Mixed stock analysis of a resident green turtle, Chelonia mydas, population in New Caledonia links rookeries in the South Pacific
Bibliographic record
Abstract
Context Migratory species are known to pose a challenge for conservation because it is essential to understand their complex life history in order to implement efficient conservation actions. Aims In New Caledonia, large seagrass habitats in the Grand Lagon Sud (GLS) are home to resident green turtles (Chelonia mydas) of unknown origins. To assess the stock composition in the GLS, 164 foraging turtles were sampled for genetic analysis of ~770 base pairs of the mitochondrial DNA (mtDNA) control region. Methods Foraging turtles ranging in size from 48.0 to 108.4 cm curved carapace length were captured at five different sites within the GLS between September 2012 and December 2013. To provide baseline data for mixed stock analysis, published data from rookeries were used in addition to 105 samples collected at rookeries in the d’Entrecasteaux Islands and Chesterfield Islands in New Caledonia and at Malekula Island in Vanuatu. Exact tests of population differentiation and pairwise FST estimates were used to test for differences in mtDNA haplotype frequencies. Key results These analyses indicated that rookeries in the d’Entrecasteaux Islands and Vanuatu form unique management units and that the Chesterfield Islands rookeries are linked to the Coral Sea management unit. Mixed stock analysis indicated the highest proportion (mean = 0.63) of foraging turtles originate from the d’Entrecasteaux stock. Conclusions The larger contribution is estimated to be from a large rookery from New Caledonia, but smaller contributions are suggested from other rookeries in the South Pacific. Implications Marine conservation policies in New Caledonia need to consider the links between the foraging and nesting populations of C. mydas in New Caledonia and other rookeries and foraging grounds in the Coral Sea.
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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 | 0.001 |
| 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".