Leaving the fish bowl: the ornamental trade as a global vector for freshwater fish invasions
Bibliographic record
Abstract
Despite its social and economic benefits, the trade in ornamental species (henceforth, ‘ornamental trade’) has become a major source of non-native fish introductions into freshwater ecosystems. However, the ornamental trade as a vector for introductions of non-native freshwater fishes is not well defined. We developed a framework incorporating elements of the biological invasion process and a typical ornamental fish trade supply chain to fill this gap. Records of non-native ornamental fishes introduced to freshwater environments of Australia, Belgium (Flanders), Canada (British Columbia), China (Guangdong), the Philippines, Poland, Singapore, the United Kingdom (England), and the United States of America (Florida) were reviewed to explore the pervasiveness of these introduced fishes in the wild. These regional case studies confirmed the prominence of the ornamental trade as a global vector for freshwater fish introductions beyond their natural range. Additionally, we examined freshwater fishes associated with the ornamental trade to identify ‘risky’ species that could establish in recipient regions based on climate match. All regions assessed were at risk of new fish introductions via the ornamental trade, with the number of ‘risky’ species ranging from seven to 256. Further, there appears to be taxonomic bias in the freshwater ornamental fish trade, with 74% of the species belonging to just 10 families (of 67). Current prevention and management approaches and associated polices, regulations and legislation on aquatic non-native species within assessed regions fit five general categories: import controls, risk assessment, whitelist, blacklist, and release ban. However, these prevention/management efforts may not be sufficient to reduce the invasion risk associated with the ornamental fish trade. Recommendations including species- and vector-based risk assessments, better recording of species import consignments, increased public education and industry engagement, and early detection and rapid response are discussed in this review.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".