When pets become pests: the role of the exotic pet trade in producing invasive vertebrate animals
Bibliographic record
Abstract
The annual trade in exotic vertebrates as pets is a multi‐billion‐dollar global business. Thousands of species, and tens of millions of individual animals, are shipped both internationally and within countries to satisfy this demand. Most research on the exotic pet trade has focused on its contribution to native biodiversity loss and disease spread. Here, we synthesize information across taxa and research disciplines to document the exotic pet trade's contribution to vertebrate biological invasions. We show recent and substantial worldwide growth in the number of non‐native animal populations introduced via this invasion pathway, which demonstrates a strong potential to increase the number of invasive animals in the future. Key to addressing the invasion threat of exotic pets is learning more about the socioeconomic forces that drive the massive growth in the exotic pet market and the socioecological factors that underlie pet release by owners. These factors likely vary according to cultural pet‐keeping traditions across regions and whether purchases were legal or illegal. These gaps in our understanding of the exotic pet trade must be addressed in order to implement effective policy solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".