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1/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Parameter Identificationin Partial Integro-Differential Equations
for Physiologically Structured Populations
Sylvia Moenickes, Otto Richter, and Klaus SchmalstiegInstitut fur Geookologie, Braunschweig
Presented at the COMSOL Conference 2008 Hannover
2/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Temporal mismatch
Population dynamics of G. pulex
Identifiability of parameters
Future Applications
3/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Temporal mismatch
Population dynamics of G. pulex
Identifiability of parameters
Future Applications
4/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Climate change
I arboreal population dynamics
I microbial population dynamics
I freshwater amphipod population dynamics
5/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Climate change
I arboreal population dynamics
I microbial population dynamics
I freshwater amphipod population dynamics
6/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Interdependency in ecology
I birthI growth
I food availabilityI temperature conditionsI . . .
I reproduction
I food availabilityI weightI . . .
I death
I food availabilityI temperature conditionsI ageI . . .
7/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Interdependency in ecology
I birthI growth
I food availabilityI temperature conditionsI . . .
I reproduction
I food availabilityI weightI . . .
I death
I food availabilityI temperature conditionsI ageI . . .
8/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Interdependency in ecology
I birthI growth
I food availabilityI temperature conditionsI . . .
I reproductionI food availabilityI weightI . . .
I death
I food availabilityI temperature conditionsI ageI . . .
9/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Interdependency in ecology
I birthI growth
I food availabilityI temperature conditionsI . . .
I reproductionI food availabilityI weightI . . .
I deathI food availabilityI temperature conditionsI ageI . . .
10/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring interdependency in ecology
I in-situ, highly dynamic
I in lab, highly specific
11/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring I
Suhling et al., 2008
12/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring I
Suhling et al., 2008
13/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring I
Suhling et al., 2008
14/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring I
Suhling et al., 2008
15/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring II
Schneider et al., 2008
16/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Measuring II
Schneider et al., 2008
17/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Temporal mismatch
Population dynamics of G. pulex
Identifiability of parameters
Future Applications
18/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Individual growth g(F , T , w)
dwdt = g(F , T , w)
dwdt = γw
23 − ρw
dwdt = γ F
F+Fhw
23 − ρw
dwdt = Φ(T )(γ F
F+Fhw
23 − ρw)
19/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Individual growth g(F , T , w)
dwdt = g(F , T , w)
dwdt = γw
23 − ρw
dwdt = γ F
F+Fhw
23 − ρw
dwdt = Φ(T )(γ F
F+Fhw
23 − ρw)
20/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Individual growth g(F , T , w)
dwdt = g(F , T , w)
dwdt = γw
23 − ρw
dwdt = γ F
F+Fhw
23 − ρw
dwdt = Φ(T )(γ F
F+Fhw
23 − ρw)
21/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Individual growth g(F , T , w)
dwdt = g(F , T , w)
dwdt = γw
23 − ρw
dwdt = γ F
F+Fhw
23 − ρw
dwdt = Φ(T )(γ F
F+Fhw
23 − ρw)
22/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Weight structured population n(w , t)
Population density n varying with time t and weight w :
∂n(w ,t)∂t = −∂g(F ,T ,w)n(w ,t)
∂w
+ λ∂2n(w ,t)∂w2
− p−(F ,T ,w , . . .)
+ p+(F ,T ,w , . . .)
23/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Weight structured population n(w , t)
Population density n varying with time t and weight w :
∂n(w ,t)∂t = −∂g(F ,T ,w)n(w ,t)
∂w + λ∂2n(w ,t)∂w2
− p−(F ,T ,w , . . .)
+ p+(F ,T ,w , . . .)
24/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Weight structured population n(w , t)
Population density n varying with time t and weight w :
∂n(w ,t)∂t = −∂g(F ,T ,w)n(w ,t)
∂w + λ∂2n(w ,t)∂w2
− p−(F ,T ,w , . . .)
+ p+(F ,T ,w , . . .)
25/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Weight structured population n(w , t)
Population density n varying with time t and weight w :
∂n(w ,t)∂t = −∂g(F ,T ,w)n(w ,t)
∂w + λ∂2n(w ,t)∂w2
− p−(F ,T ,w , . . .)
+ p+(F ,T ,w , . . .)
26/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23 n(w , t)dw
27/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)
(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23 n(w , t)dw
28/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))
(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23 n(w , t)dw
29/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23 n(w , t)dw
30/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23
n(w , t)dw
31/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23
n(w , t)dw
32/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Mortality and Fertility
Mortality
p−(F ,T ,w , . . .) = µ(F ,T ,w , . . .)n(w , t)
1− µ(F ,T ,w , . . .) = (1− µ0)(1− µT (1− Φ(T )))(1− µFFµ
Fµ+F )
Fertility
p+(F ,T ,w , . . .) = B(F ,T ,w , . . .)Π(w)
B(F , t) =∫ wmax
wminrmax
FF+Fh
w23 n(w , t)dw
33/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Temporal mismatch
Population dynamics of G. pulex
Identifiability of parameters
Future Applications
34/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Parameter identification
I Minimization of
L(θ) =∑
j
∑i
(ni (tj)− ni (tj , θ))2
I throughI direct simulation with Comsol in MatlabI optimization toolbox of Matlab
I with model efficiency ME
ME = 1−∑
j
∑i (ni (tj)− ni (tj))
2∑j
∑i (ni (tj)− n(tj))2
35/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Process analysis
I Statistical method for comparison of nested models:Likelihood ratio test
I The test statistic is −2 log(Lcomplex(θ)Lsimple(θ)
)n2 .
I It is χ2 distributed with the difference in numbers ofparameters as degrees of freedom.
I A significance level of 0.05 is applied.
36/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Process analysis
I Statistical method for comparison of nested models:Likelihood ratio test
I The test statistic is −2 log(Lcomplex(θ)Lsimple(θ)
)n2 .
I It is χ2 distributed with the difference in numbers ofparameters as degrees of freedom.
I A significance level of 0.05 is applied.
37/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Process analysis
I Statistical method for comparison of nested models:Likelihood ratio test
I The test statistic is −2 log(Lcomplex(θ)Lsimple(θ)
)n2 .
I It is χ2 distributed with the difference in numbers ofparameters as degrees of freedom.
I A significance level of 0.05 is applied.
38/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for continuity
I Experiments on temperature response are based onhomogeneity assumption over intervals.
I Adequateness depends on response itself.
I Our idea: dynamic parameter identification under controlledtemperature regimes.
O’Neill temperature response function:
Φ(T ) = k( Tmax−TTmax−Topt
)pep
T−ToptTmax−Topt
with
p = 1400W 2(1 +
√1 + 40
W )2
W = (q10 − 1)(Tmax − Topt)
39/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for continuity
I Experiments on temperature response are based onhomogeneity assumption over intervals.
I Adequateness depends on response itself.
I Our idea: dynamic parameter identification under controlledtemperature regimes.
O’Neill temperature response function:
Φ(T ) = k( Tmax−TTmax−Topt
)pep
T−ToptTmax−Topt
with
p = 1400W 2(1 +
√1 + 40
W )2
W = (q10 − 1)(Tmax − Topt)
40/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for continuity
I Experiments on temperature response are based onhomogeneity assumption over intervals.
I Adequateness depends on response itself.
I Our idea: dynamic parameter identification under controlledtemperature regimes.
O’Neill temperature response function:
Φ(T ) = k( Tmax−TTmax−Topt
)pep
T−ToptTmax−Topt
with
p = 1400W 2(1 +
√1 + 40
W )2
W = (q10 − 1)(Tmax − Topt)
41/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for continuity
42/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for continuity
γ ρ Topt q10
A 0.32 0.06 18.3 2.58 97.9B 0.30 0.07 17.7 2.04 98.5C 0.23 0.05 17.6 2.11 99.1D 0.15 0.03 17.7 1.75 99.2
0.16 0.04 17.5 1.7
43/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for unmanageability
I Experiments on food response would require food control.
I Food control is unjustifiably expensive.
I Our idea: dynamic parameter identification including fooddynamic.
Litter dynamics
dF
dt= L(t,T )− ImaxΦ(T )
F
F + Fh
∫ wmax
0w
23 n(w , t)dw
Possible approximation
F (t) = F0e−(t/t1)p1 (1− e−(t/t2)p2 )
44/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for unmanageability
I Experiments on food response would require food control.
I Food control is unjustifiably expensive.
I Our idea: dynamic parameter identification including fooddynamic.
Litter dynamics
dF
dt= L(t,T )− ImaxΦ(T )
F
F + Fh
∫ wmax
0w
23 n(w , t)dw
Possible approximation
F (t) = F0e−(t/t1)p1 (1− e−(t/t2)p2 )
45/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for unmanageability
I Experiments on food response would require food control.
I Food control is unjustifiably expensive.
I Our idea: dynamic parameter identification including fooddynamic.
Litter dynamics
dF
dt= L(t,T )− ImaxΦ(T )
F
F + Fh
∫ wmax
0w
23 n(w , t)dw
Possible approximation
F (t) = F0e−(t/t1)p1 (1− e−(t/t2)p2 )
46/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Accounting for unmanageability
Fh p1 p2 t1 t24.27 18.4 25.1 84.0 97.9
4.7 20 25.0 82 98
47/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Identifying processes
I Mortality depends onmultiple processes.
I Effect of individual processoften unknown.
I Adequate simplificationscan be tested.
48/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Identifying processes
µ0 µT µF RMSE ME (%)
• 268 99.0• 364 98.7
• 285 99.0
• • 263 99.0• • 264 99.0
• • 266 99.0
• • • 260 99.0
0.002 0.002 0.002
49/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Identifying processes
µ0 µT µF RMSE Λ
0.004 268 •0.01 364
0.008 285 ◦0.003 0.004 263 •0.003 0.002 264 •
0.005 0.005 266 •0.002 0.003 0.004 260
0.002 0.002 0.002
50/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Completing the circle
µ0 rmax ME (%)
0.006 0.007 94.1
0.01 0.008
51/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
Temporal mismatch
Population dynamics of G. pulex
Identifiability of parameters
Future Applications
52/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
What we learned
53/53 Temporal mismatch Population dynamics of G. pulex Identifiability of parameters Future Applications
What we learned