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Abstract Wildlife conservation depends on supportive social as well as biophysical conditions. Social identities such as hunter and nonhunter are often associated with different attitudes toward wildlife. However, it is unknown whether dynamics within and among these identity groups explain how attitudes form and why they differ. To investigate how social identities help shape wildlife‐related attitudes and the implications for wildlife policy and conservation, we built a structural equation model with survey data from Montana (USA) residents (n = 1758) that tested how social identities affect the relationship between experiences with grizzly bears (Ursus arctos horribilis) and attitudes toward the species. Model results (r² = 0.51) demonstrated that the hunter identity magnified the negative effect of vicarious property damage on attitudes toward grizzly bears (β = −0.381, 95% confidence interval [CI]: −0.584 to −0.178,p < 0.001), which in turn strongly influenced acceptance (β = −0.571, 95% CI: −0.611 to −0.531,p < 0.001). Our findings suggested that hunters’ attitudes toward grizzly bears likely become more negative primarily because of in‐group social interactions about negative experiences, and similar group dynamics may lead nonhunters to disregard the negative experiences that out‐group members have with grizzly bears. Given the profound influence of social identity on human cognitions and behaviors in myriad contexts, the patterns we observed are likely important in a variety of wildlife conservation situations. To foster positive conservation outcomes and minimize polarization, management strategies should account for these identity‐driven perceptions while prioritizing conflict prevention and promoting positive wildlife narratives within and among identity groups. This study illustrates the utility of social identity theory for explaining and influencing human–wildlife interactions. 

Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves ( Canis lupus ). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.