Honey Bee Colonies Research Articles

Evidence of circulating recombinants between deformed wing virus and Varroa destructor virus-1 in honey bee colonies in Türkiye.

Deformed wing virus (DWV), which is an important honey bee virus transmitted by Varroa destructor (V. destructor), causes colony losses in honey bee colonies. This study aimed to investigate the prevalence and genetic diversity of DWV in honey bees in Türkiye and to determine the role of V. destructor in the transmission of the genetic variants of DWV. Honey bee samples were collected from 62 apiaries, by simple random sampling, during March 2022 and April 2023. The presence of V. destructor in collected bee samples was examined using a stereo microscope. Real-time RT-PCR was used for the detection of DWV-A and DWV-B (Varroa destructor virus-1 (VDV-1)) viruses. Genetic characterisation of the positive samples was conducted by sequencing polyprotein genomic region. Considering the V. destructor infestation rate of 3% as relevant, out of the 62 apiaries examined, 17 (27.4%) were positive. However, DWV-A and VDV-1 specific RNA was not detected in V. destructor samples. VDV-1 specific RNA was detected in 6.5% (4/62) of the apiaries, whereas DWV-A was not detected in the sampled apiaries. Phylogenetic analysis showed that isolates detected in this study were located in a separate cluster from previously characterised DWV-A and VDV-1 isolates. According to RDP4 and GARD analyses, DWV-VDV-1 recombination breakpoints were detected in field isolates. To the best our knowledge, this is the first report of the presence of VDV-1-DWV recombinants in Türkiye. Further studies are needed to determine the impact of VDV-1-DWV recombinants and their virological and antigenic properties.

Longitudinal Analysis of Honey Bee Colony Health as a Function of Pesticide Exposure

Sixty commercial honey bee colonies were monitored over the course of one year with the goal of assessing potential correlations between measured colony strength and environmental stressors, including exposures to pesticides and pathogens. We developed a new method for assessing colony health by determining the fractional change in population of the four colonies on each pallet between peak population on 1 June and the last population assessment prior to winter on 1 October. This fractional change in population was evaluated as a function of pesticide load per pallet for each of the 37 pesticide chemicals detected, grouping pallets by beekeeper. The analysis of individual chemicals showed that both imidacloprid and cyprodinil were negatively correlated with colony health, while tau-fluvalinate and dinotefuran (at very low concentrations) were positively correlated, possibly because of effects on Varroa control. Exposure to groups of chemicals was also evaluated. Normalization of each chemical concentration to the maximum observed for that chemical provided an equal weighting for each chemical, even though their relative occurrence in the environment and their effective toxicities might differ. A total of 24 chemical groups whose members share a structural commonality, a functional commonality, or suspected synergistic actions were considered, demonstrating negative correlations between colony health and exposures to neonicotinoids as a group and neonicotinoids in combination with (1) methoxyfenozide (2) organophosphates, and (3) diflubenzuron. Analysis of several groups of fungicides applied to almonds during pollination also showed negative correlations with colony health.

Identifying and modeling the impact of neonicotinoid exposure on honey bee colony profit

Abstract Pollination by the European honey bee, Apis mellifera, is essential for the production of many crops, including highbush blueberries (Vaccinum corymbosum). To understand the impact of agrochemicals (specifically, neonicotinoids, a class of synthetic, neurotoxic insecticides) on these pollinators, we conducted a field study during the blueberry blooms of 2020 and 2021 in British Columbia (B.C.). Forty experimental honey bee colonies were placed in the Fraser Valley: half of the colonies were located within 1.5 km of highbush blueberry fields (“near” colonies) and half were located more than 1.5 km away (“far” colonies). We calculated risk quotients for these compounds using their chronic lethal dietary dose (LDD50) and median lethal concentration (LC50). Pesticide risk was similar between colonies located near and far from blueberry forage, suggesting that toxicity risks are regionally ubiquitous. Two systemic neonicotinoid insecticides, clothianidin and thiamethoxam, were found at quantities that exceeded chronic international levels of concern. We developed a profit model for a pollinating beekeeper in B.C. that was parameterized by: detected pesticide levels; lethal and sublethal bee health; and economic data. For colonies exposed to neonicotinoid pesticides in and out of the blueberry forage radii, there were economic consequences from colony mortality and sublethal effects such as a loss of honey production and compromised colony health. Further, replacing dead colonies with local bees was more profitable than replacing them with imported packages, illustrating that beekeeping management selection of local options can have a positive effect on overall profit.

The impact of landscape structure on pesticide exposure to honey bees

Pesticides may have serious negative impacts on bee populations. The pesticide exposure of bees could depend on the surrounding landscapes in which they forage. In this study, we assess pesticide exposure across various land-use categories, while targeting the Japanese honey bee, Apis cerana japonica, a native subspecies of the eastern honey bee. In a project involving public participation, we measured the concentrations of major pesticides in honey and beeswax collected from 175 Japanese honey bee colonies across Japan and quantitatively analyzed the relationships between pesticide presence/absence or pesticide concentration and land-use categories around the colonies. Our findings revealed that the surrounding environment in which bees live strongly influences pesticide residues in beehive materials, whether the pesticides are systemic or not, with a clear trend for each land-use category. Agricultural lands, particularly paddy fields and orchards, and urban areas resulted in higher pesticide exposure, whereas forests presented a lower risk of exposure. To effectively control pesticide exposure levels in bees, it is essential to understand pesticide usage patterns and to develop appropriate regulatory systems in non-agricultural lands, similar to those in agricultural lands.

Pollen foraging mediates exposure to dichotomous stressor syndromes in honey bees.

Recent declines in the health of honey bee colonies used for crop pollination pose a considerable threat to global food security. Foraging by honey bee workers represents the primary route of exposure to a plethora of toxins and pathogens known to affect bee health, but it remains unclear how foraging preferences impact colony-level patterns of stressor exposure. Resolving this knowledge gap is crucial for enhancing the health of honey bees and the agricultural systems that rely on them for pollination. To address this, we carried out a national-scale experiment encompassing 456 Canadian honey bee colonies to first characterize pollen foraging preferences in relation to major crops and then explore how foraging behavior influences patterns of stressor exposure. We used a metagenetic approach to quantify honey bee dietary breadth and found that bees display distinct foraging preferences that vary substantially relative to crop type and proximity, and the breadth of foraging interactions can be used to predict the abundance and diversity of stressors a colony is exposed to. Foraging on diverse plant communities was associated with increased exposure to pathogens, while the opposite was associated with increased exposure to xenobiotics. Our work provides the first large-scale empirical evidence that pollen foraging behavior plays an influential role in determining exposure to dichotomous stressor syndromes in honey bees.

Foraging behaviour and pollinator dynamics in lucerne crop of the Bundelkhand region: Implications for crop management in the subtropics

Abstract Pollination stands out as the paramount ecosystem service facilitated by insect pollinators, providing benefits to over three quarters of the world’s major crops. The conservation and effective management of honeybees constitute integral components for ensuring the sustainable productivity of cross-pollinated crops. The present study aimed to document the potential pollinators of lucerne in the Bundelkhand region in India, which is known for its subtropical climatic conditions. Further, the foraging behaviour, including foraging rate and speed, was meticulously documented. A total of 13 insect species from seven families across three insect orders were observed visiting lucerne flowers. Hymenopterans comprised the majority of the floral visitors (73.48%), followed by Lepidoptera (26.51%) during the study period. Among them, Apis dorsata (Hymenoptera: Apidae) emerged as the most predominant floral visitor, exhibiting peak activity at 12:00 h with a density of 7.85 bees/m2/10 min. Surprisingly, among the major floral visitors, A. florea has the highest mean foraging rate (number of flowers visited/minute/forager), i.e., 11.56, 11.59 and 10.79 bees/flowers/minute during the initial flowering stage, peak flowering stage and late flowering stage, respectively. However, the foraging speed (time spent by the forager on each flower) was longest in A. dorsata, i.e., 5.05, 3.78 and 5.41 s/flower during initial, peak and late flowering stages, respectively. The findings emphasize the importance of enhancing honeybee activity to boost lucerne seed yield. Further, the outcomes of the study can be leveraged to optimize honeybee colony management, scheduling field operations such as pesticide application and weeding during periods of minimal honeybee activity.

The Effect of Hive Type on Colony Homeostasis and Performance in the Honey Bee (Apis mellifera).

The colonies of honey bees are mostly sessile organisms. Consequently, the type of nest boxes that beekeepers provide to their bees should impact a colony's ability to maintain homeostasis, which is a key determinant of performance and fitness. Here, we used European honey bees (Apis mellifera) and provided them with two hive setups widely used and known as Langstroth and Warré. We compared colony performance in a Mediterranean climate for five months from late spring to early autumn, which covered the most active time of bees and included periods of heat and drought. We found that irrespective of hive type or season, honey bees kept hive temperature and humidity within a remarkably narrow range. Nevertheless, the hive type impacted the daily fluctuations in temperature and humidity. In Warré hives, where bees have more autonomy to build and maintain their combs, we found that bees were able to reduce daily fluctuations in temperature and humidity and kept both measures closer to the overall average. This increase in colony homeostasis found in Warré hives negatively correlated with other hive performance indicators, such as immunocompetence. We conclude that different hive types affect key areas, such as the central part of the colony with frames of developing brood or the queen, which are the most susceptible individuals. This implies that climatic changes resulting in extreme weather events are expected to impact colony performance and fitness, especially in non-managed honey bees that are limited by available nesting sites. For managed bees, adaptations to existing hive setups could be provided to help bees minimize the effects of abiotic stress.

Foraging distance distributions reveal how honeybee waggle dance recruitment varies with landscape

Honeybee (Apis mellifera) colonies use a unique collective foraging system, the waggle dance, to communicate and process the location of resources. Here, we present a means to quantify the effect of recruitment on colony forager allocation across the landscape by simply observing the waggle dance on the dancefloor. We show first, through a theoretical model, that recruitment leaves a characteristic imprint on the distance distribution of foraging sites that a colony visits, which varies according to the proportion of trips driven by individual search. Next, we fit this model to the real-world empirical distance distribution of forage sites visited by 20 honeybee colonies in urban and rural landscapes across South East England, obtained via dance decoding. We show that there is considerable variation in the use of dancing information in colony foraging, particularly in agri-rural landscapes. In our dataset, reliance on dancing increases as arable land gives way to built-up areas, suggesting that dancing may have the greatest impact on colony foraging in the complex and heterogeneous landscapes of forage-rich urban areas. Our model provides a tool to assess the relevance of this extraordinary behaviour across modern anthropogenic landscape types.

Nosema ceranae infection reduces the fat body lipid reserves in the honeybee Apis mellifera

Nosema ceranae is an intestinal parasite frequently found in Apis mellifera colonies. This parasite belongs to Microsporidia, a group of obligate intracellular parasites known to be strongly dependent on their host for energy and resources. Previous studies have shown that N. ceranae could alter several metabolic pathways, including those involved in the nutrient storage. To explore the impact of N. ceranae on the fat body reserves, newly emerged summer bees were experimentally infected, and we measured (1) the lipid percentage of the abdominal fat body at 2-, 7- and 14-days post-inoculation (p.i.) using diethyl ether lipid extraction, (2) the triglyceride and protein concentrations by spectrophotometric assay methods, and (3) the amount of intracellular lipid droplets in trophocytes at 14- and 21-days p.i. using Nile Red staining. Comparing the three methods used to evaluate lipid stores, our data revealed that Nile Red staining seemed to be the simplest, fastest and reliable method. Our results first revealed that the percentage of fat body lipids significantly decreased in infected bees at D14 p.i. The protein stores did not seem to be affected by the infection, while triglyceride concentration was reduced by 30% and lipid droplet amount by 50% at D14 p.i. Finally, a similar decrease in lipid droplet reserves in response to N. ceranae infection was observed in bees collected in fall.

Honey bee viruses in the yellow-legged hornet Vespa velutina (Lepelieter 1836): Prevalence, loads, and detection of replicative DWV and LSV forms

Apiaries in Galicia, northwestern Spain, are currently facing the invasive alien species Vespa velutina, which is well established in the region. The pressure on honey bee colonies is high, resulting in both economic and ecological losses. Honey bee colonies also face the challenge of viruses, which are becoming increasingly diverse. In recent years, honey bee viruses have been spreading across taxonomic groups beyond Apoidea, infecting the Vespoidea superfamily. This cross-species spillover has raised concerns in the scientific community due to the potential risk of viruses spreading in ecosystems. Currently, there is a lack of knowledge on this topic, and further research is needed to address this issue. This study employed qPCR and sequencing to investigate the prevalence, loads, and presence of replicative forms of important honey bee viruses in V. velutina individuals collected from 11 apiaries in Galicia. All V. velutina individuals tested positive for DWV, BQCV, AKI complex (ABPV, KBV, and IAPV), or LSV but not for CBPV. DWV showed the highest prevalence (97.0 %) and loads, with both DWV-A (67.4 %) and DWV-B (32.6 %) being detected. The AKI complex (46.3 %) and LSV (43.3 %) were also common, whereas BQCV (11.9 %) was rarer. LSV is detected for the first time in V. velutina. LSV-2 was the dominant strain (82.1 %), and two less frequent (17.9 %) unknown strains were also detected. All 44 screened V. velutina samples carried the replicative form of DWV, and six of these also carried the replicative form of LSV, raising for the first time the possibility of co-infection in the hornet. The detection of honey bee viruses in V. velutina, and the ability of these viruses to spread to other species, may indicate a potential risk of spillover in the apiaries.

Seasonal detection of pathogens in honeybees kept in natural and laboratory conditions

The honeybee is one of the most important pollinators in the world. The frequently observed poor health of honeybee colonies can be caused by various factors, e.g. environmental pollution, nutritional stress, and climate changes. Moreover, honeybees are constantly exposed to a wide spectrum of pathogens, such as parasites, bacteria, and viruses. We examined the occurrence of various diseases in different-aged worker honeybees from two colonies kept in natural and laboratory conditions during spring, summer, and autumn in Poland. The honeybees were examined by PCR to detect infection with selected pathogens: Nosema ceranae, N. apis, N. bombi, Acarapis woodi, trypanosomatids, and neogregarines (Mattesia or Apicystis species) and by RT-PCR to identify deformed wing virus (DWV), black queen cell virus (BQCV), and acute bee paralysis virus (ABPV). DWV and N. ceranae turned out to be the dominant pathogens. Trypanosomatids and BQCV were also found in several samples. We did not detect the presence of the other pathogens: N. apis, N. bombi, A. woodi, neogregarines, or ABPV. As shown in the present study, the dynamics and occurrence of pathogens are influenced by keeping conditions, honeybee age, and seasonality.

Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760-weight; 322,570-temperature; 155,373-video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.

Composition and Quality of Honey Bee Feed: The Methodology and Monitoring of Candy Boards.

The nutritional status of a honey bee colony is recognized as a key factor in ensuring a healthy hive. A deficient flow of nectar and pollen in the honey bee colony immediately affects its development, making room for pathogen proliferation and, consequently, for a reduction in the activities and strength of the colony. It is, therefore, urgent for the beekeepers to use more food supplements and/or substitutes in apiary management, allowing them to address colony nutritional imbalances according to the beekeeper's desired results. In this context, the commercial market for beekeeping products is growing rapidly due to low regulation of animal food products and the beekeeper's willingness to guarantee healthy colonies. There are numerous products (bee food additives) currently available on the worldwide market, with a highly variable and sometimes even undefined composition, claiming a set of actions at the level of brood stimulation, energy supplementation, queen rearing support, reduction of Varroa reproduction levels, improvement of the intestinal microflora of bees, Nosema prevention, and improvement of the health of honey bee colonies infested by American foulbrood, among others. To address this issue, the members of the COLOSS (Honey Bee Research Association) NUTRITION Task Force are proposing, for the first time, action on honey bee feed control and monitoring. In our common study, we focused on candy board composition and quality parameters. For that, a selected number of commercial candy boards usually found in Europe were analyzed in terms of water and ash content, pH, acidity, 5-hydroxymethylfurfural, sugars, C3-C4 sugar origin, and texture. Results revealed differences between the values found and the ones displayed on the label, demonstrating the need for regulation of the quality of these products.

Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine

In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (Apis mellifera ssps.) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9%) than RF (88.2%), CART (85.5%), or SVM (79.9%). Nonetheless, the highest overall accuracy (94.7%) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8%, 6.5%, 9.2%, and 14.8% compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.

Differences between uncapping and removal behaviors in Apis cerana from the perspective of long non-coding RNAs

BackgroundHygienic behavior, a specialized form of immune response evolved in social insects, plays a crucial role in safeguarding colonies from disease spread. In honeybee colonies, such behavior typically entails the dual steps of uncapping and removal of unhealthy and deceased brood. Although in recent years, numerous studies have examined the development of hygienic behavior, the mechanisms underlying the division in the performance of uncapping and removal have yet to be sufficiently elucidated. In this regard, long non-coding RNAs (lncRNAs) have been evidenced to be engaged in regulating the physiological activities of honeybees; however, whether lncRNAs are likewise involved in the uncapping and removal tasks has not been clarified.ResultsIn this study, the strong hygienic Apis cerana worker bees were used and the processes of uncapping and removal behaviors in three colonies were assayed with freeze-killed brood in the field. We then sequenced the antennal RNAs of honeybees to identify differentially expressed lncRNAs and performed lncRNA-mRNA association analysis to establish the differences between uncapping and removal. We detected 1,323 differentially expressed lncRNAs in the antennae, and the findings of lncRNA-mRNA association analyses revealed that the target genes of differentially expressed lncRNAs between uncapping and removal worker bees were predominantly linked to response to stimulus, receptor activity, and synapse. Notably, among the lncRNAs enriched in cellular response to stimulus, XR_001766094.2 was exclusively expressed in the uncapping worker bees. Based on these findings, we hypothesize that XR_001766094.2 plays a key role in distinguishing uncapping from removal behaviors by responding to external stimulus, thereby suggesting that the division of hygienic behaviors is governed by differential thresholds of responsiveness to environmental cues.ConclusionWe characterized differences in the uncapping and removal behaviors of worker bees from a perspective of lncRNAs. Uncapping bees may be equipped with a more rapid stimulatory response and more acute olfactory sensitivity, contributing to the rapid hygienic behavior in honeybee colonies. Our results thus establish a foundation for potential lncRNA-mediated gene expression regulation in hygienic behavior.

New insights into honey bee viral and bacterial seasonal infection patterns using third-generation nanopore sequencing on honey bee haemolymph

Honey bees are rapidly declining, which poses a significant threat to our environment and agriculture industry. These vital insects face a disease complex believed to be caused by a combination of parasites, viruses, pesticides, and nutritional deficiencies. However, the real aetiology is still enigmatic. Due to the conventional analysis methods, we still lack complete insights into the honey bee virome and the presence of pathogenic bacteria. To fill this knowledge gap, we employed third-generation nanopore metagenomic sequencing on honey bee haemolymph to monitor the presence of pathogens over almost a year. This study provides valuable insights into the changes in bacterial and viral loads within honey bee colonies. We identified different pathogens in the honey bee haemolymph, which are not included in honey bee screenings. These pathogens comprise the Apis mellifera filamentous virus, Apis rhabdoviruses, and various bacteria such as Frischella sp. and Arsenophonus sp. Furthermore, a sharp contrast was observed between young and old bees. Our research proposes that transgenerational immune priming may play a role in shaping infection patterns in honey bees. We observed a significant increase in pathogen loads in the spring, followed by a notable decrease in pathogen presence during the summer and autumn months. However, certain pathogens seem to be able to evade this priming effect, making them particularly intriguing as potential factors contributing to mortality. In the future, we aim to expand our research on honey bee transgenerational immune priming and investigate its potential in natural settings. This knowledge will ultimately enhance honey bee health and decrease colony mortality.

Main causes of producing honey bee colony losses in southwestern Spain: a novel machine learning-based approach

Honey bees assume a pivotal role as primary pollinators, but they are currently facing a growing crisis of colony losses on a global scale. This sector is important for generating essential products, preserving ecosystems, and crop pollination. This study includes the sampling of 179 beehives from three apiaries in the traditional beekeeping area of Extremadura (Spain) vital beekeeping sector and was carried out between 2020 and 2021 using the decision trees-based model. Some studies have tried to identify the primary causative factors of this issue. However, it is insufficient because the approach disregards potential nonlinear interactions among the various factors. For this reason, through meticulous exploration of different causative factors including Varroa destructor, Nosema ceranae, Deformed Wing Virus (DWV), Chronic Bee Paralysis Virus (CBPV), and strength factors, our study employed for first time machine learning methods to identify the most important variables generating colony loss. Our analysis underscores the importance of brood levels (operculated and open), pollen and honey, Varroa destructor infestation, virus (DWV), and honey bee populations as key determinants of colony survival. These findings hold promise for guiding efficacious colony management strategies and underscoring the latent potential of machine-learning applications in the realm of beekeeping.

The selection traits of mite non-reproduction (MNR) and Varroa sensitive hygiene (VSH) show high variance in subsequent generations and require intensive time investment to evaluate

The honey bee ectoparasite Varroa destructor is the main cause of honey bee colony losses worldwide. Over the last decades, several projects have focused on improving the robustness of Apis mellifera against this parasitic mite. Selection traits, such as mite non-reproduction (MNR) and Varroa sensitive hygiene (VSH), are favored selection factors in Varroa resistance projects. VSH is a trait where adult honey bees remove the Varroa-infested brood. During this process, the female mites are arrested in their reproductive cycle leading to a reduction of the Varroa population within the bee colony. From 2019 to 2022, 1402 queens were instrumentally inseminated with single or multiple drones in a breeding program. Colonies headed by these queens were established annually, and the MNR and VSH levels were analyzed. VSH was evaluated in response to cells artificially infested with Varroa, and colonies with high VSH values were used to generate our selected VSH stock. Despite crossing high VSH drones and queens, we measured a remarkable heterogeneity of MNR and VSH in the next generation(s), most likely due to the well-described, high recombination rate in the honey bee genome. When assessed multiple times in the same colony, great variance between measurements was observed. Detailed evaluations of daughter colonies are thus required if selection programs want to breed colonies with reliable VSH traits. This constant need to evaluate all offspring to ensure the desirable resistance traits are present results in high workloads and great expenses in selection programs. Furthermore, such large-scale breeding programs are inefficient due to high fluctuations between measurements and generations, indicating we need to develop new approaches and improved methods for assessing Varroa resistance.

Infectious diseases of bees and their impact on the vital activity and honey productivity of honey bee colonies in Ukraine

The article presents the results of the study of the mass mortality of honey bee colonies in different apiaries from different regions of Ukraine. The epizootic status of 102 honey bee colonies was studied and 607 samples of pathological material were analyzed in 2021–2023. According to the results of the monitoring of the epizootic situation in Ukrainian beekeeping it was found that the share of parasitic diseases (54.4%, 74.0%, 69.3%) constantly prevails over infectious diseases. It was noted that the incidence of varroosis in honey bees (34.4%, 71.4%, 41.47%) remains the highest among other diseases. Against the background of severe damage to honey bee colonies by the Varroa mite, infectious diseases began to appear in an atypical form, which significantly complicates their differential diagnosis. A probable increase in the incidence of nosemosis in adult bees and the detection of Nosema cysts in the intestines of bees, both in spring and summer, and the detection of cysts in honey indicate the spread of another pathogen, Nosema cerana, which causes nosemosis in the summer season. The reason for the periodic mass death of bees in Ukraine is the combination of a high number of parasitic Varroa mites in the honey bee colony with the presence of bee infection with microsporidia Nosema spp. These pathogens negatively affect the immunity of the honey bee colony, and cause exacerbation of latent infections in bees, which leads to a decrease in the number of honey bee colonies, weakening their viability and reducing the quality of honey products

Food supplementation does not prevent oxidative stress in forager honey bees exposed to the fungicides bixafen, prothioconazole and trifloxystrobin

As in other organisms, the antioxidant cellular defense system of bees helps maintain homeostasis. However, extrinsic factors such as pesticides and nutritional deficiencies can interfere. To determine whether nutritional supplementation can mitigate the oxidative damage caused by fungicides, five colonies were supplemented with sucrose syrup and a pollen-sucrose paste for 14 wk, while five other colonies were subjected to reduced protein intake due to the installation of pollen traps at hive entrances and were not given supplementary food. Following the food management period, forager bees were exposed by contact to 1 or 7 µg of a modern three component fungicide, or by its fungicide components, bixafen, prothioconazole, and trifloxystrobin, individually. After 24 h, treated and untreated control bees were dissected, and thorax homogenates were evaluated for signs of oxidative stress. Without fungicide treatment, food supplementation induced higher activity of the enzymatic antioxidants such as glutathione peroxidase and catalase, and altered the reduced glutathione to glutathione disulfide ratio. Increased malondialdehyde was detected in bees exposed to the three fungicides alone or in combination, except for trifloxystrobin at the lower dose, independent of nutritional condition. Food supplementation of honey bee colonies did not mitigate the oxidative stress caused by these fungicides in the bees.