Using human-dimensions research to reduce implementation uncertainty for wildlife management: a case of moose (Alces alces) hunting in northern Ontario, Canada
Bibliographic record
Abstract
Context Wildlife managers frequently use regulations to alter the preferred hunting strategies and outcomes of hunters. However, hunters can respond to changing social and resource conditions resulting from regulations in ways that can surprise wildlife managers. Aims The specific research questions were (1) how does the availability of licences (tags) required to harvest adult moose (Alces alces) relate to the success of hunters at filling these tags and (2) how do hunting pressure and the density of calf moose relate to the harvest rate of the calf population. Methods Information about hunters, harvest-related outcomes and moose abundance were estimated from social surveys and aerial inventories in 46 wildlife management units (WMUs) in northern Ontario, Canada. An information-theoretic approach was used to select regression models that predicted the average annual filling rate of tags for adult moose and for the average annual proportion of calf population harvested by hunters in the WMUs. Key results Tag filling rates were negatively and strongly associated with the availability of tags to hunters in the WMUs. The proportion of calf population harvested was positively related to hunting pressure and negatively related to the density of calf populations in the WMUs. Conclusions As tags became more scarce, hunters appeared to become more skilled at harvesting adult moose. As calf density declined, hunters harvested larger proportions of the population, indicating a possible inverse density-dependent relationship between abundance and harvest. Implications Understanding hunters and their actions and role within a larger social-ecological system are critical for helping to reduce the uncertainty of implementing regulations for managing wildlife. Without having this understanding, it is easy for managers to become trapped in situations where the intent of management actions is undermined by the abilities of hunters who respond to both changing social and resource conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".