Numerical Methods in Community Ecology

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The course is focused on common methods used by ecologists working with community data, including ordination, cluster analysis, diversity analysis etc. It combines a theoretical introduction to each method with a practical lab in R program. The course is dedicated for senior undergraduate and for graduate students. Each class will be composed of two parts: the theoretical introduction to the method, and the practical lab using the R program for all analyses. Schedule of the class: – Introduction, types of data (categorical vs quantitative, abundances, frequencies). – Pre-analysis data preparation (data cleaning, outliers, transformation, standardization, exploratory data analysis). – Ecological similarity (indices of ecological similarity and distance between samples). – Ordination (theory behind, linear vs unimodal, constrained vs unconstrained methods, PCA, CA, DCA, RDA, CCA, NMDS and some others, ordination diagrams, permutation tests, variance partitioning, forward selection, case studies). – Numerical classification (hierarchical vs nonhierarchical, agglomerative vs divisive; TWINSPAN) – Indicator value analysis (IndVal), diagnostic species, fidelity of species to sample groups. – Use of species functional traits or species indicator values in multivariate analysis (functional traits, species indicator values, community-weighted mean, fourth-corner, RLQ analysis). – Analysis of diversity (alpha, beta and gamma diversity, accumulation and rarefaction curves, true diversity, species abundance distribution, diversity estimators). – Design of community ecology experiments (manipulative vs natural experiments, avoiding pseudoreplications, problem of spatial autocorrelation, subjective vs objective sampling design). – Case studies demonstrating the use of particular analytical methods. After finishing it, you will understand the theory behind commonly used multivariate methods for analysis of community data, correctly interpret their results and apply these methods to your own datasets using R. College of Life Science Main Campus Basic statistic course (e.g. B01 34000, LS3022). Basic knowledge of R is recommended, but not required (we may plug-in an extra R-intro course for those of you who are not familiar with R at the beginning of the course). You need to bring your computer with installed R and access to the internet. David Zeleny 30 Thursday 2,3,4 EEB5083 (B44EU1950) 3