Pacific Hake Management Strategy Evaluation

1. Pacific Hake Management Strategy Evaluation Joint Technical Committee Northwest Fisheries Science Center, NOAA Pacific Biological Station, DFO School of Resource and Environmental Management, SFU

2. Main Results Default harvest control rule results in 2021-2030 median average depletions of ~28% for all cases and mean average depletions of ~36%. Median average catches range 217-284 t Incorrect year class estimates often produce forecast errors Annual vs Biennial survey benefits are marginal

4. Outline • Introduction • Review the MSE workplan objectives • Methods • Example simulations • Describe the behavior of the existing management procedure • Performance metrics • Summary figures • Discussion and Conclusion

5. Introduction Management Procedure Examples of some decisions -survey design/frequency -sampling protocols -converting backscatter to index Data - sensitivities -selectivity shape -obs/process error -areas/gender/seasons Stock Assessment - mathematical form - target harvest rate - percentiles Harvest control rule • Maximum catch • Carry-over Catch recommendation • spatial restrictions • individual quotas • other opportunities Catch that comes out of water

6. Fishery objectives Stakeholders Managers Communication Performance Trade-offs Revision Management procedure Historical Data Future data Assessment method Decision-rule Management strategy evaluation Evaluation Operating model scenarios Performance measures Closed-loop simulation Peer-review

7. MSE Workplan Objectives • Introduce the MSE process to Pacific hake • Computer simulation (most work in 2012) • Consultation (limited in 2012, but more in 2013+…) • Base simulations on the 2012 base model and current harvest control rule to evaluate: • Annual acoustic surveys • Bienniel acoustic surveys • *Alternative Fspr% values • Performance measured using specific statistics

8. Performance Statistics • Conservation objectives • Yield objectives • Stability objectives Evaluation Phase • Operating Model • Stock dynamics • Fishery dynamics • True population Feedback Loop Data Catch • Management Strategy • Data choices • Stock Assessment • Harvest control rule

9. <-- Simulation period --> ------Conditioning period ------ (2012 assessment) Short 2013-15 Long 2021-30 Med 2016-20

10. Cases Considered • No fishing • Perfect Information Case • Annual Survey • Biennial Survey • Alternative FSPR% (with perfect info)

11. No fishing case • Set catches to zero, no assessment model • Exists to provide the first reference case to describe how the stock will behave in the absence of fishing

12. Perfect Information Case • We created a reference, perfect information case where the catch applied in the management strategy was the catch given by applying the F40%-40:10 rule to the operating model. • No assessment model is fit, simulated catches come from the application of the control rule to the true stock “known” by the operating model (i.e., what if we didn’t have uncertain data and stock assessment errors?)

13. Biennial Survey Case • Every year operating model simulates dynamics of the stock (i.e. recruitments, stock size etc) • Every odd year operating model simulates and assessment model fits: • catch • survey age-composition data • commercial age-composition data • survey biomass • In even years operating model simulates and assessment model fits • catch • commercial age composition data

14. Example Simulations • These will be single iterations of the management procedure from 2013-2030 • Want to illustrate some iterations of the simulation to give you a more intuitive feeling for how the simulations work. • We’ll talk about the aggregate performance later

15. Example Simulations Biennial survey

17. Annual Survey Case • Every year operating model simulates stock dynamics (i.e. recruitments, numbers at age, etc) • Every year operating model simulates the following data: • catch • survey age composition data • commercial age composition data • survey biomass • The assessment model fits these data and returns the exploitable biomass • The harvest control rule takes the exploitable biomass calculates a catch

19. But remember – starting points are not the same for each MSE run <-- Simulation period --> ------Conditioning period ------ (2012 assessment) Short 2013-15 Long 2021-30 Med 2016-20

20. Annual Survey

21. What we learned about the current management procedure

22. The assessment sometimes chases the latest survey observation

23. Assessment errors are frequent

24. Aggregate Performance • Outcomes • catches • How variable the catch is • Proportion of years in specific zones (below 10%, between 10 and 40%, greater than 40% etc.) • The proportion of years that a management procedure closes the fishery • Probability • How often does this occur? • Time frame • Short term (2012-2015) • Medium term (2016-2020) • Long term (2021-2030)

25. Statistics Break - Medians vs Means

26. Average Annual Variability in Catch (illustration)

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

28. No fishing Perfect info Annual survey Biennial survey 10% of B0

29. Minimum Catch

30. Summary for long-term depletion

31. Summary for long term AAV

32. Summary for long-term catch

33. Key Performance Statistics Table A.6 pp 135

34. Key Performance Statistics II Table A.7 pp 135

35. Analysis of alternative target harvest rates • The hake treaty doesn't specify a target depletion level, only a target harvest rate (F40%) and a control rule (40-10). • This makes it difficult to evaluate the efficacy of the control rule (i.e. relative to what?) • One additional curiosity that we considered was what would the target harvest rate have to be in order to achieve a range of target depletion levels • The MSE can be used to explore how changes to the target harvest rate might affect depletion, AAV, and average catch. • This is an exploration of trade-offs, not a proposal to change the hake treaty.

36. Alternative target harvest rates

41. Discussion and Conclusion • The current management strategy (assessment model formulation and F40%-40:10 rule) performs as follows: • Median average depletion on the 7-17 year time horizon ~28%, mean average depletion ~37% • Benefits of annual survey marginal • Assessment design results in chasing most recent data • Since the survey is itself variable, this produces a high probability of assessment error

42. Future work • It’s not an MSE until objectives have been defined and the performance of alternative management strategies evaluated against them. • The definition of these objectives and the JMC’s key interests will determine if we consider: • Operating models that consider more complicated hake life-history (i.e. movement, Canada and US areas) • Alternative management procedures to damp variability • Etc.

44. Extra Slides

45. Other available performance metrics • 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%