PiscesLogoSmallerStill Environmental variables from biological features

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There is a paradox in gradient analysis: species respond to the environment, but they also modify the environment. For example, vegetation itself can be considered an environmental factor to which vegetation responds. There are a suite of variables derived from species data which might be useful in CCA and other constrained ordinations: maximum height of vegetation, total biomass, light penetrating through the canopy, woody plant cover, etc.


These variables might be quite informative in an exploratory analysis, though the ecologist must realize that it would be difficult to distinguish cause and effect. Species-derived variables should NOT be used in hypothesis testing, because the same data would be represented in both the dependent and the independent variables. This would lead to circular reasoning. A compromise would be to have these vegetation-derived variables considered "passive" or "supplementary" - i.e. they would be included in diagrams, but would not otherwise influence the ordination.


An extreme case of species-derived variables is to use dummy variables derived from a classification of samples. The classification could either be a result of a subjective procedure (e.g. the Braun-Blanquet approach or something less formal) or a multivariate analysis. When the centroids of these dummy variables are plotted along with species scores in a CCA biplot, you have an ideal display of the relationships between your classes, and the species that occur in them. Of course, any statistical tests would be inappropriate.