Windscapes shape seabird instantaneous energy costs but adult behavior buffers impact on offspring
Bibliographic record
Abstract
BACKGROUND: Windscapes affect energy costs for flying animals, but animals can adjust their behavior to accommodate wind-induced energy costs. Theory predicts that flying animals should decrease air speed to compensate for increased tailwind speed and increase air speed to compensate for increased crosswind speed. In addition, animals are expected to vary their foraging effort in time and space to maximize energy efficiency across variable windscapes. RESULTS: We examined the influence of wind on seabird (thick-billed murre Uria lomvia and black-legged kittiwake Rissa tridactyla) foraging behavior. Airspeed and mechanical flight costs (dynamic body acceleration and wing beat frequency) increased with headwind speed during commuting flights. As predicted, birds adjusted their airspeed to compensate for crosswinds and to reduce the effect of a headwind, but they could not completely compensate for the latter. As we were able to account for the effect of sampling frequency and wind speed, we accurately estimated commuting flight speed with no wind as 16.6 ms(?1) (murres) and 10.6 ms(?1) (kittiwakes). High winds decreased delivery rates of schooling fish (murres), energy (murres) and food (kittiwakes) but did not impact daily energy expenditure or chick growth rates. During high winds, murres switched from feeding their offspring with schooling fish, which required substantial above-water searching, to amphipods, which required less above-water searching. CONCLUSIONS: Adults buffered the adverse effect of high winds on chick growth rates by switching to other food sources during windy days or increasing food delivery rates when weather improved.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".