Citizen science surveys elucidate key foraging and nesting habitat for two endangered marine turtle species within the Republic of Maldives
Bibliographic record
Abstract
We used a citizen science-based data collection protocol to investigate foraging and nesting marine turtle populations in the Republic of Maldives. With the aid of citizen scientists, we collected nine months of data covering 12.5 % of the country, increasing the available sightings and nesting data by ~2,000 %. Data indicated that the Maldives are an important foraging habitat for juvenile and adult green ( Chelonia mydas ) and hawksbill ( Eretmochelys imbricata ) turtles, though very few adult males of either species were reported. Hawksbill turtles were the more commonly sighted species in all but one surveyed atoll, and Maldivian beaches appeared to host more green turtle nesting sites. The large number of juvenile hawksbills noted in Baa atoll, a UNESCO Biosphere Reserve, may signal faster recovery from decades of exploitation compared to other regions of the Maldives. Lhaviyani atoll appears to be an adult green turtle foraging hotspot that could warrant additional legal protection. This study provided the first estimate of green turtle nest development time and hatching success in the Maldives. Our method controlled for the data’s temporal structure and accounted for spatiotemporal differences in survey effort, allowing for the most accurate assessment of turtle distribution possible. Our results indicated that a citizen science approach can be a fast, effective way of expanding the spatiotemporal extent of a monitoring dataset and engaging the public in endangered species monitoring. Additionally, our data were used by the government to support a policy change regarding the protection of sea turtles in the Maldives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".