Trade‐offs between specificity and regional generality in habitat association models: a case study of two species of African vulture

Summary 1. The habitat requirements of various species have been evaluated by statistical models. However, recent studies have shown that models are often not transferable between regions, limiting their applicability and ability to inform management decisions. One possible cause is that models tend to reflect dominant landscape features, which vary between regions. Transferability, and thus applicability, may be increased by developing models from multiple regions. 2. We addressed this via a case study of two vulture species (white‐backed and lappet‐faced vultures, Gyps africanus and Aegypius tracheliotos ) from six biogeographically different regions across southern Africa. Logistic models, developed using an information‐theoretic approach, were used to predict nest occurrence based on explanatory variables derived from a Geographic Information System (GIS), the usual method for species with large ranges. Variables reflected key requirements at different spatial scales: food availability, human disturbance and nesting trees. We developed models using data from single and multiple regions, and tested the cross‐regional transferability. We also collected field data to asses the adequacy of the GIS variables. 3. There was a significant negative correlation between specificity and regional generality, multi‐region models tending to be more consistently transferable than single‐region models but having a weaker fit within the regions where they were developed. Multi‐region models of nesting habitat were more structurally similar to each other than single‐region models. GIS variables adequately represented the landscape but with differing adequacy between regions. There were no observed fitness benefits to the observed site selection. 4. Synthesis and applications. Models of species distribution are not transferable between regions, and use of models to inform management decisions in regions other than that used for model development should be undertaken with caution. Models are often built using GIS predictors only broadly related to the landscape properties of interest and the adequacy of such proxies can vary between regions, leading to models that emphasize dominant landscape features. Models developed from multiple regions partially overcome this problem by identifying predictors that apply across many regions and are more transferable. However, this increased generality trades off against reduced specificity. Models should be constructed with consideration to their intended use.