Controversy remains over what drives patterns in the variation of biodiversity across the planet. The resolution is obscured by lack of data and mismatches in their spatial grain (scale), and by grain-dependent effects of the drivers.
Here we introduce cross-scale models integrating global data on tree-species richness from 1,336 local forest surveys and 282 regional checklists, enabling the estimation of drivers and patterns of biodiversity across spatial grains. We uncover grain-dependent effects of both environment and biogeographic regions on species richness, with a striking positive effect of Southeast Asia at coarse grain that disappears at fine grains. We show that, globally, biodiversity cannot be attributed purely to environmental or regional drivers, as the regions are environmentally distinct even within a single latitudinal band. Finally, we predict global maps of biodiversity at local (plot-based) and regional grains, identifying areas of exceptional beta-diversity in China, East Africa and North America.
By allowing the importance of drivers of diversity to vary with grain in a single model, our approach unifies disparate results from previous studies regarding environmental versus biogeographic predictors of biodiversity, and enables efficient integration of heterogeneous data.