3x01:2x1:1
Input Sensitivity: Cubs to Plant Food
x91:3x01:2x1:1
FIGURE 10.6 Sensitivity of cubs to plant food and fruit. Plant food, principally leaves, fruits, and tubers, comprise 90% of their diets. Fruit is a
late season resource (after July) whereas plant food availability began in MayeJune. Fruit production occurs when they are approaching going into
negative energy balance.
10.4 APPLICATIONS OF NETWORK ANALYSIS AND ASCENDENCY TO SOUTH FLORIDA ECOSYSTEMS 191
Case Studies
The Everglades ecosystem in South Florida occupies a 9300 km2
basin that extends from the southern shore of
Lake Okeechobee south and southwest to the Gulf of Mexico (Hoffman et al., 1990). Currently, the basin can be
divided into three sections: Everglades Agricultural Area, Water Conservation Areas, and the Southern Everglades,
the latter of which includes the marshes south of Tamiami Trail and the Shark River Slough. There are two distinct
communities in the graminoid system that are differentiated according to short and long hydroperiod areas (Lodge,
1994) and occur in areal ratio of approximately 3:1. Short hydroperiod areas ﬂank both sides of the southern Everglades
and are occupied by a low sawgrass community of plants with a high diversity (100 species) (Lodge, 1994).
Typically, vegetation in the short hydroperiod marsh is less than 1 m tall (Herndorn and Taylor, 1986). Long hydroperiod,
deeper marsh communities are developed over peat soil (Goodrick, 1984). The long hydroperiod community
occurs more commonly in the central Everglades where they typically are straddled between sawgrass marshes and
sloughs. These inundated areas are important for ﬁsh and aquatic invertebrates, such as prawns. Long hydroperiod
areas provide an abundant reserve of prey for wading birds toward the end of the dry season (MarcheApril).
The freshwater marshes of the Everglades are relatively oligotrophic and have been typiﬁed as not being very
productivedaveraging only about 150 g mÀ2
per year in wet prairie areas according to DeAngelis et al. (1998). Graminoid
ecosystems provide valuable habitat for a wide range of animals, including species listed by the US Fish and
Wildlife Service as endangered, threatened, or of concern.
The cypress system is a 295,000 ha wetlands of the Big Cypress Natural Preserve and the adjacent Fakahatchee
Strand State Preserve. Both areas cover a ﬂat, gently sloping limestone plain (Bondavalli and Ulanowicz, 1999)
with many strands and domes of cypress trees. The cypress swamp does not have a distinct fauna but shares
many species with the adjacent communities (Bondavalli and Ulanowicz, 1999).
The Network Models of the Ecosystems
A model of the freshwater graminoid marshes was constructed by Heymans et al. (2002) and consists of 66 compartments,
of which three represent nonliving groups and 63 depict living compartments (see reference for details). The
three nonliving compartments include sediment carbon, labile detritus, and refractory detritus, all of which are utilized
mainly by bacteria and microorganisms in the sediment (living sediment) and in the water column (living
POCdparticulate organic carbon). The primary producers include macrophytes, periphyton, Utricularia, and other
ﬂoating vegetation.
Lodge (1994) suggested that: “the Everglades does not have a great diversity of freshwater invertebrates due to its
limited type of habitat and its nearly tropical climate, which many temperate species cannot tolerate.” The source of
most fauna in South Florida is from temperate areas further north. Accordingly, the invertebrate component of the
graminoid marshes are broken down into eight compartments, consisting of apple snails (Pomacea paludosa), freshwater
prawns (Palaemonetes paludosus), crayﬁsh (Procambarus alleni), mesoinvertebrates, other macroinvertebrates,
large aquatic insects, terrestrial invertebrates, and ﬁshing spiders. Loftus and Kushlan (1987) described an assemblage
of 30 species of ﬁsh in the freshwater marshes, of which 16 species are found in the sawgrass marshes.
The Everglades assemblage of herpetofauna consists of some 56 species of reptiles and amphibians. Nine compartments
of mammals were identiﬁed for the graminoid marshes. Approximately 350 species of birds have been
recorded within the Everglades National Park, and just slightly less than 300 species are considered to occur on a
regular basis (Robertson and Kushlan, 1984). Sixty percent of these birds are either winter residents, migrating
into South Florida from the north, or else visit brieﬂy in the spring or fall. The remaining 40% breed in South Florida
(Lodge, 1994), but of these only eight groups nest or breed in the graminoids. Various species of wading and terrestrial
birds roost or breed in the cypress wetlands and feed in the graminoid marshes, including anhingas, egrets,
herons, wood storks, and ibises. These birds are explicit components of the cypress network. They feed on the
aquatic and terrestrial invertebrate members of the graminoid wetland; however, this capture of prey is represented
as an export from the graminoid system and an import into the cypress swamp. Waders were not included as explicit
components in the graminoid network.
The cypress swamp model consists of 68 compartments and similar to the graminoid system, the cypress model
has three nonliving compartments (refractory detritus, labile detritus, and vertebrate detritus) and two microbial
compartments (living POC and living sediment). Ulanowicz et al. (1997), Bondavalli and Ulanowicz (1999) give a
breakdown of the construction of the model. The primary producers are more diverse than those found in the graminoids
and are represented by 12 compartments, 7 of which are essentially terrestrial producers: understory, vines,
hardwood leaves, cypress leaves, cypress wood, hardwood wood, and roots (Bondavalli and Ulanowicz, 1999).
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS192
These seven compartments ramify the spatial dimension of the ecosystem in the vertical extentdan attribute not
shared by the graminoid marshes. Other primary producer compartments include phytoplankton, ﬂoating vegetation,
periphyton, macrophytes, and epiphytes (Bondavalli and Ulanowicz, 1999).
According to Bondavalli and Ulanowicz (1999), cypress swamps do not possess a distinct faunal assemblage, but
rather share most species with adjacent plant communities. Most fauna spend only parts of their lives in the swamp.
Benthic invertebrates form the heterotrophic base of the food chain. A high diversity of invertebrates has been
recorded in cypress domes and strands, but a lack of data at the species level mandated that they resolve the invertebrates
into only ﬁve compartments (Bondavalli and Ulanowicz, 1999). Similarly, the ﬁsh component of this model
could not be resolved into more than three compartments, two containing small ﬁsh and a third consisting of large
ﬁsh (Bondavalli and Ulanowicz, 1999).
The herpetofauna compartments of the cypress model were similar to those of the graminoids. The bird community
of the cypress swamps was much more diverse than that in the graminoids. The increased diversity can be traced
to the inclusion of wading birds in the cypress model. The wading birds do not roost or nest in the graminoids,
although they do feed there; therefore, it was assumed that an export of energy and carbon ﬂowed from the graminoids
into the cypress. The 17 bird taxa in the cypress include 5 types of wading birds, 2 passerine collections, and
various predatory birds (Bondavalli and Ulanowicz, 1999). The mammals of the cypress include all the mammalian
compartments of the graminoids, as well as some terrestrial mammals unique to the cypress (shrews, bats, feral pigs,
squirrel, skunks, bear, armadillos, and foxes; Bondavalli and Ulanowicz, 1999). These species are found mostly in the
cypress trees and cypress domes, which extend the spatial extent of the ecosystem into the third dimension.
Ascendency, Redundancy, and Development Capacity
Information theory is employed to quantify how well “organized” the trophic web is (expressed in terms of an
index called the system’s “ascendency”), how much functional redundancy it possesses (what is termed the “overhead”),
what its potential for development is, and how much of its autonomy is encumbered by the necessary exchanges
with the external world (Ulanowicz and Kay, 1991).
According to the “total system throughput (TST),” the graminoid system is far more active than the cypress system
(Table 10.4). Its TST (10,978 gC mÀ2
per year) is fourfold that of the cypress system (2952 gC mÀ2
per year). The
development capacity of an ecosystem is gauged by the product of the diversity of its processes as scaled by the TST.
The development capacity of the graminoid system (39,799 gC bits mÀ2
per year) is signiﬁcantly higher than that of
TABLE 10.4 Information Indices for Both the Graminoid and Cypress Systems.
Index
Cypress Graminoids
Index % of C Index % of C
Total system throughput (gC mÀ2
per year) 2952.3 10,978
Development capacity ¼ C (gC-bits mÀ2
per year) 14,659 39,799
Ascendancy (gC-bits mÀ2
per year) 4026.1 34.3 20,896 52.5
Overhead on imports (gC-bits mÀ2
per year) 2881.6 19.7 3637 9.1
Overhead on exports (gC-bits mÀ2
per year) 75.4 0.5 606 1.5
Dissipative overhead (gC-bits mÀ2
per year) 2940 20.1 4932 12.4
Redundancy (gC-bits mÀ2
per year) 3735.8 25.5 9728 24.4
Internal capacity (gC-bits mÀ2
per year) 5443.4 18,122
Internal ascendancy (gC-bits mÀ2
per year) 1707.5 31.4 8394 46.3
Redundancy (gC-bits mÀ2
per year) 3735.8 68.6 9728 53.7
CONNECTANCE INDICES
Overall connectance 1.826 1.586
Intercompartmental connectance 3.163 1.807
Food web connectance 2.293
10.4 APPLICATIONS OF NETWORK ANALYSIS AND ASCENDENCY TO SOUTH FLORIDA ECOSYSTEMS 193
the cypress (14,659 gC bits mÀ2
per year), a difference that one might be inclined to attribute to the disparity in the
scalar factor (TST) between the systems. When one regards the normalized ascendency, however (ascendency is a
measure of the constraint inherent in the network structure), one notices that the fraction of the development capacity
that appears as ordered ﬂow (ascendency/capacity) is 52.5% in the graminoids. This is markedly higher than the
corresponding fraction in the cypress system (34.3%).
The graminoid system has been stressed by a number of modiﬁcations to the patterns of its hydrological ﬂow,
which have resulted in the loss of transitional glades, reduced hydroperiods, unnatural pooling, and overdrainage
(Light and Dineen, 1994). In comparison with the cypress community, however, the system has exhibited fewer
changes in its faunal community and is sustained by an abundance of ﬂora and microbiota. The cypress ecosystem,
like that of the graminoids, is limited by a dearth of phosphorus, which remains abundant in marine and estuarine
waters and sediments. The graminoid system compensates for this scarcity of nutrients with a profusion of periphyton.
Periphyton exhibits a high P/B ratio, even under oligotrophic conditions.
The natural stressors that affect the cypress ecosystem appear to have far greater impacts, in that they modulate
the rates of material and energy processing to a far greater extent in that system. This analysis is phenomenological,
and there is no clear reason why the modulation of rates of material and energy occur in the cypress. Thus, even
though these systems are (1) adjacent to one another, (2) share many of the same species, and (3) some of the heterotrophs
of the cypress feed off the graminoid system, the characteristic indices of the graminoid system remain
distinct from those of the cypress community.
Calculating and ranking “relative sensitivities” proves to be an interesting exercise. For example, when the
average trophic levels of the 66 compartments of the graminoid wetland ecosystem were calculated, lizards, alligators,
snakes, and mink were revealed to be feeding at trophic levels higher than some of the “charismatic megafauna,”
such as the snail kite, nighthawk, Florida panther, or bobcat (Table 10.5).
The relative contributions to ascendency by the latter actually outweighed those of the former, however. The relative
values of these sensitivities thus seemed to accord with most people’s normative judgments concerning the speciﬁc
“value” of the various taxa to the organization of the system as a whole (Table 10.5).
Similarly, in the cypress system, white ibis, large ﬁsh, alligators, and snakes feed at high effective trophic levels,
but the system performance seemed to be enhanced more by the activities of the vultures, gray fox, bobcat, and panthers
(Table 10.5).
In comparing the component sensitivities in the graminoid and cypress systems, one discovers numerous similarities
between the taxa of the two systems (Table 10.5). For example, the avian and feline predators ranked high in
both systems. The contributions of snail kites and nighthawks to the performance of the graminoid system were
highest (at ca. 14 bits), while that of the bobcat and panther were highest in the cypress (at ca. 13 bits). Both bobcat
and panther seem to be more sensitive in the cypress than in the graminoids.
The low sensitivity of crayﬁsh (0.99 bits) in the graminoids was not repeated in the cypress, although aquatic invertebrates
generally had a low sensitivity in that system, too (2.01 bits). The sensitivity of labile detritus was similar
in both systems (around 1.5 bits), while refractory detritus was more sensitive in the graminoid (1.59 bits), indicating
a greater importance in that system. The sensitivities of the primary producers are lower in the cypress (1.51 bits)
than in the graminoids (1.66 bits) and are uniform within both systems, except for Utricularia in the graminoids. Utricularia
are carnivorous plants, and, therefore, both its effective trophic level and its sensitivities are higher than those
of the other primary producers (Table 10.5). Utricularia can exhibit an interesting example of positive feedback in
ecosystems; indeed, it harnesses the production of its own periphyton via intermediary zooplankton grazers.
This subsidy to the plant apparently allows it to drive in oligotrophic environments that would stress other macrophytes
with similar direct uptake rates. As ambient nutrient level rise, however, the advantage gained by positive
feedback wanes, until a point is reached where the system collapses (Ulanowicz, 1995).
The cypress system exhibits an additional spatial dimension in comparison with that of the graminoids. The third,
vertical (terrestrial) dimension of cypress vegetation provides both additional habitat and food for the higher trophic
levels. In the cypress, the appearance of terrestrial vegetation affords increased herbivory by terrestrial fauna such as
mammals, birds, and terrestrial invertebrates. Furthermore, much of what is produced by the bacteria is consumed
by the higher trophic levels, and less production is recycled back into the detritus. With the addition of the arboreal
dimension in the cypress, one would expect that system to be more productive than its graminoid counterpart, and
that the total systems throughput (and, consequently, other systems properties) would be higher in the cypress as
well. This is not the case, however. In fact, the throughput of the graminoids exceeds that of the cypress by some
fourfold. Although the total biomass in the cypress is three times greater than that in the graminoids, the cypress
systems P/B ratio is four times lower there than in the graminoids, thereby yielding the greater throughput in
the graminoids.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS194
TABLE 10.5 Ascendency Sensitivity Coefﬁcients (Sensitivity In Bits) and Effective Trophic Levels (ETLs) for Both the Graminoid and
Cypress Systems.
Graminoids Cypress
Compartment ETL Sensitivity Compartment ETL Sensitivity
1 Crayﬁsh 2.14 0.99 Liable detritus 1.00 1.42
2 Mesoinvertebrates 2.15 1.12 Refractory detritus 1.00 1.45
3 Other macroinvertebrates 2.12 1.15 Phytoplankton 1.00 1.51
4 Flagﬁsh 2.00 1.27 Floating vegetation 1.00 1.51
5 Poeciliids 2.20 1.47 Periphyton/macroalgae 1.00 1.51
6 Labile detritus 1.00 1.55 Macrophytes 1.00 1.51
7 Refractory detritus 1.00 1.59 Epiphytes 1.00 1.51
8 Apple snail 2.12 1.60 Understory 1.00 1.51
9 Tadpoles 2.03 1.63 Vine leaves 1.00 1.51
10 Periphyton 1.00 1.66 Hardwoods leaves 1.00 1.51
11 Macrophytes 1.00 1.66 Cypress leaves 1.00 1.51
12 Floating vegetation 1.00 1.66 Cypress wood 1.00 1.51
13 Utricularia 1.03 1.69 Hardwood wood 1.00 1.51
14 Lizards 3.83 1.79 Roots 1.00 1.51
15 Freshwater prawn 2.27 2.12 Aquatic invertebrates 2.20 2.01
16 Ducks 2.20 2.32 Tadpoles 2.16 2.29
17 Blueﬁn killiﬁsh 2.57 2.34 Anseriformes 2.05 2.38
18 Other small ﬁshes 2.48 2.44 Crayﬁsh 2.26 2.46
19 Sediment carbon 1.00 2.44 Terrestrial invertebrates 2.00 2.55
20 Living sediments 2.00 2.58 Living sediment 2.00 2.64
21 Mosquito ﬁsh 2.47 2.64 Squirrels 2.00 2.72
22 Living particulate organic carbon
(POC)
2.00 2.80 Apple snail 2.26 2.74
23 Chub suckers 2.50 2.86 Prawn 2.26 2.91
24 Shiners and minnows 2.68 3.60 Rabbits 2.00 2.97
25 Gruiformes 2.01 3.76 White-tailed deer 2.00 2.97
26 Muskrats 2.00 3.83 Living POC 2.00 3.08
27 White-tailed deer 2.00 3.83 Black bear 2.26 3.30
28 Terrestrial inverts 2.00 3.91 Small herbivorous and
omnivorous ﬁsh
2.60 3.48
29 Rabbits 2.00 5.10 Galliformes 2.33 3.58
30 Killiﬁsh 2.81 5.13 Mice and rats 2.37 3.77
31 Turtles 2.74 5.57 Wood stork 3.24 3.82
32 Large aquatic insects 2.96 5.63 Raccoon 2.74 3.84
33 Salamander larvae 2.57 5.64 Great blue heron 3.24 3.85
34 Grebes 2.63 5.79 Egrets 3.23 3.90
35 Other centrarchids 3.02 6.59 Hogs 2.44 3.96
Continued
10.4 APPLICATIONS OF NETWORK ANALYSIS AND ASCENDENCY TO SOUTH FLORIDA ECOSYSTEMS 195
The increase in throughput in the graminoids increases its development capacity and ascendency. The relative
ascendency, which excludes the effects of the throughput, is perhaps a better index with which to compare these
two systems. The relative ascendency of the graminoids is exceptionally high. For example, Heymans and Baird
(2000) found that upwelling systems have the highest relative ascendency of all the systems they compared (which
TABLE 10.5 Ascendency Sensitivity Coefﬁcients (Sensitivity In Bits) and Effective Trophic Levels (ETLs) for Both the Graminoid and
Cypress Systems.dcont’d
Graminoids Cypress
Compartment ETL Sensitivity Compartment ETL Sensitivity
36 Rats and mice 2.27 6.66 Other herons 3.21 4.10
37 Raccoons 2.59 6.72 White ibis 3.58 4.19
38 Opossum 2.45 6.77 Turtles 2.82 4.28
39 Pigmy sunﬁsh 3.09 6.79 Woodpeckers 2.52 4.43
40 Bluespotted sunﬁsh 3.09 6.83 Omnivorous passerines 2.53 4.45
41 Dollar sunﬁsh 3.09 6.87 Hummingbirds 2.53 4.45
42 Seaside sparrow 2.57 7.10 Small carnivorous ﬁsh 3.07 5.56
43 Passerines 2.96 7.16 Opossum 2.35 5.61
44 Topminnows 3.10 7.47 Kites and hawks 3.37 6.10
45 Redear sunﬁsh 3.13 9.09 Owls 3.36 6.10
46 Catﬁsh 3.11 9.21 Mink 3.25 6.21
47 Spotted sunﬁsh 3.16 9.32 Otter 3.25 6.23
48 Warmouth 3.21 9.42 Medium frogs 3.21 6.24
49 Mink 3.41 9.53 Small frogs 3.21 6.24
50 Snakes 3.32 9.66 Salamanders 3.28 6.32
51 Otter 3.34 9.71 Large frogs 3.32 6.38
52 Bitterns 3.25 9.75 Gruiformes 3.35 6.53
53 Alligators 3.39 9.96 Armadillo 2.90 6.54
54 Large frogs 3.29 10.19 Pelecaniformes 3.40 6.61
55 Small frogs 3.17 10.33 Large ﬁsh 3.42 6.99
56 Other large ﬁshes 3.27 10.69 Lizards 3.00 7.64
57 Largemouth bass 3.24 10.92 Caprimulgiformes 3.00 7.64
58 Medium frogs 3.16 10.93 Bats 3.00 7.64
59 Gar 3.45 10.96 Predatory passerines 3.00 7.64
60 Cichlids 3.22 10.98 Shrews 3.00 7.65
61 Fishing spider 3.27 11.77 Alligators 3.78 8.30
62 Bobcat 3.02 12.01 Snakes 3.79 8.58
63 Salamanders 3.32 12.29 Salamander larvae 3.20 8.62
64 Panthers 3.17 12.33 Vertebrate detritus 1.00 8.82
65 Snail kites 3.13 14.38 Vultures 2.00 10.03
66 Nighthawks 3.00 14.69 Gray fox 3.41 10.29
67 Bobcat 3.03 12.96
68 Florida panther 3.36 13.48
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS196
were mostly estuarine or marine in origin), but the relative ascendency of 52% for the graminoids is higher than any
such index they had encountered. The relative ascendency of 34% reported for the cypress is lower than most of the
relative ascendencies reported by Heymans and Baird (2000).
Some reasons behind the higher relative ascendency of the graminoids can be explored with reference to the relative
contributions of the various components to the community ascendency (Table 10.5). The highest such “sensitivity”
in the cypress is more than 1 bit lower than its counterpart in the graminoids, and, on average, most
higher trophic level compartments that are present in both models exhibit higher sensitivity in the graminoids
than in the cypress. It is noteworthy also that 41 compartments in the cypress show sensitivities of less than 5
bits, while only 28 compartments lie below the same threshold in the graminoids. The higher sensitivities in the graminoids
owe mainly to the greater activity among the lowest trophic compartments, which causes the other compartments
to seem rare by comparison. Thus, in the graminoids, community performance seems sensitive to a
larger number of taxa, which accords with the analysis of dependency coefﬁcients and stability discussed in Heymans
et al. (2002). Pahl-Wostl (1998) suggested that the organization of ecosystems along a continuum of scales derives
from a tendency for component populations to ﬁll the envelope of available niche spaces as fully as possible.
This expansive behavior is seen in the cypress system, where the arboreal third dimension of the cypress trees ﬁlls
with various terrestrial invertebrates, mammals, and birds not present in the graminoids. The graminoid system,
however, appears to be more tightly organized (higher relative ascendency) than the cypress in that it utilizes primary
production with much higher turnover rates. This conﬁrms the suggestion made by Kolasa and Waltho (1998)
that niche space is not a rigid structure but rather coevolves and changes in mutual interaction with the network
components and the dynamical pattern of the environment. The graminoid system is more responsive because it
utilizes primary producers with higher turnover rates, and has, therefore, been able to track more closely environmental
and anthropogenic changes. The cypress system, on the other hand, should have more resilience over the
long term due to its higher overhead, especially its redundancy (Table 10.4).
Conclusions
According to Bondavalli et al. (2000) a high value of redundancy signiﬁes that either the system is maintaining a
higher number of parallel trophic channels in order to compensate the effects of environmental stress or that it is well
along its way to maturity. Even though these authors suggest that the cypress system is not very mature, in comparison
to the graminoids, one would have to conclude that the cypress is a more mature system. A slower turnover
rate, as one observes in arboreal systems, such as the cypress, is indicative of a more mature ecosystem. Furthermore,
the third dimension of terrestrial vegetation affords the system a greater number of parallel trophic channels to the
higher trophic levels than exists among the mainly periphyton-dominated graminoid system. Although the graminoid
system has a large throughput of carbon and a substantial base of fast-producing periphyton, it appears relatively
fragile in comparison to the cypress system, which is more resilient over the long run and has more trophic
links between the primary trophic level and the heterotrophs. In conclusion, according to ascendency indices,
scaledin the guise of the vertical dimension of the cypressdmakes that system more resilient as a whole and
less sensitive with respect to changes in material processing by many of its composite species.
10.5 THE APPLICATION OF ECO-EXERGY AS ECOLOGICAL INDICATOR FOR
ASSESSMENT OF ECOSYSTEM HEALTH
Reference where eco-exergy has been used as ecosystem health indicator:
Zaldı´var, J.M., Austoni, M., Plus, M., De Leo, G.A., Giordani, G., Viaroli, P., 2005. Ecosystem health assessment
and bioeconomic analysis in Coastal Lagoon. In: Handbook of Ecological Indicator for Assessment of Ecosystem
Health. CRC Press, pp. 163e184.
In this paragraph is reported an application of eco-exergy (see Chapters 2 and 7) to assess the ecosystem health of a coastal
lagoon.
Coastal lagoons are subjected to strong anthropogenic pressure. This is partly due to freshwater input rich in
organic and mineral nutrients derived from urban, agricultural, or industrial efﬂuent and domestic sewage, but
also due to the intensive shellﬁsh farming.
10.5 THE APPLICATION OF ECO-EXERGY AS ECOLOGICAL INDICATOR FOR ASSESSMENT OF ECOSYSTEM HEALTH 197
The Sacca di Goro is a shallow water embayment of the Po Delta. The surface area is 26 km2
and the total water
volume is approximately 40 Â 106
m3
. The catchment basin is heavily exploited for agriculture, while the lagoon is
one of the most important clam (Tapes philippinarum) aquaculture systems in Italy. The combination of all these
anthropogenic pressures call for an integrated management that considers all different aspects, from lagoon ﬂuid
dynamics, ecology, nutrient cycles, river runoff inﬂuence, shellﬁsh farming, macroalgal blooms, and sediments,
as well as the socioeconomical implication of different possible management strategies. All these factors are responsible
for important disruptions in ecosystem functioning characterized by eutrophic and dystrophic conditions in
summer (Viaroli et al., 2001), algal blooms, oxygen depletion, and sulﬁde production (Chapelle et al., 2000). Water
quality is the major problem. In fact from 1987 to 1992 the Sacca di Goro experienced an abnormal proliferation of
macroalga Ulva spp. This species has become an important component of the ecosystem in Sacca di Goro. The
massive presence of this macroalga has heavily affected the lagoon ecosystem and has prompted several interventions
aimed at removing its biomass in order to avoid anoxic crises, especially during the summer, when the Ulva
biomass start to decompose. Such crises are responsible for considerable damage to the aquaculture industry and
to the ecosystem functioning.
To carry out such an integrated approach a biogeochemical model, partially validated with ﬁeld data from 1989 to
1998, has been developed (Zaldı´var et al., 2003). To analyze its results it is necessary to utilize ecological indicators,
using not only indicators based on particular species or component (macrophytes or zooplankton) but also indicators
able to include structural, functional, and system level aspects. Eco-exergy and speciﬁc eco-exergy are used to
assess the ecosystem health of this coastal lagoon. Effects of Ulva’s mechanical removal on the lagoon’s eutrophication
level are also studied with speciﬁc exergy (Jørgensen, 1997) and costebeneﬁts analysis (De Leo et al., 2002).
Three scenarios are analyzed (for a system with clam production and eutrophication by Ulva) using a lagoon model:
(1) present situation, (2) optimal strategy based on costebeneﬁt for removal of Ulva, and (3) a signiﬁcant nutrient
loading reduction from watershed. The costebeneﬁt model evaluates the direct cost of Ulva harvesting including
vessel cost for day and damage to shellﬁsh production and the subsequent mortality increase in the clam population.
To take into account this factor, the total beneﬁt obtained from simulating the biomass increase was evaluated using
the averaged prices for clam in northern Adriatic; therefore, an increase in clam biomass harvested from the lagoon
will result in an increase of beneﬁt.
The Sacca di Goro model has several state variables for which the exergy was computed: organic matter (detritus),
phytoplankton (diatoms and ﬂagellates), zooplankton (micro- and meso-), bacteria, macroalgae (Ulva sp), and shellﬁsh
(Tapes philippinarum). The exergy and the speciﬁc eco-exergy are calculated using the data from Table 10.6 on
genetic information content and all biomasses were reduced to gDW/L.
Figs. 10.7 and 10.8 present the evolution of exergy and speciﬁc exergy under the two proposed scenarios: Ulva
removal and nutrient load reduction, in comparison with the “do nothing” alternative. As it can be seen the ecoexergy
and speciﬁc eco-exergy of both increase, due to the fact that in our model both functions are dominated
by clam biomass.
However, the optimal result from the cost/beneﬁt analysis will considerably improve the ecological status of the
lagoon in terms of speciﬁc exergy.
TABLE 10.6 Parameters Used to Evaluate the Genetic Information Content.
Ecosystem Component Number of Information Genes Conversion Factor
Detritus 0 1
Bacteria 600 2.7 (2)
Flagellates 850 3.4 (25)
Diatoms 850 3.4
Microzooplankton 10,000 29.0
Mesozooplankton 15,000 43.0
Ulva sp. 2000a
6.6
Shellﬁsh (bivalves) e 287b
a
Coffaro et al. (1997).
b
Marques et al. (1997), Fonseca et al. (2000).
From Jørgensen, S.E., 2000a. Principles of Pollution Abatement. Elsevier, Oxford, UK, 520 pp.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS198
FIGURE 10.7 Eco-exergy mean annual values: Present scenario (continuous line), removal of Ulva, optimal strategy from costebeneﬁt point of
view (dotted line) and nutrients load reduction from watershed (dashed line).
FIGURE 10.8 Speciﬁc eco-exergy mean annual values: Present scenario (continuous line), removal of Ulva, optimal strategy from costebeneﬁt
point of view (dotted line) and nutrients load reduction from watershed (dashed line).
10.5 THE APPLICATION OF ECO-EXERGY AS ECOLOGICAL INDICATOR FOR ASSESSMENT OF ECOSYSTEM HEALTH 199
Conclusions
The results show that costebeneﬁt optimal solution for removal of Ulva has the highest eco-exergy and speciﬁc
eco-exergy, followed by a signiﬁcant removal of nutrients from the watershed. In the case for removal of Ulva speciﬁc
exergy continues to increase as the number of vessels operating in the lagoon increase. The present situation had the
lowest eco-exergy and speciﬁc eco-exergy. The result shows that it is a good sustainability policy to take care of natural
resources, in this case the clams.
Eco-exergy expresses the system biomass and genetic information embedded in that biomass, while speciﬁc ecoexergy
tells us how rich in information the system is. These indicators broadly encompass ecosystem characteristics
and it has been shown that they are correlated with several important parameters such as respiration, biomass, etc.
However it has been pointed out (Jørgensen, 2000a) that eco-exergy is not related to biodiversity, and, for example, a
very eutrophic system often has a low biodiversity but high eco-exergy.
When the manager must select between different alternatives, it is difﬁcult to evaluate the optimal solution from
an ecological point of view. As eco-exergy and speciﬁc exergy are global parameters of the ecosystem, they give an
idea of beneﬁts that a measure will produce.
10.6 EMERGY AS ECOLOGICAL INDICATOR TO ASSESS ECOSYSTEM HEALTH
Reference where emery is applied as ecological indicator:
Howington, T.M., Brown, M.I., Wiggington, M., 1997. Effect of hydrologic subsidy on self-organization of a
constructed wetland in Central Florida. Ecol. Eng. 9, 137e156.
Emergy (see Chapter 7) is used to study and explain theories concerning the effect of an external subsidy on a complex system
(constructed wetland) seen by an holistic point of view.
Lake Apopka is a shallow (mean depth ¼ 1.7 m) hypereutrophic lake in Central Florida, with an area of 124 km2
(Lowe et al., 1989, 1992). In the early 1940s a hurricane removed most of the rooted macrophytes in the lake which led
to the early stages of increased nutrient availability and subsequently increased algal productivity (Schelske and Brezonik,
1992). Addressing the nutrient status of this lake, the St. Johns River Water Management District (SJRWMD)
constructed a 200 ha freshwater marsh on former agricultural lands with the goal of reducing the nutrient levels in
the lake. It was suggested that by pumping enriched lake water through a constructed marsh, ﬁltration of phosphorus
and suspended sediments could be maximized. The pump system was turned on in early 1991. The subsidized
and unsubsidized marsh maintained similar average water levels (0.76 m) throughout the study period
varying yearly by no more than 0.2 m. Theory suggests that an external subsidy should increase the carrying capacity
for wildlife of an ecosystem, all other things being equal. The increased capacity for wildlife may be an indirect
result of certain self-organizational processes such as changes in vegetative cover. Other factors inﬂuencing the relationship
between wetland productivity and hydroperiod include nutrient inputs, export, nutrient cycling, and
decomposition (Carpenter et al., 1985).
This study tested theories concerning the effect of an external subsidy on ecosystem structure and organization.
Two newly established marshes (one receiving nutrient enriched lake water and the other not receiving the subsidy)
were the areas under study. The 63 ha subsidized marsh is the ﬁrst of 2 cells that constitute the treatment wetland
receiving lake water. The unsubsidized marsh, 46 ha, was created as a result of being a borrow pit for building berms
around the treatment wetland. Vegetative cover richness and percent cover were determined using aerial photos and
GIS, and was calculated using Margalef’s index for species richness. Percent cover provided a further description of
the changes in structural complexity of each marsh over time. Also avifauna surveys were conducted. Shannon diversity
indexes were used to compare the avian communities found in the surveyed marshes. A synoptic study on
the ﬁsh population of the subsidized and unsubsidized marshes was also conduced. A model of the marsh system
(see Fig. 10.9 for energy symbols) was created to describe the role of the most important components and relationships
(Fig. 10.10). An emergy analysis was performed to evaluate on a common basis (solar energy) the contributions
of the various inputs (pumps, water, nutrients, human services, and renewable energies) driving the marshes
ecosystems.
Emergy evaluation separates inputs on the basis of the origin (local or purchased) and of their renewability (see
also Chapter 7). An environmental loading ratio (ratio of local and exogenous nonrenewable emergy to renewable
emergy) and an investment ratio (ratio of exogenous to local emergies) were calculated to compare the quantities
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS200
and qualities of the energies entering each system. Emergy analysis tables were developed separately in Tables 10.7
and 10.8 for the subsidized and unsubsidized marshes.
The environmental loading ratio showed a large contrast between the two marshes. Investment ratios for the two
marshes showed a large difference in the amount of purchased energy necessary to maintain the ﬂows of environmental
inputs.
Table 10.9 contains the ratios of free to purchased energy (environmental loading) and nonrenewable energy to
renewable energy (investment ratio). Renewable energy sources for the subsidized marsh and the unsubsidized
marsh were the same in both marshes. Lake water pumped into the subsidized marsh largely increased the emergy
of total nitrogen and total phosphorus compared to that entering the unsubsidized marsh. This implies that the
emergy ﬂux of free nonrenewable energy sources inﬂuencing self-organization contributed 26% of the total emergy
ﬂow to the subsidized marsh. Liquid fuel used to operate the hydraulic pumps and the physical structure of the
pump system itself are the two nonrenewable purchased energy sources that were included in the subsidized marsh
system and that contributed 68% of its total emergy ﬂow.
Vegetative community richness in the subsidized marsh was lower than that of the unsubsidized marsh. Fish
biomass was also signiﬁcantly different between marshes (Table 10.10).
A dynamic model was used to simulate situations in which the fuel use was increased (0% ¼ unsubsidized marsh;
100% ¼ subsidized marsh). Fig. 10.11 shows the changes in biomass carrying capacity with different levels of fuel
used. Material and energy balances, as shown in the emergy analysis, were signiﬁcantly different between the subsidized
and unsubsidized marshes.
FIGURE 10.9 Energy symbols used to make an energy diagram.
10.6 EMERGY AS ECOLOGICAL INDICATOR TO ASSESS ECOSYSTEM HEALTH 201
Lake
Apopka
nutrients
Rain
and
Nutrients
Peat
Nutrients
Sun
Water
Plants
nutrients
Detritus
Insects
Fish
Birds
Pump
System
Fuel
FIGURE 10.10 Diagram of constructed marsh. Removal of pump system simulated unsubsidized marsh.
TABLE 10.7 Annual Energy, Material, and Dollar Flows and Resulting Emergy Flows Supporting 1 Ha of the Subsidized Marsh.
Notes Energy (J, g, $) Transformity (sej/unit) Emergy (ED14)
RENEWABLES
1 Sun 5.41Eþ09 J 1.00Eþ00 0.00
2 Rain-chemical potential 6.27Eþ10 J 1.54Eþ04 9.66
NONRENEWABLES-FREE
3 Total nitrogen 7.85Eþ05 g 4.21Eþ09 33.05
4 Total phosphorus 4.31Eþ04 g 6.88Eþ09 2.96
5 Phytoplankton 2.12Eþ09 J 1.00Eþ04 0.00
6 Pumped water-chemical potential 2.39Eþ09 J 2.35Eþ04 0.56
NONRENEWABLES-PURCHASED
7 Liquid fuel 1.21Eþ11 J 6.60Eþ04 79.59
8 Construction-structure 5.11Eþ03 g 6.70Eþ09 0.34
9 Construction-services 9.34Eþ01 $ 1.60Eþ12 1.49
10 Operation and maintenance 9.55E02 $ 1.60Eþ12 15.28
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS202
Due to the nonrenewable energy sources from the lake (e.g., nutrients, phytoplankton) and the pump system itself,
the subsidized system had much higher ﬂows of available resources. This is also clearly evident in the higher
densities and biomass of the avian and ﬁsh communities. On the other hand, the complexity of the subsidized marsh
as measured by diversity and community structure was lower. High emergy subsidies may compromise the
complexity of the system in favor of high productivity. Community structure and dynamics are likely a result of
many processes, including demographics, energy cycling, habitat disturbance, and the inﬂuence of other populations
(Brown and Maurer, 1987; Maurer and Brown, 1988; Weins, 1989). In the case of the subsidized marsh, organization
and community dynamics are also controlled by the availability of energy sources with high
transformities. The importance of certain high emergy sources is their ability to facilitate the input of additional
nonrenewable energies. The nutrient enrichment seemed to speed up self-organizational processes in the subsidized
marsh increasing the rate of vegetative coverage of the marsh. Given the higher animal densities and biomass the
TABLE 10.8 Annual Energy, Material and Dollar Flows and Resulting Emergy Flows Supporting 1 Ha of the Unsubsidized
Marsh.
Notes Energy (J, g, $) Transformity (sej/unit) Emergy (ED14 sej)
RENEWABLES
1 Sun 5.41Eþ09 J 1.00Eþ00 0.00
2 Rain-chemical potential 6.27Eþ10 J 1.54Eþ04 9.66
3 Total nitrogen 1.54Eþ04 g 4.21Eþ09 0.65
4 Total phosphorus 6.35Eþ02 g 6.88Eþ09 0.04
NONRENEWABLES-PURCHASED
5 Construction-services 3.76Eþ01 $ 1.60Eþ12 0.60
TABLE 10.9 Environmental Ratio and Investment Ratio.
Subsidized (ED15sej) Unsubsidized Marsh (ED15sej)
EMERGY FLOWS
Renewable emergy 9.7 9.7
Nonrenewable emergy
Free 36.6 9.7
Purchased 96.7 0.6
Total emergy ﬂux 142.9 19.9
EMERGY INDEX
Environmental loading 13.8 0.1
Investment ratio 2.1 0.1
TABLE 10.10 Summary of Fish Community Structure.
Parameter Subsidized Unsubsidized Marsh Signiﬁcant Difference
Fish density 230 mÀ2
165 mÀ2
n ¼ 30, df ¼ 5, P ¼.01
Fish biomass 6.44 kg mÀ2
4.29 kg mÀ2
n ¼ 30, df ¼ 5, P ¼.03
10.6 EMERGY AS ECOLOGICAL INDICATOR TO ASSESS ECOSYSTEM HEALTH 203
external subsidy may have also increased the rate that these components reached their respective carrying
capacities.
This theory seemed to be validated by the computer model simulation; it suggests that carrying capacity varies
with different levels of external subsidy.
The simulation in Fig. 10.12 of changing subsidy reveals that if the subsidy is pulsed, biomass will also pulse; the
simulation model is sustained by studies in literature about the relationship between nutrients increase and biomass
increase (Price, 1992; Kerekes, 1990). Overall, the external subsidy increased the emergy ﬂux in the subsidized marsh
Fuels at 0
0
100
200
300
400
0 10 20 30 40 50 60 70 80 90 100 110 120
Month
PercentVariation
Detritus
Insects
Fish
Birds
Fuels at 10% of Use by Subsidized Marsh
0
100
200
300
400
0 10 20 30 40 50 60 70 80 90 100 110 120
Month
PercentVariation
Detritus
Insects
Fish
Birds
Fuels at 50% of Use by Subsidized Marsh
0
100
200
300
400
0 10 20 30 40 50 60 70 80 90 100 110 120
Month
PercentVariation
Detritus
Insects
Fish
Birds
Fuels at 100% (equal to fuel use of subsidized marsh)
0
100
200
300
400
0 10 20 30 40 50 60 70 80 90 100 110 120
Month
PercentVariation
Detritus
Insects
Fish
Birds
(A) (B)
(C) (D)
FIGURE 10.11 Percentage variation of biomass over time for different rates of fuel use showing: (A) 0 fuel usage represented by unsubsidized
marsh; (B) 10%; (C) 50%; (D) 100%; (E) 500%; and (F) 1000% fuel usage relative to actual usage by subsidized marsh. (100% ¼ biomass carrying
capacity of unsubsidized marsh). Vertical dashed line marks appropriate time when marsh biomass reaches a steady state.
0
100
200
300
400
500
600
700
800
1 20 39 58 77 96 115
Detritus
Insects
Fish
Birds
FIGURE 10.12 Percentage variation of biomass over time as fuel usage changes monthly as sine wave between 0% and 100% fuel usage
relative to actual fuel usage of subsidize marsh.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS204
by increasing the input of nonrenewable energy sources. As a result community parameters such as density and
overall biomass also increased in the subsidized marsh, but at a cost of lowered richness, diversity, and evenness.
Conclusions
The emergy analysis reveals that the higher emergy ﬂow in the subsidized marsh is caused by a higher input of
nonrenewable energy sources. Community parameters (density and overall biomass) are also higher in the subsidized
marsh, but at a cost of lowered richness, diversity, and evenness. Complexity of community structures is
not inﬂuenced by the external subsidy. External subsidies increase the total emergy ﬂow in an ecosystem and
may increase the rate of successional processes in both the vegetative and wildlife communities.
10.7 THE ECO-EXERGY TO EMPOWER RATIO AND THE EFFICIENCY OF ECOSYSTEMS
Reference where the ratio eco-exergy to empower is used as ecological indicator:
Bastianoni, S., Marchettini, N., 1997. Emergy/exergy ratio as a measure of the level of organization of systems.
Ecol. Model. 99, 33e40.
Bastianoni, S., 2002. Use of thermodynamic orientors to assess the efﬁciency of ecosystems: a case study in the
lagoon of Venice. Sci. World J. 2, 255e260.
Bastianoni, S., 2006. Emergy, empower and the eco-exergy to empower ratio: a reconciliation of H.T. Odum with
Prigogine? Int. J. Ecodynamics (in press).
The ratio of exergy to emergy ﬂow (empower) has been used in order to assess the efﬁciency of an ecosystem in transforming
available inputs in actual information and organization in eight aquatic ecosystems located in Argentina, Italy, and the United
States.
The Case Studies
Eight aquatic ecosystems are used to understand the importance as an indicator of the eco-exergy to empower
(emergy ﬂow) ratio. Two of these ecosystems (called “Control Pond” and “Waste Pond”) are in North Carolina
(USA) and are part of a group of similar systems built to purify sewage. Near the town of Morehead City, six artiﬁcial
lakes were created: three control ponds fed with estuary water and “clean” water from the local sewage treatment
plant, and three “waste” ponds fed with estuary water mixed with more “polluted” (i.e., richer in nutrients) efﬂuent
(Odum, 1989). Plant and animal species were introduced in and around the lakes to colonize the new areas and eventually
produce new ecosystems by natural selection.
The third ecosystem is the Lagoon of Caprolace in Latium, Italy, at the edge of the Circeo National Park. The
Lagoon of Caprolace is an ancient natural system fed by rainwater and farmland run-off rich in nitrogen, phosphorus,
and potassium.
The fourth ecosystem is Lake Trasimeno. This is the largest lake in peninsular Italy (area 124 km2
), and it is
shallow (mean depth 4.7 m, maximum 6.3 m) and accumulation processes are favored. The water level of the
lake shows strong ﬂuctuations with respect to meteorological conditions; hydrological crises occur after several
years with annual rainfall <700 mm.
The ﬁfth system is the Lagoon of Venice. With a surface area of about 550 km2
, it is the largest Italian lagoon. The
sea and the lagoon are connected through three inlets. The average daily volume of water that enters the lagoon from
the sea is about 400 million m3
, while 900 million m3
of fresh water ﬂow into the lagoon every year from the drainage
basin.
The sixth system is an artiﬁcial one, located in the central part of the Lagoon of Venice, i.e., the Figheri basin. Fish
farming basins consist of peripheral areas of lagoon surrounded by banks in which local species of ﬁsh and crustaceans
are raised. Salt water from the sea and freshwater from canals and rivers are regulated by locks and drains. The
ﬁshes of highest demand raised in basins are Dicentrarchus labrax (bass) and Sparus auratus. Various types of mullet
are also raised, as well as eels and mollusks.
Two ecosystems are located within the Esteros del Ibera´ (northeastern Argentina), one of the most pristine and
largest wetlands of South America (13,000 km2
). This subtropical wetland is located between 27360 and 28570S
and 58000e57300W. The macrosystem consists of a mosaic of marshes, swamps, and open water bodies. It is located
10.7 THE ECO-EXERGY TO EMPOWER RATIO AND THE EFFICIENCY OF ECOSYSTEMS 205
between three large rivers, the Rio Parana´ alto, the Rio Parana´ medio, and the Rio Uruguay, with a single outlet to the
Rio Corrientes that feeds into the Parana Medio (Bracchini et al., 2005; Loiselle et al., 2001).
The Galarza Lagoon is a mesotrophic, round-shaped lake with an area of 14 km2
and averages 2 m in depth. The
lagoon is fed by a small stream that originates in the large marsh area (200 km2
) directly above the lagoon and feeds
into another small stream that leads to another large shallow lagoon. The water then ﬂows out of this second lagoon
into another large marsh area.
Laguna Ibera´ (area 58 km2
, mean depth 3.2 m) has a more irregular morphology and an eutrophic status. This lake
is divided into two basins by a narrow passage that acts as a barrier reducing the interchange of wave energy and
water masses. A small river (Rıo Mirin˜ay) feeds the southern basin.
Emergy, Exergy and Their Joint Use
Why use emergy ﬂow (empower) and eco-exergy together on the same systems? Emergy and eco-exergy are complementary
concepts, the former based on the history of the system (Odum, 1988, 1996) and the latter examining the
actual state (Jørgensen, 1992, 2006). When systems follow a process of selection and organization, we can use the
ratio of exergy to emergy ﬂow in order to assess the efﬁciency of an ecosystem in transforming available inputs
in actual information and organization. The higher the ratio, the greater the efﬁciency of the ecosystem in transforming
available inputs (as emergy ﬂow) into structure and ecosystem organization (as eco-exergy). Its units are
J yr sejÀ1
. Since dimensions are those of time, it cannot be regarded as a real efﬁciency (which is dimensionless),
but more as an index of efﬁciency.
According to Svirezhev, this fact is normal, since this concept resembles that of a relaxation time, i.e., the time
necessary to recover from disturbances, so that the exergy to empower ratio should be related with concepts like
resilience and resistance of an ecosystem.
The eco-exergy/empower ratio indicates the quantity of external input necessary to maintain a structure far from
equilibrium: if the eco-exergy/empower ratio tends to increase (apart from oscillations due to normal biological cycles),
it means that natural selection is making the system follow a thermodynamic path that will bring the system to
a higher organizational level.
As an efﬁciency indicator the eco-exergy to empower ratio enlarges the viewpoint of a pure exergetic approach as
described in Fath et al. (2004), where the exergy degraded and the eco-exergy stored for various ecosystems are
compared: using emergy there is a recognition of the fact that solar radiation is the driving force of all the energy
(and exergy) ﬂows on the Biosphere, important when also important “indirect” inputs (of solar energy) are present
in a process.
To compare ecosystems different in size empower and eco-exergy densities were used. Table 10.11 shows
empower and eco-exergy density values and the ratio of eco-exergy to empower. The emergy ﬂow to Ibera´ Lagoon
has been underestimated due to lack of data about the release of nutrients from the surrounding rice farms. In a
sense this explains the highest value for eco-exergy to empower ratio, while the ecosystem does not seem to be in
ideal conditions (Bastianoni et al., 2006). Nonetheless, the important fact is that all the natural systems that are better
protected from human inﬂuence show very close ﬁgures. It seems that there is a tendency common to different ecosystems
in different areas and of different characteristics to evolve toward similar thermodynamic efﬁciencies.
Figheri basin is an artiﬁcial ecosystem, but has many characteristics typical of natural systems. This depends
partly on the long tradition of ﬁsh farming basins in the Lagoon of Venice, which has ““selected” the best
TABLE 10.11 Empower Density, Ecoexergy Density and Eco-exergy to Empower Ratio for eight Different Ecosystems.
Control
Pond
Waste
Pond
Caprolace
Lagoon
Trasimeno
Lake
Venice
Lagoon
Figheri
Basin
Ibera´
Lagoon
Galarza
Lagoon
Empower density
(sej yearÀ1
LÀ1
)
20.1 Â 108
31.6 Â 108
0.9 Â 108
0.3 Â 108
1.4 Â 109
12.2 Â 108
1.0 Â 108
1.1 Â 108
Eco-exergy density
(J LÀ1
)
1.6 Â 104
0.6 Â 104
4.1 Â 104
1.0 Â 104
5.5 Â 104
71.2 Â 104
7.3 Â 104
5.5 Â 104
Eco-exergy/
empower
(10À5
J year sejÀ1
)
0.8 0.2 44.3 30.6 39.1 58.5 73 50.0
Also the results on the entire lagoon of Venice conﬁrm the general trend, showing ﬁgures in the range of Trasimeno and Caprolace, albeit the differences in the
structure of the ecosystems and the huge inputs from the watershed.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS206
management strategies (Bastianoni, 2002). The human contribution at Figheri basin manifests as a higher emergy
density (of the same order of magnitude as that of artiﬁcial systems) than in natural systems. However, there is a
striking difference in eco-exergy density, with values of a higher order of magnitude than in any of the other systems
used for comparison: Man and nature are acting in synergy to enhance the performance of the ecosystem. The fact
that Figheri can be regarded as a rather stable ecosystem (i.e., quite regular in its behavior) makes this result even
more interesting and signiﬁcant.
It was observed that the “natural” lagoon of Caprolace had a higher eco-exergy/emergy ratio than the control and
waste ponds, due to a higher eco-exergy density and a lower emergy density (Bastianoni and Marchettini, 1997).
These observations were conﬁrmed by the study of Lake Trasimeno (Ludovisi and Poletti, 2003).
Conclusions
In general, in the more “natural” systems, where selection has acted relatively undisturbed for a long time, the
ratio of eco-exergy to empower is higher, and decreases with the introduction of artiﬁcial inputs or stress factors
that make higher the emergy ﬂow and/or lower the eco-exergy content of the ecosystem.
10.8 APPLICATION OF ECO-EXERGY AND ASCENDENCY AS ECOLOGICAL
INDICATOR TO THE MONDEGO ESTUARY (PORTUGAL)
The application of eco-exergy and ascendency as ecological indicators are presented in:
Jorgensen, S.E., Marques, J., Nielsen, S.N., 2002. Structural changes in an estuary, described by models and using
exergy as orientor. Ecol. Model. 158, 233e240.
Marques, J.C., Pardal, M.A., Nielsen, S.N., Jorgensen, S.E., 1997. Analysis of the properties of exergy and
biodiversity along an estuarine gradient of eutrophication. Ecol. Model. 102, 155e167.
Patrı´cio, J., Ulanowicz, R., Pardal, M.A., Marques, J.C., 2004. Ascendency as an ecological indicator: a case study of
estuarine pulse eutrophication. Estuar. Coast Shelf Sci. 60, 23e35.
The Mondego estuary has been used to beneﬁt of the integration of the information derived from different ecological indicators:
eco-exergy, speciﬁc eco-exergy (Chapter 2 and 7), and ascendency (Chapter 6).
Mondego Estuary: The Mondego River drains a hydrological basin of approximately 6670 km2
at the western
coast of Portugal. Urban wastewater is still discharged into the Mondego without treatment, and the estuary supports
industrial activities, desalination ponds, and aquaculture. Additionally, the lower Mondego River valley
has about 15,000 ha of farming ﬁelds (mainly rice paddies), with a signiﬁcant loss of nutrients to the estuary (Marques,
1989). The Mondego estuary is located in a warm/temperate region with a basic Mediterranean temperate
climate. It consists of two arms, north and south (Fig. 10.13), separated by an island. The two arms split in the estuarine
upstream area about 7 km from the sea, and join again near the mouth. These two arms of the estuary present
very different hydrographic characteristics. The north arm is deeper (5e10 m during high tide, tidal range about
2e3 m), while the south arm (two to four m deep, during high tide) is almost ﬁlled with silt in the upstream areas,
directing most of the freshwater through the north arm. The water circulation in the south arm is controlled by tidal
circulation and the relatively small fresh water input from the tributary, the Pranto River, which is controlled by a
sluice located 3 km from the conﬂuence with the south arm of the estuary. Due to differences in depth, the tidal
excursion is longer in the north arm, causing daily changes in salinity to be much stronger, whereas daily temperature
changes are highest in the south arm (Marques, 1989).
Seasonal intertidal macroalgae blooms (mainly of Enteromorpha spp.) have been reported in the south arm of the
estuary for several years (Marques et al., 1993 a,b) and Zostera noltii beds, which represent the richest habitat with
regard to productivity and biodiversity, are being drastically reduced in the south arm of the Mondego estuary, presumably
outcompeted by Enteromorpha (Rafaelli et al., 1991). The physical data are listed in Table 10.12.
Nutrient loading into the south arm of the estuary was estimated, assuming that the major discharge is through
the Pranto River, from the Armazens Channel (there is no freshwater discharge but, in each cycle, the tidal wave
washes out the channel, where several industries discharge waste waters), and from the downstream communication
of the south arm. The only way out of the system is the downstream communication (Fig. 10.13). The nutrient
inputs from the Pranto River and Armazens Channel, and the exchanges (input vs. output) in the downstream
communication of the south arm have been monitored from May 1993 to June 1994. The annual nitrogen loading
to the south arm of the Mondego estuary was roughly estimated to 134 tons (126 tons of nitrate and 8 tons of nitrite),
10.8 APPLICATION OF ECO-EXERGY AND ASCENDENCY AS ECOLOGICAL INDICATOR TO THE MONDEGO ESTUARY (PORTUGAL) 207
of which 14 tons are still in the system (18 tons of nitrate were imported and 4 tons of nitrite were exported) and 120
tons were transported to the sea. For phosphorus the loading was estimated to 14 tons (1 ton was imported to the
system, while 15 tons were exported to the sea, which means that 14 tons were net released from the south arm of the
estuary). In Table 10.13 are listed the chemical aspects of the area.
FIGURE 10.13 Map of Mondego River showing the ﬁeld areas and the dividing of the river in the north and south arm.
TABLE 10.12 Physical Parameters of Mondego River.
Physical Parameters Mondego River
Length and width of estuary (km2
) 10, 0.3
Area (km2
) 3.4
Volume (km3
) 0.0075
Mean depth (m) 1 and 2
Tidal range 0.35e3.3
Drainage area (km2
excluding the estuary) 6670
Discharge (m3
per year) 8.5 Â 109
Mean residence time (days) (based on fresh water discharge) North arm 2
South arm 9
Temperature range (min, max) 7e32
Mean Secchi depth in m (MayeSept.) 0.5e1.0
Annual insolation of PAR (400e700 nm) (mol. Fot. mÀ2
yearÀ1
) 3200e32,000
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS208
Maximization of Eco-exergy to Predict the Behavior of the System
It is often of interest to determine among several possibilities which structure of an ecosystem will prevail under
given environmental circumstances. Here the thermodynamic variable, eco-exergy, was used as an orientor to describe
adaptation and changes in the species composition. In the Mondego estuary two very different types of communities
have been observed: (1) An Enteromorpha dominant community with the presence of Cyathura carinata, mollusca and
crustacea. The algae community shows often a crash at early summer due to oxygen depletion. This community is
found where the salinity is not too low and the nutrient concentration is high. (2) A Zostera noltii dominant community
with the presence of oligochaeta, polychaeta, mollusca, and crustacea. This ecosystem is found where the nutrient concentration
is lower. Mollusca are more abundant in ecosystem (2) than in ecosystem (1), while for crustacea the reverse
is valid. From an ecological management point of view the Zostera dominated community is preferred because the
oxygen concentration is higher, the water is clearer, and no crash due to anaerobic conditions takes place.
Starting from the hypothesis that the ecosystem structure having the highest eco-exergy among the possible ones
would prevail, two models (one for an Enteromorpha dominant community and another one for A Zostera noltii dominant
community) were developed to compare eco-exergy for several conditions, using STELLA. The growth was
described as a function of internal concentrations of nutrients, temperature, light, and salinity (Duarte, 1995). If
the hypothesis is correct, comparing the models for the two types of ecosystems the highest exergy under eutrophied
and medium to high salinity conditions should be found for ecosystem (1), while the highest exergy should be found
for ecosystem (2) under low nutrient and low salinity conditions. The models show that if the fresh water with high
concentration of nutrients (particularly nitrogen) is discharged during the last part of the year, Enteromorpha will be
dominant. The eco-exergy calculations show that the exergy is approximately the same for the two models, which
may be interpreted as the initial value may be crucial for the ﬁnal results. The results of the ﬁve simulations suggest
that the ecological management of the freshwater discharge is a key factor for the prevailing of the two communities
(Enteromorpha, Zostera noltii). From a management viewpoint at least two possibilities can be considered: artiﬁcial
control of the freshwater discharges through the use of sluices, increasing the discharge during the ﬁrst part of
the year; or reduction of the nutrient input from fresh water (and if possible also from tide water). The joint use
of these two alternatives should give the Zostera dominated community better conditions.
Eco-exergy, Speciﬁc Eco-exergy, and Diversity
The spatial and temporal variation of eco-exergy, speciﬁc eco-exergy, species richness, and heterogeneity were
analyzed to examine in what extent these ecological indicators would capture changes in benthic communities along
the gradient of eutrophication.
TABLE 10.13 Chemical Parameters of Mondego River.
Chemical Parameters Mondego River Inner Part Outer Part
External N loading (tons N per year) 126
External P loading (tons P per year) 1
Salinity Winter
Summer
10
25
10
30
DIN (mg LÀ1
) Winter
Summer
300
50
200
40
DIP (mg LÀ1
) Winter
Summer
30
25
25
30
Tot-N (mg LÀ1
) Winter
Summer
0
0.25
0
0.2
Tot-P (mg LÀ1
) Winter
Summer
0.30
0.25
0.25
0.30
Sediment C (mg LÀ1
) 350 gC mÀ2
500 gC mÀ2
Gross sedimentation (gC mÀ2
per year)
Net sedimentation (gC mÀ2
per year)
Sediment O2 consumption (mg mÀ2
dÀ1
)
10.8 APPLICATION OF ECO-EXERGY AND ASCENDENCY AS ECOLOGICAL INDICATOR TO THE MONDEGO ESTUARY (PORTUGAL) 209
The benthic communities in the Mondego estuary (Portuguese western coast) were monitored during a yearly
cycle. Samples of macrophytes, macroalgae, and associated macrofauna were taken fortnightly at three different
sites, during low water, along an estuarine gradient of eutrophication in the south arm of the estuary, from the noneutrophicated
zone, where the Zostera noltii community is present, up to the heavily eutrophicated zone, in the inner
areas of the estuary, where Enteromorpha spp. blooms have been observed. An overview of the major taxonomic
groups contributing to the exergy in this system is provided in Table 10.14.
With regard to eco-exergy, values were consistently higher in the Zostera noltii community than in the eutrophicated
areas. Also, eco-exergy values were higher in the most heavily eutrophicated area when compared with the
intermediate eutrophicated area, especially during spring and early summer. This was related to the intensity of
the Enteromorpha bloom, which gave rise to much higher values for total biomass in the most eutrophicated area.
Speciﬁc eco-exergy was found to be consistently higher in the Zostera noltii community than in the eutrophicated
areas until late spring when the picture changed completely and values became higher in the eutrophicated areas.
This was due to a macroalgae crash in the eutrophicated areas, which determined not only a drastic reduction of the
total biomass but also a change from a primary productionebased situation toward a detritus-based food web.
Therefore, since total biomass values after late spring consisted basically of animals (consumers), primarily deposit
feeders and detritus feeders (e.g., annelid worms and crustaceans), it is clear that the abrupt increase of speciﬁc ecoexergy
in the eutrophicated areas after the algae crash do not reﬂect any augmentation of the structural complexity of
the community, but simply the different quality of the biomass involved in the calculations.
Regarding the Zostera community (data from after July 6), accounting for the primary producers and the consumers,
speciﬁc exergy is lower than in the eutrophicated areas. But if we account only for the consumers, it is
higher, following the same pattern from before the algae crash. Hence, speciﬁc eco-exergy may shift very drastically
as a function of yearly dynamics (like in communities dominated by r-strategists), providing a spatial and temporal
picture that may not be related with the long-term evolution and integrity of the system. With regard to biodiversity,
the variation of species richness and of heterogeneity (species richness þ evenness) along the gradient of eutrophication
provided quite a different picture. Through time species richness was consistently higher in Zostera community,
decreasing along the gradient of eutrophication. On the contrary, heterogeneity was always higher in the
TABLE 10.14 Major Contributors to the Exergy in the Mondego Estuary Benthic Communities Along the Gradient of Eutrophication.
Contributors
Noneutrophicated
Area
Intermediate
Eutrophicated
Area
Eutrophicated
AreadBefore the
Algae Crash
Eutrophicated
AreadAfter the
Algae Crash
Enteromorpha þ Ulva 2.099 28.211 264.642 1.273
Other macroalgae 16.141 2.138 6.152 0.165
Zostera noltii leafs 128.368 0.000 0.000 0.000
Z. noltii roots 87.975 0.000 0.000 0.000
Z. noltii-total 216.343 0.000 0.000 0.000
Anthozoa 0.003 0.000 0.000 0.000
Sipunculida 0.001 0.001 0.001 0.002
Nemertinea 0.005 0.003 0.005 0.001
Oligochaeta 0.128 0.031 0.010 0.002
Polychaeta 1.254 0.709 0.569 0.846
Mollusca 63.950 14.192 31.195 13.240
Crustacea 1.3720 1.088 14.945 3.419
Insecta 0.007 0.006 0.009 0.001
Echinodermata 0.000 0.000 0.000 0.000
Pisces 0.000 0.006 0.034 0.000
For the noneutrophicated and intermediate eutrophicated areas, the average annual biomass (g mÀ2
) of each is given. For the eutrophicated area, the average biomass
(g mÀ2
) of each group before and after the algae crash is given.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS210
eutrophicated areas, except for the decrease observed in the most heavily eutrophicated area after an algae crash.
The observed spatial variation of heterogeneity is due to the fact that the ShannoneWiener’s index integrates
two components, the number of species (species richness), and their relative abundance (evenness). Therefore,
although species richness decreased as a function of increasing eutrophication, as we expected, the dominance of
a few species (e.g., Hydrobia ulvae, a detritus feeder and epiphytic grazer gastropod, and Cerastoderma edule, a ﬁlter
feeder bivalve) in the Zostera community, probably due to the abundance of nutritional resources, decreased species
evenness and consequently heterogeneity values. In this case, lower values of heterogeneity must be interpreted as
expressing higher biological activity, and not as a result of environmental stress (Legendre and Legendre, 1984a,b).
Taking into account the yearly data series for each site along the eutrophication gradient (noneutrophicated, intermediate
eutrophicated, eutrophicated), eco-exergy and speciﬁc eco-exergy were signiﬁcantly correlated (P .05)
providing a similar picture from the system. Values were consistently higher and more stable in the noneutrophicated
area. The comparison of yearly data series (t-test, P .05) showed that using eco-exergy values it was possible
to distinguish between the three situations considered, even though differences between the intermediate and eutrophicated
areas were not signiﬁcant, which suggests that eco-exergy, an extensive function, might be more sensitive to
detect subtle differences.
Species richness and eco-exergy were signiﬁcantly correlated (P .05), following a similar pattern, both
decreasing from noneutrophicated to eutrophicated areas (Fig. 10.14B). On the contrary, heterogeneity and ecoexergy
appeared negatively correlated (although not signiﬁcantly), providing a totally distinct picture of the benthic
communities along the eutrophication gradient (Fig. 10.14A). This obviously resulted from the properties of the heterogeneity
measure, as explained above. Similar results were obtained comparing the patterns of variation of species
richness, heterogeneity, and speciﬁc eco-exergy. Species richness and speciﬁc eco-exergy appeared clearly positively
correlated (P .05) (Fig. 10.14B), while the patterns of variation of heterogeneity and speciﬁc eco-exergy showed to
be distinct (Fig. 10.14A). Moreover, from the comparison of yearly data series (t-test, P .05), heterogeneity values
were not signiﬁcantly different in the intermediately eutrophicated and eutrophicated areas and therefore did not
permit to discriminate relatively subtle differences. The hypothesis that eco-exergy and biodiversity would follow
the same trends in space and time was validated with regard to species richness, but not for heterogeneity. Actually,
eco-exergy, speciﬁc eco-exergy, and species richness responded as hypothesized, decreasing from noneutrophicated
to eutrophicated areas, but heterogeneity responded in the opposite way, showing the lowest values in the noneutrophicated
area. Their range of variation (eco-exergy and speciﬁc eco-exergy) through time was smaller in the noneutrophic
area, expressing a more stable situation, while the magnitude of the variations was stronger in the other
two areas, but especially in the intermediate eutrophic area (Marques et al., 2003). On the other hand, both ecoexergy
and species richness were able to grade situations presenting relatively subtle differences, but speciﬁc ecoexergy
and heterogeneity appeared to be less sensitive. Although biodiversity may be considered as an important
property of ecosystem structure, the relative subjectivity of its measurements and their interpretation constitutes
an obvious problem.
The spatial variation of species richness was signiﬁcantly biodiversity and may be seen as the full range of biological
diversity from intraspeciﬁc genetic variation to the species richness, connectivity, and spatial arrangement of
entire ecosystems at a landscape level scale (Solbrig, 1991). If we accept this biodiversity concept, then eco-exergy, as
system-oriented characteristic and as ecological indicator of ecosystem integrity, may encompass biodiversity.
Moreover, eco-exergy implies the existence of the transport information through scales, from the genetic to the
ecosystem level, accounting not only for the biological diversity but also for the evolutionary complexity of organisms
and ecosystem emergent properties arising from self-organization.
Ascendency Calculations
Eutrophication can be described in terms of network attributes as any increase in system ascendency (due to a
nutrient enrichment) that causes a rise in TST that more than compensates for a concomitant fall in the mutual information
(Ulanowicz, 1986). This particular combination of changes in variables allows one to distinguish between
instances of simple enrichment and cases of undesirable eutrophication. Three sampling stations representative of
the noneutrophic area, of the intermediate eutrophic area, and of the strongly eutrophic area were chosen. Estuarine
food webs were constructed at the three sites and these quantiﬁed food webs were examined using network analysis.
Taken together with Table 10.15 these provide the measures that were used to characterize the trophic status of the
three estuarine ecosystems. Although the three habitats are clearly distinct in physical appearance, network analyses
revealed both differences and similarities among their trophic structures that had not been apparent at ﬁrst glance.
10.8 APPLICATION OF ECO-EXERGY AND ASCENDENCY AS ECOLOGICAL INDICATOR TO THE MONDEGO ESTUARY (PORTUGAL) 211
It was possible to observe (Table 10.15) that the Zostera dominated community had the highest TST, followed (unexpectedly)
by the strongly eutrophic system, and ﬁnally by the intermediate eutrophic area. The development capacity
was highest in the Zostera beds and lowest in the intermediately eutrophic area.
The index differed signiﬁcantly among the three areas. Due to the logarithmic nature of this index, small differences
can represent appreciable disparities in structure. The average mutual information (AMI) was slightly higher
in the noneutrophic area, followed closely by the eutrophic area, and was lowest in the intermediate eutrophic area.
senhcirseicepSs
0
0.5
1
1.5
2
2.5
Exergy
Species richness
Specific exergy
0
350
250
300
200
150
100
50
0
350
250
300
200
150
100
50
Exergy
(g*m-2
det.energyequiv.)
30000
25000
20000
15000
10000
5000
0
Non eutrophicated
area
Intermediate
eutrophicated
area
Eutrophicated
area
Non eutrophicated
area
Intermediate
eutrophicated
area
Eutrophicated
area
30000
25000
20000
15000
10000
5000
0
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Exergy
Specific exergy
Heterogeneity
Exergy
(g*m-2
det.energyequiv.)
(A)
(B)
FIGURE 10.14 Variation of exergy and speciﬁc exergy in comparison with heterogeneity (A) and species richness (B) along the gradient of
eutrophication gradient. For each situation, respectively, noneutrophicated (ZC), intermediate eutrophicated (INT), and eutrophicated (EUT), we
indicate the average values and the standard deviation, taking into account the entire yearly data set. The spatial variation of exergy and speciﬁc
exergy was signiﬁcantly correlated (r ¼ 0.59; P .05). The spatial variation of heterogeneity was not signiﬁcantly correlated neither with exergy or
speciﬁc exergy (r ¼ À0.48 and r ¼ 0.38, respectively; P .05).
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS212
Concerning ascendency, it increased in order from the intermediate eutrophic to the heavily eutrophic zone to the
Zostera meadows, while redundancy increases in the opposite direction. The long-term study in the Mondego estuary
indicated that years of low precipitation tended to be associated with reductions in turnover rates and increases
in water column stability, temperature, salinity, and light penetration (Martins et al., 2001). These changes in habitat
conditions encouraged blooms of macroalgae that gradually replaced the resident macrophytes (Marques et al.,
1997; Martins et al., 2001). In the intermediate and strongly eutrophic areas, primary production is largely the result
of these macroalgal blooms. Production appears as a strong pulse during this speciﬁc time, but remains at very low
levels during the rest of the year. This limited temporal interval of primary production results in a signiﬁcantly lower
ﬁgure for the cumulative annual primary production and TST in these areas as compared with the corresponding
measures in the Zostera beds. Comparing the AMI values of the ﬂow structure for the three areas, it is possible to
discern a very small decrease in the measure among the three zones, suggesting that, as regards trophic structure,
these areas are indeed different. The three zones appear nevertheless much more distinct by eye than is illustrated by
the AMI values. In light of these results, the network deﬁnition of eutrophication appears to be inappropriate for the
Mondego estuarine ecosystem. It would be more accurate to describe the enrichment processes occurring in this
ecosystem as “pulse eutrophication.” This process could be characterized as a disturbance to system ascendency
in the form of an intermittent supply of excess nutrients that, when coupled with a combination of physical factors
(e.g., salinity, precipitation, temperature, etc.), causes both a decrease in system activity and a drop in the mutual
information of the ﬂow structure. Even though a signiﬁcant rise in the TST occurs during the period of the algal
bloom and at that time there is a strong increase of the system ascendency, the annual picture nevertheless suggests
that the other components of the intermediate and strongly eutrophic communities were unable to accommodate the
pulse in production. The overall result was a decrease in the annual value of the TST and, as a consequence, of the
annual ascendency as well. Regarding the results of the trophic analysis, the Zostera community has one more trophic
level than those counted in the strongly eutrophic chain, implying that this community possesses a more complex
web with additional top consumers. At the same time, the Zostera community exhibits lower transfer efﬁciency
at the ﬁrst trophic level, probably because the production of Z. noltii meadows usually cannot be eaten directly, but
needs to be decomposed ﬁrst (Lillebø et al., 1999). Concerning the analysis of cycled materials, the overall percentage
of cycled matter increases as the degree of eutrophication rises. This is indicated by the Finn cycling index (FCI) that
reveals the proportion of TST that is devoted to the recycling of carbon (Finn, 1976).
Odum (1969) suggested that mature ecosystems recycle a greater percentage of their constituent material and energy
than do pioneer or disturbed communities. Hence, according to Odum, the progressive increase in the FCI
would suggest the maturation of the ecosystem. It has been observed, however, that disturbed systems also often
TABLE 10.15 Network analysis Ecosystems Indices for the Three Areas.
Information Indices Noneutrophic Area Intermediate Eutrophic Area Strongly Eutrophic Area
Total system throughput (g AFDW mÀ2
yÀ1
) 10,852 1155 2612
Development capacity (g AFDW mÀ2
yÀ1
; bits) 39,126 5695 10,831
Ascendency (%) 42.3 30.4 36.7
Overhead on imports (%) 12.3 8.2 6.2
Overhead on exports (%) 1.3 1.5 2.5
Dissipative overhead (%) 17.7 22.1 19.9
Redundancy (%) 26.4 37.8 34.6
Average mutual information (bits) 1.52 1.50 1.52
F/TST 2.08 3.43 2.62
CONNECTANCE INDICES
Overall connectance 1.67 2.43 2.1
Intercompartmental connectance 2.41 3.57 2.63
Finn cycling index 5.75EÀ02 0.2045 0.1946
Total number of cycles 74,517 15,009 9164
10.8 APPLICATION OF ECO-EXERGY AND ASCENDENCY AS ECOLOGICAL INDICATOR TO THE MONDEGO ESTUARY (PORTUGAL) 213
exhibit greater degrees of recycling. The speculation is that such an increase in cycling in disturbed systems is the
homeostatic response that maintains in circulation resources, which before the perturbation had been stored as
biomass in the higher organisms (Ulanowicz and Wulff, 1991). This latter scenario seems consistent with the present
results. When the whole-system properties of the three areas were compared, the measures associated with the system
considered to lie between the two extremes in nutrient loading did not plot intermediate to the other two. From
this viewpoint, the intermediate eutrophic area appears to be the most disturbed of the three areas, since it has the
lowest ascendency, AMI, TST, and development capacity values and the highest ﬁgures for redundancy and FCI.
10.9 CONCLUSIONS
Chapters 2e9 have shown several ecosystem properties that need indicators to be measured. This chapter has
presented the application of these indicators. All these indicators are used to compare characteristics and performance
of different ecosystems or of an ecosystem in time, more than to give absolute measures. These indicators
cover a wide range of important properties of ecosystems, more than those shown in this chapter, for the evaluation
of ecosystem health. Here just few examples have been picked among the many to let the reader have at least an idea
of what can be done with these indicators, but nowadays a whole literature of papers and books can provide further
examples and types of applications. The use of these indicators span from agricultural to industrial systems; from
ecosystem management to ecological economics.
Especially for management purposes it is necessary to use these indicators with others, more focused on particular
aspects than on the global ecosystem. Nonetheless the approaches used in this chapter are fundamental to
describe ecosystems as “systems” and not just as the sum of singular components and therefore should always
be used. They provide information that is sometimes complementary and sometimes overlaps: in this direction
more research is needed to clarify the level of overlapping and to fully explore the essence of the indicandum provided
by every indicator, but their relevance is undoubted.
10. ECOSYSTEM PRINCIPLES HAVE ECOLOGICAL APPLICATIONS214
C H A P T E R
11
Ecosystems Carry Important Messages to
Managers and Policy Makers
“Act so that the effects of your action are compatible with the permanence of genuine human life”; or expressed negatively: “Act so that
the effects of your action are not destructive of the future possibility of such life”; . Jonas (1984)
The above quotation stems from the philosopher Hans Jonas book, “The Imperative of Responsibility,” that puts an
ethical perspective to our ever-continuing applications of technology to improve our well-being and living conditions.
To formulate his perspective in a simple manner, and taking his argument seriously, it means that every
time we implement a new technology, we also need to ensure that we do so in a responsible manner. This in turn
raises the question of what responsibility means in general, and what does it mean when dealing with the consequences
of our use of technology to our environment in particular. That problems arise from the human use of technology
is not entirely new, but the number of problems emerging from this use has been accelerating since the rise of
fossil fuel based industrialization that began around the 19th century and has been enforced with the so-called chemical
revolution throughout the 20th century.
In addition, in our attempt to achieve a sustainable society, it is no longer sufﬁcient to consider only the present,
i.e., conditions that are proximate in time and space. It is much easier to establish a responsible behavior toward our
immediate environment, which historically had been our primary focus. Jonas remarks such proximate considerations
to be characteristic for our traditional ethics, but for the rapid development of technology we need a new
and ethical planning that is more complex, a new ethics able to consider also conditions more distal in time/space
and that will include both considerations on humans, society, and nature: exactly as it is presented in the Rio declaration;
exactly as argued in the Millennium Goals and 17 Sustainable Development Goals; and exactly as it is
included in the Seventh Generation Principle of the North American Indigenous people.
Not only we as individual humans and representatives of humanity need to consider this in our private life. In
most countries, we have transferred the responsibility to formulate an organized responsible behavior to the state
government and its related authorities. This means that considerations such as the one presented above should
be at the core of any management and policy making in the environmental area, of any government or state, regardless
if it has a Marxist, conservative, or neoliberal point of view as entrance point, which does not really matter when
it deals with responsible politics and acts toward the environment. Any policy not obeying ecological principles or
rules, simply ignoring warnings or neglecting considerations, will fail and most likely will, which historically had
been our primary focus eventually lead to collapse. Examples of cases for even complex societies are legion (Diamond,
2011; Tainter, 1990, etc.).
Recent research has pointed out the importance of proper policy government to successful and sufﬁcient environmental
management (Scavenius and Rayner, 2017). The realization of a true successful development toward society
is probably likely to lie somewhere in between two radical pointsdthat of governmentality (Foucault, 2016) at one
end and true bottom up, participatory, and action research approaches. Which balance is the more beneﬁcial is likely
to differ from case to case. Meanwhile, although the number of studies is limited and knowledge in the area is rather
fragmented, some “rules of thumb” and proper indicators may be found and will be sketched in the following.
11.1 ECOSYSTEMS IN A SUSTAINABILITY PERSPECTIVE
Very often today ecosystem management can be observed to have moved away from its (natural) materialist and
scientiﬁc backgrounddor at least it seems to have been split up and is now shared with other disciplines such as the
215
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00011-5
social and economic sciences. This is perfectly illustrated with the normal view of sustainability to consist of a triadic
relation between economic, societal, and environmental perspectives. As a simple example, we notice that very early
on the introduction of the concept of sustainability, we came to talk about the economic, social, and environmental
aspects. The sequence alone serves to illustrate how quickly environmental concerns were having a secondary position
in priorities. Later, both a human and spiritual dimension has been added. But the sustainability aspect is
entering in many more disciplines, e.g., law and policy, micro- and macroeconomics, anthropology, proposals of
design and innovation in technological development, architecture, etc.
One ﬁrst point to be made here is that the whole ﬁeld of sustainability has been developeddexactly not as a
wholedbut in a rather separate (one may say atomistic) manner, each ﬁeld taken its own stance on what exactly
sustainability is or should be about, and almost always ignoring that none of the forms will exist if the physicomaterial
and geochemical basis is not there. Therefore, this needs to be ensured. Meanwhile, together with this,
each subdiscipline spawns a new set of termsda new vocabulary has been developed that tends to be so specialized
within the separate ﬁelds that makes it difﬁcult, not to say impossible, to communicate around such a critical topic,
that is, how to behave properly and hopefully improve conditions and ensure our existence on Earth, our common
place in time and space?
Second, among the terms it also has been very common to invent the new concepts around the use of metaphors.
Indeed, metaphors might have their own right, serving as summarizing and popularizing terms believed to have a
high explanatory power or value. The problem is that when often they seem to have the necessary explanatory
power, rather they serve to blur the picture of what was otherwise thought to be clear. The problem of aggregating
concepts alone may also well be illustrated by this book. For taking real action on the environment, doing proper
management, i.e., take the right decisions in a case of, for example, mitigation, restoration, remediation, it is necessary
to understand ecosystems, their properties, their behavior, and to take the right measures one need to choose the
right tools among the ones presented in the above.
11.2 MANAGEMENT WITH NATURE
The coexistence of humans and nature, as we may see from the many emergent environmental problems observed
through the last decades, is intricate and complicated in character. The construction of scenarios around the possible
impact of an increased greenhouse effect, due to emissions from human activities, and the disturbance of chemicals
on our own health and reproduction demonstrates clearly that our technological evolution and development most
often strikes back on us with unpleasant secondary effects. Our impacts are now of such a character that we have
been forced to realize that our geochemical impact in time and space is of such an order of magnitude that it exceeds
that of other organisms. Hence, we have dedicated the name Anthropocene to describe the present time on Earth.
Truly, we have an excessive and negative impact on Earth. We take up more and more space, exploit more and
more of the ﬁnite resources, and use “sophisticated” chemicals, much of it having an impact on remote places, in
what was a few decades ago believed to represent pristine nature, all with the result that we do not have any
real nature “sensu stricto” any more.
This does not mean that the anthropogenic nature we observe today does not possess any similarities with what
we would consider as proper nature. In fact, the functional principles appear to be more or less the same, although
the functionality is heavily constrained by our existence and our societies. Learning from this functionality, for
instance, through ecosystem theory and ecological engineering (EE), and obeying functionality through mimicking
(Nielsen, 2007b; Nielsen and Mu¨ller, 2009), it has often been shown to be beneﬁcial also in economic terms and in fact
examples are compiling that we should act more along this direction in the future, as suggested, for instance, in
nature-based solutions such as biomimcry, ecomimcry, green chemistry, and circular economy.
Using Ecosystem Knowledge to Improve Management
In aquatic environmental management, we have observed both improvements and critical situations over the
years. With the technological development over the latest century in general and after the Second World War in
particular, we have come to dominate, rule, and exploit nature to an extreme extent. This hasdbeyond any doubtsd
been a success in that the standard of living for most people has improved and is still improving in terms of daily
food calories available, medical care, energy use, resource consumption, and access to transportation.
Meanwhile, this development has also had its costs. Overﬁshing has been found to occur in many areas, ﬁghts
over quotas among nations at regional level and even within nations tell their own sad story. Considering that
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS216
we have even placed ourselves in a situation where we aredon one handdnot able to determine what will happen
to the oceanic ecosystems as a result of increasing greenhouse gas emissions anddon the otherdnot able to ﬁnd an
agreement on what the exact cause(s) to the conditions are, it is clear that we still have a long way to go. Scientiﬁc
evidence has not been successful in passing on the message that it is indeed possible to catch ﬁsh populations to
extinction, even if examples can be found in most ecological textbooks. And, what about the amount of plastics
ﬂoating around not only on the coast line but also concentrating in the centers of various oceanic gyres in the greater
oceans? In fact, a recent exploration to the deepest part of the planet–the Marina Trench–discovered a plastic bag on
the sea ﬂoor 11km below the surface. Have we learned to prevent and are we able to restore near coastal ecosystems,
estuaries, and lagoons that are important as breeding and nursery ground that should in turn literally feed back to
our ﬁsheries in open waters? Only little improvement if any has been made, and at the end we may have to put our
conﬁdence to the ethical and religious arguments, like the responsible behavior proposed by the philosopher Hans
Jonas or as argued by the Pope Francis in his second encyclical.
Management has been improved for many freshwater systems, and at least at regional levels, although mainly in
the industrialized countries where directives or similar instructions are found in the form of, for instance, the European
Water Framework Directive, into which many ecosystem principles have been incorporated. It is remarkable to
note that the environmental systems that are closest to our everyday life are the ﬁrst ones to receive attention. Many
lakes, rivers, and streams were polluted during the green revolution if they were allowed to exist at all and put in to
large drainage systems. It is tempting that the visual impact of green and sometimes smelling lakes, dead ﬁsh, etc.,
indirectly has led to the fact that these were the ﬁrst areas to be taken care of. In referring to the above situation of
oceans, the Convention on Fishing and Conservation of the Living Resources of the High Seas entered into force in
1966, but problems persist. We may hope that the European Marine Strategy Framework Directive and other approaches
will ratchet up efforts to be taken here.
Engineering the Natural WaydAquatics
Within the area of Ecological Engineering (EE), it has been realized that we should learn to view “humans as part
of nature and not apart from nature” (Mitsch and Jørgensen, 2003), and many of the ﬁrst efforts within EE were
concentrated on mitigation and remediating human activities by the use of constructed wetlands that might rely
on the restoration of previous conditions of the landscape or the construction of totally new wetlands in a true
mix of ecology and engineering (Mitsch and Jørgensen, 2003; Kangas, 2003; Kadlec and Wallace, 2009).
Meanwhile, less elegant solutions were chosen that are not considered to be consistent with management in accordance
to ecosystem principles. For instance, it was found that the early reliance on “the solution to pollution is dilution”
was not sufﬁcient as an operational paradigm. This approach promoted diverting pollution from small lakes to
just dilute it in larger water bodies, typically nearby coastal waters. Later, it was found that this approach just led to
the emergence of problems elsewhere. In brief, it was found that the easiest and most sensible thing to do was to go
back to the actual causes of problems by “treating pollution at its source.” This approach deals with the root cause of
the pollution and the symptoms it delivers. Other scientiﬁc disciplines were born related to these problems such as
cleaner production and industrial ecology. Those disciplines are outside the topics of this book but deﬁnitely go hand
in hand with the ecosystem principles, for instance, in their search for energy efﬁciency and resource optimality.
As an example, traditional Danish aquaculture used to be carried out in a manner that was harmful to the environment
using natural wetlands and based on water from rivers and streams. It was practiced as a throughﬂow system of
production of ﬁsh from feeds, with excess feeds and fecalia in outlets causing pollution in downstream areas, where
sometimes waters ended being of very poor quality, resulting in the absence of benthic organisms or natural ﬁsh in
theoutput environment.Today,modelsystemshavebeendevelopedtogetherwiththeaquaculturalfarmers(Svendsen
et al., 2008) that are based on almost totally recirculated water (recirculating aquaculture systems) described in Bregn-
balle(2015),andusingestablishedwastewatertechnologysuchasbioﬁltersandactivationofsludgetokeepwaterclean
in the plant. At the same time, excessive sludge is often treated through constructed wetlands. Often it is simply the
dams from the former low technology aquaculture plant that have be converted into constructed treatments wetlands,
serving to the ﬁnal polishing of the wastewater to bring it back to good river water quality. The ponds act as natural
ecosystems and support natural environment by a variety of ecotones, bringing back functionality and biodiversity.
Engineering the Natural WaydTerrestrial
For terrestrial systems to be managed in accordance with ecosystem principles, we face more obstacles as we also
want that the ever-increasing global population should be fed sufﬁciently both in quantity and quality. No one seems
11.2 MANAGEMENT WITH NATURE 217
to disagree with this goal, but the means and prejudices connected to it are abundant and often prevent adequate
communication and action. We observe mainly two fronts. On one side, we may place those who believe that the
situation needs to be handled by a new green revolution and implementation of ever-increasing monoculture agriculture
based on gene-modiﬁed organisms and basically relying on a diverse set of technological ﬁxes. On the other,
we ﬁnd people basically believing that shifting diets and diversity can solve the problems with both food quantity
and quality, lower the demand for chemicals, and thus eliminating health and environmental problems caused by its
use of which our conventional farming systems are so heavily depending on.
Several new ways of doing agricultural farming making use of ecosystem principles can be found in literature
under a variety of names such as integrated farming, agroforestry (AF), permaculture (PC), organic, or biodynamic
farming.
Integrated farms have been found to be just as competitive as conventional farming. In a study of four Filipino
farms (Dalsgaard et al., 1995; Dalsgaard and Oﬁcial, 1997), it was found that farmers still practicing in a traditional
Filipino manner and having around 30 various crops of a high number of various vegetables and even a few animals
produce at the same level or even above a farmer who has decided on modern almost monocultural farming.
Furthermore, the traditional farmers experience a stability with production of various crops dispersed over the
whole year; and thus, have a higher guarantee for food for the family both in terms of quantity and quality. This
also involves economic stability as the traditional farmer always will have something to sell even if a few crops
fail and as he has no or little need for investments other than work. Usually, such a farm can be handled by the family
alone. As opposed to this, the conventional modern farmer needs to invest in fertilizers and pesticides and takes the
risk of one successful crop: if something fails, it is crucial. If everything is sold at the same time, then tender is high
and prices are therefore lowddemands for other food sources become high and prices high. Such a farm cannot be
handled by the family alone and it is necessary to hire workers around the time of harvest. All in all, the farmer is
only marginally more productive with a much higher risk and lower income. It is hardly necessary to say that the
multidiverse system is much more in accordance with ecosystem principles which were also accounted for in terms
of diversity, cycling indices, and exergy calculations. Environmental, social, and economic sustainability thus goes
hand in hand. Similar observations were made among a set of around 37 Brazilian smallhold farmers carrying out
various farming practices in the Amazonian area around Bele`m (Siegmund-Schultze et al., 2007, 2010). Here a mix of
agricultural crops combined with extensive cattle farming seemed to offer both lower environmental impacts and
economic stability.
The two types of agriculture mentioned abovedPC and AFdwith the inclusion of diversity make use of additional
principles. First of all, conventional farming in general produces more than one crop but rarely on the
same piece of land, one crop for one year. Meanwhile, rotation is needed to avoid too many problems with diseases
and the following necessity to use pesticides. The crops in PC and AF are perennial plants as well as adding bushes
and trees to the system, reducing erosion, and thereby increasing ecotones, exploiting the space in not only two but
three dimensions (as a rain forest). One recent development (better to say rediscovery) even integrates with animal
husbandry such as growing pigs in woods (which is believed to correspond to the original and normal life of pigs) or
having geese and ducks as weeders and fertilizers at the same time with wine production.
AF can be considered as the union of two independent systems that are joined to implement an optimal use of
resources and produce the same coproducts as the originating ones. As previously seen, emergy methodology is
particularly suitable to account for coproducts. With respect to other metrics, e.g., life cycle assessment or energy
analysis, in emergy, allocation of input and output ﬂows is not allowed. By deﬁnition, the environmental cost of
two or more products obtained by the same system is the total emergy ﬂow supporting their production.
An example of the emergy evaluation of an AF system has been presented by Patrizi et al. (2017), demonstrating
that the integrated system enables, in emergy terms, a 33% of saving with respect to two separate systems producing
the same products.
The AF system assessed (Fig. 11.1C) was obtained by integrating a geese rearing system (Fig. 11.1A) in an
already productive vineyard (Fig. 11.1B). Geese have been chosen as they graze grassland without eating
grapes acting in this way as natural weeders and at the same time ensure natural fertilization through their
droppings.
Emergy evaluation has been used to appraise the beneﬁts of such an agro-livestock integrated system, although
other beneﬁts have not been included such as improvement of the soil quality due to the geese integration (Clark and
Gage, 2009). Moreover, other studies have demonstrated that the meat quality of goose produced in the integrated
system has higher nutritional quality (Sossidou et al., 2015).
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS218
In fact, all the abovementioned types make use of biodiversity to make productive ecosystems with plants that
may support each other by physical or natural chemical protection against pests such as allelopathic effects or making
use of each other’s effects on the biogeochemical cycling in soils, for instance, by use of double- or even multicropping
with legumes able to amend the soils with nitrogen.
11.3 INDICATING MANAGEMENT SUCCESS
One big question arises almost immediately from the above principles and that is how to express the eventual
success of management and thereby be able to evaluate how successful initiatives and measures may be to improve
overall well-being of those affected by the decisions (including the environment).
Three indicators have been identiﬁed that may be applied at different hierarchical levels but also for a holistic
evaluationdthat is dealing with sustainability at macroscopic level. Until now, three different approaches may be
identiﬁed and discussed in detail here: the cubes approach, the sustainability trigon, and establishing exergy budgets
and balances. Several other ways of viewing our impact on planet Earth may be developed into real indicators.
Of these ecological footprint (Rees, 1992; Rees and Wackernagel 1994), emergy (Odum, 1988, 1996), and the
numerous SDG targets have gained some popularity.
Meanwhile, using many of the indicators represents a problem in itself by being more than a dual sword. Simple
indicators often do not tell enough other than that conditions for a particular organism are poor, but do not indicate
any causality of a problem. To do this, more holistic approaches are needed. However, the calculation of such indicators
demands a high degree of expertise to be involved in both system deﬁnition and calculation of the relevant
stocks and processes. The danger is now that the more aggregate a holist indicator is, the more of its explanatory
power is also lost, i.e., again it may indicate that something is wrong but not where and certainly it is not going
to tell us exactly what to do.
The big issue is how can ecosystem principles be applied to create, understand, and test environmental management
scenarios. All in all, this should tribute not only to improved management but also lead to the introduction of
changes in policy solutions that should increase movement toward increased sustainability of our societies. Below,
we present three recent approaches which may be applied as measures in monitoring with the purpose to follow the
relative success of management initiatives.
Feed
Feed
Human
Labor
Human
Labor
Human
Labor
Fertilizers
Machineries
Machineries
R
R
R
Geese
Geese
Grapes
Grapes
Goose
production
Goose
production
Grape
production
Grape
production
Shelter
Shelter
Manure
Grass
(A)
(B)
FIGURE 11.1 Energy diagram of geese production system (A), grapes production (B), and the agroforestry system (C) Renewable and
nonrenewable input ﬂows are represented by circles. Arrows represent the ﬂows (external or internal) entering in the production systems.
11.3 INDICATING MANAGEMENT SUCCESS 219
A Framework for Sustainability AssessmentdCubes
The typical representation of sustainability focuses on the three aspects from the original Agenda 21: environment,
society, and economy. These can and should not be treated as equivalent components (as it is possibly understood
from Fig. 11.2), because they have very different space and time-scale characteristics: while the economic
system changes rapidly and something economically unfavorable can become feasible changing taxes or incentives,
the social system is much slower in changes and for the environment there is nothing we can do to make favorable
something that is harmful. A more realistic representation shows nested circles with environment at the base, with
society wholly in that circle and economy wholly in the society circle. Another version that does not use overlapping
circles is given below.
A representation has been introduced that extrapolates an input-state-output (I-S-O) framework developed for
ecosystems: an ecosystem is an open system, continuously fed by energy and matter inputs. These inputs are
captured and transformed/metabolized and synthetized by the system, whose combined efforts generate goods
and services, as outputs. Part of the outputs can be exploited for human purposes. The I-S-O framework enables
the investigation of the ability of ecosystems to attract primary energy, transform it into a structure, and generate
services.
This approach gives the idea of a relational order that links the three dimensions: the input, the state, and the
output (see Fig. 11.3). Human systems behave according to the previous scheme: the environment provides the
goods and services that are used by societies for their well-being; an organized society has a useful economic production
that contributes to the well-being as well. We can therefore adopt a pyramidal representation of sustainability
(Fig. 11.3A), which highlights the succession of stages in accordance with the I-S-O scheme. The main link
between environment and the socioeconomic system is constituted by ecosystem services, which are used in their
many forms. It is also important to stress the crucial role of feedbacks (Fig. 11.3B): the economy inﬂuences both environment
and society; and society feeds back to the environment in different ways (that can be, generally speaking,
positive or negative).
Economy
Environment
Society
FIGURE 11.2 The “usual” representation of sustainability (Barbier, 1987).
(A) (B)
Economy
Economy
outputinput
state
Society
Society
Environment
Environment
FIGURE 11.3 A pyramid represents better sustainability showing that a society is based on resources and produces an economy. There are
feedbacks from economy to society and environment.
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS220
Using such a “minimal” representation of sustainability, the status of a national system can be represented by a
three-axis diagram where three different (system) indicators are used to account for resource use, societal organization,
and goods and services produced, respectively (Pulselli et al., 2015). This approach represents a trade-off that
aims at maximizing information with the minimum number of indicators to illustrate the sustainability level of a
system. The three indicators should be representative of the system as a whole. Each of the three indicators chosen
should represent one of the sustainability aspects, environmental, societal, or economic aspects, respectively. The
aim is to depict the three different dimensions of system sustainability independently, ensuring that every indicator
maintains its identity and complementary informative capacity. This representation also avoids the use of hundreds
of indicators that are very difﬁcult to interpret.
Several choices are possible for indicators at the national/regional level. For example, in the case of environment,
ecological footprint, emergy, or indicators derived from material ﬂow analysis on the environmental axis; indicators
of social capital include equity within a society (e.g., Gini index or Palma ratio), of labor or of education for the social
axis; and indicators such as gross domestic product (GDP), the Index of Sustainable Economic Welfare or genuine
progress indicator on the economic one. A ﬁrst analysis was conducted by Pulselli et al. (2015) using the emergy ﬂow
per capita (Odum, 1988, 1996); the Gini index of income distribution, and the GDP per capita on the environmental,
social, and economic axes, respectively (Fig. 11.4).
We have seen what emergy represents; in the case of a national economy it accounts for the resource use,
measured in term of direct and indirect solar energy, which is necessary to support the country’s population,
with all its consumption. The Gini coefﬁcient is a measure of inequality ranging from 0 (in the situation of perfect
equality, where every unit has the same income) to 1 (in the situation of greatest inequality, where only one unit receives
the whole income). Even if, strictly speaking, the Gini index is an economic indicator, it has been shown that
inequality is strongly correlated with health and social problems (Wilkinson and Pickett, 2009). GDP is the sum of the
market value of the overall set of goods and services produced by an economic system in a given period of time
(generally 1 year). It can thus be intended as an indicator of the economic output.
Pulselli et al. (2015) examined 99 national economies, grouping them into eight subcubes separated by the median
value of the three axes. Data are referred to the year 2008 because it was the last one available for emergy (Fig. 11.5).
Most of the economies (85) fall in four of the eight possible subcubes, while the other 14 points fall in less populated
subcubes, but in zones that are very close to median values of at least one of the indicators. From the results, a strong
relationship between resource use per capita and GDP per capita emerges, pointing out how economic growth
drives, and depends on, an increasing requirement of energy and matter to be transformed by the economic system,
while the level of inequality on societies is quite independent of the other two metrics (Pulselli et al., 2015). The area
of the cube where there should be nations with low level of resource use and high GDP is practically empty indicating
that dematerialization is a goal hard to be reached.
The temporal dimension is a key factor of this representation: further than the ranking of the nations in 1 year, also
the evolution of a single point in the diagram is meaningful to follow the behavior of a nation in time (see Fig. 11.6).
This can be useful to evaluate the effects of national policies not only in economic but also in social and environmental
terms (Pulselli et al., 2015).
Emergy flow pc
GDPpc
Gini
FIGURE 11.4 The cube and subcubes within which we represent the national economies.
11.3 INDICATING MANAGEMENT SUCCESS 221
In fact, after the EU “beyond GDP” initiative in 2009 was taken, a number of initiatives have been implemented to
complement GDP and its prominent role in policy decisions. In Italy, the BES framework (acronym for Benessere
Equo e Sostenibile, that is equitable and sustainable well-being) was developed (see ISTAT and CNEL, 2013; 2014,
ISTAT, 2016), “a measurement tool for progress in Italy” (Riccardini and De Rosa, 2016). As a consequence, a subset
of indicators belonging to the BES framework has been selected by a Commission of experts for the Ministry of Economy
and Finance (Ministero dell’Economia e delle Finanze, 2017) to guide political economic measures; this subset is
constituted by greenhouse gases emissions (that is also a proxy for the environmental support required for economic
production), a measure of equity and the number of jobs as well as GDP. The result of the Italian parliament and
government is very much in line with the outcomes of Pulselli et al. in the choice of relevant indicators. It remains
to see if they will be used and interpreted as separate issues, as in a Barbier-like scheme, or in a relational order as
suggested by Pulselli et al.
A Framework for Sustainability AssessmentdTrigon
Integrated environmental management characterizes and follows the development of environmental problems
including, eventually, recovery processes, requires a sound diagnosis and a combination of solution methods.
This implies integrating the knowledge about such problems and their causes, as well as about the ecosystems
involved, which draws very much from systems ecology and ecosystem principles (Jorgensen et al., 2016).
Indeed, all types of environments worldwide are threatened as a consequence of pollution, over exploitation, and
impacts of climate change, and, consequently, there is a well-identiﬁed need for approaches to sustain and, where
necessary, restore ecosystems (Hughes et al., 2005). Although quite spread, the concept of sustainable developmentd
seen, for instance, as “development that satisﬁes present needs without compromising the possibility of future generations
satisfying theirs” (Brundtland, 1987)dstill remains relatively nonoperational, and its application requires
Gini
GDP
Emergy 16
17
18
14
13
0.2
0.3
0.4
0.5
0.6
TEp
2
3
4
5
FIGURE 11.5 The 99 economies in the cube. Each segment is divided using the median value (mettere ﬁgura giusta).
GDPpc
Gini
Emergy flow pc
FIGURE 11.6 Evolution of a nation within the cube framework.
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS222
additional quantiﬁcation from the scientiﬁc, cultural, and socioeconomic points of view, besides being necessary to
take into account time, relationships, and biophysical limits (Pulselli et al., 2008).
The way human society interacts with the various needed living and nonliving natural resources, which constitute
what is called natural capital, is still controversial, involving at least two main contrary perspectives regarding
the conceivable practical mean of sustainability: weak and strong sustainability. Weak sustainability admits that human
well-being must continue through intergenerational time scales, considering that natural capital and man-made
capital can be interconverted in the scope of speciﬁc production processes (Brand, 2009). Consequently, weak sustainability
also accepts the option of depleting natural capital, unless its requirement tends to decline over time
(Brand, 2009). Inversely, strong sustainability takes up a complementarity between natural capital and man-made
capital, assuming that the whole stock of natural capital has to be preserved for present and future generations in
the long run, and therefore that human society must keep each type of capital intact over time (Brand, 2009). In
any case, environmental concerns related to resource use forced the entrance of ecological sustainability into international
agendas.
Ecosystem principles provide a set of useful ecological-oriented deﬁnitions to deﬁne as clearly as possible the concepts
involved in creating environmental management scenarios, whose environmental ideologies and sustainability
perspectives are summarized, for instance, by Jørgensen et al (2016) (see Table 11.1), although such concepts are
not always used with the very same meaning, obviously depending on the ﬁeld where they are applied (e.g., ecology,
economics, policy, etc.).
Sustainable environmental management can only be attained if options and actions undertaken are environmentally
and ecologically sustainable, economically realistic, technologically feasible, socially desirable, or at least socially
tolerable, administratively manageable, legally admissible, and politically opportune (e.g., Elliott et al.,
2006; Bunce et al., 2008; Mee et al., 2008; Ojeda-Martı´nez et al., 2009). All these aspects of ecosystems are intimately
linked and are essential conditions to achieve the maintenance or even the increase of economic goods and services
demanded by a developing society and simultaneously maintaining and protecting ecological goods and services;
these together represent environmental goods and services (Kay et al., 1999).
Environmental restoration, for instance, is crucial nowadays and involves dealing with complex problems such as
(a) losses of species diversity, habitats, and a reduction in habitats’ heterogeneity and size, (b) changes in dynamics
and spatial distribution of many species, as well as diminution of their population size, (c) fragmentation of habitats
and associated increase in the vulnerability of the remaining isolated pouches, and (d) reduction of economically
signiﬁcant services and goods naturally offered by ecosystems (e.g., Elliott et al., 2007). This implies dealing with
ecological theories and concepts, of which some are well understood or at least appropriately deﬁned, like, for
instance, the nature of ecosystem structure and functioning, whereas others, like resilience, carrying capacity, or
ecosystem goods and services are not yet adequately quantiﬁed.
From the ecological point of view, health of ecosystems is most often assessed based on the abundances of few
conspicuous (or even charismatic) species, namely birds, ﬁshes, and marine mammals. Nevertheless, the mechanisms
underlying temporal or spatial variations in abundance are frequently poorly understood, and the way
changes in these species inﬂuence ecosystems as a whole is rarely addressed (Hughes et al., 2005). Ecosystem principles
and its understanding are here of crucial importance. In this sense, resilience-based management constitutes
an innovative and appropriate approach, providing a shift in focus from the conservation of targeted species to the
active management of functional groups supporting critical processes, and the maintenance of ecosystem’s services
(Hughes et al., 2005). Such focus on functional groups assumes the importance of species interactions and ecological
roles (including that of humans) in sustaining ecosystem’s resilience through spatial and temporal scales, representing
a clear change in perspective (Folke, 2006).
The resilience concept, despite its central position in sustainability science, has been suffering signiﬁcant changes
through the last three decades (Walker et al., 2004). In fact, there are confusions around the term. For instance, the
perspective that it refers to the intrinsic capacity of a system of coming back to a prior or similar state after a disturbance,
or its capacity to tolerate stressors, is often assumed (Elliott et al., 2007). Yet, there are at least three other
meanings which can be found in the literature. One refers to the dynamics of close to equilibrium systems, being
deﬁned as the time necessary to their return to an equilibrium point after a disturbance, addressing therefore system’s
recovery. This is commonly called “engineering resilience” (Holling, 1996; Folke, 2006), being in a large extent
equivalent to the stability property “elasticity” (Grimm and Wissel, 1997) or “resistance to change” (Levin and Lubchenco,
2008). A second one is deﬁned as the capacity to absorb stress and still maintain “function,” referring to the
dynamics of far from any equilibrium steady-state systems, and has been called “ecological resilience” (Gunderson
and Holling, 2002; Folke, 2006). It essentially corresponds to the capacity to maintain functioning in spite of the multiple
stressors which may affect an evolving system (Levin and Lubchenco, 2008). This second meaning addresses
11.3 INDICATING MANAGEMENT SUCCESS 223
TABLE 11.1 Environmental Ideologies and Sustainability Perspectives (Nunneri et al., 2005; Turner et al., 2003; Turner, 2008; Marques et al., 2009).
Very Weak Sustainability Weak Sustainability Strong Sustainability Very Strong Sustainability
Resource exploitative, growth
maximization position.
Resource conservationist and
managerial position.
Resource preservationist position. Extreme preservationist position.
Antigreen economy; unfettered
free markets; widening income
inequality not problematic; free
trade in international markets.
Green economy natural
capitalism and new industrial
systems green markets guided by
economic incentive instruments
(EIs) (e.g., pollution charges, etc.)
in combination with voluntary
agreements.
Deep green economy, steady-state
economy regulated by
macroenvironmental standards
and supplemented by EIs and
international agreements.
Very deep green economy, heavily
regulated to minimize resourcetake;
national environmental duty
of care formally regulated;
extensive and binding
international agreements.
Green labels
Primary economic policy
objective, maximize economic
growth (max gross national
product) (GNP); no formal policy
integration processes.
Modiﬁed economic growth
(adjusted green accounting to
measure GNP); formal policy
integration and review on
institutional structures of growth
and environmental quality.
Zero economic growth; zero
population growth; binding
policy integration.
Reduced scale of economy and
population; sustainability
accounting the primary approach.
Type of economy
Taken as axiomatic that unfettered
free markets in conjunction with
technical progress will ensure
inﬁnite substitution possibilities
capable of mitigating all local
scarcity limit constraints
(environmental sources and
sinks); voluntary approach to
environmental regulation and
intervention.
Decoupling of growth and
environmental quality important
but inﬁnite substitution rejected.
Sustainability rules, e.g., constant
natural capital rule; use efﬁciency
and productivity; sustainability
indicators; and monitoring.
Decoupling plus no increase in
scale; systems
perspectivedhealth of whole
ecosystems very important;
overcompliance with
international environmental
agreements; and sustainability
assessments and audits.
Scale reduction imperative; at the
extreme for some there is a literal
interpretation of the Gaia
hypothesis with moral
obligations.
Policies and
management strategies
Support for traditional ethical
reasoning; rights and interests of
contemporary individual
humans; instrumental value (e.g.,
recognized value to humans) in
nature.
Extension of ethical reasoning:
caring for others motive
intergenerational and
intergenerational equity (i.e.,
contemporary poor and future
people); instrumental value in
nature.
Further extension of ethical
reasoning: interests of the
collective take precedence over
those of individual primary value
of ecosystems and secondary
value of component functions and
services.
Acceptance of bioethics, i.e.,
moral rights/interests conferred
on all nonhuman species and even
the abiotic parts of the
environment: intrinsic value in
nature (i.e., valuable in its own
rights regardless of human
experience).
Ethics
Low level of environmental
awareness in the public.
Wider public education,
establishment of stakeholder
groups and of, e.g., roundtables to
increase inclusion.
Strong local/community
awareness and action campaigns.
Cultural shifts to the maintenance
of local livelihoods and
environmental stewardship.
Degree of public
inclusion
11.ECOSYSTEMSCARRYIMPORTANTMESSAGESTOMANAGERSANDPOLICYMAKERS224
more the system’s renewal, regeneration, or reorganization after a disturbance than its recovery (Folke, 2006), which
implies the assumption that disturbances and spatial heterogeneity determine the behavior of each system to be
unique. As a consequence, recovery trajectories might be difﬁcult or impossible to predict, due to the complexity
of the system combined with unexpected compounded effects of disturbance, and a recovered system may eventually
look identical to the previous one but is not the same system. As any other living system, it will be continuously
developing (Folke, 2006). Differences in degradation and recovery trajectories can be called system hysteresis (Elliott
et al., 2007), and, in practical terms, ecological resilience can only be estimated by means of resilience proxies (Carpenter
et al., 2006), which must be based on a broad resilience analysis, together with the identiﬁcation of speciﬁc
disturbance regimes and societal choices regarding desired ecosystem services (Brand, 2009). A third, recent deﬁnition
is an extension of the ecological resilience, which puts the context of resilience into the entire adaptive cycle and
a system’s ability to successfully navigate this over and over through growth, conservation, collapse, and reorganization
(Fath et al., 2015).
In simple terms, an ecosystem’s degree of ecological resilience tends to be assumed as inversely related to its degree
of threat (Brand, 2009), and therefore holding information on ecological resilience and system efﬁciency constitutes
a prerequisite to assess whether an ecosystem will reach a critical state as a response to environmental stressors
(Ulanowicz et al., 2009). Thus, a critical state regarding natural capital matches an extent in environmental degradation
that exceeds a threshold beyond which the current level of social welfare cannot be supported. Such an ecological
criticality appears therefore to be most important for the maintenance of ecosystems services and goods (Jax,
2005) and the sustainable use of their natural capital (Brand, 2009).
On the other hand, ecosystems and their modiﬁcation as a function of human stressors must be examined accounting
to their carrying capacity and its loss next to stress. Carrying capacity was originally deﬁned as the number
of individuals of a population that can be supported by a given environment without signiﬁcant negative impacts to
that population and that environment. This merely ecological deﬁnition does not entirely capture the multilayered
processes of humaneenvironment relationships, which have a ﬂuid and nonequilibrium nature, perhaps neglecting
the inﬂuence of external forces on environmental change (Moore et al., 2009). Indeed, carrying capacity must also be
related to the social and economic aspects of ecosystems (Elliott et al., 2007), looking on which level of human activities
and anthropogenic pressure can an ecosystem tolerate before undesirable changes occur, because humans’
relationships with their environment are in principle more complex than those of other species with theirs (Pulselli
et al., 2008). Actually, humans have the capacity to modify the type and degree of their impact on their environments
and candat the limitddecrease irreversibly environment productivity and/or radically occupy or transform space.
Many species can modify the carrying capacity of their systems, but because only humans can manipulate ecosystems
to such a drastic extent, the concept of carrying capacity must be expanded to include socioeconomic aspects
(MacLeod and Cooper, 2005; Elliott et al., 2007).
A holistic approach to understand, manipulate, and manage the environment may be provided by linking and
integrating ecosystem principles and the management framework. It is crucial to relate environmental management
to the dynamics of ecosystems, namely the relations between biodiversity and ecosystem function, because ecosystems
are complex adaptive systems, characterized by their historical dependency, as well as by nonlinear dynamics,
threshold effects, various basins of attraction, and limited predictability (Folke et al., 2004; Duit and Galaz, 2008;
Moore et al., 2009). For instance, it has been shown that estuarine ecosystem’s temporal stability and biodiversity
might follow different trends in relation to the systems’ ecological quality status, exhibiting therefore a nonlinear
relationship (Pinto et al., 2013b, 2014a), with the optimal condition (higher values) occurring for average values
of species richness, which may in turn inﬂuence ecosystem services’ provision. In the same estuarine system, these
ﬁndings were conﬁrmed through the analysis of food webs under distinct levels of eutrophication pressure, using
mass-balanced models: a tendency for a decrease in food web connectivity was observed during a recovery process,
from more eutrophic to less eutrophic conditions (Baeta et al., 2011), while the ecosystem’s temporal stability tended
to increase in the same period.
How can this panoply of theoretical concepts be applied in environmental management, which often involves so
many uses and users, sectorial interests, and governance regimes (Costanza et al., 1998, 1999)? Aiming at the restoration
or the sustainable use of a speciﬁc ecosystem, how to decide which will be the best possible course of management
in a multitude of driving forces which may be conﬂicting? Marques et al. (2009) proposed the use a
conceptual guidance tooldthe ecological sustainability trigon (EST)dwhich may possibly be useful as a sort of compass
to provide orientation in the process of building and testing management scenarios to approach environmental
problems. This proposal was mostly based on ideas upcoming from the marine coastal and estuarine research but
seems to be also applicable to other domains.
11.3 INDICATING MANAGEMENT SUCCESS 225
Using EST in Building Management Scenarios toward Ecological Sustainability
It is generally accepted that there is an intrinsic complexity in environmental problems, and also that these are
closely related to the development of human society. As a consequence, possible solutions to environmental problems
always require taking into account different points of view, often expressing the conﬂicting perceptions of multiple
sectors, uses, and users (Costanza et al., 1998, 1999). Different economic scenarios (Fig. 11.7) (Turner et al., 1994,
2003; Turner, 2008) have been to a certain extent trying to address the uncertainty involved in dealing with environmental
problems, and it is clear that in what way the natural or the social systems become favored will greatly
depend on the different options and systems of decision (see Table 11.1). With regard to sustainability, environmental
scenario analysis is therefore required as a tool to recognize and characterize central questions (Table 11.2), as well as
to take decisions about priorities and solutions (Kontogianni et al., 2001, 2004; Swart et al., 2004).
Although sustainability science is complex and uncertain, it is possible to encompass all the different viewpoints
and concerns in the frame of three major driving forces (Marques et al., 2009; Jorgensen et al., 2016):
a) The search for human well-being and the maintenance of human health and safety;
b) The attempt of ecological sustainability and maintenance of natural environmental well-being;
c) Systems’ resilience in face of an increasing human pressure, resulting from population size and demand for
wealth creation.
Certainly, it is needed to assume that research should complementarily cover these three views and simultaneously
fulﬁll the goals of creating knowledge and wealth and improve life quality. The search for human wellbeing,
from the governance point of view, is often endorsed as corresponding to GDP or to stakeholder beneﬁts,
which could ultimately be expressed by some sorts of metrics such as well-being indices (Diener et al., 1999; Diener,
2000) (Fig. 11.8). The straightforward societal objective has been maximizing economic goods and services, which is
usually reﬂected in governmental approaches, and at the same time, into a certain extent, protect ecological goods
and services, at least to avoid business of being accused to harm the later ones or to prevent countries from being
subject to legal violation proceedings for not fulﬁlling rules settled in common legislation, as it is the case of the European
Union Directives (Marques et al., 2009).
Sustainability indicators and composite indices have been progressively accepted as valued tools in terms of policy
making and public communication with regard to environment, economy, society, or technological improvement
(Singh et al., 2009), as well as to human quality of life (Diener and Tov, 2012). Nonetheless, in many cases,
GLOBALISATION
BAU
PT
DG
REGIONALISATION
CONSUMERISM COMMUNITY
Global
Sustainability
driven
Local
StewardshipProvincial
Enterprise
BAU - Business As Usual economy
PT - Policy target - e.g.the USA CWA and the European WFD and MSFD
DG - Deep Green economy
World
Markets
driven
Values
Governance
FIGURE 11.7 Different economic scenarios resulting from supporting the natural or to the social system, as a function of political decision
systems. Based on Turner, R.K., Pearce, D., Bateman, I., 1994. Environmental Economics: An Elementary Introduction. Harvester Wheatsheaf, London, 328 pp.
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS226
TABLE 11.2 Core Questions for Sustainability Science.
1. How can the dynamic interactions between nature and human societydincluding time lags due to inertiadbe better incorporated into
emerging models and conceptualizations that integrate the global systems, human development, and sustainability?
2. How are long-term trends and widely varying spatial scales in environment and development, including consumption and population change,
reshaping natureesociety interactions in ways relevant to sustainability?
3. What determines the vulnerability and resilience of the natureesociety system in particular kinds of places and for particular types of
ecosystem and human livelihoods?
4. Can scientiﬁcally meaningful limits or boundaries be deﬁned that would provide effective warning thresholds beyond which the
natureesociety systems are at a signiﬁcantly increased risk of serious degradation?
5. What systems of incentive structuresdincluding markets, rules, norms, and scientiﬁc informationdcan most effectively improve social
capacity to guide interactions between nature and society toward more sustainable trajectories?
6. How can today’s operational systems for monitoring and reporting on environmental and social conditions be integrated or extended to
provide more useful guidance for efforts to achieve sustainability?
7. How can today’s relatively independent activities of research, planning, observation, assessment, and decision support be better integrated into
systems for adaptive engagement and societal learning?
8. How can future changes be determined and predicted in a creative, objective, rigorous, and policy-relevant manner that reﬂects sustainability
and incorporates different perspectives?
Modiﬁed from Kates et al., 2001; Swart, R., Raskin, P., Robinson, J., 2004. The problem of the future: sustainability science and scenario analysis. Glob. Environ. Chang. 14, 137e146.
https://doi.org/10.1016/j.gloenvcha.2003.10.002; Marques, J.C., Basset, A., Brey, T., Elliott, M., 2009. The ecological sustainability trigon e a proposed conceptual framework for
creating and testing management scenarios. Mar. Pollut. Bull. 58, 1773e1779. https://doi.org/10.1016/j.marpolbul.2009.08.020.
FIGURE 11.8 The ecological sustainability trigon (EST) (Marques et al., 2009) illustrating the expected trends and relationships between
variables assumed to be correlated with ecological sustainability, human well-being, and human-induced pressures. The bottom and left-hand
axes of the EST indicate how governance and societal systems must be linked to environmental management, namely through the deﬁnition of
policy targets and the choice of ecologically sustainable economy alternatives, while the right-hand axis illustrates how increasing human
pressures imply consequences on the other two axes. Greendgood; reddbad.
11.3 INDICATING MANAGEMENT SUCCESS 227
when applied in policy, sustainability indices appear to fail in fulﬁlling fundamental scientiﬁc requirements,
turning them somewhat useless or even confusing for policy advice (Brunner and Starkl, 2004; Bo¨hringer and
Jochem, 2007; Marques et al., 2009). However, despite difﬁculties in establishing unquestionable quantitative relations,
the concept of environmental integrity is normally associated to the aim of ecological sustainability. Environmental
integrity can be perceived from different theoretical orientations, all involving inherent uncertainties
(Fig. 11.8). The panoply of tools available to evaluate environmental integrity (e.g., environmental quality indices)
is vast, although probably none of those tools is entirely appropriate (Pinto et al., 2009). On the other hand, the value
of integrated approaches has been illustrated (Pinto et al., 2014a), particularly through examples where the DPSIR
(drivers, pressures, state change, impacts, response) approach can be related to the selected indicators (McLusky
and Elliott, 2004; Aubry and Elliott, 2006; Borja et al., 2008; Gray and Elliott, 2009; Pinto et al., 2013b). Furthermore,
a new interest in deﬁning, measuring, and protecting ecosystem goods and services developed from the acknowledgment
that economic prosperity depends on ecosystem functioning and that many natural ecosystems are threatened
(Pinto et al., 2010).
Independently from such recognition regarding the importance of ecosystem services and goods, there are two
main problems in assessing them, which are (Heal and Kristrom, 2005): (1) certain functions do not become important
always at the same scale, and (2) the integration and aggregation of all temporal and spatial scales information
may originate problems due to the fact that interrelations and feedback loops may operate at scales above the level
being assessed (Pinto et al., 2010). A good illustration of this is the dependency of economic goods and services at
one area from the successful functioning somewhere else. For instance, estuarine ﬁsh nursery grounds in one area
create the conditions for the existence of marine commercial stocks in another area (McLusky and Elliott, 2004; Pinto
et al., 2010). As a consequence, scaling rules attempting to describe ecosystem service’s provision and delivery, especially
for open and dynamic systems, still require to be quantiﬁed and deﬁned (Limburg et al., 2002; Pinto et al.,
2013a, 2014a).
The human population pressure is increasing as a consequence of population growth and the associated higher
resource consumption and pollution related to the pursuit of satiating cumulative human needs. The complexity,
difﬁculty, and intrinsic costs of solutions necessary to deal with environmental problems created by such pressure
have been increasing gradually (Fig. 11.8). There is no reason to think that the needs of future generations will be
different from ours in terms of quantity and quality, and therefore we may anticipate that two changes might happen
as compared with today: (1) decrease in global carrying capacity and (2) decrease in the number of choices available,
meaning by this both the number of different resources and our capacity to exploit them. Possibly, both in terrestrial
and aquatic ecosystems, shifts in regimes in relation to resilience and the functional roles of biological diversity will
occur (Folke et al., 2004; Pinto et al., 2014a), and eventually future generations may have to adapt rapidly their ways
in accordance to ecosystem’s sustainable management (Pinto et al., 2014b).
Another aspect includes the transformation to a society that is able to understand and implement long-term
thinking into its policy and decision-making. This will require rewards to be given for different outcomes than simply
short-term gain and can be promoted through collective incentives and through a new holistic paradigm. The
ultimate goal is to ﬁnd a situation that encourages win-win situations for humans and the environment, similar
to what other organisms in the environment currently experience in their surroundings: a tree growing in the forest
improves the health and quality of the forest. One recent attempt to explain this new paradigm can be found in Fiscus
and Fath (2019). A key feature of this win-win approach is identifying and enhancing positive feedback loops
that work in the direction of gradient formation. These positive, autocatalytic cycles are critical for the continued
performance of any complex adaptive system and promote extended diversity (Ulanowicz, 2003; Hordijk and Steel,
2017; Cazolla Gatti et al., 2018).
We assume here that ecological sustainability constitutes a major goal for human society, which reﬂects in international
agreements adopted by most countries (e.g., the Convention on Biological Diversity, Sustainable Development
Goals, United Nations Framework Convention on Climate Change, etc.) and national laws approved.
Nevertheless, at the present levels of human population size and associated environmental pressure, it is not conceptually
possible to maximize at the same time ecological sustainability and stakeholder beneﬁts. Still conceptually, it
would eventually be possible to maximize ecological sustainability in a situation of extremely large human population
size, but this would entail considerably low standards of human well-being. Of course, in the short term,
we may consider maximizing stakeholders’ beneﬁts and environmental anthropogenic pressures, but only if we
renounce to the goal of ecological sustainability, compromising the future in the longer run. It is clear that in this
“game of possibilities,” there is no conceivable scenario that allows maximizing the simultaneously ecologically sustainable
solutions for human development, the search for human well-being, and environmental human-driven
pressures, although a trade-off between the three might be conceptually possible. Marques et al. (2009) called
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS228
such trade-off the “ecological sustainability trigon” (EST) (Fig. 11.9), in which the bottom and left-hand axes indicate
how governance and societal systems must be linked to environmental management to increase the chances of moving
both from MIN in the direction of MAX, which constitutes the goal. The right-hand axis illustrates how
increasing human pressures from MIN (best scenario) toward MAX (worth scenario) inevitably conditions the other
two axes determining adverse consequences.
Different issues under discussion (indicators, objectives, pressures, etc.) can be arranged along the EST corresponding
axis, which allows an immediate and intuitive rough integrated view of conceivable implications of policy
or management decisions, even when processes behind relationships are not completely understood, as it is often the
case in estuarine and coastal ecosystems. Furthermore, while relationships are assumed to be nonlinear, all imaginable
case studies roughly correspond to a position on the EST frame. At the country scale, Sweden, for instanceda
small, rich population, with good coastal ecosystem governance, few environmental problems, and a high capability
to tackle those problemsdwould be placed approximately in position A (Fig. 11.9). On the other hand, Bangladesh,
including the Sundarbans mangrove area, which have a large population and high human stress, poor funding, and
badly structured governance systems to implement resolutions, large pressures on ecosystems, and solutions almost
ecologically insensible, which distresses its population well-being, could come approximately to position B. At the
local scale, as a result of the application of some mitigation and recovery measures aiming at dealing with eutrophication
problems, the Mondego estuary, on the western coast of Portugal, which has been long last studied, showed
an improvement in its ecological quality condition during the last decade, and a positive evolution of regulation and
cultural ecosystem services, concomitant into a certain extent to an increase in stakeholders’ beneﬁts (Pinto et al.,
2013b; 2014a). This corresponded roughly to a gradual evolution from position C to position D since the mid-
1990s up to present (Fig. 11.9).
The two classic examples at the country scale plus the third one at the local scale illustrate how the EST may help
decision makers (a) in identifying the links between drivers and (b) in clarifying/deciding about the best management
options to convey any system closer to an optimal condition in the trigon. Independently, from the scale of the
FIGURE 11.9 Proposed use of the ecological sustainability trigon in building and analyzing management scenarios: analysis of the expected
variations and relationships of different indicators and variables correlated with ecological sustainability, human well-being, and human-induced
pressures (see legend of Fig. 2 for details). Greendbest scenario; reddworse scenario; yellowdacceptable scenario.
11.3 INDICATING MANAGEMENT SUCCESS 229
management scenario we may want to build, the EST may in principle be used as a conceptual guidance tool, which
constitutes one of its most interesting features.
Obviously, we must accept that certain things can be managed and other things cannot, and consequently choices
have to be made. It is regarding such choices that the EST may constitute a useful intuitive tool. Bangladesh, for
instance, at least in the short term, cannot alter its population size and concomitant human pressure, but governance
and environmental management could eventually be improved. Similarly, at a local-scale environmental management,
some pressures can be handled, such as, for instance, in the Mondego estuary, where mitigation measures
included altering the discharge of point-source polluting materials, allowing decrease in eutrophication symptoms
in estuarine areas, although the general eutrophic situation in the river basin could not be yet solved (Verı´ssimo et at.,
2013). On the other hand, climate change, as an “exogenic unmanaged pressure,” is an example of something which
cannot be managed. In fact, far from managing it, local managers can only respond to its consequences.
In general, it must be accepted that solving the increasing complexity of environmental problems cannot be the
only condition to reconcile the difﬁculties in accounting for the three driving forces simultaneously (achieve and
keep ecological sustainability human well-being goal, and dealing with human population/size pressure). Indeed,
the beneﬁts from pursuing a possible harmonization by implementing complex solutions will not increase linearly as
a function of that complexity, as the intrinsic costs (energy and money) will plausibly become unsustainable in the
long run (Fig. 11.10).
What Might Be the Advantages of Using the Ecological Sustainability Trigon?
The EST proposal (Marques et al., 2009) represented a tentative and intuitive view which has been requiring
further testing and debate (Pinto et al., 2013a; 2014a; 2014b). In fact, the number of variables that must be taken
into account when building management scenarios is often very high, as well as the uncertainties regarding their
relationships and trends. The core problem resides in understanding into what extent interactions between given
economic and ecological systems are sustainable, which involves cross-scale, transcultural, and transdisciplinary
fundamental questions, calling for innovative approaches in research, in policy, and to build social institutions
(Costanza et al., 1998, 1999). EST as a conceptual framework has been tested with interesting results at local level
case studies (Pinto et al., 2013a; 2014a), as a kind of compass to provide orientation in building management scenarios
(“In which direction do we want to go?” “How do we get there?”), alternatively to more conservative forecast
scenarios (“Where are we going to?”). In fact, besides addressing adaptations to current conditions and actions to be
FIGURE 11.10 Proposed use of the ecological sustainability trigon in building management scenarios: expected trend in costs and beneﬁts as a
function of an increasing complexity of environmental problems solutions, namely to meet policy targets, forces the adoption of environmentally
viable economic activities.
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS230
undertaken in the short term, management scenarios should also be built to accomplish transformations toward
more sustainable development pathways (Folke, 2006).
As shown in Figs. 11.8e11.10, different variables whose tendencies of variation are in principle correlated can be
arranged along the corresponding sides of the triangle, with their expected trends varying from maximum (MAX) to
minimum (MIN). Examples of variables are provided, but many other could of course be included, which may prove
particularly useful namely when policy-targeted approaches are pursued in exploring ecosystems’ resources (e.g.,
implementing the European Water Framework Directive or the European Marine Strategy Framework Directive)
(Pinto et al., 2014b).
For instance, applying governance theory, which accounts for the legal and administrative aspects of policy
implementation, implies establishing hypothesis on how different governance forms can be expected to handle processes
of change, which are characterized by nonlinear dynamics, threshold effects, and cascades, being therefore
poorly predictable (Duit and Galaz, 2008). In addition, perhaps due to scientiﬁc uncertainty and to lack of consensus
among scientists, linkages between ecological science and environmental policy are poor, which jeopardizes the
transfer of science into management (Moore, 2009; Pinto et al., 2010; Borja et al., 2011). The usefulness of the EST
as an orientation tool may possibly be more explored in testing governance hypotheses. Moreover, EST can also
be tested in analyzing and integrating, at least approximately (especially as correlations are far from being linear),
the expected variations and interactions between different variables assumed to be correlated with ecological sustainability,
human well-being, and anthropogenic pressures. Namely for decision makers, these possibilities represent
a great potential with regard to an intuitive clariﬁcation of what might be compliant and what is probably not
compliant with the goal of sustainability, as well as in establishing possible safety margins in the “game of
possibilities.”
One of the advantages of the ESTapproach is being able to address and measure the different environmental components
with a same species-speciﬁc currency, i.e., the human society view and, at the same time, of describing at the
light of ecological theory our behavior, energetics (economy), and dynamics. More than ever, the incorporation of
our behavior, energetics, and dynamics into an ecosystem integrity framework represents a crucial challenge for
the science of ecology, which requires measures of ecological status from the ecosystem organization and functioning
points of view, rather than from pressures and vulnerability. Nevertheless, the ESTapproach allows making the evaluation
criteria for environmental management scenarios more explicit, meaning that scales should match (time and
space), interactions should match (relationships), and rates should match (biophysical limits), constituting therefore
a promising tool for gap analysis (information concerning knowledge lacunae), as well as to address new research
questions.
Exergy and Carbon Budgets to Evaluate Sustainability
During the initiation and implementation of projects that are believed to bring our societies toward a more sustainable
condition, several questions often arise. Most commonly, the questions are concerned with issues such as
what is our present state? where are we at all with respects to our wishes and ambitions? and what directions should
we take? After decisions have been made on carrying out a speciﬁc project and implementing certain measures, it is
logical that one would ask even more questions like how are we proceeding? what was the actual effect? how far
have we gone along the path toward sustainability?
These questions are not speciﬁc to any stakeholders or actors in such a process but are rather common in character
and following them would be a good idea to implement a method that could lead the transition in a more generic
manner. With this, we mean a common approach that would cover all situations and have the ability to translate the
results of a given analysis into a “best practice.”
We may in this situation introduce an understanding of our societal systems in a manner that includes three major
subsystems that each need to be preserved and ensured for our sustainability in the future, namely adequate, sufﬁcient
supplies of food, energy, and water. It is important to notice that these subsystems are highly interlinked, and
the coupled system is often referred to as a NEXUS. The problems related to preserving them all and at best in an
optimal manner is known as NEXUS problems (Beck and Villarroel Walker, 2013; Vora et al., 2017).
Furthermore, the subsystems are somehow strongly dependent on physico-geographical conditions and therefore
it is impossible to point out speciﬁc, optimal solutions to all areas of the globe. The conditions vary so much that
there is no chance that “one size will ﬁt all,” so we will face a problem of ﬁnding a variety of solutions that all
may be applicable but with various degrees of importance, ways, and degrees of coupling. Solutions are speciﬁc
and nongeneric, only methods may be generic in this case.
11.3 INDICATING MANAGEMENT SUCCESS 231
The overall aims of the method will be at least three:
1) The method should allow us to tell about the state of the system and allow us to monitor our system with respect
to eventual progress or degression as consequence of initiatives
2) It should allow us to transgress the problems of the various elements of the NEXUS, i.e., to bring all elements in
the same currency
3) It should allow to give sensible and pragmatic indications on where, when, and how to take actiondin particular
when combined with economic considerations to reach the maximum impact on improving sustainability
The combined considerations on technological feasibility and impact on efﬁciency on one hand and the economic
gains and socioecological impact on the other are important in identifying what is often political language referred to
as “the lowest hanging fruits.”
In many cases, having such knowledge will provide us with a deeper insightda secondary and positive side
effect.
Unifying Matter and Energy
Following the previous, we need a method that allows us to combine both matter and energy to evaluate where
and it makes most sense to implement changes to our systems, or if it makes sense to make the changes at all.
The physical concept of exergy is believed to provide us with much information in the above directions. It gives us
an idea not only of energy used but also tells us about the energy’s capacity to do work. By indicating the quality of
energy, we get the information whether a given amount of energy could or should have been used for something
else. A primary strength is that it also applies directly to matter because raw materials and other chemical compounds
used in our society also possess an exergy value corresponding to the content of free chemical energy.
What happens during the consumption of both energy and matter in our society is that exergy is broken down,
destroyed, or dissipateddwhat term one prefers to use to describe the imperative of the second law of thermodynamics,
that all energy conversions have the price in terms of part of the energy being lost and converted into entropy.
Meanwhile, for optimization, it is exactly the amounts used and lost that are interesting. Are the high amounts
of inputs really necessary? Are the high dissipative losses really necessary? Or do we already know ways to do the
things with higher efﬁciency? Is better technology available?
Deconstruction of Society for Analysis
Implicit in the above questions is that all parts of human life are not equally important: some are more fundamental,
others are luxuries; some are more short term, and others have lasting shadows across space and time.
The NEXUS perspective takes an entrance point in shaping the basic material-energetic part of our everyday life
in society. Water is vital for drinking, irrigation, and many production processes. Energy may be provided by using
various chemical compounds of high exergy density and is needed for heating, cooking, transportation, and for industry
to provide us all with various goods and services. Food production needs both energy and water. Historically,
these two factors have been delivered to agriculture mostly by renewable resources (as ecosystem services from the
soil formation, hydrological cycle, solar radiation, and biological pollination, to name a few), but modern conventional
agriculture subsidizes these renewable ﬂows with impressive amounts of fossil fuels and fossil water.
All together, it is difﬁcult to tell exactly where one should put the greatest efforts in increasing the efﬁciencies. Is it
through optimization in industry through greener technology, cleaner production, or industrial ecology, or is it
merely by changing and decreasing existing consumption patterns? How do we decrease the need for destroying
the landscape due to increasing demands from population and consumption pressures?
Actually, we need to have better information about all sectors that are involved in shaping our living conditions.
To achieve this, we propose to acquire knowledge regarding the exergy ﬂows through a set of basic sectors to which
our activities belong, including the following (Fig. 11.11):
1) The energy sectordelectricity, heating, and transportation
2) A public sector that represents all buildings and consumption in our societal infrastructure
3) A private sector that represents our residences and domestic consumption herein
4) An industrial sector that provides us with all goods and services (other than the ones related directly to energy or
agriculture)
5) A “natural resources” sector that builds on our exploitation of terrestrial and aquatic systems, such as agriculture,
forestry, and ﬁsheries
6) A waste sector that really is a measure of wealth and all the above sectors contribute to it
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS232
7) A natural sector or Nature “per se”da set of our environment that exists and functions with a relatively low
human impact
Nielsen and Jørgensen (2015) use this conceptual framing as well as a recent study by Skytt et al. (in print). In the
latter, they point out that transport makes up a large part of our energy consumption and that splitting up this activity
on particular sectors makes it difﬁcult to decide exactly where action should be taken in future.
To simplify the case of either the society or the individual sectorsdand analyze the “sustainability state”dit is
useful to provide data on inﬂows and outﬂows but also the stored exergies of the sector (Fig. 11.12). At the same
time, inﬂows and outﬂows are split in
1) Energy ﬂows from renewable resources
2) Energy ﬂows from nonrenewable resources
3) Material ﬂows from renewable resources
4) Material ﬂows from nonrenewable resources
In the same manner, outﬂows may be distinguished. For energy, it might be useful to know if the energy leaving is
still of high exergy value or if it is no value any longer. For matter, the potential for recycling and reusing is interesting,
but one should consider that the required additional energy is needed for upgrading that is inherent in principles
like “cradle-to-cradle” or “circular economy.” The best solution is coupling processes efﬁciently together such
that the wheels spinning on one help drive the other, thereby reducing or eliminating wasteful expenditures. For
example, driving in one’s car several kilometers to recycle one aluminum can is energetically a loser (regardless
FIGURE 11.12 The conversion of energy and matter within a sector, where it is either accumulated in the sector or exported as either useful or
dissipated (nonuseful) amounts of energy and matter. Matter is considered to be dissipated when exergy expenditures exceed exergy used in the
recycling process.
FIGURE 11.11 How exergies are driving and converted in our society. Fossil fuels and renewable energy sources are converted mainly into
electricity and heat passed on to all anthropogenic sectors. Industries include all activities from production proper to commercial trade. Natural
resources (termed naturedbased in the ﬁgure) sector represents all activities that are based on a seminatural function of ecosystem.
11.3 INDICATING MANAGEMENT SUCCESS 233
of what the price of gasoline or aluminum is), but if recycling is done in an integrated fashion, then possible win-win
scenarios may emerge.
Indicating a Sustainability State
Having established balances of energy and matter ﬂow in accordance to the description above, several obvious
ways to establish indicators of sustainability emerge, such as
1) Exergy balance
2) Ratio or fraction of renewables expressed as exergies
3) Structural costs in terms of exergy
Exergy balance: In principle, the change in structure of the system should be higher than zero meaning that society
builds up its infrastructure. But again, the system should, in principle, maximize its output. At the same time,
the difference between input of exergy and output of dissipated/destroyed exergy should decrease pointing at
increased efﬁciency.
Infrastructure efﬁciency: The same structure or increasing structure should be maintained for a decreasing input,
i.e., more structure, goods, and services have been provided for less expenses, again pointing at increases in
efﬁciency and related to the previous. For more details, see Nielsen and Jørgensen, 2015.
Society should increasingly be based on renewable resources. This could be expressed as a share of either (1) the
ratio of renewable exergy to nonrenewable exergy or the fraction of renewable exergy out of the total exergy intake.
Concerning the carbon budgetdit is easy to establish a positive balance due to ecosystem services through
photosynthesisdat least for a rural society. When population density becomes higher, with more and more people
such as in urban areas and with the corresponding decreasing importance of a “natural” component, it seems almost
impossible to establish a positive carbon balance (higher uptake that emission). Meanwhile, both types of systems
should share the same goal namely a decreasing per capita emission in terms of CO2 equivalents. For more details,
see Jørgensen and Nielsen, 2014.
11.4 CONCLUSIONS
A new way of thinking is needed in environmental management. First of all, we need a management approach
which presumes an intrinsic value for nature and that nature also provides unsubstitutable services, being it (1)
renewable energies in the form of solar radiation, (2) diverse and productive ecological systems, (3) geophysical resources
such as fossil fuels, metals, or other material, (4) life-giving water and climate, and (5) recreational, psychological,
and spiritual experiences.
All of these perspectives are included in the visions of sustainability. Meanwhile, it is necessary to base management
on the biophysical components otherwise neither societal nor economic sustainability will be achieved. In
other words, socioecological management needs to be based on the concept of strong sustainability as none of the
other forms will be fully exchangeable to environmental sustainability, i.e., no money can replace biodiversity
and adjacent losses in functionality, no government can replace hierarchical structures and cybernetics of nature.
Hence, we need to redeﬁne management taking into consideration all the above features of natural functions
identiﬁed above and incorporate the considerations in elaborations of policy formulations and planning of measure.
Then, and only then, we may be able to achieve sustainable development. The ecological principles advanced
throughout this book are the starting point for that effort.
11. ECOSYSTEMS CARRY IMPORTANT MESSAGES TO MANAGERS AND POLICY MAKERS234
C H A P T E R
12
Conclusions and Final Remarks
12.1 ARE FUNDAMENTAL ECOLOGICAL PROPERTIES NEEDED TO EXPLAIN OUR
OBSERVATIONS?
Take a walk on a pleasant May day in a Chinese temperate deciduous forest, visit the Serengeti National Park in
Tanzania when the wildebeests are migrating North, paddle a canoe through a North American wetland, or hike the
alpine tundra of Austria, whatever your preference, you will be impressed by the diversity and beauty that nature
offers to you. We know that the diversity of nature is enormous, in terms of ecosystems, species, genetics, and
biochemistry. For example, focusing just on species richness, we can ﬁnd on the order of 107
different species on
earth. We also have a fairly good image of evolution from 3.8 billion years ago when the ﬁrst primitive lifeenvironment
processes emerged and evolved to the level of our own species, Homo sapiens, in possession of modern,
advanced technologies: medicines, airplanes, industrial nitrogen ﬁxation, Internet, and so on. From one perspective,
we know that all the resources we have and utilize originate from nature and end up back in nature as a throughﬂow
supporting modern civilization (as is being measured in new ﬁelds such as urban metabolism, industrial ecology, life
cycle analysis, etc.). But in addition to this ultimate dependency, we could also turn the question around and ask:
which properties do ecosystems have that explain the diversity, adaptability, and beauty of nature and evolution?
How can we explain that the interactions between matter, energy, and information lead to the complex web of
life-environment processes on earth, as we observe? We deﬁnitely do not need an intelligent designer to come up
with a clear and fully acceptable explanation. This book presents an overview of what systems-based, thermodynamic
properties are known to underpin this natural growth and development, and by extension the emergence
of human agency and actions.
12.2 PREVIOUS ATTEMPTS TO PRESENT AN ECOSYSTEM THEORY
Previously, various attempts have been made to present an ecosystem theory that could be applied to explain
quantitatively ecosystem constituting elements, processes, and regulation as well as their respective responses to
disturbance and changing impacts. While we cannot cover all the attempts here, we focus on a few based-on systems
perspectives and thermodynamics.
One of the early pioneers in Systems Ecology, Kenneth E.F. Watt, proposed his theory in the important work Ecology
and Resource Management in 1968, which opened the way for greater systems thinking in ecology. In the 1970s,
B.C. Patten edited four volumes with the title Systems Analysis and Simulation in Ecology. These volumes gave the state
of the art of and were a useful reference in systems ecology. Taken together, these volumes comprised an early
attempt to develop an ecosystem theory. During the 1980s, a number of scientists contributed to further developing
ecosystem theory: H.T. Odum, R.E. Ulanowicz, B.C. Patten, R. Margalef, C.S. Holling, and S.E. Jørgensen, to mention
a few. H.T. Odum’s book from this period, Systems Ecology: An Introduction, is probably one of the best attempts to
make a comprehensive ecosystem theory. The discussion of hierarchy theory, allometry, and scaling problems in the
1980s should also be mentioned. T.F.H. Allen and T.B. Starr in their book Hierarchy, Perspectives for Ecological
Complexity (1982) and R.V. O’Neill, D.L. De Angelis, J.B. Waide, and T.F.H. Allen in the book A Hierarchical Concept
of Ecosystems (1986) presented hierarchy theory and made it an almost fully accepted part of ecosystem theory.
Peters’(1983) publication of many allometric principles should also be mentioned in this context. Polunin (1986) edited
a book titled Ecosystem theory and application, which was an early attempt to apply ecological theory to address
some of the global environmental issues of the day. The 1980s and early 1990s saw a lot of interest in the Stream
235
A New Ecology, Second Edition
Copyright © 2020 Elsevier B.V. All rights reserved.https://doi.org/10.1016/B978-0-444-63757-4.00012-7
Ecosystem Theory (e.g., Cummins et al., 1984; Minshall et al., 1985; Minshall, 1988; Wiley et al., 1990) which focused
on streams as open systems, controlled primarily by their allochthonous riparian input.
In 1992, S.E. Jørgensen gave an overview of these contributions in his book Integration of Ecosystem Theories: A
Pattern. The various contributions to an ecosystem theory were very different, but a closer study of the proposed
theories revealed that they actually were different angles and covering different aspects, but largely were consistent,
complementary, and formed as the title of the book indicates a pattern. H.T. Odum’s theoretical contributions to systems
ecology were summarized by C.A.S. Hall (1995) in the book Maximum Power: the Ideas and Applications of H.T.
Odum. R. Margalef’s (1997) book Our Biosphere summarized his contributions to systems ecology. It was based on a
well-balanced mix of thermodynamics and ecology. Macroecology by J.H. Brown (1995) presented from this period a
quantitative ecological attempt to explain biogeophysical observations, and Reynolds (1997) expanded his ideas on
theory describing aquatic habitats.
Patten and Jørgensen (1995) edited the book Complex Ecology: The Part-Whole Relation in Ecosystems in which 31
systems ecologists contributed, presenting a wide overview of many different approaches and viewpoints, from
quantum considerations, to modeling theory, to network theory, to cybernetics and thermodynamics. Furthermore,
Jørgensen, Patten, and Straskraba published a series of papers in the journal Ecological Modeling under the title “Ecosystems
Emerging.” The paper subtitles to date are (1) Introduction, (2) Conservation, (3) Dissipation, (4) Openness,
(5) Growth, and (6) Differentiation. The remaining papers originally planned included (7) Constraints, (8) Adaptation,
(9) Coherence, and (10) Applications. Similar to this book, these papers are rooted in thermodynamic laws and
basic properties of ecosystems.
Coming from a more biogeochemical perspective, A˚ gren and Bosatta (1996) published Theoretical Ecosystem Ecology:
Understanding Element Cycles, which put emphasis on the importance of carbon and nitrogen cycling in ecosystems
and is a commonly used textbook in this ﬁeld.
In 2001, Jørgensen and Marques published “Thermodynamics and systems theory, case studies from hydrobiology”
(Hydrobiologia 445, 1e10). The paper claimed that ecosystem laws could be developed and applied similarly
to the application of physical laws in physics. Similarly, the December 2002 issue of the journal Ecological Modelling
(volume 158: 3) was based on nine papers by different authors invited to show that we could explain theoretically
many papers published in ecology, which themselves were presented as observations or rules without any theoretical
basis. The nine papers were a successful attempt to explain theoretically the phenomenological behavior
observed in ecology.
In 2004, Jørgensen and Svirezhev published Towards a Thermodynamic Theory for Ecosystems. The book covered a
major part of the ecosystem theory because thermodynamics is the foundation for understanding many ecosystem
processes. Thermodynamics is, however, a difﬁcult scientiﬁc discipline to understand, which unfortunately prevents
wider application. The presented theory is, however, coherent and is able to explain many ecological observations.
The book is important as it links the traditional thermodynamics normally dealing with ideal gases to this new
domain of a more macroscopic understanding of the energetic relations between elements quite different from molecules
(Nielsen, in print).
To claim that we have laws providing precise predictive capacity is a simpliﬁcation in the sense that ecosystem
laws inevitably will be different from physical laws due to the complexity of ecosystems compared with physical
systems. Expressed differently, it will be much harder to formulate causality in ecology than in physics, the heterogeneity
of ecosystems simply opens up to a world of multiple causalities to be possible and realized, but there seems
no doubt that ecosystems also have some general properties that can be applied to make predictions and understand
ecosystems’ responses to perturbations. Therefore, the focus in this book has been on the deduction of generalities in
processes, properties, and patterns between the many types of ecosystems.
12.3 RECAPITULATION OF THE PHENOMENOLOGICAL ECOSYSTEM THEORY
This theory integrates and extends the abovementioned initiatives, building on those contributions. As stated in
the ﬁrst chapter and carried throughout the book, the Ecosystem Theory presented here rests on seven basic principles
we observe in ecosystems:
1) Ecosystems have thermodynamic openness,
2) Ecosystems have ontic openness,
3) Ecosystems have directional development,
4) Ecosystems have connectivity,
12. CONCLUSIONS AND FINAL REMARKS236
5) Ecosystems have hierarchic organization,
6) Ecosystems have complex dynamics: growth and development, and
7) Ecosystems have complex dynamics: disturbance and decay.
Physicalechemical systems can usually be described by matter and energy relations, while biological systems in
addition need to include information relations. Biological systems can be characterized using four growth and
development forms: (1) increase of material across the system boundary, (2) structural (biomass) growth, (3)
network growth and development, and (4) information growth and development. Note the difference between
growth and development: growth terms are quantitative and extensive, while development aspects are qualitative
and intensive. Both occur within an ecosystem (and also in socioeconomic systems, which is a story for another
book). The last two forms give complex systems, including ecosystems, possibilities to move further from thermodynamic
equilibrium under the same resource ﬂow constraints. This ability to utilize energy ﬂows to create and
maintain structure and functioning is referred to in Chapter 1 as the physically driven biological aspect. Moreover,
the synergistic effects of networks give ecosystems the possibility to utilize available resources better (see Chapter
4), and thereby move further away from equilibrium. In addition to purely physical driven energetic
considerations, ecological systems are adapting and evolving, which is the principle of biologically driven biological
aspect. Through evolution, genetic, biochemical, environmental, and cultural information yields better
utilization of the available resources to move the system still further from equilibrium. While material
biomass growth form is conservation limited, network and information development are not and are far from their
possible limits.
It was demonstrated in Chapter 9 that the seven ecosystem properties presented in Chapters 2e8 can be applied
to explain a number of ecosystem rules and observations. We do not propose that we have a complete or ﬁnished
theory (no scientiﬁc discipline has a complete theory), but one that is adequate to explain many of our observations
and may have practical value in addressing our many environmental symptoms. Chapters 10 and 11 show that the
theory can be applied to assess ecosystem indicators useful in environmental management.
12.4 ARE THERE BASIC ECOSYSTEM PRINCIPLES?
Jørgensen and Fath (2004a) have discussed eight basic principles of ecosystems, their properties and processes,
and later Jørgensen (2006, Jørgensen et al., 2007) added two more. Here, as stated in Chapter 1, we hone this
down to nine key principles, which are implicitly covered by the general phenomenological properties of ecosystems
in Chapters 2e8. To the extent possible, we will mention how each of the principles are rooted in the seven ecosystem
properties presented in Section 12.3. Interpretation of the principles has, however, to be subject to the recognition
that ecosystems are ontically opendtoo complex to allow accurate and complete predictions in all details. Nevertheless,
let us try to set up the principles because they can, together with the properties presented in Chapters 2e8 and
applied in Chapters 9e11, suggest new avenues to understand ecosystems. Our research and experience have
converged on this list of nine basic ecosystem principles:
1. Mass and energy are conserved. This principle is used again and again in ecology since it allows one to write balance
equations at the core of ecosystem modeling, such as with a basic box-and-arrow diagram in which change in
mass or energy ¼ inputeoutput.
2. All ecosystem processes are dissipative and therefore irreversible (this is probably the most useful articulation of the
second law of thermodynamics). Environmental systems are open systems, embedded in an environment from
which they receive energyematter input and to which they also discharge energyematter output. This relates to
property 1, thus requiring open systems to recharge and replenish the dissipated energy, and also contributes to
the eventual collapse and decay of ecosystems described in property 7.
3. All life uses largely the same biochemical constituents and processes. This simply states the relevance of many
biochemical compounds which can be found in all living organisms. They have therefore almost the same
elemental composition derived from about 25 elements (Morowitz, 1968) where the “six-pack” of C, H, N, O, P,
and S plays the central role. This principle is widely used when stoichiometric calculations are made in ecology,
i.e., an approximate average composition way to express the Second Law of Thermodynamics in ecology.
This principle establishes a dependence between the ecosystem and its reliable, external of living matter is
applied.
12.4 ARE THERE BASIC ECOSYSTEM PRINCIPLES? 237
4. An ecosystem uses surplus energy to move further away from thermodynamic equilibrium (physically driven biological
aspect). As an open system, and as described in detail throughout this book, the ecosystem uses the energy ﬂows to
create and maintain complex structures and functions. This accords with the idea of directional development
(Property 3).
5. Ecosystems coevolve and adapt to prevailing conditions (biologically driven biological aspect). Evolution and
directionality, implicit in autocatalysis, can only be understood in light of the irreversibility principle rooted in the
Second Law of Thermodynamics. Evolution is a stepwise, path-dependent development based on previously
achieved outcomes for surviving in a changeable and very dynamic world (contributes to Properties 3 and 6).
6. Ecosystems have diversity of structure and function. Due to the principles above, planetary development has moved
in the direction of ever more complexity and diversity in terms of the type and kind of lifeeenvironment
interactions at all scales. This leads us to the next principle.
7. Ecosystems have many levels of organization and operate hierarchically. This principle is used again and again when
ecosystems are described: atoms, molecules, proteins, genes, cells, organs, organisms, populations, communities,
ecosystems, biomes, and the ecosphere (Property 5).
8. Ecosystems work together in networks that improve the resource ﬂow utilization. Simply put, this states that connectivity
is a basic property that, through transactions and relations, binds ecosystem parts together as an interacting and
often integrated and interdependent system. Both observations and ecological network analysis show the
synergistic effect of these interactions: an ecosystem is more than the sum of its parts (Property 4).
9. Ecosystems have an enormous amount of genetic, biochemical, and process information. As a result of the above
principles, ecosystems are rich with information contained in the genetic codes, but also the chemical interactions
and ecological structure and functioning. This summarizes many of the properties expressed throughout the book
and is key to ecosystem sustainability.
As seen from this short overview of the nine principles, they may be considered a useful organization of the basic
systems ecology needed to understand the ecosystem reactions and processes.
We, the nine authors, conclude that we do have a workable ecosystem theory that can be presented in different
ways but under all circumstances can be used to explain and understand ecological observations, properties, and
processes (Chapter 8) and even be applied in environmental management (Chapters 10 and 11). The theory should
be considered an early attempt to present working hypotheses about how ecosystems function from a thermodynamic
and complex systems perspective. Ecology is not just an accounting of the abundance and distribution of species,
but rather a science that investigates the interrelations and interconnections between life and environment. The
principles described herein most likely will be changed in the coming years as we gain experience by using it. We
welcome collaboration and criticism to help further improve these ideas, particularly to develop good strategies for
ecosystem management.
12.5 CONCLUSION
The earth is a nonisolated system. There is almost no exchange of matter with outer space (the earth loses a little
hydrogen and receives meteorites). The matter on earth is reused and recycled many times on times scales as
different as organismal, ecological, and geological. The rate of decomposition in cycling implies that the ecosystem
components are linked in an interacting network (Chapter 4).
Ecosystems must be, as the earth, nonisolated because otherwise they could not receive the energy needed to
maintain them far from thermodynamic equilibrium and even move further away from thermodynamic equilibrium.
Ecosystems are actually open systems (Chapter 2) because they need to exchange at least water (precipitation
and evaporation) with their environment. In addition, it is practical that evolutionary adaptations in one ecosystem
can be exported to other ecosystems (for instance, species with new emergent properties that facilitate survival under
a combination of new and emergent conditions). Moreover, it is easy to observe that ecosystems are open
systems.
The ﬂow of energy from the sun to the ecosystems is also limited. It is important that an ecosystem captures as
much sunlight as possible to supply its energy needs. Therefore, in early stages of succession, ecosystems increase
biomass to increase net primary productivity. However, even the best photosystems can only capture a certain part
of the solar radiation, which is limited to about 1017
W on average. Therefore, moving further away from thermodynamic
equilibrium requires that an ecosystem develop better utilization of the usable work capacity that it is able to
capture. Network development, where the interdependent components coevolve together, provides improved
12. CONCLUSIONS AND FINAL REMARKS238
efﬁciencies. Another possibility is to increase information in the form of better process efﬁciencies. Increased sizes of
the organisms also imply that the work energy capacity lost for respiration decreases relatively to the biomass. While
matter and energy ﬂow limit growth, the amount of information is far from its limit. Therefore, it is understandable
that information embodied in genes and in ecological networks has increased throughout evolution.
The development of life on earth has been possible because of the presence of the necessary elements to build the
biochemical compounds that support life. These include water that is essential to most life and an ideal solvent for
most biochemical reactions. In addition, the earth has a suitable temperature range such that the biochemical reactions
proceed with a certain rate and decomposition of particularly proteins is moderate. These conditions provide
the right balance between formation and destruction of high molecular weight proteins that are the enzymatic compounds
controlling life processes.
Life-environment processes take place in cells because they have a sufﬁciently high speciﬁc surface to allow an
exchange rate with the environment that is suitable. Cells are therefore the biological units that make up organs
and organisms. Nature must therefore use a hierarchical construction: atoms, molecules, cells, organs, organisms,
populations, communities, ecosystems, and the ecosphere. The addition of units in one hierarchical level to form
the next level gives the next level new and emergent properties.
The variability of life’s conditions in time and space is very high. When an ecosystem has adapted to certain conditions,
it can still be disturbed by catastrophic events. Ecosystems have the adaptability and ﬂexibility to meet these
changed conditions and still maintain their systems far from thermodynamic equilibrium. The disturbances call for
new and creative solutions for life to survive. Disturbances may therefore also be beneﬁcial in the long term for
ecosystems.
We can explain ecosystem processes, responses, and evolution with the principles and hypotheses presented in
this book. The discussion throughout the book has tried to show that the principles are sufﬁcient and the discussion
in this last section has demonstrated that the properties are also necessary. It may, however, not be the only possible
explanation of life in general. We cannot exclude that we will ﬁnd other life forms somewhere else in the universe,
for instance, based on silica or based on carbon but with another biochemistry, better suited for a different situation.
However, the properties presented above are very consistent with both direct and indirect observations, which
render them a good basis for an ecosystem theory applicable on Earth. Chapters 9e11 have shown that the ecosystem
theory presented in this book can be used to explain other ecological rules and hypotheses and have potential for
application in environmental management, which hopefully can inform our quest to manage our planet toward
sustainability.
12.5 CONCLUSION 239
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Index
‘Note: Page numbers followed by “f” indicate ﬁgures, “t” indicate tables and “b” indicate boxes.’
A
Adaptability, 149e152, 157
Adaptive cycling model, 118
Adenosine triphosphate molecules
(ATPs), 127
Adjacency matrix, 52
Agency of ecosystems, 44
Agenda 21, 1
Agroforestry (AF), 218
Agro-livestock integrated system, 218
Aquatic environmental management, 216
Allometric principles, 17e22
Ampliﬁcation hypotheses, 63e66
Anabolism, 11e12
Anergy, 12
Ascendency, 67e68, 104, 104be107b, 110,
122e126
calculations, 211e214
South Florida ecosystems, 193e197,
195te196t
Autocatalysis, 100e102, 109
Autocatalytic selection, 103e104, 108e109
Autonomy hypotheses, 73e78
Average mutual constraint (AMC),
104be107b, 108
Average mutual information, 104be107b
B
BeluzoveZhabotinsky reaction, 39
Bernard cell, 122
Bifurcation, 146, 153
Biodiversity, 2, 115, 140, 146e147, 167e170,
168f, 200, 207, 210e211
Biodynamic farming, 218
Biomanipulation, 181
Biomass
growth, 116, 136
packing, 128
storage, 178e179
Biotic communities, 47
Black-box approach, 47
Boltzmann formula, 104be107b
Bottom-up approach, 1, 50e51
Boundaries, 89e91
Boundary ampliﬁcation, 63
Boundary growth, 116
Buffer capacity, 115, 133e134
C
Canadian boreal biome, 96
Carbon budgets, 231e234
Cardinal network hypotheses, 60e81
ampliﬁcation hypotheses, 63e66
autonomy hypotheses, 73e78
grounding hypotheses, 60e61
growth and development hypotheses,
61e63
holism hypotheses, 78e81
integrative hypotheses, 66e69
relational hypotheses, 69e72
self-organization hypotheses,
73e78
Carnot efﬁciency, 119
Catabolism, 11e12
Causality, 43
Cell, 22e24
Centripetality, 67e68, 101e104, 102f,
109e110, 115, 125
Chaos, 154be155b
Chemical imperialism, 102
Classic thermodynamics, 7
Clementsian mono-climax formulation,
117e118
Closed system, 7
Competitive exclusion, 174e176. See also
Gause’s competitive exclusion
Competitive exclusion principle, 113
Complexity, 148be149b
Compton effect, 31
Conceptual modeling approach, 83
Cone Spring ecosystem network, 55e60,
104be107b
Cone Spring model, 59t, 69
Conference of Parties (COPs), 1
Conservation, 236e237
Conservation laws of energy, 113
Consistent ecosystem theory, 132e137
Constraining process, 90
Constraints, 42e43, 69, 89e91, 104be107b,
136, 139e140
Conventional ecology, 65e66
Conventional farming, 218
Convention on Biological Diversity (CBD),
1e2
Coupling, 54e55
C3-plants, 127
C4-plants, 127
Crassulacean acid metabolism (CAM)
pathway, 127
Creative selection, 34
Creativity, 141be142b
Cycling rate, 128e129
D
Darwinism, 70e71
Darwin’s ﬁnches, 130be131b, 162, 162f
Development capacity, 104be107b,
193e197
Diagonal dominance, 63
Directional selection, 160
Disruptive selection, 160
Dissipation, 8e9
Dissipative structure, 16e17
Distributed causation, 50, 50b
Distributed control, 79
Distribution, 148be149b
Diversity, 209e211
Drosophila melanogaster, 161
E
Eco-exergy, 12, 13b, 14, 122, 123be125b,
136e137, 162e164
ascendency, 207e214
diversity, 209e211
efﬁciency, 205e207
empower ratio, 205e207
genetic information content parameters,
197e200, 198t
indicator, 197e200
maximization, 208e209
speciﬁc eco-exergy, 209e211
Eco-exergy storage, 122, 125e127
Ecological communities, 110
Ecological engineering (EE), 216
Ecological network analysis, 51e52
American black bear, 189e191
Huntington Forest, 189
model’s behavior, 189
STELLA 11, 189
Ecological resilience, 223e225
Ecological selection, 160
Ecological sustainability trigon (EST),
225e231, 227f, 229fe230f
EcoNet, 55
Economic sustainability, 218
Ecosystem
connectivity, 51e52
development, 113e116, 122e126
history of, 235e236
as networks, 46e47
openness, 8
phenomenological behavior, 236e237
principles, 3e4, 4t, 237e238
trophic module, 46
257
Ecosystem theory, 5
Ecotrophic module, 46
Effective Trophic Levels (ETLs), 195te196t
Embeddedness, 83
Embodied energy, 120
Emergence, 39e40, 44
Emergy, 50, 66, 119e120, 121b
exergy, 206e207
indicator, 200e205
symbols, 201f
Endangered Species Act, 96
Energy form, 7
Engineering resilience, 223e225
Entropy, 8e11, 37
Entropy paradox, 38
Entropy production
case studies, 184
ecosystem trophic state indicator,
183e188
indices for waterbodies, 184e187,
184fe185f
Environ, 46, 50, 81
Environmental management, 1e2
Environmental sustainability, 218
Environ theory, 60, 62e63, 67e68, 70
Envirotype, 41, 81
Euproctis subnobilis, 161e162, 162f
European Water Framework Directive, 217
Evolution, 46, 103
directionality in, 109e110
Evolutionary drive, 102e103
Evolutionary theory, 160e170
Exergy, 12, 122e126, 206e207
balance, 136, 234
carbon budgets, 231e234
degradation, 136, 150
storage, 136, 164, 178
sustainability, 231e234
utilization, 131f, 134t
Exogenic unmanaged pressure, 230
Extensive network synergism, 72
F
Fick’s laws, 17e18
Finn’s cycling index, 58
First thermodynamic laws, 9
Fish community structure, 203t
Fishery management, 96
Flow analysis, 64e65
Food webs, 47e49
Foraging time, 172e173
Framework for sustainability assessment
cubes, 220e222, 222f
trigon, 222e231
Free energy, 122
G
Gause’s competitive exclusion, 176
Gause’s exclusion principle, 113
Geese rearing system, 218
Genetic determinism, 80
Genetic drift, 160
Genome size, 126
Genomic material, 109
Genotype, 41, 81
Gini coefﬁcient, 221
Globalization, 1
Goal function, 116, 116b
Gradients, 89e91, 122e126, 167e172
Grounding hypotheses, 60e61
Growth forms, 170
H
Heisenberg (uncertainty)
principle, 31, 31b, 35e36
Heterogeneity, 33e34
Hierarchical patch mosaic paradigm, 93
Hierarchy, 43, 140, 144be145b
Hierarchy theory, 235e236
High-entropy states, 10
Holism hypotheses, 78e81
Holistic memory, 34
Holocene glaciation dynamics, 88e89
Holoecology, 45
Holoevolution, 80e81
Holon, 87, 89
Homogenization, 61e62
Homo sapiens, 72, 235
Human well-being, 2
Hysteresis, 180e181
I
Ideal gas, 98e99
Indeterminacy, 42
Indeterminism, 31b, 33
Indicator, 116b, 183
eco-exergy, 197e200
emergy, 200e205
Indirect effect, 45e46, 52e53, 60e61,
189e191
Information, 113, 116, 141be142b, 164
Infrastructure efﬁciency, 234
Input-environ-based inheritance, 80e81
Input-state-output (I-S-O) framework, 220
Integral membrane proteins (IMPs), 80e81
Integral relations, 59t
Integrated environmental management,
222
Integrative hypotheses, 66e69
Interior aggradation process, 62
Interior ampliﬁcation property, 63e64
Intermediate disturbance, 146e147
International Institute of Applied Systems
Analysis (IIASA), 50
Irreversibility, 9, 15, 30e31, 112, 119,
146e147, 237e238
Island biogeography, 165e166
Isolated system, 7e11
J
Janus Enigma Hypothesis, 71e72, 71f
K
Keystone species hypothesis, 170e172
K-strategists, 170
Kullbach’s measure of information,
122e125
Kyoto Protocol, 1
L
Landscape, 153, 156
Laudate Si, 2
Laws of thermodynamics, 3
Leaf size, 128
Le Chatelier Principle, 126e127
Liebig’s Law, 177e179
Life conditions, 111e113
Life-environment processes, 239
Life expectation, 140t
Limiting similarity, 174
M
Management, nature, 216e219
aquatics systems, 217
cubes, 220e222
ecosystem knowledge, 216e217
success, 219e234
sustainability. See Sustainability
terrestrial systems, 217e219
Margalef index, 200
Masseenergy storage, 62e63
Max. entropy, 8e11
Mass-balanced models, 225
Mass extinction, 142
Maximum exergy storage principle,
62e63
Maximum power, 118e122
Melanism, 161, 161f
Metazoan organization, 81
Middle number systems, 112
Mimicry, 161e162
Monocultural farming, 218
Mondego River Estuary, 29, 207e214
Mosaic hypothesis, 117
N
Natural chain of being, 99
Negative entropy, 11
Network
aggradation processes, 62, 109
analysis, 50e52, 81
American black bear, 189e191
applications, 191e197
boundary ampliﬁcation, 63
centrifugality, 67e68
centripetality, 67e68
clockwork stockworks, 73
distributed control, 79
ecogenetic coevolution, 80e81
enfolding, 66e67
environ autonomy, 74e78
growth, 116, 136
homogenization, 61e62
interaction typing, 70
interior ampliﬁcation, 63e66, 65f
janus enigma hypothesis, 71e72
mutualism, 45e46, 70e71
nonlocality, 60e61, 70
pathway proliferation, 60
storage maximization, 62e63
synergism, 45e46, 70e71
throughﬂow maximization, 62
topogenesis, 68e69
INDEX258
trophic dynamics, 49
unfolding, 49, 67
Network environ analysis primer, 52e60
aggradation, 54e55
cardinal hypotheses of, 60e81
ampliﬁcation hypotheses, 63e66
autonomy hypotheses, 73e78
grounding hypotheses, 60e61
growth and development hypotheses,
61e63
holism hypotheses, 78e81
integrative hypotheses, 66e69
relational hypotheses, 69e72
self-organization hypotheses, 73e78
cone spring ecosystem, 55e60
Newtonian law, 97e98
Niche construction, 81
Niche theory, 173e177
Nondirectionality, 42
Nonisolated system, 7
O
Odum’s attributes, 113, 115
Ontic openness, 27e29
consequences of, 43e44
to ecology and ecologists/managers,
41e43
and emergence, 39e40
and hierarchies, 40e41
macroscopic opennesseconnections to
thermodynamics, 37e39
and physical world, 30e31
and relative stability, 36e37
Open system, deﬁnition, 8t
Operative symbolism, 35
Opthalmis lincea, 161e162, 162f
Optimal Foraging Theory, 172e173
Ordered heterogeneity, 33e34
Organic farming, 218
Orientors, 116b, 117e118, 183
Orientor optimization, 145
Output-environ-based inheritance, 81
Overhead, 104be107b
P
Parametric determination, 70
Paris Agreement, 1
Path analysis, 52
Pathway proliferation, 60
Patterns, 42e43
Permaculture (PC), 218
Phenomenology, 27
Phenotype, 41, 81
Photosynthesis, 11, 127
Physical-chemical systems, 237
Physical chemistry mechanism, 16
Physical openness, 11e14
Plant species, 97, 98f
Policy makers, 215
Probability paradox, 38e39
Protein synthesis, 23
Q
Quantiﬁcation of openness, 17e22
R
Rare events, 142e144, 144be145b
Reductionistic holism, 47
Redundancy, 193e197
Regional scales, 93e94
Relational hypotheses, 69e72
Relative stability, 36e37
Relaxation, 33b
Reorganization, 150
Resilience, 148e150
Retrogressive analysis, 155e156
Rio Declaration, 1
River continuum concept, 179e181
r-strategists, 170
Rules of thumb, 215
S
Sampling uncertainty, 35, 35b
Sapelo Island salt-marsh energy model,
74
Seasonal changes, 129e132
Seasonal intertidal macroalgae blooms,
207
Second law of thermodynamics, 10,
15e17, 122
Second thermodynamic laws, 9
Selection, natural, 136, 160
Self-organization, 73e78, 100, 103e104,
113, 115, 139e140, 156
boundaries, gradients, and constraints,
89e91
classical hierarchies in time and space,
87e89
concepts in ecology, 85
function, 91e94
inherent in biology, 85e87
interpretations above organism level,
91e94
managing ecosystems, 94e96
Sequence oxidation, 127
Serendipities, 28
Sexual selection, 160
Shannon formula, 104be107b
Size development, 162e163, 163f, 163t
Society deconstruction analysis,
232e234
Solar emergy, 121b
Solar radiation, 113, 122, 136e137,
184e186, 184fe185f
South Florida ecosystems
ascendency, 193e197, 195te196t
case studies, 192
development capacity, 193e197
network analysis applications, 191e197
network models, 192e193
redundancy, 193e197
Spatial heterogeneity, 146e147
Species number, 112, 166
Species richness, 168e169, 170f
Sphere, 111
Spin relaxation, 31
Stability, 148be149b
Stabilizing selection, 160
State-space system theory, 17
STELLA, 50, 162, 163f, 189
Stochastic processes, 100
Storage analysis, 52, 74e76
Stresses, ontic openness, 43
Structural determination, 70
Structurally dynamic models, 129
Succession, 97, 99, 149, 150f, 152, 155f
Succession theory, 117e118
Sustainability, 1e2
ecological sustainability trigon (EST),
226e230, 227f, 229f
environmental ideologies, 224t
exergy, 231e234
framework assessment, 220e222
perspectives, 215e216
science, 227t
social sustainability, 218
Sustainable Development Goals (SDGs), 2
Sustainable environmental management,
223
System Ascendency, 104
Systems analysis, 49e51
T
The Grand Synthesis, 103
Thermodynamic ecology, 8e9
Thermodynamic equilibrium, 7, 15
Thermodynamic gradients, 90
Thermodynamic openness of
ecosystems, 7
Thermodynamic systems, 7, 8t, 10,
97e99
Time and space scale, 87e89
Top-down, 50e51
Top-predator organisms, 49
Total system throughput (TST),
104be107b
Traditional Danish aquaculture, 217
Traditional succession theory, 117
Transactional network organization, 72
Transformity, 121b, 202t
Trophic indices, 186t
Tyrannosaurus rex, 49
U
Uncertainty, 35, 42
Unique events, 34
Uniqueness of ecosystems, 44
Utility analysis, 52
Utilization pattern, 129
Utricles, 100e101
Utricularia, 100e101
V
Variation, 42
Voidness, 42
W
Warning coloration, 161e162
Work capacity, 126
INDEX 259
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