Ewe are what ewe wear: bigger horns, better ewes and the potential consequence of trophy hunting on female fitness in bighorn sheep
Bibliographic record
Abstract
In polygynous species, secondary sexual traits such as weapons or elaborate ornaments have evolved through intrasexual competition for mates. In some species, these traits are present in both sexes but are underdeveloped in the sex facing lower intrasexual competition for mates. It is often assumed that these underdeveloped sexually selected traits are a vestige of strong sexual selection on the other sex. Here, we challenge this assumption and investigate whether the expression of secondary sexual traits is associated with fitness in female bighorn sheep. Analyses of 45 years of data revealed that female horn length at 2 years, while accounting for mass and environmental variables, is associated with younger age at primiparity, younger age of first offspring weaned, greater reproductive lifespan and higher lifetime reproductive success. There was no association between horn length and fecundity. These findings highlight a potential conservation issue. In this population, trophy hunting selects against males with fast-growing horns. Intersexual genetic correlations imply that intense selective hunting of large-horned males before they can reproduce can decrease female horn size. Therefore, intense trophy hunting of males based on horn size could reduce female reproductive performance through the associations identified here, and ultimately reduce population growth and viability.
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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 | 0.000 | 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 | 0.000 | 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".