Evaluation of harvest control rules (HCRs): simple vs. complex strategies

1. Evaluation of harvest control rules (HCRs):simple vs. complex strategies Dorothy Housholder Harvest Control Rules Workshop Bergen, Norway September 14, 2004

2. Introduction • Role of Models • Project Objective • Materials & Methods • Model Structure • Results & Discussion • General Simulation of HCRs • Specific Situation Simulation of HCRs • Conclusions • Future Work

3. Role of Models in Fisheries: management strategies in a mathematical computer environment • Create • Compare • Simulate • Evaluate Stochasticity (randomness and uncertainty) needed in fish population dynamics No model can accurately describe a biological process Model should be slowly built up to a certain point…

4. PROBLEM: Need for better fisheries management HARVESTCONTROLRULES! Clearly specified policy

5. Type 1 Type 2 Type 3 Fconst Fmax Fmax B* B* Definition of Terms: One parameter strategy = ‘traditional’ HCR • e.g.: constant harvest rate • Only 1 control parameter • Multi-parameter strategy = ‘complex’ HCR • Strategies with more than one parameter Fishing Mortality (F) Spawning Stock Biomass

6. CV • = (sd/(avg_yield)* 100 • coefficient of variation of mean yield as a % • Risk • Probability of biomass being below a min acceptable level (i.e. 10% of virgin biomass) HCR Performance Criteriahow to judge an HCR • Average annual yield Yield Year

7. Research Questions • Do complex HCR perform better/worse than the traditional harvesting strategies? Optimization approaches: • Single criterion optimization (i.e., yield) • Multi-criteria optimization (i.e., yield, CV, Risk) • Trade-offs among the performance criteria? • Does performance of the HCR depend on environmental/fishing mortality uncertainty?

8. Project Objective Obtain a more comprehensive & theoretical understanding of harvest control rules (HCRs) and their effect on stochastic population dynamics

9. Materials & Methods

10. this project in a nutshell: MODEL HCR Type1 Type2 Type3 GENERIC FISH STOCK Average annual yield, CV, Risk

11. P good year bad year P Model Components: Parameters

12. N0 fecundity2+ F fecundity1 s0 Vy Ey F N1 Vy M0 s1 M1 N2+ M M2+ s2+ The Model and Simulation Procedures:

13. Model Components (cont) Population equations: N0(year) = f1N1(year)+ f2+N2+(year) N1 (year+1)= s0N0(year)N2+(year+1) = s1N1(year)+ s2+N2+(year) Survival equations: s0 = exp (-M0* Ey)/ 1+kN0 s1 = exp (-(M1 + F * Vy)) s2+ = exp (-(M2+ + F * Vy))

14. B parameter loop 0-800 Intervals of 50 F parameter loop 0.0-6.0 Intervals of 0.5 Fish population ‘core’ N2+ N1 N0 Optimization approaches: • Single criterion optimization (i.e., yield) • Multi-criteria optimization (i.e., yield, CV, Risk) Simulation Procedures • Search for F and B parameters that optimize the performance criteria

15. Examining the model:

16. P good year bad year P Recruitment good year bad year

17. Results & Discussion

18. Examining the model: Stochasticity

19. RESULTS: General Simulation5,000 yearsdifferent levels of environmental and fishing stochasticity

20. Type 1 Type 2 Type 3 Fmax Fconst Fmax F B* B* BIOMASS General Simulationdifferent levels of environmental and fishing stochasticity • Best in max avg yield • Lowest CV • Lowest risk

21. HCR 1, 2 & 3 Similar yield Very high CV Small tradeoffs between CV and risk Best HCR dependent on levels of the model’s stochastic noise! Environmental variability Fishing variance Advantages and inadequacies:General Simulation

22. RESULTS: Specific Situation Simulation50,000 yearsEnvironmental variability= 0.25Fishing variance= 0.025

23. HCR Type 1:Specific Situation SimulationEnvironmental variability= 0.25 ; Fishing variance=0.025 Max Yield= 2348 Fmax= 0.4 CV= 59.3 Risk= 0.01

24. Specific Situation Simulation (cont)HCR Types 2&3:Environ. variability= 0.25Fishing variance=0.025 • Clear tradeoffs • Less risk and CV at lower F levels • Types 2&3 NOT sensitive to Threshold Biomass (B*) • resilience factor (!)

25. HCR Type 2 & 3: Environ. variability= 0.25; F variance=0.025 Max Yield= 2365 Fmax= 0.4 B*= 750 CV= 59.1 Risk= 0.0 Max Yield= 2351 Fmax= 0.4 B*= 350 CV= 59.5 Risk= 0.0

26. Type 1 Type 3 Type 2 0.4 0.4 0.4 F Mortality 350 750 BIOMASS Specific Situation Simulation: Practicalities of the HCR Yield= 2348 Yield= 2351 Yield= 2365 • Yields very similar • CVs very similar • Type 1 most practical!

27. Conclusions (but…we don’t always get it totally right…)

28. Type 1 Type 2 Type 3 Fmax Fconst Fmax F B* B* BIOMASS General Conclusions: • HCR Type 1 • best overall • practical, “simple” • robust in uncertainty • 3. HCR Type 3 • least practical for fishermen • good for conservationists • 2. HCR Type 2 • best for Risk (conservationists) • More practical than Type 3 (lower B*)

29. Research “Answers” • Do complex HCR perform better than traditional harvesting strategies? • Trade-offs among the performance criteria? • Does performance of the HCR depend on environmental/fishing mortality uncertainty? No, not for this model. Simple is best! NOTE: this model was very resilient!! Higher F gives higher CV and Risk values for all HCR Types Yes! Need good uncertainty estimates in fisheries management

30. Future Work N0 More realistic model with more age classes N1 N2 N3 N4 N5 etc… to a max age • More extensive simulations • Modelling an HCR after real data (i.e. cod, salmon, herring): different management for different life histories! Model should be slowly built up to a certain point…

31. F mortality SSB Catch Future Work: What works, what doesn’t?? current proposalto Norwegian Research Council OBJECTIVE: • Outline ways of management that seem recommendable, and highlight rules that fail • Point out factors for failure or success in worldwide fisheries management test results’ robustness with model simulations

32. “I see a major trend…towardssimpler rulesforsetting harvest levels, with thecomplex modelsbeing used primarily totest the robustnessof the rules.”- Ray Hilborn 2003. (emphasis added) Keep It Simple, Stupid! Remember to: K I S S !

33. Acknowledgements Advisors: Mikko Heino: researcher, Institute of Marine Research; Adaptive Dynamics Network, International Institute for Applied Systems Analysis, Laxenburg, Austria Øyvind Fiksen: associate professor, Department of Biology, University of Bergen