We present a one-year-long multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were collected non-stop from April 2020 to April 2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to combine a wide variety of phenotypic trait measurements annotated by apicultural science experts with multi-sensor data, which facilitate a broader scope of analysis. We first summarize the data collection procedure, sensor data pre-processing steps, and data composition. We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns. Lastly, we showcase several hive monitoring applications based on sensor data analysis and machine learning, such as winter mortality prediction, hive population estimation, and the presence of an active and laying queen.

Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensionality reduction through Principal Component Analysis (PCA), and machine learning-based segmentation and classification algorithms. Specifically, a k-Nearest Neighbors (kNNs) model distinguishes Varroa mites from other objects in the images, while a Support Vector Machine (SVM) classifier enhances shape detection. The final phase integrates a dedicated counting algorithm, leveraging outputs from the SVM classifier to quantify Varroa mite populations in hyperspectral images. The preliminary results demonstrate segmentation accuracy exceeding 99% and an average precision of 0.9983 and recall of 0.9947 across all the classes. The results obtained from our machine learning-based approach for Varroa mite counting were compared against ground-truth labels obtained through manual counting, demonstrating a high degree of agreement between the automated counting and manual ground truth. Despite working with a limited dataset, the HS-Cam showcases its potential for Varroa counting, delivering superior performance compared to traditional RGB images. Future research directions include validating the proposed hyperspectral imaging methodology with a more extensive and diverse dataset. Additionally, the effectiveness of using a near-infrared (NIR) excitation source for Varroa detection will be explored, along with assessing smartphone integration feasibility.

Since the global invasion of the ectoparasitic mite Varroa destructor (Anderson and Trueman), selection of mite-resistant honey bee (Apis mellifera L.) colonies appears challenging and has to date not broadly reduced colony mortality. The low published estimated heritability values for mite infestation levels could explain the limited genetic progresses obtained so far. We hypothesize that intercolonial horizontal mite transmission could differentially affect the single colonies located in a given apiary and therefore invisibly bias colony infestation phenotypes. This bias may be lower in regions with lower colony density, providing suitable conditions to set up evaluation apiaries. To verify these hypotheses, we monitored mite infestation and reinvasion in experimental colonies, as well as infestation in neighboring colonies belonging to beekeepers in three areas with variable colony densities in the canton of Bern, Switzerland during three consecutive beekeeping seasons. Mite immigration fluctuated between apiaries and years and significantly contributed to colony infestation level. Depending on apiary and year, 17-48% of the mites present in the experimental colonies at the time of the summer oxalic acid final treatment potentially derived from mite immigration that had occurred since mid-spring. Mite immigration was not linked to local colony density or the infestation levels of beekeepers\' colonies located within 2 km. Our results do not prove that apiaries for colony evaluation should necessarily be established in areas with low colony density. However, they highlight the high impact of beekeeping management practices on mite colony infestation levels.

Winter mortality of honey bee colonies represents a major source of economic loss for the beekeeping industry. The objectives of this prospective study were to estimate the incidence risk of winter colony mortality in southwestern Quebec, Canada and to evaluate and quantify the impact of the associated risk factors. A total of 242 colonies from 31 apiaries was selected for sampling in August 2017. The presence of Varroa destructor, Vairimorpha (Nosema) spp., Melissococcus plutonius, deformed wing virus (DWV), and viruses of the acute-Kashmir-Israeli complex (AKI complex) was investigated in each colony. Management practices of the various colonies were obtained from a questionnaire. The incidence risk of colony mortality during the winter of 2017-2018 was estimated to be 26.5% [95% confidence interval (CI): 15.4 to 40.3]. In logistic regression modeling of winter mortality in colonies, an interaction was discovered between V. destructor and DWV; the detection of ≥ 1 V. destructor mites per 100 bees was associated with higher odds of mortality (3.46, 95% CI: 1.35 to 8.90) compared to colonies with < 1 mite per 100 bees, but only in DWV-positive colonies. There were more colony losses in apiaries from beekeepers owning 1 to 5 colonies than in apiaries from beekeepers owning over 100 colonies, which suggests that beekeeper experience and/or type of management are important contributors to winter colony mortality. Assuming a causal relationship, the results of this study suggest that up to 9% of all colony mortalities in the population could have been prevented by reducing the level of V. destructor to < 1 mite per 100 bees in all colonies.
La mortalité hivernale des colonies d’abeilles est une cause importante de pertes économiques en apiculture. Cette étude prospective visait à estimer le risque d’incidence de mortalité hivernale des colonies d’abeilles et les facteurs de risque associés dans le sud-ouest du Québec (Canada). Au total, 242 colonies provenant de 31 ruches ont été sélectionnées en août 2017. La présence de Varroa destructor, de Vairimorpha (Nosema) spp., de Melissococcus plutonius, du virus des ailes déformées (DWV) et des virus du complexe AKI ont été évalués. Les pratiques de régie ont été obtenues selon un questionnaire. Le risque de mortalité des colonies à l’hiver 2017–2018 a été estimé à 26,5 % (95 % CI : 15,4 à 40,3). Dans un modèle de régression logistique, la détection de ≥ 1 mite de V. destructor par 100 abeilles était associée à des cotes plus élevées de mortalité (3,46, 95 % CI : 1,35 à 8,90) comparativement aux colonies avec < 1 mite par 100 abeilles, mais seulement pour les colonies positives au DWV. Les ruchers d’apiculteurs possédant entre 1 et 5 colonies présentaient une mortalité plus élevée comparativement à ceux d’apiculteurs possédant plus de 100 colonies, suggérant une influence de l’expérience ou du type de régie. En assumant une relation causale, les résultats de cette étude suggèrent que jusqu’à 9 % de toutes les mortalités hivernales observées dans la population auraient pu être prévenues en réduisant le niveau d’infestation par V. destructor à moins d’une mite per 100 abeilles dans toutes les colonies.(Traduit par les auteurs).

In June 2022, an exotic pest of the European honeybee (Apis mellifera), the varroa mite (Varroa destructor), was detected in surveillance hives at the Port of Newcastle, New South Wales (NSW). Previously, Australia remained the only continent free of the varroa mite. In September 2023, the National Management Group decided to shift the focus of the response from eradication to management. It is estimated that the establishment of varroa mite in Australia could lead to more than $70 million in losses each year due to greatly reduced pollination services. Currently, there are no reported studies on the epidemiology of varroa mite in NSW because it is such a recent outbreak, and there is little knowledge of the factors associated with the presence of V. destructor in the Australian context. We sourced publicly available varroa mite outbreak reports from June 22 to December 19, 2022, to determine if urbanization, land use, and distance from the incursion site are associated with the detection of varroa mite infestation in European honeybee colonies in NSW. The outcome investigated was epidemic day, relative to the first detected premises (June 22, 2022). The study population was comprised of 107 premises, which were declared varroa-infested. The median epidemic day was day 37 (July 29, 2022), and a bimodal distribution was observed from the epidemic curve, which was reflective of an intermittent source pattern of spread. We found that premises were detected to be infected with varroa mite earlier in urban areas [median epidemic day 25 (July 17, 2022)] compared to rural areas [median epidemic day 37.5 (July 29, 2022)]. Infected premises located in areas without cropping, forests, and irrigation were detected earlier in the outbreak [median epidemic days 23.5 (July 15, 2022), 30 (July 22, 2022), and 15 (July 7, 2022), respectively] compared to areas with cropping, forests, and irrigation [median epidemic days 50 (August 11, 2022), 43 (August 4, 2022), and 47 (August 8, 2022), respectively]. We also found that distance from the incursion site was not significantly correlated with epidemic day. Urbanization and land use are potential factors for the recent spread of varroa mite in European honeybee colonies in NSW. This knowledge is essential to managing the current varroa mite outbreak and preventing future mass varroa mite spread events.

The worldwide dispersal of the ectoparasitic mite Varroa destructor from its Asian origins has fundamentally transformed the relationship of the honey bee (Apis mellifera) with several of its viruses, via changes in transmission and/or host immunosuppression. The extent to which honey bee-virus relationships change after Varroa invasion is poorly understood for most viruses, in part because there are few places in the world with several geographically close but completely isolated honey bee populations that either have, or have not, been exposed long-term to Varroa, allowing for separate ecological, epidemiological, and adaptive relationships to develop between honey bees and their viruses, in relation to the mite\'s presence or absence. The Azores is one such place, as it contains islands with and without the mite. Here, we combined qPCR with meta-amplicon deep sequencing to uncover the relationship between Varroa presence, and the prevalence, load, diversity, and phylogeographic structure of eight honey bee viruses screened across the archipelago. Four viruses were not detected on any island (ABPV-Acute bee paralysis virus, KBV-Kashmir bee virus, IAPV-Israeli acute bee paralysis virus, BeeMLV-Bee macula-like virus); one (SBV-Sacbrood virus) was detected only on mite-infested islands; one (CBPV-Chronic bee paralysis virus) occurred on some islands, and two (BQCV-Black queen cell virus, LSV-Lake Sinai virus,) were present on every single island. This multi-virus screening builds upon a parallel survey of Deformed wing virus (DWV) strains that uncovered a remarkably heterogeneous viral landscape featuring Varroa-infested islands dominated by DWV-A and -B, Varroa-free islands naïve to DWV, and a refuge of the rare DWV-C dominating the easternmost Varroa-free islands. While all four detected viruses investigated here were affected by Varroa for one or two parameters (usually prevalence and/or the Richness component of ASV diversity), the strongest effect was observed for the multi-strain LSV. Varroa unambiguously led to elevated prevalence, load, and diversity (Richness and Shannon Index) of LSV, with these results largely shaped by LSV-2, a major LSV strain. Unprecedented insights into the mite-virus relationship were further gained from implementing a phylogeographic approach. In addition to enabling the identification of a novel LSV strain that dominated the unique viral landscape of the easternmost islands, this approach, in combination with the recovered diversity patterns, strongly suggests that Varroa is driving the evolutionary change of LSV in the Azores. This study greatly advances the current understanding of the effect of Varroa on the epidemiology and adaptive evolution of these less-studied viruses, whose relationship with Varroa has thus far been poorly defined.

Honey bees (Apis mellifera) play a crucial role in agriculture through their pollination activities. However, they have faced significant health challenges over the past decades that can limit colony performance and even lead to collapse. A primary culprit is the parasitic mite Varroa destructor, known for transmitting harmful bee viruses. Among these viruses is deformed wing virus (DWV), which impacts bee pupae during their development, resulting in either pupal demise or in the emergence of crippled adult bees. In this study, we focused on DWV master variant B. DWV-B prevalence has risen sharply in recent decades and appears to be outcompeting variant A of DWV. We generated a molecular clone of a typical DWV-B strain to compare it with our established DWV-A clone, examining RNA replication, protein expression, and virulence. Initially, we analyzed the genome using RACE-PCR and RT-PCR techniques. Subsequently, we conducted full-genome RT-PCR and inserted the complete viral cDNA into a bacterial plasmid backbone. Phylogenetic comparisons with available full-length sequences were performed, followed by functional analyses using a live bee pupae model. Upon the transfection of in vitro-transcribed RNA, bee pupae exhibited symptoms of DWV infection, with detectable viral protein expression and stable RNA replication observed in subsequent virus passages. The DWV-B clone displayed a lower virulence compared to the DWV-A clone after the transfection of synthetic RNA, as evidenced by a reduced pupal mortality rate of only 20% compared to 80% in the case of DWV-A and a lack of malformations in 50% of the emerging bees. Comparable results were observed in experiments with low infection doses of the passaged virus clones. In these tests, 90% of bees infected with DWV-B showed no clinical symptoms, while 100% of pupae infected with DWV-A died. However, at high infection doses, both DWV-A and DWV-B caused mortality rates exceeding 90%. Taken together, we have generated an authentic virus clone of DWV-B and characterized it in animal experiments.

Varroa mites, scientifically identified as Varroa destructor, pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set.

The grooming behavior of honeybees serves as a crucial auto-protective mechanism against Varroa mite infestations. Compared to Apis mellifera, Apis cerana demonstrates more effective grooming behavior in removing Varroa mites from the bodies of infested bees. However, the underlying mechanisms regulating grooming behavior remain elusive. In this study, we evaluated the efficacy of the auto-grooming behavior between A. cerana and A. mellifera and employed RNA-sequencing technology to identify differentially expressed genes (DEGs) in bee brains with varying degrees of grooming behavior intensity. We observed that A. cerana exhibited a higher frequency of mite removal between day 5 and day 15 compared to A. mellifera, with day-9 bees showing the highest frequency of mite removal in A. cerana. RNA-sequencing results revealed the differential expression of the HTR2A and SLC17A8 genes in A. cerana and the CCKAR and TpnC47D genes in A. mellifera. Subsequent homology analysis identified the HTR2A gene and SLC17A8 gene of A. cerana as homologous to the HTR2A gene and SLC17A7 gene of A. mellifera. These DEGs are annotated in the neuroactive ligand-receptor interaction pathway, the glutamatergic synaptic pathway, and the calcium signaling pathway. Moreover, CCKAR, TpnC47D, HTR2A, and SLC17A7 may be closely related to the auto-grooming behavior of A. mellifera, conferring resistance against Varroa infestation. Our results further explain the relationship between honeybee grooming behavior and brain function at the molecular level and provide a reference basis for further studies of the mechanism of honeybee grooming behavior.

Apis mellifera filamentous virus (AmFV) is a double-stranded DNA virus that infects Apis mellifera bees. To our knowledge, this is the first comprehensive study aiming to detect and analyse the genetic diversity and prevalence of AmFV in Korean honeybee colonies. Phylogenetic analysis based on baculovirus repeat open reading frame-N gene (Bro-N) sequences revealed that AmFV isolates from the Republic of Korea (ROK) fell into two distinct lineages, with genetic origins in Switzerland and China, with nucleotide similarities of 98.3% and 98.2%, respectively. Our prevalence analysis demonstrated a noteworthy infection rate of AmFV in 545 honeybee colonies, reaching 33.09% in 2022 and increasing to 44.90% by 2023. Intriguingly, we also detected AmFV in Varroa destructor mites, highlighting their potential role as vectors and carriers of AmFV. The presence of AmFV was correlated with an increased infection rate of sacbrood virus, deformed wing virus, Lake Sinai virus 2, black queen cell virus, and Nosema ceranae in honeybee colonies. These findings provide valuable insight into the prevalence and potential transmission mechanisms of AmFV in honeybee colonies in the ROK. The results of this study may be instrumental in the effective management of viral infections in honeybee apiaries.