Deep–Sea Research II 181-182 (2020) 104898

Contents lists available at ScienceDirect

Deep-Sea Research Part IIjournal homepage: http://www.elsevier.com/locate/dsr2

Timing of spring sea-ice retreat and summer seabird-prey associations in
the northern Bering Sea
Bungo Nishizawa a, *, Nodoka Yamada b, Haruka Hayashi b, Charlie Wright c, Kathy Kuletz c,
Hiromichi Ueno b, Tohru Mukai b, Atsushi Yamaguchi b, Yutaka Watanuki babcNational Institute of Polar Research, Midori-cho 10-3, Tachikawa, Tokyo, 190-8518, Japan
Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido, 041-8611, JapanUS Fish and Wildlife Service, 1011 E. Tudor Road, Anchorage, AK, 99503, USA

A R T I C L E I N F O

A B S T R A C T

Keywords:
ArcticSea ice
Seabird distribution
Acoustic
Water mass
Zooplankton
Fish
Northern bering sea

To understand the effect of an unusually early sea-ice retreat in the northern Bering Sea in the spring of 2018 on
the marine ecosystem of the northern Bering Sea, we compared at-sea observations of seabird density andacoustic observations of prey (fish and zooplankton) biomass during shipboard surveys around St. Lawrence
Island in the summers of 2017 and 2018. Densities of foraging seabirds in 2018 (piscivorous divers: 4.7 birds
km− 2, planktivorous divers: 5.1, shearwaters: 0.7, surface feeders: 6.6) were lower than those in 2017 (pisciv­
orous divers: 14.7 birds km− 2, planktivorous divers: 10.3, shearwaters: 11.9, surface feeders:11.9). Acousticallydetermined prey biomass in 2018 (fish: 6.4 m2nmi− 2, zooplankton: 2.3) was also lower than in 2017 (fish: 18.4
m2nmi− 2, zooplankton: 5.5). Similarly, biomass of macrozooplankton (amphipods, euphausiids, Neocalanus spp.
and Calanus marshallae) sampled using bongo nets was smaller in 2018. At scales of 5–30 km, correlations be­
tween the seabird density and prey biomass were weaker (− 0.2–0.3 of Pearson’s r) in 2018 than those in 2017
(0.4–0.9) for all seabirds except planktivorous divers. We suggest that the lack of sea ice, and the resulting lack of
ice-edge phytoplankton blooms, may weaken trophic linkages by causing a low biomass of secondary consumers
and hence a reduced density of seabirds.

1. Introduction

juvenile euphausiids, which feed on these ice-associated blooms, could
have been reduced (Sigler et al., 2016; Siddon and Zador, 2018; Duf­
fy-Anderson et al., 2019).
Other notable events in the northern Bering Sea ecosystem during
2018 included mass mortality of seabirds. In June 2018, a thousand
dead murres (common Uria aalge and thick-billed U. lomvia) washedashore along the coasts of St. Lawrence Island and around Norton Sound(Siddon and Zador, 2018; Duffy-Anderson et al., 2019; Romano et al.,2020 (this issue)). Reproductive failures, low reproductive success, anddelayed breeding were also observed in murres, black-legged kittiwakes(Rissa tridactyla), and auklets (least Aethia pusilla and crested A. crista­
tella) breeding on the islands of the northern Bering Sea in 2018 (Siddon
and Zador, 2018; Dragoo et al., 2019; Romano et al., 2020 (this issue)).
This unusual mass mortality of marine top-predators might havebeen caused by food shortage because of anomalous oceanic conditions(Jones et al., 2019; Duffy-Anderson et al., 2019) and avian influenza(Will et al., 2020a (this issue)). However, the mechanism causing thesemass mortalities of seabirds in the northern Bering Sea is not fully

The Bering Sea shelf is one of the most productive marine ecosystems
in the world (Grebmeier et al., 2006; Hunt et al., 2013). This area isexperiencing a decline in seasonal sea-ice extent and earlier sea-ice
retreat, but with large interannual variations (Perovich andRichter-Menge, 2009; Stabeno et al., 2012). Changes in the timing of
sea-ice retreat, along with increasing seawater temperatures and fresh­water content, affect the timing, magnitude, and location of phyto­plankton blooms, and thereby affect the distribution and abundance of
higher trophic levels (Li et al., 2009; Hunt et al., 2011; Renner et al.,2016; Sigler et al., 2016; Alabia et al., 2018).
In the northern Bering Sea, sea-ice extent during the winter of 2017/2018 was the lowest since satellite monitoring began in 1978 (Cornwall,
2019; Stabeno and Bell, 2019; Appendix A). The lack of sea-ice cover in
the northern Bering Sea shelf in 2018 caused an absence of ice algae andice-edge phytoplankton blooms (Siddon and Zador, 2018; Duffy-An­
derson et al., 2019). As a result, the abundance of large copepods and* Corresponding author.
E-mail address: nishizawa@salmon.fish.hokudai.ac.jp (B. Nishizawa).

https://doi.org/10.1016/j.dsr2.2020.104898
Received 16 January 2020; Received in revised form 10 October 2020; Accepted 2 November 2020
Available online 9 November 20200967-0645/© 2020 Elsevier Ltd. All rights reserved.

B. Nishizawa et al.

Deep-Sea Research Part II 181-182 (2020) 104898

known. Information on the distribution and abundance of seabirds
at-sea, their foraging behavior, and spatial associations between sea­
birds and prey is necessary to improve our understanding of the mech­anisms involved.
We used shipboard surveys around St. Lawrence Island in thenorthern Bering Sea during the summers of 2017 and 2018 to examine
spatial relationships between seabirds and prey at various spatial scales.
We compared these variables between the two study years, which had
different sea ice conditions, to better understand the linkages between
sea-ice coverage and marine top-predators in the northern Bering Sea
ecosystem. Specifically, we predicted that the lack of sea ice and its
associated phytoplankton blooms may alter trophic linkages through areduced biomass of secondary consumers and hence a reduced density ofseabirds.

(USFWS, 2014). The waters north of St Lawrence Island to the Bering
Strait, known as the Chirikov Basin, are shallow and nutrient rich,particularly on the western side through which the Anadyr Water passes.
In the study area, three distinct water masses can be discerned, which
are known to impact the distribution of zooplankton, fish, and seabirds(Elphick and Hunt, 1993; Piatt and Springer; 2003; Eisner et al., 2013),
namely Alaska Coastal Water (ACW), Bering Shelf Water (BSW), andAnadyr Water (AW). These water masses have a north-south orientation,with ACW on the east, BSW in the middle, and AW on the west
(Coachman et al., 1975). The ACW is less saline, warmer, and has lower
concentrations of nutrients and chlorophyll a than BSW and AW(Coachman et al., 1975; Walsh et al., 1989). In contrast, BSW and AW
are cooler, more saline, and have substantially higher chlorophyll a andnutrient concentrations (Coachman et al., 1975; Walsh et al., 1989).
To determine the positions of the boundaries between water masses,
we conducted CTD (SBE 911 Plus, Sea-Bird Electronics, Bellevue, WA) or
expendable CTD (XCTD, Tsurumi-Seiki, Yokohama Kanagawa, Japan)
measurements (23 stations in 2017, 24 stations in 2018, Fig. 1 a and b).
The positions of the boundaries between water masses were determinedusing salinity (Coachman et al., 1975). The ACW was defined as having
salinity below 31.8, BSW as between 31.8 and 32.5, and AW as over 32.5
(Walsh et al., 1989; Hunt and Harrison, 1990). Based on our salinity
profiles, we defined the water mass that dominated the water column ateach station (Fig. 1). The water around St Lawrence Island was stratified,
especially in the Anadyr Water in 2017, and well-mixed in 2018 (Fig. 1 cand d, see Ueno et al., 2020 (this issue)).

2. Methods
2.1. Study area and oceanography
Research cruises onboard the T/S Oshoro-Maru (Faculty of Fisheries
Sciences, Hokkaido University) were conducted around St. Lawrence
Island (ca. 63◦ 30′ N, 170◦ 30′ W) in the northern Bering Sea in July of2017 and 2018 (Fig. 1). The northern Bering Sea shelf is shallow, with asea depth of approximately 50 m (Fig. 1). Large, mixed-species colonies
support up to 12 million breeding seabirds in the region, along themainland coast, on St. Lawrence Island, and on other smaller islands

Fig. 1. The study area around St. Lawrence Island in the northern Bering Sea showing locations of seabird and acoustic survey lines [red = Alaska Coastal Water(ACW), green = Bering Shelf Water (BSW), blue = Anadyr Water (AW)], CTD stations (diamonds) with the number of stations, bongo net sampling stations (tri­angles), CTD and bongo net sampling stations (circles), and the 20- and 50-m isobath (dotted lines), for 2017 (a) and 2018 (b). The positions of the boundaries
between water masses were determined using salinity (see methods). Vertical cross section of salinity around St. Lawrence Island for 2017 (c) and 2018 (d). White
vertical lines in the cross sections indicate where CTD/XCTD measurements were conducted. The station number was also shown on the cross sections. Black arrows
on the cross sections indicate the positions of the boundaries between water masses. (For interpretation of the references to colour in this figure legend, the reader isreferred to the Web version of this article.)2

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Deep-Sea Research Part II 181-182 (2020) 104898

2.2. Seabird observations

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
10–30 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 (SA, m2nmi− 2, hereafter
acoustically-determined biomass) of fish and zooplankton were calcu­
lated at a 1-min (ca. 300 m) horizontal resolution and a 5-m verticalresolution (Reiss et al., 2008; Santora et al., 2011; Nishizawa et al.,2019). Because seabirds forage at different depths among foraging
guilds, we present acoustically-determined prey biomass at two different
water layers: an upper layer (9–24 m depth) and a lower layer (24–59 m
depth). For analyses on acoustic data, we used Echoview (version 10.0;
Echoview Software Pty Ltd.).
Zooplankton samples were collected at 11 stations in each year using
a bongo net (505 μm mesh size, 0.7 m mouth diameter) (Fig. 1). The net
was obliquely towed from 5 m above the bottom to the surface at a ship’s
speed of 2 knots. The zooplankton samples were immediately preservedwith v/v 5% borax-buffered formalin-seawater on the vessel. The
filtered water volumes were estimated from the readings of a flow-meter(Rigo Co. Ltd., Tokyo) mounted in the mouth of the net. In the labora­
tory, species identification and enumeration were performed under astereomicroscope. Zooplankton was classified into six taxonomic groups(amphipods, euphausiids, Neocalanus cristatus, N. flemingeri, Calanus
marshallae, others) following studies on the diet of two abundant auklets(least and crested) breeding on St. Lawrence Island (Gall et al., 2006;
Sheffield Guy et al., 2009). Subsequently, we measured the wet weight(WW) of each taxonomic group with a precision of 0.1 μg using anelectronic balance (Mettler AE-100). The zooplankton biomass (X: mg
WW m− 3) was calculated based on the WW and volume of water filteredthrough the net.

At-sea seabird observations were made on 16–21 July 2017 and 3–6
July 2018 around St. Lawrence Island (within 200 km from the island) in
the northern Bering Sea (Table 1, Fig. 1). We used standard strip transect
methodology (Tasker et al., 1984) while the vessel was underway at
speeds of 8.8–10.3 knots (Table 1). An observer continuously recorded
the number and behaviors (flying, sitting on water, foraging) of allseabirds using 8 × binoculars from the ship’s bridge (10 m above thewater) within a 300-m survey window (from the bow to 90◦ to port or to
starboard), from the side of 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 wereignored thereafter.
We observed 25 seabird species and classified them into four groups(piscivorous divers, planktivorous divers, shearwaters, surface feeders)according to their foraging modes (Table 2). Although we observed four
benthic-feeding seabirds (eiders and other seaducks), we did not include
them in the analyses, since we did not measure the benthos on which
they feed, nor did we record any foraging among birds in this foraging
guild. Seabird species were sometimes combined for analysis. Piscivo­rous divers included puffins (Fratercula, 2 spp), murres (common Uria
aagle and thick-billed U. lomvia were combined), pigeon guillemot(Cepphus columba), ancient murrelet (Synthliboramphus antiquus), loons(Gavia, 2 spp), and pelagic cormorant (Phalacrocorax pelagicus)(Table 2). Planktivorous divers included auklets (Aethia, 3 spp)
(Table 2). Surface feeders included northern fulmars (Fulmarus glacialis),
fork-tailed storm-petrel (Oceanodroma furcata), red phalarope (Phalar­
opus fulicarius), jaegers (Stercorarius, 3 spp), black-legged kittiwake(Rissa tridactyla), and gulls (Laridae, 5 spp) (Table 2). Short-tailed
shearwaters (Ardenna tenuirostris) feed mainly on euphausiids by both
surface seizing and pursuit diving (Ogi et al., 1980; Hunt et al., 1996;
Hunt et al., 2002), and therefore they were classified as their own group(Table 2). We defined seabirds sitting on the water or foraging as“foraging seabirds” (Hunt et al., 1996; Hunt et al., 1998; Kokubun et al.,2008) and flying seabirds (other than actively foraging from the air) as“non-foraging”. We calculated seabirds counts as densities (birds km− 2)
for all analyses.

2.4. Statistical analyses
Before statistical analysis, spatial independence of seabird densities
and acoustically-determined biomass of fish and zooplankton must be
confirmed. In clumped distributions, such as seabird flocks, adjacentobservations are often more similar than would be expected in randomly
distributed data, indicating positive autocorrelation (Schneider, 1990).
Moran’s I correlograms were calculated to examine the spatial auto­
correlation of the seabird densities and of the acoustically-determinedbiomass of fish and zooplankton (Santora et al., 2011). Lag size wasdefined as an interval of 1 km. Moran’s I statistics ranges from − 1(negative autocorrelation) to +1 (positive autocorrelation), with nullvalues being indicative of a lack of spatial structure (Sokal and Oden,
1978). In preliminary analyses all seabird foraging guilds, except
shearwaters, and acoustically-determined biomass of fish andzooplankton showed weak positive spatial autocorrelation (0.01 <Moran’s I < 0.31) at 1–4 km scales and reduced spatial autocorrelation
at larger scales between 5 and 30 km scales (Appendix B). Shearwaters
showed no evidence of spatial autocorrelation at any spatial scale bothin 2017 and 2018 (Appendix B). Therefore, we divided transects into
5-km intervals and used them as a sampling unit for statistical analysis.
We compared the seabird density and acoustically-determinedbiomass of fish and zooplankton among three water masses using the
Steel-Dwass pairwise non-parametric test. We used Mann–Whitney U
tests for two-year comparisons (2017 vs. 2018) of seabird density andacoustically-determined biomass of fish and zooplankton. Spatial re­lationships between the density of foraging seabirds and acousticallydetermined biomass of fish and zooplankton were evaluated using
Pearson’s correlation coefficient at four spatial scales (5, 10, 20, and 30
km bin sizes) because seabird-prey relationships are typically scaledependent (e.g. Hunt and Schneider, 1987; Hunt et al., 1992; Fau­chald et al., 2002).

2.3. Prey biomass estimation using acoustics and net samplings
We measured the biomass of potential seabird prey using acoustic
surveys (Fig. 1). Acoustic volume backscattering strength (SV) data werecollected along the transects where seabird observations were con­ducted, 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 calibrated once in 2016 and in
2018 using the standard sphere method (Foote et al., 1987) with a 38.1
mm tungsten carbide sphere. Calibration results were applied in the
post-processing of acoustic data. The SV data from the upper 5-m layer (i.
e. 9-m depth from the surface) and the 2-m layer above the bottom were
excluded because of the surface turbulence and bottom integrations.
Fish and zooplankton were identified using a “dB-difference” method(De Robertis et al., 2010; Korneliussen, 2018). The differences between
Table 1
Summary of at-sea seabirds surveys in 2017 and 2018 around St. Lawrence Is­
land in the northern Bering Sea. Date surveyed (Coordinated universal time:UTC), total survey length (km), area surveyed (km2), and ship speed (knots) are
shown.
Year

2017

2018

Date surveyed (UTC)
Total survey length (km)Area surveyed (km2)
Ship speed (mean ± SD) (knots)

16 July - 21 July
453.56136.078.8 ± 1.5

3 July - 6 July
513.71
154.11
10.3 ± 0.6

3

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Deep-Sea Research Part II 181-182 (2020) 104898

Table 2
Summary of species identified during seabird surveys around St. Lawrence Island in the northern Bering Sea in 2017 and 2018.
Foraging guild/species

Scientific name

Seabird density (birds km-2)
2017

2018

Foraging
Piscivorous divers
Horned Puffin
Tufted Puffin
Common/Thick-billed Murre
Ancient Murrelet
Pigeon Guillemot
Pacific Loon
Yellow-billed Loon
Unidentified Loon
Pelagic Cormorant
Planktivorous divers
Crested Auklet
Least Auklet
Parakeet Auklet
Unidentified Auklet
Shearwaters
Short-tailed shearwater
Surface feeders
Northern Fulmar
Fork-tailed Storm-Petrel
Red Phalarope
Pomarine Jaeger
Parasitic Jaeger
Long-tailed Jaeger
Black-legged kittiwake
Sabine’s Gull
Glaucous Gull
Glaucous-winged Gull
Herring Gull
Slaty-backed Gull
Total seabirds

Fratercula corniculata
Fratercula cirrhata
Uria spp.
Synthliboramphus antiquus
Cepphus columba
Gravia pacifica
Gravia adamsii
Gravia spp.
Phalacrocorax pelagicus
Aethia cristatella
Aethia pusilla
Aethia psittacula
Aethia spp.
Ardenna tenuirostris
Fulmarus glacialis
Oceanodroma furcata
Phalaropus fulicarius
Stercorarius pomarinus
Stercorarius parasiticus
Stercorarius longicaudus
Rissa tridactyla
Xema sabini
Larus hyperboreus
Larus glaucescens
Larus argentatus
Larus schistisagus

14.6760.029
0.173
14.393
0.066
0
0.015
0
0
0
10.252
2.800
6.563
0.816
0.073
11.93111.93111.854
5.751
1.051
0.838
0
0
0
4.215
0
0
0
0
0
48.714

3. Results

Non-foraging

Foraging

Non-foraging

3.822
0.088
0.254
3.355
0.110
0
0
0.007
0.007
0
5.810
1.367
3.880
0.529
0.033
7.805
7.805
22.82718.171
2.106
0.096
0
0
0.015
2.366
0.007
0.007
0.007
0.051
0
40.476

4.656
0.363
0.539
3.579
0.149
0.013
0.006
0.006
0
0
5.136
1.596
3.043
0.496
0
0.733
0.733
6.612
4.510
0.045
1.739
0.019
0
0
0.292
0
0.006
0
0
0
17.137

4.880
0.396
0.415
3.783
0.247
0.019
0
0
0
0.019
17.014
6.145
10.243
0.620
0.006
4.643
4.643
5.441
1.648
0.714
2.229
0.019
0.013
0
0.740
0.019
0.032
0.006
0.013
0.006
31.977abundant in both years (98% in 2017, 77% in 2018). Within theplanktivorous divers, least auklet was the most abundant (64% in 2017,59% in 2018). Among foraging surface feeders, northern fulmar was
most abundant (49% in 2017, 68% in 2018) (Table 2).
Water mass preference by foraging seabirds was different among
foraging guilds and between years. Piscivorous divers occurred in eachof the water masses, with higher densities in BSW in 2017 and in AW in
2018 (Table 3). Planktivorous divers showed a strong preference for AW
in both years and were absent from ACW (Table 3). Shearwaters favored
BSW in 2017 and ACW in 2018 (Table 3). Surface feeders preferred AW
in both years (Table 3).

3.1. Seabirds
We observed 25 seabird species (19 species in 2017, 22 species in2018) during our surveys (Table 2). The density of total seabirds wasnearly half in 2018 (49.1 birds km− 2) compared to 2017 (89.2 birds
km− 2); all foraging guilds but planktivorous divers declined in 2018
(Table 2).
Dominant species among foraging guilds were similar between 2017and 2018. Among foraging piscivorous divers, murres were the most

Table 3
Mean (±SE) density of foraging seabird (birds km− 2) and mean (±SE) acoustically-determined biomass of fish and zooplankton (m2nmi− 2) between water masses(ACW: Alaska Coastal Water, BSW: Bering Sea Water, AW: Anadyr Water) for 2017 and 2018. The results of the Steel-Dwass test (t-statistics and p-value) are also
shown.
Variables
Piscivorous divers
Planktivorous divers
Shearwaters
Surface feeders
Fish in upper layer (9–24 m)
Fish in lower layer (24–59 m)
Zooplankton in upper layer (9–24 m)
Zooplankton in lower layer (24–59 m)

Year
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018

ACW
1.51 ± 0.53
4.63 ± 1.34
00.04 ± 0.04
0.81 ± 0.68
2.50 ± 1.13
1.26 ± 0.34
1.38 ± 0.57
115.27 ± 30.42
11.45 ± 2.36
10.85 ± 3.56
14.37 ± 8.16
41.92 ± 13.19
6.67 ± 2.92
5.40 ± 0.97
1.59 ± 0.52

BSW

AW

16.90 ± 14.86
2.76 ± 0.53
1.48 ± 0.36
0.92 ± 0.40
12.93 ± 10.53
0.19 ± 0.08
11.34 ± 7.24
0.79 ± 0.14
161.44 ± 25.40
20.20 ± 4.04
133.34 ± 21.19
69.96 ± 9.16
47.18 ± 6.44
5.51 ± 0.89
37.97 ± 6.00
28.87 ± 12.10

4

6.65 ± 1.11
9.16 ± 2.77
40.84 ± 12.22
19.51 ± 5.45
9.39 ± 5.02
0.12 ± 0.07
11.60 ± 2.00
26.23 ± 8.63
131.81 ± 21.02
115.37 ± 23.25
232.97 ± 64.31
33.63 ± 8.89
49.46 ± 5.81
33.64 ± 8.62
53.12 ± 12.81
16.46 ± 7.14

Steel-Dwass test (t-statistics, p-value)
ACW vs. BSW

ACW vs. AW

BSW vs. AW

0.45, p = 0.89
2.10, p = 0.09
2.65, p < 0.05
1.91, p = 1.35
0.95, p = 0.61
4.43, p < 0.05
1.65, p = 0.22
0.15, p = 0.99
0.22, p = 0.97
0.84, p = 0.68
5.42, p < 0.05
4.35, p < 0.05
0.04, p = 1.00
0.51, p = 0.87
5.31, p < 0.05
4.88, p < 0.05

4.04, p < 0.05
1.02, p = 0.57
5.40, p < 0.05
4.75, p < 0.05
2.73, p < 0.05
3.38, p < 0.05
5.00, p < 0.05
3.07, p < 0.05
0.98, p = 0.59
4.88, p < 0.05
3.13, p < 0.05
3.63, p < 0.05
2.06, p = 0.10
4.23, p < 0.05
3.37, p < 0.05
4.29, p < 0.05

4.61, p < 0.05
3.47, p < 0.05
5.91, p < 0.05
5.81, p < 0.05
2.37, p < 0.05
0.01, p = 1.00
4.71, p < 0.05
4.44, p < 0.05
0.59, p = 0.83
5.60, p < 0.05
0.12, p = 0.99
2.32, p = 0.05
1.61, p = 0.24
6.03, p < 0.05
0.11, p = 0.99
0.99, p = 0.58

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Deep-Sea Research Part II 181-182 (2020) 104898

correlations were found between planktivorous divers and prey in 2017
(Fig. 4a). In contrast, in 2018, the correlations (− 0.2–0.3 of Pearson’s
correlation coefficient) were weak and non-significant, except forplanktivorous divers, which had a positive relationship (0.5 of Pearson’s
correlation coefficient) with zooplankton that year, at 20 km scale(Fig. 4b).

Except for planktivorous divers, seabird foraging guilds showed
lower densities of foraging birds in 2018 compared to 2017 (Fig. 2). The
lower density of foraging planktivorous divers in 2018 was not statis­
tically significant (Fig. 2). Except for surface feeders (34% foragers in
2017, 55% in 2018), the proportion of foraging seabirds (i.e. density offoraging birds/density of total birds) was significantly lower in 2018(piscivorous divers: 49% foragers, planktivorous divers: 23%, shear­waters: 14%) than in 2017 (piscivorous divers: 79% foragers, planktiv­
orous divers: 64%, shearwaters: 60%) (Fisher’s exact tests, p < 0.05).

4. Discussion
4.1. Seabirds and water masses

3.2. Fish and zooplankton biomass

Our findings on seabird habitat preference with respect to water
masses are mostly consistent with previous works conducted in the
Chirikov Basin during summer (July–August); planktivorous species
such as auklets and short-tailed shearwaters use Anadyr Water andBering Shelf Water more frequently and piscivorous species, including
murres, either exhibited no preference for any water mass or occurred
more often in Alaska Coastal Water (Elphick and Hunt, 1993; Haney and
Schauer, 1994). The preference of predominantly planktivorous species
for Anadyr Water and Bering Shelf Water is likely due to the presence oflarger zooplankton species such as copepods (Neocalanus spp., Eucalanus
bungii) and euphausiids (Thysanoessa spp.) (Hunt et al., 1990; Piatt andSpringer, 2003). In Alaska Coastal Water, these larger prey are notabundant and small zooplankton predominate (Hunt and Harrison,
1990; Piatt and Springer, 2003; Eisner et al., 2013).

Acoustically-determined biomasses of fish and zooplankton differedamong water masses and between years. In 2017, the biomass of fish in
the upper layer (9–24 m depth) showed similar values among the threewater masses, while that in the lower layer (24–59 m depth) was greatest
in AW or BSW (Table 3). In 2018, the biomass of fish in the upper layer
was greatest in AW, and that in the lower layer was greatest in AW or
BSW (Table 3). For zooplankton, relationships between acousticallydetermined biomass and water masses were similar to those of fish
(Table 3). In 2018, acoustically-determined biomasses of both fish andzooplankton were smaller than those in 2017 (Fig. 3). In summary, bothfish and zooplankton were highest in AW, particularly in the upper layerwater, and both had higher biomass in 2017 than in 2018.
3.3. Bongo net sample

4.2. Seabird density and prey biomass

Biomass of total zooplankton collected using the bongo net in 2018
was significantly smaller than in 2017 (Table 4). Biomass of all largesized zooplankton (amphipods, euphausiids, Calanus marshallae, andNeocalanus copepods) appeared to be smaller in 2018, but the differ­ences were not statistically significant except for Neocalanus flemingeri(Table 4).

In general, we found reduced numbers of birds in our study area in2018. Largest declines in total density were for shearwater, fulmar,
kittiwakes, and murres. The decline in murres, while greatest in 2018,
appears to have occurred in the northern Bering Sea over several years
Kuletz et al., 2020 (this issue). In contrast, planktivorous auklet densities
were up slightly in 2018. The increase in auklets also fits the patternobserved by Kuletz et al., 2020 (this issue), showing higher aukletdensities in the Chirikov Basin during 2017–2019.
We found that densities of foraging seabirds, including both pisci­
vores and planktivores, were lower in 2018 than in 2017. Similarly, theacoustic surveys and zooplankton net samples showed lower biomassesof fish and zooplankton in 2018. Thus, the low density of foraging

3.4. Spatial relationships between seabirds and prey
In 2017, positive relationships (0.4–0.9 of Pearson’s correlation co­efficient) were found between the densities of foraging seabirds andacoustically-determined biomass at scales between 5 and 30 km forpiscivorous divers, shearwaters, and surface feeders (Fig. 4a). No

Fig. 2. Mean (+SE) densities (birds km− 2) of foraging piscivorous divers, planktivorous divers, shearwaters, and surface feeders for 2017 and 2018. Asterisks
represent significance of difference (p < 0.05) for two-year comparisons using Mann–Whitney U tests.
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Deep-Sea Research Part II 181-182 (2020) 104898

Fig. 3. Mean (+SE) acoustically-determined biomass (SA) (m2nmi− 2) of fish (a) and zooplankton (b) in the upper layer (9–24 m) and lower layer (24–59 m) for 2017and 2018. Asterisks represent significance of difference (p < 0.05) for two-year comparisons using Mann–Whitney U tests.

seabirds in 2018 reflected the lower prey biomass.
We were not able to identify the prey species responsible for the
acoustic backscatter. However, acoustic surveys using the dual fre­quencies (38 and 120 kHz) can provide relative biomass of fish andzooplankton (Mitson et al., 1996; Kokubun et al., 2008; De Robertis
et al., 2010; Sigler et al., 2012). Our acoustically-determined fishbiomass could reflect the presence of midwater fish such as juvenile
walleye pollock (Gadus chalcogrammus), capelin (Mallotus villosus), andjuvenile salmon (Oncorhynchus spp.), all of which are important prey
items for murres. Murres were the most abundant piscivorous divers inour study, and they are an important component of the offshore avifaunain the region (Kuletz et al., 2020). The estimates of zooplankton biomass
that we obtained could include euphausiids and other zooplankton suchas copepods, which are the main prey species for planktivorous auklets(least, crested, and parakeet) and short-tailed shearwaters during sum­mer in the Bering Sea (Bédard, 1969; Ogi et al., 1980; Hunt et al., 2002;
Gall et al., 2006).
The lower biomasses of fish and zooplankton in 2018 could be
related to the lack of sea ice. In 2017, sea-ice coverage extended south of
St. Lawrence Island even in mid-April, while in April 2018 the sea ice
had completely retreated to the north of St. Lawrence Island (Fukai

Table 4
Zooplankton biomass (mg Wet Weight m− 3) collected using bongo net. Values
are mean ± SD with range in parentheses. Sample size and the results of Man­
n–Whitney U tests are also shown.
2017

2018

U testSamples (n)Amphipods

11
8.0 ± 20.8 (0–72.8)

11
2.4 ± 7.7 (0–26.8)

Euphausiids

91.0 ± 87.9 (0–248.7)

Neocalanus
cristatus
Neocalanus
flemingeri
Calanusmarshallae
Others20.6 ± 45.2 (0–141.2)

68.1 ± 74.5
(0–231.6)
083.3 ± 89.2 (0–245.3)

8.6 ± 17.3 (0–57.9)

268.2 ± 234.8(70.7–802.6)
195.0 ± 178.7(27.1–562.3)
666.2 ± 378.9(126.4–1275.7)

133.0 ± 116.9(6.4–441.0)
118.5 ± 119.2(2.6–349.2)
330.5 ± 233.9(19.1–703.1)–
U = 71.0, p= 0.33
U = 64.0, p= 0.84
U = 71.5, p= 0.17
U = 89.0, p< 0.05
U = 83.0, p= 0.15
U = 79.0, p= 0.24
U = 92.0, p< 0.05Total
zooplankton

Fig. 4. Pearson’s correlation coefficient between the densities of foraging seabirds and acoustically-determined biomass at fourdifferent spatial scales (5, 10, 20, and
30 km bin sizes) for 2017 (a) and 2018 (b). Squares = piscivorous divers vs. fish in the upper (9–24 m) and lower layers (24–59 m) combined; diamonds =planktivorous divers vs. zooplankton in the upper (9–24 m) and lower layers (24–59 m) combined; circles = shearwaters vs. zooplankton both in the upper (9–24 m)
and lower layers (24–59 m) combined; triangles = surface feeders vs. both fish and zooplankton in the upper (9–24 m) layers. Filled symbols represent statistically
non-significant (p > 0.05).
6

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Deep-Sea Research Part II 181-182 (2020) 104898

et al., 2019; Appendix A). Due to these sea-ice conditions, there was no
ice-edge phytoplankton bloom and only small-magnitude open water
blooms in the northern Bering Sea in 2018 (Duffy-Anderson et al., 2019).
Lack of ice algae and ice-edge blooms in warm water might lead todepressed production of large-sized zooplankton (Calanus spp., Thysa­
noessa spp., Themisto spp.) in the Bering Sea (Hunt et al., 2011; Sigler
et al., 2016; Duffy-Anderson et al., 2019).
Large-sized zooplankton (Calanus spp., Thysanoessa spp., Themistospp.) are important food for midwater forage fish such as juvenile
walleye pollock, juvenile salmon, capelin and Pacific herring (Clupea
pallasii) in the Bering Sea (Aydin et al., 2007; Coyle et al., 2011; Andrews
et al., 2016). Therefore, the acoustically-determined lower biomass offish might reflect lower biomass of large-sized zooplankton in 2018. The
trawl surveys in the summer of 2018 in the northern Bering Sea showedthat Pacific herring abundance was lower than in 2017, although theabundance of capelin and age-0 pollock were similar between these twoyears (Duffy-Anderson et al., 2019).

Declaration of competing interest

4.3. Spatial relationships for foraging seabirds

Appendix A. Supplementary data

We found that, at the scales of 5–30 km, correlations between thedensity of foraging seabirds and acoustically-determined prey biomass
were weaker in 2018, when prey biomass was small, than in 2017; thispattern occurred for all seabird foraging guilds except planktivorous
divers. The results contradicted the general conclusion of Vlietstra et al.
(2005), wherein stronger correlations between seabirds and preyoccurred when prey availability was low. These stronger correlations
between seabirds and prey can be interpreted as seabird responses tohigh and intermediate absolute prey abundance when prey-tracking
could be an energetically efficient foraging strategy (Vlietstra et al.,2005). In contrast, studies reporting weaker correlations between sea­
birds and prey when prey availability declined (Fauchald and Erikstad,
2002; Becker and Beissinger, 2003; this study) may have characterized
responses to intermediate and low levels of absolute prey abundance. In
this case, seabirds may be limited in their ability to locate prey patches
and would benefit by searching elsewhere for more abundant food (Grayand Kennedy, 1994; Spencer et al., 1996; Fauchald, 1999). Indeed, theproportion of non-foraging individuals was larger in 2018 than in 2017,indicating that seabirds may have spent more time searching for prey
within our study area, or moving to areas with better foraging
conditions.

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.dsr2.2020.104898.

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
We thank the captain, officers, and crews of the T/S Oshoro-Maru, aswell as Dr. Atushi Ooki and Dr. Toru Hirawake (chief scientists of the T/
S Oshoro-Maru cruise in 2017 and 2018) for their help with the field
operations. Support for the U.S. Fish and Wildlife (USFWS) observer wasfrom a grant from the Bureau of Ocean Energy Management (Inter­agency Agreement M17PG00039). Elizabeth Labunski (USFWS) assistedwith data compilation. This study was supported by MEXT (Japanese
Ministry of Education, Culture, Sports, Science and Technology) throughthe Arctic Challenge for Sustainability Project.

Author contributions
Bungo Nishizawa: Conceptualization, Formal analysis, Investigation,
Writing - original draft, Writing - review & editing, Nodoka Yamada:
Formal analysis, Investigation, Writing - review & editing, Haruka
Hayashi: Investigation, Writing - review & editing, Charlie Wright:
Investigation, Writing - review & editing, Kathy J. Kuletz: Conceptual­ization, Writing - review & editing, Hiromichi Ueno: Investigation,
Writing - review & editing, Tohru Mukai: Formal analysis, Writing review & editing, Atsushi Yamaguchi: Formal analysis, Investigation,
Writing - review & editing, Yutaka Watanuki: Conceptualization,
Writing - original draft, Writing - review & editing
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Compared to 2017, we found lower overall density of seabirds at sea,
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