LINKING MOVEMENT, DIVING, AND HABITAT TO FORAGING SUCCESS IN A LARGE MARINE PREDATOR
Bibliographic record
Abstract
Establishing where and when predators forage is essential to understanding trophic interactions, yet foraging behavior remains poorly understood in large marine carnivores. We investigated the factors leading to foraging success in gray seals (Halichoerus grypus) in the Northwest Atlantic in the first study to use simultaneous deployments of satellite transmitters, time depth recorders, and stomach-temperature loggers on a free-ranging marine mammal. Thirty-two seals were each fitted with the three types of instrumentation; however, complete records from all three instruments were obtained from only 13 individuals, underscoring the difficulty of such a multi-instrument approach. Our goal was to determine the characteristics of diving, habitat, and movement that predict feeding. We linked diving behavior to foraging success at two temporal scales: trips (days) and bouts (hours) to test models of optimal diving, which indicate that feeding can be predicted by time spent at the bottom of a dive. Using an information-theoretic approach, a Generalized Linear Mixed Model with trip duration and accumulated bottom time per day best explained the number of feeding events per trip, whereas the best predictor of the number of feeding events per bout was accumulated bottom time. We then tested whether characteristics of movement were predictive of feeding. Significant predictors of the number of feeding events per trip were angular variance (i.e., path tortuosity) and distance traveled per day. Finally, we integrated measures of diving, movement, and habitat at four temporal scales to determine overall predictors of feeding. At the 3-h scale, mean bottom time and distance traveled were the most important predictors of feeding frequency, whereas at the 6-h and 24-h time scales, distance traveled alone was most important. Bathymetry was the most significant predictor of feeding at the 12-h interval, with feeding more likely to occur at deeper depths. Our findings indicate that several factors predict feeding in gray seals, but predictor variables differ across temporal scales such that environmental variation becomes important at some scales and not others. Overall, our results illustrate the value of simultaneously recording and integrating multiple types of information to better understand the circumstances leading to foraging success.
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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 | 0.002 |
| 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 | 0.000 | 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".