Phytoseiid predatory mites can disperse entomopathogenic fungi to prey patches
Bibliographic record
Abstract
Recent studies have shown that predatory mites used as biocontrol agents can be loaded with entomopathogenic fungal conidia to increase infection rates in pest populations. Under laboratory conditions, we determined the capacity of two phytoseiid mites, Amblyseius swirskii and Neoseiulus cucumeris to deliver the entomopathogenic fungus Beauveria bassiana to their prey, Frankliniella occidentalis. Predatory mites were loaded with conidia and released on plants that had been previously infested with first instar prey clustered on a bean leaf. We examined each plant section to characterize the spatial distribution of each interacting organism. Our results showed that A. swirskii delivered high numbers of conidia to thrips infested leaves, thereby increasing the proportion of thrips that came into contact with the fungus. The effect was larger when thrips infestation occurred on young leaves than on old leaves. Neoseiulus cucumeris delivered less conidia to the thrips infested leaves. These patterns result from differences in foraging activity between predatory mite species. Amblyseius swirskii stayed longer on plants, especially within thrips colonies, and had a stronger suppressing effect on thrips than N. cucumeris. Our study suggests that loading certain predatory mite species with fungal conidia can increase their capacity to suppress thrips populations by combining predation and dispersing pathogens.
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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".