Not all predators are equal: miticide non‐target effects and differential selectivity
Bibliographic record
Abstract
BACKGROUND: Biological control in conventional agroecosystems involves the integration of chemical and conservation tactics, requiring knowledge of pesticide non-target effects on key natural enemies. Even for natural enemy groups such as predatory mites (Acari: Phytoseiidae), where pesticide non-target effects have been thoroughly examined, there may be significant differences in species susceptibility to specific active ingredients, including newer selective products. Using bioassays, we examined lethal (female mortality) and sublethal (fecundity, egg hatch, larval survival) effects of ten miticides on a spider mite pest (Tetranychus urticae) and three insectary-purchased predatory mites (Phytoseiulus persimilis, Neoseiulus californicus, and N. fallacis) commonly used for its management. Susceptibility of field-collected and insectary-reared populations of P. persimilis was also compared. Cumulative impacts on production of larvae by treated female spider mites and predators were compared to create a metric that simultaneously accounted for miticide efficacy and selectivity. RESULTS: Bifenthrin was the least selective, as it caused acute toxicity to all predators and had little efficacy against T. urticae. Hexythiazox and cyflumetofen were the most selectively favorable. Phytoseiulus persimilis populations were similar in which miticides they were sensitive to, although the insectary-purchased population was generally more sensitive. CONCLUSIONS: All products, including those considered selective (cyflumetofen, bifenazate, acequinocyl) had non-target effects on at least one species of predator tested. This work emphasizes that there is high variability in selectivity among species, highlighting the need to examine key natural enemies individually when creating management programs. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 | 0.001 |
| 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".