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Numerical Methods in Community Ecology

群聚生態學分析方法

EEB5083 (B44 U1950), semester 106-2, in English
Instructor: David Zelený (澤大衛) [ VegLab ]
When & Where: every Thursday from 9:10 in 生科3A
Link to CEIBA

About the class

The course is focused on numerical methods commonly used by ecologists working with multivariate community data, such as ordination, cluster analysis, diversity analysis and few others. It combines theoretical introduction to each method with practical lab in R program.

The course is for (senior) undergraduate and graduate ecology students. Basic knowledge of R program is required.

After finishing it, you will understand the theory behind commonly used multivariate methods for analysis of community data, you will be able to correctly interpret their results and apply these methods to your own datasets using the R program.

Teaching schedule (combined theoretical and practical part)

(Three hours per week)

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.

References

  • Borcard, D., Gillet, F. & Legendre, P. 2011. Numerical Ecology with R. Springer.
  • Legendre, P. & Legendre, L. 2012. Numerical Ecology. Third English edition. Elsevier Science BV, Amsterdam.
  • Šmilauer, P. & Lepš, J. 2014. Multivariate Analysis of Ecological Data using Canoco 5. Second Edition. Cambridge University Press, Cambridge, UK.
numecol/start.txt · Last modified: 2018/06/14 09:36 by david