Despite Catch-Up, Prolonged Growth Has Detrimental Fitness Consequences in a Long-Lived Vertebrate
Bibliographic record
Abstract
Individuals experiencing poor growth early in life may later make up their size deficit. Compensatory growth or growth prolongation may lead to such catch-up, involving different life-history trade-offs under natural conditions. Frequent recaptures and detailed monitoring of animals surviving to asymptotic size are required to compare growth tactics and their fitness consequences. No study to date has obtained such detailed information for wild animals. We used repeated mass measurements (mean 11.6/animal) spanning the lifetime of 104 bighorn ewes (Ovis canadensis) to quantify growth tactics and identify the determinants and life-history costs of these tactics. Growth prolongation, not compensatory growth, led to partial catch-up: mass difference at age 7 was reduced to 4%, for two groups that differed by nearly 20% as yearlings. Ewes that had been light as yearlings prolonged their growth regardless of density or age of primiparity. Growth prolongation did not affect fecundity or longevity. Ewes that experienced poor early growth prolonged growth at the expense of reproductive fitness, weaning a smaller proportion of their lambs. By tracking multiyear growth patterns and comparing events at different life-history stages, we quantified a trade-off between growth and reproduction that would be overlooked if only the adult phenotype was considered. Compensatory growth in long-lived animals appears unlikely when early growth restrictions are mostly density dependent.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".