Unexpected recovery and non-effects predicted with a mixture toxicity implementation in a population model

Current regulations in risk assessment are very substance-based and only regard effects observed in lab-standardized tests on individual organisms. However, in the environment organisms are exposed to mixtures of chemicals that vary in concentration and composition. In addition, effects on individuals will have an effect on populations and higher levels of organisation in the ecosystem. Mechanistic, individual-based models have been proposed to tackle this issue as they can integrate effects observed at the individual level to make an extrapolation of effects at the population level. In addition, mechanistic models can be used to describe the effects of mixtures. Mixture toxicity implementations of the GUTS and DEBtox theory are applied here: damage addition and independent action. Integrating these models in an individual-based implementation, a model is obtained that describes effects of mixtures on Daphnia magna populations. Two population experiment were conducted exposing Daphnia magna mixtures of different compounds (Cu, Zn, alfa-HCH, dicofol and pyrene). These compounds have been selected for their suspected toxicological mode of action. The populations were exposed for 2 months to these compounds, their binary mixtures, and a ternary mixture. The population density over time was recorded bi-weekly in all of the treatments. We calibrated an individual-based model for D. magna based on the individual-level effects of the different substances. We validated the implementation at the population level with the data from the population experiment. We evaluated the different mixture toxicity implementations (i.e. damage addition vs independent action). For Cu, the model was able to predict (unexpectedly) recovery of effects over time. The independent action approach was able to predict the effects for the Cu and Zn mixtures. Overall, we highlight the applicability of mechanistic population models for predicting mixture toxicity effects at the population, based on effects observed on the individual level with individual substances.

Jaar van publicatie:2020

Toegankelijkheid:Open