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Although managing habitats in the context of climate change is increasingly important in Western North America, management recommendations are often lacking at fine scales relevant for management. Identifying management actions for climate adaptation requires an understanding of how wildlife (i) might vary in their response to habitat conditions across their range and (ii) the spatial scale of environmental effects. We quantified breeding habitat use of the Interior population of Band-tailed Pigeons (Patagioenas fasciata) in the Southwestern U.S. by analyzing data from satellite-tagged birds with a resource selection function. We used Reversible-jump Markov chain Monte Carlo (RJMCMC) to quantify habitat use of Band-tailed Pigeons across vegetation, topography, and precipitation, examining the possibility for differences in habitat selection and estimated the most ecologically relevant spatio-temporal scale for these habitat features (i.e., the optimal "scale of effect"). Our RJMCMC results indicated that Band-tailed Pigeon intensity of use was characterized by precipitation x conifer cover and precipitation x basal area interactions. In drier areas, Band-tailed Pigeons were more likely to use areas with more conifer cover; as precipitation increased, Band-tailed Pigeons were more likely to use areas with less conifer cover. Increased precipitation facilitated greater use of forests with higher basal area, and drier areas were associated with use of forests with lower basal area. Conifer cover was primarily selected at the 1 km scale, and basal area was selected at the 2 km scale in response to precipitation during the winter preceding the breeding season. Although Band-tailed Pigeons have long been known to associate with conifer forests, we found that their use of conifer forest varied across a gradient of precipitation. Using our approach to select the scale of effect for forest habitat and basal area in response to changes in precipitation can provide more precise, spatially relevant habitat management recommendations than approaches using model selection such as Akaike's Information Criterion.
