Using Risk Assessment and Habitat Suitability Models to Prioritise Invasive Species for Management in a Changing Climate
Bibliographic record
Abstract
Accounting for climate change in invasive species risk assessments improves our understanding of potential future impacts and enhances our preparedness for the arrival of new non-native species. We combined traditional risk assessment for invasive species with habitat suitability modeling to assess risk to biodiversity based on climate change. We demonstrate our method by assessing the risk for 15 potentially new invasive plant species to Alberta, Canada, an area where climate change is expected to facilitate the poleward expansion of invasive species ranges. Of the 15 species assessed, the three terrestrial invasive plant species that could pose the greatest threat to Alberta's biodiversity are giant knotweed (Fallopia sachalinensis), tamarisk (Tamarix chinensis), and alkali swainsonpea (Sphaerophysa salsula). We characterise giant knotweed as 'extremely invasive', with 21 times the suitable habitat between baseline and future projected climate. Tamarisk is 'extremely invasive' with a 64% increase in suitable habitat, and alkali swainsonpea is 'highly invasive' with a 21% increase in suitable habitat. Our methodology can be used to predict and prioritise potentially new invasive species for their impact on biodiversity in the context of climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".