Canonical Ordination

Abstract

Canonical ordination associates two or more data sets in the ordination process itself. Consequently, if one wishes to extract structures of a data set that are related to (or can be interpreted by) another data set, and/or formally test statistical hypotheses about the significance of these relationships, canonical ordination is the way to go. in this chapter, you will learn how to choose among various canonical ordination techniques: asymmetric (RDA, db-RDA, CCA, LDA, PRC and CoCA) and symmetric (CCorA, CoIA and MFA); explore methods devoted to the study of the relationships between species traits and environment; compute them using the correct options and properly interpret the results; apply these techniques to the Doubs River and other data sets; explore particular applications of some canonical ordination methods, for instance variation partitioning and multivariate analysis of variance by RDA; and write your own RDA function.