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MSE Performance Metrics and Tentative Results Summary

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. Outline. Summarize the hake MSE Example simulations Performance metrics

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MSE Performance Metrics and Tentative Results Summary

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  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 • Summarize the hake MSE • Example simulations • Performance metrics • Summary figures

  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. Performance Statistics • Conservation objectives • Yield objectives • Stability objectives • Operating Model • Stock dynamics • Fishery dynamics • True population Feedback Loop Data Catch • Management Strategy • Data choices • *Stock Assessment • Harvest control rule Organization of MSE Simulations * Use the MPD (not posterior medians, or other quantiles) for applying the harvest control rule

  5. Example MSE run 1

  6. But remember – starting points are not the same

  7. Example MSE Run II

  8. Measuring Performance • 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.

  9. Key Performance Statistics

  10. Other available options • First quartile depletion • Third quartile depletion • Median final depletion • Median of lowest depletion • Median of lowest perceived depletion • First quartile of lowest depletion • Third quartile of lowest depletion • First quartile of AAV in catch • Third quartile of AAV in catch • First quartile of average catch • Third quartile of average catch • Median of lowest catch levels • First quartile of lowest catch levels • Third quartile of lowest catch levels • Proportion with any depletion below SB10% • Proportion perceived to have any depletion below SB10%

  11. Statistics Break - Medians vs Means

  12. Average Annual Variability in Catch (illustration)

  13. 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 as follows: • To separate observation vs process error i.e. variable data don’t affect management procedure performance • to provide a standard relative to which a comparison of the test (biennial and annual) cases could be made

  14. Perfect information (con’t)

  15. Comparisons of Depletion, Catch and AAV for All Cases

  16. Summary for long-term depletion

  17. Summary for long term AAV

  18. Summary for long-term catch

  19. Discussion • Does the groups want alternative performance statistics considered • Progress and next steps

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