Individual variation in reproductive costs of reproduction: high‐quality females always do better
Bibliographic record
Abstract
1. Although life-history theory predicts substantial costs of reproduction, individuals often show positive correlations among life-history traits, rather than trade-offs. The apparent absence of reproductive costs may result from heterogeneity in individual quality. 2. Using detailed longitudinal data from three contrasted ungulate populations (mountain goats, Oreamnos americanus; bighorn sheep, Ovis canadensis; and roe deer, Capreolus capreolus), we assessed how individual quality affects the probability of detecting a cost of current reproduction on future reproduction for females. We used a composite measure of individual quality based on variations in longevity (all species), success in the last breeding opportunity before death (goats and sheep), adult mass (all species), and social rank (goats only). 3. In all species, high-quality females consistently had a higher probability of reproduction, irrespective of previous reproductive status. In mountain goats, we detected a cost of reproduction only after accounting for differences in individual quality. Only low-quality female goats were less likely to reproduce following years of breeding than of nonbreeding. Offspring survival was lower in bighorn ewes following years of successful breeding than after years when no lamb was produced, but only for low-quality females, suggesting that a cost of reproduction only occurred for low-quality females. 4. Because costs of reproduction differ among females, studies of life-history evolution must account for heterogeneity in individual quality.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".