Feather corticosterone reveals effect of moulting conditions in the autumn on subsequent reproductive output and survival in an Arctic migratory bird
Bibliographic record
Abstract
For birds, unpredictable environments during the energetically stressful times of moulting and breeding are expected to have negative fitness effects. Detecting those effects however, might be difficult if individuals modulate their physiology and/or behaviours in ways to minimize short-term fitness costs. Corticosterone in feathers (CORTf) is thought to provide information on total baseline and stress-induced CORT levels at moulting and is an integrated measure of hypothalamic-pituitary-adrenal activity during the time feathers are grown. We predicted that CORTf levels in northern common eider females would relate to subsequent body condition, reproductive success and survival, in a population of eiders nesting in the eastern Canadian Arctic during a capricious period marked by annual avian cholera outbreaks. We collected CORTf data from feathers grown during previous moult in autumn and data on phenology of subsequent reproduction and survival for 242 eider females over 5 years. Using path analyses, we detected a direct relationship between CORTf and arrival date and body condition the following year. CORTf also had negative indirect relationships with both eider reproductive success and survival of eiders during an avian cholera outbreak. This indirect effect was dramatic with a reduction of approximately 30% in subsequent survival of eiders during an avian cholera outbreak when mean CORTf increased by 1 standard deviation. This study highlights the importance of events or processes occurring during moult on subsequent expression of life-history traits and relation to individual fitness, and shows that information from non-destructive sampling of individuals can track carry-over effects across seasons.
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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".