Bi7540 Data analysis in community ecology

Faculty of Science
Spring 2023
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
doc. RNDr. Jakub Těšitel, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jakub Těšitel, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: doc. RNDr. Jakub Těšitel, Ph.D.
Supplier department: Department of Botany and Zoology – Biology Section – Faculty of Science
Timetable
Tue 14:00–16:50 B09/316
Prerequisites
Bi5040 Biostatistics - basic course || Bi5560 Basics of statistics for biol.
The lecture expects a basic statistical knowledge in the extent of the basic Bi5040 Biostatistics course, namely correlation analysis, and (generalized) linear models. It is therefore recommended that students enroll this class after the Biostatistics course. The practicals will be taught in the popular R software. Previous basic knowledge of work in R is also recommended. Such knowledge may be achieved in the Bi7560 Introduction to R course.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course deals with basic methods of statistical analysis of the data on species composition of plant and animal communities, irrespective of their taxonomic delimitation. The focus is on numerical classification and ordination methods and analysis of the relationships between species composition and environmental factors.
At the end of this course, students should be able to apply particular methods, using a popular program R.
Learning outcomes
Students should be able to:
Choose an appropriate multidimensional method to solve a given ecological problem
Apply this method
Interpret the results
Accompany the results with an illustrative graphical output
Incorporate the analysis into a scientific text
Syllabus
  • Pre-analysis data preparation (data cleaning, outliers, transformation, standardization, exploratory data analysis), types of data (categorical vs quantitative, abundances, frequencies)
  • Ecological similarity (indices of ecological similarity and distance between samples)
  • Ordination (linear vs unimodal, constrained vs unconstrained, ordination diagrams, permutation tests, variance partitioning, forward selection, case studies)
  • Numerical classification (hierarchical vs nonhierarchical, agglomerative vs divisive, supervised vs unsupervised)
  • Use of species functional traits or species indicator values in multivariate analysis (functional traits, Ellenberg indicator values, community-weighted mean, fourth-corner)
  • Diversity indices (alpha, beta and gamma diversity, accumulation curves and rarefaction curves)
  • Case studies demonstrating the use of particular analytical methods
  • Design of ecological experiments (manipulative vs natural experiments)
    Practicals will consist of the analysis of real-world data in the software R.
Literature
    recommended literature
  • LEPŠ, Jan a Petr ŠMILAUER. Mnohorozměrná analýza ekologických dat. 2001. http://regent.jcu.cz/skripta.pdf
  • HERBEN, Tomáš and Zuzana MÜNZBERGOVÁ. Zpracování geobotanických dat v příkladech. Část I. Data o druhovém složení. http://www.natur.cuni.cz/~botanika/, 2001. info
    not specified
  • ŠMILAUER, Petr and Jan LEPŠ. Multivariate Analysis of Ecological Data using CANOCO 5. 2nd ed. Cambridge: University Press, 2014, xii, 362. ISBN 9781107694408. info
  • BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer, 2011, xi, 306. ISBN 9781441979759. info
  • ZUUR, Alain F., Elena N. IENO and Graham M. SMITH. Analysing Ecological Data. Springer-Verlag New York, 2007, 672 pp. ISBN 978-0-387-45967-7. Available from: https://dx.doi.org/10.1007/978-0-387-45972-1. URL info
Teaching methods
theoretical lessons with additional computer labs
Assessment methods
For exam, students will prepare a short study, in which they analyze their own or demonstration data, using the statistical approaches discussed in the lecture. The study should have a form of short scientific paper - more details about its structure will be published on the class website. The exam is oral discussion about the study, with additional questions targeting theoretical background of used methods.
Language of instruction
Czech
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Spring 2010, Spring 2011, Spring 2012, Autumn 2011 - acreditation, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Spring 2023, recent)
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