McAfee, A.; Alavi-Shoushtari, N.; Tran, L.; Labuschagne, R.; Cunningham, M.; Tsvetkov, N.; Common, J.; Higo, H.; Pernal, S. F.; Giovenazzo, P.; Hoover, S. E.; Guzman-Novoa, E.; Currie, R. W.; Veiga, P. W.; French, S. K.; Conflitti, I. M.; Pepinelli, M.; Borges, D.; Walsh, E. M.; Bishop, C. A.; Zayed, A.; Duffe, J.; Foster, L. J.; Guarna, M. M.

Improving our understanding of how climate influences honey bee parasites and pathogens is critical as weather patterns continue to shift under climate change. While the prevalence of diseases vary according to regional and seasonal patterns, the influence of specific climatic predictors has rarely been formally assessed. To address this gap, we analyzed how occurrence and intensity of three prominent honey bee disease agents (Varroa destructor [-] hereon Varroa [-] Melissococcus plutonius, and Vairimorpha spp.) varied according to regional, temporal, and climatic factors in honey bee colonies across five Canadian provinces. We found strong regional effects for all disease agents, with consistently high Varroa intensity and infestation probabilities and high M. plutonius infection probabilities in British Columbia, and year-dependent regional patterns of Vairimorpha spp. spore counts. Increasing wind speed and precipitation were linked to lower Varroa infestation probabilities, whereas warmer temperatures were linked to higher infestation probabilities. Analysis of an independent dataset shows that these trends for Varroa are consistent within a similar date range, but temperature is the strongest climatic predictor of season-long patterns. Vairimorpha spp. intensity decreased over the course of the summer, with the lowest spore counts found at later dates when temperatures were warm. Vairimorpha spp. intensity increased with wind speed and precipitation, consistent with inclement weather limiting defecation flights. Probability of M. plutonius infection generally increased across the spring and summer, and was also positively associated with inclement weather. These data contribute to building a larger dataset of honey bee disease agent occurrence that is needed in order to predict how epidemiology may change in our future climate.