Threats to UK freshwaters under climate change: Commonly traded aquatic ornamental species and their potential pathogens and parasites
Bibliographic record
Abstract
The aquatic ornamental industry, whilst providing socio-economic benefits, is a known introduction pathway for non-native species, which if invasive, can cause direct impacts to native species and ecosystems and also drive disease emergence by extending the geographic range of associated parasites and pathogens and by facilitating host-switching, spillover and spill-back. Although current UK temperatures are typically below those necessary for the survival and establishment of commonly-traded tropical, and some sub-tropical, non-native ornamental species, the higher water temperatures predicted under climate-change scenarios are likely to increase the probability of survival and establishment. Our study aimed primarily to identify which of the commonly-traded non-native ornamental aquatic species (fish and invertebrates), and their pathogens and parasites, are likely to benefit in terms of survival and establishment in UK waters under predicted future climate conditions. Out of 233 ornamental species identified as traded in the UK, 24 were screened, via literature search, for potential parasites and pathogens (PPPs) due to their increased risk of survival and establishment under climate change. We found a total of 155 PPPs, the majority of which were platyhelminths, viruses and bacteria. While many of the identified PPPs were already known to occur in UK waters, PPPs currently absent from UK waters and with zoonotic potential were also identified. Results are discussed in the context of understanding potential impact, in addition to provision of evidence to inform risk assessment and mitigation approaches.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".