1. MSE Performance Metrics and Tentative Results Summary Joint Technical Committee Northwest Fisheries Science Center, NOAA Pacific Biological Station, DFO School of Resource and Environmental Management, SFU

2. Outline Review of MSE Graphics of preliminary results – Omniscient case – Annual case – Biennial case Key performance statistics – discussion

3. Objectives of the MSE Use the 2012 base case as the operating model. As defined in May 2120 – Evaluate the performance of the harvest control rule – Evaluate the performance of annual, relative to biennial survey frequency.

4. Organization of MSE Simulations Operating Model *Stock dynamics *Fishery dynamics *True population Management Strategy *Data choices *Stock Assessment *Harvest control rule CatchData Performance Statistics *Conservation objectives *Yield objectives *Stability objectives Feedback Loop

6. Animation

7. Performance Measures Choose metrics that capture the tradeoffs between conservation, variability in catch and total yield for specific time periods. Define short, medium and long time periods as Short=2013-2015, Medium=2016-2020, Long=2021-2030. The main conservation metric is the proportion of years depletion is below 10% The main variability in catch metric is the Average Annual Variability in catch for a given time period. For yield we used the median average catch We’ve chosen what we think are the top six. We’d like to discuss if others are needed.

8. Average Annual Variability in Catch (illustration)

9. Medians vs Means

10. Perfect Information Case We created a reference, perfect information case where we simulated data with no error The purpose of the perfect information case was to provide: – Separate observation vs process error i.e. variable data don’t affect management procedure performance – a reference to compare the annual/biennial survey cases to.

11. Perfect information (con’t)

14. Annual Survey Case

15. Biennial Survey Case

16. Summary