Determining the sustainability of legal wildlife trade
Bibliographic record
Abstract
Exploitation of wildlife represents one of the greatest threats to species survival according to the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Whilst detrimental impacts of illegal trade are well recognised, legal trade is often equated to being sustainable despite the lack of evidence or data in the majority of cases. We review the sustainability of wildlife trade, the adequacy of tools, safeguards, and frameworks to understand and regulate trade, and identify gaps in data that undermine our ability to truly understand the sustainability of trade. We provide 183 examples showing unsustainable trade in a broad range of taxonomic groups. In most cases, neither illegal nor legal trade are supported by rigorous evidence of sustainability, with the lack of data on export levels and population monitoring data precluding true assessments of species or population-level impacts. We propose a more precautionary approach to wildlife trade and monitoring that requires those who profit from trade to provide proof of sustainability. We then identify four core areas that must be strengthened to achieve this goal: (1) rigorous data collection and analyses of populations; (2) linking trade quotas to IUCN and international accords; (3) improved databases and compliance of trade; and (4) enhanced understanding of trade bans, market forces, and species substitutions. Enacting these core areas in regulatory frameworks, including CITES, is essential to the continued survival of many threatened species. There are no winners from unsustainable collection and trade: without sustainable management not only will species or populations become extinct, but communities dependent upon these species will lose livelihoods.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".