Statistical methodologies and tools applicable to ecotoxicology have developed and improved over recent decades. Nevertheless, regulatory risk assessments are still largely based on statistical principles and approaches that are no longer state-of-the-art. SETAC meeting sessions dedicated to statistical methods have witnessed a high level of interest and discussion regarding the progress and applications of statistical methodologies. Many ecotoxicologists have expressed interest in the revision of the 2006 OECD document no. 54 ("Current approaches in the statistical analysis of ecotoxicity data: a guidance to application"). This guidance is no longer considered reflective of contemporary statistical methods or computational platforms used in ecotoxicology, and its revision process is ongoing. Against this background, a call for papers for a Special Series in Integrated Environmental Assessment and Management (IEAM) was launched in autumn 2024 with manuscript submissions until summer 2025. The purpose of this Special Series was to provide an overview of the statistical ecotoxicology landscape and reflect on recent developments, processes and opinions. Relevant topics include (but are not limited to): similarities and differences of toxicity metrics (NOEC, BMD, NEC, ECX, NSEC, and more); developments and tools for dose-response modelling; developments and tools in SSD modelling (model averaging, mixture models); Bayesian vs. frequentist approaches; and case studies of novel statistical methodologies highlighting their benefits. We welcomed and encouraged contributions and viewpoints from members of all sectors, including academia, industry and government. By the time of writing (November 2025), five papers have been accepted for the series, with several more manuscripts under evaluation. In this poster we give an overview of the final series of papers, focusing on the variety of approaches, scientific novelties, and implications for regulatory ecotoxicology and risk assessment.
Wolf et al. (Thu,) studied this question.
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