Neonicotinoid and sulfoximine pesticides differentially impair insect escape behaviour and motion detection
Bibliographic record
Abstract
Insect nervous systems offer unique advantages for studying interactions between sensory systems and behaviour given that they are complex and yet highly tractable. By examining the neural coding of salient environmental stimuli and resulting behavioural output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behaviour and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of non-target species, such as bees, has motivated development of new compounds, which could potentially mitigate putative resistance in target species (1, 2) and declines of non-target species. We used a neuroethological approach, including behavioural assays and multineuronal recording techniques, to investigate effects of imidacloprid and the novel insecticide sulfoxaflor on visual motion-detection circuits and related escape behaviour in the tractable locust system. Despite similar LD50 values, imidacloprid and sulfoxaflor evoked different behavioural and physiological effects. Imidacloprid significantly attenuated collision avoidance behaviours and impaired responses of neural populations, including a decrease in spontaneous firing and decreased neural habituation. In contrast, sulfoxaflor displayed no effect at a comparable sublethal dose. These are the first results to show that a neonic affects population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of sulfoxaflor reflect an altered mode of action to imidacloprid. More broadly, we suggest that neuroethological assays for comparative neurotoxicology are a valuable tool to fully address current issues regarding proximal effects of environmental toxicity in non-target species.
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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".