CAPAM – Selectivity Workshop La Jolla, USA, 11-14 March, 2013

1. An exploration of alternative methods to deal with time-varying selectivity in the stock assessment of YFT in the eastern Pacific Ocean Alexandre Aires-da-Silvaand Mark Maunder CAPAM – Selectivity Workshop La Jolla, USA, 11-14 March, 2013

2. Outline • Background on YFT assessment • Stock Synthesis (SS3) model • Selectivity issues: time-varying process • Retrospective pattern in recent recruitments • Explore SS3 approaches to deal with time- varying selectivity • Ignore time-varying selectivity (base case model) • Full time-varying selectivity (deviates) • Time-varying for terminal years only

3. YFT fishery definitions

4. YFT Stock Synthesis model • Quarterly time-step model • Fishery definitions: 16 fisheries • Data weighting: the CV of the southern LL fishery was fixed (0.2), others estimated (NOA, DEL) • Growth modeling: Richards curve, L2 and variance of length-at-age are fixed • Modeling of catchability and selectivity: • Catchability coefficients for 5 CPUE time series are estimated (NOA-N, NOA-S, DEL-N, DEL-I, LL-S) • Size-based selectivity curves for 11 of the 16 fisheries are estimated (fit to size composition data) • Logistic selectivity for LL-S and DEL-S, and dome-shape for other fisheries

5. YFT size selectivity

6. OBJ time-varying selectivity? F1-OBJ_S F3-OBJ_I F2-OBJ_C F4-OBJ_N

7. OBJ LF residual pattern F1-OBJ_S F2-OBJ_C F4-OBJ_N F3-OBJ_I

8. Retrospective pattern

9. Projections CATCHES SPAWNING BIOMASS Purse seine Longline

10. Numerical and convergence issues • Unstable selectivites (OBJ) • Sensitive to initial parameter values and phases • Long run times (> 4 hours) • Issues inverting hessian matrix (steepness run)

11. Objectives of study • Test approaches available in SS to deal time- varying selectivity • Improve selectivity process (time-varying) • Minimize retrospective pattern • Shortcoming: more parameters, longer run times • Simplify model • Less data, collapse fisheries (OBJ) • Some considerations • We assume that retrospective pattern is mainly driven by model misfit to recent OBJ LF data caused by misspecified selectivity • We recognize that other sources of bias and misspecifcation may exist

12. A single “lumped” OBJ fishery F1-OBJ_S F3-OBJ_I F4-OBJ_N F2-OBJ_C

13. Model 0: Constant selectivity • Selectivity: Estimate “average” constant selectivity • Data: Fit to OBJ length-frequency data for all historic period • Base case model configuration

14. Model 0: Constant selectivity

15. Model 1 - Full time-varying selectivity • Selectivity: Quarterly time-varying selectivity • Estimate quarterly deviates on base selex parameters of double normal OBJ selectivity curve • Data: Fit to OBJ LF data for all historic period • SD of quarterly deviates need to be defined: • First run: freely estimate devs with high flexibility (SD=1) • Second run: Use SD of estimated devs from first run in penalized likelihood approach

16. Model 1 - Full time-varying selectivity

17. Model 1 - Full time-varying selectivity

18. Model 1 - Full time-varying selectivity

19. Model 1 - Full time-varying selectivity Time-variant model (M1-P2fix) Constant selectivity model 0

20. Model 1 - Full time-varying selectivity

21. Model 2 – “hybrid” approach • Recent period is the most influential on management quantities (recent recruitments, Fs) • Time-varying selectivity process in recent period only • Estimate quarterly deviates on base selex parameters of double normal OBJ selectivity curve • Fit to OBJ LF data for recent period only • 3 terminal years (3-year average used for management quantities) • 5 terminal periods (a longer period) • As for early period, fix to “average” constant selectivity from terminal years (base parameters)

22. Model 2 – “hybrid” approach Tvarselex- 3 years Tvarselex - 5 years

23. Model 2 – “hybrid” approach Tvarselex- 3 years Tvarselex - 5 years

24. Model 2 – “hybrid” approach

25. Conclusions • Allowing for OBJ time-varying selectivity helped to minimize retrospective pattern in recent YFT recruitment estimates • Balance between the amount of selectivity process (# of params.) needed in the model and the OBJ LF data to include in model fit (whole series or few recent years only?) • Allowing for time-varying selectivity (quarterly deviates) in terminal years of the assessment only while fitting to LF data for this period seems a reasonable compromise • An “average” constant selectivity curve is applied to the early period while not fitting to the LF data for that period • A simulation study is needed to more rigorously investigate selectivity issues and associated bias in the YFT assessment

26. QUESTIONS?

27. Total catches

28. Models Fix selectivity • Assume “average” stationary OBJ selectivity • “Drop” (not fit) all OBJ LF data • Fix to base selectivity parameters estimated in full time-varying runs (models 1)

29. Models Fix selectivity M2-P2fixed M2-P2est

30. Models Fix selectivity M2-P2est M2-P2fixed

31. Recruitment – all models

32. SBR – all models

33. Model type 1

34. Model type 3