Temporal changes in reproductive success and optimal breeding decisions in a long-distance migratory bird
Bibliographic record
Abstract
Many avian migrants have not adjusted breeding phenology to climate warming resulting in negative consequences for their offspring. We studied seasonal changes in reproductive success of the greater snow goose (Anser caerulescens atlantica), a long-distance migrant. As the climate warms and plant phenology advances, the mismatch between the timing of gosling hatch and peak nutritive quality of plants will increase. We predicted that optimal laying date yielding highest reproductive success occurred earlier over time and that the seasonal decline in reproductive success increased. Over 25 years, reproductive success of early breeders increased by 42%, producing a steeper seasonal decline in reproductive success. The difference between the laying date producing highest reproductive success and the median laying date of the population increased, which suggests an increase in the selection pressure for that trait. Observed clutch size was lower than clutch size yielding the highest reproductive success for most laying dates. However, at the individual level, clutch size could still be optimal if the additional time required to acquire nutrients to lay extra eggs is compensated by a reduction in reproductive success due to a delayed laying date. Nonetheless, breeding phenology may not respond sufficiently to meet future environmental changes induced by warming temperatures.
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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 | 0.001 |
| 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) | 0.001 | 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".