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The Atlantic Forest is the second most diverse forest system in South America, and only a fraction of its original distribution remains. In this study, we aim to use robust datasets of small mammals along the entire forest system to disentangle the main drivers for diversity along this gradient. More specifically, we aim to disentangle whether deforestation (recent), biogeographical variables, including 19 bioclimatic variables (historic), or historical trapping bias best describe patterns of taxonomic, functional, and phylogenetic diversities using small mammal assembles, from northeastern Brazil to eastern Paraguay. For that, we applied regression tree analyses to determine what environmental variables best describe each of the dimensions of diversity. Additionally, we implemented polynomial regression to test nonlinear relationships between biodiversity metrics and patch size. We found that patterns of overall taxonomic, functional, and phylogenetic diversities; rodent taxonomic diversity; and marsupial functional diversity were better explained by temperature variables. Meanwhile, marsupial taxonomic and phylogenetic diversities, and rodent functional and phylogenetic diversities were best explained by precipitation variables. Furthermore, patch area, trapping, and latitude were never the best descriptors for any of the diversity dimensions. Although all dimensions of biodiversity are correlated, they have unique information and should be considered individually to better understand biodiversity and inform conservation strategies. We found that fragmentation is impactful at a local scale and becomes less important at a biogeographical scale. Therefore, climatic variables drove biogeographical faunal patterns for all clades, probably reflecting important historical assembly process at large spatial scales. [ABSTRACT FROM AUTHOR]

Techniques to evaluate elements of metacommunity structure (EMS; coherence, species turnover and range boundary clumping) have been available for several years. Such approaches are capable of determining which idealized pattern of species distribution best describes distributions in a metacommunity. Nonetheless, this approach rarely is employed and such aspects of metacommunity structure remain poorly understood. We expanded an extant method to better investigate metacommunity structure for systems that respond to multiple environmental gradients. We used data obtained from 26 sites throughout Paraguay as a model system to demonstrate application of this methodology. Using presence-absence data for bats, we evaluated coherence, species turnover and boundary clumping to distinguish among six idealized patterns of species distribution. Analyses were conducted for all bats as well as for each of three feeding ensembles (aerial insectivores, frugivores and molossid insectivores). For each group of bats, analyses were conducted separately for primary and secondary axes of ordination as defined by reciprocal averaging. The Paraguayan bat metacommunity evinced Clementsian distributions for primary and secondary ordination axes. Patterns of species distribution for aerial insectivores were dependent on ordination axis, showing Gleasonian distributions when ordinated according to the primary axis and Clementsian distributions when ordinated according to the secondary axis. Distribution patterns for frugivores and molossid insectivores were best described as random. Analysis of metacommunities using multiple ordination axes can provide a more complete picture of environmental variables that mold patterns of species distribution. Moreover, analysis of EMS along defined gradients (e.g., latitude, elevation and depth) or based on alternative ordination techniques may complement insights based on reciprocal averaging because the fundamental questions addressed in analyses are contingent on the ordination technique that is employed.