Information-theoretic approach for selection of spatial and temporal models of community organization

Abstract

The literature on the ecology of marine assemblages includes frequent examples of data analysis with no well-defined alternative hypotheses for the definition of environmental variables (independent matrix for multivariate methods). Alternative models, whereby spatial or temporal patterns are investigated, should be explicitly assumed. We present a parsimonious procedure for model selection in multivariate data combined with canonical correspondence analysis to determine the measure of explained variance for each tested model, using Akaike’s information criterion (AIC) for model selection. The AIC procedure is an effective tool for model selection and, in contrast to other conventional procedures that use only 1 implicit model and ignore other community patterns, it provides a framework for ranking hierarchical patterns that adds an alternative non-disjunctive perspective to assemblage analysis. Hierarchical patterns are revealed as layers in a scale-dependent framework