On the quantification of intertest variability in ecotoxicity data with application to species sensitivity distributions

Ecotoxicological hazard assessment relies on species effect data to estimate quantities such as the predicted no-effect concentration. While there is a concerted effort to quantify uncertainty in risk assessments, the uncertainty due to intertest variability in species effect measurements is an overlooked component. The European Union Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) guidance document suggests that multiple toxicity records for a given chemical-species combination should be aggregated by the geometric mean. Ignoring this issue or applying unjustified so-called harmonization methods weakens the defensibility of uncertainty quantification and interpretation about properties of ecological models, for example, the predicted no-effect concentration. In the present study, the authors propose a simple and broadly theoretically justifiable model to quantify intertest variability and analyze it using Bayesian methods. The value of data in ecotoxicity databases is maximized by using (interval-)censored data. An exploratory analysis is provided to support the model. The authors conclude, based on a large ecotoxicity database of acute effects to aquatic species, that the standard deviation of intertest variability is approximately a factor (or fold-difference) of 3. The consequences for decision makers of (not) adjusting for intertest variability are demonstrated.