The U.S. market for imported wildlife not listed in the CITES multilateral treaty
Bibliographic record
Abstract
The international wildlife trade presents severe conservation and environmental security risks, yet no international regulatory framework exists to monitor the trade of species not listed in the appendices of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). We explored the composition and dynamics of internationally regulated versus nonregulated trade, with a focus on importations of wild-caught terrestrial vertebrates entering the United States from 2009 to 2018. We used 10 years of species-level trade records of the numbers of live, wild-caught animals imported to the United States and data on International Union for the Conservation of Nature (IUCN) estimates of extinction risk to determine whether there were differences in the diversity, abundance, and risk to extinction among imports of CITES-listed versus unlisted species. We found 3.6 times the number of unlisted species in U.S. imports compared with CITES-listed species (1366 vs. 378 species). The CITES-listed species were more likely to face reported conservation threats relative to unlisted species (71.7% vs. 27.5%). However, 376 unlisted species faced conversation threats, 297 species had unknown population trends, and 139 species were without an evaluation by the IUCN. Unlisted species appearing for the first time in records were imported 5.5 times more often relative to CITES-listed species. Unlisted reptiles had the largest rate of entry, averaging 53 unique species appearing in imports for the first time per year. Overall trade quantities were approximately 11 times larger for imports of unlisted species relative to imports of CITES-listed species. Countries that were top exporters of CITES-listed species were mostly different from exporters of unlisted species. Because of the vulnerabilities of unlisted, traded species entering the United States and increasing global demand, we strongly recommend governments adapt their policies to monitor and report on the trade of all wildlife.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".