Syrphid Diversity in Sweet Alyssum Flower Strips in Quebec's Lettuce Fields: Molecular Identification and Delimitation of the <i>Sphaerophoria</i> Complex
Bibliographic record
Abstract
ABSTRACT Lettuce is an economically significant crop in Canada, with 70% of commercial production occurring in peatlands (Histosols) in southern Quebec. Insecticide application is currently the main method for managing lettuce pests, but there is a growing need for sustainable pest control alternatives. Conservation biological control, such as incorporating flowering strips into fields to attract natural enemies, is a promising strategy. This study evaluates the potential of sweet alyssum, Lobularia maritima, to attract syrphids, whose larvae are voracious predators of lettuce pests, particularly aphids. A total of 16 species were collected from flowering plants across three lettuce farms in Quebec. The most abundant species was Toxomerus marginatus, accounting for 70% of all specimens, followed by Sphaerophoria philanthus (10.3%) and Allograpta obliqua (4.6%). All other species each constituted less than 4% of the total catch. A subset of 82 females from the Sphaerophoria philanthus/asymmetrica/abbreviata species complex underwent COI DNA‐based delimitation analyses, revealing three molecular operational taxonomic units (MOTUs). Fourteen of the 16 identified species or MOTUs are aphidophagous. Analysis of diversity metrics across the three sites indicated no statistically significant differences between flower and control treatments. However, of the 16 species recorded, 10 were found exclusively or predominantly (≥ 80%) in flower plots. Our findings suggest that alyssum flowers can successfully attract natural aphid predators in lettuce fields. This approach has the potential to mitigate lettuce pest issues and reduce reliance on insecticides, thus promoting more sustainable pest management.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".