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What does each data component tell in the integrated stock assessment model under model misspecification?. Momoko Ichinokawa 1,2 , Hiroshi Okamura 1,2 , Yukio Takeuchi 2 1. National Research Institute of Fisheries Science, Japan 2. National Research Institute of Far Seas Fisheries, Japan. - PowerPoint PPT Presentation
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What does each data component tell in the integrated stock assessment model
under model misspecification?
Momoko Ichinokawa1,2, Hiroshi Okamura1,2, Yukio Takeuchi2
1. National Research Institute of Fisheries Science, Japan2. National Research Institute of Far Seas Fisheries, Japan
Integrated model
• Pros– Fully utilize multiple data sets such as
abundance indices (CPUE, survey), size compositions (length, weight), etc..
• Cons–Relative weighting among different data
sets (Francis 2011)
- log
like
lihoo
d(R
elati
ve v
alue
)
CPUESize comps
Total
likelih
ood
Parameter (such as R0)CPUE fits best Size data fit best
MLE = max (CPUE LL + size LL)
Problematic likelihood profile pattern causes the issue relative weighting
Francis (2011) address the issueFig.3 in Francis (2011)Fig.1 in Francis (2011)
Model mis-specification(e.g. time-varying selectivity)
Each component achieves minimum -LL at
the different values
An example of LP in Pacific bluefin
0123456789
10S14 S15S16 S18S19 S22
0
1
2
3
4
5
6
7
8F1 F2 F3 F4
F5 F6 F7 F8
F9 F10 F11 F13
R0 R0
Size composition CPUE
Rela
tive
-like
lihoo
d
Question: ① What types of and how does model mis-specification cause a conflict? • In particular, ignoring time varying selectivity
② Where is the true value under the conflict?• Ignoring time varying selectivity could mostly affect
likelihood in size composition of the specific fleets.
Where is true R0?
Approach: operating model
• Population dynamics– Age-structured
• Fisheries– Age selectivity
• Observation (fishery data)– Catch weight by fleets– Catch at length– CPUE
Simulation model
Estimation model (SS)
Likelihood profiles by each data components on R0• How does a conflict
occur?• Where is true value?
Equations in simulation model• Important parameters
– M=0.25 for all ages– Beverton-Holt, steepness=0.8
– Rdev=0.6– Start from equiribrium (F=0)
– Fishing intensity (F) is constant for all years
– Simulation is conducted fro 50 years
Assumed Biology and fisheryLength, weight and maturity by age
(Pacific bluefin tuna like)Age selectivity
(for older and younger)
Ages
Estimation model (SS)• Estimate – R0– recruitment
deviations– 3 x 2 selectivity
parameters (double normal, 24th option)
• Fixed– Other parameters
(growth, M, steepness, etc.) Ages
Simulation scenarios
1. Perfect case2. Estimation model ignore time-varying
selectivity (TV sel)– Not Down-weight– Down-weight
3. Other scenarios– Incorrect growth parameter (10cm smaller Linf)– Non linearity of CPUE (CPUE = q Nb, b=2.0)
1. Perfect case (Estimated vs True)
Top parameters can’t be correctly estimated even under perfect case
Comparison of various scenarios
Total
Size(older)
Size(younger)
CPUE(older)
CPUE(younger)
Perfect case
IgnoreTV-sel
Ignore TV sel (DW)
Smaller L infinity
NonlinearCPUE
R0
In our settings, CPUE of fishery targeting older fish tends to prefer R0 smaller than true
Summary 1
Ignoring time-varying
selectivity
LL of size comps of time-varying fishery are primarily sensitive to
the mis-specification, but almost all likelihood profiles
including CPUE are changed
Over-estimation of stock size and biased SSB trends Change other parameters
and stock status in SS
Summary 2• Model mis-specification would affect all
likelihood components, and likelihood profiles (because it is integrated model...)
• It is difficult to tell tendency of changes of likelihood profile shape (not straightforward)– Can simulation work help to grasp general
tendency?– Otherwise, under model mis-specification (common
cases of stock assessment), ‘which likelihood profile is true?’ might be nonsense question