Evolutionary rate explanations for latitudinal diversity gradients predict faster speciation and diversification rates in richer, older, and more stable tropical regions (climatic stability hypothesis). Numerous modern lineages have emerged in high latitudes, however, suggesting that climatic oscillations can drive population divergence, at least among extratropical species (glacial refugia hypothesis). This conflicting evidence suggests that geographical patterns of evolutionary rates are more complicated than previously thought. Here, we reconstructed the complex evolutionary dynamics of a comprehensive dataset of modern mammals, both terrestrial and marine. We performed global and regional regression analyses to investigate how climatic instability could have indirectly influenced contemporary diversity gradients through its effects on evolutionary rates. In particular, we explored global and regional patterns of the relationships between species richness and assemblage‐level evolutionary rates and between evolutionary rates and climatic instability. We found an inverse relationship between evolutionary rates and species richness, especially in the terrestrial domain. Additionally, climatic instability was strongly associated with the highest evolutionary rates at high terrestrial latitudes, supporting the glacial refugia hypothesis there. At low latitudes, evolutionary rates were unrelated to climatic stability. The inverse relationship between evolutionary rates and the modern latitudinal diversity gradient casts doubt on the idea that higher evolutionary rates in the tropics underlie the current diversity patterns of modern mammals. Alternatively, the longer time spans for diversity to accumulate in the older and more stable tropics (and not high diversification rates) may explain the latitudinal diversity gradient.

Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge. To address this gap, we introduce the sabinaNSDM R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions. sabinaNSDM streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs. This paper outlines the workflow and functions integrated into the sabinaNSDM package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.

Conserving the genetic resources of major forest species is essential for mitigating climate risks associated with global change. Species distribution models and their projections under future emission scenarios are key tools for assessing climate-related threats to populations targeted for conservation efforts. In this study, we analyzed the populations included in the Spanish Network of Forest Genetic Conservation Units, comprising 297 units across twenty main forest tree species in Spain. We used species distribution models with high-resolution environmental data to assess habitat suitability across different climate scenarios and time periods. We introduced a Climatic Vulnerability Index to identify Genetic Conservation Units at risk of maladaptation by evaluating shifts in current conditions. Our results reveal spatial and interspecific heterogeneity in the future climatic vulnerability of the conservation network, as well as a relationship between expert-defined marginality and current climatic unsuitability under the Spanish Group model. Overall, our findings provide a basis for prioritizing conservation actions, guiding adaptive forest management, and supporting the long-term monitoring and viability of forest genetic resources under accelerating climate change.