The Effect of Moonlight on Scopoli's Shearwater<i>Calonectris diomedea</i>Colony Attendance Patterns and Nocturnal Foraging: A Test of the Foraging Efficiency Hypothesis
Bibliographic record
Abstract
Abstract Moonlight is known to affect the nocturnal behaviour and activity rhythms of many organisms. For instance, predators active at night may take advantage from increased visibility afforded by the moon, while prey might regulate their activity patterns to become less detectable. Many species of pelagic seabirds attend their colony only at night, in complete darkness, avoiding approaching their nest sites under moonlight. This behaviour has been most often interpreted as an antipredator adaptation (‘predation avoidance’ hypothesis). However, it may also reflect a lower foraging efficiency during moonlit nights (‘foraging efficiency’ hypothesis). Indeed, moonlight may reduce prey availability because preferred seabird prey is known to occur at higher depths in moonlit nights. Using high‐accuracy behavioural information from data loggers, we investigated the effect of moonlight on colony attendance and at‐sea nocturnal foraging in breeding Scopoli's shearwatersCalonectris diomedea. We found that birds departing for self‐feeding trips around the full moon performed longer trips than those departing around the new moon. On nights when the moon was present only partly, nest burrow entrances took place largely in the moonless portion of the night. Moreover, contrary to predictions from the ‘foraging efficiency’ hypothesis, nocturnal foraging activity increased according to moonlight intensity, suggesting that birds increased their foraging activity when prey became more detectable. This study strengthens the idea that colony attendance behaviour is strictly controlled by moonlight in shearwaters, which is possibly related to the perception of a predation risk.
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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".