THE USE OF MECHANISTIC POPULATION MODELS IN METAL RISK ASSESSMENT: COMBINED EFFECTS OF COPPER AND FOOD SOURCE ON LYMNAEA STAGNALIS POPULATIONS.

Environmental risk assessment (ERA) of chemicals aims to protect populations, communities and ecosystems. Population models are considered more frequent in ERA as they can bridge the gap between the individual and the population level. Lymnaea stagnalis (the great pond snail) is a particularly sensitive organism to various metals, including copper (Cu). In addition, the sensitivity of this species to Cu differs between food sources. The first goal of this study was to investigate if we could explain the variability in sensitivity between food sources (lettuce and fish flakes) at the individual level with a Dynamic Energy Budget (DEB) model. By adapting an existing DEB model and calibrating it with copper toxicity data, thereby combining information from three studies and two endpoints (growth and reproduction), we put forward inhibition of energy assimilation as the most plausible physiological mode of action (PMoA) of copper. Furthermore, the variation of copper sensitivity between both food sources was considerably lower at PMoA level than at individual level. Higher copper sensitivity at individual level under conditions of lower food quality or availability appears to emerge from first DEB principles when inhibition of assimilation is the PMoA. This supports the idea that DEB explained copper sensitivity variation between food source. Our second was goal was to investigate if this food source effect propagated to the population level. By incorporating DEB in an Individual-Based Model (IBM), population level effects were predicted. Based on our simulations, the food source effect was still present at the population level, albeit less prominently. Finally, we compared predicted population-level ECx values to individual-level ECx values for different studies. Using the DEB-IBM, the range of effect concentrations decreased significantly: at the individual-level, the difference in chronic EC10 values between studies was a factor 70 (1.13 to 78 µg dissolved Cu/L), whereas at the population level this was a factor 15 (2.9 to 44.6 µg dissolved Cu/L). To improve inter-study comparability, a bioavailability correction for differences in water chemistry was performed with a biotic ligand model. This further decreased the variation, down to a factor 7.4. Applying the population model in combination with a bio-availability correction thus significantly decreased the variability of chronic effect concentrations of Cu for L. stagnalis. Overall, the results of the present study illustrate the potential usefulness of transitioning to a more modelling-based environmental risk assessment. This article is protected by copyright. All rights reserved.