Not all predators are equal: miticide non‐target effects and differential selectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it