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Time is of the essence! Tjalling Jager Dept. Theoretical Biology.

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1 Time is of the essence! Tjalling Jager Dept. Theoretical Biology

2 Challenges of ecotox  Some 100,000 man-made chemicals  For animals alone, >1 million species described  Complex dynamic exposure situations  Species interact dynamically in ecosystems We cannot (and should not) test all permutations!

3 Extrapolation “Protection goal” Laboratory tests time is of the essence!

4 concentrations over time and space environmental characteristics and emission pattern Fate modelling mechanistic fate model physico-chemical properties under laboratory conditions

5 Fate modelling oil-spill modelling pesticide fate modelling

6 prediction effects in dynamic environment Classic ecotox effects data over time for one (or few) set(s) of conditions Description for: one endpoint one timepoint constant exposure one set of conditions Description for: one endpoint one timepoint constant exposure one set of conditions EC50 NOEC summary statistics

7 proper measures of toxicity Learn from fate modelling effects data over time for one (or few) set(s) of conditions that do not depend on time or conditions prediction effects in dynamic environment mechanistic model for species A

8 model parameters for species test conditions Data analysis mechanistic model for species A effects data over time for one (or few) set(s) of conditions model parameters that do not depend on time or conditions model parameters for toxicant life-history information of the species

9 prediction life- history traits over time model parameters for species model parameters for toxicant Educated predictions mechanistic model for species A dynamic environment: exposure and conditions only for one species... model parameters that do not depend on time or conditions

10 mechanistic model for species B model parameters for species A model parameters for toxicant Community effects mechanistic model for a community simulate community effects and recovery over time mechanistic model for species A dynamic environment model parameters for species A model parameters for toxicant

11 What individual model? mechanistic model for species A dynamic environment model parameters for species A model parameters for toxicant

12 external concentration (in time) toxico-kinetic model toxico-kinetic model TKTD modelling internal concentration in time process model for the organism process model for the organism effects on endpoints in time toxicokinetics toxicodynamics

13 external concentration (in time) toxico-kinetic model toxico-kinetic model TKTD modelling internal concentration in time toxicokinetics

14 TKTD modelling internal concentration in time process model for the organism process model for the organism effects on endpoints in time toxicodynamics

15 Organisms are complex … process model for the organism process model for the organism

16 Learn from fate modellers Make an idealisation of the system  how much biological detail do we minimally need … –to explain how an organism grows, develops and reproduces –to explain effects of stressors on life history –to predict effects for untested situations –without being species- or stressor-specific

17 Dynamic Energy Budget Organisms obey mass and energy conservation –find the simplest set of rules... –over the entire life cycle... –for all organisms (related species follow related rules) –most appropriate DEB model depends on species and question resources waste products growth maintenance maturation offspring Kooijman (2010)

18 The “DEBtox” concept external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time DEB parameters in time DEB model DEB model repro growth survival feeding hatching …

19 The “DEBtox” concept external concentration (in time) toxico- kinetics toxico- kinetics internal concentration in time DEB parameters in time DEB model DEB model Internal concentration are often not measured … repro growth survival feeding hatching … DEB parameter cannot be measured …

20 “Standard” tests... mechanistic model for species A constant exposure, ad libitum food Many DEBtox examples, e.g.: model parameters for species model parameters for toxicant

21 Dynamic exposure mechanistic model for species A dynamic exposure pattern, different food levels... Daphnia magna and fenvalerate –modified 21-day reproduction test –pulse exposure for 24 hours –two (more or less) constant food levels Pieters et al (2006) model parameters for species model parameters for toxicant

22 Pulse exposure Body length Cumulative offspring Fraction surviving High food Low food mode of action: ‘assimilation’ Insights parameters independent of food chemical effects fully reversible reproduction rate slows down …

23 mechanistic model for species B Work needed For the individual level –select relevant species and appropriate DEB models –adapt/develop model code, allow time-variable inputs –collect and analyse relevant existing test data Evaluate –are DEB models useful? –what are limitations? –what are major gaps in knowledge? –what test protocol is most useful? mechanistic model for species A

24 Community level What makes community different? –dynamic interactions between species –less or more sensitive to toxicants? mechanistic model for a community mechanistic model for species A mechanistic model for species B

25 Community level Food web models can become rather complex … –results depend heavily on modelling choices –difficult to parameterise –focus on furry animals … –little general insight gained –not useful for generic RA

26 Canonical community Start simple: –each species a simple DEB model –closed system (open for energy) –include nutrient recycling

27 Canonical community producer consumer nutrients decomposer detritus light predator Start simple: –each species a simple DEB model –closed system (open for energy) –include nutrient recycling

28 consumer predator decomposer detritus Using the DEB community producer nutrients light previous project at VU-ThB (EU-MODELKEY) collaboration with SCK-CEN, Belgium (EU-STAR)

29 light producer Using the DEB community consumer predator decomposer nutrients detritus previous collaborations, e.g., Univ. Antwerp (EU-NoMiracle, EU-OSIRIS) collaboration with UFZ, Leipzig (EU-CREAM)

30 detritus consumer light producer Using the DEB community decomposer nutrients collaboration with Eawag, Switzerland (EU-CREAM) collaboration with IRSN, France predator

31 detritus consumer light producer Using the DEB community decomposer nutrients previous projects at VU-ThB predator

32 Work needed For the community level –specify interactions between the species –code a community with DEB populations –simulations for various scenarios Evaluate –what’s different at the community level? –more or less effect? –correspondence to e.g., mesocosm? –identify major gaps in knowledge mechanistic model for a community

33 Wrapping up Time is of the essence! –an organism is a dynamic system … –that interacts dynamically with others … –in a dynamic environment … –with dynamic exposure to chemicals NOEC, EC50 etc. are useless … time is of the essence!

34 Wrapping up Mechanistic models essential for the individual –to extract time-independent parameters from data –to extrapolate to untested dynamic conditions –to increase efficiency of risk assessment –learn from fate and toxicokinetics modellers … Integrate models into a simple community –study how interactions affect toxicant responses –study recovery of the community

35 Wrapping up Advantages of using DEB as basis –well-tested theory for individuals –mechanistic, dynamic, yet (relatively) simple –deals with the entire life cycle –not species- or chemical-specific –small but well-connected international DEB community

36 Wrapping up This project tries to deliver “proof of concept” –can DEB serve as a general platform? –can simple mechanistic community models help RA? –how can we modify test protocols? –where are the major stumbling blocks?

37 More information on DEB: on my work: time is of the essence!

38 Ex.1: maintenance costs time cumulative offspring time body length TPT Jager et al. (2004)

39 Ex.2: growth costs time body length time cumulative offspring Pentachlorobenzene Alda Álvarez et al. (2006)

40 Ex.3: egg costs time cumulative offspring time body length Chlorpyrifos Jager et al. (2007)


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