Hunting, age structure, and horn size distribution in bighorn sheep
Bibliographic record
Abstract
ABSTRACT Trophy hunting, the selective removal of animals for human recreation, can contribute to conservation when appropriately managed. Yet, little is known about how harvest rates or different definitions of trophy affect age structure and trophy size in harvested animals and in survivors because no controlled studies exist. To investigate the impacts of different management regimes, we developed an individual‐based model for bighorn sheep ( Ovis canadensis ), based on empirical data on survival from a protected population and data on horn growth from 2 populations that differed in their growth rates. One population showed slow horn growth and the other population fast horn growth. We subjected these model populations to varying harvest rates and 2 different hunting regulations: 4/5 curl and full‐curl definitions of a trophy male. We found that the effect of hunting regulations depends on horn growth rate. In populations with fast horn growth, the effects of trophy hunting on male age structure and horn size were greater and the effect of a change in the definition of legal male smaller than in populations with slow growth rates. High harvest rates led to a younger age structure and smaller horn size. Both effects were weakened by a more restrictive definition of trophy male. As harvest rates increased past 40% of legal males, the number of males harvested increased only marginally because an increasing proportion of the harvested males included those that had just become legal. Although our simulation focused on bighorn sheep, the link between horn growth rate and harvest effects may be applicable for any size‐selective harvest regime. © 2017 The Wildlife Society.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".