Bearing all Down Under: the role of Australasian countries in the illegal bear trade
Bibliographic record
Abstract
Context Illegal wildlife trade (IWT) is a leading concern for conservation and biosecurity agencies globally, and involves multiple source, transit, and destination countries smuggling species on a transnational scale. The contribution of non-range countries for driving demand in IWT is often overlooked. Aims We analysed the dynamics (source, type and quantity) of bear seizures in Australia and New Zealand to gain a deeper understanding of the IWT, and to raise awareness among enforcement agencies for mitigating the international smuggling of bear parts and derivatives, and reducing the global threat to bears from illegal exploitation. Methods We collated biosecurity and conservation enforcement agency records of CITES seizures from Australia and New Zealand. All of the seizures were declared for ‘personal use’. Key results We report on 781 seizures of bear parts and derivatives in Australia and New Zealand from 33 countries over the past decade. The majority of seizures were medicinal (gall bladder and bile) products, but also included a range of body parts, hunting trophies and meat. China was the source of the greatest number of seizures, however, 32 additional source and transit countries/territories (from Asia, Europe, Americas, Middle East and Africa) were also involved in the seizures of bear parts and their derivatives. Conclusions The widespread trade of bears is an example of the far-reaching consequences commercial use can have on threatened species. Australia and New Zealand have no native bear species, and yet are frequently involved in wildlife seizures, and illegal bear trade continues to be an enforcement issue. Implications IWT has a detrimental impact on the conservation of bears. Conservation research in non-range countries needs to be conducted to determine the demand and threats from IWT, and to increase collaborative strategies to counter transnational smuggling.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".