Genotyping‐by‐sequencing approach indicates geographic distance as the main factor affecting genetic structure and gene flow in Brazilian populations of <i>Grapholita molesta</i> (Lepidoptera, Tortricidae)
Bibliographic record
Abstract
The oriental fruit moth Grapholita molesta is one of the major pests of stone and pome fruit species in Brazil. Here, we applied 1226 SNPs obtained by genotyping-by-sequencing to test whether host species associations or other factors such as geographic distance structured populations of this pest. Populations from the main areas of occurrence of G. molesta were sampled principally from peach and apple orchards. Three main clusters were recovered by neighbor-joining analysis, all defined by geographic proximity between sampling localities. Overall genetic structure inferred by a nonhierarchical amova resulted in a significant ΦST value = 0.19109. Here, we demonstrate for the first time that SNPs gathered by genotyping-by-sequencing can be used to infer genetic structure of a pest insect in Brazil; moreover, our results indicate that those markers are very informative even over a restricted geographic scale. We also demonstrate that host plant association has little effect on genetic structure among Brazilian populations of G. molesta; on the other hand, reduced gene flow promoted by geographic isolation has a stronger impact on population differentiation.
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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 | 0.001 |
| 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".