Genetic differentiation of the regional <i>Plutella xylostella</i> populations across the Taiwan Strait based on identification of microsatellite markers
Bibliographic record
Abstract
Movement of individuals through events, such as storms or crop transportation, may affect survival and distribution of insect pests, as well as population genetic structure at a regional scale. Understanding what factors contribute to gene flow in pest populations remains very important for sustainable pest management. The diamondback moth (Plutella xylostella) is an insect pest well known for its capacity of moving over short to long distances. Here, we used newly isolated microsatellite markers to analyze the genetic structure of nine populations across the Taiwan Strait of China (Taiwan and Fujian). A total of 12,152 simple sequence repeats (SSRs) were initially identified from the P. xylostella transcriptome (~94 Mb), with an average of 129 SSRs per Mb. Nine SSRs were validated to be polymorphic markers, and eight were used for this population genetic study. Our results showed that the P. xylostella populations could be divided into distinct two clusters, which is likely due to the year-round airflows in this region. A pattern of isolation by distance among the local populations within Fujian was found, and may be related to vegetable transportation. Considering the complexity of the P. xylostella population genetic structure from local and regional to global levels, we propose that developing ecologically sound strategies for managing this pest will require knowledge of the link between behavioral and population ecology and its genetic structure.
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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".