Quantitative assessments of honeybee colony's response to an artificial vibrational pulse resulting in non-invasive measurements of colony's overall mobility and restfulness.

In this work we aim to provide a quantitative method allowing the probing of the physiological status of honeybee colonies by providing them with a gentle, short, external artificial vibrational shockwave, and recording their response. The knock is provided by an external electromagnetic shaker attached to the outer wall of a hive, driven by a computer with a 0.1 s long, monochromatic vibration at 340Hz set to an amplitude that occasionally yields a mild response from the bees, recorded by an accelerometer placed in the middle of the central frame of the colony. To avoid habituation, the stimulus is supplied at randomised times, approximately every hour. The method is pioneered with a pilot study on a single colony hosted indoors, then extended onto eight outdoors colonies. The results show that we can quantitatively sense the colony's overall mobility, independently from another physiological aspect, which is phenomenologically explored. Using this, a colony that is queenless is easily discriminated from the others.

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

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.

Expression of Molecular Markers of Resilience against Varroa destructor and Bee Viruses in Ethiopian Honey Bees (Apis mellifera simensis) Focussing on Olfactory Sensing and the RNA Interference Machinery.

Varroa destructor mites and the viruses it vectors are two major factors leading to high losses of honey bees (Apis mellifera) colonies worldwide. However, honey bees in some African countries show resilience to varroa infestation and/or virus infections, although little is known about the mechanisms underlying this resilience. In this study, we investigated the expression profiles of some key molecular markers involved in olfactory sensing and RNA interference, as these processes may contribute to the bees' resilience to varroa infestation and virus infection, respectively. We found significantly higher gene expression of the odorant binding protein, OBP14, in the antennae of Ethiopian bees compared to Belgian bees. This result suggests the potential of OBP14 as a molecular marker of resilience to mite infestation. Scanning electron microscopy showed no significant differences in the antennal sensilla occurrence and distribution, suggesting that resilience arises from molecular processes rather than morphological adaptations. In addition, seven RNAi genes were upregulated in the Ethiopian honey bees and three of them-Dicer-Drosha, Argonaute 2, and TRBP2-were positively correlated with the viral load. We can conclude that the antiviral immune response was triggered when bees were experiencing severe viral infection and that this might contribute to the bees' resilience to viruses.

Virus Prevalence in Egg Samples Collected from Naturally Selected and Traditionally Managed Honey Bee Colonies across Europe.

Monitoring virus infections can be an important selection tool in honey bee breeding. A recent study pointed towards an association between the virus-free status of eggs and an increased virus resistance to deformed wing virus (DWV) at the colony level. In this study, eggs from both naturally surviving and traditionally managed colonies from across Europe were screened for the prevalence of different viruses. Screenings were performed using the phenotyping protocol of the 'suppressed in ovo virus infection' trait but with qPCR instead of end-point PCR and a primer set that covers all DWV genotypes. Of the 213 screened samples, 109 were infected with DWV, 54 were infected with black queen cell virus (BQCV), 3 were infected with the sacbrood virus, and 2 were infected with the acute bee paralyses virus. It was demonstrated that incidences of the vertical transmission of DWV were more frequent in naturally surviving than in traditionally managed colonies, although the virus loads in the eggs remained the same. When comparing virus infections with queen age, older queens showed significantly lower infection loads of DWV in both traditionally managed and naturally surviving colonies, as well as reduced DWV infection frequencies in traditionally managed colonies. We determined that the detection frequencies of DWV and BQCV in honey bee eggs were lower in samples obtained in the spring than in those collected in the summer, indicating that vertical transmission may be lower in spring. Together, these patterns in vertical transmission show that honey bee queens have the potential to reduce the degree of vertical transmission over time.

qPCR assays with dual-labeled probes for genotyping honey bee variants associated with varroa resistance.

BACKGROUND: The varroa mite is one of the main causes of honey bee mortality. An important mechanism by which honey bees increase their resistance against this mite is the expression of suppressed mite reproduction. This trait describes the physiological inability of mites to produce viable offspring and was found associated with eight genomic variants in previous research. RESULTS: This paper presents the development and validation of high-throughput qPCR assays with dual-labeled probes for discriminating these eight single-nucleotide variants. Amplicon sequences used for assay validation revealed additional variants in the primer/probe binding sites in four out of the eight assays. As for two of these the additional variants interfered with the genotyping outcome supplementary primers and/or probes were developed. Inclusion of these primers and probes in the assay mixes allowed for the correct genotyping of all eight variants of interest within our bee population. CONCLUSION: These outcomes underline the importance of checking for interfering variants in designing qPCR assays. Ultimately, the availability of this assay allows genotyping for the suppressed mite reproduction trait and paves the way for marker assisted selection in breeding programs.

Heritability estimates of the novel trait 'suppressed in ovo virus infection' in honey bees (Apis mellifera).

Honey bees are under pressure due to abnormal high colony death rates, especially during the winter. The infestation by the Varroa destructor mite and the viruses that this ectoparasite transmits are generally considered as the bees' most important biological threats. Almost all efforts to remedy this dual infection have so far focused on the control of the Varroa mite alone and not on the viruses it transmits. In the present study, the sanitary control of breeding queens was conducted on eggs taken from drone brood for 4 consecutive years (2015-2018). The screening was performed on the sideline of an ongoing breeding program, which allowed us to estimate the heritabilities of the virus status of the eggs. We used the term 'suppressed in ovo virus infection' (SOV) for this novel trait and found moderate heritabilities for the presence of several viruses simultaneously and for the presence of single viral species. Colonies that expressed the SOV trait seemed to be more resilient to virus infections as a whole with fewer and less severe Deformed wing virus infections in most developmental stages, especially in the male caste. The implementation of this novel trait into breeding programs is recommended.