Taking the Bite Out of Winter: Common Murres (Uria aalge) Push Their Dive Limits to Surmount Energy Constraints
Bibliographic record
Abstract
Diving seabirds that overwinter at high latitudes experience persistent cold exposure, shorter days and associated declines in ocean productivity that can challenge their ability to balance daily energy budgets. We used dive-immersion geo-locators to test the hypothesis that pursuit-diving Common murres (Uria aalge) will respond to the challenges of winter in the North Atlantic through increased daily energy expenditures (DEE) that will be met by increased foraging effort and adjustments in dive tactics. Largely flightless in winter ( 85% resting and swimming). Accordingly, when sea surface temperatures (SST) were consistently near freezing in late winter (1.9 ± 0.8 °C), mean DEE (2463.2 ± 10.9 kJ day-1) exceeded the theoretical limit to sustainable energy expenditure in vertebrates (i.e. 7 X Basal Metabolic Rate or 2450 kJ day-1 for murres). Consistently deep (70% > 50m) and long dives in late winter, 38% of which exceeded their calculated aerobic dive limit indicate that targeted prey was distributed in deep (dark) waters. Consequently, foraging was highly constrained to daylight hours; likely because capture efficiency of deep-water prey is very poor in low light. Murres responded to late winter time and energy constraints with a nearly 2-fold increase in daily time spent diving (95.2 ± 5.6 mins and 178.3 ± 6.3 mins day-1 during early and late winter, respectively), an increase in dive bout frequency and duration, and correspondingly less time resting between bouts. Uniquely adapted for deep-diving, pursuit-diving murres push their dive limits in order to maximize daily energy intake when energy demands are high and prey are distributed in deep water. This study highlights late winter as an extremely challenging phase in the annual cycle of North Atlantic murres and provides critical insights into the behavioral mechanisms underlying their winter survival.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".