FISHERIES OCEANOGRAPHY

Fish. Oceanogr. 22:6, 496–508, 2013

Habitat differentiation between sei (Balaenoptera borealis)and Bryde’s whales (B. brydei) in the western North Pacific

HIROKO SASAKI,1,* HIROTO MURASE,2
HIROSHI KIWADA,3 KOJI MATSUOKA,3
YOKO MITANI4 AND SEI-ICHI SAITOH1,5
1

Laboratory of Marine Environment and Resource Sensing,
Faculty of Fisheries Sciences, Hokkaido University, 3-1-1, Minato, Hakodate, Hokkaido, 041-8611, Japan
2
National Research Institute of Far Seas Fisheries, 2-12-4
Fukuura, Kanazawa, Yokohama, Kanagawa, 236-8648, Japan
3
Institute of Cetacean Research, 5F, 4-5 Toyomi, Chuo, Tokyo,
104-0055, Japan
4
Laboratory of Marine Ecology Change Analysis, Filed Science
Center for Northern Biosphere, Hokkaido University, 3-1-1, Minato, Hakodate, Hokkaido, 041-8611, Japan
5
Laboratory of Marine Bioresource and Environment Sensing,
Faculty of Fisheries Sciences, Hokkaido University, 3-1-1,
Minato, Hakodate, Hokkaido, 041-8611, Japan

ABSTRACTTwo closely related baleen whale species, sei and
Bryde’s whales, in the western North Pacific were studied to identify differences in habitat use. Data wereobtained from May to August 2004 and 2005. This
study examined the relationship between oceanographic features derived from satellite data and the
distribution of sei and Bryde’s whales using basic statistics. We investigated oceanographic features including
sea surface temperature (SST), sea surface chlorophylla (Chl-a), sea surface height anomalies (SSHAs), anddepth of the habitat. These two whale species used
habitats with different SST, Chl-a, and SSHA ranges.
The 0.25 mg m3 Chl-a contour (similar to the definition of the Transition Zone Chlorophyll Front) was agood indicator that separated the habitats of sei and
Bryde’s whales. Then generalized linear models wereused to model the probabilities that the whale species
would be present in a habitat and to estimate theirhabitat distribution throughout the study area as afunction of environmental variables. The potential
habitats of the two species were clearly divided, andthe boundary moved north with seasonal progression.
*Correspondence. e-mail: hiro_sasaki@salmon.fish.hokudai.
ac.jp
Received 11 September 2012
Revised version accepted 29 April 2013496

doi:10.1111/fog.12037

The habitat partitioning results indicated that SST
contributed to the patterns of habitat-use and mightreflect differences in prey species between the two
whales. This study showed that the habitats of the sei
and Bryde’s whales were clearly divided and theirpotential habitat-use changed seasonally.
Key words: Bryde’s whale, generalized linear models,
habitat partitioning, oceanographic features, sei whale,
western North Pacific

INTRODUCTION
Marine mammals play important roles in marine ecosystems as top predators (Bowen, 1997; Hunt et al.,2000). The amount of food consumed by cetaceans is
not negligible, and they can cause top-down effects byforaging on large amounts of prey, such as zooplankton
and fishes (Tamura, 2003). Cetaceans are affected byenvironmental changes attributable to the dynamics
of complex physical and biological processes. Identification of the habitats used by cetaceans is fundamental
to understanding the interactions between cetaceans
and marine ecosystems.
Marine mammals also experience bottom-up effects
from environmental changes. Atmospheric andclimate-related processes drive changes in marine ecosystems on different timescales. Accordingly, understanding climate patterns and ecosystems, andconnections between the two, is important (Overland
et al., 2008). Interannual variation in climate is now
widely recognized to affect fish production (Baumgartner et al., 1992; Mantua et al., 1997; Bakun andBroad, 2003). Changes in fish populations are related
to changes in the amount and timing of primary production (Hunt, 2006), and temperature-relatedchanges in the growth and maturation of zooplankton(Mackas et al., 1998). Hunt and McKinnell (2006)
indicated that decadal-scale changes in atmosphericforcing (climatic regime shifts) are associated withchanges in the distribution and population size of awide variety of fishes in the North Pacific. Marine
mammals, which feed on zooplankton and fishes,
would be susceptible to such bottom-up effects.
Longer-term climate changes could affect marine© 2013 John Wiley & Sons Ltd

Sei and Bryde’s whale in western North Pacificmammals, but the extent to which they will be
impacted is not yet certain (Simmonds and Eliott,2009). To investigate the responses of marine mammals to climate and ecosystem changes at various
timescales, understanding current habitat-use patterns
in relation to oceanographic conditions is criticallyimportant. Recent improvements in satellite remotesensing techniques allow us to conduct such analyses.
In the past, habitat-use by cetaceans in the western
North Pacific was generally studied qualitatively using
commercial catch data. Uda (1954) and Nasu (1966)
suggested that the distribution of baleen whales was
closely related to the locations of eddies and fronts, inaddition to topography. Kawamura (1973) reported
that sei whales were distributed around the Emperor
seamount (Fig. 1) and that they preyed almost exclusively on copepods. Nemoto (1959) found that the distributions of prey species were related to the
distributions of baleen whales. Such studies have rarely
been conducted since 1987, when a moratorium oncommercial whaling was imposed by the International
Whaling Commission. Medium-sized baleen whales,
the sei (Balaenoptera borealis) and Bryde’s (Balaenoptera
brydei) whales, are distributed in the western North
Pacific. The two species are closely related in terms ofgenetics and morphology. Until 1955, they were collectively recorded as ‘sei whales’ in Japanese whaling
statistics because of their morphological similarity,
although sei and Bryde’s whales were recognized as
northern and southern types of ‘sei whales’, respectively (Omura and Fujino, 1954; Omura, 1959). An
examination of 901 aligned sites of the control region
sequence of mtDNA suggested that the smallest interspecific nucleotide difference in congeners was
between sei and Bryde’s whales (Wada et al., 2003).
Wada et al. (2003) also indicated that three species ofBryde’s-like whales occur worldwide, namely, Bryde’s
whale (Balaenoptera brydei), Eden’s whale (Balaenoptera
edeni), and Omura’s whale (Balaenoptera omurai).
However, some scientists consider the former two
forms to be subspecies rather than species (Kato andPerrin, 2009; Committee on Taxonomy, 2011). These
Bryde’s-like whales are still treated as B. edeni by theInternational Whaling Commission, with recognition
that the designation contains more than one species(International Whaling Commission, 2001). In agenetic study, Kanda et al. (2007) identified Bryde’slike whales distributed in the western North Pacific as
Bryde’s whales. In this paper, we assume Bryde’s whale
as B. brydei following the nomenclature proposed by
Wada et al. (2003). Sei and Bryde’s whales mainly
feed on zooplankton and small pelagic fishes in thewestern North Pacific, although their dominant preyspecies differ geographically, seasonally, and annually(Nemoto, 1959; Kawamura, 1980; Horwood, 1987;
Murase et al., 2007; Konishi et al., 2009; Watanabe
et al., 2012). Sei whales remain in low-latitude areas
for breeding in the winter and move to higher latitudes
for feeding in the summer (Horwood, 1987), whereas
Bryde’s whales show little seasonal movement andremain at low latitudes throughout the year (Kato andPerrin, 2009). The distributions of sei and Bryde’s
whales differ in relation to sea surface temperature(Omura and Nemoto, 1955; Nemoto, 1959; Horwood,1987). For example, Ohsumi (1977) reported that sea
surface temperature ranges for sei and Bryde’s whales
at catch positions were 5–25°C and >18°C, respectively. Previous reports have indicated that these two
species are found in different areas in the western
North Pacific. However, little quantitative information is available regarding their spatial and temporal
habitats. Seasonal and annual shifts in the habitats of(b)

Figure 1. Map of the North Pacificshowing (a) location of the study area
identified by black box and (b) enlarged
map depicting the tracklines covered.
The orange line indicates the JARPN II
research trackline in 2004 and the whiteline shows the trackline in 2005.

497

(a)

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498

H. Sasaki et al.

other marine life, such as albacore tuna (Thunnus alalunga) and loggerhead turtles (Caretta caretta), havebeen characterized by specific oceanographic conditions in the western North Pacific (Polovina et al.,
2001; Polovina et al., 2004; Zainuddin et al., 2004,
2008). Many marine animals utilize the transition zone
between subarctic and subtropical waters in the western North Pacific as feeding grounds and migration
corridors between these waters.
Generalized linear models (GLMs) are widely used
to predict cetacean distributions and understand theecological processes determining these distributions(Waring et al., 2001; Hamazaki, 2002; Macleod et al.,
2004; MacLeod et al., 2008; Praca et al., 2009; Anderwald et al., 2011). In these studies, the presence/
absence of animals was used as a binary response
variable.
To determine how whale habitats differ withrespect to environmental conditions, we first examined relationships between environmental conditions,which were mainly determined using satellites, andthe distribution patterns of sei and Bryde’s whales
using basic statistics. In contrast to previous studies,
the presence of sei and Bryde’s whales was used as abinary response variable to differentiate their habitats.
Finally, suitable and potential habitats for the two
whale species in the study area were estimated using
the selected habitat models.
MATERIALS AND METHODS
Study area
The study area was located in the western North Pacific, between 35° and 48°N, and between the eastern
coast of Japan and 175°E, excluding waters that areclaimed by countries other than Japan (Fig. 1). Most
regions in the study area had depths >4000 m. Topographic features in the area included the Kuril Trenchin the northwest of the basin, the Japan trench on thewest, and the Emperor seamount at around 170°E,
which runs in a north–south direction (Fig. 1). The
Shatsky Rise is located at the center of the basin anddivides the Kuroshio Extension. The Kuroshio south ofJapan and the Kuroshio Extension east of Japan are thewestern boundary currents of the North Pacificsubtropical gyre, and they transport warm saline surface
water (Yasuda, 2003). Subarctic currents, including thesouth-flowing Oyashio (Yasuda et al., 1996, 2001; Okuda et al., 2001), represent other major currents in thewestern North Pacific. Our study area was part of theKuroshio-Subarctic transition zone (Kawai and Saitoh,1986; Kawamura et al., 1986; Yasuda et al., 1992),
which is one of the world’s major fishing grounds.
Whale sighting data
Surveys were conducted as part of the second phase of
the Japanese Whale Research Program under a Special
Permit in the North Pacific (JARPN II). Surveys wereconducted in the summers (May–September) of 2004
and 2005 (Table 1, Fig. 2). From these surveys, weonly utilized the subset data from May to August.
Sighting data for sei and Bryde’s whales were collectedduring the day by a sighting survey vessel, the Kyoshin
Maru No. 2 (KS2: 368GT). The vessel traveled at a
speed of 10.5 knots (19.5 km h1). The survey was
only conducted when the wind speed did not exceed
16 knots (7.9 m s1) and the visibility was >2 nautical miles (3.7 km). Primary observers were allocatedto the top barrel (three observers) and the upper bridge(two observers). The barrel and upper bridge were 17
and 8 m above the water surface, respectively. The
observers searched for whales within 3 nautical (n.)
miles (5.6 km, perpendicular distance) within 3
n.miles boundary either side of the constructed trackline using 7 9 50 binoculars with a reticule. Zigzag
tracklines were constructed within the survey area.
The starting points of the tracklines were randomly
selected. The sighting survey was conducted during
daylight hours from 1 h after sunrise to 1 h before sunset. The dates and positions of sightings of sei and
Bryde’s whales recorded during the survey were used inthis study. It was assumed that all the schools of sei
and Bryde’s whale within the 3 n.miles boundaries
were sighted. We evaluated the pattern of spatial autocorrelation for each species using Moran’s I. Moran’s I
provides a measure of similarity of ‘presence’ as a function of spatial distance between consecutive segments(in this case 4 km spatial resolution). Values of index
range from 1 to +1, and indices close to zero suggest
a random distribution (not be spatial autocorrelated).

Table 1. Summary of survey details for sighting data of sei whales and Bryde’s whales.
No. of sightings (groups)
PeriodTotal distance (n. miles)

Survey effort (n. miles)

Sei whales

Bryde’s whales

2004 May–August
2005 May–August

10 609.54
9763.133939.425082.4180 (53)360 (181)

40 (35)15 (12)© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Sei and Bryde’s whale in western North PacificFigure 2. Tracklines and sighting pointsof the survey. (a) 2004 sei whales, (b)2005 sei whales, (c) 2004 Bryde’s whales,(d) 2005 Bryde’s whales. Colored lines
indicate the trackline for each month.
Colored circles indicate locations ofsighting points for each species.

(a)

(b)

(c)

(d)

Environmental data
Four types of environmental data were used to model
the habitats of sei and Bryde’s whales: sea surface temperature (SST; °C), sea surface chlorophyll a concentration (Chl-a; mg m3), sea surface height anomalies(SSHAs; cm), and depth (m) (Table 2).
Satellite-derived monthly mean SST data sets,
NOAA/Advanced Very High Resolution Radiometer(AVHRR) Pathfinder version 5.0, were used in thisanalysis. The data were provided by the National
Aeronautics and Space Administration (NASA)/Jet
Propulsion Laboratory (JPL) Physical Oceanography
Distributed Active Archive Center (PO-DAAC).
Monthly mean Chl-a data sets were derived from asatellite sensor Orbview-2/ Sea-viewing Wide Fieldof-View Sensor (SeaWiFS), and used as an indicatorof the concentration of primary producers. Specifically, global area coverage, monthly composite level-3
standard mapped image data were used.
The SSHA data were provided by the Maps of Sea
Level Anomalies (MSLA)/Archiving, Validation andParameter

Explanation

Dependentp(xi)Independent

SST

Probability of thepresence ofsei whales
Sea surface
temperature
Chlorophyll
a concentration
Sea surface heightanomaly
Depth

Chl-a
SSHA
Depth

Interpretation of Satellite Oceanographic Data (AVISO) scientific team of Collecte, Localisation, Satellite(CLS)/Center National d’Etudes Spatiales (CNES)
data center. The SSHA data were originally derived
from the satellite sensors Topex/Poseidon, Jason-1,
ENVISAT, and ERS altimeter. Because monthly mean
SSHA data were not provided by the data center,monthly mean data were calculated using data from
7-day periods.
A depth data set, 2-minute Worldwide Bathymetry/
Topography (ETOPO2), was provided by the NOAA/
National Geophysical Data Center (NGDC). The
data had a cylindrical projection and a spatial resolution of 2 min. Because the spatial resolutions and map
projections of the four data sets were different, they
were standardized using ARCGIS 9.2 (ESRI, Redlands,
CA, USA). First, all data were converted to an equidistant cylindrical projection. Secondly, each data set
was interpolated within ordinary kriging using theGeostatistical Analyst extension of ARCGIS. Finally,each data set was converted to a raster data set that
had a spatial resolution of 4 km.

°C

Summary statistics of selected environmental variables
To elucidate the distribution ranges of sei and Bryde’s
whales in response to four environmental variables(SST, Chl-a, SSHAs, and depth), summary statistics(mean, standard deviation, median, and interquartile
range, IQR) were calculated. Environmental variables
corresponding to sighting positions per month wereextracted using ARCGIS.

mg m3

Spatial modeling of potential habitats

Table 2. Parameters for the generalized linear model(GLM).
Type

499

Unit

cm
m

© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

A GLM was used to identify potential habitats of sei
and Bryde’s whales in the western North Pacific. In
the model, a binary response variable Y (presence ofsei whale equal to 1 and presence of Bryde’s whale

500

H. Sasaki et al.

equal to 0) was estimated using four environmental
variables (SST, Chl-a, SSHAs, and depth) as explanatory variables. As a prerequisite for our approach,when a sei or Bryde’s whale was found, the presence ofsei whales was defined as [P(Y = 1)] and that of Bryde’s
whale was defined as [P(Y = 0)]. The binary response
variable is assumed to follow a binomial error distribution in the model. Formulae for the model were as follows:PðY ¼ 1Þ
1  PðY ¼ 1Þ
¼ a þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4

logit½PðY ¼ 1Þ ¼

ð1Þ
where P(Y = 1) is the probability of presence of sei
whales varying between 0 and 1; a is the intercept; xi isthe i-th explanatory variables, namely SST, Chl-a,
SSHAs, and depth; and bi is the coefficient term for ith explanatory variables.
The probability of occurrence for Bryde’s whales
was defined as:PðY ¼ 0Þ ¼ 1  PðY ¼ 1Þð2ÞModel selection was based on Akaike’s information
criterion (AIC; Akaike, 1974; Burnham and Anderson, 2010). The model with the lowest AIC value wastaken to be the best model. We calculated AIC differences (DAIC) defined by the difference in AIC values
between the best and other models. A DAIC of 0–2 isconsidered to demonstrate substantial empirical support and values of 4–7 indicate considerably lessempirical support (Burnham and Anderson, 2010). For
cases in which multiple models have DAIC of 0–2,
substantial uncertainty exists in terms of which modelis truly the best model. Therefore, we compared pairsof models from among the models that have DAIC of
0–2 using likelihood-ratio tests. The potential habitatsof sei and Bryde’s whales in each month from May toAugust were estimated using the selected best model.
Maps of the estimated habitats were drawn using
ARCGIS.
Identification of suitable habitats
Mean values for each environmental variable at thesighting positions of sei and Bryde’s whales, with standard deviations, were calculated as suitable habitat
ranges (Zainuddin et al., 2008). If the selected environmental variables were within the suitable habitat
range in a 4-km grid, the grid was defined as containing suitable habitat. In addition, we overlaid
the 0.25 mg m3 Chl-a contour as an indicator of theTransition Zone Chlorophyll Front (TZCF) in thewestern North Pacific. The TZCF is important for the
migrations and foraging of large pelagic species (Polovina et al., 2001; Polovina and Howell, 2005; Zainuddin et al., 2008). We also investigated theprobabilities that sei and Bryde’s whales occurred ineach suitable habitat area by overlaying the potential
habitat map onto the suitable habitat map.
RESULTS
Summary statistics of environmental variables
Sei whales were distributed in areas with lower SST
values and an IQR range of 13.1–16.8°C (mean:
15.5°C), whereas Bryde’s whales were distributed inhigher SST areas with an IQR range of 19.7–22.2°C(mean: 21.5°C) (U-test: P < 0.001; Fig. 3). Sei whales
were distributed in higher Chl-a areas with an IQR
range of 0.26–0.57 mg m3 (mean: 0.50 mg m3)
and Bryde’s whales were distributed in lower Chl-aareas with an IQR range of 0.15–0.25 mg m3 (mean:
0.22 mg m3) (P < 0.001; Fig. 3). These results showthat sei whales tended to be distributed in areas withlower SST and higher Chl-a compared with Bryde’s
whales. IQR ranges for SSHA were narrower for sei
whales (–3.00 to 3.59 cm, mean: –1.35 cm) than for
Bryde’s whales (–17.8 to 18.9 cm, mean: 3.85 cm,
P < 0.001; Fig. 3). The distributions of sei and
Bryde’s whales were not different with respect todepth.
Spatial modeling of potential habitats
The result of Moran’s I shows no evidence for significant autocorrelation for either of the species (Moran’sI < 0.5). Therefore, we took no account of spatial
autocorrelation in our approach. The model that
included SST, SSHAs, and Chl-a had the lowest AIC
value (Table 3, Model-1). However, Model-1 andModel-2 had similar AIC values, and they did not significantly differ based on the likelihood-ratio test. The
only difference between the two models was whether
the variable ‘depth’ was included (Model-2) or not(Model-1). A comparison of the depth between thetwo models using a U-test indicated no significant difference (P > 0.05). Furthermore, ‘depth’ was not significant in Model-2 (P > 0.05). Therefore, we selected
Model-1 as the best model.
The order of significance for the variables in themodel was [P(SST) < 0.0001, P(SSHA) < 0.05, P(Chl-a) = 0.07]. According to the AIC value, the bestmodel with SST, SSHAs, and Chl-a was 110.9,
whereas Model-3 (without Chl-a), Model-4 (without
SSHAs), and Model-5 (without SST) were 2.60,
10.71, and 31.47 units higher, respectively (Table 3).
© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Sei and Bryde’s whale in western North Pacific501

Figure 3. Boxplots of occurrences of sei
and Bryde’s whales as a function of sea
surface temperature (SST; °C), sea surface chlorophyll a concentration (SSC;
mg m3), sea surface height anomalies(SSHA; cm), and depth (m). The box is
25% and 75% quantile range, the upperand lower horizontal lines the 95% confidence limits, the center horizontal line
the median, the red dot the mean, andthe blue line the mean ± one standard
deviation.

The order of effective variables, based on the AIC values, was SST, SSHAs, and then Chl-a.
The coefficients of the selected model indicatedthat SST and SSHAs had significant negative effects,
whereas Chl-a had a significant positive effect on thespatial distribution of sei whales.
The potential habitats of sei and Bryde’s whales
were estimated spatially using the selected model(Fig. 4). The isoline for a 50% probability of occurrence is also provided in the figure as a habitat partitioning line (HPL) between sei and Bryde’s whales.
The HPL moved northward through the summer. In
May, the HPL was around 35°N, and it moved to 40°–45°N in August in both 2004 and 2005.

Probabilities of occurrence for sei and Bryde’s
whales within their suitable habitats are shown in Figure 6. Relatively high probabilities of occurrence wereobserved for sei whales in their suitable habitats. In
contrast, probabilities of occurrence for Bryde’s whales
were relatively low within their suitable habitats, especially in May (Table 4). Probabilities of occurrence for
Bryde’s whales were high in the southern portion of
their suitable habitat range in May, but the range
moved northward as the season progressed. As a result,areas that had high probabilities of occurrence for sei
and Bryde’s whales were in close proximity in August.

Identification of suitable habitats

The results of the GLM and basic statistics demonstrated that the habitats of sei and Bryde’s whales areclearly separated with respect to oceanographic conditions. The habitats of sei whales were distributed to
the north relative to the habitats used by Bryde’s
whales during the summer season in the western North
Pacific. As suggested by Slijper (1965), habitat-use
and migrations of cetaceans are limited by the supplyof prey species. Gaskin (1982) also indicated theimportance of cetacean foraging areas having continuous biological production and short timescale foodchains. Some attempts were made to relate spatial distributions of cetaceans and their prey in the world(e.g. Friedlaender et al., 2006; Laidre et al., 2007; Torres, 2009; Skern-Mauritzen et al., 2011; Murase et al.,Suitable habitats for sei whales were located north of
the suitable habitats for Bryde’s whales throughout thesurvey season in both years (Fig. 5). The suitable habitats of both sei and Bryde’s whales shifted northward
as the season progressed. The rate of the shift was faster in 2004 than in 2005. Although suitable habitats
for Bryde’s whales were apparent regardless of month,suitable habitats for sei whales were obscure in May
and June. In general, the boundary between the suitable habitats for the two species was wider in May andJune and narrower in July and August, and sei whales
were distributed north of the TZCF, whereas Bryde’s
whales were distributed south of it regardless of themonth.
© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

DISCUSSION

–0.021 (0.014)+6.35(2.56)*+13.25 (2.77)***

SST, sea surface temperature; SSC, sea surface chlorophyll a concentration; SSHA, sea surface height anomaly; Depth, depth; K, number of variables in the model; ΔAIC,
AIC difference; LRT, P-value of likelihood ratio tests. *P < 0.1, **P < 0.05, ***P < 0.01.

102.90102.70107.50115.61
136.37–0.66
0.03<0.001<0.001
0.00
1.80
2.60
10.71
31.47
110.90112.70113.50121.61142.373
4
3
2
2–0.24 E–05 (–0.54 E–05)–0.059 (0.018)***–0.058 (0.018)***–0.068 (0.018)***–0.47 (0.11)***–0.49 (0.12)***–0.62 (0.095)***–0.32 (0.080)***+8.76 (2.58)***+7.87 (3.06)**+13.17 (1.89)***+5.41 (2.03)**2.28 (0.70)**
1
2
3
4
5

+4.25 (2.36)*+4.23 (2.32)*

SSTIntercept
Model

Coefficients (SE)

SSC

SSHA

Depth

K

AIC

ΔAIC

LRT

Residual
deviance

H. Sasaki et al.

Table 3. Results of Akaike’s information criterion (AIC)-based model selection for associations between the environmental variables and species (sei or Bryde’s whales)with the value of coefficients. Models with substantial empirical support by AIC < 3.

5022013). However, when prey data are not available atthe time of a cetacean survey (as in the case of thisstudy), statistical estimates of cetacean habitat suitability can be carried out using environmental dataobtained from satellites and assuming relationships
between prey species distributions and these environmental variables.
The sei whale IQR range for SST (13.1–16.8°C)
was lower than that of the Bryde’s whale (19.7–
22.2°C) in this study. Based on the results of summer
sighting surveys conducted in the western North Pacific from 1994 to 2003, sei whales were reportedly distributed in areas where the SST ranged between 8.0
and 25.0°C, whereas Bryde’s whales were distributed
in areas where the SST was higher than 20°C,
although their distributions overlapped in areas where
the SST was 20–25°C (Fujise et al., 2004). Previously
reported SST ranges (Ohsumi, 1977; Fujise et al.,2004) largely overlap with the results of this study,
although the SST ranges were narrower in the current
results. The suitable SST ranges for sei and Bryde’s
whales could be related to the distributions of theirprey species. Nemoto (1959) reported the prey speciesof these whales in order of preference (i.e., sei whale:
copepods–krill–pelagic fishes–squids, Bryde’s whale:
krill–pelagic fishes–copepods) and classified the sei
whale as a copepod feeder and Bryde’s whale as a krill
and fish feeder. Konishi et al. (2009) also indicatedthat the composition of prey in the stomachs of sei
and Bryde’s whales varied geographically. Based on
the same survey during 2000–2007, 56.4% of the stomach contents of sei whales consisted of copepods andkrill. On the other hand, 54.7% of the stomach contents of Bryde’s whales consisted of Japanese anchovy(Engraulis japonicus) (Konishi et al., 2009). Smallanchovy tended to occur in areas with high SST,
whereas large individuals tended to occur in low-SST
areas (Murase et al., 2012). Sei whales tend to feed onlarge anchovy individuals, whereas Bryde’s whales
tend to feed on smaller individuals (Konishi et al.,2009). In our study, the SST IQR ranges of sei and
Bryde’s whales were clearly divided, with the SST
range for Bryde’s whales being higher (median:
21.4°C) than that of sei whales. The difference in the
body length of Japanese anchovies in the stomachs ofsei and Bryde’s whales reflects the difference in their
spatial distributions with respect to SST.
The difference in the SSHA range between sei and
Bryde’s whales could also indicate differences in theirprey. SSHA is an index of water mass and current.
The Kuroshio Extension region has the highest levelof eddy variability in the North Pacific Ocean, whereas
the Oyashio area has a low level (Qiu, 2001). Sei© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Sei and Bryde’s whale in western North Pacific

(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

503

Figure 4. Probabilities of cetacean
occurrence in 2004 and 2005. (a) 2004
May, (b) 2004 June, (c) 2004 July, (d)2004 August, (e) 2005 May, (f) 2005
June, (g) 2005 July, and (h) 2005
August. Colors from blue to red indicate
the probability of occurrence and which
species can be found (sei or Bryde’s
whales). The black line indicates a probability of 50%.

whales tended to be distributed in areas within a narrow SSHA range (Fig. 3) where low variability in
SSHAs was observed. Bryde’s whales tended to occur
in areas with large anomalies (Fig. 3) and were distributed throughout the Kuroshio and Kuroshio Extension
areas. The Kuroshio and the Kuroshio Extension carryeggs and larvae of small pelagic fishes, such as Japanesesardine and anchovy, into the offshore transition area,
where biological productivity is high (Odate, 1994).
In contrast, zooplankton, such as krill and copepods,
tend to concentrate just outside of eddies, such as inareas with low variability in SSHAs. Sei and Bryde’s
whales fed on krill in the western North Pacific regardless of location and SST (Konishi et al., 2009). Many
krill species are distributed in the western North© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Pacific (Brinton, 1962) and their distribution patterns
are related to oceanographic conditions (Letessier
et al., 2011). Copepodite stage 5 (C5) of Neocalanus
spp. is the most common copepod in sei whale stomachs (Konishi et al., 2009). Neocalanus spp. is distributed around the subarctic North Pacific (Mackas andTsuda, 1999). Nagasawa et al. (2001) reported thatthe highest abundance of Neocalanus spp. was found ataround 40°–45°N in the southern subarctic North
Pacific in summer. Differences in habitat-use by preyspecies partially explain why sei and Bryde’s whale
were distributed in areas with different SSHA
conditions.
Sei and Bryde’s whales were found in areas with
Chl-a ranges of 0.26–0.57 and 0.15–0.25 mg m3

504

H. Sasaki et al.

(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

Figure 5. Suitable habitats calculatedusing boxplots and Transition Zone
Chlorophyll Front in 2004 and 2005. (a)2004 May, (b) 2004 June, (c) 2004 July,(d) 2004 August, (e) 2005 May, (f) 2005
June, (g) 2005 July, and (h) 2005
August. The green area indicates suitable
habitats for sei whales and the pink area
denotes suitable habitats for Bryde’s
whales. The black line represents theTransition Zone Chlorophyll Front.

Table 4. Averaged probabilities of the occurrence (%) ofeach species in areas of suitable habitat.
Probability (SD)Species

Year

May

June

July

AugustSei whales

2004
2005
2004
2005

95 (4.8)
94 (6.5)
59 (16)56 (15)94 (5.9)
96 (5.0)55 (19)68 (18)95 (4.4)
96 (4.3)
61 (20)57 (18)96 (4.5)
95 (4.8)64 (19)60 (20)Bryde’swhales(Fig. 3), respectively. The boundary between theirhabitats was around 0.25 mg m3 Chl-a. Polovina
et al. (2001) reported that Chl-a values for the subarctic gyre and the Transition Zone, and the subtropicalgyre, were >0.25 and <0.15 mg m3, respectively.
Accordingly, at the study site, sei whales were mainly
distributed in the subarctic gyre and the Transition
Zone, whereas Bryde’s whales were mainly found in
the subtropical gyre. These two water masses were separated by a sharp chlorophyll front with a Chl-a of
0.25 mg m–3 that spans the North Pacific. The chlorophyll front is well known and is termed the TZCF
(Polovina et al., 2001; Polovina and Howell, 2005)
and is important for the migration and foraging habitats of large pelagic species. The TZCF is defined by a
0.2 mg m3 Chl-a contour in the eastern North Pacific, and Zainuddin et al. (2004, 2008) suggested that a
Chl-a of 0.3 mg m3 is an important proxy for albacore (Thunnus alalunga) in the western North Pacific.
The TZCF can be assumed to be located around these© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Sei and Bryde’s whale in western North Pacific

(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

505

Figure 6. Probabilities of cetacean
occurrence by species in suitable habitats
in 2004 and 2005. (a) 2004 May, (b)2004 June, (c) 2004 July, (d) 2004
August, (e) 2005 May, (f) 2005 June, (g)2005 July, and (h) 2005 August. Colors
indicate the probability pattern of cetacean occurrence. The blue line represents the form of suitable habitat for sei
whales, and the red line represents suitable habitat for Bryde’s whales.

values. In this study, a 0.25 mg m3 Chl-a contour(similar to the definition of the TZCF) provided agood indicator that separated the habitats of sei and
Bryde’s whales. TZCF may also play a role as a proxyfor cetacean habitats.
Ocean depth was not selected as a covariate basedon AIC values in this study. In the past, important
relationships have been found between cetacean habitats and topography (Gaskin, 1982; Keiper et al.,2005; Morato et al., 2008). The data used in this study
were recorded offshore, at depths of 4000–5000 m.
The magnitude of the relationship between depth andcetacean distribution could depend on the area ofinterest. The exception in this study area was the© 2013 John Wiley & Sons Ltd, Fish. Oceanogr., 22:6, 496–508.

Emperor Seamount. Although no quantitative analysis
was conducted, observed densities of sei whales werehigh around the Emperor Seamount compared withother regions in the study area. The relationshipbetween sei whales and depth might be important if
one focused on the area around the Emperor
Seamount.
As already discussed, we deduced the oceanographic conditions in whale habitats and found a clear
division between the suitable habitats that were determined for the two whale species (Fig. 3). Although
the extent of suitable habitat differed depending on
the season, the suitable habitats of sei whales werelocated north of the habitats of Bryde’s whales

506

H. Sasaki et al.

(Fig. 5). The potential habitat map also indicated thatthe northern area had a high probability of occurrence
for sei whales and the southern area had a high probability of occurrence for Bryde’s whales. The habitats ofsei and Bryde’s whales were distributed in different
environments and clear habitat partitioning wasobserved in the western North Pacific during the summer (May–August) in 2004–2005. Although the magnitude of the difference changed depending on theseason, the HPL moved northward as the summer progressed. Therefore, we could quantitatively determinethat their habitats moved northward with seasonalchanges in oceanographic conditions. Because the
SST contributed strongly to the habitat-use patterns,
this transition was mainly coupled with the SST. Furthermore, our results support a previous hypothesisthat the SST plays an important role in defining whale
habitats (Kaschner et al., 2006, 2011; MacLeod,
2009).
Sei whales had higher probabilities of occurrencein their suitable habitats compared with Bryde’s
whales (Table 4, Fig. 6). Early in the summer, Bryde’s
whales could still be found in the southern portion of
the survey area because the oceanographic conditionsin the survey area were not suitable. After overlaying
the survey tracklines on the potential habitat map,
most of the tracklines were clearly set around the areaof highest probability for the occurrence of sei whales(Figs 2 and 4). In contrast, little survey effort wasallocated to areas with a high probability of occurrence for Bryde’s whales. In 2004–2005, quantifying
the primary habitat of Bryde’s whales was difficult due
to the lack of appropriate data for modeling. Consequently, the probability of sighting a Bryde’s whale
was low. Originally, Bryde’s whales were distributed inlower-latitude areas, but almost all of them migrated
seasonally (Fujise et al., 2004; Kato and Perrin, 2009).
The addition of data from the southern portion of thesurvey area in the early summer could improve themodeling results.
In our analysis, it was assumed that all the schoolsof sei and Bryde’s whales were sighted (perfect detection). Imperfect detection (not all animals are sighted)
is a common problem in wildlife sampling, although it
has only recently been considered in the studies of species occurrence (Royle and Dorazio, 2008). It is
expected that detection probability decreases withincreasing distance from the trackline (Thomas et al.,2010). Double-platform line transect data are widelyused to estimate the probability of detection on the
trackline g(0) of cetaceans (Okamura et al., 2003).
However, because no such data were collected during
our survey, estimation of g(0) could not be carried out.

Consideration of imperfect detection in modeling ofoccurrence of cetacean is required in future studies.
ACKNOWLEDGEMENTS
The surveys were conducted as part of the second
phase of the Japanese Whale Research Program undera Special Permit in the North Pacific (JARPN II).
Many people contributed to this project in importantways. Baleen whale sighting data were obtained during
the offshore survey of JARPN II. We express our sincere thanks to the captains, crews, and researchers
who were involved in the offshore components of the
JARPN II surveys in 2004 and 2005. Our study was
supported by the Institute of Cetacean Research. We
thank associate professor Toru Hirawake who provided
constructive comments on an early version of themanuscript and advised us throughout this study.
Comments from anonymous reviewers greatly
improved our manuscript.
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