Behavioural flexibility in an Arctic seabird using two distinct marine habitats to survive the energetic constraints of winter
Bibliographic record
Abstract
BACKGROUND: Homeothermic marine animals in Polar Regions face an energetic bottleneck in winter. The challenges of short days and cold temperatures are exacerbated for flying seabirds with small body size and limited fat stores. We use biologging approaches to examine how habitat, weather, and moon illumination influence behaviour and energetics of a marine bird species, thick-billed murres (Uria lomvia). METHODS: We used temperature-depth-light recorders to examine strategies murres use to survive winter in the Northwest Atlantic, where contrasting currents create two distinct marine habitats: cold (-0.1 ± 1.2 °C), shallower water along the Labrador Shelf and warmer (3.1 ± 0.3 °C), deep water in the Labrador Basin. RESULTS: In the cold shelf water, murres used a high-energy strategy, with more flying and less diving each day, resulting in high daily energy expenditure and also high apparent energy intake; this strategy was most evident in early winter when day lengths were shortest. By contrast, murres in warmer basin water employed a low-energy strategy, with less time flying and more time diving under low light conditions (nautical twilight and night). In warmer basin water, murres increased diving at night when the moon was more illuminated, likely taking advantage of diel vertically migrating prey. In warmer basin water, murres dove more at night and foraging efficiency increased under negative North Atlantic Oscillation (calmer ocean conditions). CONCLUSIONS: The proximity of two distinct marine habitats in this region allows individuals from a single species to use dual (low-energy/high-energy) strategies to overcome winter energy bottlenecks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".