Nesting dynamics of hawksbill and leatherback turtles: a four-year photo-identification study in Martinique
Bibliographic record
Abstract
One major limitation in conservation studies is accurately estimating population size to adapt management efforts. Thus, avoiding individual duplicate counts is essential to prevent any overestimation of population size. Photo-identification (photo-ID) offers a low cost and non-invasive alternative for monitoring migratory animals, and yet, it remains generally under-implemented in marine species. In this study, we applied photo-ID with sea-turtle populations in the French Antilles for the first time, thereby contributing to global population survey efforts in the Caribbean while minimising stress or harm to turtles. We focussed on two species of concern, Dermochelys coriacea (leatherback) and Eretmochelys imbricata (hawksbill), identified through a semi-automated recognition method to analyse their nesting behaviour. Our multi-annual survey involved 5292 h of night monitoring across three Martinique beaches over four years, yielding valuable data on nesting behaviours, population dynamics and conservation needs. We recorded 57 occurrences of leatherback turtles with a recapture rate of 61%, and 314 hawkbill observations with a recapture rate of 36%. The microhabitat of each nest was recorded, providing insights on nesting site preferences. Additionally, leatherbacks exhibited a longer time interval between their arrival on the beach and the start of nesting activity compared to hawksbills. These results reveal significant behavioural differences and specific nesting habits underscoring the potential of expanding photo-ID combined with ecological analysis, as a valuable resource for the conservation management of threatened sea-turtle species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".