Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage

Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage Gagandeep Brar, Lyna Ngor, Quinn S. McFrederick, Alex Torson, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4727319/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Honey bee gut microbiota play a number of specific roles in promoting host growth and physiology. While the gut microbiota of honey bee queens, workers, and larvae has been extensively studied, less is known about the role and composition of gut microbiota in the winter worker bees. This study investigates the dynamics of the gut microbiota in overwintering adult worker bees, focusing on two commercial bee strains: Bolton™ bees and Mann Lake™ bees. The strains were investigated under different storage conditions (indoor storage at 6°C and outdoor storage in natural conditions) during the winter months (October, November, and December). Utilizing 16S rRNA gene amplicon sequencing, we characterized the microbial composition of the whole gut. We observed a stable microbiota with slight variations in host-associated bacterial communities with Lactobacillus genus dominating in all the overwintering honey bee guts. Bolton bees exhibited higher abundance levels of Bartonella , Bifidobacterium , and Wolbachia , while Mann Lake bees showed increased abundance of Commensalibacter . Our results suggest that alpha diversity is influenced by the month rather than by the bee strain or storage conditions. We also found significant differences in beta diversity by month. Overall, in this study a stable gut microbiota was observed in both indoor and outdoor environmental conditions across different months with slight differences in abundance between bee strains. Considering all potential benefits of the honey bee gut microbiome to health and nutrition, this study suggests that beekeepers need not be concerned about whether overwintering storage conditions affect the gut microbiome. Biological sciences/Microbiology/Communities Biological sciences/Microbiology/Communities/Microbial ecology Honey bees gut microbiota temperature stress climate change Lactobacillus overwintering storage Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Winter presents a significant challenge for honey bee colonies in temperate regions. Over the last two decades, beekeepers have reported high colony losses during winters. These losses have been associated with low temperature stress, geographic relocation due to climate change, genotype, diseases, pesticides, and poor nutrition 1 – 4 . In preparation for overwintering, a honey bee colony undergoes notable alterations in both the behavior and physiology of the worker bees. These changes include shifts in endocrine profiles, decreased individual activity, increased nutrient reserves, extended longevity, and thermoregulating clustering at the colony level 1 . Low temperatures affect honey bee survival(Q. Wang et al., 2016) through higher larval mortality and reduced life expectancy in adulthood 6 . To mitigate these deleterious effects, honey bees have evolved life history strategies like cessation of brood rearing during the winters, increased immunity, and increased antioxidant expression 7 . Additionally, honey bees form a dense winter cluster to withstand harsh winter conditions. The thermoregulation of this winter cluster relies heavily on the insulation provided by the tightly packed outer layer of bees, known as the mantle bees, which is crucial for survival at low temperatures 8 . Moreover, to reduce the cold temperature stress on the bee colonies, and to improve overwintering survival, bee keepers often store the hives indoors. Storing hives under controlled cold climate conditions of 5°C − 7°C and 25% relative humidity has been shown to improve survival(Degrandi-Hoffman et al., 2019,Owens et al., 1971, Meikle et al., 2023). The symbiotic gut microbiota of honey bees is vulnerable to temperature stress 12 . The loss of honey bee workers during the winter is due to weather conditions as the primary stress factor, with starvation and diseases being secondary factors 13 . Moreover, honey bees experiencing cold stress have an increased risk of diseases and infections, which in turn raises the likelihood of colony losses. A recent transcriptomic study provides insight into the molecular mechanisms of cold resistance in Apis cerana . This study identified various temperature-sensitive proteins, such as heat shock proteins and zinc finger proteins, which were upregulated when bees were exposed to 0°C and are considered candidate genes for honey bees’ tolerance to cold stress 14 . Apart from the host physiology, the role played by the honey bee gut microbiome has also emerged as one of the most important physiological aspects to investigate in relation to honey bee cold tolerance. The gut bacterial species within honey bees undergo alterations due to various environmental and developmental stresses, with temperature being a crucial one 15 . The honey bee gut microbiome contains five core host-specific bacterial species that are highly conserved and comprise 95% of the total microbes 16 , 17 . The hindgut of every adult worker contains Snodgrassella alvi, Gilliamella apicola, Lactobacillus Firm-5, Lactobacillus Firm-4 , and Bifidobacterium species. Along with these core bacteria, there are non-core species including Bartonella apis, Commensalibacter spp ., and some other identified and several unidentified species that have been collected from the surrounding environment like hives and plants 18 , 19 . These bacterial species promote honey bee growth, physiology, facilitate breakdown of toxic dietary compounds, and modulate immune functions 20 .Dynamic changes to the microbiota composition have been observed throughout the seasons 21 – 23 . The gut microbiota has been reported to differ between winter and summer honey bees, with reduced α-diversity and higher levels of Bartonella and Commensalibacter during winter 24 . Moreover, temperature during different months affects the composition of the gut microbiota by gradually changing the bacterial diversity between the seasons. In subtropical conditions, precipitation affects the composition of the honey bee gut microbiota 25 . Numerous studies have been conducted to understand the seasonal dynamics of honey bee gut microbiota. However, very few studies have explored the effects of prolonged exposure to harsh climatic conditions on honey bee gut microbiome. For instance, direct exposure to high temperatures has been found to influence the gut microbiota in both honey bees and bumble bees, rendering them more resistant to heat stress 26 . Additionally, elevated temperature has shown to affect nectar microbes where the abundance of bacteria increased in the warmer temperature, influencing bumble bee forager preference 27 . Beyond seasonality and temperature, vast differences in gut microbiota at the strain level has also been observed in two closely related honey bee species, Apis mellifera and Apis cerana 28 . The gut composition exhibits greater divergence among genetically distinct bees at both phylotype- and sequence-discrete population levels 29 . Additionally, significant variation in both composition and function among diverse Asian honey bee populations has been reported, showing distinct patterns of their gut microbiota 30 . A previous study showed genetic divergence and functional convergence of gut bacteria in the eastern honey bee, Apis cerana and the western honey bee, Apis mellifera 31 and between Apis cerana and Apis florea 32 . Differences in gut microbes between strains of Apis mellifera may influence the performance of those honey bee strains under different climate conditions. The goal of this study was to assess whether overwintering storage conditions disrupt the stability and diversity of the gut microbes in overwintering honey bees. To understand long term storage effects on gut microbiota, we compared the whole gut microbiota of two commercial strains of Apis mellifera i.e., claimed cold hardy bees https://boltonbees.com/pages/mn-hardy-hives (Bolton Bees, MN) that are bred and reared in Minnesota and Italian bees (Mann lake Bees, MN) of unknown rearing. Hives were stored either at a constant 6°C indoor or kept outside in natural conditions during winter. We predicted that hives stored outside in natural fluctuating temperatures will have different gut bacterial communities due to cold stress compared to hives stored in constant temperatures. We also predicted that bacterial communities would change when sampled over a three-month period of falling temperatures, viz. October, November, and December. Results Alpha diversity To investigate alpha diversity patterns, generalized linear mixed models (LMM) that included Shannon diversity as a response variable was applied. (Table 1 ). The most parsimonious, best-fit model included month as a fixed effect, and random effects for hive ID (different hives in each treatment) (Table 2 ). In contrast to the predictions, alternative models incorporating bee strain and storage treatment did not significantly improve the best-fit model. The random effects structure in the best fit model showed relatively little variation among hives (For hives and replications, SD = 0.1604 & 0.0406, respectively), when compared to the residual error of the model (SD = 0.3560). This indicates variation between hives and the replications do not account for the majority of the variation in Shannon diversity. Furthermore, Shannon diversity index in October did not differ significantly from November (p = 0.1275), and December (p = 0.398), whereas it was significantly lower in November compared to December (p = 0.0045). Month explained only 8% of the variation in Shannon diversity, whereas the full model explained 23% of the variation in Shannon diversity. Overall, results indicate a temporal variation in Shannon diversity, but the strain of bee and whether the hives were stored inside or outside had no effect (Fig. 1 ). Table 1 Comparisons of Akaike Information Criterion (AIC) value for different Generalized Linear Mixed Models (GLMMs) with relevant predictor and random variables. Best fit GLMM with the lowest AIC value is highlighted in bold. ∆ AIC ≤ 2 indicated similar models, which leads to the selection of simpler model (i.e. with less parameters). Model AIC Model: Shannon diversity index~ Storage status + month + bee strain + 1|hive ID 110.0 Storage status * month * bee strain + 1|hive ID 112.8 Storage status * month + bee strain + 1|hive ID 106.8 Storage status + month * bee strain + 1|hive ID 114.6 Storage status * bee type + strain + 1|hive ID 113.7 Storage status + month + 1|hive ID 108.0 Month + bee strain + 1|hive ID 108.1 Storage status + bee strain + 1|hive ID 116.2 Month * bee strain + 1|hive ID 110.9 Storage status * bee strain + 1|hive 118.2 Storage status + 1|hive ID 114.3 Bee strain + 1|hive ID 116.4 Month + 1|hive ID 106.1 1|hive ID 112.5 Table 2 Results of best fit Generalized Linear Mixed Model from Shannon diversity index. Model β Estimate ± SE df t value p value Variance SD Shannon diversity index ~ Month + 1|hive ID Fixed Effects (Intercept) 3.33555 ± 0.07521 33.48419 44.352 < 2×10 –16 a Month (Reference: October November − 0.16872 ± 0.08512 95.20089 -1.982 0.0503 December) 0.11101 ± 0.08447 95.08469 1.314 0.1920 Random Effects Hive (Intercept) 0.02506 0.1583 Residual 0.1284 0.3584 Number of observations = 107; groups: Hive, 12; a Statistically significant at p ≤ 0.05. SE = standard error; SD = standard deviation Beta diversity To investigate beta diversity patterns, we conducted PERMANOVA using the adonis2 package in R, testing various models with Bray-Curtis distance matrices (phyloseq) as a response variable. The simplest and best-fitting model included storage status and month in interaction with bee strain as a fixed effect, and block (strata) as bee hive ID; Distance ~ Storage status + Month * Bee strain, strata = hive ID (Table 3 ). The overall composition of bee gut microbiomes differed by bee type (adonis F = 3.19, df = 1, R 2 = 0.0242, P = 0.002), month (adonis F = 5.09, df = 2, R 2 = 0.0872, P = 0.001), but not storage status (adonis F = 0.57, df = 1, R 2 = 0.0047, P = 0.887). The interaction between month and bee strain was significant (adonis F = 1.57, df = 2, R 2 = 0.0269, P = 0.001). Month explained only 8.7% of the variation, whereas the bee type, and interaction between month and bee strain explained 2.4%, and 2.6% of the variation, respectively. Overall, these findings suggest that the microbial dynamics in overwintering honey bee workers are influenced by bee strain over the months, with stability observed in storage conditions (Fig. 2 ). Pairwise comparison showed significant differences in gut microbiota when compared October – November ( p value = 0.002), November – December ( p value = 0.001), and October - December ( p value = 0.001). Table 3 Best fit permutation analysis of variance (PERMANOVA) model applied using the adonis function with distance matrices with 999 permutations and bee hives accounted as a blocks – strata. Model Df Sum of squares R 2 F Pr (> F) Distance ~ Storage status + Month * Bee strain, strata = hive ID) Fixed Effects Storage Status 1 0.0894 0.00487 0.5701 0.887 Month 2 1.5975 0.08702 5.0950 0.001 Bee stain 1 0.5001 0.02724 3.1903 0.001 Month * bee strain 2 0.4945 0.02694 1.5771 0.021 Residual 100 15.6767 0.85394 Total 106 18.3581 1.00000 a Statistically significant at p ≤ 0.05. Taxonomical abundance Following the removal of barcodes and primer sequences through QIIME2 (Demultiplexing), we obtained a collective sum of 34,879,494 paired-end 16S rRNA gene amplicon reads. The average read count per sample was 317,086, with an overall average quality score of 34. Across the 108 samples analyzed, the range of reads per sample varied from a minimum of 56,533 to a maximum of 1,314,819. Analyzing the taxonomic profile revealed a predominance of Firmicutes (70.96%), Proteobacteria (20.18%), and Actinobacteriota (5.83%) at the phylum level. We investigated the presence of the core gut microbiota and identified only a limited number of reads for Snodgrassella . Lactobacillus Firm-5 and Lactobacillus Firm-4 were not detected in our samples. We focused on examining the relative abundance of two non-core ( Bartonella and Commensalibacter) and two core ( Gilliamella and Bifidobacterium ) microbes, along with the endosymbiont Wolbachia . The bacterial species-specific analysis for relative abundance was performed using mixed model with formula: Abundance ~ Storage status + Month + Bee strain + (1 | hive ID ). Considering the importance of genus Bartonella in a previous study 24 , we analyzed the abundance of two Bartonella species: Bartonella apis and another denoted as uncultured Bartonella . Bartonella apis abundance did not differ by month (df = 99.11, F value = 2.27, p -value = 0.1085), bee strain (df = 38.26, F value = 0.1824, p -value = 0.6717), or storage status (df = 38.26, F value = .1824, p -value = 0.6717). The Bartonella uncultured ASV significantly differed by month ( df = 98.72, F value = 4.74, p -value = 0.0107) while storage status (df = 34.58, F value = 0.0528, p -value = 0.81954) and bee strain (df = 11.559, F value = 0.1322, p -value = 0.72274) were not significant. We found a significant difference in abundance between December and November (SE = 1.03, df = 97, p -value: 0.0085), but not between October and December (SE = 1.21, df = 112, Tuckey HSD, p -value = 0.358) or October and November (SE = 1.21, df = 112, Tuckey HSD, p -value = 0.456). For Commensalibacter (Commensalibacter Acetobacteraceae_bacterium) , bee strain (SE = 1.48, df = 102, Tukey HSD, p -value < 0.0001) was the only factor significantly influencing abundance with Mann Lake bees having a higher abundance than Bolton bees. The effect of storage status (df = 107, F value = 0.261, p -value = 0.6097) and month (df = 107, F value = 1.6663, p -value = 0.1938) was not statistically significant. Bee strain is a significant predictor of Bifidobacterium ( Bifidobacterium uncultured_Bifidobacterium ) abundance where Bifidobacterium showed higher abundance in Bolton bees compared to Mann Lake bees (SE = 0.077, df =102, Tuckey HSD, p -value = 0.0014). The storage status (df = 107, F value = 0.0682, p -value = 0.7944) and month (df = 107, F value = 0.8069, p -value = 0.4489) variables did not contribute significantly to the variation in this model. The storage status (df = 37.34, F value = 6.67, p -value = 0.013) and month (df = 99.15, F value = 14.18, p -value = 3.808×10 −6 ) significantly affected the abundance of Gilliamella ( Unclassified Gilliamella ), while Bee strain did not show a significant effect (df = 12.10, F value = 0.675, p -value = 0.426). Pairwise comparison showed significant differences in abundance during October and December (SE = 0.197, df =112, Tuckey HSD, p -value < 0.0001), November and December (SE = 0.166, df =97.5, Tuckey HSD, p -value = 0.002), but no significant difference during November and October (SE = 0.197, df =112, Tuckey HSD, p -value = 0.5361). A significant effect of the storage status on the abundance of Gilliamella was seen between the Inside and Outside bees (SE = 0.215, df =45, Tuckey HSD, p -value = 0.0272). storage status (df = 48.66, F value = 0.0377, p -value = 0.846) and Month (df = 99.42, F value = 0.97, p -value = 0.38) is not a significant predictor of abundance in Wolbachia ( Unclassified Wolbachia ). Wolbachia abundance was significantly different between the bee strains (df = 11.78, F value = 5.352 p -value = 0.03959). The results suggest a potential difference in gut microbial abundance between Bolton bees and Mann lake bees, but the significance was marginal (SE = 0.243, df =14, Tuckey HSD, p -value = 0.0570). Overall, this taxonomic data emphasizes the marginal differences in gut microbiome composition between bee types, revealing a temporally stable microbial composition that is not influenced by storage conditions in overwintering worker honey bee (Figure 3). Discussion We used a 16s rRNA gene amplicon sequencing approach to study/characterize the gut microbiota of overwintering adult worker bees between (a) two commercial bees (Bolton Bees and Mann Lake bees), (b) different storage status/conditions and, (c) different months during storage/ a season. In this study, we hypothesized that temperature would be one of the most important environmental stressors affecting the gut microbiota in overwintering honey bees. Surprisingly, we found no changes in the diversity, composition, or abundance of the microbiota when the bees were stored at constant 6°C or outside during the winter. Despite the physiological stress induced by cold temperatures on honey bee physiology, this study demonstrates that honey bees have the potential to acclimatize their gut microbiota despite the cold stress conditions. One possible explanation for these findings is that honey bees rely on specific bacterial species to maintain their health and utilize the functions performed by the gut microbiota, and therefore the bees retain and maintain these bacterial species to extract sufficient resources from them. Interestingly, the overwintering bees’ gut microbiome was dominated by Lactobacillus genus, which contributed to an increased bacterial load across all samples, regardless of storage conditions. As the most important genus in the guts of honey bees 33 , 34 , Lactobacillus plays an important role in the health of bees by showing probiotic characteristics. Lactobacilli have shown an ability to digest flavonoids and other compounds present in the pollen wall 35 , to inhibit pathogens 36 – 38 and to release short chain fatty acids and vitamins used by midgut cells as an energy resource 39 . A higher abundance of Lactobacillus during overwintering phase may support heat production by worker bees, stabilizing colonies temperature. The only differences in bacterial composition at the species level were observed between both bee types. Bolton bees exhibited significantly higher abundance of Bartonella (in some instances), Bifidobacterium , and Wolbachia , whereas Mann lake showed significantly higher abundance of Commensalibacter when compared to Bolton bees. The bees obtained from Bolton Bees represent specific genetic lines known for their resilience in Minnesota's harsh climate. This genetic line, named after the location, where the parent Queen overwintered (Bolton, Minnesota), is derived from the base stock called MN Hygienic. The queens from the MN-Hardy line, used as grafts for queen production, have successfully endured the challenges of a prolonged and frigid Minnesota winter. Typically, such severe winters could affect honey stores, cleansing flights, and brood-laying, but these MN-Hardy Queens are claimed to have demonstrated ability to thrive under these cold winter conditions ( https://boltonbees.com/pages/mn-hardy-hives ). On the other hand, “OHB Italian” Mann Lake bees, sourced from Olivarez Honey Bees (OHB) in Northern California, are accompanied by claims for their hive performance, disease resistance, and overall robust health. The Italian Queens from OHB have reportedly undergone extensive breeding and careful genetic selection. The incorporation of traits such as "Minnesota Hygienic" and "VSH" (Varroa Sensitive Hygiene) further strengthens their innate ability to resist diseases and combat mite infestations. These two distinct types of bees have been specifically employed for their resilience to withstand the challenging winters typical of the Midwest, making them interesting subjects to study the gut microbiome during the overwintering period. Among the social bees, the transmission of symbiotic bacteria in the gut occurs among successive generations of siblings through social interactions. In a previous study on honey bees, the colonization of specific sets of bacteria, such as Lactobacillus Firm5 and Bifidobacterium Bifido-1.2, in genetically varied hosts strongly suggests that the genotype significantly influences the microbiota structure 29 . Another study on honey bees has identified marked differences in the core gut microbial community when comparing different lineages, which include Maltese honey bees (lineage A) to the Italian honey bees (lineage C). Notably, Maltese honey bees exhibited an inverse proportion of Lactobacillaceae and Bartonellaceae when compared to Italian honey bees 40 . The functionality of these differences between bee strains have not been studied. This finding underscores the extent of strain-level diversity within the bacterial communities. Our study showed extensive overlapping of the gut microbial strains among both bee types and differences in specific bacterial species. The results of the current study raises several fundamental questions regarding the evolution and maintenance of stable microbiota with slight change in specific host-associated bacterial communities. The findings in this study reveal varying degrees of presence of bacterial species in overwintering honeybees across different months. There were significant differences in the richness and evenness (Shannon Diversity Index) between October and November but no differences were noted in the bees sampled in November when compared to December samples. Additionally, beta diversity shows difference in species diversity in different months. Before overwintering, foragers collect pollen, water, nectar, and propolis. Remarkably, the overwintering honeybee samples exhibited consistent dominance in species composition with no significant difference in their abundance across different months. However, a significant difference in beta diversity was observed and this is consistent with findings from prior studies 41 , 42 . These findings could be interpreted as that during the timeframe when the honey bee workers are confined to their hives without access to foraging, there is a lack of exposure to new environmental microorganisms to colonize and populate the bee gut. Previous studies have noted that the gut microbiota differs between winter and summer honey bees, with the long-lived winter bees exhibiting a stable microbiota with reduced α-diversity and higher levels of Bartonella and Commensalibacter 24 . This lower community alpha diversity with Bartonella and Commensalibacter as dominant bacterial species may confer certain physiological benefits. However, these studies also collectively reveal minor variations in gut microbial communities in temperate honey bee colonies during winter, indicating a shift in dominance, with the non-core bacterium Bartonella surpassing the core bacterial species 24 , 42 , 43 . Another recent study also highlighted the significance of Bartonella , highlighting its expanded capability to convert metabolic wastes such as lactate and ethanol into pyruvate, which potentially provides energy for the host as well as other symbionts 44 . Considering the importance of Bartonella in previous studies, we analyzed two different species of Bartonella for their taxonomic abundance. However, we found the Lactobacillus genus as dominant, present in higher abundance, contrasting with the findings of a previous study where the Bartonella genus showed dominance in winter bees. Lower diversity in the gut microbiota was observed in both summer and winter, with predominance of Gilliamella apicola and Snodgrasella alvi 25 . Overall, we observed that the honey bee gut microbiota remains stable across different months whether under storage of natural conditions in mid-western part of the United States. In this study, it is possible that seasonal- or storage condition-dependent variations in the hind gut core bacteria are either not detectable, or are being overridden by the non-core, non-stable midgut bacteria. It is possible that slight differences in the relative abundance of bee-associated bacterial species are influenced by changes in other non-core hive-associated bacteria. A previous study 45 identified co-evolutionary relationship of core gut bacteria and honey bee that is generally very similar and share the same core bacterial species. Measuring slight differences between bee strains in terms of absolute abundance would be challenging due to costs associated with increasing the depth of 16S sequencing. Additionally, 16S rRNA data cannot measure absolute abundances, only the relative abundance and general compositional nature of the microbiome. To enhance bacterial species detection resolution and functional profiling at the gene level, future studies could employ shotgun sequencing instead of 16S amplicon sequencing. Our study was also limited in timeframe, only sampling in the fall and early winter. We observed beta diversity drastically decline in the month of November, and it is likely that this pattern could change if bees were sampled during subsequent months, such as January, February, and March. In conclusion, our study shows that the gut microbiota of overwintering honey bees is dominated by one specific bacterial genus, Lactobacillus , which contribute to the bees’ ability to withstand environmental stressors due to known functionality 33 – 39 . Our study of the gut microbiota in overwintering honey bees reveals significant stability across different months and storage conditions. However, there was a significant difference in alpha and beta diversity, which was influences by month irrespective of storage conditions and different bee strain. Furthermore, our analysis reveals minor differences in the gut microbiota between different bee types, viz. Bolton and Mann Lake bees. Despite the microbial differences among the bee types, the overall stability of the gut microbiota across different months and storage conditions suggests a remarkable resilience of honey bee gut bacteria to environmental changes. This resilience may be attributed to the co-evolutionary relationship between honey bees and their gut microbiota, which has developed over millions of years 45 . Finally, our findings suggest that beekeepers need not be overly concerned about changes in the gut microbiota during the winter months, as the bacteria appear to remain stable regardless of storage conditions. Overall, our study contributes to the growing body of research on the gut microbiota of honey bees and its role in bee health during winter. By demonstrating the stability of the gut microbiota across different months of storage and storage conditions, this study provides important insights for beekeepers and researchers alike. Further research is needed to explore the mechanisms underlying the resilience of honey bee gut bacteria and its implications for bee health. Methods Insect samples Honey bee hives were managed outside in the field from the first week of May to the last week of October and then 3 hives from each bee strain were shifted to refrigerated cargo containers for overwintering storage (6°C) in November while 3 hives from each bee strain were left outdoors in the field. The first sampling was done on 10/17/2022 (base sampling of outdoor bees), followed by moving some hives inside storage on 10/21/2022. The second sampling was done on 11/15/2022 from hives both inside and outside. The third sampling was done 12/14/2022, again for both inside and outside hives (Fig. 4 , Table. 4) Table 4 Representation of honey bee sample collected from various storage environments across different months. Bee strain Months Mann lake Bees : Outside (6 hives) Bolton Bees : Outside (6 hives) October 3 bees/hive 3 bees/hive 3 bees/hive 3 bees/hive Mann lake Bees Bolton Bees Storage - Constant 6°C (3 hives) Outside (3 hives) Storage - Constant 6°C (3 hives) Outside (3 hives) November 3 bees/hive 3 bees/hive 3 bees/hive 3 bees/hive December 3 bees/hive 3 bees/hive 3 bees/hive 3 bees/hive DNA extraction Three bees from each treatment were used for whole gut dissection after surface sterilizing bees using 1% sodium hypochlorite followed by three washes using sterilized water in sterile conditions. Whole gut samples were prepared by bead-beating the samples on a Qiagen Tissuelyser for 6 min at 30 Hz to disrupt recalcitrant cells. Samples for cell lysis were prepared by combining two 3-mm chromium steel beads and approximately 50 µl of 0.1 & 0.5 mm ZR BashingBeads™ inside lysis tube (Biospec, Bartlesville, OK) with 750 µl of ZymoBIOMICS™ lysis solution and 20 µl of proteinase K. A subsequent round of bead beating involved rotating the samples for 6 minutes at 30 Hz, followed by an incubation period at 56°C for one hour. DNA was extracted using ZymoBIOMICS™ DNA microprep kit collection (Zymo research, Irvine, California) including 2 blank extractions as a no template control for further downstream analysis. PCR Amplification and Illumina Sequencing 16S rRNA gene libraries for paired-end reads were generated following a previously published protocol (McFrederick & Rehan, 2016; Russell & McFrederick, 2022). The V5-V6 region of the 16S rRNA gene was amplified using 16S rRNA gene primers (799F mod3: CMGGATTAGATACCCKGG and 1115R: AGGGTTGCGCTCGTTG), each incorporating a unique barcode sequence. PCR1 reactions were conducted with 2 µl of DNA, 10 µl of 2× Pfusion High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA), 10 µl of ultrapure water, and 0.5 µl of 10 µM 799F-mod3 and 0.5 µl of 10 µM 1115R primers. The reaction condition for PCR1 was 94°C for 3 minutes, 94°C for 45 seconds, 52°C for 1 minute, 70°C for 1:30 minutes, repeated step2 29X, and 72°C for 10 minutes to amplify this region. We ran gel electrophoresis to confirmed if 16s rRNA primers were successfully attached to our samples. To complete the Illumina adapter sequence, we initiated the process by cleaning the PCR product with exonuclease and shrimp alkaline phosphatase. Exonuclease was applied to remove excess primers, while shrimp alkaline phosphatase was used to eliminate residual deoxynucleoside triphosphates (dNTPs). Four µL of the 1x ExoSAP was mixed with each 7µL PCR1 sample. This reaction was incubated at 37˚C for 30 minutes and then 95˚C for 5 minutes. Following this initial cleanup, the purified PCR products were employed as templates for a second PCR. This subsequent PCR (PCR2) utilized 1 µl of the cleaned PCR product as a template, using the same primers (PCR2F and PCR2R), and was conducted under conditions identical to the initial PCR. For PCR2, a 1 µl aliquot of Exo-Sap PCR1 product was utilized to conduct the second step of Illumina library preparation. In PCR2, the Exo-Sap PCR1 products were further amplified, incorporating linker poly-A primers for recognition on the sequencing platform. The PCR2 reaction conditions included an initial denaturation at 94°C for 3 minutes, followed by cycles of 94°C for 45 seconds, 58°C for 1 minute, 72°C for 1 minute and 30 seconds, with step 2 repeated 14 times, and a final extension at 72°C for 10 minutes, with final volume of 25 µl. To ensure uniform DNA input quantities, Invitrogen DynaMag TM-96 Side Skirted kit was employed for the normalization step across all samples prior to submitting a final volume of 10 µl for sequencing. The 16 rRNA gene sequencing was performed on NextSeq2000 P2 600 cycle kit (2x300xN/A) that produced total of 382.53 M reads at Q30 of 88.96%. Bioinformatics QIIME 2-2019 was used for the visualization and trimming of low-quality ends in reads from raw 16S rRNA sequence libraries. Subsequently, DADA2 was utilized for the assignment of sequences to amplicon sequence variants (ASVs), which represent 16S rRNA gene sequences with 100% matches. This process involved the removal of chimeras and reads with more than two expected errors. Taxonomy was assigned to the ASVs through the sklearn classifier, trained specifically for the 799-1,115 region of the 16S rRNA gene, utilizing the SILVA database. Additionally, local BLASTn searches against the NCBI 16S microbial database (accessed October 2023) were conducted. Features were filtered from the resultant ASV table, corresponding to contaminants identified in the blanks using the R package ‘decontam’ (version 1.10.0) at a conservative threshold of 0.5. This process aimed to identify contaminants while also eliminating ASVs identified as chloroplast and mitochondria. To standardize the number of sequences per library, alpha rarefaction in QIIME2 was employed, and 4000 reads per sample were selected. This approach ensured the retention of 107 out of 108 samples while capturing most of the diversity. Generalized Linear Mixed-effects Models (GLMMs) was used to model the Shannon diversity index. All GLMMs were fitted using the lme4 package 48 and lmer Test package 49 in R 50 , using R studio version 4.2.2. Bee strain, storage treatment (exposure to natural vs. storage at 6°C temperature), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measuring of each hive 51 . A stepwise backward selection process was employed for model selection. Initially, a full model was run with all fixed effects and random effects. Then all possible interactions among fixed effects were examined, followed by the systematic removal of one fixed effect at a time. The process continued until no further model improvement (e.g., lower AIC) could be observed. A difference of ≤ 2 in the AIC indicated that models were similar, in which case the simpler model (i.e., with fewer parameters) was selected as the best-fit model 52 . The best fit model was diagnosed for model assumptions through residual diagnostic plots using the ‘DHARMa’ package 53 . Finally, package ‘ggeffects’ version 0.16.0 54 was used to extract means, standard deviations, and 95% confidence intervals for the predicted values of Shannon diversity index for different sampling times. Tukey HSD tests for multiple comparisons between treatments was employed with estimated marginal means comparisons (EMMs) using the ‘emmeans’ function (Russell V. Lenth, 2024). For beta diversity analysis, Adonis Bray–Curtis distance dissimilarities and nonmetric multidimensional scaling (NMDS) ordination were conducted in R v4.3.0 with ‘vegan’ package. The 'phyloseq’ (ver 1.34.0) R package was used for beta diversity analysis. Permutation analysis of variance (PERMANOVA) was applied using the ‘adonis’ function on distance matrices with 999 permutations. The differences between bee gut microbiomes including distance as a dependent variable and months (October, November, and December), bee strain (Bolton bees and Mann lake bees) and Storage Status (Inside and Outside) as independent variable were assessed using separate Adonis models. In all models, bee hives were accounted as a block- strata, where permutations were allowed among levels of all the variable, but within each level of bee hives (random variable), no permutations occurred. To understand the effect of different variables on the abundance of candidate bacterial species, Generalized Linear Mixed-effects Models (GLMMs) was used to model the abundance of particular bacterial species. Bee strain, storage treatment (exposure to natural vs. storage at 6°C), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measures of each hive 51 . The fitted GLMM and lm was diagnosed for issues with heterogeneity, overdispersion or missing co-variates using the residual plots. To evaluate the significance of fixed effect, analysis of variance tables for the GLMM was calculated using Wald chi-square tests. Tukey HSD tests was employed for multiple comparisons between treatments with estimated marginal means comparisons (EMMs) using the ‘emmeans’ function (Lenth, 2024). Declarations Data availability and benefit-sharing statement: https://datadryad.org/stash/share/ycQx06SicJFuCPsZ0XMkwLVX9F6DetlucISxvTQoQF0 Acknowledgments: The authors thank Melanie Tischleder and Nyle Jonason for their technical assistance through this project. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. Author contributions : Gagandeep Brar (GB) is the first author, with Lyna Ngor (LN), Quinn McFrederick (QSM), Preetpal Singh (PS), Arun Rajamohan (AR), Alex Torson (AT), Joseph Rinehart (JR), and Julia Bowsher (JB) as co-authors. GB, JR, and JB conceived and designed the study. GB and LN processed samples, and GB and PS analyzed the data. GB wrote the first version of the manuscript. All authors contributed to reviewing the final manuscript. Funding : Funding was provided by NSF RII Track-2 FEC 1826834, NSF-IOS-1557940, and USDA-ARS 3060-21220-032-00D Ethics approval and consent to participate Not applicable. Consent for publication Upon approval, we give full consent for publication Competing interests The author(s) declare no competing interests. “All authors affirm that the study confirms to the ethical standards and integrity policies of the Scientific Reports” References Genersch, E. Honey bee pathology: Current threats to honey bees and beekeeping. Appl Microbiol Biotechnol 87, 87–97 (2010). Abou-Shaara, H. F., Owayss, A. A., Ibrahim, Y. Y. & Basuny, N. K. A review of impacts of temperature and relative humidity on various activities of honey bees. Insectes Soc 64, 455–463 (2017). Giannini, T. C. et al. Crop pollinators in Brazil: a review of reported interactions. Apidologie 46, 209–223 (2015). Hristov, P., Shumkova, R., Palova, N. & Neov, B. Factors Associated with Honey Bee Colony Losses: A Mini-Review. Veterinary Sciences 2020, Vol. 7, Page 166 7, 166 (2020). Wang, Q. et al. Low-Temperature Stress during Capped Brood Stage Increases Pupal Mortality, Misorientation and Adult Mortality in Honey Bees. PLoS One 11, e0154547 (2016). Steinhauer, N., vanEngelsdorp, D. & Saegerman, C. Prioritizing changes in management practices associated with reduced winter honey bee colony losses for US beekeepers. Science of the Total Environment 753, (2021). Aurori, C. M. et al. What is the main driver of ageing in long-lived winter honeybees: Antioxidant enzymes, innate immunity, or vitellogenin? Journals of Gerontology - Series A Biological Sciences and Medical Sciences 69, 633–639 (2014). Stabentheiner, A., Pressl, H., Papst, T., Hrassnigg, N. & Crailsheim, K. Endothermic heat production in honeybee winter clusters. Journal of Experimental Biology 206, 353–358 (2003). Degrandi-Hoffman, G., Graham, H., Ahumada, F., Smart, M. & Ziolkowski, N. The Economics of Honey Bee (Hymenoptera: Apidae) Management and Overwintering Strategies for Colonies Used to Pollinate Almonds. J Econ Entomol 112, 2524–2533 (2019). Owens, C. D., States, U. & Service, A. R. The thermology of wintering honey bee colonies. Preprint at (1971). Meikle, W. G., Corby-Harris, V., Ricigliano, V., Snyder, L. & Weiss, M. Cold storage as part of a Varroa management strategy: effects on honey bee colony performance, mite levels and stress biomarkers. Scientific Reports 2023 13:1 13, 1–15 (2023). Coulibaly, K. A. S., Majeed, M. Z., Sayed, S. & Yeo, K. Simulated Climate Warming Influenced Colony Microclimatic Conditions and Gut Bacterial Abundance of Honeybee Subspecies Apis mellifera ligustica and A. mellifera sinisxinyuan. J Apic Sci 66, 15–27 (2022). Zhang, Y. et al. Mediating a host cell signaling pathway linked to overwinter mortality offers a promising therapeutic approach for improving bee health. J Adv Res 53, 99–114 (2023). Xu, K., Niu, Q., Zhao, H., Du, Y. & Jiang, Y. Transcriptomic analysis to uncover genes affecting cold resistance in the Chinese honey bee (Apis cerana cerana). PLoS One 12, e0179922 (2017). Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Current Opinion in Insect Science Preprint at https://doi.org/10.1016/j.cois.2018.02.012 (2018). Zheng, H., Steele, M. I., Leonard, S. P., Motta, E. V. S. & Moran, N. A. Honey bees as models for gut microbiota research. Lab Anim (NY) 47, 317–325 (2018). Engel, P. et al. The bee microbiome: impact on bee health and model for evolution and ecology of host-microbe interactions. Am Soc Microbiol 7, (2016). Kwong, W. K. & Moran, N. A. Gut Microbial Communities of Social Bees. Nat Rev Microbiol 14, 374 (2016). Martinson, V. G., Moy, J. & Moran, N. A. Establishment of characteristic gut bacteria during development of the honeybee worker. Appl Environ Microbiol (2012) doi: 10.1128/AEM.07810-11 . Engel, P. & Moran, N. A. The gut microbiota of insects – diversity in structure and function. FEMS Microbiol Rev 37, 699–735 (2013). Ludvigsen, J. et al. Shifts in the Midgut/Pyloric Microbiota Composition within a Honey Bee Apiary throughout a Season. Microbes Environ 30, 235–244 (2015). Almeida, E. L. et al. Geographical and Seasonal Analysis of the Honeybee Microbiome. Microb Ecol 85, 765–778 (2023). Castelli, L., Branchiccela, B., Romero, H., Zunino, P. & Antúnez, K. Seasonal Dynamics of the Honey Bee Gut Microbiota in Colonies Under Subtropical Climate: Seasonal Dynamics of Honey Bee Gut Microbiota. Microb Ecol 83, 492–500 (2022). Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. The ISME Journal 2019 14:3 14, 801–814 (2019). Castelli, L., Branchiccela, B., Romero, H., Zunino, P. & Antúnez, K. Seasonal Dynamics of the Honey Bee Gut Microbiota in Colonies Under Subtropical Climate: Seasonal Dynamics of Honey Bee Gut Microbiota. Microb Ecol 83, 492–500 (2022). Hammer, T. J., Le, E., Moran, N. A. & Hammer, T. J. Thermal niches of specialized gut symbionts: the case of social bees. Proceedings of the Royal Society B 288, (2021). Russell, K. A. & McFrederick, Q. S. Elevated Temperature May Affect Nectar Microbes, Nectar Sugars, and Bumble Bee Foraging Preference. Microb Ecol 84, 473–482 (2022). Ellegaard, K. M., Suenami, S., Miyazaki, R. & Engel, P. Vast Differences in Strain-Level Diversity in the Gut Microbiota of Two Closely Related Honey Bee Species. Current Biology 30, 2520–2531.e7 (2020). Wu, J. et al. Honey bee genetics shape the strain-level structure of gut microbiota in social transmission. Microbiome 9, 1–19 (2021). Su, Q. et al. Significant compositional and functional variation reveals the patterns of gut microbiota evolution among the widespread Asian honeybee populations. Front Microbiol 13, 934459 (2022). Wu, Y. et al. Genetic divergence and functional convergence of gut bacteria between the Eastern honey bee Apis cerana and the Western honey bee Apis mellifera. J Adv Res 37, 19–31 (2022). Khan, K. A. et al. Gut microbial diversity in Apis cerana indica and Apis florea colonies: a comparative study. Front Vet Sci 10, 1149876 (2023). Vásquez, A. et al. Symbionts as Major Modulators of Insect Health: Lactic Acid Bacteria and Honeybees. PLoS One 7, e33188 (2012). Forsgren, E., Olofsson, T. C., Vásquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 2010 41:1 41, 99–108 (2010). Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol 15, e2003467 (2017). Yoshiyama, M. & Kimura, K. Bacteria in the gut of Japanese honeybee, Apis cerana japonica, and their antagonistic effect against Paenibacillus larvae, the causal agent of American foulbrood. J Invertebr Pathol 102, 91–96 (2009). Sabaté, D. C., Carrillo, L. & Carina Audisio, M. Inhibition of Paenibacillus larvae and Ascosphaera apis by Bacillus subtilis isolated from honeybee gut and honey samples. Res Microbiol 160, 193–199 (2009). Forsgren, E., Olofsson, T. C., Vásquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 2010 41:1 41, 99–108 (2010). Den Besten, G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54, 2325–2340 (2013). Gaggìa, F. et al. Environment or genetic isolation? An atypical intestinal microbiota in the Maltese honey bee Apis mellifera spp. ruttneri. Front Microbiol 14, 1127717 (2023). Bleau, N., Bouslama, S., Giovenazzo, P. & Derome, N. Dynamics of the Honeybee (Apis mellifera) Gut Microbiota Throughout the Overwintering Period in Canada. Microorganisms 2020, Vol. 8, Page 1146 8, 1146 (2020). Liu, P. et al. Overwintering honeybees maintained dynamic and stable intestinal bacteria. Scientific Reports 2021 11:1 11, 1–10 (2021). Papp, M. et al. Natural diversity of the honey bee (Apis mellifera) gut bacteriome in various climatic and seasonal states. PLoS One 17, e0273844 (2022). Li, C., Tang, M., Li, X. & Zhou, X. Community Dynamics in Structure and Function of Honey Bee Gut Bacteria in Response to Winter Dietary Shift. mBio 13, (2022). Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, (2013). McFrederick, Q. S. & Rehan, S. M. Characterization of pollen and bacterial community composition in brood provisions of a small carpenter bee. Mol Ecol 25, 2302–2311 (2016). Russell, K. A. & McFrederick, Q. S. Floral nectar microbial communities exhibit seasonal shifts associated with extreme heat: Potential implications for climate change and plant-pollinator interactions. Front Microbiol 13, 3107 (2022). Bates, D., Mächler, M., Zurich, E., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. (2015). Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw 82, 1–26 (2017). R core team. R: The R Project for Statistical Computing. https://www.r-project.org/ (2022). Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, e4794 (2018). Meurisse, N., Pawson, S. M. & Somchit, C. Bark beetles on pine logs: forecasting winter colonisation dynamics based on trap catches and temperature records. J Pest Sci ( 2004 ) 94, 1357–1373 (2021). Hartig. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html (2018). Lüdecke, D. ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. J Open Source Softw 3, 772 (2018). Russell V. Lenth. Package ‘emmeans’ Type Package Title Estimated Marginal Means, aka Least-Squares Means. (2024) doi: 10.1080/00031305.1980.10483031 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviews received at journal 13 Aug, 2024 Reviews received at journal 08 Aug, 2024 Reviewers agreed at journal 27 Jul, 2024 Reviewers agreed at journal 21 Jul, 2024 Reviewers invited by journal 19 Jul, 2024 Editor assigned by journal 19 Jul, 2024 Editor invited by journal 16 Jul, 2024 Submission checks completed at journal 12 Jul, 2024 First submitted to journal 11 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4727319","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334485329,"identity":"35d749cd-b6e6-4ee8-a178-7a11ee8cb8b5","order_by":0,"name":"Gagandeep Brar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAwST+QCQsGBg4CFeC1sCA0OCBElaeAyI02IuffjZB4Zfdnb9s3s+Pub9ISHHz3OA8cPHHNxaLPvSjGcw9iUnz7hzdrMxT4KEsWRvA7PkzG14HHaGwZiBsYc5meFG7jZpoJbEDecZ2Jh58Wph/wzUUp8sfyPnGbFaeIwZGH4ctjO4kcMG0XK2Ab8Wyx6eYobEhuMJhjfSjA3npAH90nOwGa9fzHnYNzN8+FNtL3cj+eGDNzY2wBBLPvjhIx4tYJDYBrQIwWVswKUQCfxhsCdC1SgYBaNgFIxUAABO40t7ShtvDwAAAABJRU5ErkJggg==","orcid":"","institution":"North Dakota State University","correspondingAuthor":true,"prefix":"","firstName":"Gagandeep","middleName":"","lastName":"Brar","suffix":""},{"id":334485330,"identity":"c67ef094-6b66-4c2c-8864-c0c6548802ca","order_by":1,"name":"Lyna Ngor","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Lyna","middleName":"","lastName":"Ngor","suffix":""},{"id":334485331,"identity":"38bc6364-fb30-4c2e-b3b4-84a9a9a8efe1","order_by":2,"name":"Quinn S. McFrederick","email":"","orcid":"","institution":"University of California Riverside, Edward T. Schafer Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Quinn","middleName":"S.","lastName":"McFrederick","suffix":""},{"id":334485332,"identity":"738719c5-8059-4e15-96f5-fa1bc0606d1a","order_by":3,"name":"Alex Torson","email":"","orcid":"","institution":"Edward T. Schafer Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Torson","suffix":""},{"id":334485333,"identity":"2c9dfea5-922c-4578-b6c2-761c39d44019","order_by":4,"name":"Arun Rajamohan","email":"","orcid":"","institution":"Edward T. Schafer Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Rajamohan","suffix":""},{"id":334485334,"identity":"4be8def3-f60f-4b91-baa3-2c9e997a5de4","order_by":5,"name":"Joesph Rinehart","email":"","orcid":"","institution":"Edward T. Schafer Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Joesph","middleName":"","lastName":"Rinehart","suffix":""},{"id":334485335,"identity":"f9d0d501-651c-40ca-b6b0-e953b652b56a","order_by":6,"name":"Preetpal Singh","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Preetpal","middleName":"","lastName":"Singh","suffix":""},{"id":334485336,"identity":"bc712961-cb2b-4598-878f-8833e771f27c","order_by":7,"name":"Julia Bowsher","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Bowsher","suffix":""}],"badges":[],"createdAt":"2024-07-12 02:00:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4727319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4727319/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90763-0","type":"published","date":"2025-03-03T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61842445,"identity":"7941e969-30c3-4238-8b63-5d7127a0f9c3","added_by":"auto","created_at":"2024-08-06 07:01:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha-Diversity of Bolton Bees and Mann Lake Bees Across Different Months and Storage Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlot shows alpha-diversity: Shannon diversity Index for Bolton bees and Mann Lake bees during different months (A) and different storage conditions (B)The line inside the box represents the median, while the whiskers represent the lowest and highest values within the 1.5 interquartile range. The outliers and individual sample values are shown as dots. (C) Model predictions for effect of sampling time on Shannon diversity index. Grey shading represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4727319/v1/28510daa38e17c5ee6e57ea2.png"},{"id":61842446,"identity":"8bc1f322-6b68-4c09-8c20-5b9c79065425","added_by":"auto","created_at":"2024-08-06 07:01:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNMDS Plot of Honey Bee Gut Samples Using Bray-Curtis Dissimilarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-metric multidimensional scaling (NMDS) performed with Bray-Curtis dissimilarity. Each point on the plot represents a honey bee gut sample and samples that share greater similarity are ordinated closer together. Ellipses on the plot indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4727319/v1/e6496a4f32a1437e14b717c2.png"},{"id":61842449,"identity":"fe7abb0e-3845-426e-87a6-14e7db4678be","added_by":"auto","created_at":"2024-08-06 07:01:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative Abundances of Top Fifteen Bacterial Species in Honey Bee Gut Across Different Storage Conditions and Months\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative abundances of the top fifteen bacterial species present in honey bee gut in different storage conditions across different months. Less than 10% is a category of low abundance species that made up less than 10% of the median number of reads. Each column represents an individual bee. The relative abundance, represented in percentages, is shown on the y-axis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4727319/v1/2228bf134a576e3bc618b21f.png"},{"id":61842448,"identity":"b62296c9-bbf0-4440-9564-d7b00d084237","added_by":"auto","created_at":"2024-08-06 07:01:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverwintering Storage of Honey Bee Hives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIllustration of the management of honey bee hives across different months from October to December. In October, hives were managed outside in the field. In November, three hives from each bee strain were shifted to refrigerated cargo containers (6°C) for overwintering storage, while another three hives from each strain remained outdoors in the field. The same management was repeated in December.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4727319/v1/ebc3d4104507b6c12532fdd2.png"},{"id":78190835,"identity":"8673c041-d6f4-4aef-a789-6eaf27175d30","added_by":"auto","created_at":"2025-03-10 19:51:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202650,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4727319/v1/bafd4924-ebfd-4c49-88e9-56c5721f81f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWinter presents a significant challenge for honey bee colonies in temperate regions. Over the last two decades, beekeepers have reported high colony losses during winters. These losses have been associated with low temperature stress, geographic relocation due to climate change, genotype, diseases, pesticides, and poor nutrition \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In preparation for overwintering, a honey bee colony undergoes notable alterations in both the behavior and physiology of the worker bees. These changes include shifts in endocrine profiles, decreased individual activity, increased nutrient reserves, extended longevity, and thermoregulating clustering at the colony level \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Low temperatures affect honey bee survival(Q. Wang et al., 2016) through higher larval mortality and reduced life expectancy in adulthood \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. To mitigate these deleterious effects, honey bees have evolved life history strategies like cessation of brood rearing during the winters, increased immunity, and increased antioxidant expression \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Additionally, honey bees form a dense winter cluster to withstand harsh winter conditions. The thermoregulation of this winter cluster relies heavily on the insulation provided by the tightly packed outer layer of bees, known as the mantle bees, which is crucial for survival at low temperatures \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Moreover, to reduce the cold temperature stress on the bee colonies, and to improve overwintering survival, bee keepers often store the hives indoors. Storing hives under controlled cold climate conditions of 5\u0026deg;C \u0026minus;\u0026thinsp;7\u0026deg;C and 25% relative humidity has been shown to improve survival(Degrandi-Hoffman et al., 2019,Owens et al., 1971, Meikle et al., 2023).\u003c/p\u003e \u003cp\u003eThe symbiotic gut microbiota of honey bees is vulnerable to temperature stress \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The loss of honey bee workers during the winter is due to weather conditions as the primary stress factor, with starvation and diseases being secondary factors \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, honey bees experiencing cold stress have an increased risk of diseases and infections, which in turn raises the likelihood of colony losses. A recent transcriptomic study provides insight into the molecular mechanisms of cold resistance in \u003cem\u003eApis cerana\u003c/em\u003e. This study identified various temperature-sensitive proteins, such as heat shock proteins and zinc finger proteins, which were upregulated when bees were exposed to 0\u0026deg;C and are considered candidate genes for honey bees\u0026rsquo; tolerance to cold stress \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Apart from the host physiology, the role played by the honey bee gut microbiome has also emerged as one of the most important physiological aspects to investigate in relation to honey bee cold tolerance. The gut bacterial species within honey bees undergo alterations due to various environmental and developmental stresses, with temperature being a crucial one \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe honey bee gut microbiome contains five core host-specific bacterial species that are highly conserved and comprise 95% of the total microbes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The hindgut of every adult worker contains \u003cem\u003eSnodgrassella alvi, Gilliamella apicola, Lactobacillus Firm-5, Lactobacillus Firm-4\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e species. Along with these core bacteria, there are non-core species including \u003cem\u003eBartonella apis, Commensalibacter spp\u003c/em\u003e., and some other identified and several unidentified species that have been collected from the surrounding environment like hives and plants \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These bacterial species promote honey bee growth, physiology, facilitate breakdown of toxic dietary compounds, and modulate immune functions \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.Dynamic changes to the microbiota composition have been observed throughout the seasons \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The gut microbiota has been reported to differ between winter and summer honey bees, with reduced α-diversity and higher levels of \u003cem\u003eBartonella\u003c/em\u003e and \u003cem\u003eCommensalibacter\u003c/em\u003e during winter \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, temperature during different months affects the composition of the gut microbiota by gradually changing the bacterial diversity between the seasons. In subtropical conditions, precipitation affects the composition of the honey bee gut microbiota \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Numerous studies have been conducted to understand the seasonal dynamics of honey bee gut microbiota. However, very few studies have explored the effects of prolonged exposure to harsh climatic conditions on honey bee gut microbiome. For instance, direct exposure to high temperatures has been found to influence the gut microbiota in both honey bees and bumble bees, rendering them more resistant to heat stress \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Additionally, elevated temperature has shown to affect nectar microbes where the abundance of bacteria increased in the warmer temperature, influencing bumble bee forager preference \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBeyond seasonality and temperature, vast differences in gut microbiota at the strain level has also been observed in two closely related honey bee species, \u003cem\u003eApis mellifera\u003c/em\u003e and \u003cem\u003eApis cerana\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The gut composition exhibits greater divergence among genetically distinct bees at both phylotype- and sequence-discrete population levels \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Additionally, significant variation in both composition and function among diverse Asian honey bee populations has been reported, showing distinct patterns of their gut microbiota \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A previous study showed genetic divergence and functional convergence of gut bacteria in the eastern honey bee, \u003cem\u003eApis cerana\u003c/em\u003e and the western honey bee, \u003cem\u003eApis mellifera\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and between \u003cem\u003eApis cerana\u003c/em\u003e and \u003cem\u003eApis florea\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Differences in gut microbes between strains of \u003cem\u003eApis mellifera\u003c/em\u003e may influence the performance of those honey bee strains under different climate conditions.\u003c/p\u003e \u003cp\u003eThe goal of this study was to assess whether overwintering storage conditions disrupt the stability and diversity of the gut microbes in overwintering honey bees. To understand long term storage effects on gut microbiota, we compared the whole gut microbiota of two commercial strains of \u003cem\u003eApis mellifera\u003c/em\u003e i.e., claimed cold hardy bees \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://boltonbees.com/pages/mn-hardy-hives\u003c/span\u003e\u003cspan address=\"https://boltonbees.com/pages/mn-hardy-hives\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Bolton Bees, MN) that are bred and reared in Minnesota and Italian bees (Mann lake Bees, MN) of unknown rearing. Hives were stored either at a constant 6\u0026deg;C indoor or kept outside in natural conditions during winter. We predicted that hives stored outside in natural fluctuating temperatures will have different gut bacterial communities due to cold stress compared to hives stored in constant temperatures. We also predicted that bacterial communities would change when sampled over a three-month period of falling temperatures, viz. October, November, and December.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAlpha diversity\u003c/h2\u003e \u003cp\u003eTo investigate alpha diversity patterns, generalized linear mixed models (LMM) that included Shannon diversity as a response variable was applied. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most parsimonious, best-fit model included month as a fixed effect, and random effects for hive ID (different hives in each treatment) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast to the predictions, alternative models incorporating bee strain and storage treatment did not significantly improve the best-fit model. The random effects structure in the best fit model showed relatively little variation among hives (For hives and replications, SD\u0026thinsp;=\u0026thinsp;0.1604 \u0026amp; 0.0406, respectively), when compared to the residual error of the model (SD\u0026thinsp;=\u0026thinsp;0.3560). This indicates variation between hives and the replications do not account for the majority of the variation in Shannon diversity. Furthermore, Shannon diversity index in October did not differ significantly from November (p\u0026thinsp;=\u0026thinsp;0.1275), and December (p\u0026thinsp;=\u0026thinsp;0.398), whereas it was significantly lower in November compared to December (p\u0026thinsp;=\u0026thinsp;0.0045). Month explained only 8% of the variation in Shannon diversity, whereas the full model explained 23% of the variation in Shannon diversity. Overall, results indicate a temporal variation in Shannon diversity, but the strain of bee and whether the hives were stored inside or outside had no effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of Akaike Information Criterion (AIC) value for different Generalized Linear Mixed Models (GLMMs) with relevant predictor and random variables. Best fit GLMM with the lowest AIC value is highlighted in bold. ∆ AIC\u0026thinsp;\u0026le;\u0026thinsp;2 indicated similar models, which leads to the selection of simpler model (i.e. with less parameters).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eModel: Shannon diversity index~\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status\u0026thinsp;+\u0026thinsp;month\u0026thinsp;+\u0026thinsp;bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status * month * bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status * month\u0026thinsp;+\u0026thinsp;bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status\u0026thinsp;+\u0026thinsp;month * bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status * bee type\u0026thinsp;+\u0026thinsp;strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status\u0026thinsp;+\u0026thinsp;month\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u0026thinsp;+\u0026thinsp;bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status\u0026thinsp;+\u0026thinsp;bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth * bee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status * bee strain\u0026thinsp;+\u0026thinsp;1|hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorage status\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBee strain\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonth\u0026thinsp;+\u0026thinsp;1|hive\u003c/b\u003e ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e106.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1|hive ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of best fit Generalized Linear Mixed Model from Shannon diversity index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ Estimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eShannon diversity index\u0026thinsp;~\u0026thinsp;Month\u0026thinsp;+\u0026thinsp;1|hive ID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33555\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.48419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u0026times;10\u003csup\u003e\u0026ndash;16 a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth (Reference: October\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.16872\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.20089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecember)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11101\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.08469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHive (Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNumber of observations\u0026thinsp;=\u0026thinsp;107; groups: Hive, 12;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. SE\u0026thinsp;=\u0026thinsp;standard error; SD\u0026thinsp;=\u0026thinsp;standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBeta diversity\u003c/h2\u003e \u003cp\u003eTo investigate beta diversity patterns, we conducted PERMANOVA using the adonis2 package in R, testing various models with Bray-Curtis distance matrices (phyloseq) as a response variable. The simplest and best-fitting model included storage status and month in interaction with bee strain as a fixed effect, and block (strata) as bee hive ID; \u003cem\u003eDistance\u0026thinsp;~\u0026thinsp;Storage status\u0026thinsp;+\u0026thinsp;Month * Bee strain, strata\u0026thinsp;=\u0026thinsp;hive\u003c/em\u003e ID (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall composition of bee gut microbiomes differed by bee type (adonis F\u0026thinsp;=\u0026thinsp;3.19, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0242, P\u0026thinsp;=\u0026thinsp;0.002), month (adonis F\u0026thinsp;=\u0026thinsp;5.09, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0872, P\u0026thinsp;=\u0026thinsp;0.001), but not storage status (adonis F\u0026thinsp;=\u0026thinsp;0.57, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0047, P\u0026thinsp;=\u0026thinsp;0.887). The interaction between month and bee strain was significant (adonis F\u0026thinsp;=\u0026thinsp;1.57, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0269, P\u0026thinsp;=\u0026thinsp;0.001). Month explained only 8.7% of the variation, whereas the bee type, and interaction between month and bee strain explained 2.4%, and 2.6% of the variation, respectively. Overall, these findings suggest that the microbial dynamics in overwintering honey bee workers are influenced by bee strain over the months, with stability observed in storage conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pairwise comparison showed significant differences in gut microbiota when compared October \u0026ndash; November (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.002), November \u0026ndash; December (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.001), and October - December (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBest fit permutation analysis of variance (PERMANOVA) model applied using the adonis function with distance matrices with 999 permutations and bee hives accounted as a blocks \u0026ndash; strata.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePr (\u0026gt;\u0026thinsp;F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDistance\u0026thinsp;~\u0026thinsp;Storage status\u0026thinsp;+\u0026thinsp;Month * Bee strain, strata\u0026thinsp;=\u0026thinsp;hive ID)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStorage Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBee stain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth * bee strain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.6767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea Statistically significant at p\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomical abundance\u003c/h2\u003e \u003cp\u003eFollowing the removal of barcodes and primer sequences through QIIME2 (Demultiplexing), we obtained a collective sum of 34,879,494 paired-end 16S rRNA gene amplicon reads. The average read count per sample was 317,086, with an overall average quality score of 34. Across the 108 samples analyzed, the range of reads per sample varied from a minimum of 56,533 to a maximum of 1,314,819. Analyzing the taxonomic profile revealed a predominance of \u003cem\u003eFirmicutes\u003c/em\u003e (70.96%), \u003cem\u003eProteobacteria\u003c/em\u003e (20.18%), and \u003cem\u003eActinobacteriota\u003c/em\u003e (5.83%) at the phylum level. We investigated the presence of the core gut microbiota and identified only a limited number of reads for \u003cem\u003eSnodgrassella\u003c/em\u003e. \u003cem\u003eLactobacillus\u003c/em\u003e Firm-5 and \u003cem\u003eLactobacillus\u003c/em\u003e Firm-4 were not detected in our samples. We focused on examining the relative abundance of two non-core (\u003cem\u003eBartonella\u003c/em\u003e and \u003cem\u003eCommensalibacter)\u003c/em\u003e and two core (\u003cem\u003eGilliamella\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e) microbes, along with the endosymbiont \u003cem\u003eWolbachia\u003c/em\u003e. The bacterial species-specific analysis for relative abundance was performed using mixed model with formula: \u003cem\u003eAbundance\u0026thinsp;~\u0026thinsp;Storage status\u0026thinsp;+\u0026thinsp;Month\u0026thinsp;+\u0026thinsp;Bee strain + (1 | hive ID\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eConsidering the importance of genus \u003cem\u003eBartonella\u003c/em\u003e in a previous study \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we analyzed the abundance of two \u003cem\u003eBartonella\u003c/em\u003e species: \u003cem\u003eBartonella apis\u003c/em\u003e and another denoted as \u003cem\u003euncultured Bartonella\u003c/em\u003e. \u003cem\u003eBartonella apis\u003c/em\u003e abundance did not differ by month (df\u0026thinsp;=\u0026thinsp;99.11, F value\u0026thinsp;=\u0026thinsp;2.27, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.1085), bee strain (df\u0026thinsp;=\u0026thinsp;38.26, F value\u0026thinsp;=\u0026thinsp;0.1824, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.6717), or storage status (df\u0026thinsp;=\u0026thinsp;38.26, F value\u0026thinsp;=\u0026thinsp;.1824, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.6717). The \u003cem\u003eBartonella uncultured\u003c/em\u003e ASV significantly differed by month ( df\u0026thinsp;=\u0026thinsp;98.72, F value\u0026thinsp;=\u0026thinsp;4.74, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0107) while storage status (df\u0026thinsp;=\u0026thinsp;34.58, F value\u0026thinsp;=\u0026thinsp;0.0528, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.81954) and bee strain (df\u0026thinsp;=\u0026thinsp;11.559, F value\u0026thinsp;=\u0026thinsp;0.1322, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.72274) were not significant. We found a significant difference in abundance between December and November (SE\u0026thinsp;=\u0026thinsp;1.03, df\u0026thinsp;=\u0026thinsp;97, \u003cem\u003ep\u003c/em\u003e-value: 0.0085), but not between October and December (SE\u0026thinsp;=\u0026thinsp;1.21, df\u0026thinsp;=\u0026thinsp;112, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.358) or October and November (SE\u0026thinsp;=\u0026thinsp;1.21, df\u0026thinsp;=\u0026thinsp;112, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.456). For \u003cem\u003eCommensalibacter (Commensalibacter Acetobacteraceae_bacterium)\u003c/em\u003e, bee strain (SE\u0026thinsp;=\u0026thinsp;1.48, df\u0026thinsp;=\u0026thinsp;102, Tukey HSD, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was the only factor significantly influencing abundance with Mann Lake bees having a higher abundance than Bolton bees. The effect of storage status (df\u0026thinsp;=\u0026thinsp;107, F value\u0026thinsp;=\u0026thinsp;0.261, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.6097) and month (df\u0026thinsp;=\u0026thinsp;107, F value\u0026thinsp;=\u0026thinsp;1.6663, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.1938) was not statistically significant.\u003c/p\u003e\u003cp\u003eBee strain is a significant predictor of \u003cem\u003eBifidobacterium\u003c/em\u003e (\u003cem\u003eBifidobacterium uncultured_Bifidobacterium\u003c/em\u003e) abundance where \u003cem\u003eBifidobacterium\u003c/em\u003e showed higher abundance in Bolton bees compared to Mann Lake bees (SE = 0.077, df =102, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value = 0.0014). The storage status (df = 107, F value = 0.0682, \u003cem\u003ep\u003c/em\u003e-value = 0.7944) and month (df = 107, F value = 0.8069, \u003cem\u003ep\u003c/em\u003e-value = 0.4489) variables did not contribute significantly to the variation in this model. The storage status (df = 37.34, F value = 6.67, \u003cem\u003ep\u003c/em\u003e-value = 0.013) and month (df = 99.15, F value = 14.18, \u003cem\u003ep\u003c/em\u003e-value = 3.808\u0026times;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) significantly affected the abundance of \u003cem\u003eGilliamella\u0026nbsp;\u003c/em\u003e(\u003cem\u003eUnclassified Gilliamella\u003c/em\u003e), while Bee strain did not show a significant effect (df = 12.10, F value = 0.675, \u003cem\u003ep\u003c/em\u003e-value = 0.426). Pairwise comparison showed significant differences in abundance during October and December (SE = 0.197, df =112, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.0001), November and December (SE = 0.166, df =97.5, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value = 0.002), but no significant difference during November and October (SE = 0.197, df =112, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value = 0.5361). A significant effect of the storage status on the abundance of \u003cem\u003eGilliamella\u003c/em\u003e was seen between the Inside and Outside bees (SE = 0.215, df =45, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value = 0.0272). storage status (df = 48.66, F value = 0.0377,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value = 0.846) and Month (df = 99.42, F value = 0.97,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value = 0.38) is not a significant predictor of abundance in \u003cem\u003eWolbachia\u0026nbsp;\u003c/em\u003e(\u003cem\u003eUnclassified Wolbachia\u003c/em\u003e). \u003cem\u003eWolbachia\u003c/em\u003e abundance was significantly different between the bee strains (df = 11.78, F value = 5.352\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value = 0.03959). The results suggest a potential difference in gut microbial abundance between Bolton bees and Mann lake bees, but the significance was marginal (SE = 0.243, df =14, Tuckey HSD, \u003cem\u003ep\u003c/em\u003e-value = 0.0570). Overall, this taxonomic data emphasizes the marginal differences in gut microbiome composition between bee types, revealing a temporally stable microbial composition that is not influenced by storage conditions in overwintering worker honey bee (Figure 3).\u003c/p\u003e\u003c/div\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eWe used a 16s rRNA gene amplicon sequencing approach to study/characterize the gut microbiota of overwintering adult worker bees between (a) two commercial bees (Bolton Bees and Mann Lake bees), (b) different storage status/conditions and, (c) different months during storage/ a season. In this study, we hypothesized that temperature would be one of the most important environmental stressors affecting the gut microbiota in overwintering honey bees. Surprisingly, we found no changes in the diversity, composition, or abundance of the microbiota when the bees were stored at constant 6\u0026deg;C or outside during the winter. Despite the physiological stress induced by cold temperatures on honey bee physiology, this study demonstrates that honey bees have the potential to acclimatize their gut microbiota despite the cold stress conditions. One possible explanation for these findings is that honey bees rely on specific bacterial species to maintain their health and utilize the functions performed by the gut microbiota, and therefore the bees retain and maintain these bacterial species to extract sufficient resources from them.\u003c/p\u003e \u003cp\u003eInterestingly, the overwintering bees\u0026rsquo; gut microbiome was dominated by \u003cem\u003eLactobacillus\u003c/em\u003e genus, which contributed to an increased bacterial load across all samples, regardless of storage conditions. As the most important genus in the guts of honey bees \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e plays an important role in the health of bees by showing probiotic characteristics. \u003cem\u003eLactobacilli\u003c/em\u003e have shown an ability to digest flavonoids and other compounds present in the pollen wall \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, to inhibit pathogens\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and to release short chain fatty acids and vitamins used by midgut cells as an energy resource \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A higher abundance of \u003cem\u003eLactobacillus\u003c/em\u003e during overwintering phase may support heat production by worker bees, stabilizing colonies temperature. The only differences in bacterial composition at the species level were observed between both bee types. Bolton bees exhibited significantly higher abundance of \u003cem\u003eBartonella\u003c/em\u003e (in some instances), \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eWolbachia\u003c/em\u003e, whereas Mann lake showed significantly higher abundance of \u003cem\u003eCommensalibacter\u003c/em\u003e when compared to Bolton bees.\u003c/p\u003e \u003cp\u003eThe bees obtained from Bolton Bees represent specific genetic lines known for their resilience in Minnesota's harsh climate. This genetic line, named after the location, where the parent Queen overwintered (Bolton, Minnesota), is derived from the base stock called MN Hygienic. The queens from the MN-Hardy line, used as grafts for queen production, have successfully endured the challenges of a prolonged and frigid Minnesota winter. Typically, such severe winters could affect honey stores, cleansing flights, and brood-laying, but these MN-Hardy Queens are claimed to have demonstrated ability to thrive under these cold winter conditions (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://boltonbees.com/pages/mn-hardy-hives\u003c/span\u003e\u003cspan address=\"https://boltonbees.com/pages/mn-hardy-hives\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). On the other hand, \u0026ldquo;OHB Italian\u0026rdquo; Mann Lake bees, sourced from Olivarez Honey Bees (OHB) in Northern California, are accompanied by claims for their hive performance, disease resistance, and overall robust health. The Italian Queens from OHB have reportedly undergone extensive breeding and careful genetic selection. The incorporation of traits such as \"Minnesota Hygienic\" and \"VSH\" (Varroa Sensitive Hygiene) further strengthens their innate ability to resist diseases and combat mite infestations. These two distinct types of bees have been specifically employed for their resilience to withstand the challenging winters typical of the Midwest, making them interesting subjects to study the gut microbiome during the overwintering period.\u003c/p\u003e \u003cp\u003eAmong the social bees, the transmission of symbiotic bacteria in the gut occurs among successive generations of siblings through social interactions. In a previous study on honey bees, the colonization of specific sets of bacteria, such as \u003cem\u003eLactobacillus\u003c/em\u003e Firm5 and \u003cem\u003eBifidobacterium\u003c/em\u003e Bifido-1.2, in genetically varied hosts strongly suggests that the genotype significantly influences the microbiota structure \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Another study on honey bees has identified marked differences in the core gut microbial community when comparing different lineages, which include Maltese honey bees (lineage A) to the Italian honey bees (lineage C). Notably, Maltese honey bees exhibited an inverse proportion of \u003cem\u003eLactobacillaceae\u003c/em\u003e and \u003cem\u003eBartonellaceae\u003c/em\u003e when compared to Italian honey bees \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The functionality of these differences between bee strains have not been studied. This finding underscores the extent of strain-level diversity within the bacterial communities. Our study showed extensive overlapping of the gut microbial strains among both bee types and differences in specific bacterial species. The results of the current study raises several fundamental questions regarding the evolution and maintenance of stable microbiota with slight change in specific host-associated bacterial communities.\u003c/p\u003e \u003cp\u003eThe findings in this study reveal varying degrees of presence of bacterial species in overwintering honeybees across different months. There were significant differences in the richness and evenness (Shannon Diversity Index) between October and November but no differences were noted in the bees sampled in November when compared to December samples. Additionally, beta diversity shows difference in species diversity in different months. Before overwintering, foragers collect pollen, water, nectar, and propolis. Remarkably, the overwintering honeybee samples exhibited consistent dominance in species composition with no significant difference in their abundance across different months. However, a significant difference in beta diversity was observed and this is consistent with findings from prior studies \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These findings could be interpreted as that during the timeframe when the honey bee workers are confined to their hives without access to foraging, there is a lack of exposure to new environmental microorganisms to colonize and populate the bee gut.\u003c/p\u003e \u003cp\u003ePrevious studies have noted that the gut microbiota differs between winter and summer honey bees, with the long-lived winter bees exhibiting a stable microbiota with reduced α-diversity and higher levels of \u003cem\u003eBartonella\u003c/em\u003e and \u003cem\u003eCommensalibacter\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This lower community alpha diversity with \u003cem\u003eBartonella\u003c/em\u003e and \u003cem\u003eCommensalibacter\u003c/em\u003e as dominant bacterial species may confer certain physiological benefits. However, these studies also collectively reveal minor variations in gut microbial communities in temperate honey bee colonies during winter, indicating a shift in dominance, with the non-core bacterium \u003cem\u003eBartonella\u003c/em\u003e surpassing the core bacterial species \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Another recent study also highlighted the significance of \u003cem\u003eBartonella\u003c/em\u003e, highlighting its expanded capability to convert metabolic wastes such as lactate and ethanol into pyruvate, which potentially provides energy for the host as well as other symbionts \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Considering the importance of \u003cem\u003eBartonella\u003c/em\u003e in previous studies, we analyzed two different species of \u003cem\u003eBartonella\u003c/em\u003e for their taxonomic abundance. However, we found the \u003cem\u003eLactobacillus\u003c/em\u003e genus as dominant, present in higher abundance, contrasting with the findings of a previous study where the \u003cem\u003eBartonella\u003c/em\u003e genus showed dominance in winter bees. Lower diversity in the gut microbiota was observed in both summer and winter, with predominance of \u003cem\u003eGilliamella apicola\u003c/em\u003e and \u003cem\u003eSnodgrasella alvi\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Overall, we observed that the honey bee gut microbiota remains stable across different months whether under storage of natural conditions in mid-western part of the United States.\u003c/p\u003e \u003cp\u003eIn this study, it is possible that seasonal- or storage condition-dependent variations in the hind gut core bacteria are either not detectable, or are being overridden by the non-core, non-stable midgut bacteria. It is possible that slight differences in the relative abundance of bee-associated bacterial species are influenced by changes in other non-core hive-associated bacteria. A previous study \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e identified co-evolutionary relationship of core gut bacteria and honey bee that is generally very similar and share the same core bacterial species. Measuring slight differences between bee strains in terms of absolute abundance would be challenging due to costs associated with increasing the depth of 16S sequencing. Additionally, 16S rRNA data cannot measure absolute abundances, only the relative abundance and general compositional nature of the microbiome. To enhance bacterial species detection resolution and functional profiling at the gene level, future studies could employ shotgun sequencing instead of 16S amplicon sequencing. Our study was also limited in timeframe, only sampling in the fall and early winter. We observed beta diversity drastically decline in the month of November, and it is likely that this pattern could change if bees were sampled during subsequent months, such as January, February, and March.\u003c/p\u003e \u003cp\u003eIn conclusion, our study shows that the gut microbiota of overwintering honey bees is dominated by one specific bacterial genus, \u003cem\u003eLactobacillus\u003c/em\u003e, which contribute to the bees\u0026rsquo; ability to withstand environmental stressors due to known functionality \u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our study of the gut microbiota in overwintering honey bees reveals significant stability across different months and storage conditions. However, there was a significant difference in alpha and beta diversity, which was influences by month irrespective of storage conditions and different bee strain. Furthermore, our analysis reveals minor differences in the gut microbiota between different bee types, viz. Bolton and Mann Lake bees. Despite the microbial differences among the bee types, the overall stability of the gut microbiota across different months and storage conditions suggests a remarkable resilience of honey bee gut bacteria to environmental changes. This resilience may be attributed to the co-evolutionary relationship between honey bees and their gut microbiota, which has developed over millions of years \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Finally, our findings suggest that beekeepers need not be overly concerned about changes in the gut microbiota during the winter months, as the bacteria appear to remain stable regardless of storage conditions. Overall, our study contributes to the growing body of research on the gut microbiota of honey bees and its role in bee health during winter. By demonstrating the stability of the gut microbiota across different months of storage and storage conditions, this study provides important insights for beekeepers and researchers alike. Further research is needed to explore the mechanisms underlying the resilience of honey bee gut bacteria and its implications for bee health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInsect samples\u003c/h2\u003e \u003cp\u003eHoney bee hives were managed outside in the field from the first week of May to the last week of October and then 3 hives from each bee strain were shifted to refrigerated cargo containers for overwintering storage (6\u0026deg;C) in November while 3 hives from each bee strain were left outdoors in the field. The first sampling was done on 10/17/2022 (base sampling of outdoor bees), followed by moving some hives inside storage on 10/21/2022. The second sampling was done on 11/15/2022 from hives both inside and outside. The third sampling was done 12/14/2022, again for both inside and outside hives (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table. 4)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRepresentation of honey bee sample collected from various storage environments across different months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBee strain\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMann lake Bees\u003c/b\u003e: Outside (6 hives)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBolton Bees\u003c/b\u003e: Outside (6 hives)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMann lake Bees\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBolton Bees\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStorage - Constant 6\u0026deg;C\u003c/p\u003e \u003cp\u003e(3 hives)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutside (3 hives)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStorage - Constant 6\u0026deg;C (3 hives)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutside (3 hives)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 bees/hive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eThree bees from each treatment were used for whole gut dissection after surface sterilizing bees using 1% sodium hypochlorite followed by three washes using sterilized water in sterile conditions. Whole gut samples were prepared by bead-beating the samples on a Qiagen Tissuelyser for 6 min at 30 Hz to disrupt recalcitrant cells. Samples for cell lysis were prepared by combining two 3-mm chromium steel beads and approximately 50 \u0026micro;l of 0.1 \u0026amp; 0.5 mm ZR BashingBeads\u0026trade; inside lysis tube (Biospec, Bartlesville, OK) with 750 \u0026micro;l of ZymoBIOMICS\u0026trade; lysis solution and 20 \u0026micro;l of proteinase K. A subsequent round of bead beating involved rotating the samples for 6 minutes at 30 Hz, followed by an incubation period at 56\u0026deg;C for one hour. DNA was extracted using ZymoBIOMICS\u0026trade; DNA microprep kit collection (Zymo research, Irvine, California) including 2 blank extractions as a no template control for further downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePCR Amplification and Illumina Sequencing\u003c/h2\u003e \u003cp\u003e16S rRNA gene libraries for paired-end reads were generated following a previously published protocol (McFrederick \u0026amp; Rehan, 2016; Russell \u0026amp; McFrederick, 2022). The V5-V6 region of the 16S rRNA gene was amplified using 16S rRNA gene primers (799F mod3: CMGGATTAGATACCCKGG and 1115R: AGGGTTGCGCTCGTTG), each incorporating a unique barcode sequence. PCR1 reactions were conducted with 2 \u0026micro;l of DNA, 10 \u0026micro;l of 2\u0026times; Pfusion High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA), 10 \u0026micro;l of ultrapure water, and 0.5 \u0026micro;l of 10 \u0026micro;M 799F-mod3 and 0.5 \u0026micro;l of 10 \u0026micro;M 1115R primers. The reaction condition for PCR1 was 94\u0026deg;C for 3 minutes, 94\u0026deg;C for 45 seconds, 52\u0026deg;C for 1 minute, 70\u0026deg;C for 1:30 minutes, repeated step2 29X, and 72\u0026deg;C for 10 minutes to amplify this region. We ran gel electrophoresis to confirmed if 16s rRNA primers were successfully attached to our samples. To complete the Illumina adapter sequence, we initiated the process by cleaning the PCR product with exonuclease and shrimp alkaline phosphatase. Exonuclease was applied to remove excess primers, while shrimp alkaline phosphatase was used to eliminate residual deoxynucleoside triphosphates (dNTPs). Four \u0026micro;L of the 1x ExoSAP was mixed with each 7\u0026micro;L PCR1 sample. This reaction was incubated at 37˚C for 30 minutes and then 95˚C for 5 minutes. Following this initial cleanup, the purified PCR products were employed as templates for a second PCR. This subsequent PCR (PCR2) utilized 1 \u0026micro;l of the cleaned PCR product as a template, using the same primers (PCR2F and PCR2R), and was conducted under conditions identical to the initial PCR. For PCR2, a 1 \u0026micro;l aliquot of Exo-Sap PCR1 product was utilized to conduct the second step of Illumina library preparation. In PCR2, the Exo-Sap PCR1 products were further amplified, incorporating linker poly-A primers for recognition on the sequencing platform. The PCR2 reaction conditions included an initial denaturation at 94\u0026deg;C for 3 minutes, followed by cycles of 94\u0026deg;C for 45 seconds, 58\u0026deg;C for 1 minute, 72\u0026deg;C for 1 minute and 30 seconds, with step 2 repeated 14 times, and a final extension at 72\u0026deg;C for 10 minutes, with final volume of 25 \u0026micro;l. To ensure uniform DNA input quantities, Invitrogen DynaMag TM-96 Side Skirted kit was employed for the normalization step across all samples prior to submitting a final volume of 10 \u0026micro;l for sequencing. The 16 rRNA gene sequencing was performed on NextSeq2000 P2 600 cycle kit (2x300xN/A) that produced total of 382.53 M reads at Q30 of 88.96%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics\u003c/h2\u003e \u003cp\u003eQIIME 2-2019 was used for the visualization and trimming of low-quality ends in reads from raw 16S rRNA sequence libraries. Subsequently, DADA2 was utilized for the assignment of sequences to amplicon sequence variants (ASVs), which represent 16S rRNA gene sequences with 100% matches. This process involved the removal of chimeras and reads with more than two expected errors. Taxonomy was assigned to the ASVs through the sklearn classifier, trained specifically for the 799-1,115 region of the 16S rRNA gene, utilizing the SILVA database. Additionally, local BLASTn searches against the NCBI 16S microbial database (accessed October 2023) were conducted. Features were filtered from the resultant ASV table, corresponding to contaminants identified in the blanks using the R package \u0026lsquo;decontam\u0026rsquo; (version 1.10.0) at a conservative threshold of 0.5. This process aimed to identify contaminants while also eliminating ASVs identified as chloroplast and mitochondria. To standardize the number of sequences per library, alpha rarefaction in QIIME2 was employed, and 4000 reads per sample were selected. This approach ensured the retention of 107 out of 108 samples while capturing most of the diversity.\u003c/p\u003e \u003cp\u003eGeneralized Linear Mixed-effects Models (GLMMs) was used to model the Shannon diversity index. All GLMMs were fitted using the lme4 package \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and lmer Test package \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e in R \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, using R studio version 4.2.2. Bee strain, storage treatment (exposure to natural vs. storage at 6\u0026deg;C temperature), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measuring of each hive \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. A stepwise backward selection process was employed for model selection. Initially, a full model was run with all fixed effects and random effects. Then all possible interactions among fixed effects were examined, followed by the systematic removal of one fixed effect at a time. The process continued until no further model improvement (e.g., lower AIC) could be observed. A difference of \u0026le;\u0026thinsp;2 in the AIC indicated that models were similar, in which case the simpler model (i.e., with fewer parameters) was selected as the best-fit model \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The best fit model was diagnosed for model assumptions through residual diagnostic plots using the \u0026lsquo;DHARMa\u0026rsquo; package \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Finally, package \u0026lsquo;ggeffects\u0026rsquo; version 0.16.0 \u003csup\u003e54\u003c/sup\u003e was used to extract means, standard deviations, and 95% confidence intervals for the predicted values of Shannon diversity index for different sampling times. Tukey HSD tests for multiple comparisons between treatments was employed with estimated marginal means comparisons (EMMs) using the \u0026lsquo;emmeans\u0026rsquo; function (Russell V. Lenth, 2024).\u003c/p\u003e \u003cp\u003eFor beta diversity analysis, Adonis Bray\u0026ndash;Curtis distance dissimilarities and nonmetric multidimensional scaling (NMDS) ordination were conducted in R v4.3.0 with \u0026lsquo;vegan\u0026rsquo; package. The 'phyloseq\u0026rsquo; (ver 1.34.0) R package was used for beta diversity analysis. Permutation analysis of variance (PERMANOVA) was applied using the \u0026lsquo;adonis\u0026rsquo; function on distance matrices with 999 permutations. The differences between bee gut microbiomes including distance as a dependent variable and months (October, November, and December), bee strain (Bolton bees and Mann lake bees) and Storage Status (Inside and Outside) as independent variable were assessed using separate Adonis models. In all models, bee hives were accounted as a block- strata, where permutations were allowed among levels of all the variable, but within each level of bee hives (random variable), no permutations occurred.\u003c/p\u003e \u003cp\u003eTo understand the effect of different variables on the abundance of candidate bacterial species, Generalized Linear Mixed-effects Models (GLMMs) was used to model the abundance of particular bacterial species. Bee strain, storage treatment (exposure to natural vs. storage at 6\u0026deg;C), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measures of each hive \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The fitted GLMM and lm was diagnosed for issues with heterogeneity, overdispersion or missing co-variates using the residual plots. To evaluate the significance of fixed effect, analysis of variance tables for the GLMM was calculated using Wald chi-square tests. Tukey HSD tests was employed for multiple comparisons between treatments with estimated marginal means comparisons (EMMs) using the \u0026lsquo;emmeans\u0026rsquo; function (Lenth, 2024).\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability and benefit-sharing statement:\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://datadryad.org/stash/share/ycQx06SicJFuCPsZ0XMkwLVX9F6DetlucISxvTQoQF0\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors thank Melanie Tischleder and Nyle Jonason for their technical assistance through this project.\u0026nbsp;The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or\u0026nbsp;endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: Gagandeep Brar (GB) is the first author, with Lyna Ngor (LN), Quinn McFrederick (QSM), Preetpal Singh (PS), Arun Rajamohan (AR), Alex Torson (AT), Joseph Rinehart (JR), and Julia Bowsher (JB) as co-authors. GB, JR, and JB conceived and designed the study. GB and LN processed samples, and GB and PS analyzed the data. GB wrote the first version of the manuscript. All authors contributed to reviewing the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Funding was provided by NSF RII Track-2 FEC 1826834, NSF-IOS-1557940, and USDA-ARS 3060-21220-032-00D\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon approval, we give full consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026ldquo;All authors affirm that the study confirms to the ethical standards and integrity policies of the Scientific Reports\u0026rdquo;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGenersch, E. Honey bee pathology: Current threats to honey bees and beekeeping. Appl Microbiol Biotechnol 87, 87\u0026ndash;97 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbou-Shaara, H. F., Owayss, A. A., Ibrahim, Y. Y. \u0026amp; Basuny, N. K. A review of impacts of temperature and relative humidity on various activities of honey bees. Insectes Soc 64, 455\u0026ndash;463 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiannini, T. C. \u003cem\u003eet al.\u003c/em\u003e Crop pollinators in Brazil: a review of reported interactions. Apidologie 46, 209\u0026ndash;223 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHristov, P., Shumkova, R., Palova, N. \u0026amp; Neov, B. Factors Associated with Honey Bee Colony Losses: A Mini-Review. Veterinary Sciences 2020, \u003cem\u003eVol. 7, Page 166\u003c/em\u003e 7, 166 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Q. \u003cem\u003eet al.\u003c/em\u003e Low-Temperature Stress during Capped Brood Stage Increases Pupal Mortality, Misorientation and Adult Mortality in Honey Bees. PLoS One 11, e0154547 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinhauer, N., vanEngelsdorp, D. \u0026amp; Saegerman, C. Prioritizing changes in management practices associated with reduced winter honey bee colony losses for US beekeepers. Science of the Total Environment 753, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAurori, C. M. \u003cem\u003eet al.\u003c/em\u003e What is the main driver of ageing in long-lived winter honeybees: Antioxidant enzymes, innate immunity, or vitellogenin? Journals of Gerontology - Series A Biological Sciences and Medical Sciences 69, 633\u0026ndash;639 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStabentheiner, A., Pressl, H., Papst, T., Hrassnigg, N. \u0026amp; Crailsheim, K. Endothermic heat production in honeybee winter clusters. Journal of Experimental Biology 206, 353\u0026ndash;358 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegrandi-Hoffman, G., Graham, H., Ahumada, F., Smart, M. \u0026amp; Ziolkowski, N. The Economics of Honey Bee (Hymenoptera: Apidae) Management and Overwintering Strategies for Colonies Used to Pollinate Almonds. J Econ Entomol 112, 2524\u0026ndash;2533 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens, C. D., States, U. \u0026amp; Service, A. R. The thermology of wintering honey bee colonies. Preprint at (1971).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeikle, W. G., Corby-Harris, V., Ricigliano, V., Snyder, L. \u0026amp; Weiss, M. Cold storage as part of a Varroa management strategy: effects on honey bee colony performance, mite levels and stress biomarkers. \u003cem\u003eScientific Reports 2023 13:1\u003c/em\u003e 13, 1\u0026ndash;15 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoulibaly, K. A. S., Majeed, M. Z., Sayed, S. \u0026amp; Yeo, K. Simulated Climate Warming Influenced Colony Microclimatic Conditions and Gut Bacterial Abundance of Honeybee Subspecies Apis mellifera ligustica and A. mellifera sinisxinyuan. J Apic Sci 66, 15\u0026ndash;27 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u003cem\u003eet al.\u003c/em\u003e Mediating a host cell signaling pathway linked to overwinter mortality offers a promising therapeutic approach for improving bee health. J Adv Res 53, 99\u0026ndash;114 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, K., Niu, Q., Zhao, H., Du, Y. \u0026amp; Jiang, Y. Transcriptomic analysis to uncover genes affecting cold resistance in the Chinese honey bee (Apis cerana cerana). PLoS One 12, e0179922 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymann, K. \u0026amp; Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. \u003cem\u003eCurrent Opinion in Insect Science\u003c/em\u003e Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cois.2018.02.012\u003c/span\u003e\u003cspan address=\"10.1016/j.cois.2018.02.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, H., Steele, M. I., Leonard, S. P., Motta, E. V. S. \u0026amp; Moran, N. A. Honey bees as models for gut microbiota research. Lab Anim (NY) 47, 317\u0026ndash;325 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngel, P. \u003cem\u003eet al.\u003c/em\u003e The bee microbiome: impact on bee health and model for evolution and ecology of host-microbe interactions. Am Soc Microbiol 7, (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwong, W. K. \u0026amp; Moran, N. A. Gut Microbial Communities of Social Bees. Nat Rev Microbiol 14, 374 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinson, V. G., Moy, J. \u0026amp; Moran, N. A. Establishment of characteristic gut bacteria during development of the honeybee worker. Appl Environ Microbiol (2012) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/AEM.07810-11\u003c/span\u003e\u003cspan address=\"10.1128/AEM.07810-11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngel, P. \u0026amp; Moran, N. A. The gut microbiota of insects \u0026ndash; diversity in structure and function. FEMS Microbiol Rev 37, 699\u0026ndash;735 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudvigsen, J. \u003cem\u003eet al.\u003c/em\u003e Shifts in the Midgut/Pyloric Microbiota Composition within a Honey Bee Apiary throughout a Season. Microbes Environ 30, 235\u0026ndash;244 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeida, E. L. \u003cem\u003eet al.\u003c/em\u003e Geographical and Seasonal Analysis of the Honeybee Microbiome. Microb Ecol 85, 765\u0026ndash;778 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelli, L., Branchiccela, B., Romero, H., Zunino, P. \u0026amp; Ant\u0026uacute;nez, K. Seasonal Dynamics of the Honey Bee Gut Microbiota in Colonies Under Subtropical Climate: Seasonal Dynamics of Honey Bee Gut Microbiota. Microb Ecol 83, 492\u0026ndash;500 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKešnerov\u0026aacute;, L. \u003cem\u003eet al.\u003c/em\u003e Gut microbiota structure differs between honeybees in winter and summer. The ISME Journal 2019 \u003cem\u003e14:3\u003c/em\u003e 14, 801\u0026ndash;814 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelli, L., Branchiccela, B., Romero, H., Zunino, P. \u0026amp; Ant\u0026uacute;nez, K. Seasonal Dynamics of the Honey Bee Gut Microbiota in Colonies Under Subtropical Climate: Seasonal Dynamics of Honey Bee Gut Microbiota. Microb Ecol 83, 492\u0026ndash;500 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammer, T. J., Le, E., Moran, N. A. \u0026amp; Hammer, T. J. Thermal niches of specialized gut symbionts: the case of social bees. \u003cem\u003eProceedings of the Royal Society B\u003c/em\u003e 288, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell, K. A. \u0026amp; McFrederick, Q. S. Elevated Temperature May Affect Nectar Microbes, Nectar Sugars, and Bumble Bee Foraging Preference. Microb Ecol 84, 473\u0026ndash;482 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllegaard, K. M., Suenami, S., Miyazaki, R. \u0026amp; Engel, P. Vast Differences in Strain-Level Diversity in the Gut Microbiota of Two Closely Related Honey Bee Species. Current Biology 30, 2520\u0026ndash;2531.e7 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. \u003cem\u003eet al.\u003c/em\u003e Honey bee genetics shape the strain-level structure of gut microbiota in social transmission. Microbiome 9, 1\u0026ndash;19 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, Q. \u003cem\u003eet al.\u003c/em\u003e Significant compositional and functional variation reveals the patterns of gut microbiota evolution among the widespread Asian honeybee populations. Front Microbiol 13, 934459 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, Y. \u003cem\u003eet al.\u003c/em\u003e Genetic divergence and functional convergence of gut bacteria between the Eastern honey bee Apis cerana and the Western honey bee Apis mellifera. J Adv Res 37, 19\u0026ndash;31 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, K. A. \u003cem\u003eet al.\u003c/em\u003e Gut microbial diversity in Apis cerana indica and Apis florea colonies: a comparative study. Front Vet Sci 10, 1149876 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;squez, A. \u003cem\u003eet al.\u003c/em\u003e Symbionts as Major Modulators of Insect Health: Lactic Acid Bacteria and Honeybees. PLoS One 7, e33188 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForsgren, E., Olofsson, T. C., V\u0026aacute;squez, A. \u0026amp; Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. \u003cem\u003eApidologie 2010 41:1\u003c/em\u003e 41, 99\u0026ndash;108 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKešnerov\u0026aacute;, L. \u003cem\u003eet al.\u003c/em\u003e Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol 15, e2003467 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshiyama, M. \u0026amp; Kimura, K. Bacteria in the gut of Japanese honeybee, Apis cerana japonica, and their antagonistic effect against Paenibacillus larvae, the causal agent of American foulbrood. J Invertebr Pathol 102, 91\u0026ndash;96 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabat\u0026eacute;, D. C., Carrillo, L. \u0026amp; Carina Audisio, M. Inhibition of Paenibacillus larvae and Ascosphaera apis by Bacillus subtilis isolated from honeybee gut and honey samples. Res Microbiol 160, 193\u0026ndash;199 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForsgren, E., Olofsson, T. C., V\u0026aacute;squez, A. \u0026amp; Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. \u003cem\u003eApidologie 2010 41:1\u003c/em\u003e 41, 99\u0026ndash;108 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDen Besten, G. \u003cem\u003eet al.\u003c/em\u003e The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54, 2325\u0026ndash;2340 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGagg\u0026igrave;a, F. \u003cem\u003eet al.\u003c/em\u003e Environment or genetic isolation? An atypical intestinal microbiota in the Maltese honey bee Apis mellifera spp. ruttneri. Front Microbiol 14, 1127717 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBleau, N., Bouslama, S., Giovenazzo, P. \u0026amp; Derome, N. Dynamics of the Honeybee (Apis mellifera) Gut Microbiota Throughout the Overwintering Period in Canada. Microorganisms 2020, \u003cem\u003eVol. 8, Page 1146\u003c/em\u003e 8, 1146 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, P. \u003cem\u003eet al.\u003c/em\u003e Overwintering honeybees maintained dynamic and stable intestinal bacteria. \u003cem\u003eScientific Reports 2021 11:1\u003c/em\u003e 11, 1\u0026ndash;10 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapp, M. \u003cem\u003eet al.\u003c/em\u003e Natural diversity of the honey bee (Apis mellifera) gut bacteriome in various climatic and seasonal states. PLoS One 17, e0273844 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, C., Tang, M., Li, X. \u0026amp; Zhou, X. Community Dynamics in Structure and Function of Honey Bee Gut Bacteria in Response to Winter Dietary Shift. \u003cem\u003emBio\u003c/em\u003e 13, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngel, P. \u0026amp; Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcFrederick, Q. S. \u0026amp; Rehan, S. M. Characterization of pollen and bacterial community composition in brood provisions of a small carpenter bee. Mol Ecol 25, 2302\u0026ndash;2311 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell, K. A. \u0026amp; McFrederick, Q. S. Floral nectar microbial communities exhibit seasonal shifts associated with extreme heat: Potential implications for climate change and plant-pollinator interactions. Front Microbiol 13, 3107 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., M\u0026auml;chler, M., Zurich, E., Bolker, B. M. \u0026amp; Walker, S. C. Fitting linear mixed-effects models using lme4. (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuznetsova, A., Brockhoff, P. B. \u0026amp; Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw 82, 1\u0026ndash;26 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR core team. R: The R Project for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison, X. A. \u003cem\u003eet al.\u003c/em\u003e A brief introduction to mixed effects modelling and multi-model inference in ecology. \u003cem\u003ePeerJ\u003c/em\u003e 2018, e4794 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeurisse, N., Pawson, S. M. \u0026amp; Somchit, C. Bark beetles on pine logs: forecasting winter colonisation dynamics based on trap catches and temperature records. \u003cem\u003eJ Pest Sci (\u003c/em\u003e2004\u003cem\u003e)\u003c/em\u003e 94, 1357\u0026ndash;1373 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartig. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml;decke, D. ggeffects: Tidy Data Frames of Marginal Effects from Regression Models. J Open Source Softw 3, 772 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell V. Lenth. Package \u0026lsquo;emmeans\u0026rsquo; Type Package Title Estimated Marginal Means, aka Least-Squares Means. (2024) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00031305.1980.10483031\u003c/span\u003e\u003cspan address=\"10.1080/00031305.1980.10483031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Honey bees, gut microbiota, temperature stress, climate change, Lactobacillus, overwintering storage","lastPublishedDoi":"10.21203/rs.3.rs-4727319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4727319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHoney bee gut microbiota play a number of specific roles in promoting host growth and physiology. While the gut microbiota of honey bee queens, workers, and larvae has been extensively studied, less is known about the role and composition of gut microbiota in the winter worker bees. This study investigates the dynamics of the gut microbiota in overwintering adult worker bees, focusing on two commercial bee strains: Bolton\u0026trade; bees and Mann Lake\u0026trade; bees. The strains were investigated under different storage conditions (indoor storage at 6\u0026deg;C and outdoor storage in natural conditions) during the winter months (October, November, and December). Utilizing 16S rRNA gene amplicon sequencing, we characterized the microbial composition of the whole gut. We observed a stable microbiota with slight variations in host-associated bacterial communities with \u003cem\u003eLactobacillus\u003c/em\u003e genus dominating in all the overwintering honey bee guts. Bolton bees exhibited higher abundance levels of \u003cem\u003eBartonella\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eWolbachia\u003c/em\u003e, while Mann Lake bees showed increased abundance of \u003cem\u003eCommensalibacter\u003c/em\u003e. Our results suggest that alpha diversity is influenced by the month rather than by the bee strain or storage conditions. We also found significant differences in beta diversity by month. Overall, in this study a stable gut microbiota was observed in both indoor and outdoor environmental conditions across different months with slight differences in abundance between bee strains. Considering all potential benefits of the honey bee gut microbiome to health and nutrition, this study suggests that beekeepers need not be concerned about whether overwintering storage conditions affect the gut microbiome.\u003c/p\u003e","manuscriptTitle":"Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 07:01:52","doi":"10.21203/rs.3.rs-4727319/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-26T08:07:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-19T05:49:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65462966193796117358094421943461151142","date":"2024-08-14T07:27:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-13T16:28:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T17:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300306141853949521158975922556219767558","date":"2024-07-28T02:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158583748197089799941982516006683083070","date":"2024-07-22T01:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-19T15:05:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-19T14:03:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-16T15:41:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T06:52:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-12T01:59:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd68d63a-0203-4b21-b9b1-704b11145935","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35421568,"name":"Biological sciences/Microbiology/Communities"},{"id":35421569,"name":"Biological sciences/Microbiology/Communities/Microbial ecology"}],"tags":[],"updatedAt":"2025-03-10T19:48:59+00:00","versionOfRecord":{"articleIdentity":"rs-4727319","link":"https://doi.org/10.1038/s41598-025-90763-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-03 15:56:55","publishedOnDateReadable":"March 3rd, 2025"},"versionCreatedAt":"2024-08-06 07:01:52","video":"","vorDoi":"10.1038/s41598-025-90763-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90763-0","workflowStages":[]},"version":"v1","identity":"rs-4727319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4727319","identity":"rs-4727319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}