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Selecting - PPT Presentation

mechanistic effect models for environmental risk assessment Tjalling Jager SETAC Nantes April 2016 Contents Role of models in ERA Issues in selecting useful effects models Take home messages ID: 549183

rate models population intrinsic models rate intrinsic population ibms tktd aquatox model ecosystem individual test µg protocols goals rotection

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Slide1

Selecting mechanistic effect models for environmental risk assessment

Tjalling Jager

SETAC Nantes, April 2016Slide2

ContentsRole of models in ERAIssues in selecting useful effects models

Take home messages

exposure assessment

risk

effects assessmentSlide3

concentrations

,

time and spaceExposure assessment

mechanistic

fate modeltheoryenvironment

phys-chem propertiesrelease scenarioSlide4

Effects assessment

statistics

‘safe’

concentration

toxicity testarbitrary factorsStandardised:

exposure timetest conditionsspecies/endpointconstant exposureSlide5

Risk assessment?

mechanistic

fate

model

statistics &safety factorsSlide6

predicted

‘impacts’ over time (and

space)

mechanisticfate model

New paradigm for ERAmodel parametersmechanisticeffect model(s)environment

model parameterssee Jager (in press)dedicatedtestingrelease scenarioSlide7

Which effects model(s)?Huge range of models available …

(e.g., Galic

et al, 2010, Schmolke et al, 2010)Models differ in:level of organisation

complexitygenerality‘quality’ (e.g., GMP)underlying assumptions…Don’t look at models in isolation!Slide8

Models in their context

p

rotection goals

effect models

test protocols

defined by regulators,

too vague …

developed by modellers,

not tailored to ERA …

developed by experimenters,

not tailored to model needs …

options

constraintsSlide9

Daphnia magna and dichloroaniline

individual

population

ecosystem

TKTD modelse.g., IBMse.g., AQUATOXintrinsic rate

p

rotection goals

effect models

test protocols

options

constraintsSlide10

DEBtox (e.g., Jager & Zimmer, 2012)

individual

populationecosystem

TKTD models

e.g., IBMse.g., AQUATOXintrinsic rateSlide11

DEBtox, MoA: direct effect on reproNEC = 6.4 µg/L (5.6-7.0)

0

5

10

15

20

time (days)

0

20

40

60

80

100

120

140

Conc. 0 µg/L

Conc. 2.5

µg/L

Conc. 5

µg/L

Conc. 10

µg/L

Conc. 20

µg/L

Conc. 40

µg/L

cumulative offspring

individual

population

ecosystem

TKTD models

e.g., IBMs

e.g., AQUATOX

intrinsic rate

Data from

Klüttgen

&

Ratte

(1994)

p

rotection goals

test protocolsSlide12

p

rotection goals

Exponential growth under constant conditions

0

5

10

15

20

25

30

35

40

c

oncentration DCA (µg/L)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

intrinsic rate (d

-1

)

individual

population

ecosystem

TKTD models

e.g., IBMs

e.g., AQUATOX

intrinsic rate

food 100%

food 90%

food 80%

test protocolsSlide13

Martin et al

(2013), DEB combined with IBM

individual

population

ecosystemTKTD modelse.g., IBMse.g., AQUATOXintrinsic rateData from Sokull-Klüttgen (1998)Slide14

Population predictions (DEB-IBM)lab conditions, semi-batch feeding

Data from Preuss et al (2010)

time (d)

time (d)

abundancecontrol40 µg/Lindividual

populationecosystemTKTD modelse.g., IBMse.g., AQUATOXintrinsic rate

p

rotection goals

test protocolsSlide15

Semi-batch fed Daphnia in

isolation …

nutrients

individual

populationecosystemTKTD models

e.g., IBMse.g., AQUATOXintrinsic rate

p

rotection goals

test protocolsSlide16

Semi-batch fed Daphnia in isolation …Add

parasites, disease, migration, spatial aspects …

nutrients

individual

population

ecosystemTKTD modelse.g., IBMse.g., AQUATOXintrinsic rate

p

rotection goals

test protocolsSlide17

Summarising

individual

population

ecosystem

TKTD modelse.g., IBMse.g., AQUATOXintrinsic rate

ecological realism, specificity, complexity …Slide18

individual

population

ecosystem

TKTD models

e.g., IBMs

e.g., AQUATOX

intrinsic ratetoxicity testingSummarising

data needs, potential model integration …

ecological dataSlide19

individual

population

ecosystem

TKTD models

e.g., IBMs

e.g., AQUATOXintrinsic rate

toxicity testingtesting recoverySummarising

ecological data

landscape and mobility

options for recovery…

intrinsic rateSlide20

Take home 1Science-based ERA requires effect models

Preferably in all tiers (just like fate models)Set of standard models or sub-models e.g., Hommen

et al, 2015; Grimm & Berger, 2016Slide21

Take home 2Model selection cannot be viewed in isolation

Closely tied to protection goals and test protocolswhat exactly do we want to protect

?adjust test protocols to match model needsModel ‘quality’ and ‘realism’ is not all …link to protection goalwell-established principlestransparency…Slide22

Take home 3ERA needs more ambitious road map for the future

More structured dialogue between stakeholders …Ecotoxicology needs focus on theory and modelling

In science and education …Slide23

More information:on DEBtox/GUTS:

www.debtox.info

summercourse dynamic modelling of toxic effects, 9-17 August 2016 (DK) (register before 1 June

!)Relevant new project:

“Critical evaluation of effect models for risk assessment of plant protection products”(UBA, UFOPLAN 3715674080)