Sailfish generate foraging opportunities for seabirds in multi-species predator aggregations
Bibliographic record
Abstract
While various marine predators form associations, the most commonly studied are those between subsurface predators and seabirds, with gulls, shearwaters or terns frequently co-occurring with dolphins, billfish or tuna. However, the mechanisms underlying these associations remain poorly understood. Three hypotheses have been proposed to explain the prevalence of these associations: (1) subsurface predators herd prey to the surface and make prey accessible to birds, (2) subsurface predators damage prey close to the surface and thereby provide food scraps to birds, and (3) attacks of underwater predators lower the cohesion of prey groups and thereby their collective defences making the prey easier to be captured by birds. Using drone footage, we investigated the interaction between Indo-Pacific sailfish ( Istiophorus platypterus ) and terns ( Onychoprion sp . ) preying on schooling fish off the eastern coast of the Malaysian peninsula. Through spatio-temporal analysis of the hunting behaviour of the two predatory species and direct measures of prey cohesion we showed that terns attacked when school cohesion was low, and that this decrease in cohesion was frequently caused by sailfish attacks. Therefore, we propose that sailfish created a by-product benefit for the bird species, lending support to the hypothesis that lowering cohesion can facilitate associations between subsurface predators and seabirds.
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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) | 0.000 | 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 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".