Can leafhoppers help us trace the impact of climate change on agriculture?
Bibliographic record
Abstract
SUMMARY Climate change is reshaping agriculture and insect biodiversity worldwide. With rising temperatures, insect species with narrow thermal margins are expected to be pushed beyond their thermal limits, and losses related to herbivory and diseases transmitted by them will be experienced in new regions. Several previous studies have investigated this phenomenon in tropical and temperate regions, locally and globally; however, here, it is proposed that climate change’s impact on agriculture can be traced through the study of Nearctic migratory insects, specifically leafhoppers. To test this hypothesis, leafhoppers in strawberry fields located in the province of Québec, eastern Canada, were evaluated. The strawberry-leafhopper pathosystem offers a unique opportunity because leafhoppers can transmit, among other diseases, strawberry green petal disease (SbGP), which is associated with pathogenic phytoplasmas. Here, we found that in the last ten years, the number of leafhoppers has been increasing in correspondence with the number of SbGP cases detected in eastern Canada, reporting for the first time ten species new to eastern Canada and two to the country, although the leafhopper diversity has been seriously affected. Our model using more than 34 000 leafhoppers showed that their abundance is influenced by temperature, a factor that we found also influences the microbiome associated with Macrosteles quadrilineatus , which was one of the most abundant leafhoppers we observed. One of our most striking findings is that none of the insecticides used by strawberry growers can control leafhopper incidence, which could be linked to microbiome changes induced by changing temperatures. We suggest that Nearctic leafhoppers can be used as sentinels to trace the multilayered effects of climate change in agriculture. GRAPHICAL ABSTRACT IN BRIEF The current climate crisis is reshaping insect biodiversity and abundance, but little is known about the direct effect of this phenomenon on agriculture. In this study, we explored leafhoppers, a group of agriculturally important insect pests and disease vectors, as sentinels of the effect of climate change on agriculture. Our findings indicate that this group of insects can help us to understand the effect of the current climate crisis on insect invasions, diversity, abundance, disease dynamics and insecticide resistance and to take quick action to ensure food security while achieving more sustainable agriculture. HIGHLIGHTS Migratory leafhoppers benefit from temperature increases Leafhopper-transmitted diseases have increased in the last decade New non-migratory leafhoppers can be found now in Nearctic regions Leafhopper insecticide resistance could be linked to the insect microbiome
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".