The objective of this paper was to identify the main factors that explain the greater part of variation in bird of prey species richness (BPSR) along latitudinal gradient, particularly from Northern Europe to South Africa. For this purpose we used 26 environmental variables grouped according to the main hypotheses proposed to explain the regional variability in species richness. Because the relationship between species richness and environmental variables vary with spatial scale, we used two type of grain size (100 km x 100 km and 200 km x 200 km). To link explanatory environmental variables to BPSR, for each grain size, we used two multiple regression methods: a global model, the Ordinary Least Squares (OLS) and a local model, the Geographically Weighted Regression (GWR). Further, we designed 11 different models based on combination of the environmental variables, both for OLS and GWR methods, in order to select the best one. For OLS method, variation in BPSR across the study area was best predicted by total model (all variables), both for finer (R-2 adjusted = 0.712; AIC(c) = 22394.89) and coarser spatial scales (R-2 adjusted = 0.804; AIC(c) = 4888.98). For GWR method, the (climate + habitat heterogeneity) model was the best model regarding variation explaination and model performances, both for finer (R-2 adjusted = 0.953; AIC(c) = 16890.49) and coarser (R-2 adjusted = 0.958; AIC(c) = 3909.18) resolutions. The GWR regression models performed better than OLS models in explaining variation in BPSR, the improvement of models performance was evident. In our study, both regression methods and analysis of variance partitioning lead to the conclusion that climate (particularly PC2) and habitat heterogeneity (particularly HABNB) were the most influental factors in determining BPSR at the spatial scales analyzed in this study.
