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Resource selection analyses based on detection data are widely used to parametrize resistance surfaces used to identify ecological corridors. To successfully parametrize resistance, it is crucial to decouple resident and disperser behaviours yet to date connectivity studies using detection data have not addressed this issue. Here, we decoupled data of resident and dispersing wolves by analysing detection data collected within a natural corridor crossing a human dominated plain in Italy. To decouple residents and dispersers, we ran a Kernel Density analysis to investigate whether clusters of wolf detection points characterized by sharply higher points’ density exist and checked whether the areas outlined by these clusters (core areas) hold specific characteristics. Habitat selection analysis was then performed to compare the intensity of habitat selection carried out by putative residents and dispersers. We identified a high-density cluster of 30 detection points outlining a small core area stably located in the central part of the park. The dramatic differences of the R 2  and the AUC of the habitat selection models performed inside (R 2  = 0.506; AUC = 0.952) and outside (R 2  = 0.037; AUC = 0.643) the core area corroborated the hypothesis that the core area effectively encloses detection points belonging to residents. Our results show that through simple space use analyses it is possible to roughly discriminate between detection points belonging to resident-behaving and disperser-behaving individuals and that habitat selection models separately performed on these data have extremely different results with strong possible effects on resistance surfaces parametrized from these models.

Decoupling residents and dispersers from detection data improve habitat selection modelling: the case study of the wolf in a natural corridor