Cefic LRI s ECO19: the ChimERA project

Transcription

1 Cefic LRI s 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

2 Current practice of many ecological risk assessments Exposure Effects 1 number 1 number

3 How it should be done ChimERA Exposure fate Fate model Effects toxicokinetics/ Toxicity models -dynamics ecosystem Ecosystem dynamics model population Population dynamics models

4 ChimERA is a network of models, integrating exposure and effect assessment ChimERA Fate model Toxicity models Ecosystem model Population models

5 Part 1/3: ChimERA fate ChimERA Fate model Toxicity models Ecosystem model Population models

6 Why should we care about ecosystems when calculating chemical exposure?

7 Because biota exert a huge influence on chemical fate % applied dose Morselli et al. in press. azinphos-methyl: slow macrophyte uptake lambda-cyhalothrin: rapid macrophyte uptake Time (h) Pred Pred de Measure

8 Side view and because Box biomass 1 is a dynamic thing Conc. (ng L -1 ) Conc. (ng L -1 Conc. (ng L -1 ) Bio Mf Conc. (g (ng d.w. L -1 m ) -2 ) Mf (g d.w. m -2 ) (Emission) Box 1 (Emission) Box 3 Upstream Box 4 Box 5 Across Box 3 river Box 4 Box 5 Side view J F M A M J J A S O N D Month

9 Side view ChimERA fate predicts Box 1 how (Emission) biomass dynamics affects fate Conc. (ng L -1 ) Conc. (ng L -1 Conc. (ng L -1 ) Bio Mf Conc. (g (ng d.w. L -1 m ) -2 ) Mf (g d.w. m -2 ) Box 1 (Emission) Box 3 Upstream Side view Box 4 Box 5 Downstream Box 3 Box 4 Box 5 J F M A M J J A S O N D Month

10 Conclusions part 2: Influence of biomass on fate well captured Need to validate for biomass dynamics

11 Part 2/3: Toxicity and ecological effects ChimERA Fate model Toxicity models Ecosystem model Population models

12 Toxicokinetics & toxicodynamics predict onset and size of resulting individual-level toxic effects Example: G. pulex exposed to pyrene (Focks et al, in prep)

13 How to move from effects on an individual to population-level effects??

14 Existing ( off the shelf ) individual-based models (IBMs) are used to translate to population-level effects Properties of individuals Emerging properties

15 in ChimERA: accounting for species interactions Predation Competition

16 Why should we care about species interactions when calculating effects?

17 Because species interactions can influence chemical effects on populations

18 Predation reduces pyrene effects on Daphnia populations Control +Pyrene Predation Time (d) Viaene et al. Environ Toxicol Chem. 2015;34(8):

19 Competition is more important to rotifers than pyrene effects control competition pyrene comp + pyr Viaene et al. In prep.

20 This pattern is well captured by the population models control competition pyrene comp + pyr Viaene et al. In prep.

21 Daphnia sp. abundance For Daphnia sp., pyrene effects more important than effects of competition Competition absent/present Pyrene absent/present

22 Daphnia sp. abundance This pattern is well captured by the population models, not the raw data Competition absent/present Pyrene absent/present

23 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

24 Part 3/3: a technically sound integration of all models ChimERA Fate model Toxicity models Ecosystem model Population models

25 A screen shot

26 Chemical

27 Chimera fate predicts the bioavailable concentrations Pyrene (µg/l) Time (d)

28 Population models predict the response of the species present (example: Daphnids) Unexposed Exposed

29 accounting for competition and predation (example: G. pulex competing with A. aquaticus)

30 The output can be tracked from the ecosystem to the individual

31

32 Ongoing work = scenario analyses How does the model respond when we change its parameters?

33 A simplified ChimERA demonstrates the importance of the parameter setting Effect Recovery Different symbols and colours = different parameter settings (De Laender et al. Env. Int. 2015)

34 Conclusions part 3/3: ChimERA is technically sound How to further validate its results? How to analyse its output? Which path towards ERA tool?

35 Thank you