Charting the course of invasion: Ensemble species distribution models predict the range expansion of a newly invasive aphid pest Metopolophium festucae cerealium in North America
Bibliographic record
Abstract
Invasive species, including insect pests, pose significant threats globally. Ongoing global environmental changes may exacerbate the threats, by potentially favoring range expansion of invasive pests, altering native ecosystems, and damaging valuable crops. To reduce the spread and impact of invasive pests, monitoring and identification of their suitable habitats in the context of global environmental change (e.g., ongoing changes in land use and climate) is essential. This study examines the current and future potential habitat suitability of a newly invasive aphid, Metopolophium festucae cerealium , in North America, focusing on its potential expansion into wheat growing regions in North America. Using occurrence data collected during a decade of surveying from ∼450 sites in the Pacific Northwest (PNW) where the aphid has invaded, with the help of an ensemble modeling framework, we predicted the habitat suitability for M. f. cerealium for North America under various climate scenarios and land use conditions. The results indicate a high likelihood of further eastward and southward expansion from the PNW, particularly in wheat and cereal crop-producing regions, posing a threat to crop production. The key environmental drivers include cropland percentage, temperature, and precipitation, suggesting potential impacts of future environmental change. The study underscores the importance of considering not only climatic factors but also host plant presence and agricultural practices in pest management strategies. • Identifying potential suitable habitats for invasive pests is crucial. • We examined current and future habitat suitability of invasive M. f. cerealium in North America. • High likelihood of invasive pest expanding from Pacific Northwest, USA to other regions. • Cropland percentage, temperature, and precipitation are identified as key drivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".