Z8055 Methods in Physical Geography 3 - biogeography, pedogeography

Faculty of Science
Autumn 2024
Extent and Intensity
1/2/0. 5 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
RNDr. Jan Divíšek, Ph.D. (lecturer)
Mgr. Filip Hrbáček, Ph.D. (lecturer)
Guaranteed by
RNDr. Jan Divíšek, Ph.D.
Department of Geography – Earth Sciences Section – Faculty of Science
Contact Person: RNDr. Jan Divíšek, Ph.D.
Supplier department: Department of Geography – Earth Sciences Section – Faculty of Science
Timetable
Wed 8:00–8:50 Z2,01032
  • Timetable of Seminar Groups:
Z8055/01: Wed 9:00–10:50 Z2,01032, J. Divíšek, F. Hrbáček
Prerequisites
This course is recommended to all students who are interested in ecology, biogeography, soil science and spatial data analysis in R and GIS.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 12/20, only registered: 0/20
fields of study / plans the course is directly associated with
Course objectives
We will learn how to analyse ecological, biogeographical and soil-science data in order to correctly test hypotheses stated in bachelor or diploma theses. This course focuses on basic and advaced numerical analytical methods, which are commonly used in scientific studies. Special attention will be paid to data manupulation and application of statistical methods in R environment. After the completing of the course, students will be able to manipulate and analyse their data in R and GIS softwares. They will be also able to present and interpret the resuts of the analyses.
Learning outcomes
After the completing of the course, students will be able to manipulate and analyse their data in R and GIS softwares. They will be also able to present and interpret the resuts of the analyses.
Syllabus
  • Here is a preliminary outline of the course, however, all points are subject to change depending on students' requirements.
  • 1. Introduction to the course – ouline; literature; software; sample data; instalation of the following softwares: R, R Studio; overview of biogeographical and environmental data.
  • 2. Introduction to R – basic operations with R, vectros, matrices, data frames a lists, cycles in R, data import, descriptive statistics (boxplots, histograms etc.), simple maps in R, data visualization in R, spatial data manipulation in R
  • 3. Correlation and regression analysis in R – correlation coefficients (Pearson's and Spearman's cefficients), linear regression models, variation partitioning (R2 and adjusted R2), selection of explanatory variables (Forward selection), generalized linear models (GLM, logistic regression). Correlation of distance matrices (Mantel correlation).
  • 4. Machine-learning methods - CART + Random Forests - classification and regression trees (CART), Random Forests, model evaluation (k-fold cross-validation, bootstrapping).
  • 5. Species distribution modelling - modelling species ditribution (presence-absence data) with MaxEnt.
  • 6. Spatial autocorrelation – measuring in univariate data (Moran’s I), measuring in multivariate data (Mantel correlation), effect of spatial autocorrelation on results of statistical tests, spatial proximity/distnace in numerical analyses (XY, spatial polynoms, PCNM, MEM variables). Applications in R and SAM.
  • 7. Numerical classifications – methods of hierarchical classification (Single linkage, Complete linkage, UPGMA, Ward's method, β flexible), non-hierarchical classification (k-means), spatially constrained classification. Applications in R.
  • 8. Gradient analysis – linear vs. unimodal methods, direct vs. indirect ordination analysis with particular focus on Principal Component Analysis (PCA), Principal Coordinate Analysis (PCoA). Varibale testing (Monte Carlo permutation test), covariates and partial ordination. Applications in R.
  • 9. Statistical methods in soil science - examples of the application of appropriate statistical methods for the analysis of soil science data
  • 10. Basics of spatial interpolation – sampling types, basic interpolation methods, use of Surfer software.
  • 11. Use of remote sensing – sources of satellite data and their application in soil science, basics of processing multispectral images in a GIS environment.
Literature
    recommended literature
  • BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer, 2011, xi, 306. ISBN 9781441979759. info
  • FORTIN, Marie-Joseé and Mark R. T. DALE. Spatial analysis : a guide for ecologists. 1st pub. Cambridge: Cambridge University Press, 2005, xiii, 365. ISBN 0521009731. info
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 3rd engl. ed. Amsterdam: Elsevier, 2012, xvi, 990. ISBN 9780444538680. info
  • PEKÁR, Stano and Marek BRABEC. Moderní analýza biologických dat. 1. vyd. Praha: Scientia, 2009, x, 225. ISBN 9788086960449. info
  • LEPŠ, Jan and Petr ŠMILAUER. Multivariate analysis of ecological data using CANOCO. Cambridge: Cambridge University Press, 2003, xi, 269 s. ISBN 0-521-81409-X. info
Teaching methods
Theoretical lectures accompanied by practical applications in R and other GIS softwares.
Assessment methods
To pass the exam student must prove the ability to analyse the data using statistical methods and prepare short study. Detail instrictions will be communicated during the semester. The exam will consist of discussion on the methods used for data analysis including deeper theoretical background.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2016, Autumn 2016, Spring 2017, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2024/Z8055