Can phenotypic rescue from harvest refuges buffer wild sheep from selective hunting?
Bibliographic record
Abstract
Human harvests can unwittingly drive evolution on morphology and life history, and these selective effects may be detrimental to the management of natural resources. Although theory suggests that harvest refuges, as sources of unselected animals, could buffer the effects of human exploitation on wild populations, few studies have assessed their efficiency. We analyzed records from >7000 trophy bighorn rams (Ovis canadensis) harvested in Alberta, Canada, between 1974 and 2011 to investigate if the movement of rams from refuges toward harvested areas reduced the effects of selective harvesting on horn size through phenotypic rescue. Rams taken near refuges had horns on average about 3% longer than rams shot far from refuges and were slightly older, suggesting migration from refuges into hunted areas. Rams from areas adjacent to and far from harvest refuges, however, showed similar declines in horn length and increases in age at harvest over time, indicating a decreasing rate of horn growth. Our study suggests that the influx of rams from refuges is not sufficient to mitigate the selective effects of sheep trophy harvest. Instead, we suggest that selective hunting of highly mobile animals may affect the genetic structure of populations that spend part of the year inside protected areas.
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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 | 0.001 | 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".