Cheese and cheese infusions: ecological traps for mosquitoes and spotted wing <scp> <i>Drosophila</i> </scp>
Bibliographic record
Abstract
BACKGROUND: Harnessing insect ecology for insect control is an innovative concept that seeks to exploit, among others, insect-microbe ecological interactions for improved control of pest insects. Microbe-produced cheese odour attracts several dipterans, including host-seeking mosquitoes, but this phenomenon has not been thoroughly explored for mosquito control. Here we tested the hypothesis that attraction of mosquitoes to cheese odour can be exploited as an ecological trap for mosquito control. RESULTS: In laboratory and/or field experiments, we show that (i) each of five cheese varieties tested (Raclette, Pecorino, Brie, Gruyere, Limburger) strongly attracts female Aedes aegypti and Culex pipiens; (ii) cheese infusions, or headspace odourant extracts (HOEs) of cheese infusions, significantly affect oviposition choices by mosquitoes, (iii) HOEs contain at least 13 odourants; (iv) in field settings, cheese infusions more effectively stimulate mosquito oviposition than positive bluegrass infusion controls, and also capture (by drowning) the spotted wing Drosophila, Drosophila suzukii; and (v) home-made cheese infusions modulate oviposition choices by mosquito females and affect the survivorship of their offspring larvae. CONCLUSION: Our data show that microbial metabolites associated with cheese are attractive to mosquito females seeking hosts and oviposition sites and are likely toxic to mosquito larvae. These microbes and their metabolites could thus be co-opted for both the attract, and the kill, function of 'attract & kill' mosquito control tactics. Implementation of customizable and non-conventional nutritional media as microbe-based ecological traps presents a promising concept which exploits insect ecology for insect control. © 2021 Society of Chemical Industry.
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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".