INSIGNIA is an innovative project which will build on the wide range of expertise of the applicants developed during previous projects such as the COLOSS “CSI Pollen project”. INSIGNIA involves the development of a protocol for a citizen science monitoring programme using beekeepers to collect biweekly pollen samples from honeybee colonies for analysis for pesticide residues and botanical origin. In the first year, in four EU member states representing all authorisation zones, monitoring using the well-established technique for collecting pollen samples using pollen traps, will be compared with two innovative techniques: the collection of beebread using a novel sampling device, and the use of passive in-hive sampling devices.
The sampling sites chosen will embrace different land uses, providing a contrasting range of expected pesticide exposure. Since pollen is a biological material subject to very rapid decay, and hence degradation of any chemical residues contained within it, a variety of different methods for sample storage and transport will also be compared. The samples obtained will be analysed for residues of agricultural pesticides and veterinary products, both authorised and unauthorised, as well as identification of botanical origin, using state of the art molecular techniques. In the second year of the project, the most suitable and economical methods identified in the first year will be more extensively tested in a monitoring programme carried out in sentinel apiaries in nine EU Member states. The results of the monitoring programme will then be combined with geospatial land use data including the CORINE database, in order to develop models of plant biodiversity and pesticide exposure for honeybees, which will enable pesticide contamination to be linked to crop and other plants. The extrapolation of the results to other pollinators will also be assessed, in order to contribute to the implementation of European environmental legislation.
Key words: monitoring; pesticides; honeybees
Other key words: pollen; biodiversity; exposure modelling;