Male morphological traits are heritable but do not predict reproductive success in a sexually-dimorphic primate
Bibliographic record
Abstract
Sexual selection favours traits that increase reproductive success via increased competitive ability, attractiveness, or both. Male rhesus macaque (Macaca mulatta) morphological traits are likely to reflect the effects of multiple sexual selection pressures. Here, we use a quantitative genetic approach to investigate the production and maintenance of variation in male rhesus macaque morphometric traits which may be subject to sexual selection. We collected measurements of body size, canine length, and fat, from 125 male and 21 female free-ranging rhesus macaques on Cayo Santiago. We also collected testis volumes from males. We used a genetic pedigree to calculate trait heritability, to investigate potential trait trade-offs, and to estimate selection gradients. We found that variation in most male morphometric traits was heritable, but found no evidence of trait trade-offs nor that traits predicted reproductive success. Our results suggest that male rhesus macaque morphometric traits are either not under selection, or are under mechanisms of sexual selection that we could not test (e.g. balancing selection). In species subject to complex interacting mechanisms of selection, measures of body size, weaponry, and testis volume may not increase reproductive success via easily-testable mechanisms such as linear directional selection.
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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".