WildPosh established a pan-European site network across two major cropping systems (wheat and oilseed rape) to characterise sources and routes of pesticide exposure in the key pollinator groups. We sampled the level of contamination of six matrices (pollen, nectar, water, plant matrices, soil, and pollen from bumblebee workers).
We developed new protocols for monitoring pesticide impact, extending the novel methods for lab-based testing of pesticide on wild bees to a higher diversity of wild pollinators (18 species so far). We determined the ecotoxicology and toxicokinetic of major pesticides and their mixtures across 11 pollinator species of bees, hoverflies and butterflies in laboratory conditions, and of two species in semi-field conditions. WildPosh is also improving MALDI PolTyping®, which is a laboratory tool to monitor pollinator health through simple, non-lethal, field-collection of a pollinator’s hemolymph. We expect that this approach will become a fundamental component of future global solutions for health management plans for pollinators, and become a referenced, fast, cost-effective and automatable analytical procedure to demonstrate the presence of stressors.
We are compiling a comprehensive open-source database about distribution and about traits, which includes morphological/ecological traits reflecting the sensitivity of European pollinators to pesticides and other stressors (e.g. nutrition, climate, parasite). Combining both databases will inform about the risk to pesticide exposure. In this way, WildPosh will be able to define traits associated with sensitivity to pesticides, thereby identifying sensitive ‘umbrella’ species whose protection will benefit the broader community of pollinators. We are additionally building a database to include information on pesticide use, as well as on other stressors able to amplify the adverse effects. In this way, we develop an open-source curated database on pollinators and the use of pesticides.
We are critically reviewing all existing prediction methodologies (QSAR models, category approach, read-across), identifying the most important gaps and sources of uncertainty and will propose improved strategies for increasing their ability to predict risk, facilitating the regulatory acceptance of in silico methods and their integration the ERA process. Moreover, we will develop methodologies for risk assessment in open-source tools. WildPosh will integrate existing and newly created data and models into an open-source user-friendly web-platform interface to produce a refined systems-based risk assessment output for stakeholders. It will include exposure, toxicity (sublethal, chronic), and risk of single and multiple pesticides at individual, population, and community level across landscapes and land-use scenarios.
