A management strategy evaluation of stock recruitment proxies

Transcription

1 A management strategy evaluation of stock recruitment proxies Josh Nowlis ECS Federal contractor in support of Northwest Fisheries Science Center

2 Our challenge What is our goal? Provide advice about the consequences of various catch policies How does S-R fit into this goal? The S-R function, both its general shape and the parameters that describe it, affect our expectations of future productivity There is always uncertainty, even if we can fit a complex model How do mistakes translate into real-world consequences? Assessments and fishing policy as buffers

3 An assessment of Gadus conputatus stock-recruitment patterns and fishery consequences Josh Nowlis ECS Federal contractor in support of Northwest Fisheries Science Center

4 Why Gadus conputatus? Fully observable life cycle and fishery dynamics in mere minutes (ok, over an hour but I am working on a better fitting algorithm) Communities don t suffer when you crash the fishery Replicable experiments

5 Why Gadus conputatus? Fully observable life cycle and fishery dynamics in mere minutes (ok, over an hour but I am working on a better fitting algorithm) Communities don t suffer when you crash the fishery Replicable experiments ><)))*> ><)))*> ><)))*> ><)))*> ><)))*> ><)))*> ><)))*> ><)))*> School of Gadus conputatus in its natural habitat

6 The model Population biology Detailed true model Science Incomplete and potentially inaccurate Management Decisions based on science and policy

7 Population biology Steepness = 0.7* with N(0,0.1*) recruitment variation Natural mortality = 0.3* B-H shape parameter = 1* K = 1000* These four parameters determine all others: = MM nn 0.2 h 0.21 nn ββ = KK 11 nn MM 1 MSY- and OY-related parameters

8 Science Stock goes unassessed for 25* years, but data collection starts in year 5* Collect CPUE and catch series (assume correct data) Once begun, assessments take place every 5* years Estimate K and B 5, using fixed assumptions for hh, MM, and nn Fixed assumptions for hh, MM, and nn span a range of values: [80%*...100%...120%*] of the true values

9 Management Utilizes a policy Fishing mortality rate is FF OOOO above BB 4444, 0 below BB 1100, and linearly extrapolated between the two FF OOOO based on stock assessment estimate of KK, and assumptions about other parameters Total allowable catches rely on fishing mortality and the stock assessment estimate of BB cccccccc Catches pre-management follow with perfect information phased in over first 5 years Total allowable catch is always achieved

10 Simulation Run for 25* years (will add MC runs) Keep track of annual catches and stock biomasses, summarized into normalized performance measures Economic Productivity: Net present value of catches/npv(msy) Status: Catch at end of series/msy Crisis: Lowest catch post-assessment/msy Similar ecological indices comparing biomass to K

11 Time series

12 Time series Productivity measures: Econ: sum(present values) Ecol: mean(biomass)

13 Time series Crisis avoidance: Econ: min catch Ecol: min biomass

14 Economic productivity (large operator)

15 Economic crisis minimization

16 Ecological productivity

17 Ecological crisis minimization

18 Intriguing possiblities So far**, results appear to support a few conclusions: Economic productivity largely retained, and ecological performance fairly constrained. This result is most like a result of the robustness of stock assessments and the policy. Economic crises of greater concern: appear more sensitive to misspecification of h than M

19 Future directions Preliminary results: need validation and more study Compare across life histories (parameters, S-R curves) Economic perspective: small operations with high discount rate vs large operations with low rate How much does collecting data from early (not yet fished down) years aid performance? How are these results affected by environmental uncertainty?