“APPLICATION OF SIGNAL DETECTION METHODS TO �THE FISHERIES MANAGEMENT SYSTEM”

By, Deepak George Pazhayamadom

Emer Rogan

(Department of ZEPS, University College Cork)

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Ciaran Kelly

(Fisheries Science Services, Marine Institute)

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Edward Codling

(Lecturer in Mathematical Biology, University of Essex)

Supervisors

Department of Zoology, Ecology and Plant Science (ZEPS)

University College Cork (UCC), Cork, Ireland

Fisheries Management

(Traditional approach)

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Catch Data (Stochastic )

Fisheries Management

(New approach)

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SSB, F

Estimated Indicators

(Stock abundance)

Statistical models with assumptions

Monitor with Reference Limits

(Acceptable, Precautionary, Limit)

Regulate with HCR

(TAC , Effort (f), Other measures)

Next Year

Limit - 1000 1.5

Precautionary - 1500 0.8

Acceptable - 2000 0.5

F

SSB

Statistical Process Control

Statistical Signals:

Empirical Indicators

(Stock abundance)

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EXISTING APPROACH

Optimize Yield

Stabilize Yield

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

Data for time = ‘N’ years

Monitor Parameter

Out of Control ?

YES

NO

Correct Cause

Product

N =N+1

N =N+1

N = ‘1’ year

Scandol, J., 2003. Use of cumulative sum (CUSUM) control charts of landed catch in the management of fisheries. Fish. Res. 64, 19-36.��Scandol, J., 2005. Use of Quality Control Methods to Monitor the Status of Fish Stocks. In: Kruse, G.H., Galluci, V.F., Hay, D.EPetitgas, P. (2009). "The CUSUM out-of-control table to monitor changes in fish stock status using many indicators." Aquat. Living Resour. 22(2): 201-206.., Perry, R.I., Peterman, R.M., Shirley, T.C., Spencer, P.D., Wilson, B., Woodby, D.(Eds.), Fisheries Assessment in Data Limited Situations. Alaska Sea Grant AK-SG-05-02. ISBN:156612-093-4,pp.213-234.

Petitgas, P. (2009). "The CUSUM out-of-control table to monitor changes in fish stock status using many indicators." Aquat. Living Resour. 22(2): 201-206. �� Mesnil, B. and P. Petitgas (2009). "Detection of changes in time-series of indicators using CUSUM control charts." Aquat. Living Resour. 22(2): 187-192.

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UCL

LCL

Cµ

K= 1

Monitor a process using indicator/s and stabilize the system using corrective action if the control chart signals an “Out of Control” situation.

CUSUM Control Chart

• Standardize each indicator

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[z_t=(D-µ)/σ]

D = Indicator(Time Series), µ = Control Mean, σ = Control S.D.

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• 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 = min (0, Ф ⁺_n-1 + z_n - k), Ф⁺₀ = 0

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k = Allowance parameter

Scandol, J., 2003. Use of cumulative sum (CUSUM) control charts of landed catch in the management of fisheries. Fish. Res. 64, 19-36.��Scandol, J., 2005. Use of Quality Control Methods to Monitor the Status of Fish Stocks. In: Kruse, G.H., Galluci, V.F., Hay, D.E., Perry, R.I., Peterman, R.M., Shirley, T.C., Spencer, P.D., Wilson, B., Woodby, D.(Eds.), Fisheries Assessment in Data Limited Situations. Alaska Sea Grant AK-SG-05-02. ISBN:156612-093-4,pp.213-234.

6

UCL

LCL

Cµ

K= 1

Monitor a process using indicator/s and stabilize the system using corrective action if the control chart signals an “Out of Control” situation.

Fisheries Management

Scandol, J., 2003. Use of cumulative sum (CUSUM) control charts of landed catch in the management of fisheries. Fish. Res. 64, 19-36.��Scandol, J., 2005. Use of Quality Control Methods to Monitor the Status of Fish Stocks. In: Kruse, G.H., Galluci, V.F., Hay, D.E., Perry, R.I., Peterman, R.M., Shirley, T.C., Spencer, P.D., Wilson, B., Woodby, D.(Eds.), Fisheries Assessment in Data Limited Situations. Alaska Sea Grant AK-SG-05-02. ISBN:156612-093-4,pp.213-234.

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• Define Indicators ?
• Best indicators ?
• Reference Period (‘µ’) ?
• Reference Limits (‘h’) ?
• Inherent variability (‘k’) ?

Recommendations:

1. Empirical Indicators
2. Catch Data

- Age Based Numbers

- Age Based Weight

- Proportions

- Other measures

Recommendations:

1. Relationship with SSB
2. Best Matches (Correlations)
3. Use of Combined EIs

Recommendations: (CUSUMs)

1. From all available data
2. Years of preferred fishery state
3. Moving Average
4. Fixed goal

Recommendations:

1. Performance Evaluation

- Sensitivity

- Specificity

Data : Greenland Halibut in Subareas I and II (1964-2006)

Simulation : Age Structured Population Numbers for 100 years (1995 onwards)

Exponential Decay and Catch Equations were used.

Average Fishing Mortalities (1964-2006) with variation (C.V.=0.2)

Iterations : 1000

CUSUM

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Reference Period : 1980-1989

Indicators : CN6,CN7,CN8,CN11

Allowance (k) : 1

Reference Limit (h) : 1

Action : Triggered with Lower CUSUM Limit

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HCR-I : 20% to 50% reduction in Fishing Mortality (Random)

HCR-II : 50% to 80% reduction in Fishing Mortality (Random)

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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’

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Potential Indicator: CN11

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Illustration

CUSUM with HCR

CN6

r= 0.09310228

CN7

r= 0.27876774

CN8

r= 0.72337373

CN11

r= 0.90395651

Illustration

CUSUM with HCR

CN6

r= 0.09310228

HCR-I

HCR-II

CN7

r= 0 .27876774

CN8

r= 0.72337373

CN11

r= 0.90395651

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

Single Species

Simulation Framework

TASK 7

Multiple Stocks and/or

Ecosystem Interactions

To develop a theoretical management framework based on HCR that use SPC methods with empirical indicators from number of stocks to successfully manage model fisheries at the ecosystem level.