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By, Deepak George Pazhayamadom Emer Rogan (Department of ZEPS, University College Cork) Ciaran Kelly (Fisheries Science Services, Marine Institute) Edward.

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Presentation on theme: "By, Deepak George Pazhayamadom Emer Rogan (Department of ZEPS, University College Cork) Ciaran Kelly (Fisheries Science Services, Marine Institute) Edward."— Presentation transcript:

1 By, Deepak George Pazhayamadom Emer Rogan (Department of ZEPS, University College Cork) Ciaran Kelly (Fisheries Science Services, Marine Institute) Edward Codling (Lecturer in Mathematical Biology, University of Essex) Supervisors Department of Zoology, Ecology and Plant Science (ZEPS) University College Cork (UCC), Cork, Ireland

2 Fisheries Management (Traditional approach) Fishery Dependent Data: Catch (BIAS, NOISE ) Fishery Independent Data: Survey ( BIAS, NOISE) Fisheries Management (Alternative approach) SSB, F [Estimated Indicators] (Stock abundance and Exploitation) Population models with assumptions Monitor with Reference Limits (Acceptable, Precautionary, Limit) Regulate with output controls HCR (Eg: TAC) Other measures (Eg: Effort) Next Year Limit Precautionary Acceptable FSSB Statistical Process Control Statistical Signals [Stock Indicators] EXISTING APPROACH Maximize Yield Stabilize Yield

3 3 REFERENCE:

4 SPC (Statistical Process Control) SPC is a statistical technique concerned with stabilizing processes to fixed targets and improvements for  Making inferences about process behaviour  Decision making SPC is a statistical technique concerned with stabilizing processes to fixed targets and improvements for  Making inferences about process behaviour  Decision making Time Series Data (On Process) Monitor Indicator Out of Control ? YES NO Correct Cause Product N =N+1 N = ‘1’ year

5 5 Monitor a process using indicator/s and stabilize the system using corrective action if the control chart signals an “Out of Control” situation. UPPER CONTROL LIMIT (UCL) CONTROL MEAN (Cµ) LOWER CONTROL LIMIT (LCL) UPPER CONTROL LIMIT (UCL) Allowance Parameter (‘K’)

6 CUSUM Control Chart Standardize each indicator [z t =(D-µ)/σ] D = Indicator(Time Series), µ = Control Mean, σ = Control S.D. Standardized values (z t ) are converted to Lower and Upper CUSUMs Lower CUSUM : Ф - n = min (0, Ф - n-1 + z n + k), Ф - 0 = 0 Upper CUSUM : Ф + n = max (0, Ф + n-1 + z n - k), Ф + 0 = 0 k = Allowance parameter

7 7 UCL LCL Cµ K= 1 Fisheries Management

8 8 Recommendations: 1.Empirical Indicators 2.Catch Data - Age Based Numbers - Age Based Weight - Proportions Recommendations: 1.Empirical Indicators 2.Catch Data - Age Based Numbers - Age Based Weight - Proportions Recommendations: 1.Relationship with SSB 2.Best Matches (Correlations) 3.Use of Combined Indicators Recommendations: 1.Relationship with SSB 2.Best Matches (Correlations) 3.Use of Combined Indicators PHASE I: (Reference Period) 1.From all available data 2.Moving Average 3.SSB levels PHASE I: (Reference Period) 1.From all available data 2.Moving Average 3.SSB levels PHASE II: (Tune CUSUMs) IC-ARL, OC-ARL PHASE II: (Tune CUSUMs) IC-ARL, OC-ARL STEPS & GUIDELINES ICES Stocks 1.Scenarios of past events 2.Life histories ICES Stocks 1.Scenarios of past events 2.Life histories Phase III: Action ? Best Strategy or HCRs Phase III: Action ? Best Strategy or HCRs Data ? Define Indicators ? Best Indicators ? Control Mean (‘µ’) ? Control Limits (‘h’) ? Inherent variability (‘k’) ? HCR ?

9 Data: Greenland Halibut in Subareas I and II ( ) Simulation: Age Structured Population Numbers for 100 years (1995 onwards) Exponential Decay and Catch Equations were used. Average Fishing Mortalities ( ) with variation (C.V.=0.2) Iterations: 1000 CUSUM Reference Period: Indicators: CN6,CN11 Allowance (k): 1 Reference Limit (h): 1 Action : Triggered with Lower CUSUM Limit HCR: 20% to 50% reduction in Fishing Mortality (Random) Reference: James, L. J., (2008). M.Sc. Thesis, ‘Use of cumulative sum (CUSUM) control charts of empirical indicators to monitor the status of fisheries in the North-east Atlantic’ Potential Indicator: CN11 Illustration CUSUM with HCR CN6 r= CN11 r=

10 Illustration CUSUM with HCR CN6 r= CN11 r=

11 Project Outline TASK 1 Define Indicators TASK 2 Find Best Indicators TASK 3 Control Mean TASK 4 Control Limits TASK 4 Allowance TASK 5 Performance Evaluation TASK 6 Evaluate model HCRs TASK 6 Evaluate model HCRs Single Species Simulation Framework TASK 7 Multiple Stocks and/or Ecosystem Interactions TASK 7 Multiple Stocks and/or Ecosystem Interactions To develop a theoretical management framework based on HCR that use SPC methods with indicators from number of stocks to successfully manage model fisheries at the ecosystem level.

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