Predicting introduction, establishment and potential impacts of smallmouth bass
Bibliographic record
Abstract
Abstract Aim The introduction of non‐indigenous species has resulted in wide‐ranging ecological and economic impacts. Predictive modelling of the introduction and establishment of non‐indigenous species is imperative to identify areas at high risk of invasion to effectively manage non‐indigenous species and conserve native populations. Smallmouth bass ( Micropterus dolomieu ), a warm water fish species native to central North America has negatively impacted native fish communities, including cyprinids and salmonid populations, as a result of intentional introductions. We predicted the introduction risk; species establishment based on habitat suitability; identified lakes at high risk of invasion; and finally assessed the consequential impacts on native salmon, trout and cyprinid populations. Location Ontario and British Columbia, Canada. Methods Classification tree and logistic regression models were developed and validated to predict the introduction and establishment of smallmouth bass for thousands of lakes. Results Densely human populated areas and larger lake surface areas successfully identify lakes associated with the introduction of smallmouth bass (introduction model) in British Columbia. Climate, lake morphology and water chemistry variables were the driving environmental parameters to define suitable smallmouth bass habitat (establishment model). A combination of the introduction and establishment model identified 138 lakes that are currently at risk in British Columbia to the introduction and establishment of smallmouth bass. Of these 138 high‐risk lakes, 95% of them contain at least one species of salmon, trout or cyprinid, thereby increasing the potential impact of an invasion by smallmouth bass. Main conclusions Our framework can be applied to other terrestrial and aquatic species to obtain a better understanding of the potential risk posed by a non‐indigenous species to an ecosystem. Furthermore, our methodology can be used to focus management efforts on areas at higher risk (e.g. number of potential releases, more favourable habitats) to control future introductions of non‐indigenous species, thereby conserving native populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".