Size-dependent responses of colony-founding bumblebee (Bombus impatiens) queens to exposure to pesticide residues in soil during hibernation
Bibliographic record
Abstract
Bumblebees and other key pollinators are experiencing global declines, a phenomenon driven by multiple environmental stressors, including pesticide exposure. While bumblebee queens spend most of their life hibernating underground, no study to date has examined how exposure to pesticide-contaminated soils might affect bumblebee queens during this solitary phase of their lifecycle. We exposed Bombus impatiens queens (n = 303) to soil treated with field-realistic concentrations of two diamide insecticides (chlorantraniliprole and cyantraniliprole) and two fungicides (boscalid and difenoconazole), alone or combined, during a 30-week hibernation period. We found that exposure to boscalid residues in soil doubled the likelihood of queens surviving through the colony initiation period (after successful hibernation) and laying eggs. Our data also revealed complex interactions between pesticide exposure and queen body mass on aspects of colony founding. Among others, exposure to cyantraniliprole led to lethal and sublethal post-hibernation effects that were dependent on queen size, with larger queens showing higher mortality rates, delayed emergence of their first brood, and producing smaller workers. Our results show that effects of pesticide exposure depend on intrinsic traits of bumblebee queen physiology and challenge our understanding of how bees respond to pesticides under environmentally realistic exposure scenarios.
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 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".