Favorable spring conditions can buffer the impact of winter carryover effects on a key breeding decision in an Arctic‐breeding seabird
Bibliographic record
Abstract
Abstract The availability and investment of energy among successive life‐history stages is a key feature of carryover effects. In migratory organisms, examining how both winter and spring experiences carryover to affect breeding activity is difficult due to the challenges in tracking individuals through these periods without impacting their behavior, thereby biasing results. Using common eiders Somateria mollissima, we examined whether spring conditions at an Arctic breeding colony (East Bay Island, Nunavut, Canada) can buffer the impacts of winter temperatures on body mass and breeding decisions in birds that winter at different locations (Nuuk and Disko Bay, Greenland, and Newfoundland, Canada; assessed by analyzing stable isotopes of 13‐carbon in winter‐grown claw samples). Specifically, we used path analysis to examine how wintering and spring environmental conditions interact to affect breeding propensity (a key reproductive decision influencing lifetime fitness in female eiders) within the contexts of the timing of colony arrival, pre‐breeding body mass (body condition), and a physiological proxy for foraging effort (baseline corticosterone). We demonstrate that warmer winter temperatures predicted lower body mass at arrival to the nesting colony, whereas warmer spring temperatures predicted earlier arrival dates and higher arrival body mass. Both higher body mass and earlier arrival dates of eider hens increased the probability that birds would initiate laying (i.e., higher breeding propensity). However, variation in baseline corticosterone was not linked to either winter or spring temperatures, and it had no additional downstream effects on breeding propensity. Overall, we demonstrate that favorable pre‐breeding conditions in Arctic‐breeding common eiders can compensate for the impact that unfavorable wintering conditions can have on breeding investment, perhaps due to greater access to foraging areas prior to laying.
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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 | 0.001 | 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".