Reconstructing the European Grapevine Moth (Lepidoptera: Tortricidae), Invasion in California: Insights From a Successful Eradication
Bibliographic record
Abstract
Successfully eradicated invasions are ideal opportunities for understanding the factors governing biological invasions and developing robust management strategies should the same, or similar, organisms again invade. We used geospatial analyses and habitat suitability modeling to reconstruct the spatiotemporal dynamics of an invasive vineyard pest, the European grapevine moth (Lobesia botrana [Denis & Schiffermüller]), in northern California. L. botrana detections were most strongly autocorrelated at local spatial scales (≤250 m) and remained clustered up to ~10 km. Generalized linear model, boosted regression tree, and random forest modeling methods performed well in predicting habitat suitability for L. botrana; annual mean temperature, elevation, and distance to the nearest road were identified as important predictors. Hotspots in L. botrana occurrence were spatiotemporally dynamic, yet habitat suitability was less important than purely spatial effects in explaining hotspot persistence. Our results indicate that local regulatory response to novel L. botrana detections was appropriate; 500 m treatment zones around detections are sufficient given the apparent propensity for very local movement by L. botrana. Our results also confirm the role of anthropogenic effects in L. botrana spread and support the establishment of quarantine procedures to limit human-mediated dispersal. Lastly, ensemble predictions provide a fine-scale measure of relative risk for a portion of northern California in the event of future L. botrana introductions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".