Differences in larval pesticide tolerance and esterase activity across honey bee (Apis mellifera) stocks
Bibliographic record
Abstract
Honey bee populations in North America are an amalgamation of diverse progenitor ecotypes experiencing varying levels of artificial selection. Genetic differences between populations can result in variable susceptibility towards environmental stressors, and here we compared pesticide tolerances across breeding stocks using a mixture of seven pesticides frequently found in colonies providing pollination services. We administered the pesticide mixture chronically to in vitro reared larvae at four concentrations of increasing Hazard Quotient (HQ, or cumulative toxicity) and measured mortality during larval development. We found that different stocks had significantly different tolerances to our pesticide mixture as indicated by their median lethal toxicity (HQ50). The intensively selected Pol-Line stock exhibited the greatest pesticide sensitivity while Old World (progenitor) and putatively feral stocks were the most pesticide-tolerant. Furthermore, we found that activity of the detoxification enzyme esterase was positively correlated with pesticide tolerance when measured using two different substrate standards, and confirmed that larvae from the Pol-Line stock had generally lower esterase activity. Consistent with an increased pesticide tolerance, the Old World and putatively feral stocks had higher esterase activities. However, esterases and other detoxification enzymes (CYP450s and GSTs) were found in similar abundances across stocks, suggesting that the differences in enzyme activity we observed might arise from stock-specific single nucleotide polymorphisms or post-translational modifications causing qualitative variation in enzyme activity. These results suggest that selective breeding may inadvertently increase honey bees’ sensitivity to pesticides, whereas unselected, putatively feral and Old World stocks have larvae that are more tolerant.
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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 | 0.001 | 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".