Diverse Sublethal Effects of a Common Fungicide Impact the Behavior and Physiology of Honey Bees
Bibliographic record
Abstract
Honey bees and other pollinators are key to functioning natural and managed ecosystems. However, their health is threatened by many factors, including pesticides. Spraying fungicides during flowering of fruit trees is widespread even though it directly exposes pollinators to these fungicides. Here, we report a series of experiments designed to understand how the combination of propiconazole and carbendazim, marketed in China as Chunmanchun®, affects honey bee health. With an acute oral toxicity of 23.8 μg a.i./bee over 24 h in the laboratory, we considered the acute mortality risk from normal Chunmanchun® applications as relatively low. However, our comprehensive studies revealed other diverse effects: Chunmanchun® reduced memory after classic conditioning by approximately 25% and altered the activity of protective enzymes and the composition of the honey bees’ gut microbiota. Specifically, the genus Lactobacillus was decreased by ~13%, and Bartonella and Snodgrassella were increased by ~10% and ~7.5%, respectively. The gut metabolome was also disrupted in diverse ways, possibly as a functional consequence of the microbiome changes. Thus, we demonstrated numerous sublethal effects of the combination of propiconazole and carbendazim, which adds to the growing evidence that agrochemicals and fungicides in particular can harm pollinator health in subtle ways that are not captured in simple mortality assays.
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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 | 0.001 |
| 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".