The research relevance is determined by the decline of bee populations in Albania, as the need to understand the dynamics of colony loss and the factors contributing to it is of paramount importance. The study aimed to comprehensively investigate the prevalence and main causes of colony losses, with special attention to Varroa mite infestation, Nosema disease, viral pathogens, pesticides, and bacterial infections. Using the stratified sampling method, 15,493 beekeepers of different ages and experiences participated in the study. Both electronic and face-to-face surveys were used to collect data on bee family losses, management practices and environmental factors affecting bee health. In addition, monitoring programmes allowed a detailed assessment of bee family health and environmental conditions in the apiary, providing valuable information on temporal trends and patterns. The findings indicate alarming rates of Varroa mite infestation, prevalence of Nosema and a complex interplay of factors contributing to colony loss, particularly during the summer and winter months. For example, Varroa mite infestation was found in 61% of the 29,474 bee samples collected during summer sampling, with rates ranging from 0.5% to 70.2%. Similarly, during autumn sampling, 65% of 43,037 bee samples contained Varroa mites, with an average infestation rate of 5.3%. Moreover, Nosema disease is also a complex problem, with clinical prevalence ranging from 0.1% in autumn to 1.3% in summer and spring. These key figures highlight the urgent need to develop effective strategies to reduce Varroa mite infestation and Nosema disease, thereby maintaining bee populations and ecosystem health. The results of the study make a valuable contribution to bee management and policy development, emphasising the importance of holistic approaches to maintaining bee health and resilience in Albania

European Foulbrood (EFB) is a severe bacterial disease affecting honeybees, primarily caused by the Gram-positive bacterium Melissococcus plutonius. Although the presence of M. plutonius is associated with EFB, it does not consistently predict the manifestation of symptoms, and the role of ‘secondary invaders’ in the disease’s development remains a subject of ongoing debate. This review provides an updated synthesis of the microbial ecological factors that influence the expression of EFB symptoms, which have often been overlooked in previous research. In addition, this review examines the potential negative health consequences of prolonged antibiotic use in bee colonies for treating EFB, and proposes innovative and sustainable alternatives. These include the development of probiotics and targeted microbiota management techniques, aiming to enhance the overall resilience of bee populations to this debilitating disease.

ABSTRACT European foulbrood (EFB) is a prevalent disease in the European honey bee (Apis mellifera) in the United States, which can lead to colony decline and collapse. The bacterial components of EFB are well-studied, but the diversity of viral infections within infected colonies has not been explored. In this study, we use meta-transcriptomics sequencing of 12 honey bee hives, symptomatic (+, n = 6) and asymptomatic (–, n = 6) for EFB, to investigate viral infection associated with the disease. We assembled 41 viral genomes, belonging to three families (Iflaviridae, Dicistroviridae, and Sinhaliviridae), all previously reported in honey bees, including Lake Sinai virus, deformed wing virus, sacbrood virus, Black queen cell virus, and Israeli acute paralysis virus. In colonies with severe EFB, we observed a higher occurrence of viral genomes (34 genomes) in contrast to fewer recovered from healthy colonies (seven genomes) and a complete absence of Dicistroviridae genomes.We observed specific Lake Sinai virus clades associated exclusively with EFB + or EFB – colonies, in addition to EFB-afflicted colonies that exhibited an increase in relative abundance of sacbrood viruses. Multivariate analyses highlighted that a combination of site and EFB disease status influenced RNA virome composition, while EFB status alone did not significantly impact it, presenting a challenge for comparisons between colonies kept in different yards. These findings contribute to the understanding of viral dynamics in honey bee colonies compromised by EFB and underscore the need for future investigations to consider viral composition when investigating EFB.IMPORTANCEThis study on the viromes of honey bee colonies affected by European foulbrood (EFB) sheds light on the dynamics of viral populations in bee colonies in the context of a prevalent bacterial brood disease. The identification of distinct Lake Sinai virus and sacbrood virus clades associated with colonies affected by severe EFB suggests a potential connection between viral composition and disease status, emphasizing the need for further investigation into the role of viruses during EFB infection. The observed increase in sacbrood viruses during EFB infection suggests a potential viral dysbiosis, with potential implications for honey bee brood health. These findings contribute valuable insights related to beekeeping practices, offering a foundation for future research aimed at understanding and mitigating the impact of bacterial and viral infection in commercial honey bee operations and the management of EFB.

The Asian honey bee ( Apis cerana) is a keystone pollinator in East and South Asia, crucial to regional biodiversity and agricultural productivity. Despite its ecological significance, A. cerana has been severely impacted by viral pathogens, particularly sacbrood virus (SBV), which has decimated colonies in South Korea. Here, we report the first detection of Lake Sinai virus (LSV) in A. cerana populations in South Korea, marking a substantial addition to our understanding of LSV host range and interspecies transmission dynamics. Using high-throughput sequencing (HTS)-based virome analysis, we identified three distinct LSV species-LSV2, LSV3, and LSV4alongside eight other honey bee-associated viruses. This study provides evidence of potential LSV transmission fromA. mellifera, where it was previously detected in South Korea, expanding the virome landscape ofA. cerana. These findings highlight the urgent need for ongoing virological surveillance in A. cerana populations to track emerging viral threats. This study underpins critical strategies for enhancing honey bee health, conservation, and disease management to secure the pollination services essential for ecosystem stability.

International audience | Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.