Legal trade of threatened marine species undermines conservation commitments
Bibliographic record
Abstract
The international trade of threatened marine species as seafood poses significant challenges for biodiversity conservation and undermines global sustainability goals. While illegal fishing contributes to these threats, many national and international policies permit the legal harvest and trade of threatened species, creating a fundamental conflict with conservation objectives.The International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides the world's most comprehensive assessment of species' conservation status and extinction risks. However, threats and species' extinction risks at regional levels can differ significantly from global assessments, leading many countries to develop their own national threatened species lists. For instance, the Orange Roughy is classified as "Vulnerable" on the IUCN Red List in Europe, but listed as "Endangered" under Australia's Environment Protection and Biodiversity Conservation (EPBC) Act.While previous studies have analyzed trade patterns using IUCN listings alone, incorporating national threatened species lists can provide a more complete picture of how international trade affects endangered species. Our research compiles national threatened species lists from around the world to examine how major seafood trading nations engage in trade of species listed as threatened under their own biodiversity conservation policies, and identifies the mechanisms that enable such trade. By analyzing the interaction between national conservation frameworks and international trade patterns, we identify critical gaps where trade practices conflict with domestic species protection policies. Our findings suggest specific targets for strengthening domestic conservation measures and highlight opportunities to better align international trade policies with biodiversity protection and sustainability goals.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".