1. Identification of suitable habitats for small, endangered populations is important to preserve key areas for potential augmentation. However, replicated spatial data from a sufficient number of individuals are often unavailable for such populations, leading to unreliable habitat models. This is the case for the endangered Pyrenean brown bear Ursus arctos population, with only about 20 individuals surviving in two isolated groups.
2. We conducted habitat suitability analyses at two spatial scales (coarse and local). Given the limited available data, we used information from the nearby Cantabrian brown bear population in Spain to develop a two-dimensional model (human and natural variables) at a coarse scale, based on logistic regression, which we applied in the Pyrenees. At a local scale, we used bear presence in the Pyrenees to describe the population’s ecological niche and develop a habitat suitability model using presence-only methods. We combined these models to obtain a more integrative understanding of bear requirements.
3. The coarse-scale model showed a good transferability to the Pyrenees, identifying preference for areas with high forest connectivity, masting trees, rugged terrain and shrubs and avoidance of areas with anthropogenic structures. The local-scale model was consistent with the coarse-scale model. Bears showed a trade-off between food resources (scarcer at high elevations) and human presence (higher at low elevations).
4. Our models illustrated that there is unoccupied good habitat for bears in the Pyrenees that could host new individuals. Combining two scales allowed us to identify areas that should be prioritized for management actions and also those that should be easier to manage for bears.
5. Synthesis and applications. Our study illustrates how a nested-scale approach, combining coarse data from a different population and fine-scale local data, can aid in the management of small populations with limited data. This was applied to remnant brown bear populations to identify priorities for conservation management.