Factors influencing moose harvest success and hunter effort in Ontario, Canada
Bibliographic record
Abstract
Abstract The management of big game harvest is important for maintaining viable populations and providing recreational opportunities to hunters. There are numerous strategies used by management agencies to achieve these goals, but they are complicated by variation in factors that are difficult to control, such as harvest success rates. For harvest management decisions to have the desired effect on big game populations, the mechanisms affecting factors like harvest success rates need to be properly understood. We used Bayesian hierarchical survival models to explore the factors influencing spatial and temporal variation in moose (Alces alces) harvest success rates in Ontario, Canada. We estimated harvest success rates from hunter reports from 59 Wildlife Management Units from 2000–2019. Overall, harvest success rates were primarily influenced by variables under the control of management agencies, such as season length and tag allocations, but they were also affected by external factors like moose density and weather. Season length, while positively related to harvest success for shorter seasons (e.g., <25 days), exhibited limited influence for longer seasons (>25 days). Our results were largely consistent across spatial and temporal scales, with a similarly strong positive effect of moose density and negative effect of tag allocation between management units and across years. This study emphasizes the need for managers to recognize the inherent uncertainty in harvest outcomes beyond their control and the importance of open communication with hunters in achieving effective harvest management, while offering concrete pathways for influencing harvest success.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".