Individual quality, early‐life conditions, and reproductive success in contrasted populations of large herbivores
Bibliographic record
Abstract
Variations among individuals in phenotypic quality and fitness often confound analyses of life-history strategies assessed at the population level. We used detailed long-term data from three populations of large herbivores with generation times ranging from four to nine years to quantify heterogeneity in individual quality among females, and to assess its influence on mean annual reproductive success over the lifetime (MRS). We also determined how environmental conditions in early life shaped individual quality and tested A. Lomnicki's hypothesis that variance in individual quality should increase when environmental conditions deteriorate. Using multivariate analyses (PCA), we identified one (in sheep and deer) or two (in goats) covariations among life-history traits (longevity, success in the last breeding opportunity, adult mass, and social rank) as indexes of individual quality that positively influenced MRS of females. Individual quality was reduced by unfavorable weather, low resource availability, and high population density in the year of birth. Early-life conditions accounted for 35-55% of variation in individual quality. In roe deer, we found greater variance in individual quality for cohorts born under unfavorable conditions as opposed to favorable ones, but the opposite was found in bighorn sheep and mountain goats. Our results demonstrate that heterogeneity in female quality can originate from environmental conditions in early life and can markedly influence the fitness of females in species located at different positions along the slow-fast continuum of life-history strategies.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".