The rich get richer: Invasion risk across North America from the aquarium pathway under climate change
Bibliographic record
Abstract
Abstract Aim To evaluate how the establishment risk of freshwater fish species from the aquarium trade will change under a climate change scenario forecast for the year 2050. Location North America. Methods In order to estimate changes in the magnitude of risk across geography and across different species in the aquarium pathway, we considered an integrated approach to modelling the probability of establishment, which simultaneously included proxies of propagule pressure, environmental variables, species traits and interactions between environment and traits. We then used the parameters of our model to predict how the risk of establishment will change under a scenario of climate change forecast for the year 2050. Results Our joint model performed better than submodels, suggesting that combining all components is worthwhile. The most predictive factors were precipitation, maximum temperature tolerance, maximum fish length and minimum temperature. Our joint model forecasted a 40% increase in the average risk of establishment by 2050 in the United States. In contrast to our expectations, the absolute establishment risk associated with this pathway remained very low for the entire suite of species in the aquarium trade in northern regions, such as Quebec, Canada. Instead, Florida, which has one of the highest current risks of establishment, was also forecasted to have the greatest absolute risk increase. Main conclusions Our methodology for risk assessment allows invasive species management strategies to consider entire suites of species at a time and to forecast establishment risk for each species and location. While the aquarium pathway is likely to become more important for the United States, the Quebec government should prioritize other pathways of introduction in its exotic invasive species strategy. Our approach can be extended to be applied to different sets of species pertaining to the same or different pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".