Institut für Umweltforschung
Institute for Environ. Research
Prediction of effects from FOCUS-scenarios to populations of D. magna – Comparison of measured data and modelling results for triphenyltin – T. G. Preuss1, S. Rhiem1, E. Bruns2, D. Schäfer2, G. Görlitz2, H. T. Ratte1 1
Institute for Environmental Research, RWTH Aachen University, Germany 2 Bayer Cropscience, Monheim, Germany
[email protected]
1
PEvEP- project
Institut für Umweltforschung
Prediction of effects from variable exposure scenarios to plankton communities
FOCUS/Freiland Environment/FOCUS Zeit Time
Concentration Konzentration
Concentration
Laboratory
Konzentration Concentration
Mesokosmos Mesocosm
Zeit Time
Time
Konzentration Concentration
Mesokosmos
Zeit
Static Poster Weber et al. WE 245-247
Time
Dynamic 2
Approach - Daphnia
Institut für Umweltforschung
Individual level Concentration
Concentration
Effects models
Time
Time
IDamP (individual based population model)
Concentraton
Concentration
Concentration
Population level
Time
Time
Time
3
Effect models
Institut für Umweltforschung
Immediate Response (IR)
100 Neonates 48 h Adults 48h
Mortality [%]
80
60
40
20
0 1
10
S (t ) = 1 − f _ TWA * ∫ CW (t ) dt 0
Cw [µg/l]
60 50 40 30 20 10 0
Ashauer et al. 2006
0 -10
20
40 Time [h]
60
80
0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 0
60 µg/l 100 Hazard
70
f_TWA* Inbtegral CW (t)
Time weighted average (TWA) t
100
Concentration [µg/l]
80 60 40 20 0
20
40 Time [h]
60
80
0
20
40 Time [h]
60
80
Damage Assessment Model (mt-DAM)
Mixed from Lee et al. 2002 & Ashauer et al. 2007 4
IDamP Institut für Umweltforschung
– Prediction of population dynamics from individuals –
Individual based Daphnia magna population model Forcing functions (dynamic): • Food • Density • Temperature • Toxicant
newborn
Feeding Ageing
embryo development brood size
yes
maximal age ? no growth
no juvenile development
no yes
Adult?
born juveniles yes
Birthing?
Calibrated on life-history data for individual daphnids Preuss et al. 2009, Ecol. Model. 220, 310-329
5
Pattern oriented modelling
Institut für Umweltforschung
Population density
Feeding
10000
Algae concentration [cells/ml]
100
Population size
80 60 40 20
8000
6000
4000
2000
0 0
20
40
60
80
100 0
Time [d]
0
20
40
60
80
100
Time [d]
Population structure 50 1.4 mm < 2.2 mm
> 2.2 mm
Population size
40 30 20 10 0 0
20
40
60
80
0
20
40
60
Data: Dülmer (1998), Phd-Thesis, RWTH Aachen University
80
0
20
40
60
80
6
Impact of food conditions Starvation
100
100
80
80
60 40
60 40
20
20
0
0 0
20
40
60
80
100
Semi-static (food level & startpopulation) -1
600
-1
-1
-1
Neonates 0.5 mgC pop d
Neonates 1.3 mgC pop d
Adults & Neonates 0.5 mgC pop-1 d-1
Adults & Neonates 1.3 mgC pop-1 d-1
500 0
10
Time [d]
20
30
40
Time [d]
1000 Food conditions
Population size
Population size
Population size
Flow-through
Institut für Umweltforschung
400 300 200
0
100
600 500 Population size
Abundance Measured
100
10
400 300 200 100 0 0
1 1
10
100
Abundance Predicted
1000
10
20 Time [d]
30
40
0
10
20
30
40
Time [d]
7
Impact of temperature
Institut für Umweltforschung
300
4°C
10°C
15°C
20°C
Population size
250 200 150 100 50 0 300
Population size
250
1000
150 100 50
100
0 300
0
25°C
10
10
20
30
40
Time [d]
250 Population size
Abundance Measured
Food conditions Temperature
200
200 150 100 50
1 1
10
100
1000
0 0
Abundance Predicted
10
20 Time [d]
30
40
8
Impact of 3,4-Dichloroaniline
Institut für Umweltforschung
Flow-through 100
Control
Semi-Static
5 µg/l
300
60
Population size
Abundance
250
40 20 0 100
20 µg/l
40 µg/l
80 Abundance
2.5 µg/l
Control
80
200 150 100 50
60
0 300
40
10 µg/l
5 µg/l
20
250 0 20
40
60
80
100
0
20
40
Time [d]
60
80
100
Population size
0
Time [d]
1000
150 100 50 0
100
300
20 µg/l
40 µg/l
250 Population size
Abundance Measured
Food conditions Temperature 3,4-DCA
200
10
200 150 100 50 0 0
1 1
10
100
Abundance Predicted
1000
10
20 Time [d]
30
40
0
10
20
30
40
Time [d]
9
Impact of Nonylphenol
Institut für Umweltforschung
400
Control
Ethanol
39 µg/l
65 µg/l
85 µg/l
113 µg/l
Population size
300
200
100
0 400
Population size
300
Food conditions Temperature 3,4-DCA NP
200
100
100 0 400
300 Population size
Abundance Measured
1000
10
200
100
1 1
10
100
1000
0 0
Abundance Predicted
10
20 Time [d]
30
40
0
10
20 Time [d]
30
40
10
Modelling results for a pesticide Concentration [µg/l]
Conc-Resp Curve
250
Institut für Umweltforschung
DAM
TWA
50
50 140
30
100
20
50
Abundance
150
120
40
100 30 80 60
Immediate response can be used only for simple exposure scenarios. The TWA-approach does not reveal a better prediction as the immediate response. The mt-DAM-Model predicts the population dynamics for complex exposure scenarios quite well. Where it does not, it is protective.
20
40
10
Concentration [µg/l]
40 Concentration [µg/l]
Abundance
200
10
20
0
0
0 250
10
20
30
40
50
0
10
20
30
40
0
50
0
10
20
30
40
50
0
10
20
30
40
0
50
50
50
140
100
20
50
Abundance
30
150
120
40
100
30
80 60
20
40
10
Concentration [µg/l]
40
Concentration [µg/l]
Abundance
200
10
20
0
0
0 200
20
30
40
50
0
10
20
30
40
0
50
0
10
20
30
40
50
0
10
20
30
40
0
50
50
50
140
40
120
30
100 80
20
60
120
Abundance
140
Concentration [µg/l]
160
30
80 60
20
40
10
40
40
100
Concentration [µg/l]
180
Abundance
10
10 20
20 0
0 0
10
20
30
Time [days]
40
50
0
10
20
30
40
50
0
0 0
Time [days]
Preuss et al., SETAC Europe 2008; Poster Hommen & Preuss WE 237
10
20
30
Time [days]
40
50
0
10
20
30
40
50
Time [days]
11
Fentin - Toxicodynamics
Institut für Umweltforschung
Neonates Survival [%]/Conc. [µg/l]
100 80 60 40 20 0 0
48
96
144
192
240
288 0
48
96
Time [h]
144
192
240
288 0
48
96
Time [h]
144
192
240
288 0
48
96
144
192
240
288
192
240
288
Time [h]
Time [h]
Adult Survival [%]/Conc. [µg/l]
100 80 60 40 20 0 0
48
96
144 Time [h]
192
240
288 0
48
96
144
192
240
288 0
Time [h]
48
96
144 Time [h]
192
240
288 0
48
96
144 Time [h]
Strong delayed effects in adults 12
Institut für Umweltforschung
200
50
150
40 30
100
20
50
10
0
0 0
10
20
30
Time [days]
40
50 0
10
20
30
40
Concentration [µg/l]
Abundance
Fentin – Effects on population level
50
Time [days]
LC50 (Adults) leads to extinction of populations Delayed effects were observed
Worst-case test item for predicting the effects on population level 13
Can one of the effect models explain and predict the delayed effects?
Institut für Umweltforschung
Immediate Response (IR)
100 Neonates 48 h Adults 48h
Mortality [%]
80
Exposure concentration
60
40
20
Calibrated on toxicodynamics only
0 1
10
100
Concentration [µg/l]
Cw [µg/l]
S (t ) = 1 − f _ TWA * ∫ CW (t ) dt
60 50 40 30 20
0
10 0
Ashauer et al. 2006
0 -10
20
40
60
80
Time [h]
0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 0
60 µg/l 100 Hazard
70
t
f_TWA* Inbtegral CW (t)
Time weighted average (TWA) 80 60 40 20 0 20
40 Time [h]
60
80
0
20
40 Time [h]
60
80
Damage Assessment Model (mt-DAM) Calibrated on toxicodynamics & toxicokinetics data Mixed model from Lee et al. 2002 & Ashauer et al. 2006
Internal concentration
14
Fentin - Toxicokinetic
Institut für Umweltforschung
Elimination Phase
internal concentration [dpm/mg]
Uptake Phase 1600
Neonate Adult
1400 1200 1000 800 600 400 200 0 0
24
48 Time [h]
72
0
24
48
72
Time [h]
Toxikokinetics differs between life-stages Nearly no excretion for adults May explain the delayed effects in adults but not in neonates 15
Fentin - Size dependent toxicokinetic 3,0
ke = -0.0046 x wet wt1/2 x 0.0168
r ² = 0.808
r ² = 0.715 log (Bioconcentration after 48 h)
0,025
0,020
Elimination constant (ke)
Institut für Umweltforschung
0,015
0,010
0,005
2,5
2,0
1,5
1,0
Wurzel Biomasse vs ke Plot 1 Regr Plot 1 Conf1
0,5
log (BC) = -0.321 x log(wet wt) + 2.129 0,000 0,0
0,5
1,0
1,5
2,0
2,5
3,0
Sqrt (wet wt [mg])
First order kinetics:
4,0
0,0 -1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
log (wet wt [mg])
ku
CW
3,5
CB
BC48 h ku = ke 1× 1 − e − ke ×48
ke 16
Effect models - Results
Institut für Umweltforschung
Neonate Survival [%]
200
80
150
60 40
100
20
50 0
0 0
24
48 0
24
Time [h]
48 0
24
Time [h]
48 0
24
Time [h]
48
0
24
Time [h]
48
Time [h]
h vs CW h vs CW
40
h vs S h vs Col 6
80
Col 7 vs Col Survival 7 vs Survival measured measured
60
30
40
20
20
10
Concentration [µg/l]
50
100 Survival [%]
Concentration [µg/l]
250
100
0
0
0
48
96
0
144
48
96
144
0
48
Time [h]
Time [h]
96
144
Time [h]
The mt-DAM is able to describe effects from constant and variable exposure This holds true in neonates and adults The TWA-model failed to predict effects from variable exposure
Adult Survival [%]
200
80 60
150
40
100
20
50
0
Concentration [µg/l]
250
100
0 0
24
48
0
24
Time [h]
48 0
24
48 0
24
24
Time [h]
Time [h]
Time [h]
48 0
48 0
24
Time [h]
48
Time [h]
Survival [%]
100 200
80
150
60 40
100
20
50
0
Concentration [µg/l]
250
0 0
48
96 144 192 240 288 0 Time [h]
48
96 144 192 240 288 0 Time [h]
48
96 144 192 240 288 0 Time [h]
48
96 144 192 240 288 0 Time [h]
48
96 144 192 240 288 Time [h]
17
Prediction on population level - Single peak exposure DAM
measured
TWA
50
250
50
200
40
200
40
150
30
150
30
100
20
100
20
50
10
50
10
0
0
0 0
10
20 Time [days]
30
40
Abundance
250
Concentration [µg/l]
IR
Concentration [µg/l]
Abundance
Concentration [µg/l]
Institut für Umweltforschung
0 0
10
20
30
40
Time [days]
The mt-DAM coupled to IDamP is able to describe the effects and the recovery after single peak exposure Extinction of the populations is shown by the mt-DAM coupled to the IDamP The TWA and IR model failed to predict the extinction of the populations 18
Prediction on population level - Multi peak exposure DAM
measured
TWA
250
25
200
20
150
15
100
10
50
5
0 250
0 25
200
20
150
15
100
10
50
5
0
Concentration [µg/l]
IR
Concentration [µg/l]
Abundance
Abundance
Concentration [µg/l]
Institut für Umweltforschung
0 0
10
20
30
40
Time [days]
50
60
70 0
10
20
30
40
50
60
70
Time [days]
The mt-DAM coupled to IDamP is able to describe the effects and the recovery after multi peak exposure The TWA and IR model failed to predict the effects of the populations 19
Prediction on population level - Upscaled FOCUS-Scenarios DAM
FOCUS scenario scaled up by a factor of 250
250
pond 1
Abundance
measured
TWA
25
pond 2
pond 3
200
20
150
15
100
10
50
5
0 20
40
60
80 0
20
Time [days]
Abundance
250
40
60
80
100 0
20
40
60
80
0 100
Time [days]
Time [days] 25
stream1
stream 2
200
20
150
15
100
10
50
5
0
Concentration [µg/l]
0
Concentration [µg/l]
IR
Concentration [µg/l]
Institut für Umweltforschung
FOCUS scenario scaled up by factor of 10 and 15
0 0
20
40 Time [days]
60
80 0
20
40
60
80
Time [days]
The mt-DAM coupled to IDamP is able to describe the effects and the recovery after FOCUS stream and pond scenarios The TWA and IR model failed to predict the effects and extinction of the populations
20
Conclusion
Institut für Umweltforschung
Delayed effects in D. magna were induced by fentin on individual and population level fentin is one worst-case test substance for the prediction of effects from time-variable exposure The delayed effects can be explained by the toxicokinetics of fentin in daphnids Due to the delayed effects the IR and TWA model failed to desribe and predict the effects from time-variable exposure on individual level and population level respectively. Only the mt-DAM, taking the toxicokinetics into account, was able to describe and predict the effects from time-variable exposure on individual and population level respectively For assessing the risk of time-variable exposure, like the FOCUSscenarios, toxicodynamics and toxicokinetics have to be taken into account for some substance/species combinations The DAM coupled with individual based population models seems to be a valuable tool to predict the effects from timevariable exposure on population level 21
Damage assessment models DAM
t-DAM
Lee et al. 2002
Ashauer et al. 2007
Institut für Umweltforschung
mt-DAM
Calculate internal concentration
dC B = kin × CW − kout × C B dt
dC B = kin × CW − kout × C B dt
dC B = kin × CW − kout × C B dt
Calculate damage
dD = kk × CB − kr × D dt
dD = kk × CB − kr × D dt
dD = kk × CB − kr × D dt
Calculate hazard H = k3 × D
dH = max[D − tresh,0] dt
H = max[D − tresh,0]
Calculate survival S = e− H
S = e− H
S = e− H 22
