Neonicotinoid Seed Treatments Influence Soil Nematode Taxonomic Composition and the Soil Microbial Cooccurrence Networks
Bibliographic record
Abstract
Neonicotinoid insecticides are widely used to control early-season and foliar-feeding pests. Some studies have revealed their nontarget impacts on pollinators and other invertebrates but few investigated their effects on soil microbiota. Given the crucial role of soil prokaryotic and eukaryotic microbial communities in agroecosystem regulation and their contribution to soil fertility, it is critical to understand their structure and changes in response to disturbances such as pesticide application. Among these communities, free-living nematodes have the potential to indicate the ecological changes in soil caused by environmental stress and have a key role in forming and modulating soil microbial composition and function by feeding on other soil microorganisms or interacting with them. Here, we used 18S ribosomal RNA gene amplicon sequencing to characterize the effects of neonicotinoids on soil nematode communities in a 3-year soybean-corn crop rotation in Quebec, Canada. We also quantified the changes in nematode-bacteria cooccurrence networks in soil exposed to neonicotinoids. We found that neonicotinoid seed treatment significantly explained variation in nematode community composition and affected the relative abundance of some nematode families, such as a decrease in the omnivorous family Dorylaimidae in neonicotinoid-treated samples. Moreover, neonicotinoids altered the patterns of nematode-bacteria cooccurrence, including the structure and taxonomic composition of the networks. However, it is unclear whether neonicotinoids affected bacterial cooccurrence networks directly, or indirectly by affecting nematodes that feed on bacteria. Further research is needed to understand how neonicotinoids affect nematodes and the role of nematodes in microbial network variation in soil exposed to neonicotinoids.
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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".