Compensatory Growth Limits Opportunities for Artificial Selection in Alpine Chamois
Bibliographic record
Abstract
ABSTRACT In ungulates, big males with large weapons typically outcompete other males over access to estrous females. In many species, rapid early growth leads to large adult mass and weapon size. We compared males in one hunted and one protected population of Alpine chamois ( Rupicapra rupicapra ) to examine the relationship between horn length and body mass. We assessed whether early development and hunter selectivity affected age‐specific patterns of body and horn size and whether sport hunting could be an artificial selection pressure favoring smaller horns. Adult horn length was mostly independent of body mass. For adult males, the coefficient of variation of horn length (0.06) was <50% of that for body mass (0.16), suggesting that horn length presents a lower potential for selection and may be less important for male mating success than is body mass. Surprisingly, early development did not affect adult mass because of apparent compensatory growth. We found few differences in body and horn size between hunted and protected populations, suggesting the absence of strong effects of hunting on male phenotype. If horn length has a limited role in male reproductive success, hunter selectivity for males with longer horns is unlikely to lead to an artificial selective pressure on horn size. These results imply that the potential evolutionary effects of selective hunting depend on how the characteristics selected by hunters affect individual reproductive success.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".