Range expansion risk for a newly established invasive duckweed species in Europe and Canada
Bibliographic record
Abstract
Abstract Landoltia punctata is an invasive aquatic plant that has spread across the United States. Unlike native duckweeds, this species has developed herbicide resistance. As a result, invasion can lead to high management costs and the loss of recreational areas and natural habitats. The species has been recently found in Europe, and is also approaching the northern US border with Canada. We predicted the potential distribution of L. punctata in western Europe and Canada using presence-only data from the Global Biodiversity Information Facility as well as other literature records. We fit predictive models to this data using a Maxent approach. Since climate data based on surface lake water conditions are often more relevant to macrophytes than air temperature metrics, our models included both water and air temperature bioclimatic variables related to the life history of the species. Model comparisons confirmed a superior fit of lake temperatures to duckweed distribution records. The best fit model suggests a high habitat suitability for the species in most Western European countries and Western Canada. A moderate emission scenario suggests that in 2070 currently compatible areas will still be suitable, and that the Great Lakes region will become suitable. Preventive measures to avoid future spread of L. punctata are recommended in these locations to avoid impacts associated with this and similar duckweed species in Europe and the US.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".