SC/F16/JR16

Preliminary attempt of spatial estimation of prey
consumption by sei whales in the JARPNII survey area
using data obtained from 2002 to 2013
TSUTOMU TAMURA1, HIROTO MURASE2, HIROKO SASAKI3,4, AND TOSHIHIDE KITAKADO5
1

Institute of Cetacean Research, 4-5, Toyomi-cho, Chuo-ku, Tokyo, 104-0055, Japan
National Research Institute of Far Seas Fisheries, Fisheries Research Agency (FRA), 2-12-4 Fukuura,
Kanazawa, Yokohama, Kanagawa 236-8648, Japan
3
National Institute of Polar Research, 10-3 Midoricho, Tachikawa, Tokyo 190-8518, Japan
4
Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minatocho, Hakodate, Hokkaido 041-8611,
Japan
5
Tokyo University of Marine Science and Technology, 4-5-7 Konan, Minato-ku, Tokyo, 108-8477, Japan
2

ABSTRACT
Spatial estimation of prey consumption by sei whales in the JARPNII survey area was preliminary attempted using data from2002 to 2013. Two levels of models are constructed to achieve the goal. Firstly, relative abundance of sei whales in relation withoceanographic conditions is estimated by using a generalized additive model (GAM). Secondary, amount of prey consumed by asei whale in relation with oceanographic conditions is also estimated by using GAM. Finally, prey consumption of sei whales in
the JARPNII survey area is calculated as the product of these two models. Data obtained from 2002 to 2013 are used in theanalysis. Spatial distribution of prey consumption shifted toward north as the season progress. Estimated amount of prey
consumption by sei whales using the spatial model was comparable to estimates based on traditional methods (Tamura et al.,
2016: SC/F16/JR15). SST was selected as environmental covariates in the first and second models. However, the shape offunctional form for the first level model (prey consumption) was relatively flat in comparison with the second level model(abundance). The results indicated that spatial distribution of sei whales at meso scale were largely determined by oceanographic
conditions such as SST. Sei whales could then search for their prey with the optimal oceanographic conditions as indicated byfeeding behaviour study (Ishii et al., 2016: SC/F16/JR25). Future study on feeding ecology of baleen whales should pursuit suchan integrated approach further.

INTRODUCTION
Prey consumption of sei whales in the JARPNII survey area has been estimated based on traditional methods(Konishi et al, 2009; Tamura et al., 2016: SC/F16/JR15). These estimations have their values as the estimation
methods are relatively simple. However, these methods cannot estimate spatio-temporal changes of prey
consumption by whales. The Panel of the JARPNII expert workshop held in 2009 recommended pursuing suchan attempt as medium to long term project (IWC, 2010).
In this paper, a preliminary result of spatial estimation of prey consumption by sei whales in the
JARPNII survey area is presented to see whether the consumption is heterogeneous in both spatially andtemporally. Two levels of models are constructed to achieve the goal. Firstly, relative abundance of sei whales inrelation with oceanographic conditions is estimated by using a generalized additive model (GAM). Secondary,amount of prey consumed by a sei whale in relation with oceanographic conditions is also estimated by using
GAM. Finally, prey consumption of sei whales in the JARPNII survey area is calculated as the product of these
two models. Data obtained in JARPNII (the second phase of the Japanese Whale Research Program underSpecial Permit in the western North Pacific) from 2002 to 2013 are used in the analysis.
There are three spatiotemporal scale to link feeding ecology of cetaceans with distribution pattern ofthem and the definitions are follows; at the macro scale, cetaceans migrate seasonally between feeding andbreeding grounds; at the meso scale, cetaceans move over days and weeks in search of preferred local abundanceof food; and at the micro scale, whales dive and search for food within localised areas (IWC, 2003). This study
corresponds to the meso scale.

MATERIALS AND METHODS
Sighting survey
A brief summary of sighting survey related to this s provided hear. The details can be found in Matsuoka et al,(2016: SC/F16/JR2) and Bando et al. (2016: SC/F16/JR4). The cetacean sighting surveys were conducted in thewestern North Pacific in July from 2002 to 2013 as part of JARPNII (Fig. 1). The boundaries of the survey area

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SC/F16/JR16

were 35°N, the boundary of the economic exclusive zone (EEZ) claimed by countries other than Japan and
170°E and the eastern coast line of Japan. Sighting data obtained outside but adjacent to the area during theperiod were also used as supplemental data. Several survey vessels were engaged in the cetacean sighting
surveys but their specifications were similar. Their gross tonnages were approximately 1000 GT with two main
observation platforms: top barrels were set at 20 m above the sea surface and upper bridges were set at 10 m
above the sea surface. Data obtained by sighting vessels (SVs) and sighting and sampling vessels (SSVs) wereused in the analysis as the relative abundance of sei whales was estimated using a model based method instead ofa design based method. Three observers at each observation platform using 7 × 50 binoculars were engaged in
the sighting surveys. Sightings within 3 n.miles from the survey tracklines were counted. The survey vessels
steamed around 10.5 knots along the survey tracklines during the survey hours. The survey was conductedduring the daytime from 1 hour after sunrise to 1 hour before sunset. Surveying was stopped when the visibility
was <3.7 km (≈ 2 nm) and/or sea state >4 on the Beaufort wind force scale.
Activities aboard the ship were basically classified into two categories: on-effort and off-effort. Oneffort activities were times when a full search effort was executed within the acceptable conditions. Off-effort
activities were all times other than on-efforts. On-effort data used in the analysis. All sightings of sei whales
recorded during on-effort activities were classified as primary-sightings. All other sightings were consideredsecondary sightings and not used in this analysis. On-effort surveys were conducted in the closing and passing
mode; the survey vessels approached all sightings during the closing-mode to confirm the species and number ofindividuals in a school, and all sightings during the passing-mode were not approached although the species andestimated number of individuals in a school were recorded.
Sampling survey
The research area of the JARPNII was a part of sub-areas 7, 8 and 9, which were established by the International
Whaling Commission/Scientific committee (IWC/SC) (IWC, 1994). All sei whales of primary and secondarysighting were targeted for sampling. The order of individuals to be sampled in a school were chosen by aresearcher on board using a series of tables of random sampling numbers (TRS), which were prepared according
the size of the schools. When the sighting of the sei whales was occurred, the SSV approached to the school ofwhales within 0.2 n.miles. Observers on the top barrel counted a number of whales and estimated body length ofeach animal. If a sighting was solitary whale, it was sampled immediately after the body length estimation. If aschool was consisted of two or more animals, the researcher assigned a serial number to each individual, ranging
from left to right. The first target whale was chosen using the TRS specific to the school size. When two whales
should be sampled from a school, the second target was selected by the same manner after the first animal was
sampled. In this case, the remaining individuals were renumbered according to the latest position in the school
and TRS was used for the original school size minus one. After sampling, whales were brought to the researchbase vessel where the animals were examined by a biologist onboard. The whale sampling procedure is
described by Bando et al (2016: SC/F16/JR4) in detail. In the JARPNII surveys, the stomach contents were
removed from each compartments and weighed to the nearest 0.1kg on the ship’s flensing deck after capture.
The analysis of prey consumption in this study was based on data collected from the first compartment(forestomach) and second compartment (fundus). The sampling and treatment procedure of stomach contentsfrom whales is described by Tamura et al (2016: SC/F16/JR15) in detail.
Daily prey consumption by sei whales
Prey consumption by sei whales are being made using a same methodology described in Tamura et al. (2016:
SC/F16/JR15). In Tamura et al (2016: SC/F16/JR15), it applied three equations for estimating daily and seasonal
prey consumption. In this document, it applied for each sampled sei whale following equation:
SMR = 803.71M0.75 (Perez et al, 1990)
SMR is the daily prey consumption (expressed by KJ day-1) and M is body mass of sei whales sampled in kg.
The value from this equation was intermediate one among equations. It was considered appropriate value for thisanalysis at this stage. Daily prey consumption for dominant prey in each sampled sei whale was calculated byusing the equation.
Reproductive status of sei whales
Spatial distribution of reproductive status of sampled sei whales was plotted in maps to see whether the
distribution was segregated by reproductive status. Males were defined as sexually matured by testis weight(larger side) of more than 1,090 g. Female were defined as sexually mature by the occurrence of at least one
corpus luteum or albicans in their ovaries.

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Spatial modelling
A hierarchical structure with three levels of spatial distribution models is considered in this paper: (1) daily prey
consumption by sei whales and (2) relative abundance of sei whales. Prey consumption by sei whales was as the
product of the results from the two levels. Generalized additive models (GAMs) having a Tweedie error
distribution with logarithmic link function were used for both levels. A Tweedie random variable with 1<p<2 isa sum of N gamma random variables in which N has a Poisson distribution (Wood, 2015). If p equals 1, then it isa generalization of a Poisson distribution and a discrete distribution supported on integer multiples of the scaleparameter. If p is larger than 1 and smaller than 2 (1<p<2), then the distribution is supported on the positive reals
with a point mass at zero. If p equals 2, then it is a gamma distribution. Initially, we experimentally changed thevalue of p in each GAM and set it as 1.1. The models with the lowest GCV scores were selected. For thisanalysis, the mgcv package (Wood, 2006) version 1.8-7 of the R software version 3.2.2 (R Development Core
Team, 2015) was used. The shapes of the functional forms for the all covariates were also plotted with thatpackage.
Sea surface temperature (SST), sea surface height anomaly (SSHa) and sea surface chlorophyll-aconcentration (Chl-a) recorded by satellites and a digital seafloor depth data were used as environmental
covariates in the models. SST and Chl-a data (4x4 km grid) obtained by Moderate Resolution Imaging
Spectroradiometer aboard the Aqua satellite (Aqua MODIS) were used. Monthly mean from July 2002 to
September 2013 in Level 3 Standard Mapped Image products downloaded from Ocean Color Web(http://oceancolor.gsfc.nasa.gov/, NASA Goddard Space Flight Center "Last accessed on 10 January 2014")
were used. It should be noted that recording of MODIS data was started in July 2002. Missing data in these data
were estimated by ordinary kriging with the aid of the geographic information system (GIS), ArcGIS 10.1 (ESRI,
California, USA). Daily data of Sea Level Anomalies (MSLA) were also downloaded and used as SSHa. The
altimeter products were produced by Ssalto/Duacs and distributed by Aviso, with support from Cnes
(http://www.aviso.oceanobs.com/duacs/ "Last Last accessed on 18 September 2014"). ETOPO1 Global Relief
Model for bottom topography" (Amante and Eakins, 2009) was used as seafloor depth data. "A Global Selfconsistent, Hierarchical, High-resolution Geography Database" (Wessel and Smith, 1996) was used in figures to
depict coastline. Mean values in 1x1 longitude and latitudinal grid cell in each month in each year were
calculated using these data. Year and month were used as categorical covariates in both levels. Spatial resolution
considered in the models was 1x1 longitude and latitudinal grid cell. Data recoded from May to September ineach (2002-2013) were used in the modelling.
At the first level model, daily prey consumption of a sei whale (t/day by an individual) in a grid cell wasmodelled using a GAM. Five major prey species, namely, copepods, euphausiids, Japanese anchovy, Pacificsaury and mackerels were considered in the modelling and these were used as categorical covariates as well.
Number of individuals in a grid cell was treated as weight in the model so that prey consumption by anindividual in a grid cell could be estimated.
At the second level abundance in a grid cell was modelled using a GAM. The following procedures
were conducted to estimate abundance. Original sighting effort data were divided first into 1 km segments, andthen these segments were pooled in each grid cell (d). Because sighting effort was discontinuous in closing mode,
especially in high density area of baleen whales, a relatively short segment length (1 km) was chosen for the
analysis. The number of schools of sei whales was also pooled in each grid cell (n). Effective strip width (esw)
and mean school size (E(s)) were estimated by a program, DISTANCE version 6.2 release 1 (Thomas et al.,2010). All sighting data from 2002 to 2013 recorded by SVs and SSVs were pooled for the estimation.
Probability of detection on the trackline (g(0)) was assumed as 1. Abundance in a grid cell was first calculated
by unit density, i.e. (n ×E(s))/(d × 2 × esw × g(0)), and then multiplied by the area of the grid cell. Some gridcells had unusually high abundance. Abundance falling more than 95 percentiles were treated as outliers and notconsidered in the GAM to avoid unrealistic over estimation. Details of methods are also shown in Murase et al.
(2016: SC/F16/JR7).

RESULTS AND DISCUSSION
Maps of mean SST, SSHa and Chl-a in each month from 2002 to 2013 are shown in Figs. 2-4. These maps
showed seasonal changes of these environmental covariates. Spatial segregation by maturity stage and preyspecies of sei whales was not considered in this analysis as maps of maturity status and prey species of sampled
sei whales indicated there was no obvious concertation by these categories (Figs. 5-6). Selected GAM based on
GCV score for daily prey consumption of a sei whale is summarized in Table 1. The shapes of the functional
forms for selected covariates (SST and Chl-a) are shown in Fig. 7. Selected GAM based on GCV score forabundance of sei whales is summarized in Table 2. The shapes of the functional forms for selected covariates(SST and Chl-a) are shown in Fig. 8. Estimated spatial distribution and abundance of sei whales are shown in

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SC/F16/JR16

Fig. 9 and Table 3. Spatial distribution of prey consumption shifted toward north as the season progress.
Estimated spatial distribution of prey consumption and the amount are shown in Figs. 10-14 and Table 4-8.
Estimated amount of prey consumption by sei whales using the spatial model was comparable to estimates basedon traditional methods (Tamura et al., 2016: SC/F16/JR15). Uncertainty associated with estimated amount of
prey consumption was not considered as this is a preliminary attempt. The Sub-Committee on the RMP of the
IWC/SC is currently trying to develop a guideline for model-based abundance estimation methods, mainly
focusing on GAMs (Hedley and Bravington, 2014). The Working Group on Ecosystem Modelling (EM) of the
IWC/SC recognized the necessity for the development of a guideline on the techniques and underlying
assumptions of SDMs based on up-to-date and comprehensive knowledge (IWC, 2015). Consideration of
uncertainty of prey consumption in spatial context should be investigated in the future along with developmentof associated techniques in the IWC/SC. SST was selected as environmental covariates in the first and second
models. However, the shape of functional form for the first level model (prey consumption) was relatively flat incomparison with the second level model (abundance). The results indicated that spatial distribution of sei whales
at meso scale were largely determined by oceanographic conditions such as SST. Sei whales could then search
for their prey with the optimal oceanographic conditions as indicated by feeding behaviour study (Ishii et al.,
2016: SC/F16/JR25). Future study on feeding ecology of baleen whales should pursuit such an integrated
approach further.
ACKNOWLEDGEMENT
The authors express their thanks to the crews and researchers who participated in the surveys to collect thesevaluable data. The study was supported by the Fisheries Agency of Japan, the Fisheries Research Agency ofJapan and the Institute of Cetacean Research.
REFERENCES
Amante, C., Eakins, B.W. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical
Memorandum NESDIS NGDC-24.
Bando, T., Yasunaga, G., Tamura, T., Matsuoka, K., Murase, H., Kishiro, T. and Miyashita, T. 2016. Methodology and survey procedures
under the JARPNII - offshore component- during 2008 to 2014 with special emphasis on whale sampling procedures. Paper SC/F16/JR4
presented to the JARPNII special permit expert panel review workshop, Tokyo, February 2016 (unpublished)
Hedley, S., Bravington, M. 2014. Comments on design-based and model-based abundance estimates for the RMP and other contexts. Paper
SC/65b/RMP11presented to the 65b IWC Scientific Committee, May 2014. (unpublished). 9pp.
Ishii, M., Murase, H., Fukuda, Y., Sawada, K., Sasakura, T., Tamura, T., Bando, T., Matsuoka, K., Shinohara, A., Nakatsuka, S., Katsumata,N., Miyashita, K. and Mitani, Y. 2016. A short note on feeding behavior of sei whales observed in JARPNII. Paper SC/F16/JR25 presented
to the JARPNII special permit expert panel review workshop, Tokyo, February 2016 (unpublished)
IWC. 2003. Report of the Scientific Committee. J. Cetacean Res. Manage. 5 (suppl.):1-92.
IWC. 2010. Report of the expert workshop to review the ongoing JARPNII programme. J. Cetacean Res. Manage. 11 (suul.2): 405-49.
IWC. 2015. Annex K1 Report of the Working Group on Ecosystem Modelling. J. Cetacean Res. Manage. 16 (suppl.):Konishi, K., Tamura, T., Isoda, T., Okamoto, R., Hakamada, T., Kiwada, H. and Matsuoka, K. 2009. Feeding strategies and prey
consumption of three baleen whale species within the Kuroshio-Current Extension. J. North. Atl. Fish. Sci., 42: 27-40.
Matsuoka, K., Hakamada, T. And Miyashita, T. 2016. Methodology and procedure of the dedicated sighting surveys in JARPNII (20082013) - Offshore component -. Paper SC/F16/JR2 presented to the JARPNII special permit expert panel review workshop, Tokyo, February
2016 (unpublished).
Murase, H., Hakamada, T., Sasaki, H., Matsuoka, K. and Kitakado, T. 2016. Seasonal spatial distributions of common minke, sei and
Bryde’s whales in the JARPNII survey area from 2002 to 2013. Paper SC/F16/JR7 presented to the JARPNII special permit expert panel
review workshop, Tokyo, February 2016 (unpublished).
Tamura, T., Konishi, K. and Isoda, T. 2016. Updated estimation of prey consumption by sei, Bryde’s and common minke whales in thewestern North Pacific. Paper SC/F16/JR15 presented to the JARPNII special permit expert panel review workshop, Tokyo, February 2016
(unpublished).
Thomas, L., Buckland, S.T., Rexstad, E.A., Laake, J.L., Strindberg, S., Hedley, S.L., Bishop, J.R., Marques, T.A. and Burnham, K.P. 2010.
Distance software: design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47: 5-14.
R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.
Wessel, P. and Smith, W.H.F. 1996. A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of GeophysicalResearch: Solid Earth 101: 8741-3.
Wood, S., N. 2006. Generalized additive models: an introduction with R. Chapman & Hall/CRC, Boca Raton.

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Table 1. Selected generalized additive model (GAM) for daily prey consumption of a sei whale. Approximate
significance levels (p-value) and degrees-of-freedom (edf) are shown for each covariate.

FamilyLink function
Power (p)Tweedie
log
1.1
0.73
76.7
124.50Adjusted R2
Deviance explained (%)
GCV score
Parametric coefficientsIntercept
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Month
6
7
8
9
Prey
Euphausiids
Anchovy
Saury
Mackerels
Approximate significanceof smooth terms
SST
log(Chl-a)5

Estimate
6.69

p-value
<0.05

-0.07
-0.05
0.00
-0.07
-0.05
-0.02
-0.05
0.04
-0.03
0.01
0.03

0.12
0.25
0.96
0.10
0.27
0.56
0.28
0.28
0.53
0.75
0.60

0.00
0.04
0.03
-0.02

0.90
0.28
0.44
0.68

0.20
-0.57
-0.93
-0.57

<0.05
<0.05
<0.05
<0.05

edf
7.40
7.22

p-value
<0.05
<0.05

SC/F16/JR16

Table 2. Selected generalized additive model (GAM) for abundance of a sei whale. Approximate significancelevels (p-value) and degrees-of-freedom (edf) are shown for each covariate.

FamilyLink function
Power (p)Tweedie
log
1.1
0.08
21.1
33.60

Adjusted R2
Deviance explained (%)
GCV score
Parametric coefficientsIntercept
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Month
6
7
8
9
Approximate significanceof smooth terms
SST
SSHa
log(Chl-a)
Depth

6

Estimate-7.93

p-value
<0.05

-0.48
-0.20
-0.02
0.04
0.21
-0.30
-0.13
-0.01
-0.32
-1.11
-0.01

0.07
0.42
0.94
0.87
0.37
0.26
0.65
0.98
0.28
<0.05
0.96

0.56
0.59
0.43
0.53

<0.05
<0.05
<0.05
0.07

edf
4.39
6.99

p-value
<0.05
<0.05

SC/F16/JR16

Table 3. Estimated number of sei whale individuals from May to September in each year (2002-2013).

Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
1,368
1,842
2,443
2,556
2,871
1,866
2,206
2,312
1,721
870
2,529

June
2,719
3,305
4,364
5,116
5,798
3,490
3,809
4,600
3,413
1,637
4,417

July
3,622
2,322
2,659
4,026
4,281
5,734
3,257
3,239
3,163
3,228
1,281
3,186

August
3,134
1,744
2,403
2,809
3,382
3,325
2,253
2,150
2,152
2,166
798
2,788

September
3,525
1,997
2,943
2,801
3,443
3,540
2,005
2,547
2,508
2,134
793
2,969

Table 4. Estimated amount of copepods consumed by sei whales (t) from May to September in each year (20022013).
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
32,03843,473
61,232
59,61867,88145,35952,36360,03341,753
21,78964,678June
60,36574,700104,888
114,790
131,51782,04086,790114,430
79,721
39,591
107,983

July
92,762
54,90564,620103,183
101,257
138,645
80,874
79,337
83,62080,04032,911
82,859August80,002
41,24759,252
72,05480,71280,35756,311
52,17757,78253,651
20,77373,454September
83,04943,89165,74966,55475,307
79,18645,76457,39061,45348,652
18,94071,902

Total
255,814140,042
189,622
241,791
257,276
298,189
182,949
188,903
202,855
182,343
72,624228,215

Table 5. Estimated amount of euphausiids consumed by sei whales (t) from May to September in each year
(2002-2013).
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
39,08353,03374,69772,72882,80855,33463,87773,23450,934
26,58078,900June
73,63991,126
127,952
140,031
160,437
100,080
105,874
139,592
97,251
48,296
131,728

July
113,160
66,97878,830125,872
123,523
169,133
98,65896,783102,008
97,64040,148101,079

7

August
97,59450,31772,28187,89898,46098,027
68,69463,65070,48865,44925,34189,606

September
101,31153,54280,207
81,18991,866
96,59855,82770,00974,96659,35023,10587,713

Total
312,065
170,837
231,318
294,959
313,850
363,758
223,179
230,442
247,462
222,439
88,594
278,398

SC/F16/JR16

Table 6. Estimated amount of Japanese anchovy consumed by sei whales (t) from May to September in eachyear (2002-2013).
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
18,08424,53834,56233,651
38,31525,60329,55633,88523,567
12,29836,507

June
34,072
42,16459,20364,792
74,23446,30748,98864,58944,99822,34760,950July
52,35930,99036,47458,241
57,15478,25745,64944,78147,19945,17818,576
46,769August45,15623,28133,44440,67045,55745,357
31,78429,451
32,61530,283
11,72541,460

September
46,876
24,774
37,112
37,56642,50644,69625,831
32,39334,686
27,461
10,69040,584

Total
144,392
79,046
107,030
136,476
145,217
168,310103,264
106,625
114,500
102,92240,992
128,814

Table 7. Estimated amount of Pacific saury consumed by sei whales (t) from May to September in each year
(2002-2013).
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
12,64017,152
24,15823,522
26,782
17,896
20,65923,685
16,473
8,596
25,518June
23,81629,472
41,38245,28951,88832,36834,24245,14731,453
15,62042,603

July
36,59821,66225,49540,70939,94954,70031,90831,301
32,991
31,57812,98532,691

August31,564
16,27323,37728,42831,84431,704
22,217
20,585
22,797
21,167
8,196
28,980

September
32,766
17,31625,94026,25829,711
31,242
18,05522,642
24,24519,195
7,472
28,368

Total
100,927
55,25274,812
95,395
101,504
117,646
72,18074,52980,033
71,941
28,65390,039

Table 8. Estimated amount of mackerels consumed by sei whales (t) from May to September in each year (20032013).
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

May
18,12924,60034,65033,736
38,412
25,66829,63033,971
23,627
12,33036,599June
34,15942,27059,35364,95674,421
46,424
49,11264,752
45,112
22,403
61,104July
52,491
31,06936,56758,38857,29878,45545,76444,89447,31845,292
18,62346,887

8

August45,271
23,34033,52940,77345,67345,472
31,86529,52532,69730,36011,75541,565September
46,99524,836
37,206
37,661
42,61444,80925,89632,47534,77427,531
10,71840,687

Total
144,757
79,246
107,301
136,822
145,585
168,736
103,525
106,894
114,790103,182
41,096
129,140SC/F16/JR16

Figure 1. The survey area of JARPNII (red line). Seafloor depth is also shown.

9SC/F16/JR16

Figure 2. Sea surface temperate (SST) from May to September. Means of values in 1×1 longitude and latitude
grids from 2002 to 2013 are shown.

10SC/F16/JR16

Figure 3. Sea surface height anomaly (SSHa) from May to September. Means of values in 1×1 longitude andlatitude grids from 2002 to 2013 are shown.

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SC/F16/JR16

Figure 4. Chlorophyll-a concentrations (Chl-a) from May to September. Means of values in 1×1 longitude andlatitude grids from 2002 to 2013 are shown.

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Figure 5. Maturity of sei whales from May to September. Data from 2002 to 2013 are pooled.

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Figure 6. Prey of sei whales from May to September. Data from 2002 to 2013 are pooled

14

SC/F16/JR16

Figure 7. Smoothed fits of selected covariate modelling prey consumption of sei whales. Ticks on the x-axis areobserved data points. The y-axis represents the spline function. Shaded areas indicate 95% confidence intervals

Figure 8. Smoothed fits of selected covariate modelling number of sei whale individuals. Ticks on the x-axis areobserved data points. The y-axis represents the spline function. Shaded areas indicate 95% confidence intervals

15

SC/F16/JR16

Figure 9. Estimated spatial distribution of sei whales from May to September. Means of estimated number ofindividuals in 1×1 longitude and latitude grids from 2002 to 2013 are shown.

16

SC/F16/JR16

Figure 10. Spatial distribution of estimated amount of copepods consumed by sei whales (t/day) in 1×1
longitude and latitude grids from May to September. Means from 2002 to 2013 are shown.

17

SC/F16/JR16

Figure 11. Spatial distribution of estimated amount of euphausiids consumed by sei whales (t/day) in 1×1
longitude and latitude grids from May to September. Means from 2002 to 2013 are shown.

18

SC/F16/JR16

Figure 12. Spatial distribution of estimated amount of Japanese anchovy consumed by sei whales (t/day) in 1×1
longitude and latitude grids from May to September. Means from 2002 to 2013 are shown.

19

SC/F16/JR16

Figure 13. Spatial distribution of estimated amount of Pacific saury consumed by sei whales (t/day) in 1×1
longitude and latitude grids from May to September. Means from 2002 to 2013 are shown.

20SC/F16/JR16

Figure 14. Spatial distribution of estimated amount of mackerels consumed by sei whales (t/day) in 1×1
longitude and latitude grids from May to September. Means from 2002 to 2013 are shown.

21