The cost of trying: weak interspecific correlations among life-history components in male ungulates
Bibliographic record
Abstract
Life-history trade-offs are well known in female mammals, but have seldom been quantified for males in polygynous species. I compared age-specific mass, weapon size, survival, and reproductive success of males in eight species of ungulates, and found weak interspecific correlations among life-history traits. Young males tended to have higher reproductive success in rapidly-growing than in slow-growing species, and in species where horns or antlers reached near-asymptotic size over the first few years of life. There was no clear interspecific trade-off between early reproduction and early survival. Reproductive senescence was evident in most species. Generation length, calculated as the mean age of fathers, was negatively correlated with the reproductive success of young males and positively with life expectancy of 3-year-olds, but not with early mortality. The main determinant of male reproductive success in polygynous ungulates is the ability to prevail against competing males. Consequently, the number and age structure of competitors should strongly affect an individual’s ability to reproduce, making classic trade-offs among life-history traits very context-dependent. Most fitness costs of reproduction in male ungulates likely arise from energy expenditure and injuries sustained while attempting to mate. Individual costs may be weakly correlated with fitness returns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".