Bottom time does not always predict prey encounter rate in Antarctic fur seals
Bibliographic record
Abstract
Summary Optimal foraging models applied to breath‐holding divers predict that diving predators should optimize the time spent foraging at the bottom of dives depending on prey encounter rate, distance to prey patch (depth) and physiological constraints. We tested this hypothesis on a free‐ranging diving marine predator, the Antarctic fur seal Arctocephalus gazella, equipped with accelerometers or Hall sensors (n = 11) that recorded mouth‐opening events, a proxy for prey capture attempts and thus feeding events. Over the 5896 dives analysed (>15 m depth), the mean number of mouth‐opening events per dive was 1·21 ± 1·69 (mean ± SD). Overall, 82% of mouth‐openings occurred at the bottom of dives. As predicted, fur seals increased their inferred foraging time at the bottom of dives with increasing patch distance (depth), irrespective of the number of mouth‐openings. For dives shallower than 55 m, the mean bottom duration of dives without mouth‐openings was shorter than for dives with mouth‐opening events. However, this difference was only due to the occurrence of V‐shaped dives with short bottom durations (0 or 1 s). When removing those V‐shaped dives, bottom duration was not related to the presence of mouth‐openings anymore. Thus, the decision to abandon foraging is likely related to other information about prey availability than prey capture attempts (i.e. sensory cues) that seals collect during the descent phase. We did not observe V‐shaped dives for dives deeper than 55 m, threshold beyond which the mean dive duration exceeded the apparent aerobic dive limit. For dives deeper than 55 m, seals kept on foraging at bottom irrespective of the number of mouth‐openings performed. Most dives occurred at shallower depths (30–55 m) than the 60 m depth of highest foraging efficiency (i.e. of greatest number of mouth‐opening events per dive). This is likely related to physiological constraints during deeper dives. We suggest that foraging decisions are more complex than predicted by current theory and highlight the importance of the information collected by the predator during the descent as well as its physiological constraints. Ultimately, this will help establishing reliable predictive foraging models for marine predators based on diving patterns only. A lay summary is available for this article.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".