A novel bioassay to assess the non-target impacts of insecticide exposure on a larval endoparasitoid of the emerald ash borer
Bibliographic record
Abstract
Pest management strategies for invasive species like the emerald ash borer (EAB) must combine chemical control with biological control agents to protect vulnerable hosts. When used in tandem with biological control agents, however, systemic insecticides may impact the fitness of biological control agents, thus reducing their effectiveness. Systemic insecticides are used for EAB management in urban forests across North America, while classical biocontrol with introduced natural enemies has been an important tactic for managing EAB in natural forests in North America. We tested the non-target effects of azadirachtin on Tetrastichus planipennisi Yang, a larval parasitoid of EAB introduced to North America. A novel bioassay protocol was developed whereby EAB larvae were initially reared on host material in the laboratory and then temporarily transferred to an artificial EAB diet containing azadirachtin followed by exposure to parasitism by T. planipennisi. Exposure to azadirachtin at concentrations causing 30% and 50% mortality in EAB larvae reduced EAB larval parasitism by T. planipennisi. Exposure to azadirachtin also reduced T. planipennisi's sex ratio, adult emergence, female body size, potential fecundity, and adult longevity. These results suggest there are negative interactions between systemic insecticides and EAB biological control agents, which present challenges for the integration of tactics for long-term EAB management.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".