1 Dynamic energy budgets in individual based population modelsCross species test and application A. Gergs · H. Selck · M. Hammers-Wirtz · A. Palmqvist

2 Extrapolations in risk assement of chemicalsConstant vs. time variable exposure Extrapolation of population level effects from individual level toxicity tests Laboratory to field extrapolation … Mechanistic effect modelling

3 Individual-based population model (IBM)Individual organism Individual organism Individual organism life history traits behaviours food conditions toxic exposure

4 Conceptual illustration of the IBM approachnewborn Feeding Ageing growth juvenile development born juveniles brood size embryo development  yes no maximal age ? Adult? Birthing? Preuss et al. (2009) Ecological Modelling 220:

5 Comparability of IBMs Kulkarni et al. (2014) Chemosphere 112: 340–347Strauss et al. (2016) Ecological Modelling 321: 84-97

6 Dynamic energy budgets in IBMsAbundance [#] Time [d] Stage dependent mortality Figure: Martin et al (2013) American Naturalist 181:

7 Size dependent starvation resistanceDaphnia magna Notonecta maculata Fraction surviving [-] Fraction surviving [-] Time [d] Time [d] Assumption scaled mobilisation flux is changed in a way that somatic maintenance costs are always paid Scaled reserve density [-] Time [d] Gergs & Jager (2014) Journal of Animal Ecology 83: 758–768

8 DEB parametrization for Daphnia magnaSize dependent starvation Filtration rate Growth Reproduction Gergs et al. (2014) PlosOne 9: e91503

9 Emerging population dynamicsmean, range

10 Emerging population dynamicsdata model

11 Cross species transferabilityGrowth Reproduction Gergs et al. (in prep.)

12 Cross species test data model Food availabilityGergs et al. (in prep.)

13 Toxicokinetic-toxicodynamic effect modelsinternal concentration damage effect model Toxicokinetics Toxicodynamics scaled internal concentration x Physiological modes of action Assimilation Maintenance costs Cost for structure Cost for reproduction Hazard during oogenesis GUTS scheme modified from: Jager et al. (2011) ES&T 45, 2529–2540

14 Lethal effects Mortality Population dynamics Population size [#]Survival [-] Population size [#] Concentration [µg/L] Time [days] Time [days] Model prediction (minimum, mean, maximum) Effect data Range control data Exposure

15 Internal concentrationBioaccumulation Bioaccumulation Population dynamics Internal concentration [dpm/g] Population size [#] Concentration [µg/L] Time [h] Time [days] Model prediction size scaling Model prediction NO size scaling Effect data Range control data Exposure Gergs et al. (2016) Environmental Science and Technology 50, 6017−6024

16 Effect on reproductionSublethal effect Effect on reproduction Population dynamics 85µg/L Cummulative offspring [#] Population size [#] Time [days] Time [days] Model prediction (minimum, mean, maximum) Data Control condition Gergs et al. (in prep.)

17 Predatory aquatic insect10 m

18 Conclusion DEB models allow for the standardized development of IBMsThis facilitates the analysis of life history contributions to population dynamics across species and ecological systems When combined with process based effect models, the DEB integration with IBMs enable a straightforward propagation of population and community level effects from individual level toxicity testing

19 Thank you for your attentionModNanoTox funded by the European Union (project no ) Long-range Research Initiative (project no. ECO28)