Plasticity in laying dates of Canada Geese in response to spring phenology
Bibliographic record
Abstract
Observed phenological changes can be explained either by individual phenotypic plasticity or by evolutionary changes, but there is more evidence pointing towards phenotypic plasticity to explain the mechanism behind changes in bird phenology. However, most studies on phenology have been conducted on insectivorous bird species for which breeding is closely tied to temperature and insect emergence. In this study, we examined the consequences of climatic conditions on the nesting phenology of temperate breeding Canada Geese Branta canadensis maxima, which rely on a continuous food supply, during a 14‐year period (2003–16). We determined whether laying dates were plastically adjusted to spring environmental conditions, and whether this adjustment resulted in a laying date advancement. We further estimated the strength and shape of selection acting on breeding timing, by looking at the effect of laying date on the relative number of young successfully hatched in a nest. We found that Geese plastically adjusted their laying date to spring maximum temperature (and not to precipitation or ice break‐up), resulting in a 9‐day advancement of laying date in the population for that period. Laying date was also moderately repeatable (r = 0.23) and subject to directional selection, but stabilizing selection was negligible. We thus demonstrate how Canada Geese plastically adjust laying dates to temperature, which may further be beneficial to nesting success. Evolutionary change of laying date to selection related to climate change, however, is still possible.
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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".