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MSE of the performance of data-moderate assessment methods for U.S. West Coast groundfish

MSE of the performance of data-moderate assessment methods for U.S. West Coast groundfish. Chantell Wetzel Data-Moderate STAR Panel April 23-28, 2013. Outline. Description of Assessment Models DCAC, DB-SRA, exDB -SRA, and exSSS (AIS) MSE Simulation Setup Operating model

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MSE of the performance of data-moderate assessment methods for U.S. West Coast groundfish

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  1. MSE of the performance of data-moderate assessment methods for U.S. West Coast groundfish Chantell Wetzel Data-Moderate STAR Panel April 23-28, 2013

  2. Outline • Description of Assessment Models • DCAC, DB-SRA, exDB-SRA, and exSSS (AIS) • MSE Simulation Setup • Operating model • Projection Period • Results • Rockfish • Cases 1, 3, and 6 • Flatfish • Cases 1, 3, and 6

  3. Description of Models • exDB-SRA • Data: Index of Abundance • Delay-Difference Model • Knife-edged selectivity set at age of maturity for fishery & survey • Parameters: • M, Fmsy/M, Delta fixed year ( = 1-depletion), Bmsy/B0 • Estimates B0 • Results: Stochastic* • DB-SRA • Parameters: • M, Fmsy/M, Delta fixed year (=1-depletion), Bmsy/B0 • Results: Stochastic • exSSS • Data: Index of Abundance • Age-Structured • Knife-edged selectivity set at age of maturity for fishery & survey • Parameters • M, steepness, depletion in a fixed year • Estimates R0 • Results: Deterministic • DCAC • Parameters: • M, Fmsy/M, Delta fixed year (=1-depletion), Bmsy/B0 • Results: Stochastic

  4. Operating Model • Age-Structured Operating Model • Two Life-History Types • Flatfish • Rockfish • Recruitment • Shepherd’s Stock Recruit Relationship • Process Error: Recruitment Deviations • Single Fishery • Begins in year 1 with the operating model population in non-equilibrium • Single Survey • Observation Error • CV = 0.25

  5. Simulation Setup Population Setup Projection Period Age-Structured Operating Model Recruitment: Shepherd’s SRR Year 1:50 Conduct Assessment Index of Abundance Year 31:50 Fishery Year 1-50 Estimate OFLs Index of Abundance Year 31:(50 +4 years) Reduce to ABCs End of Projection: Year 75 Evaluate Performance of Each Assessment Method Operating Model Project Forward 4 years

  6. Adaptive Importance Sampling • Create initial parameter sets • Fix parameters in the assessment model and solve for R0 or B0 that meets the final depletion value under the assumed dynamics. • Sample from the initial parameter set based on the weights: • Regenerate parameter vectors using a multivariate student’s T distribution based on the drawn parameters • Rerun the assessment model with each of the new parameter sets • Repeat steps 2-4 until the entropy criteria (e > 0.92)is met: • Create a large final sample from the parameter sets that met the entropy criterion. • Run the assessment model for the final time with the final parameter sets.

  7. Preliminary Results

  8. Rockfish Case 1: where all priors are assumed correctly and biomass is at target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  9. Rockfish: Case 1OFL estimation

  10. Status in Year 75

  11. Rockfish: Case 1 Relative Error of Spawning Biomass exDB-SRA exSSS RE Spawning Biomass RE Spawning Biomass Year Year

  12. exDB-SRA exSSS

  13. RockfishCase 3: where all priors are assumed correctly and biomass is below target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  14. Rockfish: Case 3 OFLs

  15. Status in Year 75

  16. Rockfish: Case 3 Relative Error of Spawning Biomass exDB-SRA exSSS RE Spawning Biomass RE Spawning Biomass Year Year

  17. exDB-SRA exSSS

  18. RockfishCase 6: where productivity and status in year 50 are mis-specified, biomass is below target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  19. Rockfish: Case 6 OFLs

  20. Status in Year 75

  21. Rockfish: Case 6 Relative Error of Spawning Biomass exDB-SRA exSSS RE Spawning Biomass RE Spawning Biomass Year Year

  22. exDB-SRA exSSS

  23. FlatfishCase 1: where all priors are assumed correctly and biomass is at target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  24. Flatfish: Case 1OFL estimation

  25. Status in Year 75

  26. Flatfish: Case 1 Relative Error of Spawning Biomass exDB-SRA exSSS

  27. exDB-SRA exSSS (AIS)

  28. FlatfishCase 3: where all priors are assumed correctly and biomass is below target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  29. Flatfish: Case 3 OFLs

  30. Status in Year 75

  31. Flatfish: Case 3 Relative Error of Spawning Biomass exDB-SRA exSSS

  32. exDB-SRA exSSS

  33. FlatfishCase 6: where productivity and status in year 50 are mis-specified, biomass is below target depletion Time-trajectories of depletion for the rockfish population when the OFLs are provided by each assessment method

  34. Flatfish: Case 6 OFLs

  35. Status in Year 75

  36. Flatfish: Case 6 Relative Error of Spawning Biomass exDB-SRA exSSS

  37. exDB-SRA exSSS

  38. Conclusions • There are some advantages of exDB-SRA and exSSS over the data-limited methods; DCAC and DB-SRA • Mis-specifications • exDB-SRA generally performs better for the rockfish life-history compared to flatfish • exSSS rebuilds both life-histories towards or above the target quickly

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