Seasonal variability and individual consistency in gray seal (<i>Halichoerus grypus</i>) isotopic niches
Bibliographic record
Abstract
Although it is often assumed that individuals in generalist populations are equivalent, recent research indicates that individual dietary specialization can be common in marine predators. Gray seals (Halichoerus grypus (Fabricius, 1791)) were considered locally extinct in United States waters by 1958 but have since recolonized the region. Although considered generalists, less is known about gray seal foraging ecology in the United States. To address this, we used carbon and nitrogen stable isotope analyses to investigate the foraging niches of adult gray seals in Massachusetts, USA. We examined skin, fur, and blood components to investigate seasonal variability and individual consistency in foraging niches, and serially sampled vibrissae to quantify the degree of individual foraging specialization in this population. Our results suggest that seals shift from coastal foraging habitats before molt to offshore habitats after molt, with a coincident shift from higher to lower trophic-level prey. Adult gray seals also exhibited individual consistency in foraging niches independent of population-level shifts and reflect a generalist population composed of individual foraging specialists. These findings serve as a baseline for subsequent research on gray seals in United States waters that could help to determine the mechanisms which promote individual specialization in this population.
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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) | 0.001 | 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".