Corticosterone and timing of migratory departure in a songbird
Bibliographic record
Abstract
Bird migration entails replenishing fuel stores at stopover sites. There, individuals make daily decisions whether to resume migration, and must also decide their time of departure. Variation in departure timing affects the total time required to complete a migratory journey, which in turn affects fitness through arrival time at the breeding and wintering grounds. It is well established that stopover departure decisions are based on cues from innate rhythms, intrinsic factors and extrinsic factors. Yet, virtually nothing is known about the physiological mechanism(s) linking these cues to departure decisions. Here, we show for a nocturnal migratory songbird, the northern wheatear (Oenanthe oenanthe), that baseline corticosterone levels of birds at stopover increased both over the migratory season and with wind assistance towards the migratory destination. Corticosterone in turn predicted departure probability; individuals with high baseline corticosterone levels were more likely to resume migration on a given night. Corticosterone further predicted the departure time within the night, with high baseline levels being associated with early departures. These novel findings indicate that corticosterone may be mediating between departure cues and the timing of departure from a stopover site, which is a major step towards understanding the hormonal control of animal migration.
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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".