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. Outline Introduction Review the MSE workplan objectives Methods Example simulations General behavior of the existing management strategy Performance metrics Summary figures Discussion and Conclusion

3. Introduction Stock Assessment Data Harvest control rule Catch recommendation Catch that comes out of water - structure -selectivity shape -obs/process error -spatial -survey design -survey frequency -converting backscatter to index - mathematical form - target harvest rate - inflection points -hedging -un-quantified uncertainties -objectives/constraints -spatial restrictions -individual quotas -other fishing opportunities Examples of some decisions Management StrategyManagement Strategy

4. MSE Workplan Objectives Use the 2012 base case as the operating model. Two objectives – Evaluate the performance of the harvest control rule – Evaluate the performance of annual, relative to biennial surveys.

5. 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 Overview of the MSE Process

6. Closed-Loop Simulations Use the MPD (not posterior medians, or other quantiles) for applying the harvest control rule

7. 19601970198019902000201020202030 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Year S S B t  ------Conditioning period ------  (2012 assessment) MSE Simulations

8. Cases Considered No fishing Perfect Information Case Annual Survey Biennial Survey

9. 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

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 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

11. Perfect information case Every year operating model simulates dynamics of the stock (i.e. recruitments, stock size etc) No assessment model is fit, simulated catches come from the application of the control rule to the true stock

12. 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

13. Biennial survey

15. 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 catch given the harvest control rule back to the operating model

17. But remember – starting points are not the same for each MSE run

18. Annual Survey

19. Some lessons about the performance of the current management strategy

20. The assessment chases the latest survey observation

21. Assessment errors are frequent

22. Aggregate 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.

23. Statistics Break - Medians vs Means

24. Average Annual Variability in Catch (illustration)

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

28. Summary for long-term depletion

29. Summary for long term AAV

30. Summary for long-term catch

31. Key Performance Statistics Short Term Medium Term Long Term Percentage of years:PerAnnBiePerAnnBiePerAnnBie Depletion above 40%34.30%35.90%35.64%28.95%31.29%32.67%27.07%29.54%31.06% Depletion below 10%4.44%6.61%6.87%0.94%7.17%8.59%0.39%5.39%7.04% Depletion between 10 and 40%61.26%57.49% 70.11%61.54%58.74%72.54%65.08%61.90% MS closes fishery0.00%4.70%3.90%0.00%8.51%8.21%0.00%10.11%13.61%

32. Key Performance Statistics II Short Term Medium Term Long Term Medians of:PerAnnBiePerAnnBiePerAnn Bie Average catch251284273216226217230217218 Average depletion31.7%31.4%31.6%27.9%26.9%27.8%27.6%27.3%28.0% AAV in catch (%)36.6%35.5%32.5%23.1%34.1%34.7%23.3%32.5%33.2%

33. Additional Analyses on the general performance of the harvest control rule as a function of the default target harvest rate Characterize the conservation, yield and variability tradeoffs.

34. Alternative target harvest rates

39. 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.

40. 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

41. 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 interested 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.

42. Extra Slides

43. 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%