- Broad-scale spatial patterns in species richness have been widely investigated with spatial statistics tools in the past few years. The primary goal of these investigations has been to understand the ecological and evolutionary processes underlying such patterns. Nevertheless, most of the current (climate) explanations for these patterns actually rely on the geographical range limits of species, so that a better understanding of such processes may be achieved by coupling richness and distribution (niche) models.
- We analysed the geographical ranges and richness patterns for 115 triatomine species in the Neotropics, modelled as a function of 12 environmental variables expressing alternative hypotheses that have been used to explain richness gradients. These analyses were based on spatial [spatial eigenvector mapping (SEVM)] and non-spatial ordinary least-squares multiple regression models. The geographical ranges of species were also individually analysed using a general linear model (GLM). The coefficients of the regression models for richness and distribution were then compared.
- Spatial analyses revealed that the unique contributions of spatial eigenvectors and environmental variables to richness were, respectively, equal to 24.2% and 12.2%, with high coefficient values for temperature, actual evapotranspiration, and seasonality. Similar results were obtained using a GLM, and the mean GLM coefficients had a relatively high correlation with those obtained with SEVM (r = 0.586; P < 0.05).
- Our analyses show that the drivers of Neotropical Triatominae richness and of its species ranges show a high correlation, although the differences among the drivers may be important for understanding the emergent properties (historical processes and species-specific environmental drivers) that explain richness patterns. Moreover, although our analyses identified an important role for temperature and temperature seasonality in explaining both species richness and distributions, other spatially structured environmental variables and historical factors may explain a large part of the variation in diversity patterns.