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Climatic predictors of prominent honey bee (Apis mellifera) disease agents: Varroa destructor, Melissococcus plutonius, and Vairimorpha spp. [1]

['Alison Mcafee', 'Department Of Biochemistry', 'Molecular Biology', 'Michael Smith Laboratories', 'University Of British Columbia', 'Vancouver', 'British Columbia', 'Department Of Applied Ecology', 'North Carolina State University', 'Raleigh']

Date: 2024-09

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 that were sampled at three time points. 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.

Funding: This work was funded by Genome BC through the Genomic Innovation for Regenerative Agriculture, Food and Fisheries (GIRAFF) program to LJF, LT, MMG, CAB, and JD. Data analyzed in this work was derived from the BeeCSI project, which was funded and supported by the Ontario Genomics Institute (OGI-185) to AZ, Genome Canada (LSARP #16420) to AZ, LJF, PG, SEH, EG, RWC, MMG and SFP, the Ontario Research Fund (LSARP #16420) to AZ, Genome Quebec, and the Government of Canada through Agriculture and Agri-Food Canada (AAFC) Genomics Research and Development Initiative (GRDI) funding (AAFC J-002368) to MMG and SFP. LT and EMW received salaries in part paid from AAFC GDRI funds, and MC received a stipend as Research Affiliate from AAFC as well as funds derived from the GIRAFF program. Salaries for AM, JC, HH, SKF, IMC, and DB were derived in part from funds awarded by Genome Canada. Salaries for AM and NT were also derived in part from funds derived from the GIRAFF program. This work was also supported by the Canadian Honey Council and the Technology Transfer Program (of which DB is a member) of the Ontario Beekeepers’ Association. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Given how widespread these honey bee pathogens and parasites are and their apparent associations with weather conditions, we sought to investigate relationships between the disease agents and climatic variables while accounting for broad differences between regions and years. To this end, we used a dataset, in part previously published [ 42 , 43 ], of Varroa infestation levels, Vairimorpha spp. spore intensities, and M. plutonius occurrence across five Canadian provinces, representing a wide range of climates (including Köppen climate types of oceanic in British Columbia, cold semi-arid in southern Alberta, subarctic in northern Alberta, and humid continental in Manitoba, Ontario, and Quebec), to identify associations with temperature, precipitation, and wind speed. While regional differences were most pronounced, climatic variables were also influential and largely reveal intuitive patterns. This dataset will help build the growing body of knowledge and data necessary to facilitate predictive modelling of disease agent dynamics under projected climate conditions.

EFB disease can cause severe damage to honey bee colonies by killing developing brood, thereby weakening the colony and reducing productivity [ 16 ]. M. plutonius is often detected in asymptomatic and apparently healthy colonies, especially those proximal to symptomatic colonies and apiaries [ 16 , 24 , 39 ]; therefore, dispersal from infected units is likely an important factor governing patterns of M. plutonius prevalence. EFB is thought to be an opportunistic disease that tends to manifest when larvae are under nutritional stress, such as when colonies are producing new brood very quickly in the spring or when pollen and nectar resources are low [ 16 , 24 ]. However, larval food supply is clearly not the only factor involved, since larvae fed in excess may still develop the disease [ 40 ]. Interestingly, Milbrath [ 16 ] and Bailey [ 41 ] suggest that even favorable foraging conditions could lead to temporary nutritional stress of larvae during the spring if a strong nectar flow causes nurse bees to reallocate their labor to process honey. Rowland et al. [ 9 ] found that higher precipitation (associated with poor foraging conditions) was linked to higher EFB incidence in England and Wales, but no relationship was found with temperature nor wind speed.

Vairimorpha spp. are microsporidian endoparasites of the honey bee midgut and, in temperate climates, tend to infect bees with seasonal intensities that are normally highest in the spring and early summer, then decrease in late summer and early fall [ 18 – 21 ]. Two species of Vairimorpha are known to infect honey bees in Canada: V. apis and V. ceranae with different seasonal trends. Whereas V. apis exhibits a regular peak in average gut spore loads in the spring and often a secondary peak in in the fall [ 28 ], V. ceranae trends appear more variable across different countries [ 29 – 32 ]. Regional differences in V. ceranae and V. apis abundance has led to speculation over whether each species’ relative success is influenced by climate, but it is not clear if this is the case [ 17 , 19 ]. Some [ 18 , 33 ] but not all [ 34 ] field studies have found that higher temperatures correlate with lower spore loads, and some research suggests that geographic region (and, by extension, climate) is an important factor influencing Vairimorpha spp. epidemiology [ 35 ]. Indeed, in addition to warm temperatures inhibiting V. apis and V. ceranae proliferation in laboratory trials [ 36 ], inclement (cold, windy, or wet) weather limits opportunities for defecation flights that help clear Vairimorpha spp. infections [ 37 , 38 ], but interactions among these variables have, to our knowledge, not yet been investigated.

Varroa is broadly considered the most economically damaging parasite of honey bees, with only some remote islands remaining Varroa-free [ 14 ], and its life cycle is closely tied to seasonal changes in honey bee brood production [ 14 , 25 ]. The mites disperse to new colonies through contact between bees at forage sources as well as robbing and drift (see reviews [ 14 , 25 ] for more information about Varroa biology and control). Varroa has successfully become established in all continents where honey bees are maintained, and only a handful of island territories remain Varroa-free [ 14 , 26 ]. Many studies broadly highlight the impact of winter brood breaks on Varroa loads and differences in Varroa population growth in temperate versus tropical climates (reviewed in Rosenkranz et al. [ 25 ]). The studies that have investigated relationships with weather conditions have focused mainly on temperature [ 9 , 22 , 23 , 27 ], with extreme heat being associated with reduced Varroa reproduction [ 22 , 23 ] but otherwise a positive relationship between Varroa occurrence and temperature has been observed [ 9 , 27 ]. One study conducted by Harris et al. [ 23 ], found that rainfall had no association with Varroa population growth rates, and a recent study by Rowland et al. [ 9 ] found a negative correlation between Varroa occurrence and precipitation and wind. Further studies in different regions would be helpful to clarify and substantiate these findings in different geographies.

Several etiological agents are known to occur in seasonal cycles, including but not limited to Vairimorpha spp. (formerly Nosema spp. [ 13 ], the causative agent(s) of nosemosis), Melissococcus plutonius (the causative agent of European foulbrood disease, or EFB), and the mite Varroa destructor (hereon referred to as Varroa, the causative agent of varroosis) [ 14 – 21 ]. Such seasonal patterns may be driven by direct effects of weather on pathogen or parasite survival and reproduction, or indirect effects via population or demographic changes within the honey bee colony. As an example, high temperatures (>35°C) reduce Varroa survival and reproduction directly [ 22 , 23 ]. whereas moderately increasing temperatures during the spring favor Varroa population growth indirectly through increased honey bee brood rearing (which Varroa requires for reproduction) [ 14 ]. Moreover, favorable conditions (warm temperatures with low wind and precipitation) increase dispersal opportunities between colonies for disease agents, but might also affect the risk of colonies manifesting diseases (such as EFB [ 24 ]) that are associated with poor food availability or changes in the number of nurse bees available to tend to brood. It is thus difficult to predict, a priori, how weather conditions may translate into changes in intensity or occurrence of disease agents.

Infectious diseases and parasites are among the dominant factors affecting honey bee (Apis mellifera) colony health in commercial operations [ 1 – 3 ]. Several recent studies have investigated relationships between climatic variables and overwintering colony mortality [ 4 – 8 ]. but similarly large-scale association studies between weather patterns and specific diseases or pathogens are more rarely conducted, with the notable exception of Rowland et al. [ 9 ], who examined patterns of disease incidence associated with climatic variables in England and Wales. Weather patterns are expected to become less stable as the climate changes, with higher frequencies of extreme events along with a shifting baseline to warmer temperatures and region-specific shifts in precipitation [ 10 – 12 ]. A better understanding of relationships between honey bee disease agents and current climatic trends would be an asset for predicting how these agents might change in the vicissitudes of our future climate.

Methods

Honey bee colonies and datasets This study includes analyses of a primary dataset (Varroa, Vairimorpha, and M. plutonius data collected in 2020 and 2021) and a Varroa validation dataset (Varroa only, collected in 2016). Some, but not all, elements of the primary and validation datasets have been previously described in several publications (see details below).

Primary dataset A subset of the primary dataset’s honey bee colonies and pathogen/parasite data have been previously described by French et al. [42] (240 colonies sampled in 2020 and 2021) along with additional colonies (a further 240 in both years) described here and partially reported in McAfee et al. [43] As described further below, samples for M. plutonius and Vairimorpha spp. testing were pooled ahead of analysis (4 colonies per composite sample), whereas samples taken for Varroa detection were not pooled. The total number of samples analyzed for M. plutonius, Vairimorpha spp., and Varroa were therefore N = 120, 120, and 480, respectively, though all were derived from the same 480 colonies. As previously described [42], the colonies were located in five provinces (British Columbia (BC), Alberta (AB), Manitoba (MB), Ontario (ON), and Quebec (QC)), and eight different regions. Within these regions, colonies were derived from the same beekeeping operation; therefore, operational and regional differences are indistinguishable. Two regions/operations were represented within AB (near Grande Prairie to the north and Lethbridge to the south); three regions/operations were represented in QC (near Quebec City in 2020 and near Montreal and Lac St. Jean in 2021); and BC, MB, and ON were represented by one region/operation each. The initial purpose of the experiment from which these data are derived was to test for impacts of colony proximity to different focal crops on various colony health indicators. In the present manuscript, our preliminary analysis showed that proximity to focal crops was not an influential factor; nevertheless, we include a description of the experimental design here for full transparency. Colonies were placed near or far from one of eight possible focal crops, which depended on region, and sampled at the start (immediately before or immediately after moving into pollination), middle (peak bloom), and end (immediately before or immediately after) of the pollination period. The timing and duration of the pollination period varied depending on commercial standards for each crop, but broadly fell between the months of April and August. In the case of corn, which is not pollinated by honey bees, colonies were sampled before sowing (April), during sowing (May), and at the end of the growing season (September). An equal number (N = 240) of additional colonies produced from the same stocks, according to the same standards, as those assigned to the focal crops but located far from the pollination yards (>1.5 km, with the exception of highbush blueberries which were > 1.3 km) are included in this dataset, bringing the final number of colonies to 480 across site types (near or far from crops) in both years, each subject to three sampling events over time. A subset of these data are also described in McAfee et al. [43] Please see French et al. [42] and McAfee et al. [43] for more detailed descriptions around apiary site selection. Colony locations in each province and at each time point are illustrated in S1 Fig and sampling dates for each crop and region are exactly as described by French et al. [42] (S3 Table therewithin), and are also contained within S1 and S2 Data. We were unfortunately unable to obtain information on surrounding honey bee colony densities for this dataset. In each region, colonies were either supplied by collaborating beekeepers, owned by the relevant institution, or purchased from producers, but in all cases sampling and management during the experiment were conducted by the research teams. As previously described [42], colonies were headed by locally overwintered queens and colony sizes were standardized in deep Langstroth-style single- or double- box pollination units according to standards for commercial pollination for each focal crop. Although Varroa is one of the parasites of interest, which we subsequently measured, early season Varroa control is often necessary to ensure colony vitality throughout the season; therefore, in some cases colonies were treated with miticides before the experiment began if adult bee infestation levels exceeded the economic threshold (>1 mite per 100 bees) [44]. Specifically, in early spring, colonies in BC (2020; n = 64) were treated with Formic Pro and colonies in southern Alberta (2020 and 2021; n = 48 in both years) were treated with Apivar according to manufacturer instructions. Note that although Apivar efficacy has waned in some regions [45], initial mite abundances in southern Alberta were very low (<1%); therefore, this potential issue did not have a meaningful impact on our results. Mite treatments applied to colonies prior to being purchased from commercial suppliers (BC in 2021 [n = 80] and MB in 2020 and 2021 [n = 56 and 16, respectively]) are unknown. All other colonies in northern AB (n = 16 in both years), ON (n = 40 in 2020), and QC (n = 16 in 2020 and 80 in 2021) were under university or government research laboratory management for the full calendar year and miticides were not applied. These differences in treatment (and other operational/regional differences) were accounted for statistically by including region as an interactive term in the model and by including colony as a random intercept term (accounting for differences in baseline mite levels). No colonies were treated between study commencement and termination, and no colonies were treated with Fumagillin-B (a product for controlling Vairimorpha spp.) or oxytetracycline (an antibiotic for controlling EFB disease).

Varroa validation dataset The Varroa validation dataset consisted of previously published data quantifying Varroa infestations in Canada in 2016 (described by Borba et al. [46]). We used these data to validate our findings relating Varroa to regional, temporal, and climate variables and explore how length of beekeeping season relates to Varroa occurrence. For our analysis, we only used Varroa infestation data from overwintered colonies belonging to, and managed by, beekeeper collaborators where their mite loads were not manipulated and colonies were not otherwise managed in a way that could affect Varroa populations (colonies were not split, requeened, or allowed to swarm) for the duration of the experiment. This includes N = 480 overwintered colonies located in BC, southern AB, southern ON, and southern QC. Colonies within BC were located in 11 yards and were owned by 11 operators in coastal BC (<100 km from the coast; 8 yards) and interior BC (Thompson/Okanagan and Kootenay/Boundary regions; 3 yards), which have notably different climates. Colonies in AB, ON, and QC were also distributed across multiple yards, all of which are shown in Borba et al. [46] Each colony was sampled at up to three time points during the active beekeeping season. Data were filtered to exclude colonies from which fewer than two mite wash samples were obtained between May and September, as well as colonies which beekeepers moved to unknown locations between mite sampling events.

Pathogen testing (primary dataset) Varroa, M. plutonius, and Vairimorpha 2020 and 2021 sample analysis was performed exactly as previously described [42]. Briefly, for M. plutonius and Vairimorpha, samples were analyzed as pooled replicates (15 bees from 4 colonies each, yielding n = 5 replicates from each site type (near to or far from focal crops) for each crop in each year. The same four colonies were pooled at each time point, and pooled colonies were always located together in the same yard (and therefore experienced the same climatic conditions). M. plutonius analysis was performed by endpoint PCR (forward primer: CAG CTA GTC GGT TTG GTT CC; reverse primer: TTG GCT GTA GAT AGA ATT GAC AAT) and Vairimorpha spp. spore counts were determined by microscopy (hemocytometer counting) and expressed as number of spores per bee [42]. Samples for Varroa mite counts were not pooled across colonies, and mite counts per 100 bees were determined using the alcohol wash method [47], modified slightly as previously described [46]. Therefore, final sample sizes for M. plutonius and Vairimorpha data were N = 60 pooled samples per time point, per year (360 samples from 120 pooled four-colony units), whereas the sample size for Varroa was N = 240 colonies per time point, per year (1,440 samples from 480 colonies). Please see the previously published methods for specific details regarding the protocols used [42].

Varroa testing (validation dataset) The Varroa abundances in colonies contributing to the validation dataset were determined by the alcohol wash method [47], as described in Borba et al. [46], and expressed as mites per 100 bees.

Climatic data extraction Datasets from Environment and Climate Change Canada climate stations [48] were used to extract hourly and daily climate variables (mean, maximum, and minimum daily temperatures, average daytime and nighttime wind speeds, and total precipitation). The climate stations that were within 30 km of the colony locations were selected and the daily and hourly datasets for the corresponding time of the sampling events were downloaded. The daily climate records were used to extract the daily climate variables for the sampling dates and were averaged over three weeks (21 days) prior to the sampling date. Averaging climatic variables across time prior to sampling dates improves model fits [9], and three weeks prior (corresponding to one honey bee brood cycle) was the longest period for which we had precise knowledge of colony GPS locations. Because wind is predominantly expected to influence honey bees via changing their flight behavior, and since flight only occurs during the daylight hours, we used the hourly climate records to extract daytime and nighttime averages of wind speed for the sampling dates, which were also averaged over three weeks prior to the sampling dates. Daytime was defined as 5 am to 8 pm for all locations and dates, except for September sampling in Ontario, for which daytime was defined as 6 am to 7 pm to more closely match actual sunrise and sunset times late in the season. For the colonies with more than one climate station within a 30 km distance, the climate data were extracted for each climate station independently as described above, then averaged across all climate stations within the 30 km distance. Prior to conducting statistical modelling, correlations among climate variables in the primary dataset were assessed by computing Pearson correlation coefficients (S2 Fig). Based on these results, we included only mean temperature, average daytime wind speed, and total precipitation in our statistical models (described under “Statistical analysis,” as these variables had correlation coefficients of |r| < 0.4 in 2020 and < 0.3 in 2021. We chose to use daytime wind exclusively, because wind is expected to only impact honey bees during the day when they have an opportunity to fly. For precipitation, we used daily totals, rather than daytime totals, because precipitation is also linked to nectar production in flowers and ambient humidity regardless of when it falls, and could therefore conceivably affect honey bee behavior, thermoregulation, or nutrition at any time.

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[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000485

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