ORIGINAL RESEARCH
published: 22 February 2022
doi: 10.3389/fmars.2022.808910

Effects of Early Sea-Ice Reduction on
Zooplankton and Copepod
Population Structure in the Northern
Bering Sea During the Summers of2017 and 2018
Fumihiko Kimura 1 , Kohei Matsuno 1,2* , Yoshiyuki Abe 3 and Atsushi Yamaguchi 1,2
1

Faculty/Graduate School of Fisheries Sciences, Hokkaido University, Hakodate, Japan, 2 Arctic Research Center, Hokkaido
University, Sapporo, Japan, 3 Research Development Section, Office for Enhancing Institutional Capacity, Hokkaido
University, Sapporo, Japan

Edited by:
Daniel J. Mayor,
University of Southampton,
United Kingdom
Reviewed by:
Terry Whitledge,
Retired, Fairbanks, AK, United States
Vladimir G. Dvoretsky,
Murmansk Marine Biological Institute,
RussiaAgata Weydmann-Zwolicka,
University of Gdańsk, Poland*Correspondence:
Kohei Matsuno
k.matsuno@fish.hokudai.ac.jp
Specialty section:
This article was submitted toMarine Ecosystem Ecology,
a section of the journal
Frontiers in Marine Science
Received: 04 November 2021
Accepted: 20 January 2022
Published: 22 February 2022
Citation:
Kimura F, Matsuno K, Abe Y andYamaguchi A (2022) Effects of Early
Sea-Ice Reduction on Zooplankton
and Copepod Population Structurein the Northern Bering Sea During
the Summers of 2017 and 2018.
Front. Mar. Sci. 9:808910.
doi: 10.3389/fmars.2022.808910

A remarkable early sea-ice reduction event was observed in the northern Bering Sea
during 2018. In turn, this unusual hydrographic phenomenon affected several marine
trophic levels, resulting in delayed phytoplankton blooms, phytoplankton communitychanges, and a northward shift of fish stocks. However, the response of the zooplankton
community remains uncharacterized. Therefore, our study sought to investigate thezooplankton community shifts in the northern Bering Sea during the summers of 2017and 2018 and evaluate the effects of early sea-ice melt events on the zooplankton
community, population structure of large copepods, and copepod production. Five
zooplankton communities were identified based on cluster analysis. Further, annualchanges in the zooplankton community were identified in the Chirikov Basin. In 2017,
the zooplankton community included abundant Pacific copepods transported by theAnadyr water. In 2018, however, the zooplankton community was dominated by small
copepods and younger stages of large copepods (Calanus glacialis/marshallae andMetridia pacifica), which was likely caused by reproduction delays resulting from theearly sea-ice reduction event. These environmental abnormalities increased copepod
production; however, this higher zooplankton productivity did not efficiently reach thehigher trophic levels. Taken together, our findings demonstrated that zooplankton
community structure and production are highly sensitive to the environmental changes
associated with early sea-ice reduction (e.g., warm temperatures and food availability).
Keywords: community structure, population structure, phytoplankton bloom timing, reproduction, copepod
production, pacification

INTRODUCTION
The northern Bering Sea is a shallow shelf (approximately 50 m in depth) that connects the Bering
Sea and the Arctic Ocean. This region is one of the most productive in the world (Springer andMcRoy, 1993) owing to the continuous inflow of nutrient-rich water from Anadyr Bay (Coachman
et al., 1975). Therefore, the high primary productivity of this region supports higher trophic levels

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FIGURE 1 | Location of stations in the northern Bering Sea during 11–22 July 2017 and 2–12 July 2018. The numbers indicate the station ID.

FIGURE 2 | Horizontal distribution of environmental factors in the northern Bering Sea during 11–22 July 2017. MD, melt day.

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FIGURE 3 | Horizontal distribution of environmental factors in the northern Bering Sea during 2–12 July 2018. MD, melt day.

events have been linked to decreases in sea bird populations
due to food scarcity (Nishizawa et al., 2020), as well as adecline in the body condition (mass/length) of phocid seals(Boveng et al., 2020).
Zooplankton form a vital link from primary production tohigher trophic levels. Zooplankton species-level community
structure varies geographically and is governed by watermass inflow from the south (Springer et al., 1989; Ozaki and
Minoda, 1996). Zooplankton biomass is largely dominatedby copepods (Eisner et al., 2013), particularly the smallsized copepods Pseudocalanus spp. and the large-sized
Calanus glacialis/marshallae, Eucalanus bungii, Metridia
pacifica, and Neocalanus spp. (Hopcroft et al., 2010). Except
for Neocalanus spp., these large copepods reproduce withfeeding on phytoplankton blooms at the surface layer(Padmavati et al., 2004; Shoden et al., 2005; Søreide et al.,2010). In fact, this strong link between phytoplankton
blooms and copepod phenology has been demonstrated
from field observations. Specifically, the community
structure of copepods is affected not only by water mass
characteristics but also by phytoplankton bloom timing(Kimura et al., 2020).
The early sea-ice reduction in 2018 resulted in an increasein small copepod populations, which was potentially due towarm temperatures (Duffy-Anderson et al., 2019). Further,
this phenomenon also caused delay of the phytoplankton
blooms (Kikuchi et al., 2020), which could have affected theincluding fish, birds, and mammals (Springer et al., 1996; Kuletz
et al., 2015). However, this region has recently faced drasticchanges in sea-ice cover and hydrographic conditions during
summer (Grebmeier et al., 2015; Frey et al., 2018). The sea-ice
coevrage period in the northern Bering Sea is highly variable
and exhibits no inter-annual trends until 2000s; however, fromthe winter of 2014 and 2015, ice coverage in this region has
decreased remarkably (Stabeno and Bell, 2019; Stabeno et al.,2019). Particularly, in the winter of 2017 and 2018, the seaice coverage reached a historical minimum based on satellite
observations dating back to 1978 (Cornwall, 2019). This early sea
ice retreat has been largely attributed to relatively warm winds
from the south, accompanied by a westward shift of the Aleutian
low from its typical position (Stabeno et al., 2019; Basyuk andZuenko, 2020). This drastic change in sea ice coverage may
weaken the water column stratification of the Chirikov Basin(Ueno et al., 2020), thus decreasing the cold pool in the bottom
layer of the Bering shelf (Stabeno and Bell, 2019).
The impacts of the early sea-ice retreat are also clearlyobserved at several trophic levels in the marine ecosystem,
including phytoplankton bloom delays (Kikuchi et al., 2020)and phytoplankton community shifts from cold-water tocosmopolitan species (Fukai et al., 2020). Moreover, fish
populations have been found to migrate northward in responseto the aforementioned cold pool decrease, which functions
as a barrier to these fishes (Cornwall, 2019; Duffy-Anderson
et al., 2019; Stevenson and Lauth, 2019). Additionally, these

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FIGURE 4 | Cluster analysis based on zooplankton abundance in the northern Bering Sea during July of 2017 and 2018 (A). Five groups (A–E) were identified fromthe Bray–Curtis similarity connected with UPGMA. Mean abundance and species/taxon composition of each group (B). dbRDA plots of the five groups withenvironmental parameters (C). The direction and length of the lines indicate the relationship between groups and the strength of the relationship. The station IDs in
the plots indicate the year-station number. NO, nitrate+nitrite; P, phosphate; Si, silicate; TSM, time since sea-ice melt.
TABLE 1 | Summary table of the results of the DistLM sequential tests, results shown are for the model with the lowest AICc values for each response variable.
Variables

AICc

SS

Pseudo-F

Si (OH)4

224.9

4238

6.084

0.001

0.15970.159732

PO4 –P

223.392429.1

3.791

0.001

0.091560.251331

TSM

222.252058.3

3.468

0.004

0.07759

0.328930

NO2 +NO3

221.12

1925.5

3.517

0.002

0.072580.401529

P-value

Prop.

Cumul.

Res. df

SS, sum of squares; Prop., proportion of variance explained by each predictor variable; Cumul., cumulative of the proportion of variance explained by each predictor
variable; Res.df, residual degrees of freedom; TSM, time since sea-ice melt.

zooplankton community and population structure as potentiallychanging their reproduction timing (Kimura et al., 2020),
and this changes in zooplankton will reach higher trophic

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levels due to mis-match timing for getting their prey (DuffyAnderson et al., 2019). However, the effect of early seaice reduction on zooplankton community structure remains

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unclear because of a lack of information on zooplankton
species composition, population structure, and production in thenorthern Bering Sea.
Our study thus investigated the zooplankton community andpopulation structure of large-sized copepods in the northern
Bering Sea during the summers of 2017 (exhibiting a typicalsea-ice retreat timing after the winters of 2014 and 2015;
Stabeno et al., 2019) and 2018 (extremely early sea-ice retreat).
These 2 years were compared to evaluate the effects ofphytoplankton bloom delays coupled with an early sea-ice
reduction on zooplankton community structures. Additionally,
copepod production was estimated using metabolic equations

TABLE 2 | Mean abundance (ind. m−2 ) for all species/taxon of the zooplankton groups identified by cluster analysis (cf. Figure 4) in the northern Bering Sea during Julyof 2017 and 2018.
Taxon/species

Groups
A (9)

B (8)

C (3)

D (7)

E (7)

Appendicularians
Fritillaria spp.

374

181

39

6,814

4,379*

Oikopleura labradoriensis

1

5

2

2

823

Oikopleura vanhoeffeni

0

25

2

2

330

Oikopleura spp.

0

9

5

1

477

Chaetognaths
Eukrohnia hamata

36

6

0

112

18

Parasagitta elegans

228

3,316

1,522

3,390

513

Copepods
Acartia spp.

269

8,735

5,080

7,504

456

Calanus glacialis/marshallae

1,913*

20,665

12,255*

12,049*4,605*

Calanus spp. nauplii

1,047

39,133

9,738*

88,399*24,293*Centropages spp.

1184

4,941*

6,297

3,909

34,602*78,524*

16,365*

124,360*

64,900*3,042

Cyclopoida

1,624

16

0

400

Eucalanus bungii nauplii

0

0

0

1,007

3,290

Metridia pacifica

654

2,167

0

7,913

11,921

Microcalanus pygmaeus

408

58

0

311

532

Microsetella spp.

638

221

0

396

567

Neocalanus cristatus

23

0

0

0

60

Neocalanus flemingeri

620*

121

0

0

487

Eucalanus bungii

Neocalanus plumchrus

409

0

0

49

248

Oncaea spp.

4,905

768

215

5,222

8,907

Pseudocalanus spp.

17,667*

219,669*

50,465*

230,425*58718*

Pseudocalanus spp. nauplii

62

7,750

1,555

13,197*

3,838

Scolecithricella minor

110

0

0

0

55

Tortanus discaudatus

0

0

0

164

0

17

29

0

0

144

Barnacle cypris

541

65

2,001

7,914

2,110

Barnacle nauplii

4,013

0

802

6,333

9,077

Bivalvia larvae

3,491*

168,846*

13,098*

136,566*

6,915*

Cladocerans

0

0

4,100

7,764

0

Decapod zoea

25

36

36

0

11

Echinopluteus larvae

114

39,511

3,958

5,166

1,015

Euphausiacea

1,050

211

363

4,912

6,562*

Amphipoda

Hydrozoa

21

155

0

404

27

Limacina helicina

302

578

738

177

85

Ostracoda

0

0

0

76

48

Polychaeta

1,908*

39,816*

194,749

24,026*32,193*

Unidentified nauplii

86

3,316

3,807

6,656

839

Total copepods

64,963377,914100,613497,693

189,828

Total zooplankton

77,171

634,019

325,835

708,006

255,395

Bold indicates IndVal of greater than 25% for that group. *Represents top 50% of species in each gorup according to SIMPER analysis. Number in () represents N, numberof sampling stations.

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based on body mass and in situ temperature to evaluate theimpact of copepod community changes on higher trophic levels.

(OH)4 ) were measured using an auto-analyzer (QuAAtro 2HR,
BL-TEC Co., Ltd., Osaka, Japan).

Sample Analysis
MATERIALS AND METHODSIn the laboratory, zooplankton samples were split using a Motoda
box splitter (Motoda, 1959). The zooplankton in the aliquots was
identified and counted under a dissecting microscope. Calanoid
copepods were identified to the species and copepodid stage
level, as described by Brodsky (1967). Calanus glacialis andCalanus marshallae were treated as C. glacialis/marshallae inthis study owing to the difficulty of distinguishing between
these two organisms at the species level (Frost, 1974). The
mean copepodid stage (MCS) of the dominant large copepods(C. glacialis/marshallae, E. bungii, and M. pacifica) was calculatedusing the following equation:
P6
i = 1 i × Ai
MCS =P6
i = 1 Ai

Satellite Data
Sea-ice concentration data (10-km resolution) were obtained
from the Advanced Microwave Scanning Radiometer 2 (AMSR2)
to evaluate the extent of the sea ice coverage. These AMSR2 data
were supplied by the Japan Aerospace Exploration Agency via
the Arctic Data archive System (ADS)1 , through the cooperationof the National Institute of Polar Research and Japan Aerospace
Exploration Agency (JAXA). Based on the data, melt day (MD)was defined as the last date when the sea ice concentration (SIC)
fell below 20% prior to the observed annual sea-ice minimum
across the study region. Additionally, the time since sea-ice melt(TSM) was defined as the number of open-water days from the
MD to the sampling date at each station.

where i (1–6 indicate C1–C6) indicates the copepodid stage
for a given species and Ai (ind. m−2 ) is the abundance of a
copepodid stage (cf. Marin, 1987). For quantitative comparison of
the abundance among the stations, abundance per square meter(ind. m−2 ) was used for later analysis.

Field Samplings
A total of 34 zooplankton sample collections were taken by the
T/S Oshoro-Maru in the northern Bering Sea (62◦ 100 –66◦ 440 N,
166◦ 300 –174◦ 050 W) during 11–22 July 2017 and 2–12 July 2018
(Figure 1 and Supplementary Table 1). The study areas were thewaters south of St. Lawrence Island, the Chirikov Basin (from thenorth of St. Lawrence Island to the south of Bering Strait), andthe Bering Strait (Figure 1). Historically, this region has exhibitedcomplex hydrographic dynamics due to the inflow of multiple
currents with different hydrographic features (Danielson et al.,2017). These water masses can be identified by salinity rather
than location (Coachman et al., 1975). According to Coachman
et al. (1975), the three water masses were categorized as low-saline(S < 31.8) and low nutrient Alaskan Coastal Water (ACW), highsaline (S > 32.5) and high nutrient Anadyr Water (AnW), andBering Shelf Water (BSW, 31.8 < S < 32.5).
Zooplankton samples were collected by vertical hauls with a
NORPAC net (mouth diameter: 45 cm; mesh size: 150 µm) from
5 m above the bottom. The towing depth of the net ranged from22 to 71 m. The volume of the water filtered through the net
was estimated using a one-way flow meter (Rigosha CO., Ltd.,
Bunkyo-ku, Tokyo, Japan) mounted in the mouth of the net.
Zooplankton samples were immediately preserved using 5% v/v
borax buffered formalin. At all stations, temperature, salinity,and fluorescence were measured using vertical casts of a CTD(SBE911Plus, Sea-Bird Electronics Inc., Bellevue, WA, UnitedStates). The mixed layer depth was defined as the depth where
the density was 0.10 kg m3 greater than the value at a 5 m depth(Danielson et al., 2011). Water samples for nutrient analysis werecollected from 4 to 6 layers every 10 m from the surface to
5 m above the seafloor using a bucket and Niskin bottles (cf.
Fukai et al., 2020). The obtained unfiltered nutrient samples werefrozen on board at −80◦ C. In the laboratory, the concentrationsof major nutrients (NO2 –N + NO3 –N, NH4 –N, PO4 –P, and Si
1

Data Analysis
Abundance data (X: ind. m−2 ) for each species were transformed
to the fourth root (X−4 ) prior to cluster analysis to reduce the biasof abundant species. Similarities between samples were examinedusing the Bray–Curtis index according to differences in species
composition excluding rare species (i.e., those that occurred only
once in the study area). For grouping samples, similarity indiceswere coupled with hierarchical agglomerative clustering andthe complete linkage method (unweighted pair group methodwith arithmetic mean; UPGMA) (Field et al., 1982). Based on
the fourth root transformed abundance, similarity percentages(SIMPER) analysis was applied to determine which species
contributed to the top 50% of total abundance for each group. To
find potential indicator species in the groups that resulted fromthe cluster analysis, the program Indicator Value (IndVal) wasapplied (Dufréne and Legendre, 1997).
To evaluate the relationship between environmental
parameters and zooplankton community, a distance based linear
modeling (DistLM) and a distance based redundancy analysis(dbRDA) were carried out. Firstly, to remove multicollinearity
among the environmental parameters (mixed layer depth,mean water column temperature, mean water column
salinity, integrated water column fluorescence [the sum of
the fluorescence values from the water column], mean watercolumn nutrients [NO2 –N + NO3 –N, NH4 –N, PO4 –P, andSi (OH)4 ], MD and TSM), we calculated variance inflation
factors (VIF) for each parameter. Only MD showed higher than
5 to TSM, it was removed from the explanatory parameters.
All environmental parameters were normalized, and thenthe DistLM was run with dbRDA plot by combining theresemblance matrix (based on Bray–Curtis similarities between
the abundances of zooplankton species in the samples) and thehttps://ads.nipr.ac.jp/

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Zooplankton Change by Early Sea-Ice Reductionhydrographic variables. To run DistLM, we choose a “Step-wise”
as a selection procedure, a “AICc” as a selection criterion, and thenumber of permutations was 999. The analyses were conductedusing the Primer 7 software (PRIMER-E Ltd., Albany, Auckland,
New Zealand).
Additionally, the interannual differences in copepodite
abundance, nauplii abundance, and MCS for the dominant large
copepods (C. glacialis/marshallae, E. bungii and M. pacifica)
were compared using the Mann–Whitney U test performed in
the StatView v5 software (SAS Institute Inc., Cary, NC, UnitedStates).

Copepod Biomass and Production
Biomass of copepods at each station was estimated bymultiplying individual dry weight (mg ind.−1 ) from references
and unpublished data (cf. Supplementary Table 2), abundance(ind. m−2 ) and a conversion factor 48% from dry weight
to carbon biomass (Kiørboe, 2013). Then, we classified
C. glacialis/marshallae as Arctic copepods, and E. bungii, N.
cristatus, N. flemingeri, N. plumchrus, M. pacifica as Pacificcopepods, and the other copepods (cf. Table 2) as small copepods.
To calculate the production of copepodite stages of all
copepods, the respiration rate (µL O2 ind. −1 h−1 ) was calculatedusing the following equation (Ikeda et al., 2001).
Ln(R) = − 0.399 + 0.801 ∗ Ln (DW) + 0.069 ∗ T
where R is the respiration rate, DW is the individual dry weight(mg ind. −1 ) from references (cf. Supplementary Table 2)
and T is the ambient temperature (◦ C). The respiration rate
was converted to carbon units by assuming a respiratory
quotient of 0.97 (Gnaiger, 1983) and multiplied by 0.75
(Production = 0.75∗ Respiration, cf. Ikeda and Motoda, 1978),abundance (ind. m−2 ), and 24 (hours) to estimate production(P, mg C m−2 day−1 ). To evaluate the effect of temperature andspecies composition on copepod production, a pacification index(%) of copepod production was calculated using the following
equation:

FIGURE 5 | Horizontal distribution of the five groups identified from theBray–Curtis similarity analysis based on zooplankton abundance (cf. Figure 4)
in the northern Bering Sea during July of 2017 and 2018.

ACW. Whereas the cold and high saline water of the westernside was attributed to the AnW (Figures 2, 3). All nutrients also
exhibited high concentrations associated with the high salinityof the Chirikov Basin. Both years exhibited high chlorophyll
fluorescence at the Bering Strait.

Pacification index(%) = PPacific copepods /Pwhole copepods ∗ 100
where Ppacific copepods is the Pacific copepod production,
Pwhole copepods is the sum of the whole copepod production(including small, Arctic, and Pacific copepods). Finally, P/B ratio
was assumed by dividing the production with the biomass.

Zooplankton Community
The zooplankton abundances ranged from 31 to 1,163 × 103
ind. m−2 in 2017 and from 240 to 1,420 × 103 ind. m−2 in2018, and relatively high values were observed in the southof St. Lawrence Island (Supplementary Figure 1). Copepods
were the most dominant taxon, accounting for 11%–97% of theoverall zooplankton abundance throughout the study site. Smallsized copepods such as Cyclopoida and Pseudocalanus spp. were
dominant in the eastern side and the south of St. Lawrence
Island. Pacific copepods (i.e., E. bungii, M. pacifica, Neocalanus
cristatus, Neocalanus flemingeri, and Neocalanus plumchrus)

RESULTSSea Ice, Water Mass, and Hydrography
The MD in 2018 was much earlier than that in 2017; MD occurredfrom April 4 to May 11 in 2017 and from March 23 to April
29 in 2018 (Supplementary Table 1 and Figures 2, 3). In thisstudy, the spatial variations of in situ hydrographic parameters
showed interannual similarities. The eastern side exhibited hightemperatures and low salinity, these features belonging to the

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FIGURE 6 | T-S diagram of the five groups (A–E) identified from Bray–Curtis similarity analysis based on zooplankton abundance (cf. Figure 4) in the northern Bering
Sea during July of 2017 and 2018. The numbers in the panels indicate water density. wCW, warm coastal water; wSW, warm shelf water; IMW cCW, ice melt waterand cool coastal water; cSW, cool shelf water; AnW, Anadyr water; MWW, modified winter water; WW, winter water (cf. Danielson et al., 2020).

were categorized in nutrients (silicate, phosphate and nitrate andnitrite), and the remaining one was TSM (Table 1).
Group D exhibited the highest total zooplankton abundance(708,006 ind. m−2 ) due to the abundance of small size copepods(Cyclopoida, Pseudocalanus spp.), Calanus spp. nauplii andbivalve larva in group D (Table 2). In the group, the highest
number (total seventeen species/taxon) of the indicator species,
including the small copepods, Fritillaria spp., barnacle nauplii
and cypris, bivalve larva, were selected by IndVal (Table 2).
Whereas group E had high abundances of Oikopleura species,
Euphausiacea and Pacific copepods (especially, E. bungii and
M. pacifica) originating from AnW. The Pacific copepod

occurred from the south of the St. Lawrence Island to the Bering
Strait and were especially abundant in Chirikov Basin in 2018
(Supplementary Figure 1).
Based on cluster analysis of zooplankton community
composition, five groups (A–E) including 3–9 stations wereidentified at 55%, 62%, and 69% similarity levels (Figure 4A).
Copepoda was the most dominant taxon in the groups except
group C, which was dominated by Polychaeta (Figure 4B). Eachzooplankton assemblage was also relatively well separated in the
dbRDA plot (Figure 4C). As a best solution in DistLM, four
environmental parameters were selected and explained 40.1% of
the zooplankton variation (Table 1). In the result, three variables

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TABLE 3 | Mean and standard deviations of environmental factors between the groups identified by cluster analysis (cf. Figure 4) in the northern Bering Sea during Julyof 2017 and 2018.
Parameters

Groups
A (9)

B (8)

C (3)

D (7)

E (7)

Temperature (◦ C)

4.09 ± 0.98

2.97 ± 0.53

6.39 ± 1.83

4.80 ± 0.99

3.54 ± 0.47

Salinity

32.24 ± 0.26

31.85 ± 0.19

30.97 ± 1.38

31.82 ± 0.38

32.76 ± 0.09

Fluorescence

16.65 ± 4.81

17.31 ± 6.77

9.32 ± 3.86

19.92 ± 9.31

28.47 ± 16.32

Phytoplankton cell density (cells mL−1 )a90.01 ± 59.40

30.43 ± 31.90

126.50 ± 40.23

45.10 ± 53.48

252.30 ± 317.28

NO2 +NO3 (µM)

9.26 ± 6.26

4.40 ± 2.22

0.31 ± 0.37

3.68 ± 3.36

13.10 ± 3.77

PO4 -P (µM)

1.21 ± 0.44

1.21 ± 0.22

0.76 ± 0.16

1.11 ± 0.17

1.58 ± 0.44

NH4 -N (µM)

0.94 ± 0.35

0.76 ± 0.40

0.21 ± 0.10

0.66 ± 0.45

0.84 ± 0.37

Si (OH)4 (µM)

27.57 ± 10.94

19.74 ± 8.37

10.53 ± 1.90

9.53 ± 4.47

28.39 ± 8.29

Depth (m)45.11 ± 5.69

61.25 ± 11.39

33.33 ± 9.29

43.29 ± 7.34

52.43 ± 3.99

Mixed layer depth (m)9.33 ± 3.12

12.13 ± 2.42

8.33 ± 4.04

14.00 ± 6.20

19.25 ± 9.25

MD (day)

106.67 ± 12.29

101.38 ± 14.67

120.00 ± 6.00

106.00 ± 10.63

116.29 ± 16.33

TSM (day)

91.22 ± 14.65

95.36 ± 9.04

70.33 ± 2.89

83.43 ± 8.24

72.86 ± 14.25

The numbers in parentheses indicate the number of stations belonging to each group. Melt day (MD) is indicated by the day of the year. TSM, time since sea-ice melt.
a Data from Fukai et al. (2020).

lower in 2018 (2.10–3.44) compared to 2017 (3.02–5.24). E. bungii
mainly occurred in the north of the St. Lawrence Island withno interannual differences. A higher abundance of M. pacifica
was observed in 2018 than in 2017. The MCS of the copepod
species exhibited a clear border at the west of St. Lawrence
Island. Specifically, later stages (3.29–4.63) were observed in thenorthern area, whereas younger stages (1.46–2.32) were observedin the south. In 2018, their MCS remained low throughout
the sampling area.
The abundance of Calanus spp. nauplii ranged from 0 to
26,136 ind. m−2 in 2017 and from 12,608 to 224,316 ind. m−2 in2018 (Figure 8), and high abundances were frequently observedin 2018. Eucalanus bungii nauplii were distributed only aroundthe Bering Strait in 2017 but they were observed throughout the
study area in 2018.
To compare the interannual population structure of large
copepods excluding water mass differences, the abundanceand MCS of group E copepods were compared between 2017and 2018, as this group occurred in both years and showedsimilar hydrographic conditions (cf. Figure 6). This comparison
indicated that C. glacialis/marshallae and M. pacifica exhibited
significantly higher abundances and younger populations in2018 than in 2017 (Table 4 and Figure 7). Calanus spp.
nauplii were also significantly more abundant in 2018 thanin 2017. In contrast, E. bungii did not exhibit any significant
interannual differences.

N. flemingeri and N. plumchrus were also selected as indicatorspecies in group A. The horizontal distributions of the groups
varied between the study years, and groups A and D were only
observed in 2017 and 2018, respectively (Figure 5). Interannualchanges in group E distribution were also identified, whereas
groups B and C remained, respectively, distributed in the southof St. Lawrence Island and near the Alaskan coasts in both years.

Hydrographic Features in the ClusteringGroups
According to Danielson et al. (2020), water mass compositionin the water column was different among the groups. Warm
Coastal Water (wCW) was mainly observed in group C, all groups
presented warm Shelf Water (wSW), cool Shelf Water (cSW)
was mainly observed in groups A and B, Anadyr Water (AnW)in groups A and E, and Modified Winter Water (MWW) and
Winter Water (WW) in group B (Figure 6). The interannual
differences of the water mass composition in groups B andE were generally subtle, especially the salinity of group E. In
contrast, the T-S diagram of group C varied greatly between years(Figure 6). As hydrographic features of the groups, groups A andE exhibited high concentrations of all nutrients except NH4–N
compared to the other groups (Table 3). Group B showed thelowest temperature owing to the presence of MWW and WW.
The warmest water was observed in Group C. Group D did notshow clear difference on hydrographic parameters among thegroups, excepting slightly higher temperature comparing to thesein group A and E.

Biomass and Production of Copepods
The copepod biomass and production exhibited significant intergroup differences (Figures 9A,B). In groups B and C, Arctic
species mainly contributed to the parameters, whereas small
copepods dominated biomass and production in group D. In
groups A and E, biomass and production were dominated by
Pacific copepods, but was lower than that observed in the othergroups. Regarding to P/B ratio, small copepods showed thehighest contribution (4.54–5.32%) among the group category

Population Structure of Large Dominant
Copepods
Calanus glacialis/marshallae abundance was high in the south of
St. Lawrence Island but low in Chirikov Basin in 2017 (Figure 7).
In contrast, their abundance was high through the samplingarea in 2018. The MCS of the copepod species was substantially

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Zooplankton Change by Early Sea-Ice Reduction(Figure 9C). When comparing the effects of the pacificationindex and temperature on the copepod community, the index did
not increase the total production, and temperature had a slightly
positive effect on the production (Figure 9D).

DISCUSSION
Summer Zooplankton Community in
Alaska Coastal Region and St. Lawrence
IslandInterannual differences in the zooplankton community wereobserved in the Chirikov Basin but not near the Alaska coastal
region and the south of St. Lawrence Island.
Group C was observed in the Alaska coastal region during
both years. Given that the water column of this group exhibitedhigh temperature and low salinity, we concluded that this groupbelonged to the ACW community (Coachman et al., 1975;
Danielson et al., 2017). The ACW community is abundant incladocerans and coastal copepods (e.g., Acartia hadsonica andCentropages abdominalis) (Hopcroft et al., 2010). However, in
this study, mitraria larvae of benthic Polychaeta (Shanks, 2001)
were the most dominant taxa. Particularly, the highest abundance(581,378 ind. m−2 ) of the mitraria larvae was recorded at Stn.
19 in 2018, where the integrated water-column salinity was 29.4,
which was lower than the previous value (29.9–30.8) defined as
ACW in Hopcroft et al. (2010). Therefore, given that Stn. 19
was strongly affected by brackish water from the coastal region,
mitraria larvae of benthic Polychaeta were dominant in this study
region. In brackish water, the copepod production was extremely
low (14.68 mg C m−2 day−1 ) even at the highest temperature(8.5◦ C).
Group B was observed south of St. Lawrence Island inboth years, and was characterized with high TSM in dbRDA(Figure 4C) because their stations located in the most southernarea in whole study region. On zooplankton composition in thegroup, Acartia spp., C. glacialis/marshallae, bivalve larvae, andechinopluteus larvae were dominated. The biomass of C. glacialis
was linked to the presence of cold (<0◦ C) and saline WW(Pinchuk and Eisner, 2017). In this study, because of the presenceof WW in the near-bottom layer in 2017, C. glacialis/marshallae
was abundant in group B. The highest copepod production wasobserved in this group owing to the contribution of this species.
Moreover, the south of St. Lawrence Island is known to be a
hot spot of benthic species (Grebmeier et al., 2006), and thehigh abundances of bivalve and echinopluteus larvae in group B
are likely due to their high reproduction rates. Given the highcopepod production and benthic larva abundance observed inthis region, we concluded that the south of St. Lawrence Island
plays a key role in sustaining the zooplankton abundance of thenorthern Bering Sea.

FIGURE 7 | Horizontal distribution of dominant large copepods in thenorthern Bering Sea during July 2017 (left) and 2018 (right). The circle sizeand color denote the abundance and mean copepodite stage of eachspecies. The black frames indicate the stations identified as group E, as
demonstrated by Bray–Curtis similarity analyses based on zooplankton
abundance (cf. Figure 4).

A and E were characterized by the presence of Pacific copepods(N. flemingeri, N. plumchrus, E. bungii, and E. bungii nauplii).
These species are transported by saline and nutrient-rich AnW(Springer et al., 1989; Matsuno et al., 2011; Ershova et al., 2015).
In fact, the salinity and the concentration of all nutrients except
NH4 –N in the water column of groups A and E were higher
than those of the other groups. Based on species compositionand hydrographic results, groups A and E in 2017 were found tobelong to the AnW community. Additionally, nutrients (silicate,nitrate and nitrite) were significantly contributed to zooplankton
variability, and groups A and E were located in higher values(Figure 4C). In other words, nutrients may be a good indicator ofwater masses and zooplankton assemblages in this region during
summer (Springer et al., 1989; Matsuno et al., 2011; Ershova et al.,2015).
We next compared the Pacific copepod abundances inthis study with those reported in a previous study from thenorthern Bering Sea and the Chukchi Sea during September

Interannual Differences in Chirikov Basin
In Chirikov Basin, the zooplankton community explicitly showedinterannual changes. For instance, groups A and E were observedin 2017, whereas groups D and E were observed in 2018. Groups

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FIGURE 8 | Horizontal distribution of dominant large copepod nauplii in the northern Bering Sea during July 2017 (left) and 2018 (right). The circle size denotes theabundance of each species. Black frames indicate the stations identified as group E, as demonstrated by Bray–Curtis similarity analyses based on zooplankton
abundance (cf. Figure 4).

and an increase in Appendicularia, E. bungii, and Euphausiacea
compared to group A.
Group D, which was observed only in 2018, had thehighest abundance of total zooplankton and copepods and was
dominated by small copepods (Cyclopoida and Pseudocalanusspp.). Based on studies in the southeastern and northern Bering
Sea during the summer of 2018, the abundance of small copepods(<2 mm prosome length) increased whereas that of large
copepods (>2 prosome length) decreased owing to a reduction in
sea-ice cover, elevated temperature, and delayed phytoplankton
spring bloom (Duffy-Anderson et al., 2019). The temperaturesin group D were slightly higher than those in groups A and
E in this study, and therefore these warm conditions may
have accelerated the growth rate of small copepods (Corkett
and McLaren, 1978). Regarding copepod food availability, thenumber of diatom resting stage cells in the surface sediment,a variable that represents the time-integrated abundance of
diatoms from sea-ice melt to the sampling period (e.g., spring andsummer), was 10–100 times higher in 2018 than in 2017 (Fukai
et al., 2019). This suggests that food was much more available
for copepods in 2018 than in 2017. In turn, these unusual
conditions (warm temperatures and food abundance) associatedwith early sea-ice melt favored the reproduction, survival rate,

2007 (Eisner et al., 2013). Neocalanus species exhibited a similarabundance in both studies [23–620 ind. m−2 (this study) vs. 10–
1,718 ind. m−2 (cf., Table 4 in Eisner et al., 2013)]. However,
the abundances of E. bungii and M. pacifica were much lower
in this study than in the previous work. Particularly, group A
showed only approximately 1/20 of the abundance reported in theearlier study. It is also worth noting that the sampling methods
between the studies were almost the same (mouth diameter 45 cm
vs. 50 cm; mesh size 150 µm vs. 160 µm). The characteristics
of group A, which belonged to the AnW community withlow abundance, had not been previously reported in Chirikov
Basin. The group A water column is known to be strongly
stratified (Ueno et al., 2020), and the upper layers were occupied
by low-density wCW and wSW. These observations suggested
that low-density water masses prevent nutrient supply from thebottom layer. In turn, this restricts phytoplankton growth anddramatically decreases zooplankton abundance. In contrast, thewater column of group E exhibited thorough vertical mixing
in 2017 due to weak stratification (Ueno et al., 2020). AnW
upwells in the Chirikov Basin by bottom friction through theAnadyr Strait (Kawaguchi et al., 2020). Therefore, nutrientrich AnW upwells into the upper layer without stratification ingroup E, followed by an increase in phytoplankton production

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FIGURE 9 | Inter-group comparison of copepod biomass (A), production (B), and P/B ratio (C) by group category (cf. Supplementary Table 2) in the northern
Bering Sea during July 2017 and 2018. The values in the parenthesis are the number of stations in each group (cf. Figure 4A). Relationship between the pacificationindex, temperature, and total copepod production in the northern Bering Sea during July 2017 and 2018 (D). The pacification index in the copepod community wascalculated by dividing the Pacific-copepod production by the whole copepod production (see the “Materials and Methods” section for more details). The symbols
indicate cluster groups.

(Padmavati and Ikeda, 2002). However, given that these values
were much lower than the interannual MCS differences (2.15
MCS and 2.00 MCS for C. glacialis/marshallae and M. pacifica,
respectively) in this study, we concluded that the sampling date
was not the main reason for the observed differences.
Alternatively, reproduction timing, which is strongly linked
with spring bloom (Padmavati et al., 2004; Shoden et al., 2005;
Søreide et al., 2010) was also explored. In 2017, the spring bloomin the Chirikov Basin was observed as an ice-edge bloom on
23 April due to a late sea-ice retreat (Kikuchi et al., 2020). In
2018, however, the open-water bloom was delayed (17 May)
owing to an early sea-ice retreat and weakened water column
stratification by strong winds (Kikuchi et al., 2020). Owing tothis shift in the phytoplankton bloom timing, the number of days
from the bloom to the sampling date was calculated as 78.5 daysin 2017 and 49 days in 2018. Additionally, the abundance of thetwo species was significantly higher in 2018 than that in 2017
(U test: p < 0.05). Assuming that the natural mortality rate of
the copepods was not different in both years, the reproduction
timing of the species in 2018 was closer to the sampling datethan that in 2017. Therefore, late sea-ice retreat induces early
reproduction of large copepods with ice-edge bloom, as observedin 2017. Conversely, early sea-ice retreat delays phytoplankton
blooms resulting in copepod reproduction delays, as observed in
2018. In conclusion, the interannual changes in the populationand growth rates of small copepods (Duffy-Anderson et al., 2019),
which eventually resulted in high copepod production in the
Chirikov Basin in 2018.

Interannual Changes in the Population
Structure of Large Copepods
The zooplankton biomass in the northern Bering Sea wasdominated by the large copepods C. glacialis/marshallae,
E. bungii, M. pacifica, and Neocalanus species (Hopcroft
et al., 2010; Eisner et al., 2013). Among these species,
C. glacialis/marshallae, E. bungii, and M. pacifica reproduce inthe upper layer with active phytoplankton grazing (Padmavati
et al., 2004; Shoden et al., 2005; Søreide et al., 2010). Owing to thestrong link between copepod reproduction and phytoplankton
blooms, zooplankton population structure was expected to be
directly affected by shifts in spring bloom timing.
The interannual comparison of population structure for thethree large copepods in group E, C. glacialis/marshallae, andM. pacifica exhibited significant differences, particularly a higher
abundance and younger stages in 2018 than in 2017. This was
likely because sampling was conducted 5.5 days earlier in 2018
than in 2017. When copepod development was estimated inthese 5.5 days, we assumed +0.77 MCS for C. glacialis at 4◦ C
(Corkett et al., 1986) and +0.20–0.31 MCS for M. pacifica at 5◦ C

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TABLE 4 | Result of the Mann–Whitney U test on mean and standard deviation of copepodite abundance, nauplii abundance, and MCS of the dominant large copepods in group E identified via Bray–Curtis similarity
analysis based on zooplankton abundance (cf. Figure 4) in the northern Bering Sea during July of 2017 and 2018.
Species13

C. glacialis/marshallae

E. bungii

M. pacifica

Year

Abundance of copepodites

Abundance of nauplii

Estimated MCS
based ondifferences insampling date
(5.5 days)

MCS

Mean

SD

p-valueMeanSDp-valueMeanSDp-value

2017

1,613

1,004

0.0002

7,512

5,021

0.03444.44

0.05<0.0001

2.15

2018

8,594

733

46,66827,412

2.29

0.07

2017

2,076

1,806

0.7596

−0.07

2018

4,329

4,984

2017

811

640

0.00222.00

201826,734

14,436b Padmavati and Ikeda (2002).

0.0138

1,982

1,100

5,034

3,485

0.1516

1.86

0.35

1.93

0.20

4.31

0.26

2.31

0.64

+0.77 MCS (4◦ C)a+0.20–0.31 MCS(5◦ C)b

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a Corkett et al. (1986).

0.4314

Difference
between 2017and 2018

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Zooplankton Change by Early Sea-Ice Reduction

structure of the large copepods C. glacialis and M. pacifica arelargely driven by the timing of phytoplankton blooms associatedwith sea-ice retreat.
It is not immediately clear why E. bungii abundances andpopulations did not change between the studied years; however,
we believe this could have been due to species-specific grazing
strategies. C. glacialis and M. pacifica are believed to selectively
graze on microzooplankton, whereas E. bungii has been described
as an opportunistic grazer (Campbell et al., 2016). Thus, thesensitivity of copepods to environmental changes including preyavailability could be substantially affected by their species-specificgrazing strategy.

The northern Bering Sea and the southern Chukchi Sea areundergoing “pacification,” a borealization process related to thetransport of anomalies from sub-Arctic seas into the Arctic
Ocean, such as an increased influx of Pacific water containing
sub-Arctic species (cf. Polyakov et al., 2020). We estimated thepacification index of copepod production as the ratio of Pacificcopepod production to that of all copepod species. In casesof high pacification indices (e.g., groups A and E), the Pacificcopepods contributed half of the total copepod production;
however, the amounts did not reach the original production by
Arctic copepod C. glacialis (e.g., groups B and C). Therefore, thepacification of the copepod community will ultimately decrease
copepod productivity in the study area. The marine ecosystemof the northern Bering Sea is thus undergoing profound andunexpected ecological changes due to its unique geographicalcharacteristics (e.g., high-latitude, nutrient inflow) (Huntington
et al., 2020), which will invariably affect the local marine
biota. Therefore, continued monitoring of the trophic levels andenergy transfer in this region is required to prevent further
ecological impacts.

Ecological Impacts of Changing
Zooplankton Communities
Our findings demonstrated that early sea-ice retreat induceshigh copepod production with abundant small copepods andyoung stages of large copepods. The changes in copepod size
frequency and production can directly and indirectly affecthigher trophic levels, particularly fish and planktivorous seabirds(Heintz et al., 2013; Jones et al., 2018; Duffy-Anderson et al.,2019). Negative effects in higher trophic levels were reported
during 2018, including decreases in the population of forage
fish species associated with decreases in phytoplankton blooms
and zooplankton productivity (Cornwall, 2019; Duffy-Anderson
et al., 2019). Other studies have reported high mortality rates
and reproductive failure of sea birds due to warm temperaturesand food scarcity (Jones et al., 2018, 2019; Dragoo et al., 2019;
Nishizawa et al., 2020), whereas another study reported a decline
in phocid seal body condition (fat and mass/length) (Boveng
et al., 2020). These opposite responses between zooplankton
and higher trophic levels suggest that small copepods do notefficiently reach the higher trophic levels despite being highly
abundant, which reported in the 1980s investigations (Springer
et al., 1989). Food availability, food quality, and transfer efficiency
could thus be limiting factors that prevent the transfer of energy
from copepods to higher trophic levels. Food availability forfish species could be expressed as a ratio of small copepod
production to total copepod production, and the lowest value(31.9%) was observed in group D compared to the other groups(51.7%–81.1%). This low food availability (dominance of small
individuals) likely caused a marked decrease in predator feeding(O’Brien, 1979). Almost none of the small copepods in thisstudy store lipids in their bodies, and therefore small copepod
species are far less nutritious than their larger counterparts. In
the Bering Sea, the dominance of low-lipid (i.e., low nutrition)
zooplankton has decreased the survival rate of pollock in winterregardless of zooplankton abundance (Heintz et al., 2013). Recent
studies linked a lack of interaction between the members of theplankton food web during 2018 with a decrease in energy andmaterial transfer efficiency (Yamaguchi et al., 2021), resulting in a
substantial decrease in jellyfish populations in 2018 (Maekakuchi
et al., 2020). Therefore, even if zooplankton abundance andproduction are higher, the dominance of small-sized and lownutrition zooplankton adversely affects the higher trophic levelsin the northern Bering Sea (Springer et al., 1989).

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CONCLUSION
In this study, the patterns of inter-annual changes in thezooplankton community differed among the regions. The
zooplankton communities in the Alaska coastal region and thesouth of St. Lawrence Island were consistently affected by water
masses and input of benthic larvae without interannual changes.
Clear interannual changes in the zooplankton community
were observed in the Chirikov Basin. The AnW community,
which was characterized by pacific copepods, was dominant
in 2017, whereas the zooplankton community was dominatedby small copepods in 2018. This change was thought to be
associated with the unusual conditions (warm temperaturesand food abundance) caused by the early sea-ice reduction.
The large copepods C. glacialis and M. pacifica exhibited large
interannual differences in population structure, suggesting that
their reproduction timing shifted because of the changes inphytoplankton bloom timing.
Early sea-ice retreat resulted in a higher copepod production,
which was dominated by small copepods and young stages oflarge copepods; however, this increased copepod productivity did
not efficiently reach the higher trophic levels owing to decreasedfood availability and quality. Based on the pacification index
of the copepod community, pacification will further decrease
copepod production. Therefore, our findings demonstrated
that the zooplankton community and production was highly
sensitive to environmental changes (warm temperatures and foodabundance) associated with early sea-ice retreat, which adversely
affects the higher trophic levels in the northern Bering Sea.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will bemade available by the authors, without undue reservation.

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Kimura et al.

Zooplankton Change by Early Sea-Ice Reduction(Pioneering), JP20H03054 (B), JP20J20410 (B), JP19H03037 (B),JP18K14506 (Early Career Scientists), and JP17H01483 (A).

AUTHOR CONTRIBUTIONSKM and AY designed the study and wrote the manuscriptwith contributions from all authors. FK and YA performed theresearch. FK and KM analyzed the data. All authors contributed
to the article and approved the submitted version.

ACKNOWLEDGMENTS
We thank the captain, officers, crew, and researchers on board
the T/S Oshoro-Maru (Hokkaido University) for their crucial
contributions during the field sampling. The ADS dataset
is archived and was provided by the Arctic Data archive
System (ADS), which was developed by the National Instituteof Polar Research.

FUNDING
This work was funded by the Arctic Challenge for Sustainability(ArCS) (Program Grant Number JPMXD1300000000) and Arctic
Challenge for Sustainability II (ArCS II) (Program Grant Number
JPMXD1420318865) project. Funding was also provided by
the Environment Research and Technology Development Fund(JPMEERF20214002) of the Environmental Restoration andConservation Agency of Japan. Additionally, this work was partly
supported by the Japan Society for the Promotion of Science(JSPS) KAKENHI Grant Numbers JP21H02263 (B), JP20K20573

SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fmars.
2022.808910/full#supplementary-material

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