Nasturtium leaf compounds, diphenyl disulfide and lyral, against <i>Atta sexdens</i> (Hymenoptera: Formicidae) and their symbiotic fungi
Bibliographic record
Abstract
Social insect pests, particularly leaf-cutting ants, present a considerable challenge in terms of control. Leaf-cutting ants are significant agricultural, forestry, and pasture pests, and understanding their behavior and defense mechanisms is essential for managing their colonies effectively. While toxic ant baits are a primary control method, the limited availability of effective insecticides and concerns over their hazardous nature has spurred the search for alternative solutions, particularly natural compounds, which aligns with the goals of forest certification groups. In the light of previous evidence demonstrating the efficacy of nasturtium leaves (Tropaeolum majus L. (Brassicales: Tropaeolaceae)) in suppressing leaf-cutting ant colonies, this study investigates 2 active components of nasturtium leaf extracts: diphenyl disulfide and lyral. We tested their impact on Atta sexdens (L.) (Hymenoptera: Formicidae), the most prevalent leaf-cutter ant species in Brazil, and their symbiotic fungus, Leucoagaricus gongylophorus (Möller) Singer (Agaricales: Agaricaceae). We conducted experiments with increasing concentrations of diphenyl disulfide and lyral, assessing their effects on the symbiotic fungus and on forager workers and gardeners of A. sexdens colonies. Our findings revealed no fungicidal activity, and ant mortality was minimal in both topical and ingestion bioassays with the exception of gardeners topically exposed to diphenyl sulfide. Furthermore, the compounds did not affect leaf ingestion, but diphenyl disulfide did increase interactions among foragers. These results suggest that neither diphenyl disulfide nor lyral are the primary contributors to the suppression of leaf-cutting ant colonies by nasturtium leaves. However, they may enhance the formicidal activity of other compounds present in nasturtium leaves.
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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".