The cost of being valuable: predictors of extinction risk in marine invertebrates exploited as luxury seafood
Bibliographic record
Abstract
Extinction risk has been linked to biological and anthropogenic variables. Prediction of extinction risk in valuable fauna may not follow mainstream drivers when species are exploited for international markets. We use results from an International Union for Conservation of Nature Red List assessment of extinction risk in all 377 known species of sea cucumber within the order Aspidochirotida, many of which are exploited worldwide as luxury seafood for Asian markets. Extinction risk was primarily driven by high market value, compounded by accessibility and familiarity (well known) in the marketplace. Extinction risk in marine animals often relates closely to body size and small geographical range but our study shows a clear exception. Conservation must not lose sight of common species, especially those of high value. Greater human population density and poorer economies in the geographical ranges of endangered species illustrate that anthropogenic variables can also predict extinction risks in marine animals. Local-level regulatory measures must prevent opportunistic exploitation of high-value species. Trade agreements, for example CITES, may aid conservation but will depend on international technical support to low-income tropical countries. The high proportion of data deficient species also stresses a need for research on the ecology and population demographics of unglamorous invertebrates.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".