Person: Duquesne, Sabine
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Veröffentlichung Mechanistic effect modeling of earthworms in the context of pesticide risk assessment: synthesis of the FORESEE workshop(2020) Forbes, Valery E.; Agatz, Annika; Ashauer, Roman; Duquesne, Sabine; Pieper, SilviaEarthworms are important ecosystem engineers, and assessment of the risk of plant protection products toward them is part of the European environmental risk assessment (ERA). In the current ERA scheme, exposure and effects are represented simplistically and are not well integrated, resulting in uncertainty when the results are applied to ecosystems. Modeling offers a powerful tool to integrate the effects observed in lower tier laboratory studies with the environmental conditions under which exposure is expected in the field. This paper provides a summary of the (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop held 28â€Ì30 January 2020 in Düsseldorf, Germany. This workshop focused on toxicokineticâ€Ìtoxicodynamic (TKTD) and population modeling of earthworms in the context of ERA. The goal was to bring together scientists from different stakeholder groups to discuss the current state of soil invertebrate modeling and to explore how earthworm modeling could be applied to risk assessments, in particular how the different model outputs can be used in the tiered ERA approach. In support of these goals, the workshop aimed at addressing the requirements and concerns of the different stakeholder groups to support further model development. The modeling approach included 4 submodules to cover the most relevant processes for earthworm risk assessment: environment, behavior (feeding, vertical movement), TKTD, and population. Four workgroups examined different aspects of the model with relevance for risk assessment, earthworm ecology, uptake routes, and crossâ€Ìspecies extrapolation and model testing. Here, we present the perspectives of each workgroup and highlight how the collaborative effort of participants from multidisciplinary backgrounds helped to establish common ground. In addition, we provide a list of recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products. © 2020 SETACVeröffentlichung A critical examination of the protection level for primary producers in the first tier of the aquatic risk assessment for plant protection products(2023) Brendel, Stephan; Duquesne, Sabine; Hönemann, Linda; Konschak, Marco; Pieper, Silvia; Solé, Magali; Wogram, JörnBackground The aim of environmental risk assessment (ERA) for pesticides is to protect ecosystems by ensuring that specific protection goals (SPGs) are met. The ERA follows a prospective tiered approach, starting with the most conservative and simple step in risk assessment (RA) (so-called tier 1) using the lowest available appropriate endpoint derived from ecotoxicological tests. In 2015, for the tier 1 RA of aquatic primary producers, the recommendation was changed from using the lowest of the 50% inhibition (EC50) values based on biomass (area under the curve-EbC50), increase in biomass (yield- EyC50) or growth rate (ErC50) to only using the growth rate inhibition endpoint (ErC50) because it is independent of the test design and thus more robust. This study examines the implications of this such on the level of conservatism provided by the tier 1 RA and evaluates whether it ensures a suitable minimum protection level. Results Our analysis shows that replacing the lowest endpoint with the growth rate inhibition endpoint while maintaining the assessment factor (AF) of 10 significantly reduces the conservatism in the tier 1 RA. Comparing protection levels achieved with different endpoints reveals that the current assessment is less protective. To maintain the previous level of protection, and since the protection goals have not changed, we recommend to multiply the default AF of 10 by an extra factor of minimum 2.4 in the tier 1 RA based on ErC50. Independently of the endpoint selected in tier 1 RA, several issues in the general RA of pesticides contribute to uncertainties when assessing the protection levels, e.g., lack of appropriate comparison of the higher tier experimental studies (i.e., best achievable approximation of field situation, so-called surrogate reference tier) with field conditions or the regulatory framework's failure to consider realistic conditions in agricultural landscapes with multiple stressors and pesticide mixtures. Conclusions We advise to consider adjusting the risk assessment in order to reach at least the previous protection level for aquatic primary producers. Indeed continuing using an endpoint with a higher value and without adjustment of the assessment factor is likely to jeopardize the need of halting biodiversity loss in surface waters. © The Author(s) 2023Veröffentlichung Better define beta-optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies(2020) Alalouni, Urwa; Duquesne, Sabine; Egerer, Sina Elisabeth; Frische, Tobias; Gergs, René; Gräff, Thomas; Sahm, René; Pieper, Silvia; Wogram, JörnThe minimum detectable difference (MDD) is a measure of the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. It is a measure at a defined level of probability and a given variability of the data. It provides an indication for the robustness of statistically derived effect thresholds such as the lowest observed effect concentration (LOEC) and the no observed effect concentration (NOEC) when interpreting treatment-related effects on a population exposed to chemicals in semi-field studies (e.g., micro-/mesocosm studies) or field studies. MDD has been proposed in the guidance on tiered risk assessment for plant protection products in edge of field surface waters (EFSA Journal 11(7):3290, 2013), in order to better estimate the robustness of endpoints from such studies for taking regulatory decisions. However, the MDD calculation method as suggested in this framework does not clearly specify the power which is represented by the beta-value (i.e., the level of probability of type II error). This has implications for the interpretation of experimental results, i.e., the derivation of robust effect values and their use in risk assessment of PPPs. In this paper, different methods of MDD calculations are investigated, with an emphasis on their pre-defined levels of type II error-probability. Furthermore, a modification is suggested for an optimal use of the MDD, which ensures a high degree of certainty for decision-makers. © 2020 Springer Nature Switzerland AG