Does Prey Capture Induce Area‐Restricted Search? A Fine‐Scale Study Using GPS in a Marine Predator, the Wandering Albatross
Bibliographic record
Abstract
In a patchy environment, predators are expected to increase turning rate and start an area-restricted search (ARS) when prey have been encountered, but few empirical data exist for large predators. By using GPS loggers with devices measuring prey capture, we studied how a marine predator adjusts foraging movements at various scales in relation to prey capture. Wandering albatrosses use two tactics, sit and wait and foraging in flight, the former tactic being three times less efficient than the latter. During flight foraging, birds caught large isolated prey and used ARS at scales varying from 5 to 90 km, with large-scale ARS being used only by young animals. Birds did not show strong responses to prey capture at a large scale, few ARS events occurred after prey capture, and birds did not have high rates of prey capture in ARS. Only at small scales did birds increase sinuosity after prey captures for a limited time period, and this occurred only after they had caught a large prey item within an ARS zone. When this species searches over a large scale, the most effective search rule was to follow a nearly straight path. ARS may be used to restrict search to a particular environment where prey capture is more predictable and profitable.
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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 | 0.000 | 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".