Integrating Social, Political, and Economic Factors into Spatial Models of Grizzly Bear Conflict and Connectivity
The rapid expansion of the global human footprint is forcing humans and wildlife to share more space. There is rising concern over human wildlife conflict and its effects on human and animal wellbeing. Investigation into the biophysical and social landscape features that shape conflict or how spatia...
| Main Author: | |
|---|---|
| Format: | Text |
| Language: | unknown |
| Published: |
ScholarWorks
2022
|
| Subjects: | |
| Online Access: | https://scholarworks.boisestate.edu/td/1999 https://doi.org/10.18122/td.1999.boisestate https://scholarworks.boisestate.edu/context/td/article/3134/viewcontent/Spragg__Shannon__2022__Integrating_social__political__and_economic.pdf |
| Summary: | The rapid expansion of the global human footprint is forcing humans and wildlife to share more space. There is rising concern over human wildlife conflict and its effects on human and animal wellbeing. Investigation into the biophysical and social landscape features that shape conflict or how spatial patterns in conflict ultimately affect species’ movement or survival is limited. Characterizing landscape connectivity and identifying potential movement corridors is a key conservation strategy, but is challenged by the fact that many wildlife species navigate a mosaic of infrastructure, available habitat, land uses, and political boundaries. In this thesis, I investigated the social and biophysical factors that contribute to conflict with grizzly bears (Ursus arctos) and how this conflict may impact connectivity for bears across southern British Columbia and northern Washington. I selected this system due to its rich cultural history with grizzly bear biological and social complexity. The region has current grizzly bear populations, extirpated areas, state/provincial and international boundaries, diverse land uses, and a variety of social values towards wildlife. I used two resource selection approaches to first determine the probability of conflict reporting across all wildlife species, and then to determine the probability of bear conflict specifically. First, I used presence and background sampling in combination with Bayesian logistic regression to identify important predictors of conflict across species using 5,606 reported instances of conflict and 8,703 background points. Then, I fit a second regression treating 2,062 bear conflict occurrences as presence points and 3,544 instances of other conflict as absences to characterize how bear conflict might differ from wildlife conflict in general. I found that predictors of conflict differed between species and that the probability of general wildlife conflict was substantially different than the probability of bear conflict across the study system. The strongest ... |
|---|