Modeling Geographic Extent's Impact on Species Distribution

To study the effect of altering the geographic extent of the region modeled (i.e., the area in which the model is trained) on predicted species’ distributions, we first generated SDMs using a wide geographical extent including the entire region of Fennoscandia and north-western Russia (map shown in Figure 3B). Due to the paucity of Falco records in north-western Russia, using a full geographic extent of the region modeled may characterize the realized distribution of species in the genus poorly. Including large areas increases the chance that the model samples pseudo-absences in areas that have suitable conditions for the species but are falsely classified as unsuitable because the species has not been properly sampled in that region [8] . Indeed, choosing the correct extent is not a trivial task since the values where occurrence data are lacking are taken as pseudo-absences that are meant to provide a comparative data set to establish the conditions where a species may occur. If large extents with great environmental variation are selected, predictive models will be dominated by parameters that serve to coarsely discriminate regional conditions and weaken the ability to tease out fine-scale conditions determining presence or absence of species [27] . On the other hand, using a restricted region for selection of pseudo-absences can be a serious error when fitting models to project potential effects of climate change [37] , since future environmental conditions may not be represented. Since occurrence data for our model species was lacking for north-western Russia, we used Fennoscandia, which accurately mirrored the distribution of the occurrence data of the species (map shown in Figure 3A).