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IOTC–2014–WPB12–24 Rev_1
Page 1 of 19
Stock and risk assessments of swordfish (Xiphias gladius) in the Indian Ocean
by ASPIC incorporating uncertainties
(Revised 1)
Tom Nishida
National Research Institute of Far Seas Fisheries (NRIFSF)
Fisheries Research Agency (FRA), Shimizu, Shizuoka, Japan
Sheng-Ping Wang
Department of Environmental Biology and Fisheries Science,
National Taiwan Ocean University, Keelung, Taiwan
October, 2014
Abstract
Stock and risk assessments were conducted by ASPIC software using 64 years data (1950-2013) for swordfish in
the whole and the SW Indian Ocean. In the stock assessments, uncertainties caused by production models
(Schaefer vs. Fox) and targeting effect (SWO cluster vs. NHBF) were taken into account.
As for the Whole Indian Ocean, the stock is now at the well safe zone (green zone in the Kobe plot), i.e., Total
biomass (TB) ratio=1.64 and F ratio=0.52. Risk assessments suggests no risks violating MSY levels at all if the
current catch in 2013 (31,000 tons) or less level continues next 10 years, while the low risk with 20% increases
(37,000 tons) and high risks with 40% increase (44,000 tons) for both TB and F ratios.
As for the SW Indian Ocean, the stock is now at the recovering stage (yellow zone close to both MSY levels of TB
and F in the Kobe plot), i.e., Total biomass (TB) ratio=0.95 and F ratio=0.93. Risk assessment suggests that there
are high risks violating MSY levels of TB ratio levels next 10 years if the current catch in 2013 (7,300 tons) were
increased by 10% or more, while low or no risks if the current catch or less level were continued. As for F ratio,
even if the current catch level were reduced by 10% (6,600 tons), there are high risks violating MSY levels. If the
current catch levels were reduced more than by 20% (5,900 ton), there are less or no risks next 10 years.
Contents 1. Introduction------------------------------------------------------------------------------------------------------ 02 2. Stock assessments in the whole Indian Ocean
2.1 Data---------------------------------------------------------------------------------------------------02-05 2.2 ASPIC runs-------------------------------------------------------------------------------------------05-07 2.3 Discussion (ASPIC)---------------------------------------------------------------------------------08-10 2.4 Risk assessments (Kobe II)-----------------------------------------------------------------------11-12
3. Stock assessments in the SW Indian Ocean 3.1 Data----------------------------------------------------------------------------------------------------12-15 3.2 ASPIC runs--------------------------------------------------------------------------------------------15-16 3.3 Risk assessments (Kobe II)------------------------------------------------------------------------17-18
Acknowledgements----------------------------------------------------------------------------------------------------19
References---------------------------------------------------------------------------------------------------------------19
_______________________________________________________________________ Submitted to the IOTC 12 th WPB meeting, Oct. 21-25, 2014, Yokohama, Japan
IOTC–2014–WPB12–24 Rev_1
Page 2 of 19
1. Introduction
We attempted the stock assessment of swordfish (Xiphias gladius) (SWO) in the Indian Ocean
by A Stock-Production Model Incorporating Covariates (ASPIC) using 64 years data (1950-
2013). As in the past, we conducted stock assessments for 2 areas (whole Indian Ocean and
the SW IO) because the local depletion in the SW Indian Ocean has been detected (Nishida
and Wang, 2011) and we need to know the current situation.
2. Stock assessments in the whole Indian Ocean
2.1 Data
(1) Catch
We used the nominal catch data from the IOTC dataset (as of Oct., 15, 2014) (Fig. 1).
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IO SWO catch (1950-2013)(tons)
Fig. 1 Nominal catch of swordfish in the Indian Ocean (1950-2013) (IOTC dataset, Oct., 15, 2014 version)
(2) CPUE
We have 4 STD_CPUE (standardized CPUE) (Japan, Taiwan, Spain and Portugal) in the whole IO.
Fig. 2a and 2b shows 4 STD_CPUE in 1971-2013 and in 1999-2013 respectively. JPN STD_CPUE
shows the decreasing trend from 1971-2005 and afterward increasing trends and stabilized in
IOTC–2014–WPB12–24 Rev_1
Page 3 of 19
higher level, while they have big bumps. TWN STD_CPUE shows the increasing trend from
1980-1997, afterward constant trend, while they also have large bumps throughout. SPN and
POR STD_CPUE trends show more or less flat trend
During 1991-1998, STD_CPUE trends between Japan and Taiwan show different trends, i.e.,
Japan shows the decreasing trend, while Taiwan, increasing trend. During 1999-2013, 4
STD_CPUE show more or less similar trend (flat trend).
0
0.5
1
1.5
2
1971197319751977197919811983198519871989199119931995199719992001200320052007200920112013
scal
ed
(av
e=1
)
STD_SPUE (JPN+TWN+SPN+POR) (1971-2013)
Japan
Taiwan
Spain
Portugal
Fig 2a Comparisons of STD_CPUE among 4 fleets (Japan, Taiwan, Spain and Portugal) (1971-2013)
0
0.5
1
1.5
2
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
scal
ed
(av
e=1
)
STD_CPUE(JPN+TWN+SPA+POR)(1999-2013)Japan
Taiwan
Spain
Portugal
Fig 2b Comparisons of STD_CPUE among 4 fleets (Japan, Taiwan, Spain and Portugal) (1999-2013).
IOTC–2014–WPB12–24 Rev_1
Page 4 of 19
(3) Relation between nominal catch vs. STD_CPUE (Fig. 3)
Japan
Taiwan
Spain
Portugal
Fig. 3 Comparisons between nominal catch vs. STD_CPUE by fleet (Whole Indian Ocean)
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scal
ed
CP
UE
(ave
=1)
ton
s
Catch vs. JPN_CPUE
catch
JPN_CPUE
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scal
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch vs TWN STD_CPUE
catch
TWN_CPUE
y = -1E-05x + 1.2218R² = 0.407
0
0.5
1
1.5
2
0 10000 20000 30000 40000 50000
JPN
STD
_CP
UE
(sca
lke
d a
ve=1
)
catch (tons)
Relation between catch vs. JPN STD_CPUE
y = 2E-05x + 0.6294R² = 0.6381
0
0.5
1
1.5
2
0 10000 20000 30000 40000 50000
scla
ed C
PU
E (a
ve=1
)
catch (tons)
Relation between catch vs TWN STD_CPUE
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scla
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch vs. SPAIN STD_CPUE
catch
SPAIN STD_CPUE
y = 1E-06x + 0.9561R² = 0.0043
0
0.5
1
1.5
0 10000 20000 30000 40000 50000
scal
ed
CP
UE
(ave
=1)
catch (tons)
Relation between catch vs SPAIN STD_CPUE
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scla
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch vs. PRT STD_CPUE
y = 1E-05x + 0.6999R² = 0.0968
0
0.5
1
1.5
0 10000 20000 30000 40000 50000
scal
ed
CP
UE
(ave
=1)
catch (tons)
Relation between catch vs POR STD_CPUE
IOTC–2014–WPB12–24 Rev_1
Page 5 of 19
Based on Fig. 3, only Japan has the negative correlation between nominal catch and STD_CPUE,
hence we use JPN_CPUE for ASPIC runs.
2.2. ASPIC runs
We used the ASPIC software (ver. 5.05) developed by Prager (2004). For details of this
software, refer to “User’s Manual for ASPIC: A Stock-Production Model Incorporating
Covariates (ver. 5) and auxiliary programs, Population Dynamics Team, Center for Coastal
Fisheries and Habitat Research, National Oceanic and Atmospheric Administration, Beaufort,
North Carolina, USA: National Marine Fisheries Service Beaufort Laboratory Document BL-
2004-01” by Prager (2004).
In ASPIC, we set up 4 scenarios (Table 1) to incorporate uncertainties between models (FOX vs.
Schaefer) and also between targeting effects (SWO cluster vs. NHBF) (Fig.4). We assume
B1950 =K. Table 1 and Figs. 5-6 show results. To represent these uncertainties, we used
weighted average of RMSE (Root Mean Square Errors) and Fig. 7 and Table 1 show the results.
0
0.5
1
1.5
2
2.5
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
scal
ed (
ave=
1)
JPN STD_CPUE (Case 5 vs. 6) (All area)
case 5
case 6
Fig 4 JPN STD_CPUE used for the ASPIC runs (case 5: SWO cluster and case 6: NHBF)
(Refer to Table 1)
IOTC–2014–WPB12–24 Rev_1
Page 6 of 19
Table 1 ASPIC runs incorporating uncertainties (4 scenarios) and results (1950-2013)
Scenario
Elements of uncertainty R2
(CPUE)
RMSE
(over all)
MSY
(tons)
TB(2013)
/TB(MSY)
F(2013)
/F(MSY) Model Targeting
effect
(1) FOX SWO cluster 0.416 0.2017 44,480 1.93 0.37
(2) NHBF 0.420 0.2003 37,300 1.46 0.57
(3) SCHAEFER SWO cluster 0.455 0.2706 35,970 1.69 0.51
(4) NHBF 0.419 0.2808 34,260 1.38 0.66
Overall ASPIC results
incorporating uncertainties
(weighted average by 1/RMSE)
38,450
1.63
0.52
SWO cluster NHBF
F
O
X
Scenario (1) Scenario (2)
S C H A E F E R
Scenario (3) Scenario (4)
Fig. 5 Kobe plots of ASPIC results (4 scenarios) showing uncertainties among and within and scenarios.
IOTC–2014–WPB12–24 Rev_1
Page 7 of 19
Fig. 6 Kobe plot incorporating uncertainties in 4 scenarios (weighted average of 1/RMSE)
Table 2 Summary of ASPIC result
Management Quantity Whole Indian Ocean
Most recent catch estimate (t) (2013) 31,804
Mean catch over last 5 years (t) (2009-2013) 26,510
MSY (1000 t) (80%CI) 38.5 (26.3-56.2)
Data Period used in assessment 1950-2013 (64 years)
F(2013)/F(MSY)
(80% CI)
0.52
(0.48-0.55)
TB(2013)/TB(MSY)
(80% CI)
1.63
(1.60-1.69)
SB(2013)/SB(MSY) NA
TB(2013)/TB(1950) 0.76
SB(2013)/SB(1950) NA
TB(2013)/TB(2013, F=0) NA
SB(2013)/SB(2013, F=0) NA
IOTC–2014–WPB12–24 Rev_1
Page 8 of 19
95%
75%50%25%
5%
80 81 82 83 8485868788899091
92
9394
95
9697
989900
0102
03
04
0506
07
08
09
TB/TBmsy
3210
F/F
msy
2
1
0
2.3 Discussion (ASPIC)
(1) Why we got much more optimistic stock status this time than in last time (2011)?
In the last stock assessment by ASPIC (2011) using 30 years data (1980-2009), the Kobe plot
shows more conservative phase (Fig. 7) than those in this time (Figs. 5-6). This gap is likely
caused likely by the following reason, i.e., the previous assessment in 2011 used CPUE for 30
years [B]-[C] (1980-2009), while for this time for 43 years [A]-[D] (1971-2013). This means that
the stock assessment for this time include the recent recovery phase [D], which likely made
much optimistic stock status (Fig. 8).
Fig. 7 Kobe plot of the 2011 stock assessment by ASPIC using 1980-2009 data
(Nishida and Wang, 2011)
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40000
50000
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1952
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1958
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1984
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1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
scal
ed C
PU
E (a
ve=1
)
ton
s
Catch vs. JPN_CPUE
Fig. 8 Trend of JPN STD_CPUE (4 phases) [A]-[D] and the catch trend
[A]
[B]
[C]
[D]
IOTC–2014–WPB12–24 Rev_1
Page 9 of 19
(2) Why STD_CPUE (SWO cluster) made ASPIC results much optimistic than the one (NHBF)?
The reason is considered as follows: Targeting effect by NHBF cluster makes CPUE trends
much steeper than the one by SWO cluster. This is easily understood from the comparison of
STD_CPUE between 2 targeting effect (Fig. 9). Average slope of STD_CPUE with NHBF is much
lower than the one by SWO cluster. This means that STD_CPUE with SWO cluster will produce
much less stock reduction over the period than the one by NHBF, i.e., it makes the stock more
optimistic.
Comparison of STD_CPUE within 2014 (slope: -0.0122 vs. -0.0223)
Comparison of STD_CPUE between 2014 vs. 2011 (slope : -0.0122 vs. -0.017)
Fig. 9 Comparison JPN STD_CPUE between 2 different target effects: SWO cluster vs. NHBF
y = -0.0223x + 1.4899R² = 0.4055
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sca
led
(a
ve
=1
)
JPN STD_CPUE (Case 6) (NHBF)
y = -0.0122x + 1.2694R² = 0.3081
0
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sca
led
(a
ve
=1
)
JPN STD_CPUE (Case 5) (SWO cluster)
y = -0.017x + 1.4258R² = 0.2669
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2
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sca
led
(a
ve=
1)
JPN STD_CPUE (2011) (NHBF)
y = -0.0122x + 1.2694R² = 0.3081
0
0.5
1
1.5
2
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sca
led
(a
ve=
1)
JPN STD_CPUE (2014) (SWO cluster)
IOTC–2014–WPB12–24 Rev_1
Page 10 of 19
(3) Why JPN STD_CPUE produced more realistic trends?
Japanese tuna LL has been exploiting SWO as bycatch in the whole Indian Ocean. This means
that Japanese tuna LL likely performs “simple random sampling” for SWO, which make CPUE
more realistic (abundance) trends even they have a lot of noises (bumps).
As for TWN tuna LL, they targets SWO for sometimes and for other times, exploits as bycatch
when they target other species such as albacore, bigeye tuna, yellowfin tuna and southern
Bluefin tuna. Thus, their samplings strategies are not consistent, which make CPUE biased with
large fluctuations. It is likely that even SWO cluster as targeting correction factor are used,
large biases causes by such inconsistent samplings may be not able to be reduced. In fact,
STD_CPUE with SWO cluster (targeting correction factor) (case 4 in Fig. 8) (Wang and Nishida,
2014) are much smoother than the one with species compositions (case 2). But, the rough
trends between two are more or less same because of inconsistent samplings.
Fig. 10 Comparison of TWN STD_CPUE by different targeting correction factor
(Wang and Nishida, 2014)
(Case 2: species composition; and case 4: SWO cluster)
As Spain and Portugal LL, the time series of their CPUE are short and it is difficult to explain
reasons why their STD_CPUE and catch are not well corresponding as shown Fig. 2
IOTC–2014–WPB12–24 Rev_1
Page 11 of 19
2.4 Risk assessments (Kobe II)
Based on 500 time bootstraps by ASPIC software, we conducted risk assessments for TB ratios
and F ratios. Box 1 shows results. Box 1 suggests no risks violating MSY levels at all if the
current catch (31,000 tons) or less level continues next 10 years, while the low risk with 20%
increases (37,000 tins) and high risks 40% (44,000 tons) for both TB and F ratios.
Box 1 Kobe II risk assessment for TB ratio and F ratios (Whole Indian Ocean)
Color legends (4 probabilities ranges of risks violating MSY levels)
(1) TB (Total biomass) ratio
years later 1 2 3 4 5 6 7 8 9 10
catch level catch (tons) 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023-40% 18,650 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-20% 24,867 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
current
(2013)31,084 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
20% 37,301 0.05 0.12 0.15 0.18 0.22 0.24 0.27 0.28 0.30 0.34
40% 43,518 0.13 0.28 0.40 0.52 0.60 0.70 0.75 0.80 0.87 0.89
(2) F ratios
years later 1 2 3 4 5 6 7 8 9 10catch level catch (tons) 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
-40% 18,650 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-20% 24,867 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
current
(2013)31,084 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
20% 37,301 0.02 0.11 0.13 0.18 0.22 0.24 0.26 0.28 0.30 0.33
40% 43,518 0.20 0.36 0.47 0.53 0.63 0.71 0.77 0.81 0.87 0.89
0-0.25 0.25-0.50 0.50-0.75 0.75-1
IOTC–2014–WPB12–24 Rev_1
Page 12 of 19
3. Stock assessment in the SW Indian Ocean
3.1 Data
(1) Catch
We used the nominal catch data in the SW region available in the IOTC dataset for SWO stock
assessments published in October 15, 2014 (Fig. 11).
0
5000
10000
15000
20000
19
54
19
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12
Catch : SW Indian Ocean (tons) (1950-2013)
Fig 11 Nominal catch (ton) used for the ASPIC analyses for the SW Indian Ocean
(IOTC dataset for stock assessments, October 15, 2014)
(2) STD_CPUE
We have 4 STD_CPUE (standardized CPUE) (Japan, Taiwan, Spain and Portugal) in the whole IO.
Fig. 12a and 12b shows 4 STD_CPUE in 1971-2013 and in 1999-2013 respectively. JPN
STD_CPUE shows the decreasing trend in general with large bumps (ups and downs). TWN
STD_CPUE shows the increasing trend from 1980-1993, afterward the decreasing trend, while
they also have large bumps throughout. SPN and POR STD_CPUE trends show more or less flat
trend
During 1980-1993, STD_CPUE trends between Japan and Taiwan show different trends, i.e.,
Japan shows the decreasing trend, while Taiwan, increasing trend, while both shows the
IOTC–2014–WPB12–24 Rev_1
Page 13 of 19
decreasing trend during 1993-2013. 4 STD_CPUEs show more or less similar trend (flat trend)
except a big jump in JPN STD_CPUE in 2008.
0
1
2
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4
19
71
19
73
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sca
led
(a
ve=
1)
STD CPUE (SW) (JPN+TWN+SPN+PRT)
Japan
Tawain
Portgul
Spain
Fig 12a STD_CPUE in the SW Indian Ocean (Japan, Taiwan, Spain and Portugal) (1971-2013)
0
1
2
3
199920002001200220032004200520062007200820092010201120122013
scal
ed
(av
e=1
)
Comparisons among 4 STD_CPUE (1999-2013)
Japan
Tawain
Portgul
Spain
Fig 12b STD_CPUE in the SW Indian Ocean (Japan, Taiwan, Spain and Portugal) (1999-2013)
IOTC–2014–WPB12–24 Rev_1
Page 14 of 19
(3) Relation between nominal catch vs. 4 STD_CPUE (Fig. 13)
Japan
Taiwan
Spain
Portugal
Fig. 13 Comparisons between nominal catch vs. STD_CPUE by fleet (SW Indian Ocean)
0
1
2
3
0
5000
10000
15000
20000
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
scal
ed C
PU
E (a
ve=1
)
catc
h (
ton
s)
Catch vs TWN STD_CPUE
0
1
2
3
4
0
5000
10000
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200001
95
4
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scal
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch vs. JPN STD_CPUE (SW)
y = -1E-04x + 1.4477R² = 0.3767
0
1
2
3
4
0 5000 10000 15000 20000
scal
ed
CP
UE
(ave
=1)
catch (tons)
Relation : Catch vs. JPN STD_CPUE (SW)
y = 0.0001x + 0.2434R² = 0.6756
0
1
2
3
0 5000 10000 15000 20000
rela
tice
CP
UE
(ave
=1)
Catch (tons)
Relation: TWN STD_CPUE vs Catch
0
1
2
3
0
5000
10000
15000
20000
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19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
scal
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch vs SPAIN STD_CPUE (SW)
y = -3E-06x + 1.0223R² = 0.0013
0
0.5
1
1.5
0 5000 10000 15000 20000
scal
ed
CP
UE
(ave
=1)
Catch (tons)
Relation : STD_CPUE vs Catch (SW)
0
0.5
1
1.5
2
2.5
0
5000
10000
15000
20000
19
54
19
57
19
60
19
63
19
66
19
69
19
72
19
75
19
78
19
81
19
84
19
87
19
90
19
93
19
96
19
99
20
02
20
05
20
08
20
11
scal
ed
CP
UE
(ave
=1)
catc
h (
ton
s)
Catch and POT STD_CPUE (SW)
y = -1E-06x + 1.0352R² = 0.0002
0
0.5
1
1.5
0 5000 10000 15000 20000
Scal
ed
CP
UE
(ave
=1)
Catch (tons)
Relation: POT STD_CPUE vs Catch
IOTC–2014–WPB12–24 Rev_1
Page 15 of 19
Based on Fig. 13, only Japan has the negative correlation between nominal catch and
STD_CPUE, hence we use JPN_CPUE for the ASPIC.
3.2. ASPIC runs
We run ASPIC in the same way as in the Whole Indian Ocean using 4 scenarios (Table 3). Table
3 and Fig. 14 show the results. Scenarios (1) and (2) could not get convergences and (3) had
poor fits to the model hence reasonable confidence surface could not be obtained. Hence, for
the SW IO ASPIC, scenario (4) is considered to be the best results (Fig. 15). Table 3 shows the
summary.
Table 3 ASPIC runs incorporating uncertainties (4 scenarios) and results (SW IO) (1950-2013)
Scenario
Elements of uncertainty R2
(CPUE)
RMSE
(over all)
MSY
(tons)
TB(2013)
/TB(MSY)
F(2013)
/F(MSY) Model Targeting
effect
(1) FOX SWO cluster Not converged
(2) NHBF Not converged
(3) SCHAEFER SWO cluster 0.112 0.7714 9,965 0.43 1.13
(4) NHBF 0.418 0.4041 8,507 0.95 0.93
Scenario
selected
As (1)+(2) were not converged and (3) has lower R2 and higher RMSE (much
less fit) than in (4) and could not provide confidence surface, Scenario (4) is
selected as the best representative result of the ASPIC assessment.
IOTC–2014–WPB12–24 Rev_1
Page 16 of 19
SWO cluster NHBF
F
O
X
Not converged
Not converged
Scenario (1) Scenario (2)
S C H A E F E R
Scenario (3)
Confidence surface could not be evaluated due to
constrains in F pre-set in the ASPIC software
Scenario (4)
Fig. 14 Kobe plots of ASPIC results (4 scenarios) showing uncertainties among and within and scenarios.
Fig. 15 Magnified Kobe plot of scenario (4) as the best representative of ASPIC runs
IOTC–2014–WPB12–24 Rev_1
Page 17 of 19
Table 3 Summary of ASPIC result (SW IO)
Management Quantity SW Indian Ocean
Most recent catch estimate (t) (2013) 7,349
Mean catch over last 5 years (t) (2009-2013) 7,260
MSY (1000 t) (80%CI) 8.51(7.76-9.22)
Data Period used in assessment 1950-2013 (64 years)
F(2013)/F(MSY)
(80% CI)
0.93
(0.65-1.28)
TB(2013)/TB(MSY)
(80% CI)
0.95
(0.69-1.24)
SB(2013)/SB(MSY) NA
TB(2013)/TB(1950) 0.45
SB(2013)/SB(1950) NA
TB(2013)/TB(2013, F=0) NA
SB(2013)/SB(2013, F=0) NA
3.3 Risk assessments (Kobe II) (SW IO)
Based on 500 time bootstraps using ASPIC software, we conducted risk assessment for TB
ration and F ratios for next 10 years. Box 2 shows results. Box 2 suggests that there are high
risks violating MSY levels of TB ratio levels next 10 years if the current catch were increased by
10% or more, while low or no risks if the current catch or less level were continued.
As for F ratio, even if the current catch level were reduced by 10%, there are high risks
violating the MSY levels. If the current catch levels were reduced more than by 20%, there are
less or no risks next 10 years.
IOTC–2014–WPB12–24 Rev_1
Page 18 of 19
Box 2 Kobe II risk assessment for TB ratios and F ratios (SW Indian Ocean)
Color legends (4 probabilities ranges of risks violating MSY levels)
(1) TB ratio
years later 1 2 3 4 5 6 7 8 9 10
catch levelcatch
(tons)2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
-40% 4,409 0.24 0.09 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00-30% 5,144 0.31 0.13 0.03 0.02 0.01 0.01 0.01 0.01 0.00 0.00-20% 5,879 0.37 0.19 0.10 0.04 0.02 0.02 0.01 0.01 0.01 0.01-10% 6,614 0.43 0.28 0.18 0.12 0.07 0.05 0.04 0.04 0.03 0.03
current
(2013)7,349 0.49 0.40 0.31 0.26 0.21 0.18 0.16 0.14 0.14 0.12
10% 8,084 0.52 0.53 0.52 0.50 0.47 0.45 0.43 0.43 0.41 0.4020% 8,819 0.62 0.65 0.71 0.76 0.80 0.83 0.84 0.85 0.87 0.9030% 9,554 0.69 0.82 0.92 0.96 0.98 0.99 0.99 1.00 1.00 1.00
40% 10,289 0.71 0.86 0.94 0.98 0.99 0.99 1.00 1.00 1.00 1.00
(2) F ratio
years later 1 2 3 4 5 6 7 8 9 10
catch levelcatch
(tons)2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
-40% 4,409 0.10 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00-30% 5,144 0.10 0.06 0.02 0.01 0.01 0.01 0.00 0.00 0.00 0.00-20% 5,879 0.33 0.30 0.24 0.19 0.17 0.13 0.11 0.09 0.05 0.05-10% 6,614 0.54 0.56 0.58 0.62 0.64 0.67 0.69 0.69 0.67 0.68
current
(2013)7,349 0.72 0.81 0.84 0.87 0.90 0.91 0.91 0.92 0.92 0.93
10% 8,084 0.85 0.91 0.94 0.97 0.97 0.98 0.98 0.98 0.98 0.9820% 8,819 0.92 0.98 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.9930% 9,554 0.97 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
40% 10,289 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0-0.25 0.25-0.50 0.50-0.75 0.75-1
IOTC–2014–WPB12–24 Rev_1
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Acknowledgments
Many thanks for Mr. Miguel Herrera (IOTC data manager) to provide necessary information to
conduct the stock assessments. Thanks also for Mr. Iwasaki (Environmental Simulation
Laboratory) to provide the Kobe plot software (new version).
References
Nishida, T. and Wang, S-P. (2010) Stock assessment of swordfish (Xiphias gladius) in the Indian
Ocean by ASPIC (1980-2008). IOTC-2010-WPB-12
Prager, M. (2004) User’s Manual for ASPIC: A Stock-Production Model Incorporating
Covariates (ver. 5) and auxiliary programs, Population Dynamics Team, Center for
Coastal Fisheries and Habitat Research, National Oceanic and Atmospheric
Administration, 101 Pivers Island Road, Beaufort, North Carolina 28516 USA:
National Marine Fisheries Service Beaufort Laboratory Document BL-2004-01