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Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
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Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
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Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time

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Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time
Journal Article

Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time

2025
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Overview
Honey bee viruses are serious pathogens that can cause poor colony health and productivity. We analyzed a multi-year longitudinal dataset of abundances of nine honey bee viruses (deformed wing virus A, deformed wing virus B, black queen cell virus, sacbrood virus, Lake Sinai virus, Kashmir bee virus, acute bee paralysis virus, chronic bee paralysis virus, and Israeli acute paralysis virus) in colonies located across Canada to describe broad trends in virus intensity and occurrence among regions and years. We also tested climatic variables (temperature, wind speed, and precipitation) as predictors in an effort to understand possible drivers underlying seasonal patterns in viral prevalence. Temperature was a significant positive predictor of the total number of viruses per sample, which was highest in British Columbia (mean = 5.0). Lake Sinai virus (LSV) was the most prevalent overall (at 89%) and had the highest infection intensity, at an average of 3.9 × 10 8 copies per bee. Acute bee paralysis virus was the least prevalent virus (at 4.7%) and had the lowest infection intensity (1.9 × 10 5 copies per bee). Surprisingly, including Varroa abundance as a covariate did not significantly improve model fit for any virus. All viruses, except Kashmir bee virus, varied by region, and one or more climatic variables were significant predictors for six of the nine viruses. Although climatic effects were often inconsistent among individual viruses, we show that climatic variables can be better predictors of virus intensity and occurrence than Varroa mite abundance, at least when infestation rates are low.