Environmental variability, life‐history covariation and cohort effects in the red‐billed chough<i>Pyrrhocorax pyrrhocorax</i>
Bibliographic record
Abstract
Summary The consequences of environmental variability for life‐history evolution are predicted to depend on the pattern of covariation amongst life‐history traits. Using data from a 20‐year study of individually marked red‐billed choughs, we investigate the short‐ and long‐term life‐history consequences of population‐wide variation in reproductive conditions, and demonstrate clear among‐cohort variation and covariation in life‐history parameters. The mean number of offspring fledging per breeding event varied among years, and was correlated with environmental conditions (temperature and rainfall) during the months preceding breeding. As the variance in breeding performance did not differ among years and choughs did not miss breeding seasons, variation in environmental conditions affected the whole breeding population. Thus the quality of the chough's breeding environment varied amongst years. Juvenile survival, the probability of recruitment to the breeding population and breeding longevity varied amongst cohorts, and these were positively correlated with the quality of the cohort's natal environment. Offspring fledging under good conditions were more likely to survive to breeding age and recruit, and had longer breeding lives than offspring fledging under poor conditions. Age at first breeding varied amongst cohorts, and increased with population size at maturity rather than natal conditions. The total number of offspring that recruits ultimately fledged varied primarily with breeding longevity rather than recruitment age. Thus, the consistent positive covariation amongst life‐history traits meant that the total number of offspring fledged by recruits during their breeding life varied amongst cohorts, and was correlated with the quality of a cohort's natal conditions. Choughs fledging under good conditions themselves ultimately fledged more offspring. Such environmentally determined variation in offspring fitness is expected to influence optimal patterns of parental investment. We discuss the predictions that environmental variability should select for investment in adult survival and for reduced reproductive effort in poor years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".