Relative allocation to horn and body growth in bighorn rams varies with resource availability
Bibliographic record
Abstract
Males may allocate a greater proportion of metabolic resources to maintenance than to the development of secondary sexual characters when food is scarce, to avoid compromising their probability of survival. We assessed the effects of resource availability on body mass and horn growth of bighorn rams (Ovis canadensis) at Ram Mountain, Alberta, Canada over 30 years. The number of adult ewes in the population tripled during our study, and the average mass of yearling females decreased by 13%. We used the average mass of yearling females as an index of resource availability. Yearling female mass was negatively correlated with the body mass of rams of all ages, but it affected horn growth only during the first three years of life. Yearly horn growth was affected by a complex interaction of age, body mass, and resource availability. Among rams aged 2–4 years, the heaviest individuals had similar horn growth at high and at low resource availability, but as ram mass decreased, horn growth for a given body mass became progressively smaller with decreasing resource availability. For rams aged 5–9 years, horn growth was weakly but positively correlated with body mass, and rams grew slightly more horn for a given body mass as resource availability decreased. When food is limited, young rams may direct more resources to body growth than to horn growth, possibly trading long-term reproductive success for short-term survival. Although horn growth of older rams appeared to be greater at low than at high resource availability, we found no correlation between early and late growth in horn length for the same ram, suggesting that compensatory horn growth does not occur in our study population. Young rams with longer horns were more likely to be shot by sport hunters than those with shorter horns. Trophy hunting could select against rams with fast-growing horns.
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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 | 0.001 | 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".