Site specialists, diet generalists? Isotopic variation, site fidelity, and foraging by loggerhead turtles in Shark Bay, Western Australia
Bibliographic record
Abstract
Stable isotope data are useful for inferring foraging and niche variation in marine taxa but can be difficult to interpret, in part because different foraging patterns may result in similar isotopic values. Here, we integrate stable isotope analysis (δ13C and δ15N) with behavioral data to investigate the foraging ecology of loggerhead turtles Caretta caretta on a feeding ground in Shark Bay, Western Australia. Large loggerhead turtles showed little among-individual isotopic variance in skin samples, suggesting similar foraging or habitat use patterns over several months or more. Analysis of loggerhead foraging in video data, and comparison with isotopic variance for sympatric green turtles Chelonia mydas, suggest that low isotopic variance among large loggerheads reflects a similar, highly generalized diet within individuals. Higher isotopic variance among smaller turtles may reflect variation in diet, timing of recruitment to neritic habitat or use of food webs varying along other isotopic gradients. Loggerheads showed strong fidelity to the study site over many years, and individuals recaptured frequently showed remarkable affinity for very small geographic areas, often <5 km2. Thus, a substantial proportion of the Shark Bay loggerhead population comprises site specialists, with larger adults appearing to be diet generalists. Our results also suggest that among-individual isotopic variation found at some loggerhead nesting locations may reflect the isotopic characteristics of preferred migratory or foraging grounds owing to long-term site fidelity and less likely reflects prey specialization by individuals within specific feeding areas.
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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".