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The wolverine (Gulo gulo) is an uncommon, wide-ranging carnivore of conservation con- cern. We evaluated performance of landscape models for wolverines within their historical range at 2 scales in the interior Northwest based on recent observations (n = 421) from Washington, Oregon, Idaho, and Montana. At the sub-basin scale, simple overlays of habitat and road-density classes were effective in predicting observations of wolverines. At the watershed scale, we used a Bayesian belief network model to provide spatially explicit estimates of relative habitat capability. The model has 3 inputs: amount of habitat, human population density, and road density. At both scales, the best models revealed strong cor- respondence between means of predicted counts of wolverines and means of observed counts (P < 0.001). Our results can be used to guide regional conservation planning for this elusive animal.