Percorrer por autor "Verbeke, Wim"
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- Beekeeping in Europe facing climate change: a mixed methods study on perceived impacts and the need to adapt according to stakeholders and beekeepersPublication . Van Espen, Marie; Williams, James H.; Alves, Fátima; Hung, Yung; Graaf, Dirk C. de; Verbeke, WimThe beekeeping sector is suffering from the detrimental effects of climate change, both directly and indirectly. Despite numerous studies conducted on this subject, large-scale research incorporating stakeholders' and beekeepers' perspectives has remained elusive. This study aims to bridge this gap by assessing the extent to which stakeholders involved in the European beekeeping sector and European beekeepers perceive and experience the impacts of climate change on their operations, and whether they had to adapt their practices accordingly. To this end, a mixed-methods study including in-depth stakeholder interviews (n = 41) and a pan-European beekeeper survey (n = 844) was completed within the frame of the EU-funded H2020-project B-GOOD. The development of the beekeeper survey was informed by insights from literature and the stakeholder interviews. The results highlighted significant regional disparities in the perceived impacts of climate change, with beekeepers in Southern European regions expressing more negative outlooks, while Northern European beekeepers reported more favourable experiences. Furthermore, survey analysis revealed beekeepers who were classified as ‘heavily impacted’ by climate change. These beekeepers reported lower average honey yields, higher colony winter loss rates and a stronger perceived contribution of honey bees to pollination and biodiversity, underscoring climate change's detrimental impacts on the beekeeping sector. Multinomial logistic regression revealed determinants of the likelihood of beekeepers being classified as ‘heavily impacted’ by climate change. This analysis indicates that Southern European beekeepers experienced a 10-fold likelihood of being classified as heavily impacted by climate change compared to Northern European beekeepers. Other significant factors distinguishing ‘winners’ and ‘losers’ were self-reported level of professionalism as a beekeeper (ranging from pure hobbyist to fully professional, Odds Ratio (OR) = 1.31), number of years active in beekeeping (OR = 1.02), availability of floral resources throughout the bee season (OR = 0.78), beehives located in a forested environment (OR = 1.34), and the presence of local policy measures addressing climate change-related challenges (OR = 0.76).
- 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 coloniesPublication . Van Dooremalen, Coby; Ulgezen, Zeynep N.; Dall’Olio, Raffaele; Godeau, Ugoline; Duan, Xiaodong; Sousa, José Paulo; Schäfer, Marc O.; Beaurepaire, Alexi; Van Gennip, Pim; Schoonman, Marten; Flener, Claude; Matthijs, Severine; Claeys Boúúaert, David; Verbeke, Wim; Freshley, Dana; Valkenburg, Dirk-Jan; Van Den Bosch, Trudy; Schaafsma, Famke; Peters, Jeroen; Xu, Mang; Le Conte, Yyes; Alaux, Cedric; Dalmon, Anne; Paxton, Robert J.; Tehel, Anja; Streicher, Tabea; Dezmirean, Daniel S.; Giurgiu, Alexandru I.; Topping, Christopher J.; Williams, James Henty; Capela, Nuno; Lopes, Sara; Alves, Fátima; Alves, Joana; et al.; Alves, FátimaHoney 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.