Course focused on the analysis of community ecology data in R, organized by Institute of Ecology and Evolutionary Biology, College of Life Science, National Taiwan University
Language of the course: English
Course content: analysis of community ecology data, including ordination and cluster analysis, diversity analysis and analysis of species attributes. We will use real community ecology data (mostly vegetation and zoological datasets) and practice the analysis using the R project.
Target audience: senior undergraduate and graduate ecology students focused on botany and zoology, who are planning to do a study at the community level (i.e. not on a single species, but on the multiple species occurring at multiple localities). The class may be useful also for other disciplines handling multivariate data (e.g. microbiology), but the main focus is on ecological data. If you study PhD and want to join, you are welcome, but expect that some of the teaching materials will cover rather basic things.
Teaching strategy: The class includes presentation of theory, application of methods using R program, solving individual exercise during the class, homework assignments, test of knowledge (analogy of midterm and final test) and preparation of individual projects and their presentation (in English).
Schedule: 5 days during 3 weekends (in 2019 preliminarily set to 2/23-24, 3/23-24 and 5/4-5). In selected weekends, the course will start on Saturday morning at 9 a.m. and end on Sunday evening around 6 p.m.\\. First two weekends are focused on lectures and practice, the third weekend (including only Saturday) will be final exam and presentations of individual projects.
Location: Life Science Building, National Taiwan University, Taipei.
Information for students outside of Taipei
Disclaimer: this is rather an intensive course, focused on a theory of community data analysis, and practical exercise using R on real datasets. Along to the lectures taught in the classroom, you need to also complete homework assignments, midterm quiz, final test and prepare (and present in English) the final project focused on analysis of community data. This is not an R course for advanced use of R program (I expect you have a basic knowledge of R before you enter the class). If you want to learn R, consider taking my other class Introduction to R for Ecologists (regular 3 credit class taught every winter semester).
Five days (8.5h/day) during three weekend blocks (Saturday + Sunday)
|Topic||Number of classes|
|Introduction, types of data (categorical vs quantitative, abundances, frequencies).||1|
|Pre-analysis data preparation (data cleaning, outliers, transformation, standardization, exploratory data analysis).||1|
|Ecological similarity (indices of ecological similarity and distance between samples).||1|
|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).||3-4|
|Numerical classification (hierarchical vs nonhierarchical, agglomerative vs divisive; TWINSPAN)||1-2|
|Indicator value analysis (IndVal), diagnostic species, fidelity of species to sample groups.||1|
|Use of species functional traits or species indicator values in multivariate analysis (functional traits, species indicator values, community-weighted mean, fourth-corner, RLQ analysis).||1|
|Analysis of diversity (alpha, beta and gamma diversity, accumulation and rarefaction curves, true diversity, species abundance distribution, diversity estimators).||2|
|Case studies demonstrating the use of particular analytical methods.||as a part of each class|
Each class will be composed of two parts: theoretical introduction to the method, and practical lab, using the R program for all analyses. You need to bring your own computer with installed R and wifi access to internet.