Genomic Variations in the Tea Leafhopper Reveal the Basis of Its Adaptive Evolution
Bibliographic record
Abstract
Tea green leafhopper (TGL), Empoasca onukii, is of biological and economic interest. Despite numerous studies, the mechanisms underlying its adaptation and evolution remain enigmatic. Here, we use previously untapped genome and population genetics approaches to examine how the pest adapted to different environmental variables and thus has expanded geographically. We complete a chromosome-level assembly and annotation of the E. onukii genome, showing notable expansions of gene families associated with adaptation to chemoreception and detoxification. Genomic signals indicating balancing selection highlight metabolic pathways involved in adaptation to a wide range of tea varieties grown across ecologically diverse regions. Patterns of genetic variations among 54 E. onukii samples unveil the population structure and evolutionary history across different tea-growing regions in China. Our results demonstrate that the genomic changes in key pathways, including those linked to metabolism, circadian rhythms, and immune system functions, may underlie the successful spread and adaptation of E. onukii. This work highlights the genetic and molecular basis underlying the evolutionary success of a species with broad economic impacts, and provides insights into insect adaptation to host plants, which will ultimately facilitate more sustainable pest management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".