Environmental risk assessment of plant protection products increasingly requires methods that incorporate the complexity of agricultural landscapes and the roles of non-target arthropods in ecosystem service provision. However, existing approaches often lack spatial specificity and fail to support the development of targeted and ecologically meaningful protection goals. This study presents a proof-of-concept method designed to assess the relevance of non-target arthropods families across agricultural habitat scenarios, serving as a step toward more spatially explicit and biodiversity-sensitive environmental risk assessment frameworks. Using high-resolution landscape data from seven European countries, we randomly generated 29,500 agricultural landscapes across nine major crops and grouped them into four habitat scenarios categories based on crop diversity and the proportion of natural habitat. We used a bibliographic dataset covering 30 representative non-target arthropods families as a proxy to estimate family-level relevance scores for each habitat scenario, based on the proportion of land use types and literature-derived occurrence data. The results from this first application are exploratory and serve to evaluate the method's operational structure and scalability. They are not intended as definitive ecological patterns but rather illustrate how the proposed habitat scenario framework can be applied to generate relevance estimates. Future work should focus on validating outputs with empirical biodiversity data, integrating functional traits such as mobility and pesticide sensitivity, and expand habitat representation. Beyond its conceptual value, the framework provides a practical tool to identify relevant arthropod families within specific habitat scenarios, supporting targeted mitigation and integrated pest management strategies. It thus links functional biodiversity with regulatory decision-making to define measurable protection goals and align environmental risk assessment with the ecosystem services framework.
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