Deer populations up, hunter populations down: Implications of interdependence of deer and hunter population dynamics on management
Bibliographic record
Abstract
White-tailed deer (Odocoileus virginianus) are managed to yield diverse impacts, including effects to ecosystems. Many conventional hunting systems manage deer abundance through rules that strive to produce recreation opportunities and an equitable distribution of antlered bucks among hunters. To protect against excessive harvests, antlerless deer harvests often are regulated through quotas. This approach is effective when deer productivity does not outstrip capacity of the hunter population to harvest required numbers of antlerless deer. In many areas of North America, abundance of white-tailed deer has increased dramatically in the past two decades, which has caused many wildlife managers to ask whether deer populations can be controlled with conventional harvest strategies. We used population reconstruction modeling to simulate deer populations from mixed hardwood forests in southern New York, determined antlerless deer harvests needed to stabilize or reduce populations, and evaluated whether current hunting systems can effectively achieve potential ecosystem objectives. Current hunter willingness to seek or use antlerless deer permits likely is inadequate to stabilize or reduce deer densities. This situation may be exacerbated in the future with occurrence of diseases in deer or other factors that diminish hunter participation. We discuss implications for effectiveness of ecosystem management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".