Understanding habitat selection is challenging but key for species of conservation concern, including grizzly bears (Ursus arctos). Here we demonstrate an approach for understanding and predicting habitat use over multiple stages that test hypotheses of animal behavior, use newly gained knowledge to mechanistically simulate individual movements, translate results to predictive habitat maps, and test their predictive power across a large spatiotemporal scale. Grizzly bears in the Northern Continental Divide Ecosystem of northwest Montana served as our study system. Mechanistically modeling grizzly bear movements demonstrated that grizzly bears have highly individualistic spatial behaviors. Some individuals avoided whereas others preferred areas of vegetation green-up, terrain ruggedness, forest edge, riparian areas, building densities, and secure habitat. Such individualism supported the need for an individual-based modeling approach to understand and predict grizzly bear behavior. External validation using >375,000 GPS locations for 261 individuals over nearly 2 decades demonstrated mean Spearman rank scores of >0.90 across seasons and years, and overall scores of 1.0. The top 5 classes of our predictive habitat maps contained 73.5 % of female fixes and 83.6 % of male fixes, and the top class (comprising 10 % of mapped area) contained 25.6 % and 41.7 % of female and male fixes, respectively. Results of this research provide tools for conservation planning and serve as the basis for future grizzly bear research within our study system and beyond. Our multi-stage approach for understanding and predicting habitat use has high utility for conservation of myriad threatened species around the globe.
- Brown bears
- Grizzly bears
- Habitat selection
- Integrated step selection function
- Predictive habitat map