Unconstrained Ordination

  • Daniel Borcard
  • François Gillet
  • Pierre Legendre
Part of the Use R! book series (USE R)


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. In this chapter, you will learn how to choose among various ordination techniques (PCA, CA, MCA, PCoA and NMDS), compute them using the correct options, and properly interpret the ordination diagrams; apply these techniques to the Doubs River or the Oribatid mite data; overlay the result of a cluster analysis on an ordination diagram to improve the interpretation of both analyses; interpret the structures revealed by the ordination of the species data using the environmental variables from a second dataset; and finally write your own PCA function.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daniel Borcard
    • 1
  • François Gillet
    • 2
  • Pierre Legendre
    • 1
  1. 1.Département de sciences biologiquesUniversité de MontréalMontréalCanada
  2. 2.UMR Chrono-environnementUniversité Bourgogne Franche-ComtéBesançonFrance

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