Foraging flexibility and search patterns are unlinked during breeding in a free-ranging seabird
Bibliographic record
Abstract
In order to maximize foraging efficiency in a varying environment, predators are expected to optimize their search strategy. Environmental conditions are one important factor affecting these movement patterns, but variations in breeding constraints (self-feeding vs. feeding young and self-feeding) during different breeding stages (incubation vs. chick-rearing) are often overlooked, so that the mechanisms responsible for such behavioral shifts are still unknown. Here, to test how search patterns are affected at different breeding stages and to explore the proximate causes of these variations, we deployed data loggers to record both position (global positioning system) and dive activity (time–depth recorders) of a colonial breeding seabird, the razorbill Alca torda . Over a period of 3 years, our recordings of 56 foraging trips from 18 breeders show that while there is no evidence for individual route fidelity, razorbills exhibit higher foraging flexibility during incubation than during chick rearing, when foraging becomes more focused on an area of high primary productivity. We further show that this behavioral shift is not due to a shift in search patterns, as reorientations during foraging are independent of breeding stage. Our results suggest that foraging flexibility and search patterns are unlinked, perhaps because birds can read cues from their environment, including conspecifics, to optimize their foraging efficiency.
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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 | 0.001 | 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) | 0.000 | 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".