Detection rates of aphid DNA in the guts of larval hoverflies and potential links to the provision of floral resources
Bibliographic record
Abstract
Abstract Aphidophagous hoverflies (Diptera, Syrphidae, Syrphinae) are common flower visitors and aphid predators in a range of flowering plants, including fruit crops. Here, we investigate whether aphid prey DNA can be detected in the gut contents of hoverfly larvae from a commercial strawberry field as a proof of concept that a molecular approach can be used to measure agricultural biocontrol. We used high-throughput sequencing (HTS) to target insect DNA and compared the resulting data to reference databases containing aphid and hoverfly DNA sequences. We explored what impact incorporating wildflowers within polythene-clad tunnels may have on aphid DNA detection rates in hoverfly larvae. In a randomized block experiment, coriander (Coriandrum sativum), field forget-me-not (Myosotis arvensis) and corn mint (Mentha arvensis) plants were inserted in rows of strawberries. Their effect on aphid DNA detection rates was assessed. Aphid DNA was found in 55 of 149 specimens (37%) validating the method in principle for measuring agricultural services provided by hoverflies. Interestingly, detection rates were higher near plots with forget-me-not than plots with coriander, though detection rates in control plots did not differ significantly from either wildflower species. These findings confirm that hoverflies predate aphids in UK strawberry fields, and that HTS is a viable method of identifying aphid DNA in predatory hoverflies. We comment on the need for further method development to narrow down identifications of both predator and prey. We furthermore provide some evidence that there is an effect of intercropping strawberry crops with wildflowers which may affect aphid consumption in hoverfly larvae.
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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) | 0.001 | 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".