Statistical models that describe species-environmental relationships are important components within many wildlife conservation strategies. These models are typically developed from studies conducted on small geographic scales (hundreds of square kilometres), representing a relatively small range in environmental conditions. Such local models from local studies are often then extrapolated to predict the suitability of other unsampled regions. The value of many models would be increased by considering larger-scale processes that might be structuring spatial patterns across species distributions. We examined home-range habitat selection by burrowing owls throughout the mixed prairie grassland region of western Canada (180,000km2) to determine whether owl selection for biotic factors changes along abiotic gradients. Specifically, we classified 37 explanatory variables into five categories (geography, grassland fragmentation, land-use, soil, and climate), created models for each set of variables, and evaluated the predictive ability of each model. We then examined interaction effects to determine if the relationship between land cover variables and the probability of owl home-range selection varied within large-scale abiotic criteria. Our results showed that soil and climate produce the most predictive models of burrowing owl home-range selection and create unique environmental conditions for owls which are independent of land cover at this scale. This study provides new insight into burrowing owl habitat requirements, and strengthens the case for considering large-scale abiotic gradients when prioritizing areas for species conservation.