Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

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.

Exposure and risk assessment of acetamiprid in honey bee colonies under a real exposure scenario in Eucalyptus sp. landscapes

Honey bee colonies have shown abnormal mortality rates over the last decades. Colonies are exposed to biotic and abiotic stressors including landscape changes caused by human pressure. Modern agriculture and even forestry, rely on pesticide inputs and these chemicals have been indicated as one of the major causes for colony losses. Neonicotinoids are a common class of pesticides used worldwide that are specific to kill insect pests, with acetamiprid being the only neonicotinoid allowed to be applied outdoors in the EU. To evaluate honeybees' exposure to acetamiprid under field conditions as well as to test the use of in-situ tools to monitor pesticide residues, two honeybee colonies were installed in five Eucalyptus sp. plantations having different area where Epik® (active substance: acetamiprid) was applied as in a common spraying event to control the eucalyptus weevil pest. Flowers, fresh nectar, honey bees and colony products samples were collected and analyzed for the presence of acetamiprid residues. Our main findings were that (1) acetamiprid residues were found in samples collected outside the spraying area, (2) the amount of residues transported into the colonies increased with the size of the sprayed area, (3) according to the calculated Exposure to Toxicity Ratio (ETR) values, spraying up to 22 % of honeybees foraging area does not harm the colonies, (4) colony products can be used as a valid tool to monitor colony accumulation of acetamiprid and (5) the use of Lateral Flow Devices (LFDs) can be a cheap, fast and easy tool to apply in the field, to evaluate the presence of acetamiprid residues in the landscape and colony products.

First application of an Integrated Biological Response index to assess the ecotoxicological status of honeybees from rural and urban areas

Understanding the effects of environmental contaminants on honeybees is essential to minimize their impacts on these important pollinating insects. The aim of this study was to assess the ecotoxicological status of honeybees in environments undergoing different anthropic pressure: a wood (reference site), an orchard, an agricultural area, and an urban site, using a multi-biomarker approach. To synthetically represent the ecotoxicological status of the honeybees, the responses of the single biomarkers were integrated by the Integrated Biological Response (IBRv2) index. Overall, the strongest alteration of the ecotoxicological status (IBRv2 = 7.52) was detected in the bees from the orchard due to the alteration of metabolic and genotoxicity biomarkers indicating the presence of pesticides, metals, and lipophilic compounds. Honeybees from the cultivated area (IBRv2 = 7.18) revealed an alteration especially in neurotoxicity, metabolic, and genotoxicity biomarkers probably related to the presence of pesticides, especially fungicides. Finally, in the urban area (IBRv2 = 6.60), the biomarker results (GST, lysozyme, and hemocytes) indicated immunosuppression in the honeybees and the effects of the presence of lipophilic compounds and metals in the environment.

Honey Bee Health in Maine Wild Blueberry Production

Simple Summary

Wild blueberry is an important native North American crop that requires insect pollination. Migratory western honey bee colonies constitute the majority of commercial bees brought into Maine for pollination of wild blueberry. Currently, many stressors impact the western honey bee in the US. We designed a two-year monitoring study (2014 and 2015) to assess the potential health of honey bee colonies hired for pollination services in wild blueberry fields. We monitored the colony health of nine hive locations (three hives/location) in 2014 and nine locations (five hives/location) in 2015 during bloom (May–June). Queen health status, colony strength, rate of population increase, and pesticide residues on pollen, wax, and honey bee workers were measured. In addition, each hive was sampled to assess levels of mite parasites, viruses, and Microsporidian and Trypanosome pathogens. Different patterns in colony health were observed over the two years. Factors predicting colony growth rate over both years were Varroa mite infestation and risk due to pollen pesticide residues during bloom. In addition, recently discovered parasites and pathogens were already observed in most of the colonies suggesting that parasites and diseases spread rapidly and become established quickly in commercial honey bee colonies.

Abstract

A two-year study was conducted in Maine wild blueberry fields (Vaccinium angustifolium Aiton) on the health of migratory honey bee colonies in 2014 and 2015. In each year, three or five colonies were monitored at each of nine wild blueberry field locations during bloom (mid-May until mid-June). Colony health was measured by assessing colony strength during wild blueberry bloom. Potential factors that might affect colony health were queen failure or supersedure; pesticide residues on trapped pollen, wax comb, and bee bread; and parasites and pathogens. We found that Varroa mite and pesticide residues on trapped pollen were significant predictors of colony health measured as the rate of change in the amount of sealed brood during bloom. These two factors explained 71% of the variance in colony health over the two years. Pesticide exposure was different in each year as were pathogen prevalence and incidence. We detected high prevalence and abundance of two recently discovered pathogens and one recently discovered parasite, the trypanosome Lotmaria passim Schwartz, the Sinai virus, and the phorid fly, Apocephalus borealis Brues.

A systems-based approach to the environmental risk assessment of multiple stressors in honey bees

The European Parliament requested EFSA to develop a holistic risk assessment of multiple stressors in honey bees. To this end, a systems-based approach that is composed of two core components: a monitoring system and a modelling system are put forward with honey bees taken as a showcase. Key developments in the current scientific opinion (including systematic data collection from sentinel beehives and an agent-based simulation) have the potential to substantially contribute to future development of environmental risk assessments of multiple stressors at larger spatial and temporal scales. For the monitoring, sentinel hives would be placed across representative climatic zones and landscapes in the EU and connected to a platform for data storage and analysis. Data on bee health status, chemical residues and the immediate or broader landscape around the hives would be collected in a harmonised and standardised manner, and would be used to inform stakeholders, and the modelling system, ApisRAM, which simulates as accurately as possible a honey bee colony. ApisRAM would be calibrated and continuously updated with incoming monitoring data and emerging scientific knowledge from research. It will be a supportive tool for beekeeping, farming, research, risk assessment and risk management, and it will benefit the wider society. A societal outlook on the proposed approach is included and this was conducted with targeted social science research with 64 beekeepers from eight EU Member States and with members of the EU Bee Partnership. Gaps and opportunities are identified to further implement the approach. Conclusions and recommendations are made on a way forward, both for the application of the approach and its use in a broader context.

Review on Sublethal Effects of Environmental Contaminants in Honey Bees (Apis mellifera), Knowledge Gaps and Future Perspectives

Honey bees and the pollination services they provide are fundamental for agriculture and biodiversity. Agrochemical products and other classes of contaminants, such as trace elements and polycyclic aromatic hydrocarbons, contribute to the general decline of bees' populations. For this reason, effects, and particularly sublethal effects of contaminants need to be investigated. We conducted a review of the existing literature regarding the type of effects evaluated in Apis mellifera, collecting information about regions, methodological approaches, the type of contaminants, and honey bees' life stages. Europe and North America are the regions in which A. mellifera biological responses were mostly studied and the most investigated compounds are insecticides. A. mellifera was studied more in the laboratory than in field conditions. Through the observation of the different responses examined, we found that there were several knowledge gaps that should be addressed, particularly within enzymatic and molecular responses, such as those regarding the immune system and genotoxicity. The importance of developing an integrated approach that combines responses at different levels, from molecular to organism and population, needs to be highlighted in order to evaluate the impact of anthropogenic contamination on this pollinator species.

Shotgun sequencing of honey DNA can describe honey bee derived environmental signatures and the honey bee hologenome complexity

Honey bees are large-scale monitoring tools due to their extensive environmental exploration. In their activities and from the hive ecosystem complex, they get in close contact with many organisms whose traces can be transferred into the honey, which can represent an interesting reservoir of environmental DNA (eDNA) signatures and information useful to analyse the honey bee hologenome complexity. In this study, we tested a deep shotgun sequencing approach of honey DNA coupled with a specifically adapted bioinformatic pipeline. This methodology was applied to a few honey samples pointing out DNA sequences from 191 organisms spanning different kingdoms or phyla (viruses, bacteria, plants, fungi, protozoans, arthropods, mammals). Bacteria included the largest number of species. These multi-kingdom signatures listed common hive and honey bee gut microorganisms, honey bee pathogens, parasites and pests, which resembled a complex interplay that might provide a general picture of the honey bee pathosphere. Based on the Apis mellifera filamentous virus genome diversity (the most abundant detected DNA source) we obtained information that could define the origin of the honey at the apiary level. Mining Apis mellifera sequences made it possible to identify the honey bee subspecies both at the mitochondrial and nuclear genome levels.

Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment

Bees are exposed to a wide range of multiple chemicals "chemical mixtures" from anthropogenic (e.g. plant protection products or veterinary products) or natural origin (e.g. mycotoxins, plant toxins). Quantifying the relative impact of multiple chemicals on bee health compared with other environmental stressors (e.g. varroa, viruses, and nutrition) has been identified as a priority to support the development of holistic risk assessment methods. Here, extensive literature searches and data collection of available laboratory studies on combined toxicity data for binary mixtures of pesticides and non-chemical stressors has been performed for honey bees (Apis mellifera), wild bees (Bombus spp.) and solitary bee species (Osmia spp.). From 957 screened publications, 14 publications provided 218 binary mixture toxicity data mostly for acute mortality (lethal dose: LD₅₀) after contact exposure (61%), with fewer studies reporting chronic oral toxicity (20%) and acute oral LC₅₀ values (19%). From the data collection, available dose response data for 92 binary mixtures were modelled using a Toxic Unit (TU) approach and the MIXTOX modelling tool to test assumptions of combined toxicity i.e. concentration addition (CA), and interactions (i.e. synergism, antagonism). The magnitude of interactions was quantified as the Model Deviation Ratio (MDR). The CA model applied to 17% of cases while synergism and antagonism were observed for 72% (MDR > 1.25) and 11% (MDR < 0.83) respectively. Most synergistic effects (55%) were observed as interactions between sterol-biosynthesis-inhibiting (SBI) fungicides and insecticide/acaricide. The mechanisms behind such synergistic effects of binary mixtures in bees are known to involve direct cytochrome P450 (CYP) inhibition, resulting in an increase in internal dose and toxicity of the binary mixture. Moreover, bees are known to have the lowest number of CYP copies and other detoxification enzymes in the insect kingdom. In the light of these findings, occurrence of these binary mixtures in relevant crops (frequency and concentrations) would need to be investigated. Addressing this exposure dimension remains critical to characterise the likelihood and plausibility of such interactions to occur under field realistic conditions. Finally, data gaps and further work for the development of risk assessment methods to assess multiple stressors in bees including chemicals and non-chemical stressors in bees are discussed.

Investigating the role of landscape composition on honey bee colony winter mortality: A long-term analysis

The health of honey bee colonies is, amongst others, affected by the amount, quality and diversity of available melliferous plants. Since landscape is highly diverse throughout Austria regarding the availability of nutritional resources, we used data from annual surveys on honey bee colony losses ranging over six years to analyse a possible relationship with land use. The data set comprises reports from a total of 6,655 beekeepers and 129,428 wintered honey bee colonies. Regions surrounding the beekeeping operations were assigned to one of six clusters according to their composition of land use categories by use of a hierarchical cluster analysis, allowing a rough distinction between urban regions, regions predominated by semi-natural areas and pastures, and mainly agricultural environments. We ran a Generalised Linear Mixed Model and found winter colony mortality significantly affected by operation size, year, and cluster membership, but also by the interaction of year and cluster membership. Honey bee colonies in regions composed predominantly of semi-natural areas, coniferous forests and pastures had the lowest loss probability in four out of six years, and loss probabilities within these regions were significantly lower in five out of six years compared to those within regions composed predominantly of artificial surfaces, broad-leaved and coniferous forest.