Deep–Sea Research II 181-182 (2020) 104903

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

Spatial changes in the summer diatom community of the northern Bering
Sea in 2017 and 2018
Yuri Fukai a, *, 1, Yoshiyuki Abe b, c, Kohei Matsuno a, d, Atsushi Yamaguchi a, daFaculty/Graduate School of Fisheries Sciences, Hokkaido University, 3− 1− 1 Minato-cho, Hakodate, Hokkaido, 041-8611, Japan
Atmosphere and Ocean Research Institute, The University of Tokyo, 5− 1− 5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8564, Japanc
Research Development Section, Office for Enhancing Institutional Capacity, Hokkaido University, Kita-21, Nishi-10, Kita-ku, Sapporo, Hokkaido, 001-0021, Japand
Arctic Research Center, Hokkaido University, Kita-21, Nishi-11 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:
Northern Bering Sea
Phytoplankton community
Diatoms
Year-to-year changes

In recent years, the northern Bering Sea has experienced changes in the timing of sea-ice retreat and in hy­
drographic conditions during the summer. The influence of these environmental changes on the diatom com­munity has not been examined. In this study, we investigated the spatial changes in the diatom community of thenorthern Bering Sea during the summers of 2017 and 2018, and evaluated the effects of environmental vari­ability on these communities. We found that the diatom cell density and diversity varied with water masses. A
cluster analysis based on cell density revealed that the diatom communities were separated into four groups, andthat the distributions of three of these groups were different spatially between 2017 and 2018. In the Bering
Strait and the Chirikov Basin regions, the diatom communities differed between 2017 and 2018. In 2017, thesediatom communities were dominated by cold-water species such as Chaetoceros gelidus and Chaetoceros spp.
(subgenus Hyalochaetae), while in 2018, the community was dominated by cosmopolitan species such as Tha­lassionema nitzschioides and Chaetoceros spp. (subgenus Phaeoceros). NMDS and multiple regression analysis
indicated that the timing of the sea-ice retreat was the most important contributor to the differences in the
diatom community. In contrast, there was no year-to-year difference south of St. Lawrence Island, possibly
because nutrients were depleted and phytoplankton types other than diatoms were dominant.

1. Introduction
The northern Bering Sea is one of the most productive ocean regionsin the world (Springer and McRoy, 1993). Supported by the high pri­mary production, the area is important for higher trophic level species
such as sea birds and marine mammals (Springer et al., 1996). This re­gion is now facing drastic changes in sea-ice cover and hydrographic
conditions during summer (Grebmeier et al., 2015; Frey et al., 2018).
For example, reduced sea-ice cover in 2018 resulted in a diminisheddeep cold pool (<2 ◦ C) south of St. Lawrence Island, and groundfish
from the southeastern Bering Sea shifted northward., while the abun­dance of arctic species decreased in the region (Cornwall, 2019; Duf­
fy-Anderson et al., 2019). Given that sea-ice reduction is known to haveaffected some components of the northern Bering Sea, investigation ofother components is required to understand the effects of future changes(Huntington et al., 2020).
The northern Bering Sea is a shallow shelf region. This region has acomplicated hydrographic environment due to the inflow of multiple
currents with different hydrographic features. The mixing of these wa­
ters results in complex hydrographic environments that affect the dis­tribution of phytoplankton communities (Giesbrecht et al., 2019). In thenorthern Bering Sea, phytoplankton supports a high level of primary
production in the upper mixed layer; most of this production settles tothe seafloor due to low zooplankton grazing pressure (Grebmeier et al.,1988).
Diatoms play an important role as primary producers in high latitude
marine ecosystems. A large diatom bloom occurs in the northern Bering
Sea from the late spring to early summer, when the sea ice is melting andthe light limit is diminishing; the chlorophyll a concentration can exceed
8 μg L− 1 during these blooms (Springer and McRoy, 1993). During the* Corresponding author.
E-mail address: fukai@ees.hokudai.ac.jp (Y. Fukai).
1
Present address: Division of Earth System Science, Graduate School of Environmental Science, Hokkaido University, Kita-10 Nishi-5 Kita-ku, Sapporo, Hokkaido,
060-0810, Japan.
https://doi.org/10.1016/j.dsr2.2020.104903
Received 24 December 2019; Received in revised form 9 October 2020; Accepted 12 November 2020
Available online 27 November 20200967-0645/© 2020 Elsevier Ltd. All rights reserved.

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spring Arctic bloom, Chaetoceros gelidus sometimes dominates (vonQuillfeldt, 2000; Sergeeva et al., 2010). The composition of diatom
communities varies among the different hydrographic environments(Taniguchi et al., 1976; Sergeeva et al., 2010). Diatoms constitute animportant taxon in this marine ecosystem and require evaluation tounderstand how they respond to environmental change.
From 1978 to 2012, the timing of the sea-ice retreat (TSR) south of
St. Lawrence Island, in the Chirikov Basin and in the Chukchi Sea has
become earlier (Grebmeier et al., 2015; Frey et al., 2018). In thenorthern Bering Sea, the magnitude and timing of the phytoplankton
bloom varies with the timing of the spring sea-ice retreat (Fujiwara
et al., 2016). In 2018, the TSR was approximately two weeks earlier thanit was during the previous year; the magnitude of the ice algal bloom
was small and zooplankton abundance decreased (Cornwall, 2019;
Fukai et al., 2019). However, despite the importance of diatoms forprimary production, there is no information on how the diatom com­munity responds to changes in sea-ice dynamics in the northern BeringSea.
The purpose of our paper is to examine the phytoplankton commu­
nities, particularly with a focus on the diatom communities of thenorthern Bering Sea from 62◦ N to the Bering Strait. To this end, we
describe the species composition of diatoms, and test three hypotheses:
1) that the cell density and species composition of the diatom commu­
nity differ by water mass, 2) that the diatom community differed be­
tween 2017 and 2018, and 3) that the hydrography, including sea-ice
condition before sampling, will affect the diatom community.

2.2. Sea ice
Data on sea ice concentration (SIC) were obtained from theAdvanced Microwave Scanning Radiometer 2 (AMSR2) to evaluate theextent of the sea ice. These AMSR2 data were supplied by the Japan
Aerospace Exploration Agency via the Arctic Data archive System (ADS)(https://ads.nipr.ac.jp/), through the cooperation of the National
Institute of Polar Research and JAXA. We used the SIC data after
calculating a 5-day moving average. Sea-ice covered regions were
defined as having a SIC >20%. In addition, the TSR was defined as thelast date when the SIC was at 20% prior to the observed annual sea ice
minimum across the study region.
2.3. Physical Oceanography
Conductivity-temperature-depth (CTD) casts were conducted at 40
stations in 2017 and 28 stations in 2018 to obtain vertical profiles of thetemperature, salinity, and chlorophyll a fluorescence (see Appendix
Fig. 1). We used a CTD (SBE911, Sea-Bird Electronics, Inc.) calibrated
prior to the cruise. The mixed-layer depth was defined as the depth
where density was 0.10 kg m3 greater than the value at 5 m depth(Danielson et al., 2011).
2.4. Nutrients
At 26 of the CTD stations (14 stations in 2017 and 12 stations in2018), water samples for nutrient analysis were collected from 4–6
layers every 10 m from the surface to 5 m above the seafloor using abucket and Niskin bottles (Fig. 1). The obtained unfiltered nutrient
samples (n = 128) in Spitz tubes were frozen on board at − 80 ◦ C. In the
shore-based laboratory, the major nutrients (NO2–N + NO3–N, NH4–N,
PO4–P, and Si (OH)4) were measured by colorimetric methods using a
QuAAtro 2-HR system certified with standard reference materials fornutrient analysis (KANSO, standard Lot BT, BZ, Osaka, Japan) inaccordance with “The GO-SHIP Repeat Hydrography Manual” (Hydeset al., 2010).

2. Materials and methods
2.1. Study area
Sampling was conducted along the northern Bering Sea shelf from
July 9–21, 2017 and July 2–12, 2018 during the 40th and 56th cruises,
respectively, of the T/S Oshoro-Maru of Hokkaido University (Fig. 1).
The study areas were the waters south of St. Lawrence Island, theChirikov Basin (from the north of St. Lawrence Island to the south ofBering Strait), and the Bering Strait.

2.5. Phytoplankton
Water samples for phytoplankton counts were collected from theFig. 1. Location of stations in the northern Bering Sea from July 9–21, 2017 and July 2–12, 2018. The numbers indicate the station ID. The open and solid circles
indicate stations with hydrographic observations only taken by CTD and those with water sampling and hydrographic observations, respectively.
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same stations and layers as the nutrient samples. A total of 141 phyto­plankton samples was collected and preserved as follows: in 2017, 500
mL water samples were concentrated 50-fold using a nucleopore filter(3.0 μm) before being preserved with glutaraldehyde at a final con­centration of 1%. Note that the diatoms and dinoflagellates addressed inthis study experience little damage from filtering (Dahl and Naustvoll,
2010). In 2018, 1 L of each water sample was preserved on board withglutaraldehyde at a final concentration of 1%. The samples were thensettled and concentrated 24- to 33- fold using siphon tubes in the land
laboratory.
Aliquots (1 mL) of the concentrated samples were transferred to a
glass slide to count and identify the diatoms and dinoflagellates with aninverted microscope at 200–600× magnification. The diatoms and di­noflagellates were counted and identified from approximately 300 cells.
When the cell number count was less than 300 cells, the minimum
numbers were 18 cells in 2017 and 21 cells in 2018. In addition, thedetection limits were 20 cells L− 1 in 2017 and 30 cells L− 1 in 2018,suggesting that there was not much difference (ability to detect low
numbers of cells in a sample) between the years. As explained in Hasle
and Syvertsen (1997) and Hoppenrath et al. (2009), the diatoms wereidentified to the species or genus level and the dinoflagellates wereidentified to the genus level. Distinguishing Cylindrotheca closterium
from Nitzschia longissima was difficult (Hasle and Syvertsen, 1997), so
these species were treated as Cy. closterium. In addition, C. convolutus,
C. concavicornis and C. borealis were nearly indistinguishable because
they were damaged by current transportation, as mentioned by Tani­
guchi et al. (1976), so they were collectively counted and identified as
C. convolutus/concavicornis/borealis.
Using the counting data, the diversity of diatoms was evaluated by
Shannon-Wiener index (H’).
∑n
n
′
H =−
× ln
Ni
Ni

2.6. Statistical analyses
Differences in phytoplankton cell density among the water masses in
the upper mixed-layer was tested by Mann-Whitney U test. For com­
parison of the diatom community among the water masses, we per­formed cluster analyses, nonmetric multidimensional scaling (NMDS)
ordinations, and multiple regression analyses for each year or with eachwater mass, but the results could not be interpreted (cf. Appendix Figs. 2
and 3). Also, to compare the diatom diversity (H’) among the water
masses, a one-way analysis of variance (ANOVA) was used. If the
ANOVA identified statistically significant differences (p < 0.05), a post
hoc Tukey-Kramer test was used to clarify the interactions among thewater masses.
Differences between the years of the NO2–N + NO3–N concentration,
which was likely to be a limiting factor among nutrients, were comparedby a Mann-Whitney U test for each region. Similarly, differences of thephytoplankton cell density between 2017 and 2018 in the Bering Strait,and of diatom diversity (H’) between 2017 and 2018 were tested using a
Mann-Whitney U test.
To compare the diatom community between 2017 and 2018, we first
performed a cluster analysis based on the cell density within each watermass (Appendix Fig. 2). Several patterns of the cluster analysis were
tested in each year, by water mass, and by depth (cf. Appendix Figs. 2
and 3). The results of these tests were uninterpretable, so we used an­alyses based on all diatom samples from a given year. Thus, this analysis
focused on describing year-to-year changes in the diatom community(species composition) between 2017 and 2018. To reduce the bias forabundant species, the cell density data (X: cells mL− 1) for each species√̅̅̅̅were transformed to 4 X prior to cluster analysis (Quinn and Keough,2002). The similarities between samples were examined using the
Bray-Curtis index based on the differences in the species composition. Togroup the samples, the similarity indices were coupled using hierar­
chical agglomerative clustering with a complete linkage method (anunweighted pair group method using the arithmetic mean) (Field et al.,1982).
To delineate the sample groups on a two-dimensional map, NMDS

where n is the cell density (cells mL− 1) of ith species and Ni is the total
diatom cell density (cells mL− 1) at each station (Shannon and Weaver,1949).

Fig. 2. Cross-sectional distributions of the temperature, salinity, and fluorescence in the northern Bering Sea in 2017 (upper) and 2018 (lower).
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Fig. 3. T-S diagrams of all the stations in 2017 (upper) and 2018 (lower). Note that the symbols of the stations vary with the geographical location.

ordination was conducted. Thereafter, multiple regression analyses (Y= aX1+ bX2+c, where Y is the environmental variable and X1 and X2
are axes 1 and 2 of NMDS, respectively) were performed to clarify which
environmental variables (temperature, salinity, chlorophyll a fluores­
cence, concentrations of NO2–N + NO3–N, NH4–N, dissolved inorganic
nitrogen (DIN), PO4–P, and Si (OH)4, the ratio of the DIN concentration
to that of PO4–P (N:P ratio), the timing of the sea-ice retreat (TSR),
observation date, and sampling depth) had significant relationships withthe phytoplankton groups.
Furthermore, to test intergroup differences in the diatom cell density
and hydrographic environments (temperature, salinity, chlorophyll afluorescence, concentrations of NO2–N + NO3–N, NH4–N, DIN, PO4–P,
and Si (OH)4, the N:P ratio, and the TSR), a one-way ANOVA and a post
hoc Tukey-Kramer test were used. All the analyses were conducted with
PRIMER 7 (PRIMER-E Ltd.) or Stat View v5 (SAS Institute Inc.).

South of St Lawrence Island, in 2017, sea ice was completely gone by
May 3, whereas in 2018, ice left over a month earlier, on March 24. In
2017, the first day in the study area when the Chirikov Basin had an ice
concentration <20% was on April 5, whereas, in 2018, open water was
first detected on March 25, 11 days earlier.
3.2. Hydrography and nutrient chemistry
We identified four water masses that differed in physical character­istics; Bering Chukchi Summer Water (BCSW) (moderate/cold with highsalinity), Bering Chukchi Winter Water (BCWW) (cold with highsalinity), Alaskan coastal water (ACW) (warm with low salinity), andMelting Water (MW) (cold with low salinity) (Danielson et al., 2017)(Figs. 2 and 3). The water masses (>12 ◦ C in ACW and >7 ◦ C in BCSW)
that were unidentified by Danielson et al. (2017) were defined as ACWor BCSW based on their salinity (Fig. 3).
In 2017, the BCWW (<0 ◦ C) was present at the bottom south of St.
Lawrence Island (Fig. 4). The BCSW was present throughout the watercolumn in the Bering Strait and the northern Chirikov Basin. By contrast,in the eastern coastal area and south of 65 ◦ N in the Chirikov Basin, theACW was present in the surface layer and the BCSW was observed in thelower layer (Fig. 4).
In 2018, the BCWW (<0 ◦ C) was not present at the bottom in theregion South of St. Lawrence Island (Fig. 4). The BCSW was present
throughout the water column in the Bering Strait and the Chirikov Basin,
except for the eastern coastal area where the ACW was observed in thesurface layer (Fig. 4).
The distribution of water masses differed between the years. In 2017,

3. Results
3.1. Sea ice
In 2017, the study region south of St. Lawrence Is. was firstcompletely ice-covered on January 19, and in 2018, sea-ice cover was
not complete until February 5. The sea ice first covered the Bering Strait
and the Chirikov Basin, except for the most eastern station, on January
11, 2017 and December 28, 2018. In some stations in 2018 (St. 11 andSt. 14), the SIC was repeatedly over and below 20%. In the most eastern
stations in the Chirikov Basin (St. 5 in 2017 and St. 19 in 2018), the SIC
exceeded 20% on December 8, 2016 and 2017.
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Fig. 4. Spatial distribution of water masses as defined by Danielson et al. (2017). BCSW: Bering-Chukchi Summer Water, ACW: Alaskan Coastal Water, MW: Melting
Water, and BCWW: Bering-Chukchi Winter Water.

ACW was present in the surface layer of the southwestern Chirikov Basinand there was BCSW at the bottom south of St. Lawrence Island; thesewere not present in 2018.
In both years south of St. Lawrence Island, fluorescence above thepycnocline (avg = 0.07 and 0.16 in 2017 and 2018, respectively) was
lower than that below the pycnocline (avg = 0.39 and 0.60 in 2017 and2018, respectively) (Fig. 5). In Bering Strait, fluorescence was similar inboth years (2017, avg = 0.90: 2018, avg = 0.78) (Fig. 5). In the Chirikov
Basin, fluorescence above the pycnocline was higher in 2017 (avg =0.69) than in 2018 (avg = 0.26), while, below the pycnocline fluores­
cence was similar in both years (Fig. 5).
To the south of St. Lawrence Island, the NO2–N + NO3–N was
depleted in the upper mixed layer in both years, but it was high below
the pycnocline (Fig. 5) with no significant differences between the years(U test, p > 0.05). In the Bering Strait, NO2–N + NO3–N did not differ
significantly between years (U test, p > 0.05) (Fig. 5). In this region, thelowest concentration of nitrate plus nitrite was at the eastern station (st.
5) in 2017, whereas in 2018, the highest concentrations were found in
the northern Bering Strait (st. 29). In the Chirikov Basin, these nutrientswere not significantly different between the years (U test, p > 0.05); in
both years, the lowest concentrations were detected at the eastern sta­tions (st. 11 in 2017 and st. 19 in 2018).
Concentrations of other nutrients were generally similar between
years at the same station. In both years in each region, NH4–N concen­
trations were similar (Fig. 5). Likewise, the PO4–P concentrations did
not vary between years within regions; the highest value detected was in
the Bering Strait in 2017 (st.1, 6.62 μM) (Fig. 5). We found high con­centrations of Si (OH)4 over major portions of the study region in both
years, except for some stations in 2018 in the eastern Chirikov Basin (st.
11 in 2018) and the south of St. Lawrence Island (sts. 4, 6, 8 in 2018),
where this nutrient was not detectable (Fig. 5). In both years, the N:P
ratio was below 16 throughout most of the study area.

cell densities below the pycnocline did not differ among water masses(one-way ANOVA, p > 0.05). In both 2017 and 2018, the highest celldensities were observed in the Bering Strait (2017: 1.6 × 106 cells L− 1
and in 2018: 3.4 × 105 cells L− 1) with diatoms and dinoflagellates in thisregion more abundant in 2017 (stations 1–3) than in 2018 (stations
27–30) (Mann-Whitney U test, p < 0.05) (Fig. 6).
3.3.2. Phytoplankton species and their diversity
A total of 29 genera and 30 species of diatoms (centric diatoms: 19
genera and 25 species and pennate diatoms: 10 genera and 5 species)and 6 genera and 5 species of dinoflagellate were observed over the twoyears (Table 1).
The diversity of the diatoms (H′ ) ranged from 0–3.56 in 2017 and
0.36–2.98 in 2018. The H’ varied among water masses, with values
significantly higher in BCWW and BCSW than in ACW (one-way ANOVA,
p < 0.05). There were no significant differences in the diversity of di­atoms between the years (Mann-Whitney U test, p > 0.05).
3.3.3. Phytoplankton community by cluster analysis
Phytoplankton communities were classified into four groups (A–D)
by a cluster analysis at 27% and 37% similarity levels (Fig. 7a). Group A
was low-density (1.3 × 103–1.3 × 105 cells L− 1, avg = 4.6 × 104 cellsL− 1) and was composed primarily of C. gelidus (Fig. 7b). Group B had thehighest cell density (1.6 × 103–1.6 × 106 cells L− 1, avg = 2.5 × 105 cellsL− 1), and Hyalochaetae such as C. gelidus, C. furcellatus, and C. debilis
were dominant (64%) (Fig. 7b). The cell density of C. gelidus, C. diadema,
and Chaetoceros spp. in group B was significantly higher than it was ingroups C and D. The cell density of group C was nearly as low as it was ingroup A (3.3 × 103–1.2 × 105 cells L− 1, avg = 3.4 × 104 cells L− 1);
however, the community composition was very different. Phaeoceros
such as C. convolutus/concavicornis/borealis had a relatively high density
in group C (13%), and the pennate diatoms such as Thalassionema nitz­
schioides and Cylindrotheca closterium had a significantly higher density
in group C than it did in the other groups (Table 2). Group D had thelowest cell density (3.6 × 102–4.4 × 105 cells L− 1, mean 2.5 × 104 cellsL− 1) and Leptocylindrus spp. dominated (85%).
The phytoplankton communities were different in each region(Fig. 7c). In both years south of St. Lawrence Island, group D was present
in the upper layer (0 m or 0–20 m) and groups A, B, and C occurred in the

3.3. Phytoplankton community
3.3.1. Cell density
In the upper mixed layer, the cell density of diatoms and di­noflagellates was significantly different in each water mass, and it was
higher in BCSW than in ACW (Mann-Whitney U test, p < 0.05), whereas,5

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Fig. 5. Cross-sectional distributions of nutrient (NO2–N + NO3–N, NH4–N, PO4–P, and Si (OH)4) concentrations in the northern Bering Sea in 2017 (upper) and2018 (lower).

deeper layers. From 65◦ N (the Chirikov Basin) to the Bering Strait, thedistribution varied across years; group B was observed throughout thearea in 2017, but group C was observed in 2018. In these regions (theChirikov Basin and the Bering Strait), the spatial distribution of water
masses and phytoplankton community groups as determined by thecluster analysis did not match (Figs. 4 and 7c), thus, the groups weredifferent between years even though BCSW was occupied in both years.

In addition, the one-way ANOVA and Tukey-Kramer test indicatedthat the PO4 and DIN concentrations and the N:P ratio did not differ
significantly among phytoplankton groups (one-way ANOVA, p > 0.05).
However, the other hydrographic variables differed between groups B
and D. Group D had a higher temperature and lower salinity, nutrients(NO2+NO3,PO4–P,NH4–N,Si (OH)4), and chlorophyll a fluorescencethan the other groups. Groups B and C differed only in their salinity,
chlorophyll a fluorescence and TSR (Table 3). Especially note thattemperature was not significantly different between Group B and C.

3.3.4. Relationships between phytoplankton communities andenvironmental factors
On the NMDS ordination, phytoplankton plots had significant re­lationships with various environmental variables (p < 0.05), including
chlorophyll a fluorescence (r2 = 0.30), TSR (r2 = 0.28), observation date(r2 = 0.16), sampling depth (r2 = 0.13), salinity (r2 = 0.11), Si (OH)4 (r2= 0.08) and NO2+NO3 (r2 = 0.05) (Fig. 7d), but the other parameters
did not. Especially note that temperature did not have a significant
relationship with phytoplankton plots (p > 0.05), and that the contri­
bution of TSR to diatom groups was the highest among environmental
factors except for chlorophyll a fluorescence.

4. Discussion
4.1. The influence of water masses
Diatom community structure (i.e. species composition and their celldensity) was not consistently correlated with water mass during summer
except for the stations south of St. Lawrence Island. However, there weresignificant differences among the water masses in the cell density in theupper mixed layer and in diatom diversity. These differences may have6

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Fig. 6. Horizontal distribution of the average phytoplankton cell density in 2017 (left) and 2018 (right). The circles indicate the mean cell density in thewater column.

masses was related to difference in nutrient concentrations between
water masses, especially the DIN concentrations. As mentioned above,
DIN was the limiting factor in the nutrient concentration and thus, in thewater masses with high DIN such as BCWW and BCSW, competition for
DIN may have been minimal, thus resulting in many diatom species
surviving in these waters.

Table 1
List of phytoplankton species identified in the water samples that were collectedfrom the northern Bering Sea and Bering Strait in July 2017 and 2018.
Class Bacillariophyceae
Order Centrales
Actinocyclus spp.
Actinoptychus spp.
Attheya spp.
Bacterosira bathyomphala
Chaetoceros conturtus
C. convolutus/concavicornis/
borealis
C. curvicetus
C. danicus
C. debilis
C. decipiens
C. diadema
C. didymus
Order Pennales
Asteroplanus karianus
Asterionellopsis glacialis
Cylindrotheca closterium
Thalassionema nitzschioides

C. furcellatus
C. gelidus
C. laciniosus
C. lorenzianus
C. mitraC. subtilis

Ditylum spp.
Eucampia spp.
Lauderia annulata
Leptocylindrus
danicusL. minimus
Odontella aurita

C. teres
Chaetoceros spp.
Corethron hystrix
Coscinodiscus spp.
Detonula pumuila
Dactyliosolen
fragilissimus

Paralia sulcata
Rhizosolenia spp.
Skeletonema spp.
Stephanopyxis turris
Thalassiosira spp.
Other centric
diatoms

Pauelia taeniata
Pleurosigma spp.
Pseudo-nitzschia spp.

Navicula spp.
Nitzschia spp.
Other pennate
diatoms

Fragilariopsis spp.

4.2. The phytoplankton community of the south of St. Lawrence Island
In contrast to the Chirikov Basin and the Bering Strait regions, southof St Lawrence Island year-to-year changes in the phytoplankton com­munity were not observed. In the upper mixed layer south of St. Law­
rence Island, nutrient-poor ACW was present in both years, and the DIN
concentrations and, at some stations the Si (OH)4 concentrations, were
too low to support diatom growth (Justic et al., 1995). The lack of DIN
may have resulted in the dominance of phytoplankton group D, which
was predominately non-diatom species that can thrive in low nutrient
conditions (Parsons et al., 1978). The timing and magnitude of thespring phytoplankton bloom in the region south of St. Lawrence Islanddiffered in 2017 and 2018, and we hypothesize that this was due todifferences in the TSR (Fukai et al., 2019; Hirawake, per. comm.). Thus,
after the nutrients were depleted from the upper mixed layer, phyto­plankton, other than diatoms, dominated. A similar succession may
occur in the eastern Bering Sea where coccolithophore blooms during
the summer were reported after the diatom bloom (Stockwell et al.,
2001; Iida et al., 2002). Coccolithophore blooms were also observed bysatellite observation from the eastern Bering Sea to south of St. Lawrence
Island after 2000s (Harada et al., 2012). In our study, it is not clear
whether the phytoplankton community changed at the species level,
because dinoflagellates and phytoplankton other than diatoms were not
identified to the species level.

Class Dinophyceae
Alexandrium spp.
Ceratium spp.
Prorocentrum triestinum

Protoperidinium spp.
Heterocapsa triquetra
Dinophysis acuta

Dinophysis norvegica
Dinophysis rudgei
Otherdinofalagellates

been related to differences in the nutrient content of the water masses.
Differences in characteristics of water masses are known to influence
phytoplankton cell density (Coachman et al., 1975; Danielson et al.,2017; Giesbrecht et al., 2019). The N:P ratio was below 16 throughout
the study area, which indicated that the DIN was the limiting nutrient
concentration, and differences in DIN concentrations in the various
water masses was one of the most important factors for the growth ofphytoplankton upper the mixed layer over the study area. Thus,
phytoplankton cell density was higher in the upper mixed layer in the
BCSW, including the nutrient-rich AW, than in the nutrient-poor ACW(Coachman et al., 1975; Danielson et al., 2017).
The diversity of diatoms indicated by H’ was higher in BCWW andBCSW than in ACW in the same way as the DIN concentration was. We
suggest that the differences in the diversity of diatoms between water

4.3. The changes in the summer diatom community in the Chirikov Basinand the Bering Strait, 2017–2018
From 65◦ N (the Chirikov Basin) to the Bering Strait, between 2017and 2018, phytoplankton cell density declined and community structurechanged. In this region in 2017, the highest cell density (1.6 × 106 cellsL− 1) was nearly the same as that reported by Sergeeva et al. (2010) for
July 2003 and by Giesbrecht et al. (2019) (the highest density wasapproximately 106 cells L− 1). In 2018, the highest cell density (3.4 ×
105 cells L− 1) was only 34% of the values observed in 2017.
With respect to the phytoplankton community, in 2017, group B was
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Deep-Sea Research Part II 181-182 (2020) 104903

Fig. 7. (a) Results of the cluster analysis
based on the phytoplankton cell density
found by Bray-Curtis similarity. (b) Species
composition and cell density of each group.
(c) Spatial distribution of the phytoplankton
community in the northern Bering Sea dur­ing the summers of 2017 (left) and 2018(right). (d) Nonmetric multidimensional
scaling plots of the four groups, with arrows
and percentages indicating the directions of
the environmental parameters and the coef­
ficient of determination (r2), respectively.
Obs. date: observation date, Si: silicate, Sal:
salinity, N: nitrate and nitrite, TSR: thetiming of sea ice retreat, Fluor: fluorescence,and Dep: sampling depth.

Table 2Comparison of phytoplankton species in the phytoplankton community groups (A–D). The values are given as the mean cell density ( ×103 cells L− 1) and standard deviation in each group. The numbers in parentheses indicate the number of stations. The differences among
the phytoplankton communities were evaluated by a one-way ANOVA and a Tukey-Kramer test. Any groups not connected by theunderlines are significantly different (*: p < 0.05, **: p < 0.01, and ***: p < 0.005).

widely distributed and had a high cell density that was dominated (64%)
by Hyalochaetae such as C. gelidus and C. furcellatus. C. gelidus andC. furcellatus are cold-water species and are common in the Arctic (Hasle
and Syvertsen, 1997; Hoppenrath, 2009). These species are typicallyfound in the Chukchi Sea adjacent to the study area (von Quillfeldt et al.,2003). In 2018, Group C, which was mostly composed of Thalassionema

nitzschioides, was widely distributed. The abundance of Chaetoceros spp.
waslow(20%),
andPhaeoceros
suchas
C. convolutus/concavicornis/borealis made up most of the Chaetoceros
spp. T. nitzschioides and C. convolutus are known as cosmopolitan species;
the former does not occur in the high Arctic and the latter is common intemperate waters (Hasle and Syvertsen, 1997; Hoppenrath, 2009).
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Table 3
Hydrographic environmental factors among the phytoplankton communities (A–D). The values are given as the average and standard
deviation of each factor. The numbers in parentheses indicate the number of stations. The differences among the phytoplankton
communities were evaluated by a one-way ANOVA and a Tukey-Kramer test. Any groups not connected by the underlines are signifi­
cantly different (*: p < 0.05, **: p < 0.01, and ***: p < 0.005).

T. nitzschioides has also been reported in temperate water after themelting of the sea ice (Neeley et al., 2018), and it is a characteristic
species of the Pacific-Arctic region in the autumn (Matsuno et al., 2014).
These results suggest that between 2017 and 2018, the summer diatom
community in the Bering Strait and the Chirikov Basin changed fromcold water to cosmopolitan species.
Interestingly, the NMDS, the multiple regressions, and the one-way
ANOVA did not suggest any significant differences in temperature be­
tween 2017 and 2018 in the region north of 65◦ N. By contrast, the TSR
was the most important contributing factor among the environmental
factors for explaining the differences in the diatom communities be­
tween the two years. There was also a significant difference in the TSR
between group B in 2017 and C in 2018. Because the TSR differed be­
tween 2017 and 2018 in the northern Bering Sea (Cornwall, 2019; Fukai
et al., 2019; Grebmeier et al., 2019), the effect of the TSR on the summer
diatom community cannot be ignored.
The TSR affects the magnitude and timing of the spring bloom in theseasonal sea ice area (Hunt et al., 2002; Fujiwara et al., 2016). The
magnitude of the phytoplankton bloom in the open water is usually large
when the TSR is early (Hunt et al., 2002; Fujiwara et al., 2016). In 2018,
when the TSR was early, large increases in sea surface chlorophyll a in
the Chirikov Basin were observed with satellite remote sensing fromearly to late May after the sea ice had completely retreated (Hirawake,per. comm.). Hence, in 2018, when the TSR was early, the available
nutrients may have been consumed by the large open-water bloom
during early spring after the sea ice retreat, resulting in the wide dis­tribution of phytoplankton community group C during summer, with alow cell density. In summer 2018, the cosmopolitan species compositionof group C, some of which, such as T. nitzschioides, are species charac­
teristic of the autumn (Matsuno et al., 2014), and might been the resultof the early TSR in 2018 and an early depletion of nutrients.

the diatom community between 2017 and 2018 were found, dependingon the region examined. South of St. Lawrence Island, we found nochanges in the diatom community between 2017 and 2018. Nitrate andnitrite were depleted in the upper mixed layer in both years, andphytoplankton types other than diatoms dominated this region. Since wefocused our study on diatoms, it is possible that there were interannualchanges in other elements of the phytoplankton community that we did
not detect.
In the Chirikov Basin and Bering Strait, diatom communities in 2017and 2018 differed, even though the same water masses were present inboth years. The TSR was much earlier in 2018 than in 2017, thoughsummer water temperatures were similar in the two years. Since
nutrient concentrations were lower in 2018, we hypothesize that the
open water bloom in 2018 may have depleted the nutrients, with theresult that in 2018 several sub-arctic or cosmopolitan species wereabundant compared to 2017, when arctic species predominated. It will
be important to evaluate the influence of the changing diatom com­munity on the marine ecosystem of the northern Bering and Chukchi
Seas. To this end, focus on the entire phytoplankton community,
including dinoflagellates, coccolithophores and others, will be required.
Author statement
Yuri Fukai: Formal analysis, Investigation, Writing - Original Draft,
Visualization, Yoshiyuki Abe: Investigation, Kohei Matsuno: Conceptu­
alization, Writing - Review & Editing, Supervision, Project administra­tion, Atsushi Yamaguchi: Conceptualization, Investigation, Project
administration.
Declaration of competing interest
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.

5. Conclusions
This study described and compared northern Bering Sea diatom
communities in the summers of 2017 and 2018. The diatom cell densityand diatom diversity differed by water mass. Year-to-year differences in9

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Deep-Sea Research Part II 181-182 (2020) 104903

Acknowledgments

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We thank the captain, officers, crew, and researchers on board the T/
S Oshoro-Maru, of Hokkaido University, for their contributions during
field sampling. We thank Toru Hirawake, chief scientist of the cruises.
We also thank two anonymous reviewers for their helpful comments.
This study was conducted by the Arctic Challenge for Sustainability(ArCS) project and Arctic Challenge for Sustainability II (ArCS II) proj­
ect. The ADS dataset is archived and was provided by the Arctic Dataarchive System (ADS), which was developed by the National Institute ofPolar Research. Part of this study was supported through Grants-in-Aid
for Scientific Research 17H01483 (A), 19H03037 (B), 20H03054 (B),
18K14506 (Early-Career Scientists) and 20K20573 (Challenging
Research (Pioneering)) from the Japan Society for the Promotion of
Science.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.dsr2.2020.104903.
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