Harvest regulations and artificial selection on horn size in male bighorn sheep
Bibliographic record
Abstract
Abstract Wild sheep in North America are highly prized by hunters and most harvest regulations restrict legal harvest to males with a specified minimum horn curl. Because reproductive success is skewed toward larger males that are socially dominant, these regulations may select against high‐quality, fast‐growing males. To evaluate potential selective effects of alternative management strategies, we analyzed horn increment measures of males harvested over 28 yr (1975–2003) in 2 bighorn sheep (Ovis canadensis) ecotypes in British Columbia, Canada. Using mixed‐effect models we examined variation in hunter selection for horn size, early horn growth, and male age under different harvest regulations (Full Curl, Three Quarter Curl, Any Ram). Under all regulations, males with the greatest early horn growth were harvested at the youngest ages, before the age at which large horns influence reproductive success. Early growth decreased with harvest age and until ≥7 yr of age it was greatest in males harvested under Full Curl regulation. Permit type (General vs. Limited Entry Hunt) and hunter origin (British Columbia Resident vs. Non‐Resident) had little effect on horn size of harvested males. Full Curl regulations increased the average age of harvested males by <1 yr relative to Three‐Quarter Curl regulations. Age‐specific horn measures in the California ecotype harvested under Three‐Quarter Curl regulations declined over time but we observed no temporal declines in the Rocky Mountain ecotype, primarily harvested under Full Curl regulations. Management strategies that protect some males with greater early horn growth or provide harvest refuges to maintain genetic diversity are likely to reduce potential for negative effects of artificial selection. © 2010 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 | 0.002 |
| 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 | 0.000 |
| 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".