It's about time: moving away from statistical analysis of ecotoxicity data

Environmental risk assessment (ERA) of chemicals relies on single-species laboratory testing to establish the toxic properties of a compound. However, ERA is not concerned with toxicity under laboratory conditions: it needs to assess the impacts of the compound in the real world. Data-driven statistical analyses (e.g., hypothesis testing and interpolation) are the common approaches for analyzing toxicity data, but such approaches are the wrong tool for the job at hand. Environmental risk assessment does not need a statistical description of the effects in the toxicity test (at the end of the standardized test duration); it needs to extrapolate from the laboratory test to longer and time-varying exposure. Such extrapolation requires mechanistic process models, providing a simplified representation of the mechanisms underlying toxicity. Any useful model for the toxicity process should explicitly consider both dose (e.g., exposure concentration) and time. In the history of effects analysis for ERA, the factor of time does not get as much attention as the dose, hence the common use of the term "dose-response analysis." However, this is a historical oversight: time is a crucial factor for understanding toxicity and thereby essential for meaningful extrapolation from laboratory to field. Mechanistic models for ecotoxicity, considering both dose and time, have been around for quite some time and are classified as toxicokinetic-toxicodynamic (TKTD) models. Toxicokinetic-toxicodynamic models are starting to find their way into pesticide ERA in Europe next to the classical statistical approaches. In this opinion paper, I argue that it is about time to leave statistical analysis of toxicity data behind us. Statistics remains important for ERA effects assessment, but its role lies in the definition of appropriate "error models," explaining the deviations between model output and observations, which is essential for parameter estimation, uncertainty quantification, and error propagation. The "process model," essential for extrapolation, firmly belongs to TKTD modeling.