Coping with the commute: behavioural responses to wind conditions in a foraging seabird
Bibliographic record
Abstract
Movement is a necessary yet energetically expensive process for motile animals. Yet how individuals modify their behaviour to take advantage of environmental conditions and hence optimise energetic costs during movement remains poorly understood. This is especially true for animals that move through environments where they cannot easily be observed. We examined the behaviour during commuting flights of black-legged kittiwakes Rissa tridactyla breeding on Middleton Island, Alaska in relation to wind conditions they face. By simultaneously deploying GPS and accelerometer devices on incubating birds we were able to quantify the timing, destination, course and speed of flights during commutes to foraging patches, as well as how wing beat frequency and strength relate to flight speeds. We found that kittiwakes did not preferentially fly in certain wind conditions. However, once in the air they exhibited plasticity by increasing their air speed (the speed at which they fly relative to the wind) when travelling into headwinds and decreasing their air speed when flying with tailwinds. This strategy maximises flight range, whereby the greatest air distance is covered per unit of energy expenditure. Furthermore, we identified a biomechanical link behind this behaviour: that to achieve these changes in flight speeds, kittiwakes altered their wing beat strength, but not wing beat frequency. Using this information, we demonstrate that the cost of flying into a headwind outweighs the benefit of flying with a tailwind of equivalent speed, therefore exploiting a tailwind when commuting to a foraging patch would not be beneficial if having to return in the same direction with the same conditions. Our findings suggest that extrinsic factors, such as prey availability, have a more influential role in determining when and where birds fly during foraging trips than do wind conditions. However, once flying, kittiwakes exhibit behavioural plasticity to minimise transport costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".