Spatial incongruence in the species richness and functional diversity of cricetid rodents

Abstract

Biodiversity is multidimensional and different mechanisms can influence different dimensions. The spatial distribution of these dimensions can help in conservation decisions through the location of complementary areas with high diversity. We analyzed congruence in spatial patterns of species richness and functional diversity of cricetid rodents in the state of Oaxaca, southern Mexico, at different scales, and environmental variables related. Potential distribution models were produced for 49 species of cricetids in Maxent and superimposed to obtain potential communities in cells of 25, 50,100, 200 and 400 km ². We estimated species richness (SR) and functional diversity (SES.FD) eliminating the species richness effect through null models. The patterns and spatial congruence of species richness and functional diversity are described. The relationships between the environmental variables (elevation, temperature, precipitation, net primary productivity and potential evapotranspiration) and the SR and SES.FD were explored using Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). The highest species richness was found in mountainous ecosystems while the highest functional diversity was in tropical forests, revealing a spatial incongruence among these components of biodiversity (r = -0.14, p = 0.42; Pearson correlation). The locations of the cells of low congruence varied according to spatial resolution. In univariate models, elevation was the variable that best explained species richness (R ² = 0.77). No single variable explained the functional diversity; however, the models that included multiple environmental variables partially explained both the high and low functional diversity. The different patterns suggest that different historic, ecological and environmental processes could be responsible for the community structure of cricetid rodents in Oaxaca. These results indicate that one great challenge to be met to achieve more effective planning for biological conservation is to integrate knowledge regarding the spatial distribution of different dimensions of biodiversity.