Honey Bee Colonies Research Articles

Effect of supplementary diets on Apis mellifera colonies

Effect of supplementary diets on Apis mellifera colonies

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.

Nurse honey bees filter fungicide residues to maintain larval health.

Residues of plant protection products (PPPs) are frequently detected in bee matrices1,2,3,4,5,6 due to foraging bees collecting contaminated nectar and pollen, which they bring back to their hive. The collected material is further used by nurse bees to produce glandular secretions for feeding their larvae.7 Potential exposure to PPPs occurs through direct oral ingestion, contact during foraging, or interaction with contaminated hive material.8,9 Contaminants can pose health risks to adult worker bees,10,11 queens,12,13 drones (males),14 or larvae,15,16 potentially impacting colony health and productivity. However, residue concentrations can vary significantly between analyzed matrices, and potential accumulation or dilution steps have not been widely investigated. Although research has provided valuable insights into contamination risks, there remain gaps in our understanding of the entire pathway from field, via foragers, stored products, nurse bees, and finally to food jelly, i.e., royal, worker, and drone jelly, and the larvae, including all possible processing steps.17 We collected samples of bee-relevant matrices following the in-field spray application of the product Pictor Active, containing the fungicides boscalid and pyraclostrobin. The samples were analyzed for residues along this entire pathway. Fungicide residues were reduced by a factor of 8-80 from stored product to nurse bees' heads, suggesting a filtering function of nurse bees. Furthermore, detected residues in larval food jelly resulted from added pollen and not from nurse bee secretions. Calculated risk quotients were at least twice as low as the threshold values, suggesting a low risk to honey bee colonies from these fungicides at the tested application rate.

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.

The Diagnostic Value of qPCR Quantification of Paenibacillus larvae in Hive Debris and Adult Bees for Predicting the Onset of American Foulbrood.

American foulbrood (AFB) is a serious infectious disease of honeybees (Apis mellifera) caused by Paenibacillus larvae. Increased P. larvae count in hive-related material is associated with an increased risk of AFB. Here, we quantified P. larvae cells in 106 adult bee and 97 hive debris samples using quantitative PCR (qPCR); 66/106 adult bee and 66/97 hive debris samples were collected simultaneously from the same bee colony (paired-sample design). The corresponding bee colonies were also examined for the presence of AFB clinical signs. A binary logistic regression model to distinguish between AFB-affected and unaffected honeybee colonies showed a strong diagnostic accuracy of both sample types for predicting the onset of AFB based on P. larvae counts determined by qPCR. The colonies with a P. larvae count greater than 4.5 log cells/adult bee or 7.3 log cells/mL hive debris had a 50% probability of being clinically affected and were categorized as high-risk. The AFB-unaffected colonies had significantly lower P. larvae counts than the AFB-affected colonies, but the latter did not differ significantly in P. larvae counts in relation to the severity of clinical signs. Both bee-related sample types had a high diagnostic value for predicting disease outcome based on P. larvae counts. These results improve the understanding of the relationship between P. larvae counts and AFB occurrence, which is essential for early detection of high-risk colonies.

Causal network linking honey bee (Apis mellifera) winter mortality to temperature variations and Varroa mite density

Winter season is a critical time for honey bees (Apis mellifera) colonies when individual mortalities may lead to total colony losses or diminish productivity in subsequent seasons. A deeper understanding of the causes and consequences of winter mortality is required. In this study, we analyzed winter (November–March) individual bee mortality in an apiary in Central Europe from 1991 to 2023. We observed consistency in mortality times among years, but also some systematic departures from the shared trend. We distinguished four clusters of year-specific mortality trajectories. However, we found no statistically significant differences in means of spring (March–May), autumn (October), winter (November–March) temperatures, or autumn Varroa destructor density among clusters. Nevertheless, our insights into the dynamics of individual bee mortality may be important for determining critical moments during wintering when implementing additional protective measures could prove beneficial. Hypothesis-driven path analysis indicated causal links in our study system, including both direct and indirect influences. The density of V. destructor in autumn was positively related to temperature, especially in the preceding spring, but to a lesser extent also in autumn. Increased winter mortality was related to lower winter temperatures and a higher mite infestation in autumn. We found no significant effects of individual winter mortality on honey harvests in subsequent seasons. Honey harvest was determined by bee abundance in spring, and the latter, unexpectedly, was not related to winter mortality. Our study adds to accumulating evidence of the major role of weather and climatic conditions in the resilience of honey bee colonies and improves our understanding of mortality processes. We highlighted the importance of causative factors, especially seasonal temperatures and V. destructor density, and their potential as predictive indicators of individual winter mortality, bee colony fate, and honey productivity.

Analysis of contaminant residues in honey bee hive matrices

Pollinators provide ecological services essential to maintaining our food supply and propagating natural habitats. Populations are in decline due to environmental stressors including pesticides, pathogens, and habitat loss. To better understand the impacts of pesticide exposures on colony health, a field survey in Ohio, USA was conducted to monitor the potential contamination of honey bee colonies by pesticides. Apiaries (n = 10) were situated across an agricultural gradient and samples were collected over a 4-week period encompassing corn planting. Dead bees from entrance traps (DBT), pollen, and in-hive (IH) matrices including bee bread, honey, larvae, and nurse bees were analyzed for a whole suite of pesticides. Out of 210 pesticides targeted, 68 residues were quantified across 306 samples. Neonicotinoids, miticides, and fungicides were the dominant pesticide classes identified throughout all the matrix types. Neonicotinoids were detected at higher concentrations and at higher frequencies compared to fungicides, specifically in field pollen samples. DBT also contained high concentrations of these two contaminant classes, although detection frequencies for neonicotinoids were typically lower. Overall, herbicides and non‑neonicotinoid insecticides were found with low frequency and at low concentrations. For most pesticide classes, trends for the mean concentrations were DBT > IH nurse bees > field pollen > IH larvae > IH honey. Pesticides were detected in 100 % of samples with concentrations ranging from 0.01 ppb (diphenylamine) to 2790 ppb (clothianidin). All samples were contaminated with at least two pesticide residues, while 19 samples presented over ten detects and maximum detections of 20 in DBT. Pesticide residues were positively correlated with agricultural gradients across sites and sampling periods. These findings reveal that foraging leads to the exposure of the entire colony to a wide range of pesticides. Moreover, residues determined in DBT serve as an effective proxy for monitoring hive matrices with significantly less disturbance to active hives.

Multivariate optimization of the extraction and cleaning procedure to determine neonicotinoids in honey from intensive livestock and agricultural production areas by UHPLC-MS/MS

Neonicotinoid insecticides have been associated with the decline of bee population. This study aimed to investigate the occurrence of neonicotinoids in honey samples from apiaries located in areas of intensive livestock and agricultural production in Argentina. For this purpose, a QuEChERS-based analytical method was developed and optimized using multivariate strategies to reduce the processing time and the necessary amount of salts and sorbents. Validation was based on SANTE 11312/2021 guidelines, with recovery percentages between 79 and 120 %, and limits of quantitation between 0.25 and 1.00 µg kg−1. The present study provides a highly sensitive and reliable method for ascertaining residue levels of eight insecticides in 151 honey samples using liquid chromatography coupled to mass spectrometry (UHPLC-MS/MS). The presence of three of the target analytes was determined in 13 % of the samples, indicating that honey bee colonies were exposed to neonicotinoids, especially imidacloprid and thiamethoxam. The maximum residue limits were not exceeded in any of the samples; therefore, there is no risk to consumers from the public health point of view.

ICP-OES analysis of Lithium in honey, royal jelly, bee bread, propolis, and bees following microwave-assisted sample preparation

AbstractLithium is a natural, ubiquitously-occurring alkali metal found in varying amounts in foods like honey. Recently, lithium chloride (LiCl) was described to be effective against varroosis, a parasitic disease leading to loss of honey bee colonies with limited therapy options. However, LiCl treatment is not currently authorized for use in honey bee colonies. Such treatment might result in elevated lithium amounts in honey. To address this, a robust method for quantifying lithium in honey was validated using a microwave-assisted digestion technique combined with Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), achieving detection levels as low as 0.151 mg/kg. The method was applied to 65 commercially available, randomly chosen honey samples, all of which had lithium levels below the limit of quantitation (LOQ). Furthermore, the method was successfully adapted for use with more complex bee matrices, including royal jelly, bee bread, propolis, and whole bees.

Population Dynamics of the Mite Varroa destructor in Honey Bee (Apis mellifera) Colonies in a Temperate Semi-Arid Climate.

This study aimed to analyze the population dynamics of the mite Varroa destructor in honey bee (Apis mellifera) colonies in a temperate semi-arid climate in Mexico. Ten colonies homogeneous in population, food stores, and levels of mite infestation were used. The mite infestation rate in brood and adult bees, total number of mites, daily mite fall, brood and adult bee population, and food stores were determined periodically for 10 months. There was a significant effect (p < 0.05) of sampling period on the population of V. destructor in adult bees, brood, total mite population, and daily fallen mites. The total mite population increased by 26% on average per colony. The increase in brood amount reduced the mite infestation rate in adult bees, and the opposite occurred when the brood decreased. Monitoring V. destructor populations by recording fallen mites is more reliable than determining mite infestation rates in bees, as mite fall has a dynamic pattern similar to that of the total mite population. The best period to apply an acaricide treatment in the region of study is between November and December because most mites were in the phoretic phase, since there was less brood in the colonies compared to other times.

Evaluation of Some Pollen Substitutes on Productivity of Honey bee Colonies (Apis cerana F.)

This study examined the effects of different pollen substitutes on the performance of honey bee colonies in Assam, India. Natural Corbicula pollen (CPF) and six artificial diets were compared with a sugar syrup control for 15 days. CPF consistently outperformed other treatments in all parameters. Regarding the honey area, the CPF reached 22.63 cm2 and 22.65 cm2 in 2021 and 2022, respectively, while in the control group it was 15.61 cm2 and 15.72 cm2, respectively. CPF also had the highest pollen area (15.59 cm² and 15.68 cm²) and brood area (20.19 cm² and 20.35 cm²) in both years. Colony strength was maximized in the CPF (7.55 and 7.59 for frames 2021 and 2022). Among artificial foods, OUAT-PS (soy flour, honey, yeast, multivitamin) generally performed best, with no significant difference from CPF in colony strength. All pollen substitutes stimulated honey production and brood development with foods rich in proteins, vitamins and amino acids, and significantly improved colony performance compared to controls. These results can help beekeepers select appropriate additional nutrients in times of pollen shortage.

The Status of Beekeeping in Simada District, Amhara, Ethiopia, with Its Challenges and Opportunities

The study was designed to assess the existing beekeeping practices, constraints, and potential of honeybee production in Simada district. The study was carried out in four proportionally selected kebeles of highland, midland, and lowland agro-ecology. Accordingly, a total sample size of 146 beekeepers, depending on their potential, was interviewed using a structured and semi-structured questionnaire. A semi-structured questionnaire, field observation, and focal group discussion were employed to collect primary data. Descriptive statistics such as mean, frequency, and standard deviation were used to analyze the data. The majority of beekeepers in the district are mail-headed, and the majority can read and write. Beekeepers practiced three hive types but mostly used traditional hives. The majority of honeybee colonies are found in midland agro-ecology, but they are not significantly different (P&amp;lt;0.05). About 57.5% of beekeepers obtain their colony through buying, and their colony increases through reproductive swarming. Beekeepers construct both traditional and top-bar hives from the surrounding available material. Frame hives were obtained from GOs on a credit basis. Beekeepers indicated that the majority of honey was harvested in October and November. The second minor harvesting period was from May to June, which depends on the nature of the yearly rainfall conditions. As the respondent&amp;apos;s described, they stored honey below one year in a plastic jar, clay jar, and plastic sack when plenty of products were obtained and for medicinal value, unless they used honey during harvesting as a source of income. Predators and pests are major constraints on honey bee production, followed by pesticides and herbicides in the study area. Other identified beekeeping constraints were shown in relative order of importance: drought, death of colony, lack of water, migration, and disease are some of the problems that hinder productivity. Honeybees required feed supplementation during the dry season; about 28% of beekeepers fed their colonies with higher supplements made from February to April. The commonly used supplements were peas and bean flour (Shiro), barley flour (Besso), sugar, honey, and others, including Niger.

Colonies under dysbiosis benefit from oxalic acid application: the role of landscape and beekeeping practices in microbiota response to treatment

AbstractThe Varroa destructor mite causes severe losses of Apis mellifera colonies, requiring recurring treatments. One such treatment is oxalic acid (OA), considered ecological. However, it is unclear whether OA affects the honey bee gut microbiota or other hive-associated microbiotas. Herein, we studied the effect of three OA treatments (trickling at 2.1% or 4.2%, and sublimation through Varrox®) upon microbial communities associated with workers’ gut, hive bee bread and pupae, sampled from conventionally or ecologically managed colonies under different anthropization levels (located in urban, rural or mountainous landscapes). We hypothesized that treatment with OA would impact the diversity and composition of bacteria and/or eukaryotic communities, and that the effect would be dose-dependent and specific to the beehive niche. Results showed that the microbiomes of apiaries under different anthropization levels and management strategies differed prior to OA application. Neither the bacterial nor the fungal communities of bee bread and pupae shifted due to OA treatment. Independent of the dosage and the application method (trickling or sublimation), OA induced slight compositional changes in the bacterial profiles of honeybee guts. Those changes were stronger the higher the anthropization (in colonies from urban areas under conventional management). OA treatment reduced the relative abundance of several pathogens, such as Nosema ceranae, and decreased the overall bacterial diversity down to values found in less anthropized colonies. Thus, our results suggest that, aside from managing Varroa infestations, OA could have beneficial effects for stressed colonies while not impairing honey bee resilience from a microbial point of view.

Effect of some natural compounds and varroacides on varooa mites (varroa destructor) in honeybee (apis mellefra) colonies during winter season in el gharbiya governorate, egypt

Effect of some natural compounds and varroacides on varooa mites (varroa destructor) in honeybee (apis mellefra) colonies during winter season in el gharbiya governorate, egypt

Prevalence and Phylogenetic Network Analysis of Nosema apis and Nosema ceranae Isolates from Honeybee Colonies in Türkiye.

Nosemosis is a disease that infects both Western honeybees (Apis mellifera L.) and Asian honeybees (Apis cerana) and causes colony losses and low productivity worldwide. In order to control nosemosis, it is important to determine the distribution and prevalence of this disease agent in a particular region. For this purpose, a national study was conducted to assess the prevalence of Nosema ceranae and N. apis throughout Türkiye, to perform network analyses of the parasites, and to determine the presence of nosemosis. In this study which aimed to assess the prevalence of N. apis and N. ceranae in different colony types and regions where beekeeping is intensive in Türkiye, specimens were collected from hives with no clinical signs. A total of 1194 Western honeybee colonies in 400 apiaries from 40 provinces of Türkiye were examined by microscopic and molecular techniques. Nosemosis was found in all of 40 provinces. The mean prevalence ratio was 64.3 ± 3.0, with 95% CI in apiaries and 40.5 ± 2.9, 95% CI in hives. Nosema ceranae DNA was detected in all of positive hives, while N. ceranae and N. apis co-infection was detected in only four colonies. This study showed that nosemosis has spread to all provinces, and it is common in every region of Türkiye. All of the N. ceranae or N. apis samples examined were 100% identical within themselves. Network analysis showed that they were within largest haplotype reported worldwide.