Consequences of a changing environment on the breeding phenology and reproductive success components in a long‐distance migratory bird
Bibliographic record
Abstract
Abstract Migratory birds have a narrow time window to breed, especially in the Arctic, where early nesting typically yields the highest reproductive success. We assessed temporal changes (1991–2015) in reproductive success components in relation to timing of breeding in greater snow geese (Chen caerulescens atlantica). This species breeds in the Canadian Arctic, a region that has experienced a strong warming trend. We tested the effect of laying or hatching date, year and their interaction on six reproductive components: Total clutch laid, nesting success, egg survival, hatching success, prefledging, and postfledging survival. Over 25 years, mean laying date changed little, even though it advanced 1.8 days in early breeders and was delayed 3 days in late breeders. Likewise, the number of eggs in nests initiated early in the season decreased by 0.6 egg, whereas in late nests it increased by 0.3 egg. Success of nests initiated early and late in the season was lower than nests initiated near the population mean, and consistently increased over time. The proportion of eggs surviving to partial predation and postfledging survival decreased with laying date but the pattern did not change over time. In contrast, prefledging survival was not affected by laying date initially but declined in nests initiated late in the season toward the end of the study. Overall, nests initiated close to the population mean showed little temporal change for most components of reproductive success and seem to be less affected by environmental change than nests initiated early and late in the season.
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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 | 0.000 |
| 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".