Polar Science 27 (2021) 100555

Contents lists available at ScienceDirect

Polar Sciencejournal homepage: http://www.elsevier.com/locate/polar

Between-year comparison of interactions between environmental
parameters and various plankton stocks in the northern Bering Sea during
the summers of 2017 and 2018
Atsushi Yamaguchi a, b, *, Fumihiko Kimura a, Yuri Fukai c, Yoshiyuki Abe d, Kohei Matsuno a, b,
Atsushi Ooki a, b, Toru Hirawake a, b
aFaculty/Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido, 041-8611, Japan
Arctic Research Centre, Hokkaido University, Kita-21 Nishi-11 Kita-ku, Sapporo, Hokkaido, 001-0021, Japanc
Graduate School of Environmental Science, Hokkaido University, Kita-10 Nishi-5 Kita-ku, Sapporo, Hokkaido, 060-0810, Japand
Research Development Section, Office for Enhancing Institutional Capacity, Hokkaido University, Kita-21, Nishi-10, Kita-ku, Sapporo, Hokkaido, 001-0021, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
The northern Bering sea
Plankton stock
Yearly change
Structural equation modelling (SEM) analysis

In the northern Bering Sea, ice coverage and retreat timing were low and early, respectively, in the 2017–2018
winter. To evaluate the effect of these anomalous ice conditions, we quantified various environmental param­
eters (temperature, salinity, mixed-layer depth, nutrients) and the standing stocks of various planktonic taxa
(phytoplankton counts, meso- and macrozooplankton mass, jellyfish abundance) during July of 2017 and 2018.
For each year, the interaction between each parameter was evaluated by structural equation modelling (SEM)
analysis. Large yearly differences were detected for the interactions between environmental parameters andplanktonic stocks. Thus, for 2017, a total of fifteen interactions were present between environmental parametersand various planktonic stocks. In 2018, however, only eight interactions were present. Among the interactions,four were common to the two years. It is notable that the path coefficients of these four interactions were all
lower in 2018 than in 2017. These findings suggest that the small magnitude and short pulse of the phyto­plankton bloom in 2018 may have failed to transfer production and energy to a higher trophic level even within
the planktonic food web. Indeed, in 2018, mass mortality was reported for seabirds (two murre species) feedingon planktivorous fishes.

1. Introduction
In the northern Bering Sea, the sea ice coverage area for the
2017–2018 winter was reported to be at the minimum level recorded bysatellite observations initiated in 1978 (Cornwall, 2019). For seabirds,
mass mortality of two murre species (common murre and thick-billedmurre) and reproductive failure of two auklet species (crested auklet
and least auklet) were reported during the summer of 2018 (Dragoo
et al., 2019; Duffy-Anderson et al., 2019). Lack of food resources andwarming sea temperature are possible causes of mass seabird mortality
and reproductive failure (Jones et al., 2018, 2019). In a normal year,
more than 70% of the area of the southern shelf region of the St. Law­
rence Island is covered by a cold pool characterized by < 2 � C bottom
temperature (cf. Stabeno and Bell, 2019). However, the cold pool wasabsent in the summer of 2018, and the extent of the northern

distribution limit of pelagic fish fauna was affected (Cornwall, 2019;
Duffy-Anderson et al., 2019). For recent years, changes in horizontal
distribution of walruses and frequency and seasonal duration of whales
are also reported (Jay et al., 2017; Stafford, 2019). Consequently,
through changes in timing shifts in marine mammal hunting andwhaling, there is a large effect on the life of indigenous people (Hun­tington et al., 2020). Despite the pronounced between-year difference inthe sea-ice conditions, temperature, fishes, sea-bird and marine mam­mals in 2017–2019 (Huntington et al., 2020), little information isavailable concerning the mechanisms and interactions between envi­
ronmental variables and the planktonic food web.
The evaluation of the mechanisms and effects of low ice coverage
and early ice retreat on the marine ecosystem in the northern Bering Sea
is similar to an effort to gather pieces of a puzzle that vary between taxa,
regions, and years and attempting to provide a highly likely hypothesis* Corresponding author. Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido, 041-8611, Japan.
E-mail address: a-yama@fish.hokudai.ac.jp (A. Yamaguchi).
https://doi.org/10.1016/j.polar.2020.100555
Received 27 October 2019; Received in revised form 11 May 2020; Accepted 29 May 2020
Available online 28 June 2020
1873-9652/© 2020 Elsevier B.V. and NIPR. All rights reserved.

A. Yamaguchi et al.

Polar Science 27 (2021) 100555

that explains the whole phenomenon. For the evaluation of thesemechanisms, there are three principal approaches. The first is the hor­izontal characterization of pelagic biota, which varied with the target
area (Questel et al., 2013; Ershova et al., 2015a). The second is toperform comparisons between years with great differences in environ­mental and ice conditions (Matsuno et al., 2011; Pinchuk and Eisner,2017). Third, the comparison of recent biotic conditions with historicalconditions applying historical data archives has been successful forzooplankton and seabirds (Ershova et al., 2015b; Gall et al., 2017). Thismethod is direct, and accurate evaluation of yearly differences may bepossible by considering yearly differences in methodology (station,mesh size, quantification method, unit of stock [abundance orbiomass]), but this method is inevitably limited to certain targeted taxa.
Bearing in mind the above three approaches, the structural equation
modeling (SEM) analysis is a powerful tool to evaluate interactions be­
tween environmental parameters and plankton stocks (Matsuno et al.,2016; Amano et al., 2019).
In the present study, we evaluate the mechanisms by which yearly
differences in ice conditions may affect various plankton stocks using
SEM analysis based on directly obtained environmental (temperature,salinity, mixed-layer depth, nutrients, fluorescence) and plankton stock(phytoplankton, meso- and macro-zooplankton biomass, jellyfish
abundance) data during July of 2017 and 2018.
2. Materials and methods
During the 40th and 56th cruises of the T/S Oshoro-Maru (Hokkaido
University), environmental measurements and plankton stock assess­ment were performed at 21 stations (2017) and 13 stations (2018) in thenorthern Bering Sea from 9–22 July 2017 and 2–12 July 2018 (Fig. 1).
All the stations were located at 62� 100 N-66� 440 N latitude and 174� 050 W166� 300 W longitude, and their water depths were 27–76 m. For eachstation, temperature, salinity, and fluorescence were measured by CTD(Sea-bird Electronics 911Plus) and fluorescence sensor (Seapoint Sen­sors, Inc.). Mixed layer depth (MLD) was defined as the depth where theseawater density was increased by 0.1 kg m 3 from that at 5 m depth(Danielson et al., 2011). Water samples from 10-m intervals throughout
3
the water column were frozen, and nutrients (NO3 , NO2 , NHþ
4 , PO4 , Si
(OH)4) were measured with an autoanalyser QuAAtro 2-HR system(BL-tec, Osaka, Japan; Seal Analytical, Norderstedt, Germany). These
environmental parameters were calculated with depth-averaged or
depth-integrated values at each station. Thus, we abbreviated as

Fig. 1. Location of the sampling stations in 2017 and 2018 along with the bathymetry and main circulation patterns (Danielson et al., 2017). Purple: Anadyr Current;
Black: Alaskan Coastal Current.
2

A. Yamaguchi et al.

Polar Science 27 (2021) 100555

following: depth-averaged temperature (T: � C), depth-averaged salinity(S), depth-integrated fluorescence (Flu), depth-integrated nitrate þ ni­
trite (NOx: μM), ammonium (NH4: μM), phosphate (P: μM), silicate (Si:μM).
For phytoplankton, 500–1000 mL water samples collected at each
depth were preserved with 1% glutaraldehyde and concentrated to 24to 50-fold, and diatoms and dinoflagellates were examined for 1-mL
subsamples mounted on glass slides with an inverted microscope with
40 600x magnification. Phytoplankton stock was integrated
throughout the water column and expressed as x 106 cells m 2 (abbre­
viated as Phyto). For mesozooplankton, samples were collected fromvertical hauls of a flowmeter-mounted NORPAC net (45-cm mouthdiameter equipped with 335-μm mesh). For macrozooplankton, samples
were collected by oblique hauls of a flowmeter-mounted Bongo net (70cm diameter with 505-μm mesh). These zooplankton samples were
preserved in borax-buffered 5% formalin-seawater, and their wet masses
were measured with a precision of 0.1 mg using a microbalance (Met­tler-Toledo). Masses of mesozooplankton (Meso) and macrozooplankton(Macro) were expressed as standing stock of the water column (g WM
m 2). The abundance of macro-to mega-sized jellyfish was quantified at
1-m depth intervals from video images captured by slow vertical tows
(0.1 m s 1) of the video camera (Marine Arkas, Kowa Co. Ltd) down to
the 50-m depth at each station. For the quantification of jellyfish, theeffect of horizontal advection was also considered (Marie Maekakuchi,
personal communication). Jellyfish abundance in 1-m intervals was in­tegrated over the 0–50 m water column, then expressed as the standing
stock (Jelly: ind. m 2).
To evaluate interactions between each environmental parameter andplankton stock, SEM analysis was performed for each year (Grace, 2006;
Grace et al., 2010). Through SEM analysis, interactions between pa­
rameters composing ecosystems are evaluated as positive or negative
path coefficients (Grace et al., 2010). For SEM analysis, environmental
parameters (T, S, and MLD) were set at the first row. Nutrients (NOX,
NH4, P, and Si) and plankton (Flu, Phyto, Meso, and Macro) were set atthe second and third rows, respectively. Jelly was set for the fourth row.
Subsequently, the parameters characterized by non-significant relations(p > 0.05) with the other parameters (P, Si, Flu and Macro) were
removed from the final model. The overall fit of the final model was
evaluated by the goodness-of-fit index (GFI) and the adjunctgoodness-of-fit index (AGFI). The SEM analysis was performed using
add-in software for MS-Excel (http://www.ohmsha.co.jp/data/link/978-4-274-06925-3/).

Fig. 2. Horizontal distribution of depth-averaged temperature (T), salinity (S), mixed-layer depth (MLD), depth-integrated fluorescence (Flu), nitrate þ nitrite (NOx),ammonia (NH4), PO4 (P), SiO4 (Si), phytoplankton cell stock (Phyto), wet mass of mesozooplankton (Meso), wet mass of macrozooplankton (Macro), and jellyfish
standing stock (Jelly) in the northern Bering Sea during July of 2017.
3

A. Yamaguchi et al.

Polar Science 27 (2021) 100555

3. Results(2.53 � 1.15 ind. m 2) (p < 0.01, U test).

3.1. Horizontal distribution

3.2. SEM analysis

The horizontal distributions of various environmental parametersand various plankton stocks in 2017 and 2018 are shown in Figs. 2 and3, respectively. In both years, T and S had opposite gradients for the eastwest direction. Thus, T ranged from 2.1 to 8.5 � C and was high at theeastern side (Alaska). On the other hand, S ranged from 29.4 to 32.9 andwas high for the western side (Siberia). MLD and Flu exhibited northsouth gradients (Figs. 2 and 3). MLD was at 6–33 m and was deeper at
the northern side (Chukchi Sea). Flu ranged from 4.9 to 52.4 (arbitrary
units) and was higher for the northern side (Chukchi Sea). Nutrients(NOx, NH4, PO4, SiO4) showed pronounced east-west gradients; thus
they were higher for the western side (Siberia) and lower for the easternside (Alaska). These east-west patterns were prominent in 2017, while
NH4 in 2018 showed a relatively high value even at the Alaskan side
(Fig. 3). Phyto was high near the Bering Strait in 2017. Meso ranged
from 5.29 to 88.93 g WM m 2, Macro (expressed in the same units)ranged from 0.27 to 40.82 g WM m 2, and Meso was significantly higher
than Macro throughout the region and in both years (p < 0.01, U test). A
high between-year difference was detected for Jelly; thus Jelly wassignificantly higher in 2017 (mean 10.54 � 2.03 ind. m 2) than in 2018

The results of SEM analysis between environmental parameters andvarious classes of plankton stock in 2017 and 2018 are shown in Fig. 4.
The most prominent difference between the two years was that the
number of interactions was greater in 2017 than in 2018. Thus, fifteeninteractions were present between environmental parameters andvarious plankton stocks in 2017, while only eight interactions weredetected in 2018 (Fig. 4). Among the interactions, four were common inboth years (Table 1, colored red in Fig. 4). Thus, negative interactions
were present between temperature and NH4, NH4 and Phyto, and Phyto
and Jelly, while positive interaction was observed for salinity and NOX.
The path coefficients of these four common interactions were all lower
in 2018 than in 2017. Three interactions showed opposite patterns be­
tween years. Thus, temperature and Jelly were negatively related in2017 but positively related in 2018. Salinity and Phyto were positively
related in 2017 but negatively related in 2018. For Meso and Jelly,
negative (2017) and positive (2018) interactions were detected.

Fig. 3. Horizontal distribution of depth-averaged temperature (T), salinity (S), mixed-layer depth (MLD), depth-integrated fluorescence (Flu), nitrate þ nitrite (NOx),ammonia (NH4), PO4 (P), SiO4 (Si), phytoplankton cell stock (Phyto), wet mass of mesozooplankton (Meso), wet mass of macrozooplankton (Macro), and jellyfish
standing stock (Jelly) in the northern Bering Sea during July of 2018.
4

A. Yamaguchi et al.

Polar Science 27 (2021) 100555

Fig. 4. Results of structural equation model (SEM)
analysis between environmental parameters andvarious plankton taxa in the northern Bering Sea
during July of 2017 (left) and 2018 (right). The
values along the pathways represent standardized
path coefficients. Solid and dashed lines indicate
positive and negative relationships, respectively. The
four common interactions in both years are coloredwith red. MLD: mixed-layer depth, S: depth-averaged
salinity, T: depth-averaged temperature, Meso: wet
mass of mesozooplankton, Phyto: phytoplankton cellstanding stock, Jelly: jellyfish standing stock.

Horizontal distribution of MLD and Flu found in this study corresponded
well with these north-south patterns (Figs. 2 and 3). Diatoms such as
Chaetoceros spp. were dominated in the phytoplankton community ofthis region (Fukai et al., 2019).
The most important zooplankton species in mesozooplankton
biomass of this region is the copepod Calanus glacialis (Ershova et al.,2015a). From the viewpoint of food for higher trophic levels, large-sized
Pacific copepods (e.g., Neocalanus spp. and Eucalanus bungii) are also
important (Eisner et al., 2013; Pinchuk and Eisner, 2017). The Pacificcopepods are known to be abundant in the Anadyr Current (Pinchuk andEisner, 2017; Sigler et al., 2017). Within the macrozooplankton,
euphausiid Thysanoessa raschii is known to be the most important in thisregion (Eisner et al., 2013). All sampling in this study was conductedduring July, which corresponded to the season of the midnight sun.
Since euphausiids may perform great diel vertical migration (cf.
Mauchline, 1980), they are expected be distributed in the near-bottom
layer during that season. This may explain the lower biomass ofMacro than that of Meso (Figs. 2 and 3). As a large dominant jellyfish ofthis region, scyphomedusa Chrysaora melanaster is known to be impor­tant (De Robertis and Taylor, 2014). For Jelly, yearly changes were more
prominent than horizontal changes. Add to C. melanaster, ctenophore
Bolinopsis infundibulum was the dominant component of jellyfish in thisstudy (Marie Maekakuchi, personal communication).

Table 1Significant path coefficients detected between environmental parameters andvarious plankton stocks in the northern Bering Sea during the summers of 2017and 2018 (cf. Fig. 4). T: depth-averaged temperature, S: depth-averaged salinity,
MLD: mixed-layer depth, NOX: NO2þNO3, Phyto: phytoplankton cell stock,
Meso: mesozooplankton biomass, Jelly: jellyfish stock.
Interactions
Common pattern for both year
T-NH4
S-NOX
NH4-Phyto
Phyto-Jelly
Only in 2017
T-PhytoS–NH4
S-MeSoS-Jelly
MLD-NH4
MLD-Meso
NH4-NOX
NH4-Jelly
Only in 2018
MLD-Phyto
Oppose pattern with year
T-jelly
S-PhytoMeso-Jelly

2017

2018

0.59
0.67
0.74
0.88

0.56
0.66
0.36
0.57

0.33
0.30
0.52
0.50
0.41
0.42
0.51
0.79
0.89
0.44
0.70
0.57

0.55
0.79
0.40

4.2. Yearly changes in the planktonic food web (SEM analysis)
4. Discussion

From SEM analysis, significant interactions between environmental
parameters and various plankton stocks were present for fifteen in 2017
but only eight in 2018 (Fig. 4). It is also notable that the path coefficientsof the four common interactions of both years were all lower in 2018
than in 2017 (Table 1). These findings suggest that the interactions
within the lower trophic level were weak in 2018.
In the study region (northern Bering Sea), the ice coverage area forthe 2017–2018 winter was reported to be at a minimum level by satellite
observation initiated in 1978 (Cornwall, 2019). This anomalous ice
condition was caused by strong southern wind during autumn of 2017,
warm winter atmosphere and water, vertical mixing yielding low ice
coverage area, and then rapid ice melting in the spring of 2018 (Stabeno
and Bell, 2019). Due to these ice conditions, phytoplankton had noice-edge bloom and weak stratification, and only a small-magnitudeopen-water spring bloom was reported in 2018 (Duffy-Anderson et al.,2019). For mesozooplankton, dominance of small copepods and a few
abundant large copepods, which are important foods for seabirds andhigher trophic levels, were reported in 2018 (Duffy-Anderson et al.,2019). Since the cold pool, characterized by low temperature (<2 � C)
and normally present for the southern St. Lawrence Island during

4.1. Horizontal distribution
The horizontal distribution of environmental parameters, phyto- andzoo plankton stocks, and fish communities in this region was extensively
studied by the Arctic Ecosystem Integrated Survey (Arctic Eis) (Mueter
et al., 2017). The water mass present on the eastern side (Alaska), theAlaskan Coastal Current is characterized as warm and fresh, while thewater mass on the western side (Siberia), the Anadyr Current is char­acterized as cold and saline (Danielson et al., 2017). In the present study,
the east-west gradient in T and S may be related to this east-west dif­ference in water masses (Figs. 2 and 3). Horizontal distributions of nu­
trients and phytoplankton stock in this region are known to have a wellparallel with that of water masses (Danielson et al., 2017). Within thewater masses in this region, strong current and front between two water
masses in Bering Strait lead to strong mixing and deep MLD are reported
for the Bering Strait Water (Eisner et al., 2013). In the Bering Strait, theMLD of Bering Strait Water and nutrient-rich Bering Sea Water is known
to be weak and deep with high chlorophyll a (Danielson et al., 2017).
5

A. Yamaguchi et al.

Polar Science 27 (2021) 100555

summer, was completely absent during summer of 2018, pelagic fishes
were reported to extend their distribution into the northern area due tothe absence of a northern barrier of the cold pool in 2018 (Cornwall,
2019; Duffy-Anderson et al., 2019). The most prominent changes athigher trophic levels in 2018 were observed for seabirds, where
breeding failure occurred for two auklet species (crested auklet and least
auklet), and a die-off was reported for two murre species (common
murre and thick-billed murre) (Dragoo et al., 2019; Duffy-Anderson
et al., 2019). These observations may be related to a small-magnitudeopen-water spring bloom in 2018. Thus, low primary production andenergy would reduce energy and material transfer to higher trophic
levels, and the effect might be prominent for the highest trophic level(seabird) in the marine ecosystem. In the present study, the paucity ofinteractions between environmental variables and plankton stocks in2018 relative to those in 2017 (Fig. 4) may reflect the lower productionof materials and energy in 2018. Since the mesozooplankton wet massesof 2017 were comparable for those collected in 2007 and 2008 in thesame region (Matsuno et al., 2011), the plankton stocks in 2017 areconsidered to be an ordinary year.
While under these conditions, the four common interactions
observed for both years (Table 1) are considered to be the strong in­teractions that characterize the plankton food web in this region. Amongthese four interactions, strong positive interaction between salinity andNOX corresponds with that the high nutrient levels of the saline Anadyr
Current (Danielson et al., 2017; Sigler et al., 2017). Negative interaction
between temperature and NH4 indicates that NH4 is also high for thecold Anadyr Current. Since NH4 is a product of decomposition and ishigh for the lower layer in this region (Brown et al., 2015), NH4 may
have a negative interaction with fresh phytoplankton. Phytoplankton
abundance was high around the Bering Strait, which is characterized by
the fast current in this region (Abe et al., 2019). Such high current ve­
locity is not conducive to the presence of large-sized jellyfishes (DeRobertis and Taylor, 2014). Thus, negative interaction may be detected
between Phyto and Jelly (Table 1). From these viewpoints, the fourcommon interactions observed in both years may be related to theregional topography and the water masses and are considered to befundamental interactions of the planktonic food web in the northern
Bering Sea.
Among interactions with opposing patterns between years, it isremarkable that salinity showed strong positive interaction with Phyto
in 2017, while strong negative interaction was seen for 2018 (Table 1).
As mentioned above, salinity had a strong positive interaction withnutrient (NOX) levels in both years (Table 1). Thus, in 2018, while
nutrient levels were high in the high saline region, Phyto levels were low
for the region. For spring bloom in 2018, because of the early ice retreat,no ice-edge bloom and only a small-magnitude open-water bloom wasreported (Duffy-Anderson et al., 2019). This relationship between ice
and phytoplankton blooming is consistent with the Oscillating Control
Hypothesis (OCH) reported for the southeastern Bering Sea shelf (Huntet al., 2002, 2011). Observations of Meso in 2018 (the dominance ofsmall-sized copepods during the late spring phytoplankton bloom in thewarm year) may also reflect OCH (Duffy-Anderson et al., 2019). The low
production and paucity of interactions within the planktonic food web in2018 (Fig. 4) may have induced the failure of material and energy
transfer to the higher trophic level. Since a low ice coverage area wasalso reported for the winter of 2018–2019 (Duffy-Anderson et al., 2019),
if similar hydrography and ice conditions continue for successive years,
serious effects may be inevitable for the marine ecosystem in thenorthern Bering Sea.
As the alternative possible forces in the marine ecosystem, while thisstudy did not quantify the higher trophic levels, add to the bottom-up
controls focusing in this study, the factors of top-down control should
be considered. Based on the acoustic surveys, standing stocks of the agezero Arctic cod in 2017 is reported to be 5–16 times greater than those in
2012–2013 (Huntington et al., 2020). From surface trawl surveys, thecatch per unit effort for juvenile pink salmon is reported to be two times

greater in 2017 than the previous years (Huntington et al., 2020). Basedon the bottom trawl surveys, because of the absence of cold pool(near-bottom shelf waters cooler than 2 � C, located south of the Bering
Strait) which act as a thermal barrier that prevents north-wards migra­
tion of subarctic groundfish, increased biomass of walleye pollock, Pa­
cific cod and northern rock sole are reported for the northern Bering Sea
in 2017 (Stevenson and Lauth, 2019). Thus, all this information on
standing stocks of fishes (both pelagic and benthic and juvenile toadults) indicates an increase of stocks in the northern Bering Sea. In thisstudy, while we considered only for bottom-up control interactions
within the lower trophic levels, the effects of top-down controls byhigher-trophic fishes may be a possible cause of low biomasses of Meso,
Macro and Jelly in 2018 (Fig. 3).
5. Conclusions
Interactions between environmental parameters and various
plankton stocks in the northern Bering Sea were evaluated andcompared for the two successive years (2017 and 2018). In this study,
there were four common interactions observed in both years, while threeinteractions were opposite in direction between the two years. The fourcommon interactions are considered to be fundamental within theplanktonic food web in this region, while the latter three interactions arevariable with yearly differences in ice coverage and ice retreat timing.
The low ice coverage and early ice retreat were reported not only in the
2017–2018 winter but also for the 2018–2019 winter. If such ice con­ditions continue for more years, material and energy transport within
the planktonic food web may be diminished, causing a serious effect on
the higher trophic level.
Acknowledgements
We thank the captains, officers, and crews of the T/S Oshoro-Maru for
their help with zooplankton sampling and hydrographic data collection.
This work was conducted by the Arctic Challenge for Sustainability(ArCS) Project (Program Grant Number JPMXD1300000000). Part ofthis study was supported by a Grant-in-Aid for Scientific Research
20H03054 (B), 19H03037(B), 18K14506 (Early Career Scientists), and
17H01483 (A) from the Japan Society for the Promotion of Science(JSPS).
References
Abe, H., Sampei, M., Hirawake, T., Waga, H., Nishino, S., Ooki, A., 2019. Sedimentassociated phytoplankton release from the seafloor in response to wind-induced
barotropic currents in the Bering Strait. Front. Mar. Sci. 6, 97. https://doi.org/
10.3389/fmars.2019.00097.
Amano, K., Abe, Y., Matsuno, K., Yamaguchi, A., 2019. Yearly comparison of theplanktonic chaetognath community in the Chukchi Sea in the summers of 1991 and2007. Pol. Sci. 19, 112–119. https://doi.org/10.1016/j.polar.2018.11.011.
Brown, Z.W., Casciotti, K.L., Pickart, R.S., Swift, J.H., Arrigo, K.R., 2015. Aspects of themarine nitrogen cycle of the Chukchi Sea shelf and Canada Basin. Deep-Sea Res. Part
II Top. Stud. Oceanogr. 118, 73–87. https://doi.org/10.1016/j.dsr2.2015.02.009.
Cornwall, W., 2019. Vanishing Bering Sea ice poses climate puzzle. Science 364 (6441),
616–617. https://doi.org/10.1126/science.364.6441.616.
Danielson, S., Eisner, L., Weingartner, T., Aagaard, K., 2011. Thermal and haline
variability over the central Bering Sea shelf: seasonal and interannual perspectives.
Continent. Shelf Res. 31 (6), 539–554. https://doi.org/10.1016/j.csr.2010.12.010.
Danielson, S.L., Eisner, L., Ladd, C., Mordy, C., Sousa, L., Weingartner, T.J., 2017.
A comparison between late summer 2012 and 2013 water masses, macronutrients,
and phytoplankton standing crops in the northern Bering and Chukchi Seas. DeepSea Res. Part II Top. Stud. Oceanogr. 135, 7–26. https://doi.org/10.1016/j.
dsr2.2016.05.024.
De Robertis, A., Taylor, K., 2014. In situ target strength measurements of thescyphomedusa Chrysaora melanaster. Fish. Res. 153, 18–23. https://doi.org/
10.1016/j.fishres.2014.01.002.
Dragoo, D.E., Renner, H.M., Kaler, R.S.A., 2019. Breeding Status and Population Trendsof Seabirds in Alaska, 2018. U.S. Fish and Wildlife Service Report AMNWR 2019/03.
Homer, Alaska.
Duffy-Anderson, J.T., Stabeno, P., Andrews III, A.G., Cieciel, K., Deary, A., Farley, E.,
Fugate, C., Harpold, C., Heintz, R., Kimmel, D., Kuletz, K., Lamb, J., Paquin, M.,
Porter, S., Rogers, L., Spear, A., Yasumiishi, E., 2019. Responses of the northern
Bering Sea and southeastern Bering Sea pelagic ecosystems following record-

6

A. Yamaguchi et al.

Polar Science 27 (2021) 100555
Jones, T., Parrish, J.K., Peterson, W.T., Bjorkstedt, E.P., Bond, N.A., Ballance, L.T.,
Bowes, V.J., Hipfner, M., Burgess, H.K., Dolliver, J.E., Lindquist, K., Lindsey, J.,
Nevins, H.M., Robertson, R.R., Roletto, J., Wilson, L., Joyce, T., Harvey, J., 2018.
Massive mortality of a planktivorous seabird in response to a marine heatwave.
Geophys. Res. Lett. 45 (7), 3193–3202. https://doi.org/10.1002/2017GL076164.
Jones, T., Divine, L.M., Renner, H., Knowles, S., Lefebvre, K.A., Burgess, H.K., Wright, C.,
Parrish, J.K., 2019. Unusual mortality of tufted puffins (Fratercula cirrhata) in theeastern Bering Sea. PloS One 14 (5), e0216532. https://doi.org/10.1371/journal.
pone.0216532.
Matsuno, K., Yamaguchi, A., Hirawake, T., Imai, I., 2011. Year-to-year changes of themesozooplankton community in the Chukchi Sea during summers of 1991, 1992 and2007. Polar Biol. 34, 1349–1360. https://doi.org/10.1007/s00300-011-0988-z,
2008.
Matsuno, K., Abe, Y., Yamaguchi, A., Kikuchi, T., 2016. Regional patterns and controlling
factors on summer population structure of Calanus glacialis in the western Arctic
Ocean. Pol. Sci. 10, 503–510. https://doi.org/10.1016/j.polar.2016.09.001.
Mauchline, J., 1980. The biology of mysids and euphausiids. Adv. Mar. Biol. 18, 1–677.
Mueter, F.J., Weems, J., Farley, E.V., Sigler, M.F., 2017. Arctic ecosystem integrated
survey (Arctic Eis): marine ecosystem dynamics in the rapidly changing PacificArctic Gateway. Deep-Sea Res. Part II Top. Stud. Oceanogr. 135, 1–6. https://doi.
org/10.1016/j.dsr2.2016.11.005.
Pinchuk, A.I., Eisner, L.B., 2017. Spatial heterogeneity in zooplankton summer
distribution in the eastern Chukchi Sea in 2012–2013 as a result of large-scaleinteractions of water masses. Deep-Sea Res. Part II Top. Stud. Oceanogr. 135, 27–39.
https://doi.org/10.1016/j.dsr2.2016.11.003.
Questel, J.M., Clarke, C., Hopcroft, R.R., 2013. Seasonal and interannual variation in theplanktonic communities of the northeastern Chukchi Sea during the summer andearly fall. Continent. Shelf Res. 67, 23–41. https://doi.org/10.1016/j.
csr.2012.11.003.
Sigler, M.F., Mueter, F.J., Bluhm, B.A., Busby, M.S., Cokelet, E.D., Danielson, S.L., De
Robertis, A., Eisner, L.B., Farley, E.V., Iken, K., Kuletz, K.J., Lauth, R.R.,
Logerwell, E.A., Pinchuk, A.I., 2017. Late summer zoogeography of the northern
Bering and Chukchi seas. Deep-Sea Res. Part II Top. Stud. Oceanogr. 135, 168–189.
https://doi.org/10.1016/j.dsr2.2016.03.005.
Stabeno, P.J., Bell, S.W., 2019. Extreme conditions in the Bering Sea (2017–2018):
record-breaking low sea-ice extent. Geophys. Res. Lett. 46 (15), 8952–8959. https://doi.org/10.1029/2019GL083816.
Stafford, K.M., 2019. Increasing detections of killer whales (Orcinus orca), in the PacificArctic. Mar. Mamm. Sci. 35 (2), 696–706. https://doi.org/10.1111/mms.12551.
Stevenson, D.E., Lauth, R.R., 2019. Bottom trawl surveys in the northern Bering Sea
indicate recent shifts in the distribution of marine species. Polar Biol. 42, 407–421.
https://doi.org/10.1007/s00300-018-2431-1.

Breaking low winter sea ice. Geophys. Res. Lett. 46 (16), 9833–9842. https://doi.
org/10.1029/2019GL083396.
Eisner, L., Hillgruber, N., Martinson, E., Maselko, J., 2013. Pelagic fish and zooplankton
species assemblages in relation to water mass characteristics in the northern Beringand southeast Chukchi seas. Polar Biol. 36 (1), 87–113. https://doi.org/10.1007/
s00300-012-1241-0.
Ershova, E.A., Hopcroft, R.R., Kosobokova, K.N., 2015a. Inter-annual variability ofsummer mesozooplankton communities of the western Chukchi Sea: 2004–2012.
Polar Biol. 38 (9), 1461–1481. https://doi.org/10.1007/s00300-015-1709-9.
Ershova, E.A., Hopcroft, R.R., Kosobokova, K.N., Matsuno, K., Nelson, R.J.,
Yamaguchi, A., Eisner, L.B., 2015b. Long-term changes in summer zooplankton
communities of the western Chukchi Sea, 1945–2012. Oceanography 28 (3),100–115. https://doi.org/10.5670/oceanog.2015.60.
Fukai, Y., Matsuno, K., Fujiwara, A., Yamaguchi, A., 2019. The community compositionof diatom resting stages in sediments of the northern Bering Sea in 2017 and 2018:
the relationship to the interannual changes in the extent of the sea ice. Polar Biol. 42,
1915–1922. https://doi.org/10.1007/s00300-019-02552-x.
Gall, A.E., Morgan, T.C., Day, R.H., Kuletz, K.J., 2017. Ecological shift from piscivorous
to planktivorous seabirds in the Chukchi Sea, 1975–2012. Polar Biol. 40 (1), 61–78.
https://doi.org/10.1007/s00300-016-1924-z.
Grace, J.B., 2006. Structural Equation Modeling and Natural Systems. Cambridge
University Press, Cambridge.
Grace, J.B., Anderson, T.M., Olff, H., Scheiner, S.M., 2010. On the specification of
structural equation models for ecological systems. Ecol. Monogr. 80 (1), 67–87.
https://doi.org/10.1890/09-0464.1.
Hunt, G.L., Stabeno, P., Walters, G., Sinclair, E., Brodeur, R.D., Napp, J.M., Bond, N.A.,
2002. Climate change and control of the southeastern Bering Sea pelagic ecosystem.
Deep-Sea Res. Part II Top. Stud. Oceanogr. 49 (26), 5821–5853. https://doi.org/
10.1016/S0967-0645(02)00321-1.
Hunt, G.L., Coyle, K.O., Eisner, L.B., Farley, E.V., Heintz, R.A., Mueter, F., Napp, J.M.,
Overland, J.E., Ressler, P.H., Salo, S., Stabeno, P.J., 2011. Climate impacts oneastern Bering Sea foodwebs: a synthesis of new data and an assessment of theOscillating Control Hypothesis. ICES J. Mar. Sci. 68 (6), 1230–1243. https://doi.org/
10.1093/icesjms/fsr036.
Huntington, H.P., Danielson, S.L., Wiese, F.K., Baker, M., Boveng, P., Citta, J., De
Robertis, A., Dickson, D., Farley, E., George, J.C., Iken, K., Kimmel, D., Kuletz, K.,
Ladd, C., Levine, R., Quakenbush, L., Stabeno, P.J., Stafford, K.M., Stockwell, D.,
Wilson, C., 2020. Evidence suggests potential transformation of the Pacific Arctic
ecosystem is underway. Nat. Clim. Change 10, 342–348. https://doi.org/10.1038/
s41558-020-0695-2.
Jay, C.V., Taylor, R.L., Fischbach, A.S., Udevitz, M.S., Beatty, W.S., 2017. Walrus haulout and in water activity levels relative to sea ice availability in the Chukchi Sea.
J. Mammal. 98 (2), 386–396. https://doi.org/10.1093/jmammal/gyw195.

7