Do non‐native ornamental fishes pose a similar level of invasion risk in neighbouring regions of similar current and future climate? Implications for conservation and management
Bibliographic record
Abstract
Abstract Global trade in non‐native ornamental species coupled with high connectivity among countries is well known to result in worldwide biological invasions, which pose challenges for the conservation and management of biodiversity. There are few studies aimed at implementing management strategies that have examined differences in the potential invasiveness of non‐native species between neighbouring political regions within the same ecoregion. To compare the potential risk of invasiveness of non‐native ornamental fishes with high commercial value in the river basins of two neighbouring regions of East and Southeast Asia, 32 extant and horizon species were screened with the aquatic species invasiveness screening kit (as‐isk) for the lower Pearl River basin (South China) and the Chao Phraya River basin (Thailand). Both regional (i.e. basin level) and combined risk‐ranking thresholds were determined by receiver operating characteristic curve analysis. Of the 32 species screened, 14 were categorized as posing a high risk and seven were categorized as posing a medium risk of being invasive in both regions, under current and future climate conditions. These species have a history of invasiveness and the climate of their native ranges is similar to that of the two study regions. Pterygoplichthys pardalis received the highest risk score in both regions. The risk ranks of 11 species differed between the two regions, based on either the combined or regional thresholds, and this was partly related to the different risk of invasiveness between the two regions, coupled with cognitive subjectivity among the assessors. The results of the present study suggest that the invasion of non‐native ornamental fish species could pose similar threats to biodiversity in neighbouring regions. This will serve to inform policy makers of neighbouring countries in the development of coordinated, mutually beneficial regulations and management strategies to enhance the conservation of native species.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".