Indirect exposure to insect growth disruptors affects honey bee (Apis mellifera) reproductive behaviors and ovarian protein expression
Bibliographic record
Abstract
Pesticide exposure and queen loss are considered to be major causes of honey bee colony mortality, yet little is known regarding the effects of regularly encountered agrochemicals on honey bee reproduction. Here, we present the results of a two-generational study using specialized cages to expose queens to commonly used insect growth disrupting pesticides (IGDs) via their retinue of worker bees. Under IGD exposure, we tracked queen performance and worker responses to queens, then the performance of the exposed queens' offspring was assessed to identify patterns that may contribute to the long-term health and stability of a social insect colony. The positive control, novaluron, resulted in deformed larvae hatching from eggs laid by exposed queens, and methoxyfenozide, diflubenzuron, and novaluron caused a slight decrease in daily egg laying rates, but this was not reflected in the total egg production over the course of the experiment. Curiously, eggs laid by queens exposed to pyriproxyfen exhibited increased hatching rates, and those larvae developed into worker progeny with increased responsiveness to their queens. Additionally, pyriproxyfen and novaluron exposure affected the queen ovarian protein expression, with the overwhelming majority of differentially expressed proteins coming from the pyriproxyfen exposure. We discuss these results and the potential implications for honey bee reproduction and colony 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 | 0.000 |
| 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".