In June 2023, a new Horizon 2020 project name Better-B was initiated. Grouping many members of B-GOOD as well as new colleagues from all over Europe, the project aims at continuing findings innovative solutions to improve resilient beekeeping. The main focus is on protecting honey bee colonies against abiotic stressors (e.g., physical rather than biological), including climate change, chemicals and habitat loss.During the four years of Better-B, researchers and other stakeholders will work together to find ways to use the power of nature to restore balance and harmony inside colonies, but also outside (i.e., in the environment). One way to use this power is to take advantage of natural selection to promote natural traits improving the resilience of honey bees (“Darwinian beekeeping”). Indeed, resilient traits have been observed in feral and abandoned colonies found throughout Europe. These colonies do not need human management to survive, and it is likely that they have developed ways to cope with the multiple stressors they face. However, such colonies generally lack other traits, such as high honey yields and lack of aggressiveness, which make them less productive and harder to manage.To find new ways to combine resilience and beekeeping traits, Better-B aims at understanding the mechanisms behind resilience and testing ways to implement them in modern beekeeping practices.

From June 2019 to November 2023, the B-GOOD project provided the opportunity to develop new perspectives for beekeeping in Europe. While the use of advanced technologies for beekeeping initially looked very futuristic and perhaps even daring, it has now become really concrete and a growing enthusiasm among the actors in the beekeeping sector is now evident.Healthy and sustainable beekeeping, which were central in the project, means an end to the over-treatment of honey bee colonies (see Practice Abstract 30). This can be guided by tracking the health status of individual colonies. To do so, hive monitoring systems play a pivotal role (see Practice Abstract 31). Healthy and sustainable beekeeping also involves the genetic ability to cope with stressors that bees are exposed to through protective traits and genetic diversity. In B-GOOD, we have helped pave the way for broadscale use of marker-assisted selection to breed more resilient honey bee colonies in Europe (see Practice Abstracts 33-36). The project has also contributed to the virtual world of dynamic landscapes and the digital twin of the honey bee colony, in particular the ApisRAM model (see Practice Abstracts 42-48). Finally, the project has nicely mapped out the socio-economic aspects of the sector (see Practice Abstracts 53-58).Honey bee colony risk assessments will rely more and more on novel methods, including the numerous technologies developed by the project.

Beekeepers played a central role in the B-GOOD project, notably by testing the technologies developed and providing input and feedback through surveys (see Practice Abstract 6).A series of quantitative surveys were carried out to assess the expectations and experiences of partner beekeepers during their involvement in the B-GOOD project. Responses from beekeepers highlighted that access to scientific information was a strong expectation, as well as gaining knowledge to benefit their own beekeeping management. Subsequently, beekeepers’ responses indicated their involvement in the project had strengthened their ‘readiness and willingness to share knowledge with scientists’, as well as their understanding of how digital monitoring technology can benefit their beekeeping operations and management.Acting on feedback gained at consortium meetings and responding to survey results, consortium efforts were stimulated to inform beekeepers and other key actors of relevant results. For example, beekeepers’ feedback led to continued improvements of the BEEP base and associated BEEP app through improving the devices, adding new features and reporting modules.

An aim of the B-GOOD project was to develop a dynamic learning platform for knowledge exchange and feedback. Integral to this has been the establishment and convening of the Multi-actor Forum (MAF) and involvement of key actors in direct exchanges with project partners (see Practice Abstract 54).The B-GOOD website, along with social media channels and dissemination activities conducted by Pensoft and UBERN have also been instrumental as mechanisms for informing and supporting knowledge exchange with MAF members, as well as other interested parties in B-GOOD developments. The B-GOOD website has acted as a resource hub for a wider audience, helping to ensure dissemination of project activities and results, as well as beekeepers’ individual perspectives and challenges on keeping honeybees to foster learning and understanding by wider audiences. In addition, the B-GOOD website offers a library providing open access to B-GOOD results and scientific publications, as well as providing links to other beekeeping related initiatives e.g., EU Bee Partnership.The B-GOOD website (https://b-good-project.eu/) will continue to be a source of information even after the project ends, disseminating scientific learning from the B-GOOD project. In addition, the Research Ideas and Outcomes (RIO, https://riojournal.com/) platform maintains an open collection of the project’s scientific articles and other documents, ensuring a long-lasting legacy of access to project results.

During the B-GOOD project, a number of surveys were sent to EU beekeepers and other actors of the beekeeping sector (see Practice Abstracts 55 and 56).An analysis of beekeepers’ use and interest in adopting digital hive monitoring technology revealed that the proportion of European beekeepers currently using some kind of digital monitoring (21%) is still low. The use of digital hive monitoring differed significantly between European regions, type of beekeeper (professional vs. hobbyist, and associated size of the beekeeping operation), number of years’ experience as a beekeeper, and social embeddedness through active involvement in the board of beekeepers’ associations. When using digital monitoring technology, the use was mostly limited to the monitoring of a single hive parameter (mostly hive weight) and implemented on a very limited number of hives. The main motive of beekeepers for using such technologies is to facilitate hive management.The data of the beekeeper survey suggested that the so-called ‘social tipping point,’ where a minority group of early adopters of a technology (typically 25% of the population) initiate a cascading change of social behaviour, is within reach in the European beekeeping sector.

During the B-GOOD project, a number of surveys were sent to EU beekeepers and other actors of the beekeeping sector (see Practice Abstracts 55 and 57).Insights and data from the stakeholder interviews and beekeeper survey were used to assess the extent to which stakeholders and beekeepers perceive and experience the impacts of climate change on their beekeeping operations, and whether and to what extent they had to adapt their beekeeping practices.The analysis confirmed that climate change is clearly perceived and experienced to be impacting beekeeping in Europe, although the impacts differ substantially from region to region, as well across different types of beekeepers. Climate change is therefore likely to create winners and losers within the European beekeeping sector. Among the possible losers are mainly beekeepers located in Southern Europe and professional beekeepers who indicated to experience the strongest impacts from climate change. Major impacts concern changes in local weather conditions and food resource availability. More severe perceived impacts from climate change are associated with lower honey yields and higher colony winter loss.Altogether, the knowledge from this study may provide guidance to policy makers to take appropriated and targeted policy actions and help the European beekeeping community to become more resilient to climate change and its adverse impacts.

During the B-GOOD project, a number of surveys were implemented with EU beekeepers and other actors of the beekeeping sector (see Practice Abstracts 56 and 57).Based on cross-sectional data collected through a beekeeper survey gathering 844 responses from 14 European countries, consortium members of B-GOOD from UGENT investigated the diversity among European beekeepers. Based on an analysis of beekeepers’ motivations for keeping honeybees (i.e., a rather natural vs. a rather business oriented motivation) five distinct types of beekeepers were identified. Motivations ranged from merely a passion for honeybees and nature to an economic interest in the production of apiary products and related business opportunities. Associated beekeepers’ orientations towards honeybees and beekeeping have allowed identifying five segments or distinct types of beekeepers, which have consecutively been profiled and characterised in terms of socio-demographics, beekeeping managerial characteristics, outputs and business models. The five segments have been referred to as Urban-explorer, Average-cool, Passionate-hobbyist, Passionate-skilled and Professional.

One of the main objectives of the project B-GOOD is to provide practical solutions to diverse actors of the EU beekeeping sector (see Practice Abstracts 4, 5 and 6). To do so, a multi-actor approach (MAA) was placed at the centre of the project. This approach was designed to integrate the expertise and interests of a wide range of relevant actors, helping to foster knowledge exchange to guide the development of innovative and practical solutions for the beekeeping community.A keystone of B-GOOD’s multi-actor approach was the establishment of a Multi-actor Forum (MAF). This forum enabled B-GOOD consortium members to interact directly with a variety of key actors, from beekeepers to EU authorities (e.g. authorities, beekeeping associations, environmental NGOs, service providers, agri- and horticultural actors, …). MAF members were invited and actively participated in all consortium meetings. In addition, several workshops were held at consortium meetings. These workshops were used to explore a number of themes and gain input from MAF members to guide project activities (e.g., how digital hive monitoring technology could improve hive management for more healthy and sustainable beekeeping). MAF activities were beneficial in promoting interactions between the project partners and interested external actors, facilitating knowledge exchanges and fostering B-GOOD project developments that are relevant and have a lasting legacy for the beekeeping community.

Scales allowing to weigh honey bee colonies have been used for many years by beekeepers to monitor colony growth and honey production. Yet, these tools can be used for many more purposes, e.g., they have also allowed detecting fluctuations of weight caused by foraging of workers (see Practice Abstracts 9 and 21).In a recent study, B-GOOD consortium members from TNTU and AU used scales on several honey bee colonies from a single apiary and investigated changes in weight over time. Their results show that outlier hives (i.e., a colony that does not behave like the others from the same apiary) can be detected using machine learning techniques.With this approach, it is possible to detect honey bee colonies facing a diversity of issues (e.g., absence of queens) or displaying health deterioration (e.g., high levels of infection with Nosema or infestation with Varroa) by comparing changes in weights between colonies of the same apiary. This technique is particularly accurate in summer, when colony weights fluctuate more. This non-invasive technique can allow detecting colonies with deteriorating health without the need to open them, providing very useful tools for beekeepers to quickly detect abnormalities in their apiaries and act accordingly. Allowing to pinpoint individual colonies with deteriorating health in an apiary also gives the possibility to tailor management practice and avoid unnecessary prophylactic treatments (see Practice Abstract 31).

Pesticides, including neonicotinoids, can harm honey bees and are suspected to be involved in honey bee colony losses. Acetamiprid is the only neonicotinoid applied outdoors in the European Union, however there is still lack of data regarding bees’ exposure to this product in real world scenarios.To fill this knowledge gap, B-GOOD consortium members from UCOI and WUR conducted a recent study. The aim of the study was to measure exposure of honey bee colonies to acetamiprid after a spraying event in Eucalyptus landscapes in Portugal, and to calculate their possible negative effects. To detect the presence of the pesticide, they used the Lateral Flow Device systems (LFDs) developed within the project B-GOOD (see Practice Abstract 11) and to measure the amount present in flowers, nectar, honey, pollen, bee bread, and bees they used a novel technology (xMAP acetamiprid immunoassay) that it is much faster and cheaper than conventional methods.Their results indicate that it is possible to find traces of Acetamiprid in samples collected within but also outside of the spraying area (evidencing pesticide drift), and that the bigger the size of the sprayed area, the higher the concentration of pesticides detected in the colony samples. This study shows the potential of using quick and practical detection tools, that can be applied directly in the field, such as the LFDs, to better understand the presence and impact of pesticides on biodiversity.

The BEEP base is a multi-sensor, autonomous and energy efficient measurement system for beehives (see Practice Abstract 9). The bases are placed under the beehive and the built-in scale, temperature sensor and microphone measure different parameters and send them live to the BEEP app (see Practice Abstract 10)., where the data is analysed and becomes available to users.The sensors provide a range of possibilities for practical and research purposes. A recent feature of the BEEP app is the creation of alerts on events such as a bee swarm (i.e., when the colony suddenly loses weight, the BEEP app can warn beekeepers that bees have left).Future perspectives for BEEP app are the implementation of warnings based on predictions, such as the trend in colony weight compared to other colonies or algorithm or prediction model. This will allow beekeepers to act before critical events take place, instead of react once the event has taken place.Thus, the alerts and warnings enable users of the platform to take care of the colonies when it is required, with very little disturbance by manual inspections.

Data sharing is a cornerstone of science. Making data available to others enables large-scale analyses and reproducibility. To keep an overview of datasets and share these with partners for research purposes, a data portal website was designed, built, and implemented by the B-GOOD consortium members from BEEP. This tool allows all partner organisations in the consortium to see available datasets, and to give access to external users. That way, the portal forms the central data repository to publicly share the datasets towards the end of the project.With the data portal, results generated within B-GOOD will be easily retrieved by B-GOOD members, who can access it using a private account. At present, the data portal includes a total of 63 datasets, each including 1-20 files. The B-GOOD ‘publication and data sharing’ policy describes which data is accessible, and to whom. Accordingly, the different datasets of the platform can be uploaded and retrieved depending on the access rights. Access can also be requested by interested parties. Where and when possible, datasets will be shared openly.Link: https://beehealthdata.org/

ApisRAM is a highly innovative and complex model for honeybee colonies, aiming to simulate individual bees within a colony and their behaviours and their own interactions and interactions with the environments in fine detail. The development of ApisRAM is generously funded by the European Food Safety Authority (EFSA) and is anticipated to reach its completion in 2027. Within the framework of the B-GOOD project, ApisRAM has undergone further refinement, placing specific emphasis on enhancing its foraging model. Notably, a dynamic and detailed flower resource model (refer to Practice Abstracts 17) has been seamlessly integrated into The Animal, Landscape, and Man Simulation System (ALMaSS).ALMaSS serves as the foundational platform for ApisRAM, furnishing a dynamically simulated landscape for the model bees to engage with. The refined foraging model within ApisRAM strives for a realistic simulation of the interactions between the model bees and the dynamic landscape model, especially the flower resource, embedded in ALMaSS. This incorporation enables the assessment of the effects of diverse landscape management practices, such as the introduction of flower strips, on honeybee colonies.This incorporation enables the assessment of the effects of diverse landscape management practices, such as the introduction of flower strips, on honeybee colonies.

Machine learning harnesses the power of algorithms to enable computers to execute diverse tasks through the process of learning from data. In the context of the B-GOOD project, machine learning models, such as neural networks, have been designed to predict the survival rates of bee colonies, leveraging hive weight data obtained from both the BEEP automatic monitoring base and manual inspections (see Practice Abstracts 9, 10, and 17).The model takes the hive weight as input while the output is the monitored colony survival rate. Various colony property changes and activities, such as brood size, adult population, and foraging activities, can induce alterations in hive weight. Consequently, hive weight serves as a rich source of information regarding the colonies' development. The incorporation of machine learning in this context facilitates the extraction of meaningful insights into colony development, which are encoded in the dynamics of hive weight. Uncovering such insights would prove challenging through conventional methods.These advanced models provide beekeepers with valuable early warning tools, enabling them to proactively support the well-being of their bee colonies.

Unsustainable losses of insects call for adequate conservation measures. Bees rely on diverse food and nesting sites to thrive (see Practice Abstracts 43 and 44). However, access to these resources change in space and time, making conservation effort challenging. Models are great tools to understand how resources fluctuate, and can therefore help guiding conservation measures.In this regard, B-GOOD members from the Coimbra University (Portugal) have developed models grouping information on the food resources available for honeybees across Europe. These models include intrinsic habitat considerations but also external variables. For instance, the models incorporate floral resource availability, but also climatic data, water availability, and altitude, which can all play a role on honey bee colony development and health.The resulting suitability map can effectively depict varying degrees of suitability for honeybees across different regions of Europe, helping stakeholders identify priority areas for conservation and habitat enhancement. Ultimately, the landscape suitability map for honeybees serves as a powerful tool for informed decision-making and strategic planning.

Access to balanced food resources is essential for the development, health, and reproduction of pollinators, including honey bees. However, the availability of nectar, pollen and sugars varies considerably in space (i.e., between regions and landscapes) and time (during and between seasons).To better understand changes in patterns of food resources available to pollinators, and consequences of these changes on bee health, B-GOOD consortium members from the Jagiellonian University in Kraków (Poland) have developed a model to assess pollen, nectar and sugar production at habitat and landscape scales in space and time (also see Practice Abstract 18). In this model, the composition of bee-friendly plants within habitat types is defined and for each of the plants its contribution to the production of bee food resources at the habitat level per day in a year is predicted, combining information on the production levels from single floral units, the density of floral units per habitat unit area and the tome of flowering. Using this model, it is possible to analyse the consequences of fluctuation in food resources, such as “hungry gaps” (when food is not available), for pollinator health (see Practice Abstract 44). In another study, they investigated how landscapes can be made more hospitable to pollinators (see Practice Abstract 45). Ensuring access to diverse floral resources is crucial for bee health, and benefits the crops and flowers that depend on these insects.

European honeybee colonies establish themselves in natural and man-made dark cavities and cannot usually be seen unless the beekeeper inspects the hive by opening it invasively. Although the traffic of bees at the entrance of nests gives an indication of their statuses, in the wintertime all foraging can cease for weeks or even months, and it is not even possible to tell whether the colony is alive or dead. In the past, many beekeepers have been simply knocking on their hives, with their hand, to check and listen for a positive buzzing response, indicating the liveliness of the colony.In a recent study, B-GOOD members from TNTU aimed to provide a method to test the physiological status of hives by providing them with a gentle, short, external artificial vibrational shockwave, and recording their response. The knock was provided by an external electromagnetic shaker attached to the outer wall of a hive, and bee colony’s responses was recorded by an accelerometer placed in the middle of the central frame of the colony. To avoid habituation, the stimulus was supplied at randomised times, approximately every hour. The method was first tested on a single colony hosted indoors, then extended onto eight outdoors colonies. The results of this study show that it is possible to quantitatively sense the colony’s overall mobility, and that a colony that is queen-less is easily discriminated from the others, paving the way towards the use of this technique to help quick assessment of colony statuses with minimal disturbance.

Honey bees perceive vibrations over a broad range of frequencies and are known to actively build and shape the comb of their hives to promote vibration transmission. Several honey bee vibrations are known to science, but only a handful have so far been explored and characterised in detail. B-GOOD consortium members from TNTU have recently developed tools to measure vibrations in a honey bee colony (see Practice Abstract 13).Analysing the vibrational data measured in a honey bee colony has recently revealed a new vibration, called the “purring bee” signal. To better understand the function of this signal, machine learning was used to automatically detect these vibrations in measurements taking place over the long-term. The results of this monitoring revealed that these purring signals are most commonly very short (0.2 sec long), but can be as long as 20 sec. Although the signal can also be artificially stimulated by gently pressing on a bee’s wing joints, its potential function in the colony remains unknown. Further exploring vibrational measurements using machine learning will undoubtedly provide more insights into the secret life of honey bees.

Over the years, numerous studies have shown the importance of different gases to the health of honeybee colonies, however, much of the data have been collected using expensive laboratory-based analysis or controlled environments. The lack of availability of low cost, small, and highly specific gas sensors continues to be a limitation in this area of study. Recently developed sensors have made measurement of carbon dioxide (CO2) in honeybee hives attainable with a temporal resolution typically allowing one measurement a per minute.Members of the B-GOOD consortium from TNTU have developed a new tool to measure carbon dioxide levels in honey bee hives, and compared these measures to the hive mass data obtained with BEEP systems (see Practice Abstracts 9 and 10) to assess whether CO2 emission in the colonies is linked to colony strength.The study showed that the sensors are very efficient in measuring the gas emission, and that CO2 fluctuations in honey bee colonies are high, and way above those observed in humans. Moreover, both parameters measured (CO2 emissions and colony mass) are tightly linked and CO2 level drops also seem to indicate colony health deterioration. The measurement of gas emissions is therefore a new promising tool to track honey bee colony health parameters.

BEEP is a research platform with digital tools assisting honeybee research. It consists of multiple modules: the app, the hive monitoring system and the Application Programming Interface (API) (see Practice Abstracts 9 and 10). The platform is designed for beekeepers and researchers. Beekeepers use the BEEP webapp and provide high quality data to your research project and can use it for their own administration. This enables researchers to collect quantitative and qualitative data with a combination of the hardware and software.What is unique about the BEEP platform?• The BEEP webapp is multilingual, user-friendly, and device- and operating system-independent.• You can choose to use the BEEP bases (including weight, temperature and sound sensors) or connect your own measurement system. An API is included in the subscription to automatically add (push) or download (pull) data. Various data formats such as JSON and CSV are supported. A weather data service is already included.• The standardized beekeeping data category tree allows you to choose from 500+ terms to compile your specific checklists for high quality data collection.• The research module provides access to research settings, monitoring progress reports and bulk download options.The code and BEEP base designs are open source, allowing for code review and additional software modules (e.g. for educational projects).For more information: https://beep.nl/research

The Asian hornet (Vespa velutina) has recently invaded and spread across parts of Europe and the UK, posing a new threat to honeybee colonies. Current methods of hornet identification for management and control purposes are expensive and/or complex. Remotely monitoring hornet presence at apiaries would offer fast and simple identification, allowing beekeepers to take quick action when their colonies are threatened by these predators.Using microphones, such as those equipped on the BEEP bases used in the B-GOOD project (see Practice Abstract 9), is inexpensive and members of the B-GOOD consortium from TNTU have shown that hornet flight sounds contain features specific to the species for reliable identification.Using machine learning to analyse sound captured at the entrance of honey bee colonies, B-GOOD members from TNTU have managed to find specific sound features associated with the presence of Asian hornets, discriminating it from other hornets. Asian hornets were correctly detected in a great majority of the tests (98.7%).This direct remote detection method has excellent potential for use in detecting hornets at apiaries and will be incorporated in the next update of the alert system of the BEEP app (see Practice Abstract 10).

Viruses stand among the most important stressors of honey bee colonies. These pathogens can be transmitted by ectoparasitic mites, between workers, and also from queens to their offspring.Based on the findings of a new trait causing virus infection suppression in the eggs laid by honey bee queens (see Practice Abstract 35), a study was conducted within the B-GOOD project to investigate virus infection in honey bee eggs across Europe. Taking advantage of the European network of the project, two major honey bee viruses (Deformed Wing Virus and Black Queen Cell Virus) were screened in eggs collected in colonies located in Belgium, Croatia, France, the Netherlands, Norway, Portugal, Romania, Slovenia, Spain and Sweden. Some of these colonies were naturally-surviving (or resilient, see Practice Abstracts 30 and 31), while others were traditionally managed.The results of this study showed that queens from naturally-surviving colonies laid infected eggs more frequently than traditionally managed ones. Additionally, older queens were generally transmitting less viruses to their eggs, suggesting that they have the potential to reduce this route of infection over time.

Recently, members of the B-GOOD project discovered a new trait causing virus infection suppression in honey bee eggs (see Practice Abstract 35). Following this finding, a study was conducted within the project B-GOOD to evaluate the infection levels of queens coming from colonies that were expressing or not expressing this trait. To explore this, twenty queens from each group were reared, and infections with two strains of the Deformed Wing Virus (DWV-A and DWV-B) were quantified in their head, thorax, ovaries, spermatheca, guts and abdomen.The results of this follow-up study showed that all queens were infected by both strains of Deformed Wing Virus. However, the queens coming from colonies expressing the virus resistance trait had lower infections with Deformed Wing Virus in all body parts investigated. The results therefore suggest that breeding for virus resistance can have short-term effect, only in one generation, and provide colonies with lower virus infections. This finding provides new perspectives for the development of sustainable breeding for resilient honey bee colonies (see Practice Abstract 30).Further research will be conducted to explore how this promising trait for selective breeding works, and what are the mechanisms that allow queens to reduce infections in their offspring.

Viruses stand among the most important stressors of honey bee colonies. These pathogens can be transmitted by ectoparasitic mites, between workers, and also from queens to their offspring.In 2012, B-GOOD members from Ghent University started a sanitary control of the breeding queens from Flemish beekeepers in the North of Belgium following a non-destructive approach. First, the presence of bee viruses in freshly laid worker eggs was monitored. Later, virus in drone eggs were investigated. At the start of the B-GOOD project, the results of this monitoring were used to estimate how the virus infections vary between honey bee generations, allowing to estimate whether this trait is heritable (i.e., passed from one generation to the next) and and therefore can be used for breeding or not.The results of the study show that queens infected with viruses can lay eggs that are free of infection, resulting in a newly described resistance trait, called “suppressed in ovo virus infection”. This trait has a moderate heritability through queens, meaning that breeding for it might allow selecting colonies with higher resilience to virus infections. These promising findings have been further explored through the B-GOOD project (see Practice Abstracts 36 and 37).

Fostering honey bee colony resilience is a promising approach to promote sustainable beekeeping (see Practice Abstract 30). Some of the technologies developed in B-GOOD, such as the Taqman assays (see Practice Abstract number 16), facilitate the detection and use of honey bee adaptations to breed resilient bee stocks. These tools aim at detecting the genetic mutations associated with mite resistance in honey bee colonies.In the framework of the B-GOOD project, a European-wide screening (including Belgium, Germany, Greece, Italy, Finland, France, Latvia, Netherlands, Poland, Portugal, Romania, Sweden, Switzerland and the UK) of the mutations associated with drone brood resistance (i.e., mite non-reproduction in the drone brood) was conducted. Out of eight mutations targeted by the assay, one had the same levels in the 14 countries screened. The other seven mutations were found in different levels across the countries, suggesting that distinct honey bee populations may rely on different mechanisms to survive mite infestations.The findings of this study will be compared with honey bee colony genetics to assess whether mite resistance correlates with specific honey bee races found in Europe.

A diversity of parasites and pathogens can affect honey bees and some may lead to colony losses. However, the distribution of these pathogens and their impact are not always known.In this study, members of the B-GOOD consortium from Sciensano and FLI investigated the distribution of six viruses (DWV-A, DWV-B, ABPV, CBPV, BQCV and SBV), Nosema spp. (N. apis and N. ceranae), Malpighamoeba mellificae, foulbroods (European and American) and Varroa destructor in more than 1500 samples of honey bee colonies of the apiaries of the project members and participating beekeepers, covering 14 countries (Finland, Sweden, Latvia, Denmark, Poland, Romania, Germany, the Netherlands, Switzerland, France, Italy, Portugal, Greece and the UK). Honey bee samples were collected during three seasons (Spring, Summer and Fall) over one, two or three years (2020-22) and pathogens and parasites infecting them were assessed.The results of this study give an overview of the honey bee health situation in Europe in the period 2020-2022 and show that many pathogens are found in honey bee hives, but some (e.g., N. apis and DWV-A) have become very rare. Notably, viruses differed a lot in their distributions, and high loads of at least one virus were found in each country.These data will be further analysed to identify high risk factors for colony losses and can be merged later on with data from within and around beehives obtained by other working groups of the B-GOOD project (e.g., see Practice Abstract 46).

Fostering honey bee colony resilience is a promising approach to foster sustainable beekeeping (see Practice Abstract 30). Advanced technologies can help tackle this important challenge.Because new technologies such as remote hive monitoring devices (see Practice Abstracts 9 and 10) can provide direct information on the changes in colony health, it becomes possible to detect stressors and act accordingly at the level of a colony. In fact, these tools allow to tailor colony intervention when help is needed.Such an approach allows reducing the need for prophylactic management, in which treatments are routinely given, even though they are not necessarily needed. For example, a colony that has low levels of infestation with parasites might not need to be as intensively treated with acaricide compared to another colony which has high levels of pests. In traditional beekeeping, all colonies of an operation are systematically treated the same way despite these differences in parasite infestations. Tailoring treatments to the colony’s health status through the use of advanced technologies will allow considering each colony separately. Removing unnecessary treatments also means that natural selection can act more effectively, and adaptations have more chances to emerge.

Beekeepers in many regions of the world are experiencing unsustainable colony losses. These losses have been linked to the exposure of honey bee colonies to multiple stressors, including several biotic (e.g., parasites) and abiotic (e.g., pesticides) factors interacting synergistically (i.e., in combination) or antagonistically (i.e., in opposition). In consequence, beekeepers must manage their colonies more and more intensively to reduce the negative impact of these stressors.However, some honey bee colonies exposed to these stressors are surviving with little or no beekeeper management because they have developed adaptations through natural selection. Such colonies are called resilient, as they can survive despite being exposed to stressors that are lethal for other colonies. Several traits can be involved in their survival, including a diversity of adaptations to the local environment, protective traits (immunity, detoxification, behaviour…).Research is underway to understand how to use these mechanisms to foster colony survival and sustainable beekeeping (see Practice Abstract number 31). Some of the technologies developed in B-GOOD, such as the Taqman assays (see Practice Abstract number 16), facilitate the detection and use of these honey bee adaptations to breed resilient bee stocks.

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
For this purpose, we developed protocols for field observations and data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). They are continuously evaluated by end-users, give adequate insights of the (health) status of a colony, refrain from disturbance of the bees, and are user friendly. Protocols that are found to be sufficient will be made publicly available to end-users at the end of the project. 
The current protocol is about estimating brood pattern consistency. This measurement gives information about the quality of the brood in a colony. If the brood is ‘spotty’, this may suggest the presence of disease, or low sperm quality. This measurement is visually estimated by inspecting brood frames in colonies and rating the overall brood pattern consistency based on a 5-point scale based on the percentage of empty cells in areas with sealed brood.
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
This protocol documents how to categorize the presence of queen cells. For this measurement, colonies are visually inspected for queen cells. The presence of queen cells in colonies provides insight on reproduction (swarming tendency) and/or queen quality. The type of queen cells that are identified are:
1) Queen cup: It is a small cup, with an opening on the bottom. For the purposes of the project, we define queen cups as empty queen cells (without eggs or larvae) 
(2) Swarm cells: Are built when the colony is preparing to reproduce and swarm. These cells are usually present on the edges of a comb. 
(3) Supersedure cells: Are built when the colony wants to replace the current queen. These cells are generally found on the center of a comb. 
(4) Emergency cell: Are built if the old queen is dead. Like supersedure cells, they are usually found on the center of a comb. 
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
For this purpose, we developed protocols for field observations and data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). They are continuously evaluated by end-users and give adequate insights of the (health) status of a colony, refrain from disturbance of the bees and be user friendly. Protocols that are found to be sufficient will be made publicly available to end-users at the end of the project. 
The ‘Sampling drone brood eggs’ protocol details how to conduct the sampling of drone brood eggs. The purpose is to identify and analyze the ‘suppressed in ovo virus infection’ (SOV) trait in honeybee colonies. The SOV trait describes the virus free state of drone eggs. Recent research found that this trait is heritable and that colonies expressing it are more resilient to virus infections as a whole, with fewer and less severe DWV infections in most honeybee developmental stages, especially in the male caste (De Graaf, et al. 2020). 
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
In the ‘Clinical signs of diseases’ protocol, clinical symptoms are being monitored for potential diseases. Honeybees are being threatened by a variety of pests and pathogens. Most of the pathogens create clinical signs within a colony that can be recognized by inspections. Potential diseases that may be observed in colonies are: varroosis (Varroa mites are visually present on honeybees or on bottom boards), American Foulbrood (caused by the bacteria Paenibacillus larvae and causes sticky brood), European Foulbrood (Melissococcus plutonius, a bacterium that create hail shot pattern in brood), nosemosis (Nosema spp. that affects the mid gut), and the viruses Acute Bee Paralysis Virus, Chronically Bee Paralysis Virus (shivering), Black Queen Cell Virus (affects the Queen cells), Deformed Wing Virus (damaged wings) and Sacbrood Virus (larvae) and maybe (but hopefully not) small hive beetle (Aethina tumida). 
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
For this purpose, we developed protocols for field observations and data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). They are continuously evaluated by end-users, give adequate insights of the (health) status of a colony, refrain from disturbance of the bees and are user friendly. Protocols that are found to be sufficient will be made publicly available to end-users at the end of the project. 
The ‘Atypical worker behaiour’ protocol aims at identifying atypical behaviour in colonies. Atypical behaviour by workers is one of the first signals of diminished health within the colony as it may indicate e.g., presence of diseases or starvation. This measurement is done by visual inspections of worker bees. It assumes a basic level of normal typical behaviour of honeybees. Some examples of atypical behaviours include running quickly over the comb for long periods, trembling (aside from the trembling dance) or shaking.
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum.
The current protocol documents the sampling bees for lab analysis on genotyping and diseases. Samples by project participants are collected three times a year (spring, summer and autumn) and sent to European Reference Labs for analysis. Diseases of main interests are: Varroa mites, Deformed Wing Virus (DWV), Nosema spp., American foulbrood (AFB), European foulbrood (EFB), Acute Bee Paralysis (ABPV) and Chronic Bee Paralysis (CBPV), sacbrood virus (SBV). These honey bee diseases are known to be mostly wide spread in all colonies. Once a colony is weakened due to a variety of circumstances (i.e. old queen, bad weather conditions, forage/food shortage), pathogens may become more prevalent and can have devasting consequences on bee health. 
Genotyping is mostly done to seek for genetic make-up and differences in bee samples. Using molecular tools, currently being developed in WP2, we aim to find genetic variations associated with Varroa-resistance of the colony. The results of these analyses are shared with apiary owners.
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
For this purpose, we developed protocols for field observations and data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). They are continuously evaluated by end-users, give adequate insights of the (health) status of a colony, refrain from disturbance of the bees, and are user friendly. Protocols that are found to be sufficient will be made publicly available to end-users at the end of the project. 
The ‘Mite infestation’ protocol aims at determining the mite infestation level of the parasitic mite Varroa destructor, of each colony by quantifying the naturally falling mites. Although we apply standard Varroa control measurements in the project, it is important to measure mite infestation levels of the hive as Varroa is considered to be one of the most harmful stressors for honeybees and treatments against it are not 100% effective. This is done by placing a bottom board underneath colonies, with the a sticky surface facing up and covering the entire bottom, catching falling mites from the colony. For accurate data, the bottom boards need to be inspected weekly, throughout the whole year. The total amount of mites is be scored as mite fall per day.
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. For this purpose, we developed protocols for field observations and/or data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). 
The ‘Top photo analysis’ protocol is designed for estimating colony size. With this method, colony size is estimated by taking a photo of the topside of the hive. Estimates are made by calculating the ratio of bees covering the top frame and the overall area available in the box. There are several benefits of this method. Notably, it is more user-friendly for end-users because it requires less labour for the beekeeper compared to traditional methods that estimates colony sizes. In addition, the colony is hardly disturbed, and it can be used in winter as well when temperatures are too low for removing frames in honeybee colonies. The results of this protocol can also be compared to other measurements of colony size taken during the season.
For more information about this, please visit: www.b-good-project.eu/

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
The ‘Colony dynamics’ protocol relies on two methods to estimate the colony demography and resources: 1) Digital photography method and 2) Liebefeld. The number of honeybees within colonies and the amount of food resources (honey and pollen) and brood size are key determinants of colony development and survival. For this measurement, colony traits that are estimated are: colony size (honeybees), pollen stores, capped honey, capped and open brood, eggs and drone brood.
The first method uses the DeepBee software to automatically detect cells and classify their contents in comb images from digital photographed frames [1]. The software is capable of reaching a high level of accuracy and is therefore less observer biased compared to the Liebefeld method. The Liebefeld method uses a grid, etched in square centimeters, where observants visually sum the surface area of bees, brood and food resources, making this method less invasive and less time consuming [2].
For more information about this, please visit: www.b-good-project.eu/
[1] https://doi.org/10.1016/j.compag.2020.105244
[2] https://www.tandfonline.com/doi/ref/10.3896/IBRA.1.52.1.03?scroll=top

An essential task of Work Package 1 is to optimize and standardize data collection methods for identifying and monitoring the health status of honeybee colonies. We strive for all actions to be harmonized, and that differences due to the manipulator and/or the procedure are brought to a minimum. 
For this purpose, we developed protocols for field observations and data management. These protocols are selected by reference work and key scientific publications and contribute to the operationalization of the Health Status Index (HSI). They are continuously evaluated by end-users, give adequate insights of the (health) status of a colony, refrain from disturbance of the bees, and are user friendly. Protocols found to be sufficient will be made publicly available to end-users at the end of the project.
In this protocol, the presence of queens and brood in all stages (i.e., eggs, larvae, and pupae) are examined. Presence and health status of the queen guarantees for healthy colonies, while presence of worker brood gives information on queen fecundity, viability of worker force and the ability of the colony to rear the eggs until adulthood.
For more information about this, please visit: www.b-good-project.eu/

The aim of B-GOOD is to develop support for beekeepers in order to keep honey bee colonies healthy in a sustainable way, preferably through autonomous hive monitoring to minimize beekeepers’ labour as well as disturbance on the hive. To facilitate and optimize this process, the project is divided into several Work Packages. 
In Work Package 1 (WP1), the overall aim is to contribute to the operationalization of the Health Status Index (HSI) by collecting data of different health components. The main tasks of Work Package 1 are i) collection of data on honeybee health indicators and ii) validation of tools developed within the project by end-users. 
WP1’s infrastructure follows a 3-tier approach, which consists of a step-by-step expansion of participating apiaries, calling successively on partner research institutions (Tier 1), selected beekeepers (Tier 2), and the broader beekeeper community (volunteers, Tier 3). This structure allows to use outcomes from the previous year(s) and tier(s) to improve and fine-tune protocols for the following year(s). To do so, protocols and manuals are continuously updated, and new information is added according to needs and new developments. Alternatively, information may be discarded if it is found to be not sufficiently useful, and/or bee-friendly and/or user-friendly in implementation. 
By developing structured and standardized protocols, monitoring is becoming more automated with the passing of years of the project. Once the project is finished, tools can be made available to end-users. 
For more information about this, please visit: www.b-good-project.eu/

Healthy bee colonies depend on abundant and diverse flower resources that they need to forage for pollen and nectar. Having this as a key driver for heathy bees, two of the goals of B-GOOD project are to develop a dynamic landscape model across the EU capturing the major floral resources for bees, and to construct landscape suitability maps for honeybees and beekeeping at EU scale. 
To achieve these goals, B-GOOD scientists have classified over 8000 plant species according to their “bee-friendliness” value (mostly in terms of pollen and nectar contents) using information from beekeepers, from databases (i.e., pollination trait syndromes) and pollinator visitation rates. All this information was then used to create a unique database containing spatially explicit data on the important plant species used as resources in different landscape elements (using the EUNIS habitat classification), followed by ranking of habitats in terms of potential to provide abundant and diverse resources. 
This database is the base for the ongoing development of a dynamic resource model with the ability to predict the spatial and temporal dynamics of flower resources for major habitat types at regional and national scales across Europe, and for the construction of suitability maps for honeybees and beekeeping. 
These will be key tools not only for beekeepers, but also for decisionmakers allowing a better planning of the implementation strategies for apiaries at their territories (considering the carrying capacity of a particular region) and/or a better planning of changes in land-use and land-management and still maintain or improve the desired beekeeping potential.
For more information about this, please visit: www.b-good-project.eu/

ApisRAM is a mechanistic, agent-based honey bee colony model for risk assessment that explicitly considers interactions and feedbacks among various components including bees, colony, pesticide, Varroa, Nosema, disease, weather, bee resources in the landscape and bee keeping practices, of which effects on individual bees are linked through effects on their vitality index.
ApisRAM is a model within the (Animal Landscape and Man Simulation System) ALMaSS, a landscape scale simulation system for modelling the impact of management on animals using detailed agent-based models. ALMaSS provides a detailed dynamic environment where bees develop and perform activities at an individual level based on interactions and feedbacks. Together with the landscape model component, it allows the evaluation of impacts of bee resource availability and farm management on bees. 
The goal of ApisRAM is to copy the reality as closely as possible in silico. With ApisRAM in silico experiments, the model should be able to:
(1) predict colony development using landscape information, farming practicing, weather information and bee keeping practice,
(2) assess the risk of pesticides to honey bee colonies at landscape level with multiple stressors.
ApisRAM development was funded by EFSA and continues as part of B-GOOD, the first version should be available in 2022 with a fully working version planned for 2025
For more information about this, please visit: www.b-good-project.eu/
ApisRAM: www.projects.au.dk/sess/projects/apisram/
ALMaSS: www.projects.au.dk/almass/

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 (mutations in the genome of the honey bee) in previous research.
With our research team, we developed and validated an accurate assay for discriminating these eight genomic mutations. This enables us to screen genotypically for the presence of the suppressed mite reproduction trait. In comparison with the standard phenotypic test, screening genotypically is not dependent on elaborate testing and is thus easier to organize and faster.
Within the B-GOOD project we will screen colonies genotypically across Europe for the presence of suppressed mite reproduction. This information will then be compared with indicators on the health status of each colony and with the presence of varroa in each colony. Performing this research will increase our understanding on the link between the genotype and the phenotype of the trait and might open the way for marker assisted selection.
For more information about this, please visit: www.b-good-project.eu/

Honeybees are currently under the threat of growing anthropogenic pressures. Consequently, the monitoring of their population is crucial for developing sustainable protective policies and foster the conservation of these important pollinators. Yet, tracking the impact of environmental pressures on honeybees is a demanding research challenge due to large gaps in monitoring capacity and accuracy. In the B-GOOD project, we therefore propose to develop a bee counter providing a real-time recording of bee activity at the hive entrance (in- and out- activity of bees).
The automatic recognition of different bee castes (worker, queen and drone) and pollen foragers is targeted. At the end of the project, a ready-to-use bee counter should be available for the in situ monitoring of daily bee activity and mortality rates. By allowing to compare different estimates of colony dynamics (e.g., difference between exiting and re-entering honeybees), such tool will greatly improve and benefit the monitoring of honeybee colonies and environmental risk assessment by stakeholders, policymakers, beekeepers and scientists.
For more information about this, please visit: www.b-good-project.eu/

Honeybees are known to sustain their brood at 35 degrees, at anytime of the year. This can require considerable expenditure of available resources, particularly in colder weather.
In B-GOOD, we aim to better understand the use of resources that honeybees make, by measuring the distribution of the temperature all over the colony. To do so, we have built a hive with 48 temperature sensors per frame, for all 10 frames. The system has been tested and is about to be given a live colony of bees.
We will track the evolution of the temperature of the colony, over the entire hive, for more than a year.
The data will be fed to our research partner in Denmark, developing the APISRAM model (see PA#23 - ApisRAM: Developing a digital twin of a colony), for them to further understand the colony’s sophisticated use of resources depending on the external weather, available resources, presence of brood, size of the colony, etc..
Eventually the work will yield deeper understanding of the colony’s ability to sustain difficult, stressful times, to indicate to the beekeeper the important parameters that make a colony fail or succeed. 
For more information about this, please visit: www.b-good-project.eu/

Honeybees are known to communicate with pheromones, volatile molecules that are released in air, for a vast range of different purposes. They are also known to communicate with the waggle dance, and other cues often comprising of vibrational signals.
In the B-GOOD project, we place vibration sensors (accelerometers) in the centre of colonies, in order to record the vibrations originating from honeybees taking place within the honeycomb. This allows us to pick up previously known, as well as new, vibrational pulses released by individual honeybee individuals passing the vicinity of the sensor. Notably, we focus on collecting and analysing worker pipes, clear worker bee vibrational signals that typically last just under a second, the function(s) of which is/are not yet known.
Together with the array of other data collected on the colonies that we are monitoring (links to other PAs), we are aiming to correlate frequent instances of worker pipes with one or more particular colony condition (e.g., requeening). This way, new light will be shed on the meaning/function of the worker pipes, and a new tool might arise for the beekeeper to find out about his/her colony status, without having to open it.
For more information about this, please visit: www.b-good-project.eu

Symptoms of numerous trembling bees in front of the hives, unable to fly, can be caused by pesticide exposure or may result from high virus infection with honey bee paralysis viruses.
Current detection of these viruses requires samples to be sent for laboratory analysis using quantitative PCR methods, molecular methods that are expensive, time-consuming and need specific equipment.
In order to discriminate pesticide exposure and virus infection, B-GOOD members are developing a rapid diagnostic kit that can be used in the field by beekeepers or technicians. This research aims at developing a serological test, based on antibodies raised against the main paralysis virus species. Specificity and sensitivity of this method is being assessed and compared to the current qPCR methods. If suitable for virus detection, a ready-to-use kit (Lateral Flow Device) will be set up. 
B-GOOD is expected to provide the proof of concept: a simple crude extract to be tested for the presence of paralysis viruses when visiting the colonies, without complex lab experiments.
For more information about this, please visit: www.b-good-project.eu

The application of pesticides including fipronil, neonicotinoids, avermectin, pyrethroid and chlorpyrifos are suspected to harness honeybees. Standardized instrumental analysis for detecting these pesticides is expensive and time consuming.
At Wageningen Food Safety Research (WFSR) we develop rapid, simple, robust strip tests (Lateral Flow Devices, LFDs) for on-site detection of the bee-harming pesticides and validate these tests in bee-related matrices (e.g. honey, pollen…). This enables fast on-site screening of hazardous pesticides by beekeepers themselves.
In WFSR, we focus on the development and validation of pesticide LFDs. We developed and validated neonicotinoid LFD prototypes (from our Chinese partner), for application in plants, pollen, honey, bee bread and bees. We also aim to develop and validate such on-site user-friendly LFDs for other pesticides, including avermectins, pyrethroids, chlorpyrifos and fipronil.
With fast, simple and cheap LFD screening of the pesticides in bee related materials, beekeepers can frequently and efficiently monitor the apiary environment at the point-of-need.
For more information about this, please visit:
https://b-good-project.eu/news/2789_lateral-flow-device-for-neonicotino…;
https://www.youtube.com/watch?v=eZQQakPxEFE

The BEEP app is a digital checklist app in which users can register inspections. It is a digital alternative to the paperwork that is part of beekeeping. It can be used to manage information on multiple apiaries and hives using a mobile phone, laptop, computer or tablet. Typical information include observations when inspecting a colony and management actions performed on hives. The beekeeper has an overview, can share data and connect hive sensors. 
The BEEP app can be applied in research projects such as B-GOOD. It eases data collection. Key features include: set up of research projects including inspection checklists, data consent by beekeepers and export features including via an API (application programming interface). The available, optional data categories are very structured and standardized, which facilitates (scientific) data processing.
Some key figures: The app is available in nine languages and other languages can easily be added, thousands of users are using the app already, three research projects use the BEEP platform, and a multilingual knowledge base is available to support users. 
The BEEP app can work in conjunction with the BEEP base, a multi sensor, autonomous and energy efficient measurement system for beehives (add link to PA about BEEP bases), or other sensor systems. 
BEEP is GDPR compliant. And both the app and the system are shared under an open source license. 
For more information about this, please visit: https://beep.nl/home-english

The BEEP base is a multi sensor, autonomous and energy efficient measurement system for beehives. The bases are placed under the beehive. The built-in scale, temperature sensor and microphone measure every 15 minutes and send the values to the BEEP app. 
The sensors provide a range of possibilities for practical and research purposes. For example: how much nectar and pollen do the bees collect, how much do they use, are there swarming or robbery events going on, how much brood is present, what is the flight activity, and so on.
The custom-built computer is very energy efficient and the two AAA batteries last 1.6 years in average when using standard measurements settings. Data is transmitted wirelessly via LoRa (Long Range) data connection. The standard installation uses a free network called TTN (The Things Network). Both the sensor system and the free app are shared under an open source license and an API (Application Programming Interface) is available, allowing to make adjustments to the system to make it fit their own (research) needs. 
In the B-GOOD project, the BEEP bases and app are being extended and improved based on a European collaboration between beekeepers and scientists. A total of 384 bases are field-tested in 12 countries across Europe. 
The BEEP systems can be ordered via the BEEP webshop (see below).
For more information about this, please visit: https://beep.nl/home-english 
https://www.beep-shop.nl/en_GB/a-61266000/products/beep-base-complete

The B-GOOD project comprises of a specific work package, WP2, dedicated to exploring innovative technologies to monitor honeybee colonies, and their health.
In this work package, scientists are exploring the usefulness of monitoring (i) the vibrations originating from within a honeybee colony, (ii) the temperature all around the volume of the colony and the gases released in it, (iii) the absolute numbers of bees leaving and entering the hive at any point in the time of the day, (iv) specific pesticides in the colony’s matrix, (v) specific viruses in the colony, (vi) mutations in the honeybee genome related to varroa tolerance.
The scientific work is taking place into a small selection of thoroughly monitored colonies (the ‘Tier1’ mini-apiaries, including eight apiaries of eight colonies each across institutes of the B-GOOD consortium, in eight separate countries).
The explorations demonstrated to be useful to the beekeeper are being tested into larger group of users, the ‘Tier2’ group followed by the ‘Tier3’ group (add links to practice abstracts of WP1).
For more information about this, please visit: www.b-good-project.eu

Socio-economic researchers within B-GOOD completed 41 depth interviews with a diversity of stakeholders involved in the EU beekeeping sector. One of the aims of these interviews was to identify strengths, weaknesses, opportunities and threats – also called SWOT-elements.
Twenty-four internal characteristics of the beekeeping sector were identified and evaluated in a scoring survey completed by another sample of 28 stakeholders. In a similar vein, 29 external factors shaping the technological, natural, political, economic and sociocultural environment for beekeeping in the EU were identified and evaluated. 
The analysis yielded a consensus set of five strengths, five weaknesses, nine opportunities and nine threats. These were consecutively confronted with each other to yield 18 key attention points for policy and strategy development aiming at healthy and sustainable beekeeping in the EU. 
One example of the identified key attention points states that the EU beekeeping sector might strive to capitalise on the fact that locally produced honey has a favourable image as a high quality and premium product (strength) for which consumers show an interest and are willing to pay premium prices as a healthy, sustainable, natural and locally produced food (opportunity). This strength is to be carefully protected, notably the image of local honey as a healthy, sustainable and natural product. 
Advancements in product quality assessment and analysis – another important external factor identified as an opportunity – can help in this respect, e.g. by providing services to the beekeeping sector (quality control and certification aiming at consumer reassurance).
For more information about this, please visit: www.b-good-project.eu

A key element of the B-GOOD project is to provide end-users with high quality tools in order to gain insight on the health of honeybee colonies. For this purpose, protocols for hive monitoring are evaluated within the project by end-users (i.e., beekeepers). The aim of the evaluation framework is to produce protocols to 1) give adequate insights of the (health) status of a colony, 2) refrain from disturbance of the bees, and 3) be user friendly.
There are two pathways set-up within the framework for evaluation. First, yearly sessions are held with end-users to assess methods of data collection. In parallel to evaluation sessions, feedback is being continuously gathered by end-users via the BEEP helpdesk (see PA#16 - BEEP app) set up by the project. Based on the feedbacks, protocols are updated and implemented within the project for further testing. Improvements on BEEP based on evaluations are directly implemented in the BEEP app and available to the public throughout the project. This feedback process allows for constant improvements, engaging with end-users and quality check.
For more information about this, please visit: https://beepsupport.freshdesk.com/nl/support/solutions/

The EU beekeeping sector is very diverse with many organizations associated with it. We undertook a study to investigate and understand the structure of the EU bee keeping sector.
We carried out in-depth of interviews with people representing organizations belonging to the EU Bee partnership, asking them to provide details of organizations / individuals they interacted with for technical knowledge. In total 41 interviews were undertaken (January and April 2020). From these interviews, we have begun to map networks for technical knowledge exchanges.
Our analysis indicated there are several organizations that are well-connected, with links to 3 or more other organizations. Of these organizations, EFSA, BeeLife and PAN would seem to be central. EFSA and BeeLife seem to have prominent positions sought for their knowledge. PAN has numerous links, but these are both ‘receiving’, and ‘giving’ ties suggesting an influencing position, sought both for their knowledge as well as disseminating it. There several other interconnected veterinarian and technical advisory organizations e.g. EU Reference Laboratory for Bee Health (ANSES), French National Centre for Scientific Research (CNRS), Food and Agriculture Organization (FAO) and APIMONIDA.
Our analysis presented here is not a complete network analysis of the EU beekeeping sector, it represents the networks of the people we interviewed. However, it has highlighted some of the key organizations that B-GOOD will endeavor to engage with for expert knowledge, but who are also likely to be key in influencing honey bee related technical and policy developments within Europe.
For more information, please visit: www.b-good-project.eu

A core component of the B-GOOD project is its multi-actor approach. The project sets out to engage with and integrate the expertise and interests of a wide range of relevant actors, from scientists to bee keepers, within the EU bee keeping sector. We want to avoid science and innovation taking place in a bubble, by bring the right people together to generate innovative and practical solutions to ensure healthy honey bee colonies.
As part of the project’s actor engagement activities, we established the B-GOOD Multi-Actor Forum (MAF), a platform for knowledge exchange and dialog. MAF members are people who represent the varied interests within the EU beekeeping sector e.g. beekeeping associations, environmental NGOs, public authorities, farming associations, veterinary services and honey processors.
Restrictions and disruptions caused by Covid-19, has meant that the MAF has not meet physically, but other ways to interact have been realized. MAF members have attended, so far, two virtual project meetings in June and December 2020. These project meetings provided an opportunity to disseminate details of project progress and results. In addition, MAF members actively contributed by asking questions and providing feedback, helping to guide project developments.
MAF involvement has been a success and their input appreciated. Holding meetings on-line has provided an opportunity to extend the reach of the project’s engagement activities, enabling many people to participate from different locations. The use of on-line tools and platforms will be exploited for the remaining period of the project to further our engagement with a variety of actors.
For more information, please visit: www.b-good-project.eu

The B-GOOD project is composed of ten integrated and interconnected work packages (WPs), where each WP has a set of specific and clearly defined objectives. 
WP5 is establishing the relationship between environmental, biological and management drivers and bee health status, to be incorporated into a holistic predictive simulation model of bee colonies in a range of agricultural landscapes. 
In order to achieve this, WP5 receives data originating from other WPs: 
- WP1: bee health indicators from the colony 
- WP3: digital phonological maps of pollen and nectar resources for major land-use types 
- WP4: socio-economic data and business models for sustainability of beekeeping. 
In WP1, data acquisition occurs preferentially in an automated way, using the BEEP pro remote sensor device, in addition to manual data entry and laboratory analyses. Novel tests and tools for health monitoring are being developed in WP2. In WP6 we utilise and further expand the classification of the open source IT-application for digital beekeeping, BEEP, to streamline the flow of data related to beekeeping management, the beehive and its environment (landscape, agricultural practices, weather and climate) from various sources. This feeds the EU-wide bee health and management data platform that is being established. WP8 focusses on the engagement of and the collaboration between multiple actors from various beekeeping systems within the EU. In order to maximize the impact of research WP7 is dedicated to dissemination and exploitation of the results of B-GOOD. Finally, coordination and project management resorts under WP9, and WP10 is dealing with ethical issues.
For more information about this, please visit: www.b-good-project.eu

The B-GOOD consortium has a multidisciplinary membership. Prof. de Graaf from Ghent University coordinates the B-GOOD project with 17 different institute members.
Eight bee research labs (UGENT, WR, INRAE, MLU, UCLUJ, UCOI, TNTU, UBERN) guarantee a network to monitor honeybees in a research setting and have excellent relationships with national beekeeper associations, which provide a base for participant recruitment and dissemination during the project.
The reinforcement with two institutions closely linked to beekeeping (SML in the north and BSOUR in the south) enlarge the north-south axis. We additionally brought together socio-economists (UGENT, UCOI) and ecologists (UCOI, UJAG), allowing us to identify viable and sustainable business models for EU beekeepers and providing a dynamic landscape model across the EU. Furthermore, the multi actor approach of B-GOOD is facilitated by AU. In the latter institution, modelers help in understanding the relationship between environmental, biological and management drivers and bee health. Moreover, as data is gathered in an automated way, the SME BEEP, with their bee hive sensor systems, is participating. In addition to this, new features like the application of accelerometers to monitor honeybee colony activity (TNTU) and the bee counter system (INRAE) are being further developed. Two National Reference Laboratories perform routine bee disease diagnosis (FLI, SCIEN). The bees’ genetic profile is being studied by MLU and UGENT. Finally, exploitation and dissemination of the research results is provided by PENSOFT and UBERN, and SCIPROM will assist with the management.
For more information about this, please visit: www.b-good-project.eu/partners

The overall aim of the 4-year B-GOOD project is to pave the way towards healthy and sustainable beekeeping within the European Union by following a collaborative and interdisciplinary approach. The project aims to test and implement a common index for measuring and reporting honey bee health status (= Health Status Index, HSI). This index will aid risk assessors, authorities and the plant protection and veterinary medicines industries to measure honey bee health status in real time and across geographical locations, as well as evaluating the effect of (beekeeping) management decisions and actions. It is an essential building block for the development of targeted guidance for healthy and sustainable beekeeping. Semi-automated and/or automated hive monitoring will add to its utility by reducing workload and colony disturbance.
The main objectives of B-GOOD are:
• Facilitate decision making for beekeepers and other stakeholders by establishing ready-to-use tools for operationalising the HSI
• Test, standardise and validate methods for measuring and reporting selected indicators affecting bee health
• Explore the various socio-economic and ecological factors beyond bee health
• Foster an EU community to collect and share knowledge related to honey bees and their environment
• Engender a lasting learning and innovation system (LIS)
• Minimise the impact of biotic and abiotic stressors
For more information about this, please visit: www.b-good-project.eu