The spatial frequency of climatic conditions affects niche composition and functional diversity of species assemblages: the case of Angiosperms.

Climatic conditions vary in spatial frequency globally. Spatially rare climatic conditions provide fewer suitable environments than common ones and should impose constraints on the types of species present locally and regionally. We used data on 467 North American angiosperms to test the effects of the spatial frequency of climatic conditions on ecological niche specialisation and functional diversity. We predicted that rare climates should favour generalist species that are able to inhabit a broader range of climatic conditions. Our results show that climate frequency filters species that differ in niche breadths and rare environments host species combinations with greater functional diversity. The proposed analytical approaches and hypotheses can be adapted to investigate different aspects of ecological assemblies and their biodiversity. We discuss different mechanisms regarding how spatial frequency of environments can affect niche composition and functional diversity. These should be useful while developing theoretical frameworks for generating a deeper understanding of its underpinnings.

Beyond neutrality: disentangling the effects of species sorting and spurious correlations in community analysis.

The methods of direct gradient analysis and variation partitioning are the most widely used frameworks to evaluate the contributions of species sorting to metacommunity structure. In many cases, however, species are also driven by spatial processes that are independent of environmental heterogeneity (e.g., neutral dynamics). As such, spatial autocorrelation can occur independently in both species (due to limited dispersal) and the environmental data, leading to spurious correlations between species distributions and the spatialized (i.e., spatially autocorrelated) environment. In these cases, the method of variation partitioning may present high Type I error rates (i.e., reject the null hypothesis more often than the pre-established critical level) and inflated estimates regarding the environmental component that is used to estimate the importance of species sorting. In this paper, we (1) demonstrate that metacommunities driven by neutral dynamics (via limited dispersal) alone or in combination with species sorting leads to inflated estimates and Type I error rates when testing for the importance of species sorting; and (2) propose a general and flexible new variation partitioning procedure to adjust for spurious contributions due to spatial autocorrelation from the environmental fraction. We used simulated metacommunity data driven by pure neutral, pure species sorting, and mixed (i.e., neutral + species sorting dynamics) processes to evaluate the performances of our new methodological framework. We also demonstrate the utility of the proposed framework with an empirical plant dataset in which we show that half of the variation initially due to the environment by the standard variation partitioning framework was due to spurious correlations.