1

Deepak George Pazhayamadoma, Emer Rogana, Ciaran Kellyb and Edward Codlingc

aSchool of Biological, Earth and Environmental Sciences (BEES), University College Cork, Ireland; bFisheries Science Services, Marine Institute, Ireland; cDepartment of Mathematical Sciences, University of Essex, United Kingdom

Can we manage a fishery if no previous data are available?

5 10 15 20

0.0

e+

00

1.0

e+

07

2.0

e+

07

Years

Sp

aw

nin

g S

tock

Bio

ma

ss

5 10 15 20

02

04

06

08

01

00

Years

La

rge

Fis

h In

dic

ato

r (L

FI)

5 10 15 20

-4-2

02

4Years

SS

-CU

SU

M

Control LimitUpper SS-CUSUM (Positive deviations)Lower SS-CUSUM (Negative deviations)Out of Control

YES

No historical data at 0th year

2

SS-CUSUM

• SS-CUSUM is an indicator monitoring tool.

• SS-CUSUM do not need a reference point.

• SS-CUSUM calculate the cumulative deviations of indicator from running mean

Parameters

• Allowance (k) accommodate the inherent variability in observations

• Control limit (h) produce signal if the indicator is in an out-of-control (OC) situation

• Winsorizing constant (w) make self starting CUSUM robust to outliers

5 10 15 20

92

93

94

95

96

97

Years

LF

I ru

nn

ing

me

an

EVALUATION OF SS-CUSUM USING A STOCHASTIC SIMULATION TEST

• A stable fish stock was overfished and indicators were monitored using SS-CUSUM

• Signals obtained from SS-CUSUM were used to calculate sensitivity and specificity

• Sensitivity is the probability of getting a true signal when overfishing was applied

• Specificity is the probability of getting a true signal when there was no overfishing

Indicator observations corresponding to out-of-control situations are omitted while calibrating the running mean

PERFORMANCE MEASURES USED•Receiver Operator Characteristic (ROC) curves

3

• SS-CUSUM was successful in detecting the fishing impact.

• An indicator is best when the apex of ROC curve is closer to upper left corner.

•The method performed best with Large Fish Indicators (LF catch numbers, LF catch weight and LF CPUE).

RESULTS (ROC CURVES)

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

Se

nsi

tivity

RecruitmentLandingsCPUELarge Fish CPUEYoung Fish CPUE

CONCLUSION

All stock indicators in the study were useful in detecting fishing impact and hence

SS-CUSUM can be potentially used for monitoring data poor fisheries

REFERENCE:Hawkins, D.,Olwell, D., 1998. Cumulative sum charts and charting for quality improvement: Springer Verlag, pp:162-168.

BEST

GOODWORS

T

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

Se

nsi

tivity

Large Fish Catch NumbersLarge Fish Catch WeightMean AgeMean LengthMean Weight