Abstract
While cluster analysis looks for discontinuities in a dataset, ordination extracts the main trends in the form of continuous axes. It is therefore particularly well adapted to analyse data from natural ecological communities, which are generally structured in gradients. Practically, you will: Learn how to choose among various ordination techniques (PCA, CA, PCoA and NMDS), compute them using the correct options, and properly interpret the ordination diagrams Apply these techniques to the Doubs river data Overlay the result of a cluster analysis on an ordination diagram to improve the interpretation of the clustering results Interpret the structures in the species data using the environmental variables from a second dataset Write your own PCA function
Notes
- 1.
Comparison of a PCA result with the broken stick model can also be done by using function PCAsignificance() of package BiodiversityR.
- 2.
Note, however, that vegan uses an internal constant to rescale its results, so that the vectors and the circle represented here are not equal but proportional to their original values. See the code of the cleanplot.pca( ) function.
References
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Legendre, P. and Gallagher, E. D.: Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001)
Legendre, P. and Legendre, L.: Numerical Ecology. 2nd English edition. Elsevier, Amsterdam (1998)
ter Braak, C. J. F and Smilauer, P.: CANOCO reference manual and CanoDraw for Windows user’s guide: software for canonical community ordination (ver. 4.5). Microcomputer Power, New York (2002)
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Borcard, D., Gillet, F., Legendre, P. (2011). Unconstrained Ordination. In: Numerical Ecology with R. Use R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7976-6_5
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DOI: https://doi.org/10.1007/978-1-4419-7976-6_5
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