time is of the essence! tjalling jager dept. theoretical biology

Time is of the essence!

Tjalling Jager

Dept. Theoretical Biology

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!

Extrapolation

“Protection goal”

Laboratory tests

time is of the essence!

concentrations over time and

space

environmental characteristics and emission pattern

Fate modelling

mechanisticfate model

physico-chemical properties under laboratory conditions

Fate modelling

oil-spill modelling

pesticide fate modelling

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

EC50NOEC

summary statistics

proper measures of

toxicity

Learn from fate modelling


that do not depend on time or conditions

prediction effects in dynamic

environment

mechanisticmodel forspecies A

model parameters for

species

test conditions

Data analysis



model parameters that do not depend on time or conditions


toxicant

life-history information of the species

prediction life-history traits

over time


species


toxicant

Educated predictions


dynamic environment: exposure and

conditions

only for one species ... model parameters that do not depend on time or conditions

mechanisticmodel forspecies B

model parameters

for species A

model parameters for toxicant

Community effects

mechanisticmodel for

a community

simulate community effects and recovery over time


dynamic environment

model parameters

for species A


What individual model?


dynamic environment

model parameters

for species A


externalconcentration

(in time)

toxico-kineticmodel

toxico-kineticmodel

TKTD modelling

internalconcentration

in time

process modelfor the organism


effects onendpoints

in timetoxicokinetics

toxicodynamics


(in time)

toxico-kineticmodel

toxico-kineticmodel

TKTD modelling


in time

toxicokinetics

TKTD modelling


in time



effects onendpoints

in time

toxicodynamics

Organisms are complex …



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


in time


effects onendpoints

in time

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)

The “DEBtox” concept


(in time)

toxico-kinetics

toxico-kinetics internal

concentrationin time DEB

parametersin time

DEBmodel

DEBmodel

repro

growth

survival

feeding

hatching

…

The “DEBtox” concept


(in time)

toxico-kinetics

toxico-kinetics internal

concentrationin time DEB

parametersin time

DEBmodel

DEBmodel

Internal concentration are often not measured …

repro

growth

survival

feeding

hatching

…DEB parameter cannot be measured …

“Standard” tests ...


constant exposure, ad libitum food

Many DEBtox examples, e.g.: http://www.bio.vu.nl/thb/users/tjalling


species


toxicant

Dynamic exposure


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)


species


toxicant

Pulse exposure

0

1

2

3

4

0

1

2

3

4

0 5 10 15 200

1

2

3

4

0 5 10 15 200

1

2

3

4

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

0 5 10 15 200

10

20

30

40

50

60

70

0 5 10 15 200

10

20

30

40

50

60

700

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

0 5 10 15 200

0.2

0.4

0.6

0.8

1

0 5 10 15 200

0.2

0.4

0.6

0.8

1

Body length Cumulative offspring Fraction surviving

Hig

h f

oo

dL

ow

fo

od

mode of action: ‘assimilation’mode of action: ‘assimilation’

Insights• parameters independent of food• chemical effects fully reversible• reproduction rate slows down …


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?


Community level

What makes community different?– dynamic interactions between species– less or more sensitive to toxicants?


a community



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

Canonical community

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

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

consumer

predatordecomposer detritus

Using the DEB community

producernutrients light

previous project at VU-ThB (EU-MODELKEY)collaboration with SCK-CEN, Belgium (EU-STAR)

lightproducer


consumer

predatordecomposer

nutrients

detritus

previous collaborations, e.g., Univ. Antwerp (EU-NoMiracle, EU-OSIRIS)collaboration with UFZ, Leipzig (EU-CREAM)

detritus

consumer

lightproducer


decomposer

nutrients

collaboration with Eawag, Switzerland (EU-CREAM)collaboration with IRSN, France

predator

detritus

consumer

lightproducer


decomposer

nutrients

previous projects at VU-ThB

predator

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


a community

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 …


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

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

resources

waste products

growth

maintenance

maturation

offspring

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?

More information

on DEB: http://www.bio.vu.nl/thb

on my work: http://www.bio.vu.nl/thb/users/tjalling


Ex.1: maintenance costs

time

cum

ula

tive

off

spri

ng

time

bo

dy

len

gth

TPT

Jager et al. (2004)

Ex.2: growth costs

time

bo

dy

len

gth

time

cum

ula

tive

off

spri

ng Pentachlorobenzene

Alda Álvarez et al. (2006)

Ex.3: egg costs

time

cum

ula

tive

off

spri

ng

time

bo

dy

len

gth

Chlorpyrifos

Jager et al. (2007)