SC/64/NPM3

Abundance estimate for western North Pacific minke
whales based on JARPNII dedicated sighting survey
data from 2008 to 2009
12

TAKASHI HAKAMADA and TOSHIHIDE KITAKADO

1: The Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo 104-0055
2: Tokyo University of Marine Science and Technology, 5-7, Konan 4, Minato-ku, Tokyo 108-8477, Japan
Contact e-mail:hakamada@cetacean.jp

ABSTRACT
Abundance estimate for common minke whales (Balaenoptera acutorostrata) in the western North
Pacific are obtained for each management sub-area based on JARPN II 2008 and 2009 dedicated sightingsurvey data. Estimates were based on standard IWC SC methodology. Detection function with covariate
such as year, sea state (Beaufort scale) and sub-area was considered. Abundance estimate in sub-area 9 is
1,840 (CV=0.576) in 2008. Abundance estimates in sub-areas 8 and 9 are 507 (CV=0.830) and 1,693(CV=0.701), respectively in 2009. These estimates could be used for the simulating CLA. For sub-area
7CS, 7CN, 7WR and 7E in 2008 and 2009 and sub-area 8 in 2008 sighting data may not be suitable forabundance estimation for the simulating CLA because considerations of the survey coverage and survey
timing.
INTRODUCTION
In 2008 and 2009, dedicated sighting survey was conducted in north of 35oN and west of 170oE. Cruise
track was designed in accordance with original definition of sub-areas 7, 8 and 9 for western North
Pacific common minke whales (Balaenoptera acutorostrata) (International Whaling Commission (IWC),1994). Sub-areas used in present Implementation review (IR) were agreed at First Interssesional
Meeting for the Implementation Review of the common minke whales in 2010 (IWC, 2011). Allocation ofcruise track may not be suitable for abundance estimation in some sub-areas due to the modification toboundary for these sub-areas. At the second ‘First Intersessional Workshop’ for the Implementation
Review of the common minke whales, it was agreed that it is also desirable for abundance estimates fromeach of these surveys to be presented for review at the 2012 Annual Meeting (IWC, 2012) in order toabundance estimates can be used in simulating Catch limit Algorithm (CLA) application. This paper
provides abundance estimate of the minke whales by the sub-areas used in present IR based on JARPN II
survey data during 2008-2009 that could be used in simulating CLA application.
MATERIALS AND METHODS
Survey area
Sighting data were collected by dedicated sighting vessels which operated independently from Sightingand Sampling vessels during JARPN II surveys. Survey was conducted in line transect method. Summaryof both year surveys are shown in Table 1. Two dedicated sighting vessels conducted survey. Survey area
was sub-areas 7CS, 7CN, 7WR, 7E, 8 and 9 in 2008. In 2009, survey area was same as in 2008 exceptthat north of 45oN in sub-area 9 was not included.
Survey procedure
Starting point of the cruise track was selected at random. Zigzag track lines were systematically allocatedin each survey stratum uniformly. Survey procedures were following the RMP requirements andguidelines for sighting surveys (IWC, 1997; 2005; 2008). For more details on survey procedure aregiven in Hakamada et al. (2012)
Survey mode
In 2008, sighting survey was conducted in closing mode. In 2009, sighting survey was conducted in
Closing and Passing with abeam closing mode. Because there are few very high density areas andcommon minke school size are nearly all 1, it can be assumed that difference in survey mode would notcause bias in abundance estimate and therefore we didn’t estimate abundance for closing and passing

1

mode, separately.
Cruise track
Plot of cruise track and primary sightings in 2008 and 2009 are shown in Figures 1 and 2, respectively.
From these figures, survey coverage was not good in coastal area and north of 40oN in sub-area 7. The
figure also shows cruise track by two vessels. The cruse track was allocated so that they covered whole ofthe planned survey area only once and the two vessels surveyed along the cruise track independently.
Analysis procedure
Procedure conducted in this paper is similar to Hakamada and Kitakado (2011). It is assumed that g(0)=1.
Detection function is considered covariates such as sea state (Beaufort scale), sub-area and year. In order
to consider the effect of the covariates on estimated detection functions for JARPN II data, MCDS(Multiple Covariates Distance Sampling) module in DISTANCE ver. 6.0 is used. MCDS methods arebased on a Horvitz-Thompson like estimator of abundance (Thomas et al., 2010). Hazard-rate model isconsidered. Full model of the detection function is provided byb g ( x)  1  exp   xa exp( Beaufort  Year  SA)   (1).

AIC is used to select the best model to estimate effective half search width (ESW). In fitting detection
function, Beaufort scale ranges from 0 to 5 because sighting survey was not conducted when Beaufort
scale was 6 or more. Beaufort scale is grouped into 2 groups. One group is 2 or less and another group is3 or more.
Searching distance and the number of the primary sightings are stratified by the sub-areas used in presentRMP/IST. The Horvitz-Thompson like estimator is applied for the abundance estimation, withconsideration of the covariate effects. Detection function is modelled globally, and estimated separately ineach sub-area, given the covariate values of the observations in the sub-area to estimate ESW for eachsub-area.
RESULTS
Detection function considering covariates
Table 2 compares AIC for each model. Selected model is the hazard-rate model with Beaufort scale andYear as covariates. Estimated coefficients and their precision are shown in Table 3. Plot of detection
function for each Beaufort group are shown in Figure 3. The estimated coefficients and shape of thedetection functions reflect the fact that the schools are easier to detect as Beaufort scale is less. Estimated
ESHW using the selected model are applied to estimate abundance estimates.
Abundance estimates
Table 4 shows abundance estimates by sub-areas during 2008-2009. For 2008, Abundance estimate insub-area 9 is 1,840 (CV=0.576). For 2009, abundance estimates in sub-areas 8 and 9 are 507 (CV=0.830)
and 1,693 (CV=0.701), respectively. CV of encounter rate was high probably because primary sightings
were distributed in some concentrated area. North of 45oN in sub-area 9 was not surveyed in 2009,
therefore abundance estimate for sub-area 9 in 2009 could be a minimum estimated.
DISCUSSION
Suitability of abundance estimate by sub-areas for simulating CLA
It is necessary to consider which abundance estimate can be used for CLA among abundance estimatesobtained in this paper. Table 5 shows abundance estimates with the best model by sub-area and year using
JARPN II data including some information on sighting surveys such as survey month, survey
coverage, % of realized trackline and so on. Table 6 shows abundance estimates provided by Miyashitaand Fujise (1997) including information on the sighting surveys.
Table 5 indicates that searching effort was allocated insufficiently in sub-areas 7CS and 7CN,respectively in 2008 and 2009 comparing to previous survey. As suggested Figures 1 and 2, northern part2

(north of 40oN) of sub-areas 7WR and 7E were not covered sufficiently in the both year.
From sighting information during JARPN II coastal component, it is suggested that high density of theminke whale distribution occur in sub-area 7CS in April, and in sub-area 7CN from September to October.
Abundance estimate in sub-areas 7 and 8 was much less in July and August than those in May and June
based on 2006 and 2007 JARPN II surveys (Hakamada et al., 2009), it was suggested that high density ofthe minke whales did not occur in those sub-areas in 2008 survey during July - August.
Therefore, abundance estimate in sub-areas 7CS, 7CN, 7WR, 7E and 8 for 2008 and 7CS, 7CN, 7WR
and 7E for 2009 may not be suitable to use for simulating CLA application considering survey design andsurvey timing.
Underestimate of abundance
Abundance estimate examined in this paper are thought to be underestimate because it was assumed thatg(0)=1 given that Okamura et al (2010) showed that g(0) for Top barrel & Upper bridge was 0.798 forcommon minke whales.
ACKNOWLEDGEMENTAuthors would like to express gratitude for researchers and crews involved JARPN II surveys. Authors
would like to express gratitude to Drs Seiji Ohsumi, Hiroshi Hatanaka and Luis A. Pastene for theirvaluable comments to improve this manuscript.
REFERENCES
Hakamada, T. and Kitakado, T. 2011. Abundance estimation for the western North Pacific common minke
whales based on sighting information from JARPN and JARPN II. Paper SC/63/RMP18 submitted toIWC Scientific Committee, 2011 May, Tromso, Norway (unpublished). 25pp.
Hakamada, T., Matsuoka, K. and Miyashita, T. 2009. Distribution and the number of western North
Pacific common minke, Bryde’s, sei and sperm whales distributed in JARPN II Offshore component
survey area. Paper SC/J09/JR15 submitted to Workshop to review JARPN II.
Hakamada, T., Matsuoka, K. and Miyashita, T. 2012. Updated summary of information on sighting surveyprocedures during Japanese dedicated sighting surveys. Paper SC/64/NPM2 submitted to thismeeting. (unpublished) 44pp.

International Whaling Commission. 1994. Report of the Scientific Committee, Annex G. Report ofthe working group on the north Pacific minke whale management trials. Rep. int Whal. Commn
44: 120-44.
International Whaling Commission. 1997. Report of the Scientific Committee, Annex K.
Requirements and guidelines for conducting surveys and analysing data within the Revised
Management Scheme. Rep. int. Whal. Commn 47:227-35.
International Whaling Commission. 2005. Report of the Scientific Committee. Annex D. Report ofthe sub-committee on the Revised Management Procedure. Appendix 3. Requirements andGuidelines for Conducting Surveys and Analysing Data within the Revised ManagementScheme. J. Cetacean Res. Manage. (Suppl.) 7:92-101.
International Whaling Commission. 2008. Report of the Scientific Committee. Annex D. Report ofthe sub-committee on the revised management procedure. Appendix 7. New text forrequirements and guidelines for conducting surveys and analysing data within the Revised
Management Scheme (RMS). J. Cetacean Res. Manage. (Suppl.) 10:117.
International Whaling Commission. 2011. Report of the First RMP Intersessional Workshop forwestern North Pacific common minke whales. Paper SC/63/Rep3 submitted to IWC ScientificCommittee. May 2011, Tromso, Norway (unpublished) 65pp.
International Whaling Commission. 2012. Report of the second ‘First Intersessional Workshop’ for theImplementation Review of western North Pacific common minke whales. SC/64/Rep2 submitted tothis meeting.
Miyashita, T and Fujise, Y. 1997. Abundance estimate of the western North Pacific minke whale usingsighting data of the Japanese Whale Program under special permit in sub-area 9 and noted on theresults of the dedicated sighting surveys. Rep. Int. Whal. Commn 47: 543-51.
Okamura, H., Miyashita, T. and Kitakado, T. 2010. g(0) estimates for western North Pacific common
minke whales. Paper SC/62/NPM9 presented to the IWC Scientific Committee, June 2010, Agadir,Morocco (unpublished). 7pp.

3

Thomas, L., S.T. Buckland, E.A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R.B. Bishop, T. A.
Marques, and K. P. Burnham. 2010. Distance software: design and analysis of distance sampling
surveys for estimating population size. Journal of Applied Ecology 47: 5-14.

4

Table 1. Summary of sighting survey in 2008 and 2009 JARPN IIyear
2008
2009

vessels
KK1, KS2
KK1, YS1

period
5 Jul - 25 Aug
23 May - 23 Junsurvey mode
closing
closing, passing

survey area
7CS,7CN,7WR,7E,8,9
7CS,7CN,7WR,7E,8,9**: excluding north of 45N

Table 2. AIC for each model. The selected model is indicted by an asterisk.
covariates
AIC
Beaufort+Year+Sub-Area 797.4
Beaufort+Sub-Area
798.9
Beaufort+Year*
791.7
Year+Sub-Area
804.8
Sub-Area
802.6
Year806.2
Beaufort
792.2
no covariate
799.2
Table 3. Estimates of coefficients and their standard errors (SE) for the selected model. The parameters aand b are those for Beaufort scale 3 or more.
abBeaufort0-2
Year2002
Year2003
Year2004
Year2005
Year2006
Year2007
Year2008

coefficient
0.232
2.622
0.519
0.923
-0.010
0.241
0.806
0.611
0.858
0.110

SE
0.053
3.042
0.170
0.628
0.350
0.388
0.465
0.360
0.634
0.489

5

Table 4. Abundance estimate for each sub-area in 2008 and 2009.
Year sub-area
2008
92009
82009
9Area
499,235
162,789
362,113n837

Ln /LCV
2678.9 0.0030 0.468
1084.5 0.0028 0.631
2274.1 0.0031 0.586

ESW
0.405
0.444
0.423

CV E (s ) CVDPCV
0.335 1.00 0.000 0.004 1,840 0.576
0.540 1.00 0.000 0.003 507 0.830
0.356 1.29 0.143 0.005 1,693 0.701

Table 5. Abundance estimates with the best model by sub-area and year using JARPN II data including
some information on sighting surveys.
sub-area

7CS

7CN

7WR

7E

8

9year

Aerialcovarage

TimingArea size(n.miles2)

effort(n.miles)

n2003
2004
2006
2007
2008
2009
2003
2008
2009
2002
2003
2004
2006
2007
2008
2009
2003
2004
2006
2007
2008
2009
2002
2003
20042005
2006
2007
2008
2009
2002
2003
2004
2005
2006
2007
2008
2009

62.6%
100.0%
100.0%
100.0%
100.0%
100.0%
75.4%
75.4%
75.4%
30.5%
54.2%
88.8%
88.8%
88.8%
88.8%
88.8%
26.3%
57.1%
57.1%
57.1%
57.1%
57.1%
65.0%
13.1%
40.5%
65.0%
65.0%
65.0%
65.0%
65.0%
62.4%
33.2%
42.6%
63.0%
86.9%
86.9%
86.9%
63.0%

May
May
Jun - JulJun - JulAug
May - Jun
MayAugMay
Aug
May - Jun
May - Jun
Jun - JulJun - JulAug
May - Jun
May - Jun
May - Jun
May - Jun
Jun - Jul
AugJunJun - JulJulJun
May - JulMay - JulJun - Jul
Jul-Aug
May - Jun
Jun - Jul
Jul - Sep
Jun-JulMay -Aug
May - AugMay - JulJun - AugMay - Jun

16,78926,826
26,826
26,826
26,826
26,826
18,281
18,281
18,281
25,05944,58972,991
72,991
72,991
72,991
72,991
22,16648,20848,20848,20848,20848,208
162,689
32,857
101,373
162,789
162,789
162,789
162,789
162,789358,530
190,676
244,759
362,113499,235
499,235
499,235
362,113

367
199
264
10
160
153
247
19
38
244
986
789
411
465
395
601535
390
225
360
313
315
1,184
272
917
1,434
1,039
914
892
1,085
1,866
2,533
1,542
3,502
3,238
2,067
2,679
2,274

6
7
23
0003000
1070
3016320000
1813203340
413
17
187

Encounter
ESW
Mean
rate (/100(n.miles)
school sizen.miles)
1.636 (1.092) 0.431 (0.179) 1.00 (-)
3.511 (1.451) 0.606 (0.171) 1.14 (0.14)
8.718 (9.001) 0.431 (0.263) 1.36 (0.11)
0 (-)
0 (-)
0 (-)
1.214 (0.837) 0.604 (0.251) 1.00 (-)
0 (-)
0 (-)
0 (-)
1.014 (0.348) 0.431 (0.263) 1.00 (-)
0.887 (0.323) 0.484 (0.24) 1.29 (0.18)
0 (-)
0.645 (0.525) 0.431 (0.214) 1.00 (-)
0 (-)
0.166 (0.144) 0.330 (0.310) 1.00 (-)
1.121 (0.929) 0.642 (0.107) 1.33 (0.21)
0.77 (0.423) 0.422 (0.233) 1.00 (-)
0.888 (0.762) 0.423 (0.101) 1.00 (-)
0 (-)
0 (-)
0 (-)
0 (-)
0.368 (0.304) 0.431 (0.214) 1.00 (-)
0.872 (0.404) 0.461 (0.139) 1.14 (0.14)
0.07 (0.046) 0.431 (0.152) 1.00 (-)
0.289 (0.152) 0.761 (0.324) 1.00 (-)
0.219 (0.208) 0.456 (0.161) 1.00 (-)
0 (-)
0.277 (0.175) 0.440 (0.240) 1.00 (-)
0.161 (0.124) 0.31 (0.109)
2.00 (-)
1.579 (0.38) 0.609 (0.081) 1.03 (0.03)
0.259 (0.157) 0.538 (0.192) 1.00 (-)
0.371 (0.214) 0.686 (0.208) 1.11 (0.11)
0.525 (0.146) 0.484 (0.24)
1.00 (-)
0.048 (0.049) 0.497 (0.172) 1.00 (-)
0.299 (0.14) 0.405 (0.136) 1.00 (-)
0.308 (0.181) 0.423 (0.151) 1.29 (0.18)

PCV(P)

319
886
3,690
0
00184
000524
863
0546
0184
257
440
506
00
0
0140
1,093
132
309
391
0
507
1,859
2,546
590
1,088
2,708
243
1,840
1,693

0.786
0.502
1.199
0.805
0.700
0.648
0.953
1.278
0.866
0.779
0.891
0.964
0.576
0.746
0.677
1.013
0.830
0.847
0.276
0.703
0.716
0.569
1.078
0.576
0.701

plannedtrackline
524301453
98
243
270
562
172
156
618
1,725
1,558
519
764
777
777
610
683
517
480
546
431
1,736
306

1,636
1,915
1,680
1,623
1,570
1,560
3,504
3,619
3,180
4,593
5,107
4,903
4,551
3,341

% of
realised
trackline

70.0%
66.2%
58.2%
9.8%
65.6%
56.8%
43.9%
11.1%
24.5%
39.5%
57.2%
50.7%
79.2%
60.8%
50.8%
77.4%
87.7%
57.1%
43.5%
74.9%
57.3%
73.0%
68.2%
88.7%
56.1%
74.9%
61.8%
56.3%
56.8%
69.5%
53.3%
70.0%
48.5%
76.2%
63.4%42.2%
58.9%
68.1%

Conditioning
NoYes
Yes
NoYes

NoMin
Yes
NoYes

No
Yes
Yes
Yes

Yes
NoYes
Yes
Yes
Yes

NoMin
No
No
No
No

Table 6. Abundance estimates in sub-area 9 by strata using JARPN data (Miyashita and Fujise, 1997)
including some information on sighting surveys.
sub-areayear

Aerialcovarage

TimingArea size(n.miles2)

effort(n.miles)

91994 1st1994 2nd1995 1st
1995 2nd
1995 3rd

42.5%
32.9%
54.7%
13.2%
28.5%

Jul - AugAug - Sep
Jun
Jul - AugAug

244,172
189,012314,082
75,635
163,610

1,608
2,118
2,907
791
1,706

Encounter
ESW
Mean
rate (/100(n.miles)
school sizen.miles)
12 0.746 (0.228) 0.321 (0.091) 1.08 (0.08)
7 0.331 (0.185) 0.321 (0.091) 1.00 (-)
12 0.413 (0.076) 0.481 (0.095) 1.00 (-)
10 1.264 (0.436) 0.481 (0.095) 1.00 (-)
4 0.234 (0.142) 0.481 (0.095) 1.00 (-)
n6

P

CV(P)

plannedtrackline% ofrealised
trackline

Conditioning

3,065
973
1,348
994
399

0.423
0.628
0.272
0.396
0.636

6,884
5,807
5,035
3,119
2,47023.4%
36.5%
57.7%
25.4%
69.1%

No
No
No
No
No

53N
51N
49N
47N
45N
43N
41N
39N
37N
35N
33N
140E

145E

150E

155E

160E

165E

170E

Figure 1. Plot of cruise track and primary sightings of the minke whales during the JARPN II surveys for2008. Red line indicates cruise track by Kaiko-Maru (KK1). Blue line indicates cruise track byKyoshin-Maru No. 2(KS2). Black bold line indicates boundary of planned survey areas. Green squareindicates a primary sighting of the minke whales.

53N
51N
49N
47N
45N
43N
41N
39N
37N
35N
33N140E

145E

150E

155E

160E

165E

170E

Figure 2. Plot of cruise track and primary sightings of the minke whales during the JARPN II surveys for2009. Red line indicates cruise track by Kaiko-Maru (KK1). Blue line indicates cruise track byYushin-Maru No. 1(YS1). Green squaere indicates a primary sighting of the minke whales.

7B=3-5

B=0-2
1.4

1.2

1.2

11
0.8

0.8
0.6

0.6
0.4

0.4
0.2

0.2

000

0.5

11.5

00.5

11.5

Figure 3. Plot of the selected detection function and distribution of the perpendicular distance of the
primary sightings for Beaufort scale 0-2 and 3-5.

8