SC/66a/IA

Abundance estimate for sei whales in the North Pacificbased on sighting data obtained 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 whales (Balaenoptera borealis) was estimated using sighting data obtained
during the 2010-2012 International Whaling Commission-Pacific Ocean Whale and Ecosystem Research(IWC-POWER) Cruise, taking into account of comments at last IWC Scientific Committee (IWC/SC)
meeting. Sensitivity analyses were also conducted to investigate the robustness of the abundance estimate
to alternative detection functions. The abundance in the central and eastern North Pacific (north of 40°N,
south of the Alaskan coast including both the US and Canadian EEZs between 170°E-135°W), from Julyto August was estimated as 27,197 (CV=0.236) for the base case scenario. In the sensitivity analysis using
alternative detection function, the abundance estimates is 33,725 (CV=0.281) when Hazard rate model withno covariates was used. The abundance estimate would not be substantially changed by covariate used in
the detection function. Abundance information of sei whales based on IWC-POWER cruise data will be
useful for the in-depth assessment of this species in the North Pacific that is planned to start this year by
the 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; 2015). Preliminary abundance estimatesfor sei whales were calculated for each IWC-POWER survey in the period 2010-2012 (Hakamada et al.,2011; 2012; Hakamada and Matsuoka, 2013; 2014). There were no primary sightings of sei whales in the2013 survey and only one primary sighting in the 2014 survey (Matsuoka et al., 2014; 2015).
This paper presents the results of abundance estimates of sei whales for the combined data of the period
2010-2012, considering some comments on previous analyses (Hakamada and Matsuoka, 2014) at the
IWC/SC meeting last year.

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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. Track line within The US EEZ were surveyedfirst and then track lines in high sea area were surveyed (Figure 2). In 2011, the eastern North Pacific northof 40oN, south of Alaskan Peninsula, and between 170oW and 150oW 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 EEZs, between 150oW and
135oW was surveyed. The survey area was divided into northern and southern strata by the EEZ line of 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, 2012).
Planned cruise tracks and survey order for 2010-2012 IWC-POWER are shown in Figure 2. Observed
distance and angles were corrected 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 the 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 etal (2012; 2013). Because the use of passing mode in the IWC-POWER would result in very highproportions of unidentified cetaceans, the POWER technical advisory group (TAG) recommended that
Passing with abeam closing mode (NSP) is the most appropriate survey mode, both with respect toconfirming species identity and school size (IWC, 2013). Sighting and effort data in both survey modes
were pooled for abundance estimation because of the limited sample size 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 based on a Horvitz-Thompson like estimator of abundance expressed by formula (1) and(2), respectively.

P==

A n si∑
2WL i =1 p i ( z i )A n ˆ∑ s i f (0 | z i )2 L i =1

(1)

where P is abundance estimate, A is area size of the surveyed area, W is truncation distance (3.0 n.miles),
L is searching effort, n is the number of schools detected within perpendicular distance of W, si is school2

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size of ith detection, pi(zi) is the probability that school i is detected given that it is within the perpendiculardistance W and given the covariate zi. f(0|zi) is conditional probability density function of distance 0 given
covariates zi

2
2
K
r
r
 PCk PC 
∂PC ∂PC −1 1 A  
var(P ) = l−+
H jm (θ ) (2)
 
∑k
∑∑
L 
 2WL   L( K − 1) k =1  l k
j =1 m =1 ∂θ j ∂θ mwhere K is the number of transect, lk is searching distance in kth transect, PCk is abundance estimate incovered region (within 3 n.miles from track line surveyed) in kth transect, PC is abundance estimate in the
covered region, Hjm-1(θ) is the jmth element of inverse of Hessian matrix of detection function for covariateθ.
Multiple Covariate Distance Sampling (MCDS) Engine in DISTANCE program was used (Thomas et al.,2010). Given discussions at the IA sub-committee on detection function (IWC, 2015), we reconsider andrefit Half Normal and Hazard Rate models as candidate models for the detection function. Full model ofthe detection function was provided by
−b

 
g ( x) = 1 − exp−  x
a exp(Size + Beaufort + Year ) 
 

 2

g ( x ) = exp − x
22a exp{2( Size + Beaufort + Year )}


(3)

(4)

where x is perpendicular distance, a and b (b≧1) are parameter, Size is observed school size, Beaufort iscategorical variable for Beaufort sea state (good: 0-3, bad: 4-5) and Year is categorical variable for year.
AIC was used to select the best model to estimate detection probability of 1/Wf(0|zi).
Smearing was not conducted on running MCDS because MCDS doesn’t deal with smearing. Perpendiculardistance was not binned on fitting detection function because selection of cut point could affect results ofmodel selection and coefficient estimates of detection function different from previous analysis.
Mean school size is estimated from the primary sightings whose school size was confirmed.
Sensitivity Analysis
How the selection of detection function affects the abundance estimate was examined. Abundance estimates
were compared among the detection function examined in this analysis.
RESULTS
Searching effort and primary sightings
Searching effort and primary sightings for each stratum are summarized in Table 1. Searching effort was
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 conditions (Matsuoka et al., 2011; 2012; 2013). The truncatednumbers of sightings are 49, 37 and 76 in 2010, 2011 and 2012, respectively. Figure 3 shows the plot of
track lines actually surveyed and the position of sei whale primary sightings during the 2010- 2012 IWCPOWER 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, the Half normal model with no covariates
was selected to estimate the detection probability for the base case in this study. Table 4 shows estimate of3

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coefficient for detection function selected by AIC. Table 5 shows that the averaged detection probability
was 0.633 (CV=0.067). Figure 4 shows the detection function and observed frequency of detection. The
Chi-square statistic is 17.534 with 11 degrees of freedom (p=0.093). Figure 5 shows QQ plot of thedetection function that suggests that the residuals are almost normally distributed.
Abundance estimate
Table 1 shows abundance estimates in each stratum for the base case. Correlation among abundance
estimates by strata are shown Table 6. The total abundance estimate was 27,197 (CV=0.236) for the basecase.
Sensitivity analysis
Table 7 compares abundance estimates and their CVs for each detection function examined. This tablesuggests that the abundance estimate is not substantially different due to the selection of covariates. The
abundance estimate would be different depending on the model used (Hazard rate model or Half normal
model).
Table 8 shows parameters estimated for Hazard rate with no covariates. Shape parameter b was estimatedat the lower bound of the range for the parameter. This probably causes lower precision in the abundance
estimate based on the Hazard rate model than that based on the Half normal model. Table 9 shows that theestimated average detection probability was 0.511 with CV=0.166. The estimate is lower and precision isworse than the base case. Chi-square statistic is 14.194 with 10 degrees of freedom (p=0.164) and the fit issimilar to the base case. Figure 5 shows QQ plot of the detection function that suggests that the residuals
are almost normally distributed.
Total abundance estimate is 33,725 with CV=0.281 (Table 10). Abundance estimate is larger than the basecase and its CV is higher than the base case.
DISCUSSIONSDistribution of sei whale
There were few sightings in the northern strata during the 2010-2012 surveys. This may be similar to thecase of few sightings of the sei whales in the coastal area of the USA and Canada recently (Barlow andForney, 2007; Zerbini et al., 2006; Williams and Thomas, 2007). Commercial catch of sei whales were
distributed the coastal areas (Kanda et al., 2015). The reason for the apparent change in distribution should
be investigated in the future.
Covariates of detection function
It was discussed that the weather conditions were more favourable on average in 2012 than in the previous
surveys (waters in the Gulf of Alaska tend to be calmer than the waters to the west) which might be a factor
influencing the differences in the shapes of the annual detection functions (IWC, 2015) at the IWC/SC
meeting last year. Taking this into account, it was investigated whether detection function could be different
among the year. Table 2 suggests that detection functions that allows detectability could different among
years were not the best model by AIC. Using detection function with year as a covariate, abundance
estimates were not substantially different from that using detection function with no covariate (Table 6).

Averaged abundanceDifference in AIC estimate is about 1 between Hazard rate model with no covariates and Half normal modelwith no covariates (i.e. the best model). This suggests that the Hazard rate model with no covariates is also
a good model, though it is not the best model. The fit of both detection functions is similar from the Chisquare statistics. Average of the two abundance estimate could be used alternative scenario for abundance
estimation in in-depth assessment of this species. The average of the two abundance estimates by Akaike
weights (Buckland et al., 1997; Burnham and Anderson, 2002) is 29,632 and its CV is 0.242.
Abundance estimate for North Pacific sei whales incluing outside of POWER research area.
The abundance estimate in the IWC-POWER research area was 27,197 (CV=0.236). Sei whale abundance
estimate in the western North Pacific (east of Japanese coast, west of 170oE, north of 35oN, south of Russian4

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EEZ) based on JARPN II data was 5,406 (CV=0.300) in July and August (Hakamada et al., 2009). The
abundance estimate for sei whales will be updated using data obtained during 2008-2013 JARPN II surveys.
Estimation of stock abundance requires information on stock structure and distribution. Kanda et al. (2009;2011; 2013) suggested that the open water area of the North Pacific was mainly occupied by individuals
from a single stock of sei whales. Murase et al. (2009; 2013) suggested that distribution of the sei whales
predicted by a generalized additive model (GAM) is continuous in the southern part of the survey area
during 2010-2012 IWC-POWER.
Under these assumptions and the assumption that there are no correlations among the estimates, thecombined abundance estimates for the JARPN II and IWC-POWER research areas in July-August was
32,603 (CV=0.203).
ACKNOWLEDGEMENT
We thank the captain, crews and international researchers onboard the Kaiko-Maru and the Yushin-Maru
No.3 for their hard work during the survey. We thank Toshihide Kitakado for his comments on analyticalmethod and Don Goodman for his comments to improve an earlier version of this manuscript. Marion
Hughes is thanked for her work on data validation and for kindly responding to our queries on IWCPOWER data. We also thank Greg Donovan, the Head of Science of the IWC, the steering group (Hidehiro
Kato (Convener), An Yong-Rock, John Bannister, Robert Brownell, Phillip Clapham, Paul Ensor, Tomio
Miyashita, Hiroto Murase, Luis A. Pastene and Paul Wade) and the POWER Technical Advisory Group(Mark Bravington, Sharon Hedley, Natalie Kelly, Toshihide Kitakado and Debra Palka) for their scientificcontributions 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, ns and nw are thenumber of schools detected and the number of individuals detected within perpendicular distance of 3.0
n.miles, l is searching distance, P is abundance estimate and CI is abbreviation for confidence interval.
Year
2010
2011
2012
T otalStratum

Period

A

L

ns

nw

n w /L

CV(n w /L )

P

CV(P)

95%LL

Northern

7/8-16, 8/1-3,20

238,627

490.5

4

4

0.008

0.502

512

0.507

185

95%UL
1,415

Southern

7/18-8/1, 8/3-25

365,244

1,325.7

45

84

0.063

0.441

6,093

0.446

2,520

14,733

Northern

7/22-8/1

193,560

723.8

0

0

-

-

0

-

-

-

Southern

8/2-8/31

569,167

1,674.0

37

72

0.043

0.452

6,445

0.457

1,066

38,985

Northern

7/24-8/3

142,427

767.5

2

4

0.005

0.751

195

0.754

45

853

Southern

8/3-8/30

529,362

1,358.6

74

136

0.100

0.337

13,952

0.343

6,927

28,099--2,038,387

6,340.0

162

300

0.047

0.230

27,197

0.236

16,841

43,923

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

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

HR

HN

School size+Beaufort+Year

327.7

326.9

School size+Beaufort

327.8

327.5

School size+Year

328.1

327.4

Beaufort+Year

327.7

326.5

School size

327.5

326.7

Beaufort

327.6

327.2

Year

327.7

326.5

No covariate

326.9

325.9

Table 4. Parameter estimate for detection function selected by AIC.
Parameter

Estimate

SE

a

1.619

0.152

Table 5. Estimate of average pi =1/W fˆ (0 | z i ) , its standard error (SE) and its CV.

Average p

Estimate

SE

CV

0.633

0.043

0.067

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Table 6. Correlation matrix for abundance estimates by strata.
2010N

2010S

2011S

2012N

2012S

2010N

1.000

0.020

0.020

0.012

0.026

2010S

0.020

1.000

0.022

0.014

0.030

2011S

0.020

0.022

1.000

0.013

0.029

2012N

0.012

0.014

0.013

1.000

0.018

2012S

0.026

0.030

0.029

0.018

1.000

Table 7. Sensitivity of abundance estimate for sei whales applying different detection functions
Model

Covariates

P

CV(P)

Covariates

P

S+B+Y

32,264

0.262

S+B+Y

26,1450.225

S+B

31,4710.265

S+B

25,921

0.229

Hazard Rate

Model

CV(P)

S+Y

30,652

0.262

S+Y

25,9070.230

B+Y35,6470.287

B+Y

27,2870.230

S31,1460.266

S25,9430.234

B34,8780.282

B27,381

0.234

Y33,4540.283

Y27,024

0.234

None

33,725

0.281

None

27,197

0.236

Half Normal

Table 8. Parameter estimate for detection function of Hazard rate model with no covariates. Parameter b
estimate is at the lower bound of the range for b.
Parameter

Estimate

SE

a

0.835

0.342

b*

1.000

0.312

Table 9. Estimate of average pi=1/W fˆ ( 0 | z i ) , its standard error (SE) and its CV for Hazard rate with nocovariate.
Average p

Estimate

SE

CV

0.511

0.085

0.166

Table 10. The abundance estimates for the sei whales and their CV’s for using Hazard rate detection
function with no covariate. The Notations are as for Table 1.
Year
2010
2011
2012
T otalStratum

Period

A

L

ns

nw

n w /L

CV(n w /L )

P

CV(P)

95%LL

Northern

7/8-16, 8/1-3,20

238,627

490.5

4

4

0.008

0.502

635

0.529

224

95%UL
1,801

Southern

7/18-8/1, 8/3-25

365,244

1,325.7

45

84

0.063

0.441

7,555

0.471

3,018

18,915

Northern

7/22-8/1

193,560

723.8

0

0

-

-

0

-

-

-

Southern

8/2-8/31

569,167

1,674.0

37

72

0.043

0.452

7,992

0.482

1,613

39,589Northern

7/24-8/3

142,427

767.5

2

4

0.005

0.751

242

0.769

55

1,069

Southern

8/3-8/30

529,362

1,358.6

74

136

0.100

0.337

17,300

0.375

8,205

36,477--2,038,387

6,340.0

162

300

0.047

0.230

33,7250.281

19,39558,6448

SC/66a/IA

Figure 1. Survey area for 2010-2014 IWC-POWER surveys. Data for 2010-2012 IWC-POWER surveys
were analyzed because there were no primary sightings of sei whales in the 2013 survey and only one
primary sighting of the sei whales in the 2014 survey.

Figure2. Planned track line for 2010-2012 IWC-POWER surveys (bold black lines). Arrows show survey
order in 2010 (light blue), 2011 (orange) and 2012 (yellow green), respectively. Yellow lines represent
boundary for survey strata. Areas in EEZs were surveyed first then areas in high seas were surveyed.
Dotted light blue arrow indicates transit. Blue dotted lines indicate boundary for Foreign Economic
Exclusive Zones (EEZs). Yellow zone indicate survey areas.

9SC/66a/IA

Figure 3. Plot of actually surveyed track line (black lines) and position of primary sightings of the sei whales(orange circles) during 2010-2012 IWC-POWER surveys.

Figure 4. Plot of the estimated detection function 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 (rightpanel).

10

SC/66a/IA

Figure 5. QQ plot of detection functions for the Half normal model with no covariates (Base case, left
panel) and Hazard rate model with no covariates (right panel).

11