Cefic LRIs ECO19: the ChimERA project Frederik De Laender, Karel Viaene, Colin Janssen, Hans Baveco, Melissa Morselli, Marco Scacchi, Andreas Focks, Antonio Di Guardo, Paul Van den Brink Current practice of many ecological risk assessments Exposure 1 number Effects How it should be done Exposure fate toxicokinetics/ -dynamics population dynamics ecosystem dynamics Effects ChimERA is a network of models, integrating exposure and effect assessment Part 1/3: ChimERA fate Why should we care about ecosystems when calculating chemical exposure? Because biota exert a huge influence on chemical fate azinphos-methyl: slow macrophyte uptake lambda-cyhalothrin: rapid macrophyte uptake Morselli et al. in press. and because biomass is a dynamic thing Upstream Across river Upstream Downstream ChimERA fate predicts how biomass dynamics affects fate Conclusions part 2: Influence of biomass on fate well captured Need to validate for biomass dynamics Part 2/3: Toxicity and ecological effects Toxicokinetics & toxicodynamics predict onset and size of resulting individual-level toxic effects Example: G. pulex exposed to pyrene (Focks et al, in prep) How to move from effects on an individual to population-level effects? ? Properties of individuals Emerging properties Existing (off the shelf) individual-based models (IBMs) are used to translate to population-level effects in ChimERA: accounting for species interactions Competition Predation Why should we care about species interactions when calculating effects? Because species interactions can influence chemical effects on populations Predation reduces pyrene effects on Daphnia populations Control+Pyrene Viaene et al. Environ Toxicol Chem. 2015;34(8):17519. Predation Time (d) Competition is more important to rotifers than pyrene effects Viaene et al. In prep. This pattern is well captured by the population models Viaene et al. In prep. For Daphnia sp., pyrene effects more important than effects of competition Daphnia sp. abundance Competition absent/present Pyrene absent/present Daphnia sp. abundance Competition absent/present Pyrene absent/present This pattern is well captured by the population models, not the raw data Conclusions part 2: Species interactions x chemical stress = complicated There is no simple yes/no answer! Off the shelf models will rarely fit data perfectly Patterns are well-captured Part 3/3: a technically sound integration of all models A screen shot Chemical Chimera fate predicts the bioavailable concentrations Exposed Unexposed Population models predict the response of the species present (example: Daphnids) accounting for competition and predation (example: G. pulex competing with A. aquaticus) The output can be tracked from the ecosystem to the individual Ongoing work = scenario analyses How does the model respond when we change its parameters? A simplified ChimERA demonstrates the importance of the parameter setting EffectRecovery Different symbols and colours = different parameter settings (De Laender et al. Env. Int. 2015) Conclusions part 3/3: ChimERA is technically sound How to further validate its results? How to analyse its output? Which path towards ERA tool? Thank you