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

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.

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

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  1. What does each data component tell in the integrated stock assessment model under model misspecification? Momoko Ichinokawa1,2, Hiroshi Okamura1,2, Yukio Takeuchi21. National Research Institute of Fisheries Science, Japan2. National Research Institute of Far Seas Fisheries, Japan

  2. 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)

  3. Problematic likelihood profile pattern causes the issue relative weighting CPUE Size comps - log likelihood (Relative value) Total likelihood Parameter (such as R0) Size data fit best CPUE fits best MLE = max (CPUE LL + size LL)

  4. Francis (2011) address the issue Fig.1 in Francis (2011) Fig.3 in Francis (2011) Each component achieves minimum -LL at the different values Model mis-specification (e.g. time-varying selectivity)

  5. An example of LP in Pacific bluefin Size composition CPUE Relative -likelihood R0 R0

  6. 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?

  7. Approach: operating model Simulation model • Population dynamics • Age-structured • Fisheries • Age selectivity • Observation (fishery data) • Catch weight by fleets • Catch at length • CPUE • Likelihood profiles by each data components on R0 • How does a conflict occur? • Where is true value? Estimation model (SS)

  8. 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

  9. Assumed Biology and fishery Length, weight and maturity by age (Pacific bluefin tuna like) Age selectivity (for older and younger) Ages

  10. Generated fishery data Length composition (multinomial) CPUE (lognormal) Length (cm)

  11. Estimation model (SS) • Estimate • R0 • recruitment deviations • 3 x 2 selectivity parameters (double normal, 24th option) • Fixed • Other parameters (growth, M, steepness, etc.) Ages

  12. Simulation scenarios • Perfect case • Estimation model ignore time-varying selectivity (TV sel) • Not Down-weight • Down-weight • Other scenarios • Incorrect growth parameter (10cm smaller Linf) • Non linearity of CPUE (CPUE = q Nb, b=2.0)

  13. 1. Perfect case (Estimated vs True) Top parameters can’t be correctly estimated even under perfect case

  14. 1. Perfect case (Likelihood profiles) True Median

  15. 2. Ignore TV sel (Estimated vs True) Over-estimated Fits become worse

  16. 2. Ignore TV sel (Likelihood profiles) Time-varying selectivity 5 True Median

  17. 2’. Ignore TV sel & DW (Estimated vs True) Improved CPUE fits improved

  18. 2’. Ignore TV sel & DW (Likelihood profiles) Time-varying selectivity 5 True Median

  19. Comparison of various scenarios Nonlinear CPUE Ignore TV sel (DW) Perfect case Ignore TV-sel Smaller L infinity Total Size (older) Size (younger) CPUE (older) CPUE (younger) R0

  20. Summary 1 LL of size comps of time-varying fishery are primarily sensitive to the mis-specification, but almost all likelihood profiles including CPUE are changed Ignoring time-varying selectivity Over-estimation of stock size and biased SSB trends Change other parameters and stock status in SS In our settings, CPUE of fishery targeting older fish tends to prefer R0 smaller than true

  21. 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

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