Best-fit global multi-predictor spatial GLS models of species richness. (All variables refer to models for which human impact predictors were additionally fitted; Environmental variables only refer to the global model for which human impact predictors were not fitted. 1°, 2° and 4° refer to equal-area spatial resolutions approximately equivalent to 1°×1°, 2°×2° and 4°×4° longitude×latitude, respectively. At 1° resolution, MDE-fit refers to the best-fit models from sets that additionally fit species richness predictions based on geometric constraints (i.e. mid-domain effect, MDE).+ and − indicate significant positive and negative slopes, respectively, while * indicates significance of a categorical predictor. +++/−−−/***, P<0.001;++/−−/**, 0.001≤P<0.01;+/−/*, 0.01≤P<0.05. The most significant F-values and slopes for each model are highlighted in bold. The best-fit models presented are those from the set of all combinations of the four main environmental predictors that have the lowest value for Akaike's information criterion (AIC). Elevation range, NDVI, population density and agricultural land area were all log-transformed.)