Search Results (57)

One of the common causes of mass death in bee colonies is the infectious disease nosemosis, which is caused by two types of microsporidia, Nosema apis and Nosema ceranae. Of the many factors contributing to the spread of nosemosis, in this paper we consider the hybridization of subspecies of Apis mellifera L. In most of Russia, the native subspecies is the dark forest bee Apis mellifera mellifera, which is representative of the evolutionary lineage M. The export of bee packages and queens from the southern regions of Russia and other countries has led to the fragmentation of the range of these subspecies. First, we determined the maternal and paternal ancestry of 349 honey bee colonies across 12 beekeeping regions of Russia using the mitochondrial tRNAleu-COII locus and nine nuclear SSR markers (Ap243, 4a110, A024, A008, A43, A113, A088, Ap049, and A028). Among them, 140 colonies belonged to subspecies A. m. mellifera, 58 colonies were of hybrid origin, and 151 colonies belonged to evolutionary lineage C. Then, using microscopy and PCR analysis, we performed diagnostics of nosemosis in the studied colonies: N. apis was detected in 87 colonies, N. ceranae in 102 colonies, and coinfection was observed in 36 colonies. The results of our study indicate that the main reservoir of Nosema microsporidia was bees of evolutionary lineage C. Full article

In Argentina, various studies have reported the detection of multiple viruses in honey-producing and queen-rearing apiaries, with Aparavirus apisacutum, the causal agent of acute bee paralysis (ABP), demonstrating a particularly high prevalence. The potential of RNA interference (RNAi) as a strategy to control honey bee viruses has been explored, with initial findings indicating that RNAi could aid in mitigating the economic losses associated with viral infections. This study aimed to evaluate the effect of RNAi technology mediated by double-stranded RNA (dsRNA) on the dynamics of ABPV infection in adult honey bees. Fragments of the ABPV replicase and VP1 genes were used as templates for dsRNA synthesis via in vitro transcription. A gene silencing experiment was conducted through oral administration using five treatments: control, specific dsRNA + Virus, Virus alone, specific dsRNA alone, and non-specific dsRNA + virus. Bee survival was recorded over 10 days for all treatments, and samples were subsequently processed for viral quantification using quantitative real-time PCR. The oral administration of specific dsRNA reduced the viral replication curve, decreased the average viral loads and increased bee survival. This is the first report demonstrating the reduction in ABPV infection in adult honey bees through post-transcriptional gene silencing achieved via oral administration of dsRNA. Full article

In this study, honeybee viruses were identified in naturally infected honeybee colonies (Apis mellifera carnica). From nine selected samples of clinically affected and ten samples of healthy honeybee colonies, different strains of honeybee viruses were first detected using quantitative real-time RT-PCR methods. Twenty-two nucleotide sequences of the complete genomes of honeybee viruses were identified using the Illumina Next-Generation Sequencing (NGS) method: acute bee paralysis virus (ABPV) (n = 4), black queen cell virus (BQCV) (n = 3), chronic bee paralysis virus (CBPV) (n = 2), deformed wing virus (DWV) (n = 5), Lake Sinai virus (LSV) (n = 4), sacbrood bee virus (SBV) (n = 1), Apis rhabdovirus-1 (ARV-1) (n = 1), bee macula-like virus (BeeMLV) (n = 1) and Hubei partiti-like virus 34 (HPLV34) (n = 1). The nucleotide sequences of ABPV, BQCV, DWV and SBV are the first complete genomes of these viruses identified in Slovenia and they represent an important contribution to our understanding of the genetic diversity of honeybee viruses. ARV-1, BeeMLV and HPLV34 were detected and sequenced for the first time in Slovenia. Full article

The Megaselia scalaris and Senotainia tricuspis parasitoid flies of the honeybee Apis mellifera were found to infest apiaries of different European and Mediterranean countries but their prevalence and impact on apiary health are little known. Therefore, in this study, quantitative PCR (qPCR)-based methods were developed for their rapid detection directly in hive matrices. The newly developed qPCR assays were targeted at the mitochondrial cytochrome oxidase subunit I (COI) gene for the M. scalaris and the cytochrome B (cytB) gene for the S. tricuspis. The tests were preliminarily applied to 64 samples of adult honeybees and hive debris collected in the Abruzzo and Molise regions, Central Italy, and the Republic of Kosovo showing that both flies occur in the two countries and more frequently in Italy. The positive apiaries in Italy were re-sampled by capturing viable forager bees and isolating emerging flies to carry out the genotyping and analyses aimed at defining if these flies can transmit honeybee pathogens. Genotyping based on the COI and cytB gene sequencing for M. scalaris and S. tricuspis, respectively, identified one S. tricuspis genotype and diverse genotypes of M. scalaris highly similar to those from distant countries. Some fly isolates harbored the DNA or RNA of honeybee microbial pathogens Paenibacillus larvae, deformed wing viruses A and B (DWVA and B), black queen cell virus (BQCV), chronic paralysis virus (CBPV), and Nosema ceranae. The results indicated that these parasites should be efficiently controlled in apiaries by using rapid detection methods to facilitate the large screening studies and early detection. Full article

The Asian honey bee (Apis cerana) plays a crucial role in providing abundant bee products and in maintaining ecological balance. Despite the availability of the genomic sequence of the Asian honey bee, its transcriptomic information remains largely incomplete. To address this issue, here we constructed three pooled RNA samples from the queen, drone, and worker bees of A. cerana and performed full-length RNA sequencing using Nanopore single-molecule sequencing technology. Ultimately, we obtained 160,811 full-length transcript sequences from 19,859 genes, with 141,189 being novel transcripts, of which 130,367 were functionally annotated. We detected 520, 324, and 1823 specifically expressed transcripts in the queen, worker, and drone bees, respectively. Furthermore, we identified 38,799 alternative splicing (AS) events from 5710 genes, 44,243 alternative polyadenylation (APA) sites from 1649 gene loci, 88,187 simple sequence repeats (SSRs), and 17,387 long noncoding RNAs (lncRNAs). Leveraging these transcripts as references, we identified 6672, 7795, and 6804 differentially expressed transcripts (DETs) in comparisons of queen ovaries vs drone testes, worker ovaries vs drone testes, and worker ovaries vs queen ovaries, respectively. Our research results provide a comprehensive set of reference transcript datasets for Apis cerana, offering important sequence information for further exploration of its gene functions. Full article

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the “BeeTogether” dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives’ extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony’s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture. Full article

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. Full article

In the last few years, the isolation and amplification of DNA or RNA from the environment (eDNA/eRNA) has proven to be an alternative and non-invasive approach for molecular identification of pathogens and pests in beekeeping. We have recently demonstrated that bee pollen and bee bread represent suitable biological material for the molecular identification of viral RNA. In the present study, we extracted total RNA from different bee products (pollen, n = 25; bee bread, n = 17; and royal jelly, n = 15). All the samples were tested for the presence of six of the most common honey bee-associated viruses—Deformed wing virus (DWV), Acute bee paralysis virus (ABPV), Chronic bee paralysis virus (CBPV), Sacbrood virus (SBV), Kashmir bee virus (KBV), and Black queen cell virus (BQCV)—using a reverse transcription polymerase chain reaction (RT-PCR). We successfully detected six records of DWV (10.5%, 6/57), four of ABPV (7.0%, 4/57), three of Israeli acute paralysis virus (IAPV) (5.3%, 3/57), and two of BQCV (3.5%, 2/57). Using ABPV primers, we also successfully detected the presence of IAPV. The obtained viral sequences were analyzed for phylogenetic relationships with the highly similar sequences (megablast) available in the GenBank database. The Bulgarian DWV isolates revealed a high homology level with strains from Syria and Turkey. Moreover, we successfully detected a DWV strain B for the first time in Bulgaria. In contrast to DWV, the ABPV isolates formed a separate clade in the phylogenetic tree. BQCV was closely grouped with Russian isolates, while Bulgarian IAPV formed its own clade and included a strain from China. In conclusion, the present study demonstrated that eRNA can be successfully used for molecular detection of honey bee-associated viruses in bee products. The method can assist the monitoring of the health status of honey bee colonies at the local, regional, and even national levels. Full article

In lowbush blueberry fields, we conducted residue analysis comparing flowers, trapped pollen (honey bee and Osmia spp.), and collected bees (honey bee workers, bumble bee queens, and non-Bombus spp. wild native bees). The study was conducted from 2012 to 2014. The number of pesticide residues, total concentrations, and risk to honey bees (Risk Quotient) on flowers were not significantly different from those determined for trapped honey bee pollen (except in one study year when residues detected in flower samples were significantly lower than residue numbers detected in trapped pollen). The compositions of residues were similar on flowers and trapped pollen. The number of residues detected in honey bee pollen was significantly greater than the number detected in Osmia spp. pollen, while the total concentration of residue was not different between the two types of pollen. The risk to honey bees was higher in trapped honey bee pollen than in trapped Osmia spp. pollen. The analysis of honey bee workers, native bumble bee queens, and native solitary bees showed that although more pesticide residues were detected on honey bee workers, there were no differences among the bee taxa in total residue concentrations or risk (as estimated in terms of risk to honey bees). Full article

Verifying the inclusivity of molecular detection methods gives indications about the reliability of viral infection diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed at selecting inclusive probes based on reverse transcription–quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, previously described detection methods were re-evaluated in silico for their specificity and inclusivity. Based on this evaluation, selected methods were modified, or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and the viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared with previously described tests and two viral pathogens in a single PCR reaction. Full article

The COLOSS research association has been assessing honey bee colony losses, associated risk factors and management, focusing on Western countries but with a progressive international expansion. Here, we report the first survey on the loss rates of colonies in 2022/2023 in Ethiopia using COLOSS monitoring survey tools. A face-to-face interview questionnaire survey was conducted on 64 beekeepers selected from Oromia and Tigray regions. This covered 1713 honey bee colonies distributed in 68 apiaries. The percentages of colonies lost were significantly different between Oromia (24.1%) and Tigray (66.4%) regions. Colony losses were attributed as unsolvable queen problems (8% in Oromia; 10% in Tigray), natural disaster (32%; 82%), and empty hives or dead colonies (60%; 8%). The loss rate was significantly affected by queen replacement (p < 0.0001), use of natural comb (p < 0.0001), feed supplementation (p < 0.0001), region (p < 0.0001), varroa treatment (p < 0.0001), colony splitting (p < 0.01), and merging (p < 0.01). Beekeepers in Oromia managed more colonies and implemented improved practices compared to those in Tigray. However, all beekeepers in Oromia detected at least some bees with signs of deformed wing virus, compared to 76% of beekeepers in Tigray. In conclusion, the colony loss rate was significantly different between Oromia and Tigray regions due to differences in natural disasters, management, environment and health factors. Full article

Honeybee diseases are one of the most significant and most common causes of honeybee colonies’ weakness and death. An early diagnosis of subclinical infections is necessary to implement precautionary and control measures. Sampling debris from hive bottom boards is simple, non-invasive, and cheap. In this study, we collected winter debris samples in apiaries located in the continental part of Croatia. We used molecular methods, PCR and qPCR, for the first time to analyze those samples. Laboratory results were compared with the health condition and strength of honeybee colonies at an apiary in spring. Our study successfully identified the presence and quantity of various pathogens, including the presence of Vairimorpha spp. (Nosema spp.), quintefied Paenibacillus larvae, Acute Bee Paralysis Virus (ABPV), Black Queen Cell Virus (BQCV), Deformed Wing Virus (DWV), and Sacbrood Virus (SBV). However, our analysis did not detect Melissococcus plutonius, Crithidia mellificae, Lotmaria passim, and Aethina tumida. Samples of winter debris were also examined for the presence and quantification of the V. destructor mites, and their natural mite fall was observed in spring. Honeybee colonies were simultaneously infected by an average of four to six pathogens. Some observed honeybee colonies developed characteristic symptoms, while others did not survive the winter. Full article

The queen bee is a central and pivotal figure within the colony, serving as the sole fertile female responsible for its reproduction. The queen possesses an open circulatory system, with her ovaries immersed in hemolymph. A continuous and intricate transportation and interchange of substances exist between the ovaries and hemolymph of queen bees. To determine the characteristic metabolites in the hemolymph and ovary, as well as understand how their rapid metabolism contributes to the process of egg-laying by queens, we reared Apis mellifera queens from three different age groups: newly emerged queen (NEQ), newly laying queen (NLQ), and old laying queen (OLQ). Using widely targeted metabolomics, our study revealed that the laying queen (NLQ and OLQ) exhibited faster fatty acid metabolism, up-regulated expression of antioxidants, and significant depletion of amino acids compared to the NEQ. This study revealed that the levels of carnitine and antioxidants (GSH, 2-O-α-D-glucopyranosyl-L-ascorbic acid, L-ascorbic acid 2-phosphate, etc.) in the NLQ and OLQ were significantly higher compared to NEQ. However, most of the differentially expressed amino acids, such as L-tryptophan, L-tyrosine, L-aspartic acid, etc., detected in NLQ and OLQ were down-regulated compared to the NEQ. Following egg-laying, pathways in the queens change significantly, e.g., Tryptophan metabolism, Tyrosine metabolism, cAMP signaling pathway, etc. Our results suggest that carnitine and antioxidants work together to maintain the redox balance of the queen. Additionally, various amino acids are responsible for maintaining the queen’s egg production. Full article

Honey bee viruses in combination with varroa mite are very damaging for honey bee colonies worldwide. There are no effective methods to control the viral load in honey bee colonies except regular and effective control of mites. Integrated Pest Management strategies are required to effectively control mites with veterinary medicines based on organic compounds. We evaluated the effect of two brood interruption techniques, queen caging (QC) and trapping comb (TC), followed by an oxalic acid treatment, on the mite fall, colony strength, and viral load of Deformed Wing Virus (DWV) and Acute Bee Paralysis Virus (ABPV). In this paper, we report the data obtained in two experimental sites, in Slovenia and Italy, in terms of the varroacide efficacy, colony strength, and viral load. The number of adult bees after the adoption of the two techniques showed similar decreasing trends in both locations. The viral load of Acute Bee Paralysis Virus did not show any significant reduction after 25 days, reported as the number of Real-Time PCR cycles needed to detect the virus. The viral load of DWV also did not show a significant reduction after 25 days. The acaricidal efficacy of the applied protocols was high in both experimental groups and in both apiaries. Both the queen caging and trapping comb techniques, followed by an oxalic acid treatment, can be considered effective varroa treatment strategies, but further studies should be carried out to evaluate the long-term effects on viral loads to plan the Integrated Pest Management strategy with the right timing before wintering. Full article

The recent advances in smart beekeeping focus on remote solutions for bee colony monitoring and applying machine learning techniques for automatic decision making. One of the main applications is a swarming alarm, allowing beekeepers to prevent the bee colony from leaving their hive. Swarming is a naturally occurring phenomenon, mainly during late spring and early summer, but it is extremely hard to predict its exact time since it is highly dependent on many factors, including weather. Prevention from swarming is the most effective way to keep bee colonies; however, it requires constant monitoring by the beekeeper. Drone bees do not survive the winter and they occur in colonies seasonally with a peak in late spring, which is associated with the creation of drone congregation areas, where mating with young queens takes place. The paper presents a method of early swarming mood detection based on the observation of drone bee activity near the entrance to a hive. Audio recordings are represented by Mel Frequency Cepstral Coefficients and their first and second derivatives. The study investigates which MFCC coefficients, selected by the Least Absolute Shrinkage and Selection Operator, are significant for the worker bee and drone bee classification task. The classification results, obtained by an autoencoder neural network, allow to improve the detection performance, achieving accuracy slightly above 95% for the chosen set of signal features, selected by the proposed method, compared to the standard set of MFCC coefficients with only up to 90% accuracy. Full article