Climatic effects on the breeding phenology and reproductive success of an arctic‐nesting goose species
Bibliographic record
Abstract
Abstract Climate warming is pronounced in the Arctic and migratory birds are expected to be among the most affected species. We examined the effects of local and regional climatic variations on the breeding phenology and reproductive success of greater snow geese (Chen caerulescens atlantica), a migratory species nesting in the Canadian Arctic. We used a long‐term dataset based on the monitoring of 5447 nests and the measurements of 19 234 goslings over 16 years (1989–2004) on Bylot Island. About 50% of variation in the reproductive phenology of individuals was explained by spring climatic factors. High mean temperatures and, to a lesser extent, low snow cover in spring were associated with an increase in nest density and early egg‐laying and hatching dates. High temperature in spring and high early summer rainfall were positively related to nesting success. These effects may result from a reduction in egg predation rate when the density of nesting geese is high and when increased water availability allows females to stay close to their nest during incubation recesses. Summer brood loss and production of young at the end of the summer increased when values of the summer Arctic Oscillation (AO) index were either very positive (low temperatures) or very negative (high temperatures), indicating that these components of the breeding success were most influenced by the regional summer climate. Gosling mass and size near fledging were reduced in years with high spring temperatures. This effect is likely due to a reduced availability of high quality food in years with early spring, either due to food depletion resulting from high brood density or a mismatch between hatching date of goslings and the timing of the peak of plant quality. Our analysis suggests that climate warming should advance the reproductive phenology of geese, but that high spring temperatures and extreme values of the summer AO index may decrease their reproductive success up to fledging.
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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 | 0.001 | 0.001 |
| 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".