Environmental and evolutionary effects on horn growth of male bighorn sheep
Bibliographic record
Abstract
The development of male secondary sexual characters such as antlers or horns has substantial biological and socio‐economic importance because in many species these traits affect male fitness positively through sexual selection and negatively through trophy hunting. Both environmental conditions and selective hunting can affect horn growth but their relative importance remains unexplored. We first examined how a large‐scale climate index, the Pacific Decadal Oscillation (PDO), local weather and population density influenced both absolute and relative annual horn growth from birth to three years of male bighorn sheep Ovis canadensis over 42 years. We then examined the relative influence of environmental conditions and evolution mainly driven by trophy hunting on male horn length at three years of age. Horn growth was positively influenced by low population density and warm spring temperature, suggesting that ongoing climate change should lead to larger horns. Seasonal values of PDO were highly correlated. Horn growth increased with PDO in spring or summer at low density, but was weak at high density regardless of PDO. The interaction between population density and PDO in spring or summer accounted for a similar proportion of the observed annual variation in horn growth (32% or 37%) as did the additive effects of spring temperature and density (34%). When environmental conditions deteriorated, males allocated relatively more resources to summer mass gain than to horn growth, suggesting a conservative strategy favoring maintenance of condition over allocation to secondary sexual characters. Population density explained 27% of the variation in horn length, while evolutionary effects explained 9% of the variance. Thus, our study underlines the importance of both evolution and phenotypic plasticity on the development of a secondary sexual trait.
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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".