Integrating Social, Political, and Economic Factors into Spatial Models of Grizzly Bear Conflict and Connectivity
Bibliographic record
Abstract
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 predictors of conflict for all species were human population density and both livestock and row-crop operation density. The strongest predictors of bear conflict were public opinion of bears, proximity to existing grizzly bear populations, and suitable bear habitat. Generating spatial predictions on these models indicates that the urban centers of the Okanagan (e.g., Kelowna, Vernon) are hotspots for general wildlife conflict while the semi-urban and rural agriculture landscapes (e.g., outside Penticton and along the US and Canada border) are hotspots for bear conflict (Chapter 1). I then incorporated spatial predictions of the probability of bear conflict into the resistance surface for a connectivity model across the traditional territory of the Sylix people to investigate how spatial patterns of conflict may impact bear movement and the ability to recolonize the traditional Sylix territory. I used Omniscape to model connectivity as a function of biophysical variables to identify the most likely available movement pathways and compared this to model outputs that incorporated the probability of conflict into the resistance surface to determine the effects of conflict on grizzly bear connectivity. My research highlights the role that social and institutional variables play in conflict and how these effects may differ between species. Further, my results illustrate the potential for conflict to constrain wildlife movement and highlight the need to treat connectivity conservation as a socio-ecological issue rather than just an ecological one.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".