1. Simulation analysis using stock synthesis Maunder, Piner, and Lee

2. Objective Use the SS2/SS3 program as a simulator to test the ability to estimate key model parameters

3. SS2 bootstrap Built in bootstrap procedure to generate artificial data files Data files have randomly generated data based on the same characteristics of the real data Data file in the SS2 format and can be used directly to run the model Data files are in a single file one on top of the other and need to be separated Use data files to run bootstraps, and estimate uncertainty and bias Can also be used to generate data based on fixed parameter values (note in SS3 first data set is the actual and second is with no error)

4. Simulation algorithm for estimating M (based on Ian Stewart’s R code) Run the original model to generate the bootstrap files (M fixed) –Set the number of bootstrap files to 500 in the starter.ss file Split out the data files into separate files, put them in separate directories with the model files –Set the bootstrap files to 0 –Set M to a positive phase –Set it to start from par file in the starter.ss file to make it run faster Run the assessment in each directory –Do this by creating a batch file that moves through the directories and runs the model Read in the results from each run

5. Show R code

6. Methods Use current peer reviewed SS2 assessments Use the exact setup used in the assessment, including model assumptions, parameters estimated, and data Estimate any Ms that are different (e.g. old/young, male/female)

7. Species Arrowtooth flounder Black rockfish-North Black rockfish-South Chilly pepper rockfish Pacific cod Cowcod Dark blotch rockfish English sole Hake Longnose skate Blue rockfish Sablefish Yelloweye rockfish

8. Good estimates

9. Biased estimates

10. Estimating male and female

11. Estimating old and young

12. Indication of model miss-specification

13. Indication of statistical assumption violation (incorrect sample size or residual patterns)

14. Modification for estimating steepness of the stock recruitment relationship 1.Run the original model 2.Set the maximum phase to 0 in the starter file and use the par file (i.e. simulate data from fixed parameter values) 3.Replace the recruitment deviates in the par file with a N(0,Rsd) 4.Run one bootstrap 5.Estimate the model parameters 6.Repeat 3-5 500 times 7.Summarize results

15. Results for Pacific cod Estimates of steepness equal to one Even if h=0.5 Only gets estimates < 1 if h=0.5 and Rsd = 0.2 Need to try on the other stocks

16. Other examples Estimating the growth parameters Does iterative reweighting improve the results Can observation error adjust for process error MSE

17. Simon, talk about Condor

18. Acknowledgements Stacey Millar and all the assessment authors