Seabird Migration Strategies: Flight Budgets, Diel Activity Patterns, and Lunar Influence
Bibliographic record
Abstract
Every year, billions of birds undertake extensive migrations between breeding and non-breeding areas, facing challenges that require behavioural adjustments, particularly to flight timing and duration. Such adjustments in daily activity patterns and the influence of extrinsic factors (e.g., environmental conditions, moonlight) have received much more research attention in terrestrial than marine migrants. Taking advantage of the widespread deployment in recent decades of combined light-level geolocator-immersion loggers, we investigated diel organisation and influence of the moon on flight activities during the non-breeding season of 21 migrant seabird species from a wide taxonomic range (6 families, 3 orders). Migrant seabirds regularly stopped (to either feed or rest) during migration, unlike some terrestrial and wetland birds which fly non-stop. We found an overall increase for most seabird species in time in flight and, for several species, also in flight bout duration, during migration compared to when resident at the non-breeding grounds. Additionally, several nocturnal species spent more of the day in flight during migration than at non-breeding areas, and vice versa for diurnal species. Nocturnal time in flight tended to increase during full moon, both during migration and at the non-breeding grounds, depending on species. Our study provides an extensive overview of activity patterns of migrant seabirds, paving the way for further research on the underlying mechanisms and drivers.
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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 | 0.001 |
| 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".