Seasonal spatial distributions of common minke, sei and
Bryde’s whales in the JARPNII survey area from 2002 to
2013
HIROTO MURASE1, TAKASHI HAKAMADA2, HIROKO SASAKI3,4, KOJI MATSUOKA2 AND TOSHIHIDE KITAKADO5
1
National Research Institute of Far Seas Fisheries, Fisheries Research Agency (FRA), 2-12-4 Fukuura,
Kanazawa, Yokohama, Kanagawa 236-8648, Japan
2
The Institute of Cetacean Research, 4-5, Toyomi-cho, Chuo-ku, Tokyo, 104-0055, 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

ABSTRACT
Seasonal spatial distributions of common minke, sei and Bryde’s whales in the JARPNII survey area from 2002 to 2013 were
estimated by using generalized additive models (GAM). All species shifted their distribution area toward the north of the survey
area as season progress but the extents were different among species. Relative abundance of common minke whales was high incoastal area of Japan. Relative abundance of sei whales was high in the offshore area of the survey area where SST was moderate
within the area. Relative abundance of Bryde’s whales was high in the southern part of the survey area where SST was high. The
results suggested that spatial distributions of three baleen whale species were segregated in the JARPNII survey area althoughsome overlaps were occurred. Extent of direct competition (e.g. competitive exclusion of feeding area) could be minimal among
the species but indirect competition for prey might be occurs as they share same prey species.

INTRODUCTION
A preliminary result of spatial modelling of sei whales was presented to the JARPNII expert workshop held in2009 (Murase et al., 2009) and the Panel of the workshop recommended to expand the analysis further as a
medium to long term project (IWC, 2010).Spatial distribution of whales has been investigated further by using
JARPNII data since then and some of the results were published in scientific literatures. Sasaki et al. (2013)
investigated that spatial habitat differentiation between sei and Bryde’s whales using generalized liner model(GLM) taking presence of these whales as binomial response variable. Murase et al. (2014) investigated spatial
distribution of sei whales in relation to oceanic fronts by using generalized additive model (GAM) taking relative
abundance as response variable. However, seasonal changes in spatial distributions of common minke, sei and
Bryde’s whales in relation to oceanographic conditions have not been investigated fully in the JARPNII survey
area
In this paper, results of estimation of seasonal spatial distributions of common minke, sei, and Bryde’s
whales in the JARPNII survey area are presented to see whether the distributions is heterogeneous in both
spatially and seasonally.
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
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 abundances were estimated using a model based method instead of a design
based method. Three observers at each observation platform using 7 × 50 binoculars were engaged in thesighting 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.

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Activities aboard the ship were basically classified into two categories: on-effort and off-effort.
On-effort 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 wereconsidered secondary 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 of individuals in a school, and all sightings during the passing-mode were not approached although
the species and estimated number of individuals in a school were recorded.
Spatial modelling
Generalized additive models (GAM) having a Tweedie error distribution with logarithmic link function wereused to estimate spatial distributions of common minke, sei and Bryde’s whales. A Tweedie random variablewith 1<p<2 is a sum of N gamma random variables in which N has a Poisson distribution (Wood, 2015). If p
equals 1, then it is a generalization of a Poisson distribution and a discrete distribution supported on integer
multiples of the scale parameter. 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 the value of p in each GAM and set it as 1.1. The models with the lowest GCVscores were selected. For this analysis, 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 that package. When the slopes of the functional forms were positive, the covariates wererelated positively to the response variable, and vice versa.
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 Map 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
Self-consistent, Hierarchical, High-resolution Geography Database" (Wessel and Smith, 1996) was used infigures to depict coastline. Year and month were used as categorical covariates in the model. 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. Mean values in 1x1 longitude and latitudinal grid cell in eachmonth in each year were calculated using these data.
Relative abundances of common minke, sei and Bryde’s whales in a grid cell was used as response
variables. The following procedures were conducted to estimate relative abundance. Original sighting effort data
were divided first into 1 km segments, and then these segments were pooled in each grid cell (d). Becausesighting 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 ineach 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 thenmultiplied by the area of the grid cell. Some grid cells had unusually high abundance. Abundances of common
minke, sei and Bryde’s whales falling more than 99, 95 and 97 percentiles, respectively, were treated as outliers
and not considered in the GAM to avoid unrealistic over estimation.

RESULTS AND DISCUSSION
Totals sighting effort in grids in each month from 2002 to 2013 are shown in Fig. 2. Totals of number of sighted
schools of common minke, sei and Bryde’s whales in grids in each month from 2002 to 2013 are shown in Figs.
3-5. Maps of mean SST, SSHa and Chl-a in each month from 2002 to 2013 are shown in Figs. 6-8.Estimated esw
and E(s) are summarized in Table 1. Selected GAMs based on GCV score for relative abundance of three species
are summarized in Table 2. Selected environmental covariates were different among species but SST was used2

for all species. The shapes of the functional forms for selected covariates for three species are shown in Figs.
9-11. The shapes of SST for three species showed pronounced differences. Relative abundance of common
minke whales increased as SST was decreased. A peak of relative abundance of sei whales occurred around
15°C. Relative abundance Bryde’s whales increased as SST increased. Relative abundance of Common minke
whales was high in shallow water depth while that of sei whales was high in deep depth. Depth was not selected
for Bryde’s whales. Estimated spatial distributions of three species are shown Figs. 12-14. All species shifted
their distribution area toward the north of the survey area as season progress but the extents were different
among species. Relative abundance of common minke whales was high in coastal area of Japan. Relative
abundance of common minke whales was also high around 50°N and 170°E where seafloor depth is relatively
shallow because of existence of the Emperor Sea mounts. Relative abundance of sei whales was high in theoffshore area of the survey area where SST was moderate within the area. Relative abundance of Bryde’s whales
was high in the southern part of the survey area where SST was high. The results suggested that spatial
distributions of three baleen whale species were segregated in the JARPNII survey area although some overlaps
were occurred. Extent of direct competition (e.g. competitive exclusion of feeding area) could be minimal among
the species but indirect competition for prey might be occurs as they share same prey species.
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 procedure
under the JARPN II - offshore component- 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)
IWC 2010. Report of the expert workshop to review the ongoing JARPNII programme. J. Cetacean Res. Manage. 11 (suul.2): 405-49.
Matsuoka, K., Hakamada, T. And Miyashita, T. 2016. Methodology and procedure of the dedicated sighting surveys in JARPN II. Paper
SC/F16/JR2 presented to the JARPNII special permit expert panel review workshop, Tokyo, February 2016 (unpublished)
Murase, H., Hakamada, T., Kiwada, H., Inagake, D., Okazaki, M., Tojo, N. and Matsuoka, K. 2009. Preliminary results of estimation of sei
whale (Balaenoptera borealis) distribution and abundance in the whole North Pacific basin. Appendix 2. 11pp. In: Hakamada, T.
Examination of the effects on whale stocks of future JARPN II catches. Paper SC/J09/JR36 presented to the Expert Workshop to Review
the Ongoing JARPN II Programme, Yokohama, January 2009 (unpublished).51pp.
Murase, H., Hakamada, T., Matsuoka, K., Nishiwaki, S., Inagake, D., Okazaki, M., Tojo, N. and Kitakado, T. 2014. Distribution of sei
whales (Balaenoptera borealis) in the subarctic–subtropical transition area of the western North Pacific in relation to oceanic fronts. Deep
Sea Res. II 107: 22-8.
Sasaki, H., Murase, H., Kiwada, H., Matsuoka, K., Mitani, Y. and Saitoh, S. 2013. Habitat differentiation between sei (Balaenoptera
borealis) and Bryde's whales (B. brydei) in the western North Pacific. Fish. Oceanogr. 22: 496-508.
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.
Wood, S. 2015. Package ‘mgcv’ version 1.8-7. R Foundation for Statistical Computing, Vienna.

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Table 1. Estimated effective search widths (esw) and mean school size E(s) for common minke, sei and Bryde’s
whales used in this study. Coefficient of variation (CV) fro esw and E(s), and truncation distances are also
shown.

Species

Truncation
(km)

ESW
(km)

CV

E(s)

CV

Minke whale

2.78

0.84

0.05

1.05

0.01

Sei whale

5.56

2.73

0.03

1.30

0.01

Bryde's whale

5.56

3.03

0.03

1.55

0.01

Table. 2. Selected generalized additive models (GAMs) for common minke, sei and Bryde’s whales.
Approximate significance levels (p-value) and degrees-of-freedom (edf) are shown for each covariate.

Species
FamilyLink function
Power (p)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

Minke whale
Tweedie
log
1.1
0.14
31.3
41.55
Estimate
p-value
-8.05
<0.05

Sei whale
Tweedie
log
1.1
0.08
21.1
33.60
Estimate
p-value
-7.93
<0.05

Bryde's whale
Tweedie
log
1.1
0.21
48.8
26.79
Estimate
p-value
-10
<0.05

0.61
0.22
-0.20
0.22
-0.36
-0.41
-0.57
-1.82
-0.76
-0.02
-1.02

0.06
0.51
0.57
0.50
0.33
0.33
0.18
<0.05
0.12
0.97
0.08

-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

0.95
0.80
0.78
1.09
1.33
1.19
0.94
0.95
1.15
1.06
0.71

0.07
0.11
0.12
<0.05
<0.05
<0.05
0.09
<0.05
<0.05
<0.05
0.15

0.44
0.43
0.37
0.82

<0.05
0.06
0.14
<0.05

0.56
0.59
0.43
0.53

<0.05
<0.05
<0.05
0.07

-0.37
-0.67
-0.82
-1.819

0.46
0.20
0.13
<0.05

edf
5.48
8.29

p-value
<0.05
<0.05

edf
4.39
6.99

p-value
<0.05
<0.05

edf
4.60
5.43
6.36
-

p-value
<0.05
<0.05
<0.05
-

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

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Fig. 2. Sighting effort (km) from May to September. Totals from 2002 to 2013 are shown.

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Fig. 3. Number of sighted schools of common minke whales in 1×1 longitude and latitude grids from May to
September. Totals from 2002 to 2013 are shown.

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Fig. 4. Number of sighted schools of sei whales in 1×1 longitude and latitude grids from May to September.
Totals from 2002 to 2013 are shown.

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Fig. 5. Number of sighted schools of Bryde’s whales in 1×1 longitude and latitude grids from May to September.
Totals from 2002 to 2013 are shown.

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Fig. 6. Sea surface temperate (SST) from May to September. Means of values in 1×1 longitude and latitude grids
from 2002 to 2013 are shown.

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Fig. 7. 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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Fig. 8. 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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Fig. 9. Smoothed fits of selected covariate modelling number of common minke whale individuals. Ticks on thex-axis are observed data points. The y-axis represents the spline function. Shaded areas indicate 95% confidence
intervals

Fig. 10. 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

13

Fig. 11. Smoothed fits of selected covariate modelling number of Bryde’s whale individuals. Ticks on the x-axis
are observed data points. The y-axis represents the spline function. Shaded areas indicate 95% confidence
intervals

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Fig. 12. Estimated spatial distribution of common minke whales from May to September. Means of estimatednumber of individuals in 1×1 longitude and latitude grids from 2002 to 2013 are shown.

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Fig. 13. 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.

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Fig. 14. Estimated spatial distribution of Bryde’s whales from May to September. Means of estimated number ofindividuals in 1×1 longitude and latitude grids from 2002 to 2013 are shown.

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