RESEARCH ARTICLE
10.1029/2021JC017223

Special Section:
Uncovering the hidden links
between dynamics, chemical,
biogeochemical and biologicalprocesses under the changing
ArcticKey Points:• D iatom resting stage assemblages
were quantified and theirrelationship to the sea-ice dynamics
in the Pacific Arctic region was
explored• Diatom composition follows spatial
patterns that depend upon thevariable timing of sea-ice retreat andaccompanying light conditions• Abundance of resting stage diatom
cells in sediments varied by several
orders of magnitude across the study
regionCorrespondence to:Y. Fukai,
fukai@ees.hokudai.ac.jp
Citation:
Fukai, Y., Matsuno, K., Fujiwara, A.,
Suzuki, K., Richlen, M. L., Fachon,E., & Anderson, D. M. (2021). Impactof sea-ice dynamics on the spatial
distribution of diatom resting stages
in sediments of the Pacific Arctic
region. Journal of Geophysical Research:
Oceans, 126, e2021JC017223. https://doi.org/10.1029/2021JC017223
Received 9 FEB 2021
Accepted 14 JUN 2021

Impact of Sea-Ice Dynamics on the Spatial Distribution of
Diatom Resting Stages in Sediments of the Pacific Arctic
RegionYuri Fukai1,2 , Kohei Matsuno2,3 , Amane Fujiwara4 , Koji Suzuki1,5
Mindy L. Richlen6, Evangeline Fachon6, and Donald M. Anderson6,

1

Graduate School of Environmental Science, Hokkaido University, Sapporo, Japan, 2Faculty/Graduate School ofFisheries Sciences, Hokkaido University, Hakodate, Japan, 3Arctic Research Center, Hokkaido University, Sapporo,
Japan, 4Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan, 5Faculty of Environmental Earth
Science, Hokkaido University, Sapporo, Japan, 6Biology Department, Woods Hole Oceanographic Institution, Woods
Hole, MA, USA

Abstract The Pacific Arctic region is characterized by seasonal sea-ice, the spatial extent and durationof which varies considerably. In this region, diatoms are the dominant phytoplankton group during springand summer. To facilitate survival during periods that are less favorable for growth, many diatom species
produce resting stages that settle to the seafloor and can serve as a potential inoculum for subsequent
blooms. Since diatom assemblage composition is closely related to sea-ice dynamics, detailed studies ofbiophysical interactions are fundamental to understanding the lower trophic levels of ecosystems in the
Pacific Arctic. One way to explore this relationship is by comparing the distribution and abundance of
diatom resting stages with patterns of sea-ice coverage. In this study, we quantified viable diatom restingstages in sediments collected during summer and autumn 2018 and explored their relationship to seaice extent during the previous winter and spring. Diatom assemblages were clearly dependent on thevariable timing of the sea-ice retreat and accompanying light conditions. In areas where sea-ice retreatedearlier, open-water species such as Chaetoceros spp. and Thalassiosira spp. were abundant. In contrast,
proportional abundances of Attheya spp. and pennate diatom species that are commonly observed in seaice were higher in areas where diatoms experienced higher light levels and longer day length in/under thesea-ice. This study demonstrates that sea-ice dynamics are an important determinant of diatom species
composition and distribution in the Pacific Arctic region.
Plain Language Summary The Pacific Arctic region is characterized by seasonal sea-ice,and there is considerable interannual variation in the timing and quality of ice presence. In this region,
diatoms are the dominant phytoplankton group during spring and summer. Under conditions unfavorable
for growth, such as low light or limiting nutrients, many diatom species produce resting stages thatare similar to “seeds” of plants. These resting stages settle to the seafloor and can reflect the diatom
assemblages in the overlying water column. Since diatom species distribution is closely related to seaice dynamics, detailed studies of this relationship are fundamental to understanding the basis of marine
ecosystems in the Pacific Arctic region. In this study, we explored the relationship by comparing the
distribution of diatom resting stage assemblages with patterns of sea-ice coverage. Diatom assemblages
detected in sediments were dependent on the variable timing of the sea-ice retreat and accompanying light
conditions. In areas where sea-ice retreated earlier, open-water species were abundant, while proportional
abundances of ice-associated diatoms were higher in areas where diatoms experience favorable light
conditions in/under the sea-ice. This study demonstrates that sea-ice dynamics are an important
determinant of diatom composition in the Pacific Arctic region.

1. Introduction© 2021. American Geophysical Union.
All Rights Reserved.

FUKAI ET AL.

The Pacific Arctic region extends from the northern Bering Sea to the Chukchi and Beaufort Seas. Within
this region, the northern Bering and Chukchi Seas display among the highest daily rates of productivity inthe world (Springer et al., 1996). Phytoplankton are responsible for high primary productivity in the euphotic layer and delivery of particulate organic carbon (POC) to the benthos. The sinking POC flux measured

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in 2018 in the northern Bering and Chukchi Seas was the among highest ever documented across global
oceans (O'Daly et al., 2020). This POC supports patchy distributions of high benthic biomass known as“benthic hotspots” (Grebmeier et al., 1988, 2006). In contrast, primary productivity in the southwestern
Beaufort Sea is low (Frost & Lowry, 1984).
In the Arctic Ocean, ice algae production occurs in and under the sea ice, and is followed by phytoplankton
blooms during the summer retreat of sea-ice (Horner, 1984; Horner & Schrader, 1982). Mean daily watercolumn integrated primary productivity in the southwestern Beaufort Sea is about half of that of the Chukchi Sea, even during peak periods in June and July (Hill et al., 2018). Overall, annual primary production ismuch higher in the Chukchi shelf than on the Beaufort shelf (Grebmeier et al., 2006).
The Pacific Arctic region is characterized by the presence of seasonal sea-ice, which varies considerably
in extent and duration from year to year. The extent of sea-ice has been shown to influence regional phytoplankton assemblages (Neeley et al., 2018), but this relationship is not fully understood. Sea-ice decline
has been reported in the region (Frey et al., 2018; Grebmeier et al., 2015; Markus et al., 2009), and Arrigo
et al. (2008) used satellite observations to show that this decline was associated with increasing annual
primary production. However, changes in phytoplankton assemblages and particularly in ice-associatedassemblages, cannot be evaluated by satellite observations only, necessitating field-based studies to examine
the structure of these communities in more detail.
Phytoplankton assemblages during spring and summer blooms in the Pacific Arctic region are dominatedby diatoms (von Quillfeldt, 2000; Sergeeva et al., 2010), which drive sinking POC flux (Lalande et al., 2020).
Some diatom genera, Chaetoceros spp. and Thalassiosira spp., are known to form dense blooms in this region (von Quillfeldt, 2000; Sergeeva et al., 2010). In particular, C. socialis s.l. can constitute more than 90% of
phytoplankton assemblages during blooms in the northern Bering and Chukchi Seas (Sergeeva et al., 2010).
The centric diatoms Attheya spp. are reported to be present in the sea-ice of the Arctic (Campbell et al., 2018;
Melnikov et al., 2002; von Quillfeldt et al., 2003; Szymanski & Gradinger, 2016; Werner et al., 2007) andcan comprise over 60% of diatom abundance in the sea-ice during spring (Campbell et al., 2018). Pennate
diatoms are also known to constitute a large proportion of the sea-ice algal community (von Quillfeldt
et al., 2003; Szymanski & Gradinger, 2016).
Many diatom species form resting stages under unfavorable growth conditions such as nutrient limitation (Durbin, 1978; Garrison, 1984; McQuoid & Hobson, 1996; Smetacek, 1985), Fe limitation (Sugie &Kuma, 2008) and low light conditions (McQuoid & Hobson, 1996). High cell concentrations in water column assemblages can also induce formation of resting stages (Pelusi et al., 2020). Resting stages that sink
to and accumulate in bottom sediments can germinate and resume growth in response to favorable lightlevels (Hollibaugh et al., 1981). The ability to form resting stages is thus an important life cycle strategy forsurvival under low temperature and light conditions during winter in seasonal sea-ice areas (Tsukazaki
et al., 2013, 2018).
The distribution of diatom resting stage assemblages in sediments is thought to reflect the extent and magnitude of past blooms (Itakura et al., 1997; Pitcher, 1990) and can be used to investigate determinants ofcommunity structure and bloom dynamics. For example, in the northern Bering Sea, analysis of the diatom
resting stages in sediments showed that diatom assemblages in early spring were dependent upon the timing of the sea-ice retreat (TSR): ice-associated diatoms were abundant in 2017 when the sea-ice remained
until early April, but open-water diatoms dominated in 2018 when the TSR was approximately two weeks
earlier than the previous year (Fukai et al., 2019).
In this study, we enumerated viable diatom resting stages in sediments collected in a broad area across the
Pacific Arctic region, from the northern Bering Sea to the Chukchi Sea and the southwestern Beaufort Sea.
We describe the features of diatom resting stage assemblages over these regions, and discuss two hypotheses: (a) the concentrations of diatom resting stage assemblages are correlated with primary productionin the water column, and (b) the extent and duration of sea-ice during the previous winter and spring determines community structure of diatom resting stage assemblages. In addition, we discuss how observed
variations in diatom assemblages may impact organisms at higher trophic levels that rely on diatoms as animportant food source.

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Figure 1. Sediment sampling locations in the northern Bering Sea, Chukchi Sea, and Beaufort Sea in 2018.
Color contours indicate the timing of the sea-ice retreat (rainbow contour) and the bottom depth (blue contour).
Abbreviations indicate the transect names during Healy cruises 1801 and 1803. SLI: St. Lawrence Island.

2. Materials and Methods
2.1. Sea-Ice, Primary Production and Daylight Hours
To evaluate the sea-ice extent in each region, the Advanced Microwave Scanning Radiometer 2 (AMSR2)
standard sea-ice concentration (SIC) product was obtained from the Japan Aerospace Exploration Agency(JAXA) web portal (https://gportal.jaxa.jp/gpr/) at a 10-km resolution. The TSR was defined as the last day
when the SIC fell below 20% prior to the observed annual sea-ice minimum across the study region during
summer. Here, we used the SIC data after calculating a 5-day moving average.
To obtain a continuous primary production time-series, we used Level-3 standard mapped image (9-kmresolution) of Aqua-MODIS data downloaded as spectral remote sensing reflectance (Rrs) and daily photosynthetically available radiation (PAR) from the Goddard Space Flight Centre/Distributed Active Archive
Centre, NASA. The absorption coefficient for 443 nm (aph(443)) and euphotic zone depth (Zeu) were computed from Rrs(λ) using Quasi-Analytical Algorithm version 5 (Lee et al., 2007, 2009) and daylength (DL) for
the study area calculated according to Brock (1981). We then computed the daily euphotic-depth-integrated
primary production (PPeu) using aph(443), Zeu, PAR, and DL as inputs to an absorption-based productivity
model (ABPM (Hirawake et al., 2012)). Missing values in aph(443) and Zeu due to cloud cover were interpolated using their annual medians and hence PPeu was derived for the cloud-covered pixels. From thesevalues we calculated cumulative PPeu (IPeu) from TSR to the date of the in situ sediment sampling was conducted for each shipboard observation site.
2.2. Sampling
Sediment sampling was conducted in the shallow Pacific Arctic region at stations ranging from 24–194 mbottom depths (the northern Bering Sea, Chukchi Sea and the southwestern Beaufort Sea; Figure 1, Table 1)
from July 2–12, 2018 aboard T/S Oshoro-Maru of Hokkaido University, and from August 9–23, 2018 andOctober 30 to November 15 2018 aboard the U.S. Coast Guard icebreaker Healy (HLY 1801 and HLY 1803,
respectively) (Figure 2a). Sediment samples were collected using a multiple corer (Oshoro-Maru cruise), a
Van Veen Grab sampler, or a HAPS core sampler (Healy cruises) at each station. A portion of the 0–1 cmof each sediment core was extruded and stored in darkness at 5°C for Oshoro-Maru samples, and for Healy
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Table 1
Locations of Sediment Sampling Stations in the Bering, Chukchi, and Beaufort Seas From July to November in 2018
CruiseStationDate (Julian day)

Latitude(°N)

Longitude(°W)

Bottom depth(m)Sample type

TSR (Julian day)IPeu
(mg C m−2)GP(hours)Oshoro-maru

OS4

2018/7/2 (183)63.15

173.8375

Core

2018/3/22 (81)80,253.9

103.2

OS6

2018/7/3 (184)62.88

172.1655

Core

2018/3/23 (82)56,305.7

158.4

HLY 1801

HLY 1803

FUKAI ET AL.

OS8

2018/7/3 (184)62.49

170.0037

Core

2018/3/25 (84)77,752.8

234.2

OS14

2018/7/5 (186)64.51

170.8746

Core

2018/4/17 (107)

66,311.6

414.7

OS19

2018/7/6 (187)64.51

166.5128

Core

2018/5/1 (121)39,181.0

755.1

OS20

2018/7/6 (187)65.08

168.0046

Core

2018/4/20 (110)32,002.1

581.4

OS22

2018/7/7 (188)65.07

169.7051

Core

2018/4/17 (107)

106,377.2

469.0

OS30

2018/7/11 (192)66.73

168.9642

Core

2018/5/17 (137)59,460.1

1033.1

DBO2-1

2018/8/9 (221)64.67

169.9348

Van Veen

2018/4/16 (106)104,868.8

427.2

DBO2-42018/8/9 (221)64.96

169.9049

Van Veen

2018/4/17 (107)143,193.9

469.0

DBO3-62018/8/10 (222)67.90

168.2559

Core

2018/5/20 (140)95,858.0

935.9

DBO3-7

2018/8/11 (223)

67.79

168.6051

Core

2018/5/20 (140)104,109.1

935.1

IC-3

2018/8/13 (225)71.60

165.3043

Van Veen

2018/6/26 (177)

32,304.9

1829.9

IC-8

2018/8/14 (226)70.97

163.5646

Van Veen

2018/6/30 (181)48,394.7

1391.3

DBO4-2

2018/8/15 (227)71.22

161.2950

Core

2018/7/14 (195)

17,824.0

1891.3

DBO4-42018/8/15 (227)71.48

161.5049

Core

2018/7/15 (196)

15,406.3

2377.8

DBO4-5

2018/8/15 (227)71.61

161.6247

Core

2018/7/14 (195)

19,747.3

2477.9

DBO5-92018/8/17 (229)71.58

157.8266

Van Veen

2018/7/22 (203)2915.42667.8

DBO5-10

2018/8/17 (229)71.63

157.9064

Core

2018/7/22 (203)2813.12669.9

LB-11

2018/8/22 (234)70.06

167.6650

Van Veen

2018/5/13 (133)

95,614.2

973.8

LB-9

2018/8/23 (235)69.88

166.8247

Van Veen

2018/5/12 (132)

105,276.1

877.1

LB-7

2018/8/23 (235)69.68

166.0942

Van Veen

2018/5/11 (131)96,511.2

800.6

DBO6-1

2018/10/30 (303)71.16

152.2632

Van Veen

2018/7/29 (210)

42,213.8

2824.9

DBO6-3

2018/10/30 (303)71.25

152.1748

Van Veen

2018/7/29 (210)

55,326.5

2829.3

DBO6-5

2018/10/30 (303)71.34

152.1071

Van Veen

2018/8/5 (217)

63,070.1

2899.2

DBO6-7

2018/10/31 (304)71.42

152.04194

Van Veen

2018/8/5 (217)

52,153.7

2902.0

PRB-1

2018/11/2 (306)70.69

148.4426

Van Veen

2018/8/26 (238)―

3,347.2

PRB-2

2018/11/2 (306)70.77

148.3335

Van Veen

2018/9/3 (246)

17,622.4

3,465.0

PRB-4

2018/11/2 (306)70.90

148.1445

Van Veen

2018/9/4 (247)

16,083.4

3,405.6

PRB-7

2018/11/2 (306)71.02

147.9858

Van Veen

2018/9/6 (249)

19,897.2

3,490.4

MCK-1

2018/11/4 (308)69.82

139.61

38

Van Veen

2018/8/3 (215)

40,557.1

2240.4

MCK-2

2018/11/4 (308)69.90

139.4944

Van Veen

2018/8/3 (215)

47,209.6

2111.3

MCK-3

2018/11/4 (308)69.94

139.3955

Van Veen

2018/8/3 (215)

47,343.2

2113.9MCK-4

2018/11/4 (308)69.97

139.3060

Van Veen

2018/8/3 (215)

46,782.5

2113.9

KTO-2

2018/11/5 (309)70.28

143.9338

Van Veen

2018/8/18 (230)

30,870.8

3,191.8

KTO-3

2018/11/5 (309)70.37

143.7948

Van Veen

2018/8/21 (233)

14,101.3

3,247.5

KTO-5

2018/11/5 (309)70.56

143.61

110

Van Veen

2018/8/19 (231)

13,958.0

3,221.7

PRW-1

2018/11/7 (311)70.68

148.91

24

Van Veen

2018/8/28 (240)

115,525.9

3,369.1

PRW-4

2018/11/7 (311)70.82

148.8433

Van Veen

2018/8/28 (240)

76,570.9

3,373.2

PRW-7

2018/11/7 (311)70.95

148.7838

Van Veen

2018/9/3 (246)

26,140.8

3,474.0

DBO5-1

2018/11/14 (318)71.25

157.1347

Van Veen

2018/7/22 (203)

79,117.8

2661.3

DBO5-3

2018/11/14 (318)71.33

157.31

91

Van Veen

2018/7/21 (202)59,407.8

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Table 1
Continued
CruiseStationDate (Julian day)

Latitude(°N)

Longitude(°W)

Bottom depth(m)Sample type

TSR (Julian day)IPeu
(mg C m−2)GP(hours)DBO5-5

2018/11/14 (318)71.41

157.49128

Van Veen

2018/7/21 (202)44,093.7

2639.4

DBO5-7

2018/11/14 (318)71.50

157.6685

Van Veen

2018/7/22 (203)

35,765.2

2667.8

DBO5-92018/11/14 (318)71.58

157.8366

Van Veen

2018/7/22 (203)

35,857.3

2667.8

DBO3-1

2018/11/15 (319)68.31

166.9235

Van Veen

2018/5/5 (125)

196,800.9

812.4

DBO3-5

2018/11/15 (319)68.01

167.8854

Van Veen

2018/5/6 (126)

212,878.6

828.9

DBO3-8

2018/11/15 (319)67.67

168.9550

Van Veen

2018/5/21 (141)

179,194.6

1124.6Note. In sample type column, “core” and “Van Veen” indicate that the samples were collected by multiple corer and Van Veen grab sampler, respectively. The
timing of sea ice retreat (TSR) indicates the last day when the SIC fell below 20% prior to the observed annual sea-ice minimum across the study region during
summer. IPeu indicates daily integrated values of primary production from TSR to the date of the in situ sediment sampling was conducted. The growth periodof the ice-associated assemblages (GP) indicates the integrated daylength during the periods with SIC > 20% after the daylight hours exceed 10 h.

samples, a portion of the 0–3 cm layer was collected from the grab or the core and stored in air-tight amberjars at 1–4°C. The sediment samples were stored for more than one month in order to eliminate vegetative
cells. Since the main driver of diatom resting stage formation is considered to be nitrogen depletion (McQuoid & Hobson, 1996), most resting stages are thought to form in the water column rather than near thebenthos, as nutrient concentrations are higher near the seafloor.
2.3. Quantification of Diatom Resting Stages
The abundance of viable resting stages of diatoms in the sediment samples was analyzed using the mostprobable number (MPN) method (Imai et al., 1984, 1990). Homogenized wet sediment samples were suspended in Whatman GF/F filtered sterile seawater at a concentration of 0.1 g mL−1 (=100 dilution), and thesubsequent serial tenfold dilutions (10−1 to 10−6) were made with modified SWM-3 medium (Table 2) (Chenet al., 1969; Itoh & Imai, 1987). Then 1 mL aliquots of diluted suspensions were inoculated into five replicate wells of disposable tissue culture plates (48 wells). Incubation was carried out at a temperature of 5°C
and under white fluorescent light of 50 or 116 μmol photons m−2 s−1 with a 14 h light:10 h dark photocyclefor 10 days. The appearance of vegetative cells of planktonic diatoms in each well was examined using an
inverted optical microscope. The most probable number (MPN for a series of 5 tenfold dilutions) of diatomsof each species in the sediment sample (MPN cells g−1 wet sediment) was then calculated according to thestatistical table by Throndsen (1978). Since we observed wells with 10−2–10−6 dilutions, the detectable cellnumbers by the MPN method were from 1.8 × 102 to 2.4 × 107 MPN cells g−1 for each species. Note that weused the data set of Fukai et al. (2019) for the Oshoro-Maru expedition.
2.4. Statistical Analyses
The diatom resting stage communities were distinguished by cluster analysis. To reduce the bias for abundant species, the cell concentration data (X: MPN cells g−1 wet sediment) for each species were transformed
to 4 X prior to cluster analysis (Quinn & Keough, 2002). Dissimilarities between samples were examined using the Bray-Curtis index based on the differences in the species composition. To group the samples, the dissimilarity indices were coupled using hierarchical agglomerative clustering with a complete linkage method (an unweighted pair group method using the arithmetic mean). A Mann-Whitney U-test was conducted
to evaluate environmental factors (the TSR, IPeu, and the growth period of ice-associated assemblages (GP))
between the distinguished groups. The GP was defined as the integrated daylength during the periods with
SIC >20% after the daylight exceeded 10 h, as Gilstad and Sakshaug (1990) indicated that ice-associatedassemblages could increase their growth rate when daylight hours exceeded 10 h.
We defined the open-water assemblages as the community with centric diatoms, excluding Attheya spp.,
and the ice-associated assemblages as the community with pennate diatoms and Attheya spp., as Attheya

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Figure 2. (a) Horizontal values of the observation date (b) and the daily cumulative euphotic-depth-integrated primary production from the TSR to theobservation date (IPeu).

spp. and pennate diatoms are often reported to be present in the sea-ice (e.g., Campbell et al., 2018; Melnikov et al., 2002; von Quillfeldt et al., 2003; Szymanski & Gradinger, 2016; Werner et al., 2007). Based on
this definition, we analyzed the relationships of ice-associated assemblages with the TSR and the GP using
Spearman's rank correlation coefficient.
All statistical analyses were conducted using R software (version 3.6.1, R Development Core Team, 2019).

Table 2Components of the Modified SWM-3 Medium (Left Column) and Detail Components of P1-Metals and S-3 Vitamin(Right Column).
Component

Concentrations in final medium /Amounts per litter

Component
P-1 metal in10 mL

Concentrations/
Amounts

NaNO3

2.0 mM

NaH2PO4·2H2O

0.1 mM

H3BO3

Na2SiO3·9H2O

0.2 mM

MnCl2·4H2O

3.5 × 10−2 mM

Na2EDTA

30.0 µM

ZnCl2

4.0 × 10−3 mM

Fe-EDTA

2.0 µM

CoCl2·6H2O

1.0 × 10−4 mM

Na2SeO3

2.0 nM

CuCl2·2H2O

1.0 × 10−6 mM

Na2MoO4·2H2O

100 nMS-3 Vitamin in 2 mL

TRIS

500 mg

B1-HCl

0.5 mgP1-metals

10.0 mL

Ca-Pantothenate

0.1 mgS-3 Vitamins

2.0 mL

Nicotinic acid

0.1 mgP-Aminobenzoic acid10.0 µg

1.0 mM

Biotin1.0 µg

Inositol

5.0 mgFolic acid

2.0 µgThymine

3.0 mgVitamin B12

1.0 µgNote. Solvent of the medium is natural filtered sea water. Medium pH is 7.8.

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Figure 3. Horizontal distribution of diatom resting stages in the north Bering, Chukchi and Beaufort Seas in 2018.
Squares indicate benthic hot spots indicated by Grebmeier et al. (2015).

3. Results
3.1. Sea-Ice and Primary Production
The TSR was different among regions (Table 1). The sea-ice retreated from south to north in the northern
Bering and the Chukchi Seas, and from west to east in the southwestern Beaufort Sea (Figure 1).
The IPeu had a regional feature in which high values were observed in the southern Chukchi Sea and low
values in the southwestern Beaufort Sea (Table 1, Figure 2b).
3.2. Diatom Concentrations and Species Composition
The viable diatom resting stages determined by the MPN method ranged over four orders of magnitude,from 1.21 × 103 to 6.1 × 107 MPN cells g−1 wet sediment (Figure 3). Highest concentrations were found to
the south of St. Lawrence Island (3.4 × 106–6.1 × 107 MPN cells g−1 wet sediment). In the Chirikov Basin,
which extends northwards from St. Lawrence Island to the Bering Strait (DBO2-1, DBO2-4, OS14, OS19,
OS20, OS22), diatom concentrations were relatively high (2.8 × 105–3.0 × 106 MPN cells g−1 wet sediments).
Diatom concentrations near Utqiagvik (DBO5-10) were also relatively high (1.2 × 106 MPN cells g−1 wet sediments). In contrast, cell concentrations were lower in samples from the coastal region of the southwestern
Beaufort Sea (DBO6-5, PRW-7, PRB-4, PRB-7, KTO-5, MCK-1, MCK-2, MCK-3, MCK-4) (1.2 × 103–7.8 × 103
MPN cells g−1 wet sediments). 19 genera and 20 species were observed over the study region - 12 generaand 14 species of centric diatoms and 7 genera and 6 species of pennate diatoms. Centric diatoms were
dominant at almost all stations, although dominant species varied geographically; proportional abundanceof Chaetoceros spp. and Thalassiosira spp. were found in samples collected from the northern Bering Sea
and Chukchi Sea, whereas Attheya spp. were highest in the southwestern Beaufort Sea (Figure 4). Pennate
diatoms comprised over 50% of the diatom assemblages at some stations (DBO4-4, MCK-1, MCK-2, MCK3), with highest proportional abundance found in samples from the southwestern Beaufort coastal region(Figure 4). Total cell concentration in sediments were positively correlated with the cell concentrations of
Chaetoceros spp. and Thalassiosira spp. (Spearman, ρ = 0.97, p < 0.05) (Figure 5).
In order to test for seasonal effects, diatom assemblages were compared over time in stations in the northern
Bering Sea and Southern Chukchi Sea, which included locations from each sampling period (OS14, 19, 20,
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Figure 4. Cell concentrations and species composition of diatom resting stages in the northern Bering, Chukchi and
Beaufort Seas in 2018.

22, 30 by Oshoro-Maru, DBO2-1, 2–4, 3–6, 3–8 in HLY 1801, and DBO 3–1, 3–5, 3–7 in HLY 1803). There
were no significant differences in species or genera among these samples (one-way ANOVA, p > 0.05), withthe exception of Attheya spp. and C. debilis (one-way ANOVA, p < 0.05).

3.3. Diatom Assemblages by Cluster Analysis
Cluster analysis based on concentrations of diatom resting stages classified the diatom assemblages into twogroups (A, B) and four outgroups at 52% dissimilarity levels. Group A was distributed from the northern
Bering Sea to the Chukchi Sea near Utqiagvik (Figure 6a). Cell concentrations in group A were very high(7.9 × 104–1.1 × 107 MPN cells g−1 wet sediments, avg = 1.2 × 106 MPN cells g−1 wet sediment), and samples
in this group with dominated by Chaetoceros spp. and Thalassiosira spp. (35% and 51%, respectively) (Figure 6b). Group B included stations from the southwestern Beaufort Sea, where cell concentrations ranged
from 3.2 × 103 to 2.1 × 105 MPN cells g−1 wet sediment (avg = 5.8 × 104 MPN cells g−1 wet sediment) andAttheya spp. were dominant (47%) (Figure 6b). All stations from the easternmost transect in the study region(MCK) were classified as outgroups (Figure 6a).

3.4. Relationships With Environmental Factors
Environmental factors differed between samples comprising diatom
groups A and B. The TSR was significantly later at the group B locations
compared to group A (U-test, p < 0.05) (Figure 7a), and the GP was significantly longer at group B locations than group A (U-test, p < 0.05)(Figure 7b). The switching between the two diatom groups occurred
around 200 Julian day of the TSR and approximately 2500 h of the GP(Figures 7a, 7b and 8). By contrast, the IPeu and the sampling depth were
not significantly different between groups (U-test, p > 0.05) (Figures 7cand 7d).

Figure 5. The relationship between the abundances of Chaetoceros spp.
and Thalassiosira spp. and total cell concentrations in most probable
number.

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In addition, the TSR and the GP were significantly positively correlatedwith the proportion of pennate diatoms and Attheya spp., which are defined as the ice-associated assemblages (ρ = 0.63 and 0.29, respectively,
p < 0.05) (Figure 8).

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Figure 6. (a) Spatial distribution of diatom resting stage communities by group. (b) Species composition and cellconcentrations in each group.

4. Discussion
Examination of the distribution and abundance of diatom resting stages in Pacific Arctic sediments demonstrated a strong correlation with the timing of sea ice retreat and the growth period of ice-associated assemblages. Details regarding spatial community dynamics and relationships between diatom assemblages andTSR, GP, and environmental parameters are discussed below.

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4.1. Distribution of Diatom Resting Stages and the Relationships
With Primary Production in the Pacific Arctic Region
Cell concentrations of diatom resting stages exhibited geographic variability that roughly corresponded with levels of primary production
previously reported in the region. However, primary production values
estimated by satellite remote sensing in this study did not have any statistically significant relationships with diatom resting stage assemblages.

Figure 7. Comparison of environmental factors between diatom restingstage groups. (a) The timing of the sea-ice retreat (TSR). (b) The growth
period of ice-associated assemblages (GP). (c) The daily cumulative
euphotic-depth-integrated primary production from the TSR to theobservation date (IPeu). (d) The bottom depth of sampling station.

This study found high concentrations of diatom resting stages in thenorthern Bering Sea and the Chukchi Sea (avg = 3.1 × 106 MPN cells
g−1 wet sediments), but low concentrations (avg = 6.2 × 104 MPN cells
g−1 wet sediments) in the southwestern Beaufort Sea. This is consistent
with prior studies of primary productivity that documented high annual
water-column integrated primary production in the northern Bering and
Chukchi Seas and low productivity in the western Beaufort Sea (Grebmeier et al., 2006; Hill et al., 2018). However, we did not find a significant relationship between diatom resting stage assemblages and primary
production values estimated by satellite. O'Daly et al. (2020) presented
similar findings; that higher rates of primary productivity were correlated with higher rates of POC flux, including viable diatoms, but that
there is variability in the export efficiency. Thus, while the hypothesisthat diatom resting stage concentrations reflect primary productivity inthe water column (Imai et al., 1990; Itakura et al., 1997; Pitcher, 1990)
holds up over broad features of the Pacific Arctic region, additional data
are needed to justify this using resting stage assemblages as a strict proxyfor productivity.

Another cause of high concentrations of resting stages observed in the northern Bering and Chukchi Seas
is related to the high sinking flux in this region (O'Daly et al., 2020). Lalande et al. (2020) used a time seriesof sediment trap observations at single station in the Chukchi Sea to show that diatoms make up a highproportion of POC flux. The characteristically high sinking flux in the northern Bering and Chukchi Seas
could drive high concentrations of sediment diatom assemblages reported here, though diatom losses byzooplankton grazing also need to be considered (Campbell et al., 2009; Sherr et al., 2009).
We considered the impact that variable sampling times may have had upon the assemblages observed inthis study. The sediments were obtained over several different time periods (July 2–12, 2018, August 9–23,
2018, and October 30 to November 15, 2018), and it is possible that the community structure changed fromsummer to fall. However, there were no significant differences in species or genera except for Attheya spp.
and C. debilis between samples at replicated stations in the northern Bering Sea and Southern Chukchi Sea,

Figure 8. Relationships between the proportion of the ice-associated species (Attheya spp. and pennate diatoms) inmost probable number and the timing of the sea-ice retreat (a), and the GP (b). Each color indicate the diatom groups(pink: group A, green: group B, and gray: out groups).

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where sampling was conducted over multiple time periods. Dissimilarity among almost all the samples wasless than 40%, and they were also grouped in the cluster analysis. Water temperature is known to influence
resting stage survival time, with colder water increasing survival time length (McQuoid & Hobson, 1996).
Hargraves and French (1983) reported that Chaetoceros diadema, Detonula confervacea, Leptocylindrus danicus, and Thalassiosira nordenskioeldii, which sometimes appear in the Pacific Arctic sediments, survived
for 291, 220, 400, and 220 days, respectively, at temperatures between 5–6°C. These periods are sufficiently
longer than the difference in sampling periods (the longest difference is 136 days). Given that temperaturesat the seafloor of the Pacific Arctic are lower than 5–6°C, we do not believe that the survival time of restingstages impacted our results. For these reasons, differences over sampling periods appeared to be almost
negligible in this study.

4.2. The Relationship of Diatom Resting Stage Assemblages With the TSR and the GP
Prior investigators have shown that the magnitude and composition of diatom assemblages in the Arctic
spring bloom are influenced by the presence of the sea-ice and the timing of the sea-ice retreat (Fujiwara
et al., 2016; Fukai et al., 2019; Neeley et al., 2018). In this study, the distribution of diatom resting stage
assemblages were clearly related to spatial differences in the TSR. In locations where the ice retreat was
early, such as the northern Bering and the Chukchi Seas, Chaetoceros spp. including C. socialis s.l. andThalassiosira spp. were dominant in sediments (C. socialis s.l.: 0.36–93.1%, Chaetoceros spp.: 0.76–93.6%,
Thalassiosira spp.: 2.0–96.4%). Because they are known to form dense spring blooms in these regions (vonQuillfeldt, 2000; Sergeeva et al., 2010), these data suggest that diatom resting stages were formed and settled
to the seafloor after spring blooms of Chaetoceros spp. and Thalassiosira spp. in the northern Bering Sea andthe Chukchi Sea. In addition, the positive correlation between Chaetoceros spp. and Thalassiosira spp. cellconcentrations with total cell concentrations indicates that where the TSR was early and the open-water
period was long, large diatom blooms of Chaetoceros spp. and Thalassiosira spp. produced high quantities
of resting stage cells (Fukai et al., 2019).
The TSR had also an effect on the diatom community composition, especially the proportion of ice-associated diatoms in diatom assemblages. In the southwestern Beaufort Sea, where the TSR was late, diatom
assemblages were dominated by ice-associated species (Groups B and outgroups in the transect MCK),
again demonstrating that sea-ice is a driver of benthic community structure among the exported diatoms.
In addition, the prevalence of ice-associated species was positively correlated with the TSR, suggesting thatthe proportion of ice algae in diatom assemblages is higher when sea-ice persists. This is likely due in part totheir ability to sustain growth under low light levels (<1 μmol photon s−1 m−2) (Cota & Smith, 1991; Mock
& Gradinger, 1999); notably, Tsukazaki et al. (2018) demonstrated that the centric genus Attheya spp. could
survive in dark for more than six months, and thus can withstand low light conditions in the Arctic. It is
possible that this study underestimated the concentrations of pennate diatoms in sediments compared with
Attheya spp. and other centric diatoms, as few marine pennate diatoms are known to form resting stages,
while many centric diatoms do (McQuoid & Hobson, 1996), and the fate of the pennate diatoms in sedimentis largely unknown. Despite this potential bias, these data indicate that the proportion of ice-associated species was higher where the TSR occurred later. Interestingly, a spatial change from the assemblage dominated by open-water species to that with high proportion of ice-associated diatoms occurred at stations where
the TSR was around the 200th Julian day (mid-July). This indicates a potential threshold between dominantdiatom groups based on the TSR parameter.
For ice-associated assemblages in the surface sediments, the length of the growth period during which
algae receive sufficient light before the TSR is important (Fukai et al., 2019). The proportional abundanceof ice-associated diatoms was significantly higher when GP was longer, suggesting that photoperiod during
sea-ice presence is another important driver of diatom community structure (Cota & Home, 1989; Gosselin
et al., 1990; Smith et al., 1988). In addition, a GP boundary of 2500 h may be an important parameter for the
distribution of ice-associated assemblages due to corresponding changes that were observed. Future efforts
to evaluate and predict diatom assemblages should consider both the TSR and the GP.

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4.3. Connecting Diatom Distribution to Higher Trophic Levels
The diatom assemblages had clear relationships with the TSR and the GP. In the northern Bering Sea, theearly timing of the sea-ice retreat and subsequent changes in diatom assemblages in the water column andthe sediment was reported in 2018 (Fukai et al., 2019, 2020). This indicates that the recent drastic reductionof sea-ice in the Pacific Arctic region may induce a shift in diatom assemblages from relative dominance of
ice-associated species to open-water species.
The distribution and composition of diatom species in this study were associated with the zooplankton
feeding environment in the Pacific Arctic region. As diatoms comprise the largest portion of the mesozooplankton diet, especially in spring (Campbell et al., 2016), changes in diatom species composition will
perturb prey environments of higher trophic-level organisms. The spatial trend of diatom resting stage
concentrations exhibited a similar gradient to zooplankton δ13C values showed by Pomerleau et al. (2014),
which reported values that were more enriched in the western Bering Strait and less enriched on the Beaufort shelf. Nakatsuka et al. (1992) reported an increase in δ13C of POC during a diatom bloom in a mesocosm
experiment in Saanich Inlet, Canada. Additionally, they showed that the main factor influencing the variation of δ13C of POC during a phytoplankton bloom in a mesocosm experiment was the specific production
rate of POC, which can be proportional to the specific growth rate of phytoplankton, rather than carbon
dioxide system or community composition (Nakatsuka et al., 1992). Typically, fast-growing diatoms (e.g.,
Chaetoceros spp. and Thalassiosira spp.) and zooplankton that feed on these diatoms are enriched with 13C(Fry & Wainright, 1991). The inflow of nutrient rich Anadyr waters from the western Bering Strait is known
to fuel huge blooms of Chaetoceros spp. and Thalassiosira spp. (Danielson et al., 2017; Sergeeva et al., 2010),
explaining the high concentrations of these species in sediments of the northern Bering and the Chukchi
Seas reported here. In addition, regions of high diatom resting stage concentrations roughly corresponded
to benthic hotspots, which include waters to the south of St. Lawrence Island, the Chirikov Basin, thesoutheastern Chukchi Sea and the northeastern Chukchi Sea (Grebmeier et al., 2015). In these regions withmean depths from 43 to 65 m, benthic primary production can be lower than pelagic production due to light
limitation, and accumulation of microalgae in sediments are the main food source for benthic communities(Grebmeier et al., 2015). In particular, diatoms are valuable taxa because they are rich in polyunsaturated
fatty acids (PUFAs) (Brown et al., 1997). Furthermore, Wang et al. (2016) analyzed the blubber fatty acidcomposition and stable carbon isotope ratios of ice seals, who feed on pelagic and benthic fishes, in thenorthern Bering and the southern Chukchi Seas to show that ice algae production contributed up to 80% of
ice seal diets through trophic transfer. Therefore, changes in diatom assemblages caused by sea-ice dynamics will directly influence zooplankton and benthos production, with indirect effects upon higher trophic
levels.

5. Conclusions
This study demonstrated that the distribution and community composition of diatom resting stages in the
Pacific Arctic region were significantly influenced by the presence of sea-ice and the light environment.
Diatom resting stages appear to follow broad spatial patterns of primary productivity across the region, suggesting the potential use of diatom resting stages as one of the proxies for productivity, despite the fact that
there was not a significant relationship between diatom assemblages and primary productivity estimated bysatellite observations. The TSR and the GP were important drivers of diatom assemblages, and significantly
influenced the composition of diatoms in sediments. In particular, diatom assemblages changed spatiallyfrom composition dominated by open-water species to a high proportion of ice-associated diatoms in theregion where the TSR occurred after mid-July (around the 200th Julian day) and the GP was over 2500 h.
This result may indicate that a shift to earlier TSR under future climate conditions could induce not only
delayed bloom timing (Hirawake & Hunt, 2020; Kikuchi et al., 2020) but also a change in the compositionof diatom assemblages forming the spring bloom. The distribution of diatom resting stages is a valuableapproach for investigating the diatom community, particularly on the Arctic shelves where it is logisticallychallenging to characterize the rapid seasonal succession in community composition that occurs across thisremote and dynamic geographic region. Moreover, this approach provides species-level resolution lacking
in satellite observations, providing a more robust assessment of the ecosystem implications of communitychanges. On the ecosystem level, it is interesting that the distribution of diatom resting stages corresponded
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spatially with benthic hot spots and the feeding environment of zooplankton. Based on this research, it isclear that future changes in sea-ice extent and duration will impact diatom communities, and that resulting fluctuations in primary productivity and community structure will affect other components of Arctic
marine ecosystems.

Data Availability Statement
The data reported in this paper have been deposited in the data archive site of Hokkaido University, http://
hdl.handle.net/2115/80394.
Acknowledgments
We thank the captain, officers, crews
and researchers on board the U.S. Coast
Guard icebreaker Healy and the T/SOshoro-Maru, Hokkaido University
for their great efforts during the fieldsampling. We also acknowledge the
JAXA Earth Observation ResearchCenter and NASA/GSFC for providing
satellite data. This work was conductedby the Arctic Challenge for Sustainability (ArCS) project, Arctic Challenge
for Sustainability II (ArCSII) projectand ArCS program for overseas visitsby young researchers. In addition, thiswork was partly supported by the Japan
Society for the Promotion of Science(JSPS) KAKENHI Grant Number
JP20J20410 and JP21H02263. We thankAnderson laboratory members for theirsupport of our study at WHOI, and also
thank Robert Pickart, Leah McRaven,and Jacqueline Grebmeier for their support and assistance on the Healy cruises. Funding for DA, EF, and MR wasprovided by the NOAA Arctic Research
Program through the Cooperative
Institute for the North Atlantic Region(CINAR Award NA14OAR4320158),by the NOAA ECOHAB Program (NA20NOS4780195) and by the National
Science Foundation Office of Polar Programs (OPP-1823002). This is ECOHAB
contribution number ECO986.

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References
Arrigo, K. R., van Dijken, G., & Pabi, S. (2008). Impact of a shrinking Arctic ice cover on marine primary production. Geophysical Research
Letters, 35(19), L19603. https://doi.org/10.1029/2008GL035028
Brock, T. D. (1981). Calculating solar radiation for ecological studies. Ecological Modelling, 14(1), 1–19. https://doi.
org/10.1016/0304-3800(81)90011-9
Brown, M. R., Jeffrey, S. W., Volkman, J. K., & Dunstan, G. A. (1997). Nutritional properties of microalgae for mariculture. Aquaculture,151(1–4), 315–331. https://doi.org/10.1016/S0044-8486(96)01501-3
Campbell, K., Mundy, C. J., Belzile, C., Delaforge, A., & Rysgaard, S. (2018). Seasonal dynamics of algal and bacterial communities in
Arctic sea ice under variable snow cover. Polar Biology, 41(1), 41–58. https://doi.org/10.1007/s00300-017-2168-2
Campbell, R. G., Ashjian, C. J., Sherr, E. B., Sherr, B. F., Lomas, M. W., Ross, C., et al. (2016). Mesozooplankton grazing during spring
sea-ice conditions in the eastern Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography, 134, 157–172. https://doi.
org/10.1016/j.dsr2.2015.11.003
Campbell, R. G., Sherr, E. B., Ashjian, C. J., Plourde, S., Sherr, B. F., Hill, V., & Stockwell, D. A. (2009). Mesozooplankton prey preference
and grazing impact in the western Arctic Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 56(17), 1274–1289. https://doi.org/10.1016/j.dsr2.2008.10.027
Chen, L. C.-M., Edelstein, T., & McLachlan, J. (1969). Bonnemaisonia Hamifera Hariot in Nature and in culture. Journal of Phycology, 5(3),211–220. https://doi.org/10.1111/j.1529-8817.1969.tb02605.x
Cota, G. F., & Home, E. (1989). Physical control of Arctic ice algal production. Marine Ecology Progress Series, 52, 111–121. https://doi.
org/10.3354/meps052111
Cota, G. F., & Smith, R. E. H. (1991). Ecology of bottom ice algae: II. Dynamics, distributions and productivity. Journal of Marine Systems,
2(3), 279–295. https://doi.org/10.1016/0924-7963(91)90037-U
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. Deep Sea Research Part II:
Topical Studies in Oceanography, 135, 7–26. https://doi.org/10.1016/j.dsr2.2016.05.024
Durbin, E. G. (1978). Aspects of the biology of resting spores of Thalassiosira nordenskioeldii and Detonula confervacea. Marine Biology,
45(1), 31–37. https://doi.org/10.1007/BF00388975
Frey, K. E., Comiso, J. C., Cooper, L. W., Grebmeier, J. M., & Stock, L. V. (2018). Arctic ocean primary productivity: The response of marine
algae to climate warming and sea ice decline, 7.
Frost, K. J., & Lowry, L. F. (1984). Trophic relationships of vertebrate consumers in the Alaskan Beaufort Sea. In P. W. Barnes, D. M. Schell,& E. Reimnitz (Eds.), The alaskan Beaufort Sea: Ecosystems and environments (pp. 381–401). Academic Press. https://doi.org/10.1016/
b978-0-12-079030-2.50024-1
Fry, B., & Wainright, S. C. (1991). Diatom sources of 13 C-rich carbon in marine food webs. Marine Ecology Progress Series, 76(2), 149–157.
https://doi.org/10.3354/meps076149
Fujiwara, A., Hirawake, T., Suzuki, K., Eisner, L., Imai, I., Nishino, S., et al. (2016). Influence of timing of sea ice retreat on phytoplankton
size during marginal ice zone bloom period on the Chukchi and Bering shelves. Biogeosciences, 13(1), 115–131. https://doi.org/10.5194/
bg-13-115-2016
Fukai, Y., Abe, Y., Matsuno, K., & Yamaguchi, A. (2020). Spatial changes in the summer diatom community of the northern Bering Sea in2017 and 2018. Deep Sea Research Part II: Topical Studies in Oceanography, 181–182, 104903. https://doi.org/10.1016/j.dsr2.2020.104903
Fukai, Y., Matsuno, K., Fujiwara, A., & Yamaguchi, A. (2019). The community composition of diatom resting stages in sediments of thenorthern Bering Sea in 2017 and 2018: The relationship to the interannual changes in the extent of the sea ice. Polar Biology, 42(10),1915–1922. https://doi.org/10.1007/s00300-019-02552-x
Garrison, D. L. (1984). Chapter 1, Planktonic diatoms. Marine Plankton Life Cycle Strategies (Vol. 1–17).
Gilstad, M., & Sakshaug, E. (1990). Growth rates of ten diatom species from the Barents Sea at different irradiances and day lengths. Marine Ecology Progress Series, 64, 169–173. https://doi.org/10.3354/meps064169
Gosselin, M., Legendre, L., Therriault, J.-C., & Demers, S. (1990). Light and nutrient limitation of sea-ice microalgae (Hudson Bay, Canadian Arctic)1. Journal of Phycology, 26(2), 220–232. https://doi.org/10.1111/j.0022-3646.1990.00220.x
Grebmeier, J. M., Bluhm, B. A., Cooper, L. W., Danielson, S. L., Arrigo, K. R., Blanchard, A. L., et al. (2015). Ecosystem characteristics andprocesses facilitating persistent macrobenthic biomass hotspots and associated benthivory in the Pacific Arctic. Progress in Oceanography, 136, 92–114. https://doi.org/10.1016/j.pocean.2015.05.006
Grebmeier, J. M., Cooper, L. W., Feder, H. M., & Sirenko, B. I. (2006). Ecosystem dynamics of the Pacific-influenced Northern Bering and
Chukchi Seas in the Amerasian Arctic. Progress in Oceanography, 71(2–4), 331–361. https://doi.org/10.1016/j.pocean.2006.10.001
Grebmeier, J. M., McRoy, C., & Feder, H. (1988). Pelagic-benthic coupling on the shelf of the northern Bering and Chukchi Seas. I. Foodsupply source and benthic bio-mass. Marine Ecology Progress Series, 48, 57–67. https://doi.org/10.3354/meps048057
Hargraves, P. E., & French, F. W. (1983). Diatom resting spore: Significance and strategies. In G. A. Fryxell (Ed.), Survival strategies of thealgae (pp. 49–68). Cambridge University Press.
Hill, V., Ardyna, M., Lee, S. H., & Varela, D. E. (2018). Decadal trends in phytoplankton production in the Pacific Arctic Region from 1950
to 2012. Deep Sea Research Part II: Topical Studies in Oceanography, 152, 82–94. https://doi.org/10.1016/j.dsr2.2016.12.015

13 of 15

Journal of Geophysical Research: Oceans

10.1029/2021JC017223

Hirawake, T., & Hunt, G. L. (2020). Impacts of unusually light sea-ice cover in winter 2017-2018 on the northern Bering Sea marine
ecosystem—An introduction. Deep Sea Research Part II: Topical Studies in Oceanography, 181–182, 104908. https://doi.org/10.1016/j.
dsr2.2020.104908
Hirawake, T., Shinmyo, K., Fujiwara, A., & Saitoh, S. (2012). Satellite remote sensing of primary productivity in the Bering and Chukchi
Seas using an absorption-based approach. ICES Journal of Marine Science, 69(7), 1194–1204. https://doi.org/10.1093/icesjms/fss111
Hollibaugh, J. T., Seibert, D. L. R., & Thomas, W. H. (1981). Observations on the survival and germination of resting spores of three Chaetoceros (Bacillariophyceae) species1, 2. Journal of Phycology, 17(1), 1–9. https://doi.org/10.1111/j.1529-8817.1981.tb00812.x
Horner, R. (1984). Phytoplankton abundance, chlorophyll a, and primary production in the western Beaufort Sea. In P. W. Barnes, D.
M. Schell, & E. Reimnitz (Eds.), The alaskan Beaufort Sea: Ecosystems and environments (pp. 295–310). Academic Press. https://doi.
org/10.1016/b978-0-12-079030-2.50020-4
Horner, R., & Schrader, G. C. (1982). Relative Contributions of Ice Algae, Phytoplankton, and Benthic Microalgae to Primary Productionin Nearshore Regions of the Beaufort Sea. ARCTIC, 35(4), 485–503. https://doi.org/10.14430/arctic2356
Imai, I., Itakura, S., & Itoh, K. (1990). Distribution of diatom resting cells in sediments of Harima-Nada and northern Hiroshima Bay, theSeto Island Sea, Japan. Bulletin on coastal oceanography, 28(1), 75–84. https://doi.org/10.32142/engankaiyo.28.1_75
Imai, I., Itoh, K., & Anraku, M. (1984). Extinction dilution method for enumeration of dormant cells of Red Tide organisms in marine
sediments. Bulletin of Plankton Society of Japan, 312, 123–124.
Itakura, S., Imai, I., & Itoh, K. (1997). “Seed bank” of coastal planktonic diatoms in bottom sediments of Hiroshima Bay, Seto Inland Sea,
Japan. Marine Biology, 128(3), 497–508. https://doi.org/10.1007/s002270050116
Itoh, K., & Imai, I. (1987). Rafido-So. In The Japan fisheries resources conservation association. (Ed.), A guide for studies of red tide organisms (pp. 122–130). Shuwa.
Kikuchi, G., Abe, H., Hirawake, T., & Sampei, M. (2020). Distinctive spring phytoplankton bloom in the Bering Strait in 2018: A yearof historically minimum sea ice extent. Deep Sea Research Part II: Topical Studies in Oceanography, 181–182, 104905. https://doi.
org/10.1016/j.dsr2.2020.104905
Lalande, C., Grebmeier, J. M., Hopcroft, R. R., & Danielson, S. L. (2020). Annual cycle of export fluxes of biogenic matter near Hanna
Shoal in the northeast Chukchi Sea. Deep Sea Research Part II: Topical Studies in Oceanography, 177, 104730. https://doi.org/10.1016/j.
dsr2.2020.104730
Lee, Z., Lubac, B., Werdell, J., & Arnone, R. (2009). An update of the quasi-analytical Algorithm (QAA_v5). Retrieved January 6, 2021, fromhttp://www.ioccg.org/groups/software.html
Lee, Z., Weidemann, A., Kindle, J., Arnone, R., Carder, K. L., & Davis, C. (2007). Euphotic zone depth: Its derivation and implication to
ocean-color remote sensing. Journal of Geophysical Research: Oceans, 112(C3). https://doi.org/10.1029/2006JC003802
Markus, T., Stroeve, J. C., & Miller, J. (2009). Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. Journal ofGeophysical Research: Oceans, 114(C12). https://doi.org/10.1029/2009JC005436
McQuoid, M. R., & Hobson, L. A. (1996). Diatom resting stages. Journal of Phycology, 32(6), 889–902. https://doi.
org/10.1111/j.0022-3646.1996.00889.x
Melnikov, I. A., Kolosova, E. G., Welch, H. E., & Zhitina, L. S. (2002). Sea ice biological communities and nutrient dynamics in the Canada Basin of the Arctic Ocean. Deep Sea Research Part I: Oceanographic Research Papers, 49(9), 1623–1649. https://doi.org/10.1016/
S0967-0637(02)00042-0
Mock, T., & Gradinger, R. (1999). Determination of Arctic ice algal production with a new in situ incubation technique. Marine Ecology
Progress Series, 177, 15–26. https://doi.org/10.3354/meps177015
Nakatsuka, T., Handa, N., Wada, E., & Wong, C. S. (1992). The dynamic changes of stable isotopic ratios of carbon and nitrogen in suspended and sedimented particulate organic matter during a phytoplankton bloom. Journal of Marine Research, 50(2), 267–296. https://doi.org/10.1357/002224092784797692
Neeley, A. R., Harris, L. A., & Frey, K. E. (2018). Unraveling phytoplankton community dynamics in the Northern Chukchi sea under
sea-ice-covered and sea-ice-free Conditions. Geophysical Research Letters, 45(15), 7663–7671. https://doi.org/10.1029/2018GL077684
O'Daly, S. H., Danielson, S. L., Hardy, S. M., Hopcroft, R. R., Lalande, C., Stockwell, D. A., & McDonnell, A. M. P. (2020). Extraordinary
carbon fluxes on the shallow Pacific Arctic shelf during a remarkably warm and low sea ice period. Frontiers in Marine Science, 7, 986.
https://doi.org/10.3389/fmars.2020.548931
Pelusi, A., Margiotta, F., Passarelli, A., Ferrante, M. I., d'Alcalà, M. R., & Montresor, M. (2020). Density-dependent mechanisms regulate spore formation in the diatom Chaetoceros socialis. Limnology and Oceanography Letters, 5(5), 371–378. https://doi.org/10.1002/
lol2.10159
Pitcher, G. C. (1990). Phytoplankton seed populations of the Cape Peninsula upwelling plume, with particular reference to resting sporesof Chaetoceros (Bacillariophyceae) and their role in seeding upwelling waters. Estuarine, Coastal and Shelf Science, 31(3), 283–301.
https://doi.org/10.1016/0272-7714(90)90105-Z
Pomerleau, C., Nelson, R. J., Hunt, B. P. V., Sastri, A. R., & Williams, W. J. (2014). Spatial patterns in zooplankton communities and stable
isotope ratios (δ13C and δ15N) in relation to oceanographic conditions in the sub-Arctic Pacific and western Arctic regions during thesummer of 2008. Journal of Plankton Research, 36(3), 757–775. https://doi.org/10.1093/plankt/fbt129
Quinn, G. P., & Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge University Press.
Sergeeva, V. M., Sukhanova, I. N., Flint, M. V., Pautova, L. A., Grebmeier, J. M., & Cooper, L. W. (2010). Phytoplankton community in theWestern Arctic in July–August 2003. Oceanology, 50(2), 184–197. https://doi.org/10.1134/S0001437010020049
Sherr, E. B., Sherr, B. F., & Hartz, A. J. (2009). Microzooplankton grazing impact in the Western Arctic Ocean. Deep Sea Research Part II:
Topical Studies in Oceanography, 56(17), 1264–1273. https://doi.org/10.1016/j.dsr2.2008.10.036
Smetacek, V. S. (1985). Role of sinking in diatom life-history cycles: Ecological, evolutionary and geological significance. Marine Biology,
84(3), 239–251. https://doi.org/10.1007/BF00392493
Smith, W. O., Keene, N. K., & Comiso, J. C. (1988). Interannual Variability in Estimated Primary Productivity of the Antarctic Marginal Ice
Zone. In D. Sahrhage (Ed.), Antarctic ocean and resources variability (pp. 131–139). Springer. https://doi.org/10.1007/978-3-642-73724-4_10
Springer, A. M., McROY, C. P., & Flint, M. V. (1996). The Bering Sea Green Belt: Shelf-edge processes and ecosystem production. Fisheries
Oceanography, 5(3–4), 205–223. https://doi.org/10.1111/j.1365-2419.1996.tb00118.x
Sugie, K., & Kuma, K. (2008). Resting spore formation in the marine diatom Thalassiosira nordenskioeldii under iron- and nitrogen-limited conditions. Journal of Plankton Research, 30(11), 1245–1255. https://doi.org/10.1093/plankt/fbn080
Szymanski, A., & Gradinger, R. (2016). The diversity, abundance and fate of ice algae and phytoplankton in the Bering Sea. Polar Biology,
39(2), 309–325. https://doi.org/10.1007/s00300-015-1783-z
Throndsen, J. (1978). The dilution-culture method. In A. Sournia (Ed.), Phytoplankton manual (pp. 218–224). Unesco.

FUKAI ET AL.

14 of 15

Journal of Geophysical Research: Oceans

10.1029/2021JC017223

Tsukazaki, C., Ishii, K.-I., Matsuno, K., Yamaguchi, A., & Imai, I. (2018). Distribution of viable resting stage cells of diatoms in sediments
and water columns of the Chukchi Sea, Arctic Ocean. Phycologia, 57(4), 440–452. https://doi.org/10.2216/16-108.1
Tsukazaki, C., Ishii, K.-I., Saito, R., Matsuno, K., Yamaguchi, A., & Imai, I. (2013). Distribution of viable diatom resting stage cells inbottom sediments of the eastern Bering Sea shelf. Deep Sea Research Part II: Topical Studies in Oceanography, 94, 22–30. https://doi.
org/10.1016/j.dsr2.2013.03.020
von Quillfeldt, C. H. (2000). Common diatom species in Arctic spring blooms: Their distribution and abundance. Botanica Marina, 43(6),499–516. https://doi.org/10.1515/BOT.2000.050
von Quillfeldt, C. H., Ambrose, W. G., & Clough, L. M. (2003). High number of diatom species in first-year ice from the Chukchi Sea. Polar
Biology, 26(12), 806–818. https://doi.org/10.1007/s00300-003-0549-1
Wang, S. W., Springer, A. M., Budge, S. M., Horstmann, L., Quakenbush, L. T., & Wooller, M. J. (2016). Carbon sources and trophic
relationships of ice seals during recent environmental shifts in the Bering Sea. Ecological Applications, 26(3), 830–845. https://doi.
org/10.1890/14-2421
Werner, I., Ikävalko, J., & Schünemann, H. (2007). Sea-ice algae in Arctic pack ice during late winter. Polar Biology, 30(11), 1493–1504.
https://doi.org/10.1007/s00300-007-0310-2

FUKAI ET AL.

15 of 15