Abstract
Questions: Mechanisms of community assembly are increasingly explored by combining community and species trait data with null models. By investigating if the traits of co-existing species are more similar (trait convergence) or more dissimilar (trait divergence) than expected by chance, these tests relate observed patterns to different co-existence mechanisms. Do null models accurately detect trait convergence and divergence? Are different null models equally good at detecting these two opposing patterns? How important are the species pool and other constraints that are considered by different null models?
Methods: We applied ten common randomizations to communities that were simulated in a process-based model.
Results: Null models good at detecting biotic processes differed from those null models that revealed abiotic processes. In particular, limiting similarity (detected through divergence) was better detected by randomizations that release the link between species abundance and trait values, whereas environmental filtering (detected through convergence of an environmental response trait) was identified by randomizations that keep this link. In general, using species abundance data provided better results than using presence-absence data, particularly within given limited environmental conditions. Weaker competitor exclusion (detected through convergence of a competition-related trait) was only detected when no environmental filtering was acting on the simulated assembly, which points to difficulties in disentangling biotic and abiotic convergence in natural communities, especially when data are randomized across habitats.
Conclusions: Overall the results manifest the importance of the pool of species over which randomizations are applied; in particular whether randomizations are conducted across or within given habitats. Taken together, our findings show that different null models must be combined and applied to a carefully chosen pool of species and species abundance data to ensure that co-existence mechanisms can be properly assessed. We utilize the results to (1) discuss how different constraints implied in the different null models affect the outcomes of our tests, and (2) provide some basic recommendations on how to choose null models, given the data available and questions being asked.