Where the leatherbacks roam: movement behavior analyses reveal novel foraging locations along the Northwest Atlantic shelf
Bibliographic record
Abstract
Leatherback sea turtles ( Dermochelys coriacea ) migrate along the east coast of the United States, traversing the South and Mid-Atlantic Bights (SAB and MAB) while traveling to and from well-known northern foraging areas off Southern New England (SNE) and Nova Scotia. However, there is limited information on leatherback movement behavior in these regions. To identify leatherback movement patterns, we fit hidden Markov models (HMMs) to satellite transmitter data from 52 leatherbacks tagged between 2017 and 2022 off the coasts of Massachusetts and North Carolina to estimate locations of area restricted searching (ARS) and transient behaviors. Depth-temperature profiles were then paired to locations associated with ARS behavior to understand the vertical use of the water column. We observed leatherbacks displaying ARS behavior in SNE as expected, but also in the MAB and SAB. The HMM results indicated that leatherbacks were primarily foraging in SNE between Nantucket and Long Island Sound and depth-temperature plots from ARS behavior on Nantucket Shoals implied turtles foraging throughout the entire water column. In the MAB, ARS behavior was concentrated between Cape Hatteras, North Carolina, and the mouth of Delaware Bay during the summer. Turtles were closely associated with a well-defined thermocline, but still appeared to dive to deeper cooler waters, which may be a sign of thermoregulatory behavior. There was evidence of foraging in the SAB along the coast as well as along the continental shelf edge. The ARS behavior we documented within the MAB and SAB is the first published empirical evidence that both areas may be important foraging grounds. Our results lay a path for future research to understand how leatherbacks use these areas and the potential anthropogenic threats encountered while moving through these regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".