Please use the computer not a tablet because R is difficult to install on a tablet. Additional module information: Modulbeschreibung SoSe24_Computational-Bio_Quantitative-Community-Ecology_May Schließen

Inhalte:
Vorlesung: The lectures provide an introduction to Community Ecology, and give basics of analyzing biodiversity and community composition using methods in the statistical software R. The lectures are accompanied by applied examples for different community types (i.e. plants vs animals, terrestrial vs aquatic communitites) and cover the following topics:

• Analysis of ecological communities using taxonomic and functional approaches (total and relative abundances, biodiversity indicators, functional traits, functional diversity, taxonomic and functional community composition)
• Graphical visualization of variation within and between communities
• Statistics for multivariate data including Principal Component Analysis (PCA ) ordination Nonparametric Multidimensional Scaling (NMDS) and other ordination methods and cluster analysis
• Testing effects of environmental variables on community diversity and changes in community composition
• Scale-dependence of biodiversity and approaches for standardized biodiversity comparisons
• Analysis of ecosystem functions
Qualifikationsziele:
After participation in this module the students will be able to approach research questions in community ecology independently. Specifically, in this module the students acquire the following knowledge and skills using R programming language:
• Gain skills to prepare, visualize and analyse multivariate data of ecological communities and their composition using modern graphical and statistical methods and tools, including PCA, NMDS, and cluster analysis
• Master basic skills in analysing similarity and dissimilarity withing and beetwing ecological communitites
• Master skills to analyze biodiversity indices and functional trait measures of ecological communitites
• Gain basic knowledge and skills to analyse ecosystem functions
• Also, the students will gain experience in critically interpreting results in publications as well as in presenting findings of their own analyses in oral and written form.


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Borcard, D., Gillet, F. and Legendre, P., 2018. Numerical ecology with R. (second edition) New York: Springer. De Bello, F., Carmona, C.P., Dias, A.T., Götzenberger, L., Moretti, M. and Berg, M.P., 2021. Handbook of trait-based ecology: from theory to R tools. Cambridge University Press. Swenson, N.G., 2014. Functional and phylogenetic ecology in R. New York: Springer. Schließen