Data from: Testing the importance of harvest refuges for phenotypic rescue of trophy hunted populations
Bibliographic record
Abstract
Sustainable exploitation must minimize its impact on the ecology and evolution of exploited wildlife. Intense phenotype-based selective harvests can induce evolutionary change. Refuges could mitigate those evolutionary effects if individuals not subject to selective hunting in harvest refuges migrated and reproduced in hunted areas. The role of harvest refuges on phenotypic rescue of trophy-hunted species, however, has rarely been tested. We investigated spatial and temporal variation in the effect of refuges on horn size and age at harvest in bighorn sheep (Ovis canadensis). We analyzed data on 5 826 males harvested over 39 years in Alberta, Canada. Horn length, a trait targeted by hunters, and age at harvest increased with the amount of protected areas 5-40 km around each kill. Horn base circumference, however, was independent of proximity to refuges. The number of males harvested increased during the last 10 days of the hunting season in late October, corresponding with the timing of bighorn male breeding migrations. Males shot during those 10 days were on average 17% closer to a refuge than males shot earlier in the season. Apparently, some large males exit refuges late in the hunting season, are shot, and cannot contribute to rescue. Uncertainty remains about the proportion of males exiting refuges after the hunting season and how many survive to reproduce. Synthesis and applications. Harvest refuges are unlikely to rescue hunted populations of bighorn sheep in Alberta, because some males exiting refuges are at risk of harvest before they mate. For phenotypic rescue to be effective, unselected males must reproduce before they are shot. Closing the hunting season 10 days earlier would increase survival of unselected rams exiting refuges.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".