Quantifying exposure of bumblebee (Bombus spp.) queens to pesticide residues when hibernating in agricultural soils
Bibliographic record
Abstract
Exposure to pesticides is a major threat to bumblebee (Bombus spp.) health. In temperate regions, queens of many bumblebee species hibernate underground for several months, putting them at potentially high risk of exposure to soil contaminants. The extent to which bumblebees are exposed to residues in agricultural soils during hibernation is currently unknown, which limits our understanding of the full pesticide exposome for bumblebees throughout their lifecycle. To generate field exposure estimates for overwintering bumblebee queens to pesticide residues, we sampled soils from areas corresponding to suitable likely hibernation sites at six apple orchards and 13 diversified farms throughout Southern Ontario (Canada) in fall 2019-2020. Detectable levels of pesticides were found in 65 of 66 soil samples analysed for multi-pesticide residues (UPLC-MS/MS). A total of 53 active ingredients (AIs) were detected in soils, including 27 fungicides, 13 insecticides, and 13 herbicides. Overall, the frequency of detection, residue levels (median = 37.82 vs. 2.20 ng/g), and number of pesticides per sample (mean = 12 vs. 4 AIs) were highest for orchard soils compared to soils from diversified farms. Ninety-one percent of samples contained multiple residues (up to 29 different AIs per sample), including mixtures of insecticides and fungicides that might lead to synergistic effects. Our results suggest that when hibernating in agricultural areas, bumblebee queens are very likely to be exposed to a wide range of pesticide residues in soil, including potentially harmful levels of insecticides (e.g., cyantraniliprole up to 148.82 ng/g). Our study indicates the importance of empirically testing the potential effects of pesticide residues in soils for hibernating bumblebee queens, using field exposure data such as those generated here. The differences in potential exposure that we detected between cropping systems can also be used to better inform regulations that govern the use of agricultural pesticides, notably in apple orchards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".