Experimental test of the efficacy of hunting for controlling human–wildlife conflict
Bibliographic record
Abstract
Abstract Human–wildlife conflict can cause major declines in wildlife populations and pose a threat to human safety and livelihoods. Large carnivores are among the most conflict‐prone species because they range widely, eat human‐associated foods, and can pose a risk to human safety. Legal harvest of carnivores by licensed hunters is an attractive method to attempt to reduce conflict; however, there is mixed evidence for its effectiveness. We leveraged a unique management project in Ontario, Canada in which a new spring American black bear ( Ursus americanus ) hunting season was implemented in selected wildlife management units in addition to the existing fall season. We examined human–bear interactions and incidents before (2012 and 2013) and after (2014 and 2015) this implementation in treatment and control areas. Further, using data from 2004–2019, we examined the longer‐term patterns of human–bear interactions and incidents before and after this management project when a spring season was implemented throughout the entire province beginning in 2016. Harvest increased significantly upon the implementation of the spring season in selected units, but there was no concomitant reduction in interactions or incidents, and these were higher in areas with the new spring season relative to control areas. Human–bear interactions, incidents, and harvest were strongly related to the availability of natural foods in all analyses. Regulated, presumably sustainable harvest was ineffective at reducing human–bear interactions and incidents in the near‐term and might have increased both. Our results support a long history of research showing that natural food availability is a primary driver of human–wildlife conflict. Programs promoting coexistence between people and wildlife, including education, capacity building, and management of unnatural food sources are likely to be the most successful at reducing conflicts between people and bears.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".