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Record W4411154341 · doi:10.3390/insects16060603

Diverse Sublethal Effects of a Common Fungicide Impact the Behavior and Physiology of Honey Bees

2025· article· en· W4411154341 on OpenAlexafffund
Xufeng Zhang, Qian Cao, Feng Wang, Yali Du, Wen Zhao, Yuan Guo, Olav Rueppell

Bibliographic record

VenueInsects · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaShanxi Agricultural UniversityChina Scholarship Council
KeywordsBiologyHoney BeesFungicideHoney beeToxicologyZoologyEcologyPhysiologyBotany

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.290
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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