Fisheries Science (2022) 88:109–118
https://doi.org/10.1007/s12562-021-01576-9

ORIGINAL ARTICLE
BiologyTwo species of seabirds foraged in contrasting marine habitats
across the cold‑water belt along the coast of northern Hokkaido
in the southwestern Okhotsk Sea
Bungo Nishizawa1
Yutaka Watanuki2· Jumpei Okado2 · Yoko Mitani3,5 · Tomohiro Nakamura4 · Atsushi Yamaguchi2 · Tohru Mukai2 ·Received: 12 April 2021 / Accepted: 24 November 2021 / Published online: 30 January 2022
© Japanese Society of Fisheries Science 2021

Abstract
To understand the environmental factors affecting the density of foraging seabirds across the cold-water belt in the southwestern Okhotsk Sea, we conducted a 1-day (180-km transect length) shipboard seabird survey off the northeastern coastof Hokkaido during summer in 2019, along with acoustic observations of potential prey (zooplankton and fish) biomass,
thermosalinograph measurements, and CTD observations. Planktivorous short-tailed shearwaters Ardenna tenuirostris (66%of total seabirds) and piscivorous rhinoceros auklets Cerorhinca monocerata (28%) were predominant, but foraged in contrasting habitats. A large foraging flock of shearwaters was observed in the cold-water belt zone, including its front with coastal
Soya Warm Current Water and the offshore Fresh Surface Okhotsk Sea Water, where surface chlorophyll a concentrations
were the highest but not related to their prey (zooplankton) biomass at any spatial scale between 4.6 and 9.2 km. In contrast,
the density of auklets was high in the coastal Soya Warm Current Water, where the acoustically determined fish biomass
was large, and showed a positive relationship with the fish biomass especially in the lower layer (29–104 m depth) at any
spatial scale. This species-specific difference in response to prey biomass might be related to prey-searching behaviors; i.e.,rhinoceros auklets search prey underwater visually, but short-tailed shearwater can use both visual and olfactory cues tolocate zooplankton patches from the air.
Keywords Rhinoceros auklets · Short-tailed shearwater · Acoustics · Spatial scale · Biophysical interaction · Foraging
behavior

Introduction* Bungo Nishizawa
nishizawa@salmon.fish.hokudai.ac.jp
1

National Institute of Polar Research, Midori‑cho 10‑3,
Tachikawa, Tokyo 190‑8518, Japan

2

Graduate School of Fisheries Sciences, Hokkaido University,
3‑1‑1 Minato‑cho, Hakodate, Hokkaido 041‑8611, Japan

3

Field Science Center for Northern Biosphere, Hokkaido
University, 20‑5 Benten‑cho, Hakodate, Hokkaido 040‑0051,
Japan

4

Institute of Low Temperature Science, Hokkaido University,
Sapporo, Japan

5

Present Address: Wildlife Research Center, Kyoto University,
2‑24 Tanaka‑Sekiden‑cho, Sakyo, Kyoto 606‑8203, Japan

Many marine apex predators often rely on small- to mesoscale oceanographic features such as fronts, seamounts,
and canyons where prey is highly available as a result of
upwelling, convergence, or other mechanisms (Hunt andSchneider 1987; Sims and Quayle 1998; Bost et al. 2009).
Consequently, regions of the ocean with these features
are characterized by elevated energy transfer rates fromlower trophic levels to the top predators and are known
as biological hotspots (Sydeman et al. 2006; Hazen et al.
2013). It is important to identify biological hotspots andunderstand the biophysical processes driving high trophic
energy transfer, particularly those connecting oceanographic features with mid- and upper trophic levels, for theconservation of biodiversity and management of marine

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protected areas (Hooker and Gerber 2004; Sydeman et al.
2006).
In the southwestern Okhotsk Sea off Hokkaido, Japan,
there is a unique oceanographic system characterized by acold-water belt (CWB) in the summer. The CWB extends
from the southwest coast of Sakhalin to the offshore sideof the Soya Warm Current Water (SWCW), flowing to
the south along the northeastern coast of Hokkaido, andis characterized by a temperature lower than that of the
SWCW or the offshore Fresh Surface Okhotsk Sea Water(FSOSW) (Ishizu et al. 2006, 2008) (Fig. 1). Thus, welldeveloped temperature fronts are formed on both thecoastal and offshore sides of CWB (Iida et al. 2018). The
CWB region shows higher chlorophyll a concentrations inthe spring and summer due to the nutrient supply associated with upwelling (Ishizu et al. 2006, 2008).
Observation and tracking studies of seabirds and marine
mammals in the southwestern Okhotsk Sea off Hokkaido
suggest that this region provides important foraging
habitats for marine top predators (Tanaka and Kajihara
1979; Yamamoto et al. 2015; Nakanowatari et al. 2017;
Kazama et al. 2018). However, studies investigating thelinks between oceanographic environments and the upper
trophic levels are lacking in this region, and the underlying mechanisms that form these foraging habitats of the
top predators are unknown. Information on the distribution and abundance of seabirds at sea, and their spatial
associations with oceanographic environments and preyFig. 1  The study site in the
Okhotsk Sea off northeastern
Hokkaido. Seabird, acoustic,
and thermosalinograph survey
transect shown by the blackline are overlaid on the 8-day
average SST image from 25
June to 2 July 2019, derived
from moderate-resolution spectroradiometer/Aqua standard
mapped images with a spatial
resolution of approximately
4 km provided by the Ocean
Color website (http://​ocean​color.​gsfc.​nasa.​gov). CTD andNORPAC net sampling stations
are shown as white circles withthe station number. Isobaths are
represented in meters by dotted lines. Soya Warm Current
Water (SWCW), Fresh Surface
Okhotsk Sea Water (FSOSW),and cold-water belt (CWB) are
shown

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Fisheries Science (2022) 88:109–118

abundance is necessary to improve our understanding of
the mechanisms involved.
We conducted a ship-based observation of seabirds,
acoustic surveys of potential prey biomass, and thermosalinograph and conductivity-temperature-depth (CTD)
profiler measurements around the CWB in the southwestern
Okhotsk Sea in the summer to examine spatial relationships
between seabirds, prey, and oceanographic environments.
Specifically, we expected that foraging seabirds would
aggregate in or near the CWB where high chlorophyll aconcentrations and frontal systems can enhance prey (zooplankton and fish) availability (Hunt and Schneider 1987;
Suryan et al. 2006; Bost et al. 2009).

Materials and methods
Study site and oceanographic measurements
This research was conducted in the Okhotsk Sea off northeastern Hokkaido (Fig. 1) on 27 June 2019 onboard the
T/S Oshoro-Maru (Faculty of Fisheries Sciences, Hokkaido University). Surface water (4-m depth) environments including sea surface temperature (SST), salinity(SSS), and chlorophyll (SSC) were continuously recorded
using a thermosalinograph (JFE Advantech Co., Ltd.,
Hyogo, Japan) installed in the vessel’s sea chest when
the seabird observations were made. The 180-km-longFisheries Science (2022) 88:109–118

transect was classified into three areas based on SST and
SSS: the coastal Soya Warm Current Water (SWCW,
T > 7 °C, S > 33.6), offshore Fresh Surface Okhotsk Sea
Water (FSOSW, T < 18 °C, S < 32.5) (Takizawa 1982;
Ishizu et al. 2008), and the cold-water belt zone (CWBZ)(Fig. 2, Online Resource, Fig. S1). The CWBZ included
the CWB that is at a lower temperature (8–10 °C) near thesea surface (Ishizu et al. 2006, 2008) and its boundary with
the SWCW and FSOSW (Fig. 2, Online Resource, Fig.
S1). Vertical distributions of temperature, salinity, andchlorophyll (a proxy for primary production) were measured at 8 stations (3 stations in SWCW, 2 in CWBZ, and3 in FSOSW) along the transect using a CTD probe (SBE
911plus, Sea-Bird Electronics, Bellevue, WA) (Fig. 1).

Seabird observations
We used a standard strip transect methodology (Tasker
et al. 1984) while the vessel was underway at speeds of
9.9 ± 1.4 knots. An observer continuously recorded thenumber and behaviors (flying, sitting on water, foraging)of all seabirds using 8× binoculars from the ship’s bridge(10 m above the water) within a 300-m survey window(from the bow to 90° to port or to starboard), from the sideof the vessel that offered the best observation conditions(i.e., lowest sun glare) during daylight hours. Ship-following birds were recorded when they first entered the survey
range and were ignored thereafter. We defined seabirds sitting on the water or foraging as “foraging seabirds” (Huntet al. 1998; Kokubun et al. 2008) and flying seabirds (otherthan actively foraging from the air) as “non-foraging.” The
total area surveyed was 54 ­km2.
Short-tailed shearwaters Ardenna tenuirostris (66% by
number) and rhinoceros auklets Cerorhinca monocerata(28%), which accounted for 94% of total seabirds observed(Table 1), were used in the analysis. Short-tailed shearwaters migrate annually from their breeding colonies insoutheastern Australia and Tasmania to spend their nonbreeding period in the northern North Pacific during
May–October. They feed mainly on euphausiids by surface-seizing and pursuit-diving in the Okhotsk Sea and thenorthern North Pacific (Ogi et al. 1980; Hunt et al. 1996,
2002). Rhinoceros auklets are a diving seabird breeding in the middle latitude of the North Pacific between
early spring and late summer (Gaston and Jones 1998).
Rhinoceros auklets breeding on Teuri Island (44°25′N,
141°52′E), northern Hokkaido, Japan, deliver forage fish
such as arabesque greenling Pleurogrammus azonus, sandlance Ammodytes spp., and Japanese anchovy Engraulisjaponicus to their chicks during the chick-rearing periods
between May and July (Watanuki 1987; Takahashi et al.
2001; Ito et al. 2009).

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Prey biomass estimation using acoustic and net
sampling
For analyses on acoustic data, we used Echoview (version
10.0; Echoview Software Pty Ltd.) following Nishizawa
et al. (2020). We measured the biomass of potential seabird prey using acoustic surveys (Fig. 1). Acoustic volume
backscattering strength (SV) data were collected along thetransects where seabird observations were made, using a
SIMRAD EK80 scientific echosounder at 38 and 120 kHz.
The transducers were mounted on the bottom of the vessel(4 m below the sea surface). The echosounder was calibratedusing the standard sphere method (Foote et al. 1987) with
a 38.1 mm tungsten carbide sphere. The SV data from theupper 5-m layer (i.e., 9-m depth from the surface) and the
2-m layer above the bottom were excluded because of thesurface turbulence and bottom echo integration. Fish andzooplankton were identified using a “dB-difference” method(De Robertis et al. 2010; Korneliussen 2018). The differences between backscatter at 120 and 38 kHz (SV 120 kHz− SV 38 kHz) in the range of −12 to 10 dB were assigned
to the fish category, and those in the range of 10 to 30 dB
were assigned to the zooplankton category (De Robertis
et al. 2010). To compare the regional differences in the biomass of potential prey, the nautical area scattering coefficient (NASC, m­ 2nmi−2, hereafter acoustically determinedbiomass) of fish and zooplankton was calculated at a 1-min(ca. 300 m) horizontal resolution and a 5-m vertical resolution (Reiss et al. 2008; Santora et al. 2011). As different
seabird species forage at different depths, we present acoustically determined prey biomass at two different water layers:
an upper layer (9–29 m depth) and a lower layer (29–104 m
depth).
Zooplankton samples were collected during daylight
hours at the CTD stations using vertical tows with a North
Pacific standard net (NORPAC; mouth diameter 45 cm,
mesh size 335 μm) from 5 m above the bottom to the surface (depths of stations ranged between 20 to 121 m). The
zooplankton samples were immediately preserved with v/v
5% borax-buffered formalin-seawater on the vessel. The filtered water volumes were estimated from the readings of aflowmeter (Rigo Co. Ltd., Tokyo) mounted in the mouth of
the net. The total zooplankton biomass (mg wet weight m­ −3)
at each station was measured in the laboratory.

Statistical analyses
Before statistical analysis, it is necessary to confirm the spatial independence of seabird densities and acoustically determined biomass of fish and zooplankton. In clumped distributions, such as seabird flocks, adjacent observations are often
more similar than would be expected in randomly distributed
data, indicating a positive autocorrelation (Schneider 1990).

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Fig. 2  Changes in sea surface
salinity (SSS), sea surface
temperature (SST), sea surface
chlorophyll (SSC), acousticallydetermined zooplankton biomass in the upper layer (9–29 m
depth) (Zoop-up) and lower
layer (29–104 m depth) (Zooplow), acoustically determinedfish biomass in the upper layer(9–29 m depth) (Fish-up) andlower layer (29–104 m depth)(Fish-low), and densities ofshort-tailed shearwaters (STSH,
log-transformed) and rhinoceros
auklets (RHAU) at 1-min interval along the transect. Definedareas based on water massproperties; Soya Warm Current
Water (SWCW) and Fresh
Surface Okhotsk Sea Water(FSOSW) are shown on the topof panels and the cold-water
belt zone (CWBZ) between
SWCW and FSOSW are shadedin grey

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Fisheries Science (2022) 88:109–118

Fisheries Science (2022) 88:109–118
Table 1  Seabird density (ind.
­km−2) observed during oursurveys

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Species

Latin nameDensity (ind. ­km−2) %density

Short-tailed shearwater
Rhinoceros auklet
Fork-tailed storm-petrel
Northern fulmar
Black-tailed gull
Flesh-footed shearwater
Slaty-backed gullParakeet aukletSpectacled guillemot
Ancient murrelet
Leach's storm-petrel
Pomarine jaeger
Thick-billed murre
Unidentified murre
Total seabirds

Ardenna tenuirostris
Cerorhinca monocerata
Oceanodroma furcata
Fulmarus glacialis
Larus crassirostris
Ardenna carneipes
Larus schistisagus
Aethia psittacula
Cepphus carbo
Synthliboramphus antiquus
Oceanodroma leucorhoa
Stercorarius pomarinus
Uria lomvia
Uria spp.

103.75
44.64
4.19
2.43
2.26
0.35
0.11
0.06
0.04
0.02
0.02
0.02
0.02
0.02
157.92

65.70
28.27
2.66
1.54
1.43
0.22
0.07
0.04
0.02
0.01
0.01
0.01
0.01
0.01
100.00All individuals (foraging and non-foraging) were included

Fig. 3  Spatial variability (Moran’s I correlogram) for acousticallydetermined zooplankton biomass in the upper layer (9–29 m depth)(Zoop-up) and lower layer (29–104 m depth) (Zoop-low), acousticallydetermined fish biomass in the upper layer (9–29 m depth) (Fish-up)
and lower layer (29–104 m depth) (Fish-low), and densities of shorttailed shearwaters (STSH) and rhinoceros auklets (RHAU)

Moran’s I correlograms were calculated to examine the spatial autocorrelation of the seabird densities and of the acoustically determined biomass of fish and zooplankton (Santora
et al. 2011; Nishizawa et al. 2020). Lag size was defined
as an interval of 1 km. Moran’s I statistics range from −1(negative autocorrelation) to +1 (positive autocorrelation),with null values being indicative of a lack of spatial structure (Sokal and Oden 1978). In the preliminary analyses, thedensities of rhinoceros auklets and acoustically determined
zooplankton biomass in the lower layer showed positive
spatial autocorrelations (0.28 < Moran’s I < 0.46) at 1–3-km
scales and reduced spatial autocorrelation (Moran’s I < 0.28)
at larger scales (> 3 km) (Fig. 3). Therefore, we divided thetransects into 15-min (= 4.6 km long) intervals and usedthem as the smallest sampling unit for statistical analysis.
We compared the seabird density, acoustically determined
prey biomass, net-sampled zooplankton biomass, and oceanographic variables among three areas (i.e., SWCW, FSOSW,
and CWBZ) using the Steel–Dwass pairwise non-parametric test, because these data were not normally distributed(Shapiro–Wilk normality test, p < 0.05). The spatial relationships between seabird density, acoustically determined
prey biomass, and surface oceanographic variables wereassessed using Spearman’s correlation matrix. Further, spatial relationships between the density of foraging seabirds
and acoustically determined biomass of fish and zooplankton
were evaluated using Spearman’s correlation coefficient atfour spatial scales (4.6-, 6.1-, 7.6-, and 9.2-km bin sizes),
as seabird–prey relationships are typically scale-dependent(e.g., Hunt and Schneider 1987; Hunt et al. 1992; Fauchald
et al. 2002). All statistical analyses were done using R software (v.4.0.2, R Development Core Team 2020).

Results
Oceanographic parameters and lower trophic levels
SST was highest in SWCW and lowest in the CWBZ(Table 2). SSS was highest in SWSW and lowest in
FSOSW (Table 2). For the entire study region, there was apositive relationship between SST and SSS (Table 3). The
lowest SST and highest SSC occurred within the CWBZ,
indicating the upwelling of the CWB (Table 2, Fig. 2).
Within the CWBZ, SST and SSS showed steep gradients,
especially at the boundary of the SWCW (Fig. 2). Acoustically determined zooplankton biomass was higher in both

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Fisheries Science (2022) 88:109–118

Table 2  Mean (± SD) values, with the range in parentheses, for foraging seabird density (ind. ­km−2), acoustically determined biomass of fishand zooplankton ­(m2nmi−2), and surface oceanographic variables between areas
Variable

SST (°C)

SSS

SSC (mg ­m−3)
Zooplankton biomass via net (mgwet weight m­ −3)

Zoop-up (9–29 m
depth)
Zoop-low (29–
104 m depth)

Fish-up (9–29 m
depth)

Fish-low (29–
104 m depth)

Foraging STSH
density (ind.
­km−2)

Foraging RHAU
density (ind.
­km−2)

Area

Steel–Dwass test (t-statistics, p-value)

SWCW​

CWBZ

FSOSW

SWCW vs. CWBZ SWCW vs.
FSOSWCWBZ vs. FSOSW

12.58 ± 0.58
(11.28–13.23),
n = 1833.99 ± 0.02
(33.95–34.02),
n = 180.93 ± 0.29
(0.55–1.70), n = 18
227.92 ± 53.52

9.90 ± 1.26
(8.09–12.08),
n = 1133.13 ± 0.56
(32.24–33.94),
n = 11
3.76 ± 1.87
(1.06–6.50), n = 11219.62 ± 14.6

9.91 ± 0.58
(8.54–10.66),
n = 1132.15 ± 0.06
(32.08–32.31),
n = 111.31 ± 0.32
(1.00–2.07), n = 11229.81 ± 37.04

4.18, p < 0.01

4.45, p < 0.01

0.62, p = 0.81

4.45, p < 0.01

4.45, p < 0.01

3.91, p < 0.01

4.22, p < 0.01

2.92, p < 0.01

4.44, p < 0.01

0.00, p = 1.00

0.22, p = 0.97

0.58, p = 0.83

(185.18–287.96),
n=30.82 ± 1.20

(209.30–229.94),
n=20.34 ± 0.19

(187.39–255.74),
n=30.29 ± 0.19

0.04, p = 0.99

0.58, p = 0.83

0.76, p = 0.73

(0.09–4.61), n = 18 (0.11–0.77), n = 11 (0.13–0.77), n = 11
0.91 ± 1.88
0.23 ± 0.35
2.24 ± 3.77
1.21, p = 0.45

1.17, p = 0.47

1.87, p = 0.15

(0.00–7.14), n = 18 (0.04–1.25), n = 11 (0.05–12.16),
n = 112.70 ± 3.32
1.00 ± 0.33
1.01 ± 0.64

1.26, p = 0.42

1.30, p = 0.39

0.69, p = 0.77

(0.26–13.15),
n = 183.70 ± 8.40

(0.41–1.53), n = 11 (0.44–2.63), n = 11
1.52 ± 3.53

1.44 ± 1.81

1.17, p = 0.47

0.09, p = 0.99

1.35, p = 0.37

(0.00–36.05),
n = 1856.08 ± 105.14(0.13–12.10),
n = 11203.42 ± 656.70(0.11–6.35), n = 11
0.19, p = 0.98

0.78, p = 0.72

1.26, p = 0.42

(0–294.52), n = 18
35.51 ± 50.58(0–2183.10),
n = 1110.45 ± 11.94

2.64 ± 3.00

1.15, p = 0.48

1.50, p = 0.29

0.60, p = 0.82

(0–167.26), n = 18

(0–32.00), n = 11

(0–8.73), n = 1111.04 ± 11.67(0–36.36), n = 11

SWCW: Soya Warm Current Water, FSOSW: Fresh Surface Okhotsk Sea Water, CWBZ: cold-water belt zone between SWCW and FSOSWThe results of the Steel–Dwass test (t-statistics and p-value) are also shown. SST: sea surface temperature, SSS: sea surface salinity, SSC: sea
surface chlorophyll, Zoop-up (low): acoustically determined biomass of zooplankton in the upper (lower) layers, Fish-up (low): acousticallydetermined biomass of fish in the upper (lower) layers, STSH: short-tailed shearwater, RHAU: rhinoceros auklets

the SWCW and FSOSW than in the CWBZ on average,
although these trends were not statistically significant(Table 2, Fig. 2). No strong signal of acoustically determined zooplankton biomass was observed in the CWBZ
(Fig. 2). Similarly, net-sampled zooplankton showed lower
biomass in the CWBZ (Table 2). Acoustically determinedfish biomass was higher in SWCW than in FSOSW orCWBZ (Table 2; Fig. 2). Acoustically determined biomass

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of zooplankton and fish was not correlated with SST or
SSC (Table 3).

Seabirds
We observed 13 seabird species (Table 1). Short-tailed
shearwaters were the most abundant (66% by number), followed by rhinoceros auklets (28%) (Table 1). The density

Fisheries Science (2022) 88:109–118
Table 3  Spearman’s
correlation matrix for surface
oceanographic variables,
acoustically determined biomassof zooplankton and fish, andseabird densities at 4.6-km scale(n = 40)

115
Variables

SST

SSS

SSC

Zoop-up

Zoop-low

Fish-upFish-low

STSH

RHAU

SST
SSS
SSC
Zoop-up
Zoop-low
Fish-upFish-low
STSH
RHAU

1
0.69**−0.67**
−0.16
−0.02
0.06
0.08
−0.17
0.04

1
−0.54**
0.23
−0.02
0.39*
0.15
−0.11
0.40**

1
−0.13
−0.29
−0.19
−0.38
0.08
−0.20

1
0.52**
0.78**
0.55**
0.23
0.47**

1
0.32*
0.82**
0.24
0.23

1
0.34*
0.17
0.28

1
0.23
0.34*

1
0.37*

1

**Correlation is significant at the 0.01 level. *Correlation is significant at the 0.05 level. SST: sea surface
temperature, SSS: sea surface salinity, SSC: sea surface chlorophyll, Zoop-up (low): acoustically determined biomass of zooplankton in the upper (lower) layers, Fish-up (low): acoustically determined biomassof fish in the upper (lower) layers, STSH: short-tailed shearwater density, RHAU: rhinoceros auklets density
0.8

STSH vs. Zoop-up

Correlaon coefficient(Spearman's Rho)

0.7

STSH vs. Zoop-low
RHAU vs. Fish-up

0.6

RHAU vs. Fish-low

0.5
0.4
0.3
0.2
0.1
0.0

15
(=4.6 km)20(=6.1 km)25(=7.6 km)30(=9.2 km)

Bin size (min)Fig. 4  A rhinoceros auklet holding an adult (> 1-year-old) sandlancein the Soya Warm Current Water in the southwestern Okhotsk Sea on
27 June 2019 (photo by B. Nishizawa)of foraging short-tailed shearwaters seemed to be higher in
CWBZ than in SWCW or FSOSW, although the trends were
not statistically significant (Table 2). In CWBZ, we observed
a dense flock (up to 3300 ind. ­km−2, Fig. 2) of short-tailed
shearwaters where individuals showed head-dipping foraging behavior. In contrast, higher density of foraging rhinoceros auklet was observed in SWCW than in FSOSW or
CWBZ, although the trend was not statistically significant(Table 2, Fig. 2). In SWCW, we observed ~ 10 individuals of
the rhinoceros auklets capturing adult (> 1-year-old) sandlance (Fig. 4). The densities of short-tailed shearwaters andrhinoceros auklets were low in FSOSW (Table 2, Fig. 2).
The density of foraging short-tailed shearwaters was notcorrelated with oceanographic environments (Table 3) or
prey (i.e., acoustically determined zooplankton biomass) at
any spatial scale between 4.6 and 9.2 km (p > 0.05, Fig. 5).
In contrast, positive relationships (0.34–0.55 of Spearman’s

Fig. 5  Spearman’s correlation coefficient between the densities
of foraging seabirds and acoustically determined prey biomass atfour different spatial scales (4.6-, 6.1-, 7.6-, and 9.2-km bin sizes).
Squares = short-tailed shearwater (STSH) vs. zooplankton in theupper layer (9–29 m depth) (Zoop-up); diamonds = short-tailed shearwater (STSH) vs. zooplankton in the lower layer (29–104 m depth)(Zoop-low); triangles = rhinoceros auklets (RHAU) vs. fish in theupper layer (9–29 m) (Fish-up); circles = rhinoceros auklets (RHAU)
vs. fish in the lower layer (29–104 m) (Fish-low). Filled symbols represent statistically non-significant (p > 0.05)

correlation coefficient) were found between the densities offoraging rhinoceros auklets and their prey (i.e., acousticallydetermined fish biomass) in the lower layer at scales between
4.6 and 9.2 km (Fig. 5).

Discussion
This is the first study to provide detailed information on thelinks between hydrographic features and top predators (i.e.,seabirds) at a unique oceanographic system characterized
by a cold-water belt in the southwestern Okhotsk Sea off

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Hokkaido, Japan. We found that two seabird species, planktivorous short-tailed shearwaters and piscivorous rhinoceros auklets, mainly used our study region for their foraging
habitats. However, both seabirds and prey were not always
associated with the CWB.
The CWBZ between the coastal SWCW and offshore
FSOSW showed elevated chlorophyll a concentration, as inprevious studies (Ishizu et al. 2006, 2008). This high primary production may be explained by the supply of nutrients associated with upwelling (Ishizu et al. 2006, 2008).
This elevated surface chlorophyll a concentration, however,
was not always related to the acoustically determined zooplankton biomass. Our continuous recording of surface (4-mdepth) chlorophyll a often overlooks subsurface chlorophylla maximum, which can occur at depths > 20 m, as it did at
a CTD station in FSOSW (Online Resource, Fig. S1). A
variety of zooplankton species are associated with these subsurface chlorophyll a maxima (Townsend et al. 1984; Harris
1988; Moeller et al. 2019). Consequently, the limited overlapof phytoplankton and zooplankton indicated in our study
may only be an apparent mismatch. Another possible explanation is a temporal lag between phytoplankton and zooplankton abundance. Continuous echo data collected by themooring system in the southeastern Bering Sea showed thatpeak measures of copepod biovolume occurred in advance ofthe springtime bloom, while the highest euphausiid aggregations occurred in mid-summer (Stafford et al. 2010). Further,
the herbivorous zooplankton biomass dynamics in the Barents Sea followed that of spring phytoplankton with a lag of
about 1 month (Dalpadado et al. 2020).
During the summer months, short-tailed shearwaters feedon euphausiids and small fish (such as juvenile Pleurogrammus sp.) in the Okhotsk Sea and the northern North Pacific(Ogi et al. 1980). Therefore, it was expected that short-tailed
shearwaters in our study region would feed on macroplankton that could be detected acoustically (e.g., Korneliussen
and Ona 2002). However, there was no association between
the density of foraging short-tailed shearwaters and thezooplankton biomass at any spatial scale between 4.6 and9.2 km. The average prey availability at larger scales (i.e.,
10–100 km) might be more important in determining theshearwater density, although our study area was too small(20 sample size at 9.2-km scale) for testing the association
at larger scales. The short-tailed shearwaters in the southeastern Bering Sea were assumed to stay in foraging within
a 10- to 100-km area for several weeks (Baduini et al. 2006).
The density of short-tailed shearwaters in the Japan Sea wascorrelated with an acoustic index of zooplankton biomass
at a scale of 10 km (Kurasawa et al. 2011). The density ofthese birds in the northern Bering Sea was greater in 50-km
grids with euphausiids than in those without (Nishizawa
et al. 2017).

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Fisheries Science (2022) 88:109–118

Rhinoceros auklets tended to feed in coastal SWCW
and were likely to feed in regions where the fish biomass
was high in the lower layer (29–104 m depth). The acoustically determined fish biomass likely reflects the presenceof forage fish in this region, including sandlance (Safruddin 2013; Hokkaido National Fisheries Research Institute2017). In the northern Japan Sea, rhinoceros auklets diveup to 50-m depth and feed on these forage fish (Watanuki
1987; Takahashi et al. 2001; Kuroki et al. 2003). We foundrhinoceros auklets feeding on sandlance in SWCW (Fig. 3).
Shallower water (< 80 m seabed depth) with coarse sandsediments (Wright et al. 2000; Holland et al. 2005) is thepreferred habitat for sandlance and presents an importantfishing ground for the fisheries targeting this species (Hokkaido National Fisheries Research Institute 2017) as well
as for rhinoceros auklets. This area is likely to be a morestable and predictable foraging ground for rhinoceros auklets
feeding sandlance compared to those in Japan Sea feeding
mainly on pelagic fish including Japanese anchovy, wherea weak relationship between the auklets and their prey wasreported (Kurasawa et al. 2011).
In this study, piscivorous rhinoceros auklets foraged
mainly in the coastal SWCW and planktivorous short-tailed
shearwaters did not show a distinct habitat preference. In
addition, while the rhinoceros auklet density was sensitive
to changes in fish biomass, short-tailed shearwaters were
unlinked to zooplankton biomass. These species-specific differences might be related to their prey-searching behaviors.
Alcids, including rhinoceros auklets, search visually for thefish school itself underwater (Burger et al. 1993), while tubenosed Procellariiformes, including short-tailed shearwaters,
can use both visual and olfactory cues to locate prey patchesfrom the air and therefore search broader areas (Nevitt et al.
1995; Savoca and Nevitt 2014). Another explanation for the
species-specific response to their prey might be differencesin life-history stages. In the study area and period, shorttailed shearwaters were in the non-breeding stage in which
they were not restricted by energy requirements from chicks.
On the other hand, observed rhinoceros auklets were likely
in the chick-rearing stage, since our study site was locatedwithin the foraging range (~ 164 km from the colony, Katoet al. 2003) of the auklets rearing chicks in Teuri, Rebun,and Moneron islands (Watanuki 1987; Kondratyev et al.
2000; Hasebe and Senzaki 2016; Biodiversity Center ofJapan 2017). Therefore, the prey-searching area of those
breeding auklets may have been restricted to a smaller spacethan that for non-breeding shearwaters and may have shown
clearer concordance with underwater prey biomass. Since
our result showed that the spatial autocorrelations of auklets
were higher in the smaller spatial scale whereas those ofshearwaters were consistently low (Fig. 3), this result may,in part, support the above hypotheses.

Fisheries Science (2022) 88:109–118

Our study suggests that different seabird species use theenvironment in different ways, and that the heterogeneous
marine environment is an important factor for creating various foraging hotspots of top predators.
Acknowledgements We thank the captain, officers, and crews of
the T/S Oshoro-maru for their help with field surveys. Keizo Ito andSarufutsu Fishery Cooperative provided marine information on the
study region. Nodoka Yamada helped with acoustic data analysis. We
also thank two anonymous referees for their helpful comments that
greatly improved the manuscript. This study was supported by JSPS
KAKENHI Grant Number 20H03054 (Y. Mitani) and 19J01267 (B.
Nishizawa) and by the Environment Research and Technology Development Fund (JPMEERF20214002) of the Environmental Restoration
and Conservation Agency of Japan.

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