Neonicotinoid-induced signature dysbiosis identified via metagenomic sequencing of the honey bee gut microbiome
Bibliographic record
Abstract
The Western honey bee (Apis mellifera) plays an essential role in agriculture around the world. In Canada, honey bees contribute up to $7 billion in economic value annually by pollinating crops and producing honey. However, since 2006–2007 North American beekeepers have lost more than a quarter of their colonies each winter. In recent years, the losses have been up to 50% in some regions. The causes of losses are complex, including the interacting effects of nutrition, pathogens, and pesticides. Although the bee gut microbiome plays a crucial role in colony health and disease, studies on the effects of agricultural pesticides on the bee microbial community are sparse. We report the use of shotgun metagenomic sequencing to investigate bee gut microbiota changes, or dysbiosis, in response to two neonicotinoid insecticides, clothianidin and thiamethoxam. Common dysbiosis signatures included an increase in Bifidobacterium spp. after chronic sublethal exposure and an increase in Apibacter adventoris after short-term acute exposure. Other dysbiosis signatures were unique to each compound, such as an increase in Snodgrassella alvi for clothianidin and a decrease in Lactobacillus spp. for thiamethoxam. These findings enhance our understanding of how the honey bee gut microbiome responds to stressors and highlight identifiable microbial profile signatures which underscores the potential utility of gut microbiome profiling as a bee health diagnostic tool. Access to timely and accurate bee health diagnosis will inform regulatory actions to decrease and mitigate exposure to stressors and will facilitate managing and improving bee health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".