Data Sheet 1_Tree diversity patterns along an elevational gradient in Durango, Mexico.pdf

Understanding the factors that shape biodiversity is crucial for predicting species responses to environmental change and for effective conservation planning. Broad-scale diversity patterns have been widely studied, yet the mechanisms determining biodiversity remain debated. In this study, we investigate the effect of environmental factors on plant diversity patterns along a subtropical elevational gradient in Durango, Mexico. Using a database of 820 permanent forest inventory plots distributed along a gradient ranging from 1300 m to 3000 m in elevation, we assessed how tree taxonomic diversity (TD) and phylogenetic diversity (PD) change with elevation and identified their key environmental drivers. We quantified TD and PD using Hill-Chao numbers and tested soil, topographic, and climate variables as drivers of diversity using generalized linear models. Our results revealed a unimodal pattern for both TD and PD, with peak diversity at mid-elevations (2290 m). Climate variables—mean annual precipitation, mean annual temperature, precipitation seasonality, and relative humidity—emerged as the most important drivers of diversity. The unimodal patterns observed for TD and PD in relation to annual precipitation and temperature align with the predictions of the Water-Energy Dynamics model, highlighting the importance of water-energy dynamics in shaping diversity patterns. The strong positive correlation between TD and PD across all levels of diversity suggests that evolutionary processes and shared environmental tolerances likely contribute to shaping both facets of diversity. These findings have important implications for conservation and forest management, particularly for identifying diversity hotspots along the elevational gradient and understanding the role of environmental filtering in shaping community assembly and phylogenetic diversity. Future research should expand on these findings by incorporating higher-resolution environmental data and explicitly accounting for forest management practices to further refine our understanding of diversity patterns.