Aphid-induced Defences in Chilli Affect Preferences of the Whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae)
Bibliographic record
Abstract
The sweetpotato whitefly (WF), Bemisia tabaci, is a major pest that damages a wide range of vegetable crops in Malaysia. WF infestation is influenced by a variety of factors, including previous infestation of the host plant by other insect pests. This study investigated the effects of previous infestation of host chilli plants by the green peach aphid (Myzus persicae) on the olfactory behavioural response of B. tabaci, using free-choice bioassay with a Y-tube olfactometer. We analysed volatile organic compounds (VOCs) emitted by non-infested and M. persicae-infested chilli plants using solid-phase microextraction and gas chromatography-mass spectrometry. Our results showed that female WFs preferred non-infested to pre-infested plants. Collection and analysis of volatile compounds emitted by infested plants confirmed that there were significant increases in the production of monoterpenes (cymene; 1,8-cineole), sesquiterpenes (β-cadinene, α-copaene), and methyl salicylate (MeSA) compared to non-infested plants. Our results suggest that host plant infestation by aphids may induce production of secondary metabolites that deter B. tabaci from settling on its host plants. These results provide important information for understanding WF host selection and dispersal among crops, and also for manipulating WF behaviour to improve IPM in chilli.
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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".