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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

Momoko Ichinokawa 1,2 , Hiroshi Okamura 1,2 ,

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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

Generated fishery dataCPUE (lognormal) Length composition

(multinomial)

Length (cm)

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

1. Perfect case (Likelihood profiles)

True Median

2. Ignore TV sel (Estimated vs True)Over-estimated

Fits become worse

2. Ignore TV sel (Likelihood profiles)

True Median

Time-varying selectivity

5

2’. Ignore TV sel & DW (Estimated vs True)Improved

CPUE fits improved

Time-varying selectivity

5

2’. Ignore TV sel & DW (Likelihood profiles)

True Median

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