1 deepak george pazhayamadom a, emer rogan a, ciaran kelly b and edward codling c a school of...
TRANSCRIPT
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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
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02
04
06
08
01
00
Years
La
rge
Fis
h In
dic
ato
r (L
FI)
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-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
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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
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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
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• 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