Stable isotope analysis successfully identifies wild-caught individuals of threatened Asian freshwater turtles in illegal trade
Bibliographic record
Abstract
Laundering of wild-caught animals as captive-bred is a frequent practice in the illegal wildlife trade. Stable isotope analysis is a promising tool for distinguishing wild and captive-bred animals. We use Hong Kong freshwater turtles to test the effectiveness of using stable isotopes to differentiate wild and captive-bred individuals. In this study, we compared five stable isotope signatures (δ 13 C, δ 15 N, δ 34 S, δ 2 H, and δ 18 O) in claw samples across four highly threatened species: Cuora trifasciata , Mauremys reevesii , Platysternon megacephalum , and Sacalia bealei . We found non-overlapping δ 13 C and δ 15 N values for all species; combined δ 13 C and δ 15 N isotopic profiles resulted in a 100% accuracy in identifying the sources of turtles. Through repeated sampling of seized P. megacephalum , we estimate 95% turnover rates of 46.3 months for δ 13 C and 32.8 months for δ 15 N, suggesting that wild-caught individuals can be identified up to two years after capture. Lastly, we apply the stable isotope method in true wildlife seizures. These seizures are unique because some individuals possessed microchips from our long-term population study, so were unambiguously from the wild. The isotopic profiles of seized turtles clustered with those of wild populations, providing forensic evidence that supported the prosecution of suspects for illegal trade and/or possession. Overall, our study demonstrates the effectiveness of δ 13 C and δ 15 N in differentiating wild and captive freshwater turtles. We advocate for using isotopic profiling in future seizures and expanding its application to more taxa and geographic locations to support wildlife trade management and prevent illegal exploitation of wild organisms globally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".