Z8055 Methods in Physical Geography 3 - biogeography, pedogeography

Faculty of Science
autumn 2017
Extent and Intensity
1/2/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
RNDr. Jan Divíšek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Rudolf Brázdil, DrSc.
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
Mon 18. 9. to Fri 15. 12. Tue 12:00–12:50 Z6,02006
  • Timetable of Seminar Groups:
Z8055/01: Mon 18. 9. to Fri 15. 12. Tue 13:00–14:50 Z6,02006, J. Divíšek
Prerequisites
This course is recommended to all students who are interested in ecology, biogeography and spatial data analysis in R and ArcGIS.
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: 0/20, only registered: 0/20
fields of study / plans the course is directly associated with
Course objectives
We will learn how to analyse ecological and biogeographical 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 biogeographical 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 ArcGIS and R 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 ArcGIS and R 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, because I expect deeper focus on R or GIS, depending on students' requirements.
  • 1. Introduction to the course – ouline; literature; software; sample data; instalation of the following softwares: R, R Studio, AcrGIS, SAM; overview of biogeographical and environmental data.
  • 2. GIS in biogeography and ecology – basic operations in ArcMap, vector vs. raster data, data import and conversions, ArcGIS database, coordinate systems, table joins in ArcMap.
  • 3. Geographical analyses in ArcMap – digital evelation models + derivation of ecologically menaningful indices (Terrain Ruggedness, Heat load index, Topographic wetness index), landscape metrics, Toolboxes for ArcGIS, overlay algebra (Intersect etc.), extrat values to points, zonal statistics, Model Builder.
  • 4. Introduction to R – basic operations with R, vectros, matrices, data frames a lists, cycles in R, data import from ArcGIS, descriptive statistics (boxplots, histograms etc.), simple maps in R, data transformations and standardizations.
  • 5. 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). Calculations in R and SAM.
  • 6. Alpha and Beta diversity – measuring alpha diversity (Shannon's diversity index), measuring beta diversity - similarity and distance indices (Jaccard, Sørensen, Bray-Curtis, βsim, Euclidean distance, Hellinger distance), calculation of distance matrix in R, NMDS for visualization of distance matrices.
  • 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), Redundancy Analysis (RDA) and Principal Coordinate Analysis (PCoA). Varibale testing (Monte Carlo permutation test), covariates and partial ordination. Applications in R.
  • 9. Species distribution modelling (Machine-learning methods) - classification and regression trees (CART), Random Forests, model evaluation (k-fold cross-validation, bootstrapping), MaxEnt. Applications in R and MaxEnt.
  • 10. 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.
  • 11. Discussion – examples of methods' applications, discussion on results of bachelor or diploma theses.
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 softwares (ArcGIS, SAM, etc.).
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 (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2016, Autumn 2016, Spring 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (autumn 2017, recent)
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