Work packages

WP1 will: 

1) follow a traits-based approach to determine species' vulnerability to pesticides by linking specific traits (morphological, physiological, ecological) to external exposure and intrinsic sensitivity; 

2) assess critical pesticides (singularly or in combination) to identify potential interactions that may lead to deviations from dose addition (potentiation, synergism, antagonism) and potential time-reinforced toxicity properties; 

3) generate toxicological data to calibrate the models in the other WPs, including targeted experiments to support TKTD models, and to develop standardised ecotoxicological tests for new test organisms suitable for the risk assessment.

WP2 will:

1) use machine learning to develop in silico models to assess the effects of pesticides and other pollutants towards pollinators;

2) include theoretical information on the modes of action and experimental values on related substances (read-across) within the machine learning models;

3) develop models to group substances according to their common mode of action;

4) implement and make the developed models freely available within the open-source platform.

WP3 will: 

1) develop TKTD models integrating multiple exposure pathways and link to the pollinator species models;

2) develop pollinator species models, representing the key pollinator groups (wild bees, butterflies, hoverflies, and moths), in ALMaSS, consisting of two hoverflies, one butterfly, and one moth;

3) prepare existing bee species models (honey bee (Apis mellifera), bumble bee (Bombus terrestris), and solitary bee (Osmia bicornis) and new pollinator species models for the integration of TKTD models.

WP4 will:

1) develop and test a co-monitoring scheme for tracking pesticide exposure and communities of key pollinator groups across European cropping systems; 

2) develop pesticide risk indicators for these pollinators.

WP5 will:

1) integrate the toxicology, in silico toxicological models, animal modelling, and monitoring into a framework for systems ERA for pollinators;

2) evaluate how these tools might assist in drawing management from all relevant policy instruments together to maximise synergy and increase the impact of the systems ERA for pollinators;

3) explore alternative approaches in ERA scenarios and models to identify relevant risk drivers and ensure robustness and generic applicability of tools and ERA outcomes for high-level decisions;

4) adopt a system perspective thus optimising resource use, reducing risk, and ensuring data and knowledge reusability across regulatory and societal sectors.

WP6 will:

1) ensure the broad recognition of project results by supplying PollinERA with a strong visual identity and coherently tailored promotion;

2) set up a clear, relevant, and effective PEDR, including a communication strategy and an engagement strategy, the update of the same and the delivery of the corresponding activities and reporting thereof;

3) stimulate discussion on new policies or implementation of existing ones based on PollinERA results;

4) maximise the visibility, reputation, as well as project impact, with a clear pathway towards selected KERs.

WP7 will:

1) provide the methods, tools, and infrastructure to manage the project efficiently, facilitating internal communication and collaboration to ensure PollinERA reaches its objectives and fulfils its contractual commitments.

2) facilitate the coordination with an external advisory board used to help guide the project.