Identifying Diagnostic Genetic Markers for a Cryptic Invasive Agricultural Pest: A Test Case Using the Apple Maggot Fly (Diptera: Tephritidae)
Bibliographic record
Abstract
Abstract Insect pests destroy ~15% of all U.S. crops, resulting in losses of $15 billion annually. Thus, developing cheap, quick, and reliable methods for detecting harmful species is critical to curtail insect damage and lessen economic impact. The apple maggot fly, Rhagoletis pomonella, is a major invasive pest threatening the multibillion-dollar apple industry in the Pacific Northwest United States. The fly is also sympatric with a benign but morphologically similar and genetically closely related species, R. zephyria, which attacks noncommercial snowberry. Unambiguous species identification is essential due to a zero-infestation policy of apple maggot for fruit export. Mistaking R. zephyria for R. pomonella triggers unnecessary and costly quarantines, diverting valuable control resources. Here we develop and apply a relatively simple and cost-effective diagnostic approach using Illumina sequencing of double-digest restriction site-associated DNA markers. We identified five informative single-nucleotide polymorphisms (SNPs) and designed a diagnostic test based on agarose gel electrophoresis of restriction enzyme-digested polymerase chain reaction amplification products (RFLPs) to distinguish fly species. We demonstrated the utility of this approach for immediate, 1-d species identification by scoring apple- and snowberry-infesting flies from known hosts, reared from fruit collected at 11 sites throughout Washington. However, if immediate diagnosis is not required, or hundreds to thousands of specimens must be assessed, then a direct Illumina-based sequencing strategy, similar to that used here for diagnostic SNP identification, can be powerful and cost-effective. The genomic strategy we present is effective for R. pomonella and also transferable to many cryptic pests.
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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 | 0.000 | 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 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".