We introduce a method that enables the estimation of species richness–environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation.
The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species–area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by downscaling richness from 2° to 0.25° (∼250 km to ∼30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness–environment relationship, but accurately predicted only relative (rank) values of richness.
The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness–environment relationships, and for the provision of high-resolution maps for basic science and conservation.