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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.

Abstract Land conversion is causing habitat loss and fragmentation worldwide, particularly in Africa, where the proliferation of agricultural development corridors may threaten vital areas for ecological connectivity and wildlife survival. To conserve connectivity, careful landscape planning is necessary, which strongly relies on remotely sensed land cover maps. Here, we present a remote sensing‐based framework that efficiently identifies priority locations for connectivity conservation. We applied the framework in the Kilombero catchment and development cluster of the Southern Agricultural Growth Corridor (SAGCOT) in Tanzania, where new agricultural development projects could act as barriers for connectivity. Using satellite imagery from Sentinel‐1 and 2, we mapped the mixture of mountain and lowland land covers and habitats with an overall accuracy of 75%. Then, we assessed ecological connectivity to predict African elephant corridors and prioritize them in two ways. First, we identified elephant corridors that contribute the most to current landscape connectivity, and second, we identified those whose restoration would significantly enhance landscape functionality and improve the current connectivity level. We mapped 214 potential elephant corridors, identified 43 of them as priority for conservation, and 43 as target for restoration. Our model predicted four already known corridor areas in and around the Kilombero valley floodplain, and other important corridors not yet identified by previous studies in the south of the basin. Priority elephant corridors inside the floodplain showed narrow widths and low permeability, indicating low functionality in the connectivity network. Nevertheless, the abundance of priority corridors for restoration suggests that connectivity could be recovered if the recommended measures are applied during SAGCOT planning and implementation process. Our findings demonstrate the possibilities of combining multispectral and radar data for guiding biodiversity management in development corridors and for assessing ecological connectivity worldwide.