Impacts of antibiotic use, air pollution and climate on managed honeybees in Canada
Bibliographic record
Abstract
Antimicrobial resistance is a critical global health threat, significantly exacerbated by the overuse of antibiotics in agriculture. Here we investigate how recent antibiotic regulatory changes have impacted the usage of several level 2 (that is, World Health Organization ‘Watch’ list) antibiotics within the Canadian beekeeping sector. Utilizing national survey data, we examined trends in antibiotic usage and overwintering mortality rates from 2015 to 2023. Our findings reveal a significant reduction in the use of oxytetracycline, tylosin, lincomycin and fumagillin, decreasing from approximately 50% to 25% following regulatory restrictions implemented in 2018. Notably, this decrease was inversely associated with rising overwintering mortality rates, suggesting that withdrawal of antibiotics in the absence of effective alternatives may negatively impact colony health. Furthermore, multivariate analysis accounting for environmental confounders (based on 119,244 data points collected from 234 unique locations across Canada) identified nitrogen dioxide (NO2), a common air pollutant from diesel exhaust, as a strong predictor of mortality. This finding warrants urgent attention given that NO2 can degrade floral odours, rendering them undetectable to honeybees during foraging flights. These results highlight a complex interplay between antibiotic regulation, environmental stressors and honeybee health, emphasizing the need for comprehensive management strategies that mitigate antimicrobial resistance while safeguarding pollinator health. Antibiotic use in managed bee populations prevents losses from infectious diseases but can lead to the emergence of antibiotic resistance. Here the authors study the impact of recent regulatory restrictions on antibiotic use, in addition to climate and air pollution, on the beekeeping sector in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".