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Numerical Methods in Community Ecology


EEB5083 (B44 U1950), semester 105-2, 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 common methods used by ecologists working with community data, including ordination, cluster analysis, diversity analysis etc. It combines theoretical introduction to each method with practical lab in R program.

The course is for senior undergraduate and for graduate students.

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.

Teaching schedule (combined theoretical and practical part)

(Three hours per week)

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 access to internet.

  • 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).
  • Case studies demonstrating the use of particular analytical methods.


Criteria for the final evaluation consist of these parts:

Homework assignments 30%
Midterm quiz 20%
Final presentation of independent project 20%
Final exam 30%


  • 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: 2017/02/22 15:43 by david