Fine‐Scale Movement Data Reveal Primarily Surface Foraging and Nocturnal Flight Activity in the Endangered Bermuda Petrel
Bibliographic record
Abstract
ABSTRACT Foraging behaviour plays a fundamental role in animal fitness and population dynamics., particularly for central‐place foragers like breeding seabirds. Among Procellariiform seabirds, petrels exhibit a wide range of foraging strategies finely tuned to the patchy and unpredictable distribution of resources. The extent and remote nature of their foraging grounds makes direct observation of foraging behaviour impractical, thereby requiring the use of remote tracking technologies. We deployed miniaturised multi‐sensor biologgers and collected fine scale movement data to investigate the at‐sea behaviours of the Bermuda petrel Pterodroma cahow , a poorly studied and highly threatened gadfly petrel, specialised on mesopelagic prey. GPS‐tracking data revealed extensive foraging trips (mean ± SD: 1207 ± 305 km), in consistent directions, over remote oceanic regions. Time‐depth‐recorders provided new insights into Bermuda petrel feeding techniques suggesting that the meso‐bathypelagic prey targeted by petrels must be available in the very upper layer of the water surface, given their very limited diving activity (maximum dive depth of 1.57 m). We identified three flight‐related and three water‐associated behaviours using supervised classification approach to classify behaviour from tri‐axial acceleromtetry. Flying behaviours reflected the expected dynamic soaring flight strategy of Procellariiformes; individuals spent more than 75% of their time in flight (dynamic soaring and flap‐gliding) with dynamic soaring flight being the most common behaviour under all conditions. The behaviour classified as ‘Intensive flight’ was infrequently observed but could indicate aerial dipping, a characteristic foraging technique of Pterodroma species. The remaining time was spent in three water behaviours: active, inactive and intensive, with the latter being less common but thought to reflect scavenging and prey seizing. Flight increased during dusk and in the night, highlighting greater flight activity during night compared to the day, while water behaviours were more common during the day. While some of our findings may require further validation to confirm their relevance to foraging behaviour, our work offers new and valuable insights to consider when assessing the ecological needs of this endangered species and its potential vulnerability to offshore anthropogenic activities.
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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 | 0.001 | 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".