Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale

AIM: The species–area relationship (SAR) is a prominent concept for predicting species richness and biodiversity loss. A key step in defining SARs is to accurately estimate the slope of the relationship, but researchers typically apply only one global (canonical) slope. We hypothesized that this approach is overly simplistic and investigated how geographically varying determinants of SARs affect species richness estimates of vascular plants at the global scale. LOCATION: Global. METHODS: We used global species richness data for vascular plants from 1032 geographical units varying in size and shape. As possible determinants of geographical variation in SARs we chose floristic kingdoms and biomes as biogeographical provinces, and land cover as a surrogate for habitat diversity. Using simultaneous autoregressive models we fitted SARs to each set of determinants, compared their ability to predict the observed data and large‐scale species richness patterns, and determined the extent to which varying SARs differed from the global relationship. RESULTS: Incorporating variation into SARs improved predictions of global species richness patterns. The best model, which accounts for variation due to biomes, explained 46.1% of the species richness variation. Moreover, fitting SARs to biomes produced better results than fitting them to floristic kingdoms, supporting the hypothesis that energy availability complements evolutionary history in generating species richness patterns. Land cover proved to be less important than biomes, explaining only 36.4% of the variation, possibly owing to the high uncertainty in the data set. The incorporation of second‐order interactions of area, land cover and biomes did not improve the predictive ability of the models. MAIN CONCLUSIONS: Our study contributes to a deeper understanding of SARs and improves the applicability of SARs through regionalization. Future models should explicitly consider geographically varying determinants of SARs in order to improve our assessment of the impact of global change scenarios on species richness patterns.