A Multi-Criteria Decision Analysis methodology for quantitatively scoring the relevance and reliability of ecotoxicological data

The advances in the field of ecotoxicology suggest exploring new ways of applying the current methods for the evaluation of toxicity as well as identifying ways to make more efficient use of the existing ecotoxicological datasets. In this scope and context, a new methodology is presented which intends to assist the optimization of existing methods, by providing a tool for assessing the relevance and reliability of ecotoxicological data for the definition of Species Sensitivity Distributions (SSDs), within the framework of ecological risk assessments (ERA). In order to estimate a single aggregated reliability score for a given ecotoxicological datum, a ‘Multi-Criteria Decision Analysis (MCDA)-based’ Weight of Evidence (WoE) methodology has been developed, including a hierarchical structure of 57 evaluation criteria, which was created based on the review of the state of the art frameworks for the assessment of ecotoxicological data. The methodology is based on the Multi-Attribute Value Theory (MAVT) and is able to integrate different types of inputs. It incorporates the use of Fuzzy Logic operators for handling the inherent uncertainty, which appears in the form of unreported information as well as possible lack of knowledge of the experts. A panel of scientific experts on ecotoxicology was involved throughout the development of the methodology for identifying, if any, the relations between criteria and evaluating the hierarchical structure to be used in the aggregation process. Expert Knowledge is incorporated in the methodology and was extracted from the panel with the use of direct techniques, i.e. questionnaires and dedicated meetings. The methodology is planned to be used and tested in case studies, as part of the evaluation and follow up process of the research project “AMORE” (Multi-Criteria Analysis for the Development of a Decision Support Tool for the prevention of Environmental Risks), funded by the National French Research Academy (ANR)