Numerous processes operating at landscape scales threaten bats (e.g., habitat loss, disease). Temperate bat species are rarely examined at commensurate scales because of logistical and modeling constraints. Recent modeling approaches now allow for presenceonly datasets, like those often available for bats, to assist with the development of predictive distribution models. We describe the use of presence-only data and rigorous predictive distribution models to examine habitat selection by bats across Colorado, USA.We applied hierarchical Bayesian models to bat locations from 1906-2018 to examine relationships of 13 species with landscape covariates. We considered differences in type of activity (foraging, roosting, hibernation), seasonality (summer vs. winter), and scale (1, 5, 10, and 15-km buffers). These findings generated statewide probability of use models to guide management of bat species in response to threats (e.g., white-nose syndrome [WNS]). Analysis of buffers suggest selection of land cover and environmental covariates occurs at different scales depending on the species and activity. Pinyon (Pinus spp.)-juniper (Juniperus spp.) appeared as a positive association in the highest number of models, followed by montane woodland, supporting the importance of these forest types to bats in Colorado. Other covariates commonly associated with bats in Colorado include westerly longitudes, and negative associations with montane shrubland. Mechanical treatments within pinyon-juniper and montane woodlands should be conducted with caution to avoid harming bat communities. We developed hibernation models for only 2 species, making apparent