Standard methods for Apis mellifera anatomy and dissection

An understanding of the anatomy and functions of internal and external structures is fundamental to many studies on the honey bee Apis mellifera. Similarly, proficiency in dissection techniques is vital for many more complex procedures. In this paper, which is a prelude to the other papers of the COLOSS BEEBOOK, we outline basic honey bee anatomy and basic dissection techniques

Introducing the INSIGNIA project: environmental monitoring of pesticide use through honey bees

INSIGNIA aims to design and test an innovative, non-invasive, scientifically proven citizen science environmental monitoring protocol for the detection of pesticides by honey bees. It is a 30-month pilot project initiated and financed by the EC (PP-1-1-2018; EC SANTE). The study is being carried out by a consortium of specialists in honey bees, apiculture, statistics, analytics, modelling, extension, social science and citizen science from twelve countries. Honey bee colonies are excellent bio-samplers of biological material such as nectar, pollen and plant pathogens, as well as non-biological material such as pesticides or airborne contamination. Honey bee colonies forage over a circle of 1 km radius, increasing to several km if required, depending on the availability and attractiveness of food. All material collected is accumulated in the hive.The honey bee colony can provide four main matrices for environmental monitoring: bees, honey, pollen and wax. Because of the non-destructive remit of the project, for pesticides, pollen is the focal matrix and used as trapped pollen and beebread in this study. Although beeswax can be used as a passive sampler for pesticides, this matrix is not being used in INSIGNIA because of its polarity dependent absorbance, which limits the required wide range of pesticides to be monitored. Alternatively, two innovative non-biological matrices are being tested: i) the “Beehold tube”, a tube lined with the generic absorbent polyethylene-glycol PEG, through which hive-entering bees are forced to pass, and ii) the “APIStrip” (Absorbing Pesticides In-hive Strips) with a specific pesticide absorbent which is hung between the bee combs.Beebread and pollen collected in pollen traps are being sampled every two weeks to be analysed for pesticide residues and to record foraging conditions. Trapped pollen provides snapshots of the foraging conditions and contaminants on a single day. During the active season, the majority of beebread is consumed within days, so beebread provides recent, random sampling results. The Beehold tube and the APIStrips are present throughout the 2-weeks sampling periods in the beehive, absorbing and accumulating the incoming contaminants. The four matrices i.e. trapped pollen, beebread, Beehold tubes and APIStrips will be analysed for the presence of pesticides. The botanical origin of trapped pollen, beebread and pollen in the Beehold tubes will also be determined with an innovative molecular technique. Data on pollen and pesticide presence will then be combined to obtain information on foraging conditions and pesticide use, together with evaluation of the CORINE database for land use and pesticide legislation to model the exposure risks to honey bees and wild bees. All monitoring steps from sampling through to analysis will be studied and rigorously tested in four countries in Year 1, and the best practices will then be ring-tested in nine countries in Year 2. Information about the course of the project, its results and publications will be available on the INSIGNIA website www.insignia-bee.eu and via social media: on Facebook (https://www.facebook.com/insigniabee.eu/); Instagram insignia_bee); and Twitter (insignia_bee). Although the analyses of pesticide residues and pollen identification will not be completed until December 2019, in my talk I will present preliminary results of the Year 1 sampling.info:eu-repo/semantics/publishedVersio

Introducing the INSIGNIA project: Environmental monitoring of pesticides use through honey bees

INSIGNIA aims to design and test an innovative, non-invasive, scientifically proven citizen science environmental monitoring protocol for the detection of pesticides via honey bees. It is a pilot project initiated and financed by the European Commission (PP-1-1-2018; EC SANTE). The study is being carried out by a consortium of specialists in honey bees, apiculture, chemistry, molecular biology, statistics, analytics, modelling, extension, social science and citizen science from twelve countries. Honey bee colonies are excellent bio-samplers of biological material such as nectar, pollen and plant pathogens, as well as non-biological material such as pesticides or airborne contamination. Honey bee colonies forage over a circle of about 1 km radius, increasing to several km if required depending on the availability and attractiveness of food. All material collected is concentrated in the hive, and the honey bee colony can provide four main matrices for environmental monitoring: bees, honey, pollen and wax. For pesticides, pollen and wax are the focal matrices. Pollen collected in pollen traps will be sampled every two weeks to record foraging conditions. During the season, most of pollen is consumed within days, so beebread can provide recent, random sampling results. On the other hand wax acts as a passive sampler, building up an archive of pesticides that have entered the hive. Alternative in-hive passive samplers will be tested to replicate wax as a “pesticide-sponge”. Samples will be analysed for the presence of pesticides and the botanical origin of the pollen using an ITS2 DNA metabarcoding approach. Data on pollen and pesticides will be then be combined to obtain information on foraging conditions and pesticide use, together with evaluation of the CORINE database for land use and pesticide legislation to model the exposure risks to honey bees and wild bees. All monitoring steps from sampling through to analysis will be studied and tested in four countries in year 1, and the best practices will then be ring-tested in nine countries in year 2. Information about the course of the project and its results and publications will be available in the INSIGNIA website www.insignia-bee.eu.info:eu-repo/semantics/publishedVersio

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript

Emerging threats and opportunities to managed bee species in European agricultural systems: a horizon scan

Managed bee species provide essential pollination services that contribute to food security worldwide. However, managed bees face a diverse array of threats and anticipating these, and potential opportunities to reduce risks, is essential for the sustainable management of pollination services. We conducted a horizon scanning exercise with 20 experts from across Europe to identify emerging threats and opportunities for managed bees in European agricultural systems. An initial 63 issues were identified, and this was shortlisted to 21 issues through the Horizon Scanning process. These ranged from local landscape-level management to geopolitical issues on a continental and global scale across seven broad themes - Pesticides & pollutants, Technology, Management practices, Predators & parasites, Environmental stressors, Crop modification, and Political & trade influences. While we conducted this Horizon Scan within a European context, the opportunities and threats identified will likely be relevant to other regions. A renewed research and policy focus, especially on the highest-ranking issues, is required to maximise the value of these opportunities and mitigate threats to maintain sustainable and healthy managed bee pollinators within agricultural systems

The use of 'temporary confinement' and 'pollen transfer devices' to increase pollination potential of honey bees

Includes bibliographical referencesAvailable from British Library Document Supply Centre- DSC:DX219345 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Towards the development of an index for the holistic assessment of the health status of a honey bee colony

Honey bees play an important role in the maintenance of both, biodiversity and food security through pollination services and also represent an important source of income for rural areas. Despite several studies and monitoring projects gathering a large amount of data on the main factors/stressors influencing honey bee colonies, there is a lack of holistic and multidimensional statistical tools integrating different aspects which define honey bee colony health. Such tools are important to support the sustainable management of honey bees. In this study, we designed and tested a methodological framework based on Structural Equation Models for the development of a honey bee Health Status Index. The index accounts for the main abiotic (e.g. pesticide contamination, landscape characteristics) and biotic (e.g. parasitosis, virosis) factors influencing honey bee health, including the beekeepers role in managing the colony. The proposed methodology was validated against a dataset representing seven scenarios generated through Expert Knowledge Elicitation. The validation procedure showed that the Health Status Index integrates different sources of data and it can quantify the health status of a honey bee colony based on the characteristics of each scenario. Furthermore, it allows the investigation of the influence of different drivers/stressors on the health of the honey bee colony. The Health Status Index provides flexibility in the selection of variables, making it a valuable tool for holistic and harmonised assessment of honey bee health. Once validated, the index can support the evaluation at different spatial (from local- to area-wide management) and temporal (medium- to long-term management) scales to support stakeholders’ (e.g. beekeepers, risk assessors, risk managers) decision-making

More Insight Into The Chemical Composition Of Greek Propolis; Differences And Similarities With Turkish Propolis

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