Jellyfish distribution in space and time predicts leatherback sea turtle hot spots in the Northwest Atlantic
Bibliographic record
Abstract
Leatherback sea turtles (Dermochelys coriacea) migrate to temperate Canadian Atlantic waters to feed on gelatinous zooplankton ('jellyfish') every summer. However, the spatio-temporal connection between predator foraging and prey-field dynamics has not been studied at the large scales over which these migratory animals occur. We use 8903 tows of groundfish survey jellyfish bycatch data between 2006-2017 to reveal spatial jellyfish hot spots, and matched these data to satellite-telemetry leatherback data over time and space. We found highly significant overlap of jellyfish and leatherback distribution on the Scotian Shelf (r = 0.89), moderately strong correlations of jellyfish and leatherback spatial hot spots in the Gulf of St. Lawrence (r = 0.59), and strong correlations in the Bay of Fundy (r = 0.74), which supports much lower jellyfish density. Over time, jellyfish bycatch data revealed a slight northward range shift in the Gulf of St. Lawrence, consistent with gradual warming of these waters. Two-stage generalized linear modelling corroborated that sea surface temperature, year, and region were significant predictors of jellyfish biomass, suggesting a climate signal on jellyfish distribution, which may shift leatherback critical feeding habitat over time. These findings are useful in predicting dynamic habitat use for endangered leatherback turtles, and can help to anticipate large-scale changes in their distribution in response to climate-related changes in prey availability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".