Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data

Resistance models provide a key foundation for landscape connectivity analyses and are widely used to delineate wildlife corridors. Currently, there is no general consensus regarding the most effective empirical methods to parameterize resistance models, but habitat data (species' presence data and related habitat suitability models) and genetic data are the most widely used and advocated approaches. However, the practical consequences of applying one or the other approach have not been well studied. To address this knowledge gap, we performed a comparative study on the implications of using habitat suitability versus genetic data for determining effective landscape distances (a proxy inversely related to isolation among patches) based on least-cost and circuit-theoretic approaches, and for identifying potential movement corridors. For our comparison, we used data for the Cantabrian brown bear in Spain, an endangered population for which connectivity has been identified as a major conservation concern. Our results show that for brown bears, habitat models tend to overestimate resistance to movement through non-optimal areas, whereas genetic data yield higher estimates of effective distances within habitat areas. Therefore, our results suggest that (1) dispersal might be generally less constrained by landscape conditions than habitat utilization in home ranges, and that (2) dispersing animals might be more flexible in their movement behavior than residents are in their habitat resource utilization behavior, with records for residents dominating species occurrence data and subsequent habitat models. The assessed approaches provided dissimilar connectivity models with notable differences in patterns of predicted corridors across the study area, mainly due to differences in predicted connections between subpopulations. Our results highlight that the functional differences in habitat vs. genetic data, as well as the assumptions and potential limitations of different analytical approaches that use these data, need to be considered more carefully in connectivity modeling and subsequent corridor delineation.