Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic
Bibliographic record
Abstract
Somatic growth is an integrated, individual-based response to environmental conditions, especially in ectotherms. Growth dynamics of large, mobile animals are particularly useful as bio-indicators of environmental change at regional scales. We assembled growth rate data from throughout the West Atlantic for green turtles, Chelonia mydas, which are long-lived, highly migratory, primarily herbivorous mega-consumers that may migrate over hundreds to thousands of kilometers. Our dataset, the largest ever compiled for sea turtles, has 9690 growth increments from 30 sites from Bermuda to Uruguay from 1973 to 2015. Using generalized additive mixed models, we evaluated covariates that could affect growth rates; body size, diet, and year have significant effects on growth. Growth increases in early years until 1999, then declines by 26% to 2015. The temporal (year) effect is of particular interest because two carnivorous species of sea turtles-hawksbills, Eretmochelys imbricata, and loggerheads, Caretta caretta-exhibited similar significant declines in growth rates starting in 1997 in the West Atlantic, based on previous studies. These synchronous declines in productivity among three sea turtle species across a trophic spectrum provide strong evidence that an ecological regime shift (ERS) in the Atlantic is driving growth dynamics. The ERS resulted from a synergy of the 1997/1998 El Niño Southern Oscillation (ENSO)-the strongest on record-combined with an unprecedented warming rate over the last two to three decades. Further support is provided by the strong correlations between annualized mean growth rates of green turtles and both sea surface temperatures (SST) in the West Atlantic for years of declining growth rates (r = -.94) and the Multivariate ENSO Index (MEI) for all years (r = .74). Granger-causality analysis also supports the latter finding. We discuss multiple stressors that could reinforce and prolong the effect of the ERS. This study demonstrates the importance of region-wide collaborations.
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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 | 0.000 | 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".