Pest and Host Associations That Transcend Time: Assessing the Impact of Climate Change on Grape Berry Moth ( <i>Paralobesia viteana</i> ) and Its Hosts <i>Vitis riparia</i> and <i>Vitis labrusca</i> in North America
Bibliographic record
Abstract
ABSTRACT The grape berry moth (GBM) Paralobesia viteana (Clemens, 1860) (Lepidoptera: Tortricidae) is an important pest of grapes in Eastern North America. The insect is native to this region and co‐evolved with wild grapevine hosts long before the beginning of viticulture. The geographic distribution of this pest is influenced by the distribution of its hosts and by unknown environmental factors. In agriculture, species distribution models (SDMs) can help predict the effects of environmental variables and changing climate on the geographic suitability of pests and their hosts, guiding preparation for potential pest expansions. This study predicted the potential geographic distribution of GBM and two of its host plants, Vitis labrusca and Vitis riparia , across the United States (U.S.) and Canada in the current time and under two climate change scenarios (SSP2–4.5 and SSP5–8.5) and periods (2021–2040 and 2041–2060) using the Random Forest algorithm. The results show that habitat suitability for the three species was primarily determined by temperature and precipitation. The temperature annual range and the precipitation of the driest month were the variables with the greatest influence on GBM distribution, whereas the mean temperature of the warmest quarter contributed the most to V. labrusca and V. riparia SDMs . Shared suitable areas for GBM and its two hosts in current time predictions were 9.7% and 1.76% in the U.S. and Canada territories, respectively. In future climatic scenarios, these shared suitable areas are predicted to increase by 3.3%–4.5% in the Northeast and Midwest U.S. and by 7.8%–13% in Eastern Canada. These findings predict an increase in pest pressure in the U.S. and Canada in future climatic conditions, providing the basis for proactive pest monitoring, breeding for drought and cold grapevine tolerance, and adaptive vineyard management to mitigate the risks associated with climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".