Predicting Combined Effects of Chemical Stressors: Population-Level Effects of Organic Chemical Mixtures with a Dynamic Energy Budget Individual-Based Model

Most regulatory ecological risk-assessment frameworks largely disregard discrepancies between the laboratory, where effects of single substances are assessed on individual organisms, and the real environment, where organisms live together in populations and are often exposed to multiple simultaneously occurring substances. We assessed the capability of individual-based models (IBMs) with a foundation in the dynamic energy budget (DEB) theory to predict combined effects of chemical mixtures on populations when they are calibrated on toxicity data of single substances at the individual level only. We calibrated a DEB-IBM for Daphnia magna for four compounds (pyrene, dicofol, alpha-hexachlorocyclohexane, and endosulfan), covering different physiological modes of action. We then performed a 17-week population experiment with D. magna (designed using the DEB-IBM), in which we tested mixture combinations of these chemicals at relevant concentrations, in a constant exposure phase (7-week exposure and recovery), followed by a pulsed exposure phase (3-day pulse exposure and recovery). The DEB-IBM was validated by comparing blind predictions of mixture toxicity effects with the population data. The DEB-IBM accurately predicted mixture toxicity effects on population abundance in both phases when assuming independent action at the effect mechanism level. The population recovery after the constant exposure was well predicted, but recovery after the pulse was not. The latter could be related to insufficient consideration of stochasticity in experimental design, model implementation, or both. Importantly, the mechanistic DEB-IBM performed better than conventional statistical mixture assessment methods. We conclude that the DEB-IBM, calibrated using only single-substance individual-level toxicity data, produces accurate predictions of population-level mixture effects and can therefore provide meaningful contributions to ecological risk assessment of environmentally realistic mixture exposure scenarios. Environ Toxicol Chem 2022;00:1-19. (c) 2022 SETAC