Trends in Reproductive Indicators of Green and Hawksbill Sea Turtles over a 30-Year Monitoring Period in the Southern Gulf of Mexico and Their Conservation Implications
Bibliographic record
Abstract
Long-term monitoring programs of species at risk are efficacious tools to assess population changes, evaluate conservation strategies, and improve management practices to ensure populations reach levels at which they can fulfill their ecological roles. For sea turtles, annual nesting beach surveys are the most accessible method to estimating the population abundance and reproductive output, especially when these are done in primary nesting sites. However, little data exist on the long-term assessment of these parameters. Here, we present the trends of the nest abundance, female size, hatching, and emergence success of hawksbill (Eretmochelys imbricata) and green (Chelonia mydas) turtles at key nesting beaches in the southern Gulf of Mexico over 31 years (from 1990 to 2021). The nest abundance showed an increasing trend in both species as a result of the sustained protection and conservation effort, but there was no significant temporal trend in the annual female size, clutch size, hatching, and emergence success. However, these indicators showed decreasing mean values over the last decade and should be closely monitored. We suggest these decreases link to the combined effects of ocean warming and anthropogenic pressures affecting the sea turtle foraging grounds. Aside from protecting key nesting sites, protecting and restoring crucial foraging habitats should be an immediate priority requiring international cooperation.
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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) | 0.000 | 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 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".