Predicting the impacts of an introduced species from its invasion history: an empirical approach applied to zebra mussel invasions
Bibliographic record
Abstract
SUMMARY 1. Quantitative models of impact are lacking for the vast majority of known invasive species, particularly in aquatic ecosystems. Consequently, managers lack predictive tools to help them prioritise invasion threats and decide where they can most effectively allocate limited resources. Predictive tools would also enhance the accuracy of water quality assessments, so that impacts caused by an invader are not erroneously attributed to other anthropogenic stressors. 2. The invasion history of a species is a valuable guide for predicting the consequences of its introduction into a new environment. Regression analysis of data from multiple invaded sites can generate empirical models of impact, as is shown here for the zebra musselDreissena polymorpha.Dreissena's impacts on benthic invertebrate abundance and diversity follow predictable patterns that are robust across a range of habitat types and geographic regions. Similar empirical models could be developed for other invaders with a documented invasion history. 3. Because an invader's impact is correlated with its abundance, a surrogate model may be generated (when impact data are unavailable) by relating the invader's abundance to environmental variables. Such a model could help anticipate which habitats will be most affected by invasion. Lack of precision should not be a deterrent to developing predictive models where none exist. Crude predictions can be refined as additional data become available. Empirical modelling is a highly informative and inexpensive, but underused, approach in the management of aquatic invasive 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".