SC/65b/IA04

Preliminary analysis of abundance estimate for sei
whale in the North Pacific based on sighting dataobtained during IWC-POWER surveys in 2010-2012
TAKASHI HAKAMADA and KOJI MATSUOKA
Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055
Contact e-mail:hakamada@cetacean.jp
KEYWORD: ABUNDANCE ESTIMATE, NORTH PACIFIC, SEI WHALE, SURVEY-VESSEL,
IWC-POWER
ABSTRACT
Abundance of North Pacific sei whale (Balaenoptera borealis) was preliminary estimated using sighting
data obtained during the 2010-2012 International Whaling Commission-Pacific Ocean Whale andEcosystem Research (IWC-POWER) Cruise. Sensitivity analyses were also conducted to investigate
robustness of the abundance estimate to alternative assumptions on detection functions and mean school
size. Abundance in the central and eastern North Pacific (north of 40°N, south of the Alaskan coast
including both the US and Canadian EEZ between 170°E-135°W), from July to August was estimated as34,150 (CV=0.270) for the base case scenario. In the sensitivity analysis, abundance estimates ranged from26,926 (CV=0.217) to 33,664(CV=0.269) and there were no significant differences among thre sensitivity
estimates. Abundance information of sei whale based on IWC-POWER cruise data will be useful for thein-depth assessment of this species in the North Pacific being planned by the IWC Scientific Committee(IWC/SC).
INTRODUCTION
Commercial whaling of sei whale (Balaenoptera borealis) was conducted in the North Pacific until 1976
when the commercial take of this species was banned by the International Whaling Committee (IWC). The
population size of the North Pacific sei whale after the exploitation period was estimated at 9,000 whales(or 21% of the initial size) (IWC, 1977).
In recent years sighting data of sei whales have been collected in the western North Pacific throughdedicated Japanese whale sighting surveys, and the sighting component of the Japanese Whale Research
Program under Special Permit in the western North Pacific and its second phase (JARPN and JARPN II).
Distribution of sei whales based on Japanese Scouting Vessels (JSV), JARPN and JARPN II sighting data
was summarized by Miyashita et al., (1995), Matsuoka et al. (2000; 2009). Sei whale abundance in thewestern North Pacific (east of Japanese coast, west of 170oE, north of 35oN, south of Russian EEZ) basedon JARPN II sighting data was estimated at 5,406 whales (CV=0.300) in July and August (Hakamada etal., 2009).
The IWC Pacific Ocean Whale Ecosystem Research (IWC-POWER) surveys started in 2010 and have been
conducted every year (Matsuoka et al., 2011; 2012; 2013 2014). Abundance for the sei whales waspreliminary estimated for each IWC-POWER survey in the period 2010-2012 (Hakamada et al., 2011;2012; Hakamada and Matsuoka, 2013).
This paper presents the results of abundance estimates of sei whales for the combined data of the period
2010-2012, and examines if abundance estimates would be improved in comparison with the previous
individual survey estimates. This paper responds to some recommendations for additional analyses madeby Hakamada and Matsuoka (2013).

1

MATERIALS AND METHODS
Survey area
Survey areas for the 2010-2012 IWC-POWER surveys are shown in Figure 1. In 2010, the central North
Pacific (north of 40oN, south of Aleutian Islands, between 170oE and 170oW) was surveyed, and the survey
area was divided into two strata by the latitudinal line of 47oN. In 2011, the eastern North Pacific north of
40oN, south of Alaskan Peninsula, and between 170oW and 150oW area was surveyed. The survey area wasdivided into northern and southern strata by the EEZ line of the USA. In 2012, the eastern North Pacificnorth of 40oN, south of the Alaskan coast including both the US and Canadian EEZ, between 150oW and
135oW area was surveyed. The survey area was divided into northern and southern strata by the EEZ lineof the USA and Canada.
Research vessel
The sighting survey was conducted by the research vessel Kaiko-Maru (KK1) in 2010 and Yushin-Maru
No.3 (YS3) in 2011 and 2012. Specifications of KK1 and YS3 are provided in Matsuoka et al. (2011) andMatsuoka et al. (2013), respectively.
Survey design
Cruise tracks were designed using the program DISTANCE (Thomas et al., 2010) following the principles
outlined in the IWC Scientific Committee’s Requirements and Guidelines for Surveys (IWC, 2005).
Planned cruise tracks and survey order were shown in Figure 2. Observed distance and angles werecorrected using data obtained from the Angle and Distance Experiment.
Survey mode
Closing mode and IO mode were conducted in the 2010 survey. Passing with abeam closing mode was usedduring the survey in 2011 and 2012 survey. More details of closing and IO modes are provided in Matsuoka
et al. (2011), and more details of passing with abeam closing mode are provided in Matsuoka et al (2012;2013). Because the use of passing mode in the IWC-POWER would result in very high proportions of
unidentified cetaceans, the POWER technical advisory group (TAG) recommended that Passing with abeamclosing mode (NSP) is the most appropriate survey mode, both with respect to confirming species identity
and school size (IWC, 2013). Sighting and effort data in both survey modes were pooled for abundance
estimation because of limited sample size same as in the case of IDCR/SOWER based abundance
estimation for large baleen whales (Branch and Butterworth, 2001; Branch, 2011). Duplicate sightings in
IO mode are excluded from the analysis.
Data used
Analyses in this paper are based on sighting data obtained during the POWER surveys during 2010-2012.
Data validation by the IWC Secretariat was completed for the data obtained in 2010 and 2011 but thevalidation work has not been completed for the 2012 data. In this analysis, data that were not validated by
IWC Secretariat was used.
Analytical procedure
For this analysis it is assumed that g(0)=1. Detections are truncated at 3.0 n.miles. Abundance and its CV
were estimated by formula (1) and (2), respectively. DISTANCE ver 6.0 (Thomas et al., 2010) is used forabundance estimation.

N=

AnE ( s )
(1)
2 wl

2

  n 2
2
CV ( N ) = CV   + {CV (w)} + {CV (E ( s ) )} (2)  l where N is abundance estimate, A is area size of the surveyed area, n is the smeared number of schools
detected within perpendicular distance of 3.0 n.miles, E(s) is estimated mean school size, w is effective2

strip half width (ESW) and l is searching distance.
The truncated sighting data for schools are grouped into intervals of 0.3 n. miles to estimate the detection
function. Multiple Covariate Distance Sampling (MCDS) Engine in DISTANCE program was used. MCDS
methods are based on a Horvitz-Thompson like estimator of abundance (Thomas et al., 2010). We consider
Half Normal and Hazard Rate models as candidate models for the detection function. Full model of thedetection function was provided by
−b

 
g ( x) = 1 − exp−  x
a exp(Beaufort + Year ) 
 

 2

g ( x ) = exp − x
22a exp{2( Beaufort + Year )}


(3)

(4)

where x is perpendicular distance, a and b are parameter, Beaufort is categorical variable for Beaufort sea
state and Year is categorical variable for year.
AIC was used to select the best model to estimate ESW.
When MCDS Engine is executed, smearing cannot be conducted in DISTANCE. If the models with nocovariate was selected, the observed data of radial distance and angle would be smeared using the method
II of Buckland and Anganuzzi (1988) so as to improve fitting of the detection function.
Mean school size is estimated from the primary sightings whose school size was confirmed. Regression
method in Buckland et al. (1993) is applied to estimate mean school size .In order to examine if meanschool size should be estimated for each stratum or for all data combined, analysis of variance (ANOVA)
was conducted.
Sensitivity Analysis
Some sensitivity analysis were conducted to examine the effect of alternative assumptions for detection
function and mean school size.
1.
2.
3.

Estimating ESW using alternative models (Half Normal model)
Estimating ESW for each survey
Estimating mean school size for each stratum.

RESULTS
Searching effort and primary sightings
Searching effort and primary sightings for each stratum are summarized in Table 1. Searching effort are
1816.2, 2397.8 and 2126.1 in 2010, 2011 and 2012, respectively. Survey coverage for each stratum during
the 2010-2012 IWC-POWER surveys is shown in Table 2. Coverage is lower in the northern strata in 2010
and 2011 due to poor weather conditions in those years (Matsuoka et al., 2011; 2012). In the southern strata,
coverage is good due to good weather condition (Matsuoka et al., 2011; 2012; 2013). The truncated andsmeared numbers of sightings are 48.9, 36.9 and 76.0 in 2010, 2011 and 2012, respectively. Figure 3 shows
the plot of track line actually surveyed and the position of sei whale primary sightings during the 20102012 IWC-POWER surveys. Most of the primary sightings occurred in the southern strata.
Model selection of detection function
Table 3 gives AIC for each candidate model. Among the models, Hazard Rate model with no covariates
was selected. Therefore, Hazard Rate model with no covariates were used to estimate ESW for the basecase in this study. Figure 4 shows the detection function and observed frequency of detection. Chi-squarestatistics is 4.245 with 7 degrees of freedom (p=0.751), which suggests that fitting model is good.
Mean school size

3

Figure 5 shows distribution of observed school size for all primary sighting of the sei whales whose school
size was confirmed. Results of ANOVA suggests that data should be pooled to estimate mean school size
rather estimate by strata (Table 4). As shown in Table 5, effect of school size on detection was not significant
at 15% level (i.e. p-value was 0.158) therefore observed mean school size was used to estimate abundance.
Abundance estimate
Table 1 shows abundance estimates in each stratum for the base case. ESW estimate was 1.518 (CV=0.185),
the estimated mean school size was 1.852 (CV=0.057) and the total abundance estimate was 34,150(CV=0.270) for the base case.
Sensitivity analysis
1. Estimating ESW using alternative detection function
Abundance estimates for this sensitivity are shown in Table 6a. Abundance estimate and its CV were 26,948
and 0.230, respectively. Abundance estimate is not significantly different from that in the base case. Rightpanel in Figure 4 shows the plot. Chi-square statistics is 7.280 with 8 degrees of freedom (p=0.507), whichsuggests that fitting is worse than the base case.
2. Estimating ESW for each survey
Half Normal model was selected for all surveys, which is different from the base case. This agrees with theresults showing that AIC is smaller for Half Normal model than Hazard Rate model when year is used as acovariate. Abundance estimates and its CV were 26,926 and 0.217, respectively (Table 6b). Abundance
estimate is not significantly different from that for base case. Detection function for each survey is shownin Figure 6. The fitting of the detection functions are not substantially different from the base case.
3. Estimating mean school size for each stratum
Abundance estimates would not differ from that in the base case (Table 6c). Abundance estimates of 33,664(CV=0.269) is not substantially different from that for the base case. Difference in stratification to estimate
mean school size would not affect the abundance estimates.
DISCUSSIONS
Precision of abundance estimates in northern strata
In northern strata, there were few sightings of the sei whales, which lead lower precision of abundance
estimates in northern strata. In the northern stratum, there were some poor weather conditions (heavy winds
or poor visibility) in the US EEZ in 2010 and 2011 due to a strong low pressure system moving eastwards(Matsuoka et al, 2011; 2012). Possible causes of small number of sightings in northern stratum may be (1)poor weather condition and/or (2) lower density than in the southern strata.
Detection function
Figure 7 shows distance and angles for each sei whale primary sightings during the 2010-2012 POWER
surveys. Maximum of forward distance is larger than that of perpendicular distance. This could affect theshape of frequency data of sightings by intervals of perpendicular distance. There is not significant
differences between ESW estimates for the base case and sensitivities 1 and 2. Small ESW estimatesdifferences would be arise from the selection of the detection function. A possible solution for this is to
apply model averaging (Buckland et al., 1997). Further consideration should be given to the adjustment
terms of the detection function.
Abundance estimate for North Pacific sei whales incluing outside of POWER research area.
Estimate in the IWC-POWER research area was 34,150 (CV=0.270). Sei whale abundance estimate in thewestern North Pacific (east of Japanese coast, west of 170oE, north of 35oN, south of Russian EEZ) basedon JARPN II data was 5,406 (CV=0.300) in July and August (Hakamada et al., 2009).
Estimation of stock abundance requires information on stock structure and distribution. Kanda et al. (2009;2011; 2013) suggested that the open water of the North Pacific was mainly occupied by the individuals
from a single stock of the sei whales. Murase et al. (2009; 2013) suggested that distribution of the sei whales
predicted by a generalized additive model (GAM) is continuous in southern part of survey area during
2010-2012 IWC-POWER.

4

Under these assumptions and the assumption that there is no correlations among the estimates, the combined
abundance estimates for the JARPN II and IWC-POWER research areas in July-August was 39,556(CV=0.237).
ACKNOWLEDGEMENT
We thank the captain, crews and international researchers onboard the Yushin-Maru No.3 for their hard
work during the survey. We thank Shigetoshi Nishiwaki, Luis A. Pastene and Yu Kanaji for their comments
to improve earlier version of this manuscript. Marion Huge is thanked for her work for data validation andkindly responding our queries on IWC-POWER data. We also thank Greg Donovan of the Head of Scienceof the IWC, the steering group (Hidehiro Kato (Convener), An Yong-Rock, John Bannister, Robert
Brownell, Phillip Clapham, Paul Ensor, Tomio Miyashita, Hiroto Murase, Luis Pastene and Paul Wade)and the POWER Technical Advisory Group (Mark Bravington, Sharon Hedley, Natalie Kelly, Toshihide
Kitakado and Debra Palka) for their scientific contributions to organize the POWER cruises.
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Table 1. Abundance estimates for the sei whales and their CV’s for each stratum based on 2010-2012 IWCPOWER cruises from July to August for base case. A is area size of the surveyed area, n is the smeared
number of schools detected within perpendicular distance of 3.0 n.miles, l is searching distance, ESW iseffective strip half width, E(s) is estimated mean school size, D is density (individual/n.miles2), P isabundance estimate and CI is abbreviation for confidence interval.
Year

StratumAn(n.miles 2 )
2010

2011

2012

CV

n /l *100

l

CV

ESW

CV

E(s )

(n.miles)(n.miles)D(ind./n.m

P

95% CI 95% CI

CV

LL(ind.)

iles 2 )UL

Northern

238,627

4.0

490.5

0.816

0.502

1.518

0.185

1.852

0.057

0.005

1,187

0.535

444

3,176

Southern

365,24444.9

1325.7

3.390

0.326

1.518

0.185

1.852

0.057

0.021

7,555

0.379

3,687

15,480

Northern

193,560

0.0

723.8

0.000

0.000

-

-

-

-

0.000

0

-

-

-

Southern

569,16736.9

1,674.0

2.207

0.386

1.518

0.185

1.852

0.057

0.013

7,665

0.421

3,471

16,926

Northern

142,427

1.8

767.5

0.238

0.661

1.518

0.185

1.852

0.057

0.001

207

0.657

64

668

Southern

529,36274.2

1,358.6

5.429

0.293

1.518

0.185

1.852

0.057

0.033

17,536 0.354

8,939

34,403

Total

2,038,387

161.8

6,340.1

2.552

-

-

-

-

-

0.017

34,150 0.270

20,28657,489

Table 2. Survey coverage for each stratum during the 2010-2012 IWC-POWER surveys.
Survey year

Northern

Southern

2010

34%

70%

2011

58%

78%

2012

80%

85%

Table 3. AIC estimate for each model of detection functions for base case. For selected model, AIC is
indicated by bold letters. HN: Half Normal and HR: Hazard Rate
Model

HN

HR

Beaufort+Year

718.40722.71

Beaufort

719.59719.14

Year

716.67719.00No covariate

716.16714.63

Table 4. Results for ANOVA to examine if null hypothesis that mean school size are equal among the strata
can be rejected.
Sum of Squares

Degree offreedom

Mean square

F-statistics

p -value
0.751

Between strata

3.865

4

0.966

0.479

Within strata

324.593

161

2.016

-

-

T otal328.458

165

-

-

-

Table 5. Results for regression of log of observed school size and detection probability for base case. SE isstandard error of the estimate slope.
Slope

SE

Student's-t

p -value-0.1730.172-1.0060.158

7

Table 6. The abundance estimates for the sei whales and their CV’s for each stratum for sensitivity tests.
The Notations is as for Table 1.
a.
Year

Sensitivity 1
StratumAn(n.miles 2 )l

n /l *100

CV(n.miles)

ESW

CV

E(s )

CV

(n.miles)D(ind./n.miles 2 )P

CV(ind.)

95% CI 95% CI
LL

UL

Northern
Southern
Northern
2011
Southern
Northern
2012
Southern

238,627
365,244
193,560
569,167
142,427
529,362

4.0
44.9
0.0
36.9
1.8
74.2

490.5
1325.7723.8
1,674.0
767.5
1,358.6

0.816
3.390
0.000
2.207
0.238
5.429

0.502
0.326
0.000
0.386
0.661
0.293

1.590
1.590
1.842
2.195
2.195

0.117
0.117
0.144
0.109
0.109

1.852
1.852
1.852
1.852
1.852

0.057
0.057
0.057
0.057
0.057

0.005
0.020
0.000
0.013
0.001
0.033

1,133
7,210
0
6,316
143
12,124

0.516
0.350
0.421
0.657
0.354

438
3,701
3,471
64
8,939

2,935
14,047
16,926668
34,403

Total

2,038,387

161.8

6,340.1

2.552

-

-

-

-

-

0.013

26,926 0.217

17,69040,983l

n /l *100

CV

ESW

CV

E(s )

CV

D
(ind./n.m

2010

b.
Year

Sensitivity 2
StratumAn2(n.miles)(n.miles)(n.miles )iles 2 )P

CV(ind.)

95% CI 95% CI
LL

UL

Northern
Southern
Northern
2011
Southern
Northern
2012
Southern

238,627
365,244
193,560
569,167
142,427
529,362

4.0
44.9
0.0
36.9
1.8
74.2

490.5
1325.7723.8
1,674.0
767.5
1,358.6

0.816
3.390
0.000
2.207
0.238
5.429

0.502
0.326
0.000
0.386
0.661
0.293

1.923
1.923
1.923
1.923
1.923

0.070
0.070
0.070
0.070
0.070

1.852
1.852
1.852
1.852
1.852

0.057
0.057
0.057
0.057
0.057

0.004
0.016
0.000
0.013
0.001
0.033

937
5,962
0
6,048
163
13,838

0.507
0.338
0.421
0.657
0.354

367
3,130
3,471
64
8,939

2,393
11,354
16,926668
34,403

Total

2,038,387

161.8

6,340.1

2.552

-

-

-

-

-

0.013

26,948 0.230

17,25342,090nl

n /l *100

CV

ESW

CV

E(s )

CV

D
(ind./n.m

2010

c.
Year

Sensitivity 3
StratumA
2(n.miles)CV

95% CI 95% CI
LL

UL

Northern
2010
Southern
Northern
2011
Southern
Northern
2012
Southern

238,627
365,244
193,560
569,167
142,427
529,362

4.0
44.9
0.0
36.9
1.8
74.2

490.5
1325.7723.8
1,674.0
767.5
1,358.6

0.816
3.390
0.000
2.207
0.238
5.429

0.502
0.326
0.000
0.386
0.661
0.293

1.518
1.518
1.518
1.518
1.518

0.185
0.185
0.185
0.185
0.185

1.000
1.867
1.682
2.000
1.838

0.000
0.086
0.099
0.500
0.095

0.003
0.021
0.000
0.013
0.001
0.033

641
7,615
0
7,665
207
17,536

0.535
0.384
0.421
0.657
0.354

240
3,681
3,471
64
8,939

1,715
15,756
16,926668
34,403

Total

2,038,387

161.8

6,340.1

2.552

-

-

-

-

-

0.017

33,664 0.269

20,05956,496(n.miles )(n.miles)P

iles 2 )Figure 1. Survey area for 2010-2012 IWC-POWER surveys.

8(ind.)

Figure2. Planned track line for 2010-2012 IWC-POWER surveys. Arrows show survey order. Left panel isfor 2010, middle panel is for 2011 and right panel is for 2012.

Figure 3. Plot of actually surveyed track line and position of primary sightings of the sei whales during
2010-2012 IWC-POWER surveys.

Figure 4. Plot of the estimated detection function and fitted to the number of schools as a function of
perpendicular distance (n. miles) from the track line for base case (left panel) and sensitivity 2 (right panel).

9Figure 5. Frequency of observed school size of sei whales during the 2010-2012 IWC-POWER surveys.
Only sightings whose school size was confirmed are used.

Figure 6. Plot of the estimated detection function and fitted to the number of schools as a function of
perpendicular distance (n. miles) from the track line for each survey in sensitivity 2. Left upper panel is for
2010, right panel is for 2011 and left lower panel is for 2012.

10

9
8

Forward distance (n.miles)

7
6
5
4
3
2
1

-5

-3

-1

0

1

3

5

Perpendicular distance (n.miles)
Figure 7. Distribution of perpendicular and forward distances for sei whale primary sightings during the
2010-2012 IWC-POWER surveys.

11