Are fungicides a driver of European foulbrood disease in honey bee colonies pollinating blueberries?
Bibliographic record
Abstract
Introduction Blueberry producers in Canada depend heavily on pollination services provided by honey bees (Apis mellifera L.). Anecdotal reports indicate an increased incidence of European foulbrood (EFB), a bacterial disease caused by Melissococcus plutonius, is compromising pollination services and colony health. Fungicidal products are commonly used in blueberry production to prevent fungal diseases such as anthracnose and botrytis fruit rot. Pesticide exposure has been implicated in honey bee immunosuppression; however, the effects of commercial fungicidal products, commonly used during blueberry pollination, on honey bee larval susceptibility to EFB have not been investigated. Methods Using an in vitro infection model of EFB, we infected first instar honey bee larvae with M. plutonius 2019 BC1, a strain isolated from an EFB outbreak in British Columbia, Canada, and chronically exposed larvae to environmentally relevant concentrations of fungicide products over 6 days. Survival was monitored until pupation or eclosion. Results We found that larvae chronically exposed to one, two, or three fungicidal products [Supra® Captan 80WDG (Captan), low concentration of Kenja™ 400SC (Kenja), Luna® Tranquility (Luna), and/or Switch® 62.5 WG (Switch)], did not significantly reduce survival from EFB relative to infected controls. When larvae were exposed to four fungicide products concurrently, we observed a significant 24.2% decrease in survival from M. plutonius infection (p = 0.0038). Similarly, higher concentrations of Kenja significantly reduced larval survival by 24.7–33.0% from EFB (p < 0.0001). Discussion These in vitro results suggest that fungicides may contribute to larval susceptibility and response to M. plutonius infections. Further testing of other pesticide combinations is warranted as well as continued surveillance of pesticide residues in blueberry-pollinating colonies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".