Variation partitioning of species data matrices: estimation and comparison of fractions.
Establishing relationships between species distributions and environmental characteristics is a major goal in the search for forces driving species distributions. Canonical ordinations such as redundancy analysis and canonical correspondence analysis are invaluable tools for modeling communities through environmental predictors. They provide the means for conducting direct explanatory analysis in which the association among species can be studied according to their common and unique relationships with the environmental variables and other sets of predictors of interest, such as spatial variables. Variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure. Although variation partitioning in canonical analysis is routinely used in ecological analysis, no effort has been reported in the literature to consider appropriate estimators so that comparisons between fractions or, eventually, between different canonical models are meaningful. In this paper, we show that variation partitioning as currently applied in canonical analysis is biased. We present appropriate unbiased estimators. In addition, we outline a statistical test to compare fractions in canonical analysis. The question addressed by the test is whether two fractions of variation are significantly different from each other. Such assessment provides an important step toward attaining an understanding of the factors patterning community structure. The test is shown to have correct Type I. error rates and good power for both redundancy analysis and canonical correspondence analysis.
Peres-Neto
Pedro R
PR
Department des sciences biologiques, Université de Montréal, C.P. 6128, succursale Centreville, Montrial, Québec H3C 3J7, Canada. pedro.peres-neto@uregina.ca
Legendre
Pierre
P
Dray
Stéphane
S
Borcard
Daniel
D
D000818:Animals; D004463:Ecology / Q000379:methods*; D005843:Geography; D008925:Mites; D008954:Models, Biological