Analysis of single-copy, nuclear microsatellite markers from flies collected on sticky traps
Bibliographic record
Abstract
Sticky traps can provide large numbers of spatially referenced samples for use in molecular ecological studies of insects. However, the adhesives used on these traps, and the methods used to clean adhesive off trapped individuals, could potentially interfere with downstream molecular analyses. Specimens captured on sticky traps have been successfully used to analyse mitochondrial or multiple-copy ribosomal DNA markers, but not single-copy nuclear markers. Furthermore, the effects of trap adhesive and cleaning protocol on the success of molecular analyses have not been explored. Here, we examine the effects of trap adhesive, sample cleaning method, and sample storage condition on DNA concentration and purity, and on the ability to amplify single-copy, nuclear microsatellite loci, using specimens of the western cherry fruit fly, Rhagoletis indifferens (Diptera: Tephritidae) captured on sticky traps in an orchard. We could extract DNA of high purity, and amplify microsatellite loci in multi-plex polymerase chain reaction (PCR), under all combinations of treatments. However, DNA yield, DNA purity and the yield of PCR products were affected by treatment, with complex interactions among trap adhesive, sample cleaning method, and storage condition. Samples that were cleaned with acetone and stored dry had the highest DNA concentration. With respect to PCR amplification, samples cleaned with Histo-clear produced much less product than those cleaned with acetone or not cleaned at all, whereas samples that were stored dry produced more PCR product than samples stored in ethanol. Insects captured on sticky traps can thus provide genetic data appropriate for molecular ecological analyses under a wide range of treatment conditions. However, potential interactions among adhesives, cleaning protocols and storage conditions suggest that any novel combination for treatment of samples from sticky traps should be tested on a small scale prior to collecting large numbers of samples for genetic studies.
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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 | 0.000 |
| 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) | 0.001 | 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".