Functional and phylogenetic diversity are increasingly used to infer the important community assembly processes that have structured local communities, which is one of the most fundamental issues in ecology. However, there are critical assumptions and pitfalls associated with these analyses, which can create ambiguity in interpreting results.<br> Here, we present a conceptual framework which integrates three approaches to reduce the likelihood of drawing incorrect conclusions from analyses of functional and phylogenetic diversity (FD and PD, respectively):<br> <br> <br> <br> testing hypotheses for how diversity measures and ecological processes vary along an environmental gradient;<br> <br> <br> analysis of both FD and PD in concert; and<br> <br> <br> careful selection of traits related to processes of interest for inclusion in FD analyses.<br> <br> <br> <br> testing hypotheses for how diversity measures and ecological processes vary along an environmental gradient;<br> analysis of both FD and PD in concert; and<br> careful selection of traits related to processes of interest for inclusion in FD analyses.<br> We describe the utility of each of these recommendations and show, using hypothetical examples, how combining these approaches can strengthen one’s ability to correctly infer community assembly. We present this framework in the context of identifying the signatures of interspecific competition and environmental filtering, important processes that operate in many systems across different taxa and are most often referred to in the FD and PD literature. We provide examples showing how our framework can be used to test general hypotheses such as the Stress-Dominance Hypothesis, which predicts a shift in the relative importance of environmental filtering and competition along a gradient of environmental stress, using PD and FD calculated separately for alpha (competition-related) traits and beta (environmental filtering-related) traits. Our approach can be applied to other processes besides competition and environmental filtering. This framework has the potential to enhance comparability between studies, allow for testing of alternative hypotheses regarding changes in community assembly processes along gradients, and improve interpretations of FD and PD analyses.
