Aim We tested the hypothesis that distributions of Mexican bats are defined by shared responses to environmental gradients for the entire Mexican bat metacommunity and for each of four metaensembles (frugivores, nectarivores, gleaning insectivores, and aerial insectivores). Further, we identified the main environmental factors to which bats respond for multiple spatial extents.
Methods Using bat presence–absence data, as well as vegetation composition for each of 31 sites, we analysed metacommunity structure via a comprehensive, hierarchical approach that uses reciprocal averaging (RA) to detect latent environmental gradients corresponding to each metacommunity structure (e.g. Clementsian, Gleasonian, nested, random). Canonical correspondence analysis (CCA) was used to relate such gradients to variation in vegetation composition.
Results For all bat species and for each ensemble, the primary gradient of ordination from RA, which is based on species data only, recovered an axis of humidity that matched that obtained for the first axis of the CCA ordination, which is based both on vegetation attributes and on species composition of sites. For the complete assemblage as well as for aerial and gleaning insectivores, analyses revealed Clementsian or quasi-Clementsian structures with discrete compartments (distinctive groups of species along portions of an environmental gradient) coincident with the humidity gradient and with the Nearctic–Neotropical divide. Within-compartment analysis further revealed Clementsian or quasi-Clementsian structures corresponding to a gradient of elevational complexity that matched the second ordination axis in CCA. Frugivores had quasi-nested structure, whereas nectarivores had Gleasonian structure.
Main conclusions Our hierarchical approach to metacommunity analysis detected complex metacommunity structures associated with multiple environmental gradients at different spatial extents. More importantly, the resulting structures and their extent along environmental gradients are determined by ensemble-specific characteristics and not by arbitrarily circumscribed study areas. This property renders compartment-level analyses particularly useful for large-scale ecological analyses in areas where more than one gradient may exist and species sorting may occur at multiple scales.