Combining information on otolith morphometrics and larval connectivity models to infer stock structure of Plectropomus leopardus in the Philippines
Bibliographic record
Abstract
The leopard coral grouper Plectropomus leopardus is a high-value fish species in the live reef fish for food trade (LRFFT), with stocks declining continually due to increasing market demands and unsustainable fishing practices. Managing this resource is difficult due to lack of biological information on local stocks. This paper utilized phenotypic- and model-based methods to discriminate stocks of P. leopardus in 3 LRFFT hubs in the Philippines (Taytay, Quezon, and Tawi-Tawi). Phenotypic variation among sites was assessed using shape descriptors and landmark data of otoliths, while patterns of connectivity were inferred from a dispersal model of coral grouper larvae. Inferences suggest (1) the presence of distinct phenotypic stocks and (2) limited larval connectivity among sites. There was an inconsistency with how otolith shape discriminated stocks. While shape descriptors identified Tawi-Tawi as a separate unit, landmark data differentiated Quezon from the other sites. This suggests that different processes may influence otolith shape, thereby presenting a caveat when using otolith morphometrics in stock discrimination. Meanwhile, the dispersal model showed that Quezon and Tawi-Tawi distribute larvae primarily to the West Philippine Sea and Celebes Sea, respectively. Although the model showed that Taytay supplies larvae to both Quezon and Tawi-Tawi, these connections were weak. Model inferences showing all sites as important larval sources to different reefs, coupled with the presence of distinct phenotypic stocks based on otolith shape, suggest that each LRFFT hub is an independent management unit. Thus, identifying key drivers of stock decline is crucial in developing site-specific fishery management approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".