Prey-dependent foraging tactics and prey profitability in a marine mammal
Bibliographic record
Abstract
Predators face decisions about which prey to include in their diet in order to maximize fitness. The foraging tactics used to capture prey and the resulting profitability of prey influence these decisions. We present the first evidence of prey-dependent foraging tactics and prey profitability in a free-ranging pinniped. We studied 39 adult male harbour seals Phoca vitulina at Sable Island, Nova Scotia using an animal-borne video system. Each male wore the camera system for 3 d during which 10 min video samples were recorded every 45 min from 06:00 h, resulting in approximately 3 h of videotape per male and a total of 1094 capture attempts of identified prey. Males foraged mainly on sand lance Ammodytes dubius and flounders (Pleuronectids), but salmonid and gadoid fishes were occasionally pursued. Foraging tactics differed among and within prey types based on differences in prey behaviour. Sand lance was both a cryptic prey, when in the bottom substrate, and a conspicuous schooling prey. Seal swimming speed, handling time and capture success differed between cryptic and conspicuous sand lance. The highest capture success and handling time was recorded for flounders. Estimated profitability, i.e. net energy intake per unit time, also differed with prey type and prey size. Our results suggest that diet selection may have important implications on the foraging energetics of pinnipeds.
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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".