Bears in the backyard : understanding the human-black bear interactions in British Columbia
Bibliographic record
Abstract
This thesis examines the dynamics of the Human-Black Bear Conflict (HBC) using Wildlife Alert Reporting Program (WARP) data from 2013 to 2021. First, I investigate the spatial and temporal patterns of conflict involving black bears and five other wildlife species. I identify key locations and seasons for these incidents and subsequent euthanizations, revealing that conflict peaks during the summer and is concentrated in areas such as Greater Vancouver, Prince George, and Sunshine Coast. Human-black bear conflict is the most prevalent, with 66,846 incidents reported and 3,698 bears euthanized during our study period. Secondly, I explore the encounter types that most frequently result in bears being euthanized. Using a Two-Way Fixed Effects model, I find that bears exhibiting aggressive behavior toward humans or posing a threat to humans and pets are more likely to be killed. There is a strong association between bear killings and bears conditioned to non-natural food sources or exhibiting nuisance behavior. In contrast, non-aggressive bear sightings do not significantly contribute to bear fatalities. Thirdly, we evaluate the effectiveness of lethal interventions in reducing future conflict events. Models with lags reveal that while there is a temporary decline (5 to 7 months) in conflict following bear killings, this reduction is not sustained over the long term, with some models even indicating an increase in 11-12 months. Notably, Aggressive conflict initially spikes after a month, then decreases until the sixth month, only to rise again thereafter. These findings indicate that lethal interventions fail to achieve a lasting decrease in conflict, highlighting the need for alternative strategies to manage human-bear interactions effectively. This thesis provides three key insights into Human-Wildlife Conflict (HBC): (1) It establishes a baseline of spatiotemporal patterns in BC; (2) It identifies various encounter types leading to bear killings; and (3) It reveals that lethal interventions are ineffective for long-term conflict reduction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".