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.

Honey vs. Mite-A Trade-Off Strategy by Applying Summer Brood Interruption for Varroa destructor Control in the Mediterranean Region.

In this study, we investigated the effect of queen caging on honey bee colonies' post-treatment development and the optimal timing of method application on honey production during the main summer nectar flow. We conducted the study in nine apiaries (N = 9) across six Mediterranean countries, with a total of 178 colonies. The colonies were divided into three test groups: QC1, QC2, and C. The QC1 group involved queens caged for a total of 28 days before the expected harvesting day. In the QC2 group, queens were caged for 28 days, but only 14 days before the expected harvesting day. The C group consisted of queens that were not caged, and the colonies received common local treatments. In both the QC1 and QC2 groups, the colonies were treated with a 4.2% oxalic acid (OA) solution by trickling after the queen release. Our findings revealed no significant adverse effects (p > 0.05) on colony strength at the end of the study resulting from queen caging. However, significantly lower amounts of honey were extracted from the QC1 group compared to both the QC2 group (p = 0.001) and the C group (p = 0.009). Although there were no initial differences in Varroa destructor infestation between the groups, ten weeks later, a significantly higher infestation was detected in the C group compared to both the QC1 group (p < 0.01) and the QC2 group (p = 0.003). Overall, our study demonstrates that queen caging, in combination with the use of OA, is an effective treatment for controlling V. destructor. However, the timing of caging plays a crucial role in honey production outcomes.

Impact of Different Storage Temperature on the Enzymatic Activity of Apis mellifera Royal Jelly.

Royal Jelly is a nutrient secretion of nurse bees and a high interest functional food in human nutrition. Very little information is available on its chemical composition integrity and enzymatic activity during shelf life and assessment of new freshness markers are desirable for its conservation. In this study, the activity of glucose oxidase, five proteases and two antioxidant enzymes in refrigerated and frozen Royal Jelly for different storage times was preliminary investigated. Refrigeration determined a significantly reduction in glucose oxidase and carboxypeptidase A-like activity in Royal Jelly after one year of storage while no differences were recorded in the activity of these enzymes in frozen samples. After one year of storage glucose oxidase and carboxypeptidase A-like activity resulted higher in frozen samples frozen than in refrigerate ones. Results obtained suggest that the activities of these enzymes may be good markers of Royal Jelly freshness within 1 year at refrigeration condition. Freezing could be a valid alternative storage method to ensure a higher preservation of glucose oxidase and carboxypeptidase A-like activities for at least 1 year. Further investigation to determine the timing of glucose oxidase inactivation/degradation under refrigerated conditions and the enzymatic activity trend under prolonged frozen conditions are desirable.

Using Citizen Science to Scout Honey Bee Colonies That Naturally Survive Varroa destructor Infestations.

Citizen Science contributes significantly to the conservation of biodiversity, but its application to honey bee research has remained minimal. Even though certain European honey bee (Apis mellifera) populations are known to naturally survive Varroa destructor infestations, it is unclear how widespread or common such populations are. Such colonies are highly valuable for investigating the mechanisms enabling colony survival, as well as for tracking the conservation status of free-living honey bees. Here, we use targeted Citizen Science to identify potentially new cases of managed or free-living A. mellifera populations that survive V. destructor without mite control strategies. In 2018, a survey containing 20 questions was developed, translated into 13 languages, and promoted at beekeeping conferences and online. After three years, 305 reports were collected from 28 countries: 241 from managed colonies and 64 from free-living colonies. The collected data suggest that there could be twice as many naturally surviving colonies worldwide than are currently known. Further, online and personal promotion seem to be key for successful recruitment of participants. Although the survivor status of these colonies still needs to be confirmed, the volume of reports and responses already illustrate how effectively Citizen Science can contribute to bee research by massively increasing generated data, broadening opportunities for comparative research, and fostering collaboration between scientists, beekeepers, and citizens. The success of this survey spurred the development of a more advanced Citizen Science platform, Honey Bee Watch, that will enable a more accurate reporting, confirmation, and monitoring of surviving colonies, and strengthen the ties between science, stakeholders, and citizens to foster the protection of both free-living and managed honey bees.

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

BACKGROUND: With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. RESULTS: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. CONCLUSIONS: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

A novel TaqMan® assay for Nosema ceranae quantification in honey bee, based on the protein coding gene Hsp70.

Nosema ceranae is now a widespread honey bee pathogen with high incidence in apiculture. Rapid and reliable detection and quantification methods are a matter of concern for research community, nowadays mainly relying on the use of biomolecular techniques such as PCR, RT-PCR or HRMA. The aim of this technical paper is to provide a new qPCR assay, based on the highly-conserved protein coding gene Hsp70, to detect and quantify the microsporidian Nosema ceranae affecting the western honey bee Apis mellifera. The validation steps to assess efficiency, sensitivity, specificity and robustness of the assay are described also.

Estimating the density of honeybee colonies across their natural range to fill the gap in pollinator decline censuses.

Although pollinator declines are a global biodiversity threat, the demography of the western honeybee (Apis mellifera) has not been considered by conservationists because it is biased by the activity of beekeepers. To fill this gap in pollinator decline censuses and to provide a broad picture of the current status of honeybees across their natural range, we used microsatellite genetic markers to estimate colony densities and genetic diversity at different locations in Europe, Africa, and central Asia that had different patterns of land use. Genetic diversity and colony densities were highest in South Africa and lowest in Northern Europe and were correlated with mean annual temperature. Confounding factors not related to climate, however, are also likely to influence genetic diversity and colony densities in honeybee populations. Land use showed a significantly negative influence over genetic diversity and the density of honeybee colonies over all sampling locations. In Europe honeybees sampled in nature reserves had genetic diversity and colony densities similar to those sampled in agricultural landscapes, which suggests that the former are not wild but may have come from managed hives. Other results also support this idea: putative wild bees were rare in our European samples, and the mean estimated density of honeybee colonies on the continent closely resembled the reported mean number of managed hives. Current densities of European honeybee populations are in the same range as those found in the adverse climatic conditions of the Kalahari and Saharan deserts, which suggests that beekeeping activities do not compensate for the loss of wild colonies. Our findings highlight the importance of reconsidering the conservation status of honeybees in Europe and of regarding beekeeping not only as a profitable business for producing honey, but also as an essential component of biodiversity conservation.