Age‐dependent relationship between horn growth and survival in wild sheep
Bibliographic record
Abstract
1. Trade-offs in resource allocation underline the evolution of life-history traits but their expression is frequently challenged by empirical findings. In large herbivores, males with large antlers or horns typically have high mating success. The fitness costs of large horns or antlers have rarely been quantified although they are controversial. 2. Here, using detailed longitudinal data on n = 172 bighorn (Ovis canadensis, Shaw) and the capture-mark-recapture methodology, we tested whether early horn growth leads to a survival cost in rams ('trade-off' hypothesis) or if males that can afford rapid horn growth survive better than males of lower phenotypic quality ('phenotypic quality' hypothesis). We also quantified how hunting increased survival costs of bearing large horns. 3. We found an age-specific relationship between horn growth and survival. In all age classes, natural survival was either weakly related to (lambs, adult rams) or positively associated (yearling rams) with early horn growth. Hunting mortality was markedly different from natural mortality of bighorn rams, leading to an artificial negative association between early horn growth and survival. Beginning at age 4, the yearly harvest rate ranged from 12% for males with the smallest horns up to more than 40% for males with the largest horns. 4. Growing large horns early in life is not related to any consistent survival costs, hence supporting the phenotypic quality hypothesis in males of a dimorphic and polygynous large herbivores. Rapid horn growth early in life is, however, strongly counter selected by trophy hunting. We suggest that horn size is a very poor index of reproductive effort and that males modulate their mating activities and energy allocation to horn growth to limit its impact on survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".