TYPE Original Research
PUBLISHED 29 November 2023
DOI 10.3389/fmars.2023.1233492

OPEN ACCESSEDITED BY

Letterio Guglielmo,
Anton Dohrn Zoological Station Naples,
Italy
REVIEWED BY

Vladimir G Dvoretsky,
Murmansk Marine Biological Institute,
RussiaEmilia Trudnowska,
Polish Academy of Sciences, Poland
Gvm Gupta,
Centre for Marine Living Resources andEcology (CMLRE), India

Zooplankton size compositionand production just after drastic
ice coverage changes in
the northern Bering Sea
assessed via ZooScan
Shino Kumagai 1*, Kohei Matsuno 1,2 and Atsushi Yamaguchi 1,2
1

Faculty/Graduate School of Fisheries Sciences, Hokkaido University, Hakodate, Hokkaido, Japan,
Arctic Research Center, Hokkaido University, Sapporo, Hokkaido, Japan

2*CORRESPONDENCE

Shino Kumagaishino2189@eis.hokudai.ac.jp
RECEIVED 02 June 2023
ACCEPTED 13 November 2023
PUBLISHED 29 November 2023
CITATION

Kumagai S, Matsuno K and Yamaguchi A(2023) Zooplankton size composition andproduction just after drastic ice coveragechanges in the northern Bering Sea
assessed via ZooScan.
Front. Mar. Sci. 10:1233492.
doi: 10.3389/fmars.2023.1233492
COPYRIGHT© 2023 Kumagai, Matsuno and Yamaguchi.
This is an open-access article distributed
under the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in otherforums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication inthis journal is cited, in accordance withaccepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.

Drastic environmental changes were noted in the northern Bering Sea in 2018. A
reduction in sea ice affected several trophic levels within the ecosystem; thisresulted in delayed phytoplankton blooms, the northward shifting of ﬁsh stocks,
and a decrease in the number of seabirds. Changes in the community
composition of zooplankton were reported in 2022, but changes inzooplankton interactions and production have not been reported to date.
Therefore, this study examined predator-prey interaction, secondary
production, and prey availability for ﬁsh to understand the effect of early sea
ice melt. Zooplankton size data were estimated from the size spectra obtainedusing ZooScan based on samples collected in 2017 and 2018. A cluster analysis
based on biovolume showed that the zooplankton community could be divided
into three groups (Y2017N, Y2017S, Y2018). Y2017N, characterized by low
abundance, biomass, and production, Y2017S, characterized by highbiovolume and production, which contributed with Calanus spp., and Y2018,
characterized by low biovolume but high production, contributed with small
copepods, and Bivalvia. In 2017, the highest biovolume group was observed
south of St. Lawrence Island, and it was dominated by Calanus spp. and
Chaetognatha. Normalized size spectra of this group showed the highest
secondary production with present predator-prey interactions, suggesting thatthe area provides high prey availability for ﬁsh larvae and juveniles. In contrast,
small copepods and bivalve larvae were dominant in this area in 2018, whichcontain less carbons and energy, suggesting the prevalence of low-nutrient
foods in this year in relation to early sea ice melt.
KEYWORDS

sea-ice reduction, zooplankton biomass, normalized biomass size spectra, predatorprey interactions, secondary production

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10.3389/fmars.2023.1233492

particles. When NBSS slope is steep, is means small particles are lots
more than large particles. Therefore, NBSS slope can reﬂects energy
transfer from zooplankton to ﬁsh (Sprules et al., 1988). The opticalplankton counter (OPC) and laser optical plankton counter (LOPC)
are the most common instruments used in NBSS analysis (e.g.,
Herman and Harvey, 2006; Barid et al., 2008); however, these
instruments are only used to measure the size of zooplankton,
and conducting an analysis of size alone may not correlate with theactual trophic level in the food web structure (e.g., large jellyﬁshfeeding on phytoplankton). In addition, if NBSS analysis based only
on size is used to interpret energy transfer within the food chain, thefunction of the food chain may be misinterpreted due to the opacityof the prey-predator relationship (Yurista et al., 2014). In contrast,
ZooScan can be used to acquire and classify digital images ofzooplankton (Gorsky et al., 2010), and the combination of size
data and taxon information obtained from image analysis makes itsuitable for use in conducting food chain analyses, such as thosedescribed above (Grosjean et al., 2004; Gorsky et al., 2010; GarciaComas et al., 2011).
Therefore, in this study, the zooplankton community structureand size composition in 2017 (normal sea ice melt year) and 2018
(early sea ice melt year) were investigated using ZooScan, and theresults obtained from ZooScan for both years were compared toassess the effect of sea ice reduction on zooplankton size
composition. Production of whole taxa was then estimated using
size, taxon information, and in situ temperature, with the aim ofclarifying whether production was passed on to higher
trophic levels.

1 Introduction
The northern Bering Sea lies on a shallow shelf and has amaximum depth of 50 m. This region is inﬂuenced by the inﬂow of
three different water masses: warm Alaskan Coastal Water (ACW)with low salinity and nutrient levels; cold Anadyr Water (AnW)with high salinity and nutrient levels; and Bering Shelf Water(BSW), the characteristics of which are between those of the
ACW and AnW (Coachman et al., 1975). The continuous inﬂow
of AnW supports high primary productivity, resulting in highbiomass and trophic levels in this area (Springer et al., 1993).
Decreases in both the period and area of seasonal ice coverage innorthern Being Sea have been recently reported (Grebmaier et al.,2015). In the winter of 2018, the area of sea ice coverage reached anhistorical minimum based on satellite observations recorded over
40 years (Cornwall, 2019), and the reduced sea ice extent resulted inearly sea ice melting (Stabeno and Bell, 2019). The early sea-ice
retreat has been largely attributed to relatively warm winds from thesouth accompanied by a westward shift of the Aleutian Low from its
typical position (Stabeno and Bell, 2019; Basyuk and Zuenko, 2020).
Irregular phenomena associated with early sea melt have been
observed at several trophic levels: phytoplankton bloom delay(Kikuchi et al., 2020), the northward migration of subarctic ﬁshsuch as pollack and Paciﬁc cod (Stevenson and Lauth, 2019), and adecrease in the number of sea birds (Cornwall, 2019).
Zooplankton connect primary production and higher trophic
levels. The abundance of small copepods increased during thesummer of 2018 (Duffy-Anderson et al., 2019) because thedelayed phytoplankton bloom resulted in delayed reproduction
and large copepods did not reach their late developmental stage(Kimura et al., 2022). However, although the community structureof zooplankton and the population structure of copepods have beenreported, changes in the size composition of zooplankton have notbeen reported. As another problem, copepod production is only
estimated in the paper, but production of whole zooplankton taxa is
not assumed due to lack of size data.
The size of zooplankton is reﬂective of the trophic level, andonce the biomass of one size range is known, the abundance of othersize ranges can be estimated (Sheldon et al., 1977). Obtaining suchinformation is therefore important for understanding the ecosystem
structure. In addition, ﬁsh select prey based on zooplankton size,and differences in prey species affect the growth rates of juveniles
and the overwintering rates (Sheldon et al., 1977; O’Brien, 1979;
Meeren and Næss, 1993). Therefore, determining the zooplankton
size composition is not only important for assessing the ecosystem
structure but also for determining its potential as a food resource forhigher trophic levels.
Normalized Biomass Size Spectra (NBSS) were developed toanalyze the size composition of marine organisms (Sheldon et al.,1977). They have been used in various oceans to analyze marine
ecosystem structures and food chains (i.e. Garcia-Comas et al.,2011; Forest et al., 2012; Cornils et al., 2022). Characteristics of
NBSS analysis can be explained by its slope and intercepts. Slopereﬂects abundance of zooplankton, and production and energy
transportation can be explained by the slopes (Zhou, 2006). Thisis because NBSS slope explains ratio of large particles and small

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2 Materials and methods
2.1 Satellite data
Sea ice concentration (10-km resolution) was downloaded fromthe Advanced Microwave Scanning Radiometer 2 (ASMR2)
supplied by the Japan Aerospace Exploration Agency via the
Arctic Data Archive System (ADS), with the cooperation of the
National Institute of Polar Research and Japan Aerospace
Exploration Agency (JAXA). The data were then used to evaluate
the sea ice coverage extent. Based on these data, the melt day (MD)was deﬁned as the last date on which the sea ice concentration fell
below 20% prior to the observed annual sea-ice minimum across thestudy region. Time since sea-ice melt (TSM) was also deﬁned as thenumber of open-water days from the MD to the sampling date ateach station.

2.2 Field samplings
Sampling was conducted by the 40th and 56th cruises of the T/SOshoro-maru in the northern Bering Sea (62˚10’–66˚44’N, 166˚30’–
174˚05’W) during July 11–22, 2017, and July 2–12, 2018 (Figure 1).
Zooplankton samples were collected by towing a NORPAC net(0.45 m mouth diameter, 150 mm mesh) vertically from 5 m (22–71 m)
just above the seaﬂoor to the surface. The volume of water ﬁltered

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10.3389/fmars.2023.1233492

67˚ N

Chukchi Sea 2017
1
2

Siberia

29

BeringStrait

5

2018

30

27r

Alaska

6
7

65˚ N
15
16

18

22

9

24

16r

144

14 13 12 11 10
17
Anadyr Strait

17

19

AnadyrCurrent 11

19

63˚ N

20

Chirikov Basin

20

4

St. Lawrence
25 m
Island

21

22

6
8

23
AlaskaCoastalCurrent

50 m
100 m

Bering Sea

61˚ N
175˚ W

170˚ W

165˚ W 175˚ W

170˚ W

165˚ W

FIGURE 1

Location of stations in the northern Bering Sea during 11–22 July 2017and 2–12 July 2018. The numbers indicate station ID. Color arrows indicate
main currents in this region.

through the net was estimated using a one-way ﬂow meter (Rigosha
Co., Ltd., Bunkyo-ku, Tokyo, Japan) attached to the center of themouth. The collected zooplankton samples were immediately
ﬁlled with 5% buffered formalin and transported to thelaboratory. At the same station, the water temperature, salinity,and chlorophyll-a ﬂuorescence values were measured using CTD(Conductivity Temperature Depth Proﬁler; Sea-Bird Electronics
Inc., SBE911 plus).

Copepods were divided into three groups: Calanus spp., Other
large copepods (Eucalanus spp., Metridia spp., and Neocalanusspp.), and small copepods (Acartia spp., Centropages spp.,
Microcalanus spp., Microsetella spp., Oithona spp., Oncaea spp.,
Pseudocalanus spp., and Copepoda nauplii).
The size of each zooplankton was measured based on the studyof Vandromme et al. (2012), using the ellipse major axis (L major,mm) and minor axis length (L minor, mm), which best ﬁt thezooplankton image shape, and the volume of the ellipse (volume,mm3) and an equivalent spherical diameter (ESD, mm) with thesame volume as the ellipse were identiﬁed using the following
respective equations,

2.3 Sample analysis
A total of 34 zooplankton samples were divided into 1/16–64
sections using a Motoda splitter (Motoda, 1959) and image data
were obtained using ZooScan (Hydrooptic Inc.). First, the ZooScan
scanning cell was ﬁlled with deionized water to scan the backgroundimage using VueScan (Version 9.5.24), and the divided zooplankton
samples were then poured into the cell and scanned using VueScan.
The scans of the samples were digitized using a minute function at
2400 dpi, where one pixel corresponded to 10.6 mm. Image data
were semi-automatically separated per object using ZooProcess(Version 7.23) in Image J software (Version 1.410). All images
scanned through ZooScan were uploaded onto the website Ecotaxa,
where the gene level was semi-automatically identiﬁed based ondatasets made from ﬁve manually identiﬁed samples per sampling
year. All the images were manually checked to ensure that they had
been correctly identiﬁed. Copepods were identiﬁed up to the genus
level, and all other species were identiﬁed at the taxonomic level(from Order to Phylum). Images that could not be identiﬁed wereclassiﬁed as “unidentiﬁed organisms,” and those containing morethan one object were classiﬁed as “multiple.” Images identiﬁed as“multiple” and “detritus” were excluded from further analysis.

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Volume =4
L majorL minor 2
p
,
3
2
2

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
3 Volume  3
ESD =
:
4p
The biovolume (B: mm3 m-2) was calculated using the ﬁlteredwater volume (F: m-3) calculated by using data of ﬂowmeter, the tow
distance (L), split ratio (s) and the elliptical volume (Volume, mm3)
via the following equation.
B=

Volume  L
Fs

2.4 Data analysis
Based on vertical proﬁles in temperature and salinity, thicknessof water mass (according to Danielson et al., 2020) was calculated ateach station.

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10.3389/fmars.2023.1233492

Biomass = o

Zooplankton biovolumes (mm3 m-2) of 34 stations were used to
conduct a cluster analysis, and they were transformed into fourthroots (∜X) to reduce the bias of abundant species. The similarities
between the samples were examined using the Bray-Curtis
similarity index. Dendrograms were created using the mean
linkage method (unweighted pair group method using arithmetic
means) and were classiﬁed into multiple groups by separating themat speciﬁc similarity levels (Field et al., 1982). Accompanying thisanalysis, similarity proﬁle analysis (SIMPROF) was added todetermine if groupings of the stations were statistically signiﬁcant(at 5% signiﬁcance level).The relationship between each sample andnormalized hydrographic data (water temperature, salinity,
chlorophyll-a ﬂuorescence, MD, TSM and thickness in water
masses) was evaluated using DistLM (distance based linear
modeling) and a redundancy analysis (RDA). The parameters
were selected by procedures consisting of Step-wise, r2 and 999
permutations were used. The cluster analysis, DistLM, and RDA
were conducted using the Primer 7 software (PRIMER-E Ltd.,
Albany, Auckland, New Zealand).
Differences in hydrographic (water temperature, salinity,
chlorophyll-a ﬂuorescence, and MD) and zooplankton size
spectra parameters among the groups (identiﬁed by the cluster
analysis based on zooplankton biovolume) were tested using the
max-t method with a heteroscedastic consistent covarianceestimation (HC3) (Herberich et al., 2010). The tests wereconducted using R software with the packages “multcomp” and“sandwich” (version 4.1.2, R Core Team, 2021).
The NBSS analysis was conducted based on the size
composition data obtained from ZooScan. Size data were usedlater analysis in the 300–3400 mm ESD range. Zooplankton larger
than 3400 mm ESD (i.e., Cnidaria) were excluded from the analysis
because they were not quantitatively sampled by net and were easily
underestimated in ZooScan (Naito et al., 2019). Three ranges were
set to categorize the sizes of zooplankton: small size (ESD 267–1024mm), medium size (ESD 1024–2673 mm), and large size (ESD 2673–
5759 mm). The NBSS X-axis is a log10 transformation of thebiovolume for every log10 (0.25) ESD, (X: log10 zooplankton
biovolume, [mm 3 ind. - 1 ]) and the Y-axis is the log10transformation of the integrated biovolume of the size classes
classiﬁed by 100 mm ESD divided by the biovolume difference
between adjacent size classes (Dbiovolume [mm3]) (Y: log10
zooplankton biovolume [mm3 m-3]/Dbiovolume [mm3]). From X
and Y, the linear equation of the NBSS is deﬁned as Y = aX + b.
Comparing the NBSS between the clustered groups, ANCOVA was
performed using StatView, with the NBSS slope (a) applied as a
response variable and intercept and (b) cluster groups applied as
explanatory variables.
Zooplankton biomass and production were determined using
ZooScan image data. First, the individual dry weight (DWind: mg
DW ind-1) was calculated from the biovolume per individual (mm3ind.-1), assuming that the density was equivalent to water (mm3 =mg), and the result was multiplied by the water content for eachtaxon (cf. Omori, 1969; Kiørboe, 2013; Nakamura et al., 2017;
Matsuno unpublished data). The biomass per taxon was calculatedusing the following equation,

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​

DWind
L
sF

;

where s is the split ratio, F is the volume of ﬁltered water (m3),and L is the towing distance (m). To estimate production (mg C m-2
day-1), the respiration rate (R: mL O2 ind.-1 h-1) was calculated using
the following equation (cf. Ikeda, 1985),
Ln(R) = −0:399 + 0:801*Ln (DWind) + 0:069  T,
where DWind is the dry weight per individual, and T is theintegrated mean water temperature at the sampling station. From
the respiration rate, the production per individual, Pind (mg C ind-1
day 2) was estimated using the following equation (cf. Ikeda andMotoda, 1978),
Pind =

R  1224
 0:75  0:97 
:
22:4
1000

To calculate production (mg C m-2 day-1), Pind was summed
for each taxon, divided by the volume of ﬁltered water (m3), andthen multiplied by the towing depth.

3 Results
3.1 Hydrography
In both years, there were similar variations in water temperatureand salinity from east to west and with cold saline waters distributedon the western side (cf. Figure 2 and 3 in Kimura et al., 2022). On
the other hand, water mass distributions were slightly different
between the years (Figure 2). Warm Shelf Water (wSW) covered atsurface layer in both years, and cold Shelf Water (cSW) and Anadyr
Water (AnW) occupied below the water mass. However, Modiﬁed
Winter Water (MWW) and Winter Water (WW) were only
observed in the south of the St. Lawrence Island during 2017.
Chlorophyll a ﬂuorescence was higher in the Bering Strait during
both years (cf. Figure 2 and 3 in Kimura et al., 2022). The MD
occurred from March 23 to April 29 in 2018, which was earlier thanin 2017 (April 4 to May 11) (cf. Figure 2 and 3 in Kimura
et al., 2022).

3.2 Zooplankton community
Cluster analysis based on the biovolumes of all zooplankton
taxa revealed three groups (Y2017N, Y2017S, Y2018) withsimilarities of 62% and 65% (Figure 3A). By plotting the groups
on a geological map, their spatial and interannual distributions wereseparated (Figure 3B). Y2017N was found from the Bering Strait to
the Chirikov Basin in 2017 (Figure 3B); Y2017S was distributed
along the Alaskan coast and southwest of St. Lawrence Island
during both years; and Y2018 was spread widely from the Bering
Strait to the southwest of St. Lawrence Island in 2018. Y2017N was
dominated by Calanus spp. and other large copepods in similar
proportions (Figure 3C); Y2017S (with the highest observed
biovolume) was dominated by Calanus spp. and characterized by

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67˚ N

2018

2017

65˚ N

1
18

63˚ N
wCW
wSW
cSW
AnW
MWW
WW

61˚ N
175˚ W

170˚ W

165˚ W 175˚ W

170˚ W

165˚ W

FIGURE 2

Spatial distribution of water masses in the northern Bering Sea during 11–22 July 2017 and 2–12 July 2018. Deﬁnition of water mass is according toDanielson et al. (2020). wCW, warm Coastal Water; wSW, warm Shelf Water; cSW, cool Shelf Water; AnW, Anadyr Water; MWW, Modiﬁed Winter
Water; WW, Winter Water.

larvae specimens in the predator size classes (Figure 6). In Y2017N,
other large copepods and Euphausiacea dominated the late larvaland early juvenile stage predator size class; in Y2017S, large Calanus
spp. dominated the late larvae to early juveniles predator size class
but Chaetognatha was dominant in the late juvenile stage; and in
Y2018, small copepods were dominant in the late larval stage
predator size class.
Biomass ranged from 4.60–11.49 g DW m-2 and was the highest
and lowest in Y2017S and Y2017N, respectively (Figure 7A,
Supplementary Table 1). Y2017N was characterized by a highproportion of Calanus spp., other large copepods, andEuphausiacea (Figure 7A). Y2017S, which had the highest
biomass among the three groups, was dominated by Calanus spp.
In Y2018, small copepods dominated, followed by Calanus spp. andother large copepods (Figure 7A), and the proportion of Bivalvia
was higher (9.14%) than that of the other groups (0.05–0.2%).
Production ranged from 52.6–119 mg C m-2 day-1 in the threegroups, with the highest in Y2017S and the lowest in Y2017N(Figure 7B; Supplementary Table 2). Y2017N, which showed thelowest production rates, showed a high contribution from Calanus
spp., other large copepods, and Euphausiacea. Production was
dominated by copepods in Y2017S (66%), whereas small
copepods supported the production of Y2018, which was alsocharacterized by the production of Bivalvia. Euphausiacea showed
the second highest production following copepods in all threegroups, ranging from 6.68–13.93 mg C m-2.

the contribution of Euphausiacea, small copepods, andChaetognatha; and Y2018 contained the lowest biovolume but
was dominated by small copepods and other large copepods.
Each group was separately distributed in the RDA (Figure 4).
Comparing hydrographic data among the groups, only salinity
exhibited signiﬁcant difference; higher value was seen in theY2017N (32.3) than in the Y2018 (31.9) (Table 1).
An NBSS analysis based on size composition was performed forthe three groups classiﬁed by the cluster analysis. The results showedthat the slope of Y2018 was signiﬁcantly steeper (-0.8241) than that ofY2017N (-0.6843) (Figure 5, Table 1). Intercepts in NBSS were notdifferent among the group signiﬁcantly (Table 1). Biovolumes weresummarized by 0.25 on the log10 axis and compared to NBSS plots,
which clearly showed the contribution of each taxon to size classes(Figure 6). For all taxa, ESD 311–771 mm (-1.75–0 in log10 (mm3ind.-1)) with small copepods dominated, but Calanus spp. showed ahigh contribution at an ESD range of 1132–2439 mm (0–1 in log10(mm3 ind.-1)). In Y2018, with the signiﬁcantly steeper NBSS slope),
Bivalvia highly contributed to the size class of ESD 300–311 mm (-2 to-1.75, log10 (mm3 ind.-1)) (Figure 6). other large copepods were
dominant in the larger size classes of groups A and C, whereas
Chaetognatha was dominant in the same size class of Y2017S.
Euphausiacea was particularly abundant in the middle size class ofgroups Y2017N and Y2018.
A comparison between the size range and the available prey forﬁsh (Paciﬁc cod) (cf. Jacobsen et al., 2020) revealed no yolk-sac

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A

50

Similarity

60

62%

65%

70
80
90

17-01

17-07

18-27r
18-16r

18-22
18-29

18-30
18-14

18-20

18-11
18-17

18-06
18-08

18-04
18-19

17-22

17-10
17-21

17-23
17-05

C

2017

80

64˚ N

62˚ N

2018

Biovolume (103 mm3 m-2)

66˚ N

100
80

60

60
40
40
20

20

0

66˚ N

0
Y2017N

64˚ N

62˚ N
175˚ W

170˚ W

Y2017SGroupsCalanus spp.
Small copepods
Chaetognatha
Euphausiacea
other taxa

GroupsY2017NY2017SY2018
OUT

Composition (%)

B

17-20

17-24
17-19

17-16
17-18

17-17

17-09
17-02

17-14
17-13

17-12

17-11

17-06
17-15

100

Y2018

Other large copepods
Bivalvia
Cnidaria
Polychaeta

165˚ W

FIGURE 3(A) Results of cluster analysis based on mesozooplankton biovolume using Bray–Curtis similarity connected with UPGMA in the northern Bering Sea
during July 2017 and 2018. Three groups (Y2017N, Y2017S, Y2018) were identiﬁed with a similarity of 62% and 65% (dashed lines). The samples werenamed using the year and station ID. (B) Horizontal distribution of the three groups in the northern Bering Sea during July 2017 and 2018 identiﬁedby cluster analysis (cf. A). (C) Total biovolume and composition of each group.

the organic matter content of gelatinous zooplankton (Lehette andHernandez-Leon, 2009; Naito et al., 2019; Cornils et al., 2022). Also,
they are not efﬁciently caught by nets. Therefore, a considerable
amount of attention to detail is required when analyzing such taxa
with ZooScan.

4 Discussion
4.1 Limitation of ZooScan
It is not always possible to classify small objects (especiallybelow 200 μm ESD) via image analysis with ZooScan, as it has apixel resolution of 10.6 μm (Gorsky et al., 2010). Many studies using
ZooScan have focused on > 300 μm ESD zooplankton (i.e., Colaset al., 2017), and we therefore also only analyzed this size range. The
parameters of abundance and biovolume accuracy obtained using
ZooScan are largely consistent with the microscopic counts and size
measurements of copepods (Gorsky et al., 2010; Garcia-Comaset al., 2011; Cornils et al., 2022), and signiﬁcant regression ispresent for ESD 1–6 mm-sized copepods, in particular (Forest
et al., 2012). However, abundance and biomass based on
ZooScans of Chaetognatha, Appendicularia, and Cnidaria arelikely to be underestimated because it is difﬁcult to analyzetransparent objects using ZooScan and there is large variability in

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4.2 NBSS analysis
The slope and intercept of the NBSS explain the zooplankton
size composition characteristics: the NBSS intercept is reﬂected byzooplankton standing stock and primary production, whereas theslope is reﬂected by productivity and trophic levels (Zhou, 2006). Ina typical marine ecosystem, the NBSS slope approaches -1 (Plattand Denman, 1977; Kerr and Dickie, 2001). In this study, all slopes
were less than -1: this indicated that larger objects were dominant,which is commonly observed after phytoplankton blooms(Vandromme et al., 2014). NBSS slopes can be easily altered by

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FIGURE 4

dbRDA plot of the three groups with environmental parameters. The direction of the lines indicate the relationship between environmental
parameters and groups identiﬁed by cluster analysis (cf. Figure 3A) in the northern Bering Sea during July in 2017 and in 2018. Tem, water-column
integrated mean temperature; Sal, water-column integrated mean salinity; Flu, water-column integrated ﬂuorescence; MD, melt day; TSM, timesince melt day to ﬁeld sampling; wCW, warm Coastal Water; wSW, warm Shelf Water; cSW, cool Shelf Water; AnW, Anadyr Water; MWW, Modiﬁed
Winter Water; WW, Winter Water.

top-down and bottom-up effects within an ecosystem (Suthers et al.,2006). In the top-down case, the slope of the NBSS becomes steeper
as middle-to large-sized plankton decrease with predation bymammals and ﬁshes. Therefore, a negative relationship isassumed between predator abundance and slope (Suthers et al.,2006). In a bottom-up effect, the slope becomes steeper as primary
production increases, consequently increasing the size of small
zooplankton (Moore and Suthers, 2006). When discussing topdown and bottom-up effects, it is important to clarify the actual
prey-predator relationship (Yurista et al., 2014). As a result, an
increasing number of reports have combined NBSS analysis with
ZooScan to reveal taxonomic information (i.e., Cornils et al., 2022).

4.3 Characteristics of clustering groups
A cluster analysis based on the biovolume was used to reveal
regional and interannual changes in zooplankton communities(Matsuno et al., 2012). The distribution and composition showedsimilar tendencies to those of microscopic analyses (cf. Figure 4 and5 in Kimura et al., 2022), which suggests that identiﬁcation at the
taxon level (genus level for copepods) using the ZooScan approach
can be used to detect trends at a level similar to that detected via
microscopic analysis.
In northern Bering Sea, zooplankton community changes under
the inﬂuence of water masses (Kimura et al., 2020). The water

TABLE 1 Inter-group comparison of hydrographic and zooplankton size spectra parameters in the northern Bering Sea during July in 2017 andin 2018.

GroupsParameters

Max-t test
Y2017N (10)Y2017S (9)Y2018 (14)Temperature

3.92 ± 0.87

3.44 ± 1.17

4.50 ± 1.52

Salinity

32.3 ± 0.30

31.9 ± 0.17

32.0 ± 0.93

Fluorescence

21.8 ± 13.2

15.2 ± 7.57

19.9 ± 10.3

Not detected

Melt Day108 ± 13.2

108 ± 14.4

107 ± 14.8

Not detected

TSM

88 ± 16.4

88 ± 13.7

81 ± 12.9

Slope in NBSS

-0.683 ± 0.089-0.705 ± 0.215-0.901 ± 0.260

Intercept in NBSS

0.828 ± 0.155

1.005 ± 0.261

0.845 ± 0.338

Not detected
Y2017S2

Y20181,2

Y2017N1

Not detected
Y2018

2

Y2017S1,2

Y2017N1

Not detected

Different superscript numbers in the Max-t test + HC3 column indicate signiﬁcant differences between regions. NS, not signiﬁcant.

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FIGURE 5

NBSS of three groups identiﬁed from cluster analysis of mesozooplankton biovolume (cf. Figure 3A) in the northern Bering Sea during July 2017 and2018. Numbers in parenthesis indicate the number of stations belonging to each group.

Y2017N was observed in 2017 from the Bering Strait to the
Chirikov Basin, and it corresponds to the Y2017N presented in ourprevious study (Kimura et al., 2022). This group was characterized
by low abundance (Table 2 in Kimura et al., 2022) and biomass, andsmall copepods dominated the small size classes. The group showed
higher salinity than the other group, which means AnW mainly
occupied with water column at the station observed Y2017N. The
middle class of this group was dominated by other large copepods

masses changes seasonally (Danielson et al., 2020), so it is wellreported that zooplankton communities are different between
months. However, since there is no report about difference
between shorter time scale, such as week. Thus, in this study,
sampling time between two years differ only 11 days, which
potentially inﬂuences zooplankton community, but quantitativeevaluation (or back calculation) is not possible due to the lackof information.

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FIGURE 6

Species composition and biovolume within the interval of 0.25 in log10, and the NBSS line of the three groups identiﬁed from conducting a cluster
analysis of mesozooplankton biovolume (cf. Figure 3C) in the northern Bering Sea during July 2017 and 2018. The species composition was
calculated by summarizing the biovolume for all specimens in each size class. Prey size range for each stage of ﬁsh are shown at the bottom
horizontal axis (cf. Jacobsen et al., 2020).

Eucalanus spp., Metridia spp., and Neocalanus spp.) was
consistent with the characteristics of a community found during
the summer in a previous study (Eisner et al., 2013). In the Chirikov
Basin, the zooplankton community structure showed monthly
changes accompanying surface water mass changes from June to
September (Kimura et al., 2020). During June and July, other large
copepods and Euphausiacea were abundant due to the inﬂow of
AnW, Calanus spp., and Euphausiacea. In contrast, in August, thenumber of Bivalvia larvae, barnacle larvae, and Appendicularia

and Euphausiacea transported from Anadyr Bay (Springer et al.,
1989; Ashjian et al., 2010; Moore et al., 2010). Euphausiacea
dominated 60%–90% of the stomach contents of cetaceans in the
study area; they are considered to play an important role in the food
web but are not likely to establish expatriates in the present state(Moore et al., 2010). In Euphausiacea, Thysanoessa inermis, T.
raschii, Meganyctiphanes norvegica are carnivorous, which canlimit the abundance of Calanus spp. (Bamstedt and Karlson,
1998). The occurrence of other large copepods (composed of

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A

B

FIGURE 7(A) Species composition and mean biomass and (B) production in each group as identiﬁed from conducting a cluster analysis of mesozooplankton
biovolume (cf. Figure 3A) in the northern Bering Sea during July 2017 and 2018.

Calanus spp. were dominant in the middle-sized class in Y2017S,
but not in Y2017N. South of St. Lawrence Island, winter water isdistributed in the lower layers where Calanus glacialis is abundant(Pinchuk and Eisner, 2017). It is also known that in cold years (latesea-ice retreat), the abundance of large copepods (C. glacialis)increases while that of small copepods and Cnidaria decreases(Ershova et al., 2015). In 2017, late sea ice melt (compared to2018) induced ice-edge blooms in late April (Kikuchi et al., 2020);
therefore, large copepods had grown to the C5 stage by July when
our survey was conducted (Kimura et al., 2022). In other words,

increased signiﬁcantly. The community was then dominated by
Calanus nauplii and other small copepods (Centropages spp. andCyclopoida) (Kimura et al., 2020). The NBSS slope of Y2017N isgentle due to the dominance of Calanus spp., other large copepods,
and Euphausiacea in the middle- and large-size classes, and it has
been suggested that the slope of the NBSS will become steeper withan increase in benthic larvae (Matsuno et al., 2012; Kimura
et al., 2020).
The NBSS slopes of Y2017N and Y2017S (seen south of St.
Lawrence Island) were similar, but their compositions differed:

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low nutrient content (Norrbin et al., 1990). The small copepods inthis study contained minimal amounts of lipids (Kimura et al.,2022). This group was also characterized by the dominance ofBivalvia larvae in the small class due to the seasonal reproduction ofmeroplankton. The carbon content of Bivalvia larva was 1.4–1.5%,
which was much lower than that of copepods (Viverberg and Frank,
1976; Steidinger and Walker, 1984) and indicated that the foodquality was too low for higher trophic levels. Lowering the nutrientsand fats of prey decreases the winter survival rate of ﬁsh such as
salmon or Arctic cod (Heintz et al., 2013). Therefore, it is suggested
that in Y2018, the production of low fat meroplankton and small
zooplankton was not transferred sufﬁciently to higher trophic levels,
which might have affected their abundance (Springer et al., 1989;
Huntington et al., 2020).
Fish generally exhibit grazing selectivity (for example, O’Brien,1979). It is difﬁcult for ﬁsh to detect small prey (and they contain
low levels of nutrients); therefore, they tend to prey on larger foods(O’Brien, 1979). Walleye pollack (Theragra chalcogramma) is anabundant species in the study area and it preys on small copepods
when they dominate in warm years, whereas it prays on large
copepods and Euphausiacea when they dominate in cold years. Thisis likely because they graze on larger species during summer to
efﬁciently store lipids for overwintering (Coyle et al., 2011; Heintz
et al., 2013). Therefore, the prey-size composition provides
important information about the survival rate of ﬁsh. To compare
the growth stage of the ﬁsh larvae with the prey size, we determined
the available prey size range for each growth stage. In this study, theavailable prey size for yolk-sac larvae was not detected, possibly
because ZooScan could not analyze copepod eggs, which are themain food source for yolk-sac larvae. From late larvae to early
juveniles, the most important period for survival (Jacobsen et al.,2020), the available food differed across the groups. Fat-rich otherlarge copepods and Euphausiaceae with rich fats were relatively
abundant in Y2017N and Y2018; however, their biovolumes were
low. Therefore, because the rate at which ﬁsh encountered thesefoods was low and they experienced low food availability. When ﬁsh
do not receive sufﬁcient energy, their populations and survival rates
are affected (Springer et al., 1989; Heintz et al., 2013; Huntington
et al., 2020). In contrast, large and fat-rich Calanus spp. was foundto be abundant in Y2017S, which suggested that it was readily
available to ﬁsh. In addition, sufﬁcient Chaetognatha populations
for ﬁsh were seen in Y2017S; therefore, production by small andlarge copepods was efﬁciently transferred to ﬁsh in Y2017S.
In conclusion, the zooplankton community and size
compositions in the northern Bering Sea differed between 2018
(an early sea ice melt year) and 2017 (a normal sea ice melt year). In
2017, Y2017N (dominated by other large copepods andEuphausiacea) was observed in the Chirikov Basin and wasinﬂuenced by ACW, whereas Y2017S (dominated by Calanus spp.
and Chaetognatha) was distributed to the south of St. Lawrence
Island. In 2018, however, Y2017S was observed in the same area asthat in 2017, and Y2018 (dominated by small copepods andBivalvia) was distributed widely in the northern Bering Sea. The
NBSS analysis showed that the slopes of groups Y2017N andY2017S were similar; however, the species composition differed
between the groups. Y2017N was dominated by medium- or large-

Y2017S, as seen in this study, is the community generally foundsouth of St. Lawrence Island during the summer of cold years.
According to the results of the size analysis, the zooplankton
abundance in the middle-sized class was low, and the large-sizedclass was dominated by Chaetognatha. Chaetognatha is a typical
copepod predator (Feigenbaum, 1982; Kimmel et al., 2006); it istherefore likely that it fed on middle-sized copepods in Y2017S,
which resulted in their decreased abundance. This suggests that the
food chain was functional in Y2017S.
The small size glass of Y2018 (which produced the steepest
NBSS slope) was dominated by Bivalvia larvae, and this is consistent
with the large seasonal occurrence of Bivalvia larvae observed in the
study area (Moore et al., 2003; Moore et al., 2006). Changes in theslope of the NBSS due to the dominance of small copepods have also
been reported in the Chukchi Sea (Matsuno et al., 2012) andAtlantic sector of the Arctic Ocean (Basedow et al., 2010). As
Bivalvia larvae typically settle 2–6 weeks after emergence (Steidinger
and Walker, 1984), the steep slope of the NBSS in Y2018 does notindicate high productivity, but it rather indicates the seasonal
recruitment of meroplankton.

4.4 Impact of early sea-ice melt on higher
trophic levels
Sea ice melted from April to May in 2017, resulting in an iceedge bloom and early copepod reproduction that induced a highabundance of large (late stage) copepods during summer (Kimura
et al., 2022). However, sea ice melted earlier (from March to April)in 2018; therefore, an ice-edge bloom did not occur, and an openwater bloom occurred instead (Kikuchi et al., 2020). The
reproduction of copepods was delayed in relation to the lateopen-water bloom, which resulted in a decreased abundance oflarge copepods during summer (Duffy-Anderson et al., 2019;
Kimura et al., 2022). In this study, the results of the ZooScan
analysis showed clear annual variations, with groups Y2017N andY2017S dominating in 2017 and Y2018 dominating in 2018.
Y2017S, observed in 2017, exhibited the highest biomass.
Calanus spp. largely contributed to this group, and this was
attributed to late sea ice melting in cold years, as mentioned
above (Ershova et al., 2015; Kimura et al., 2022). A highabundance of benthic larvae (Grebmeier et al., 2006) and a highbiomass of copepods have been observed South of St. Lawrence
Island (Kimura et al., 2022). According to the NBSS results, Y2017S
was the most productive community with a functioning food chain
among the three groups. This result means that the south of St.
Lawrence Island is not only an important area for maintaining thezooplankton population, but also for efﬁciently transferringproduction to higher trophic levels.
Y2018 had the lowest biovolume, and this group corresponded
to group D reported by Kimura et al. (2022). The biomass of Y2018
in this study was similar to that of Y2017N. However, production
was higher than that of Y2017N because of the high production ofsmall copepods. Large copepods in the study area are efﬁcient preyfor higher trophic levels, as they store rich lipids in their body (Lee,1974), whereas small copepods rarely store lipids because of their

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Challenge for Sustainability II (ArCS II) (Program Grant Number
JPMXD1420318865) projects. This work was also supported by theEnvironment Research and Technology Development Fund(JPMEERF20214002) of the Environmental Restoration andConservation Agency of Japan. Furthermore, this work waspartially supported by the Japan Society for the Promotion of
Science (JSPS) KAKENHI, Grant Numbers JP21H02263 (B),JP20K20573 (Pioneering), JP20H03054 (B), JP20J20410 (B),
JP19H03037 (B), JP18K14506 (Early Career Scientists), andJP17H01483 (A).

sized plankton (other large copepods and Euphausiacea)
transported from Anadyr Bay. In contrast, the middle-sized classof Y2017S was dominated by Calanus spp., which is consistent withthe community observed during summer in cold years. The
abundance of the middle-sized class in this group was low, andthis may have been due to Chaetognatha grazing on middle-sized
copepod classes. The NBSS slope of Y2018 was the steepest in
relation to the seasonal occurrence of Bivalvia larvae. In 2018,secondary production in the study area increased due to theincreased abundance of small copepods, and production was
contributed to by small copepods and Bivalvia larvae. As thesesmall zooplankton are considered low-nutritional prey, the numberof higher trophic levels may have decreased.

Acknowledgments
We thank the captain, ofﬁcers, crew, and researchers onboard
the T/S Oshoro-Maru (Hokkaido University) for their crucial
contributions to ﬁeld sampling. The archived ADS dataset wasprovided by the Arctic Data archive System (ADS) developed by the
National Institute of Polar Research.

Data availability statement
The original contributions presented in the study are includedin the article/Supplementary Material. Further inquiries can bedirected to the corresponding author.

Conﬂict of interest
Ethics statement
The manuscript presents research on animals that do not
require ethical approval for their study.

The authors declare that the research was conducted in theabsence of any commercial or ﬁnancial relationships that could beconstrued as a potential conﬂict of interest.

Author contributions

Publisher’s note

KM and AY designed the study; AY performed ﬁeld sampling;
SK analyzed the samples; SK and KM analyzed the data; and SK and
KM wrote the paper with contributions from all authors. All
authors contributed to the article and approved thesubmitted version.

All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their afﬁliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.

Funding
Supplementary material
The author(s) declare ﬁnancial support was received for the
research, authorship, and/or publication of this article. This study
was funded by the Arctic Challenge for Sustainability (ArCS)(Program Grant Number JPMXD1300000000) and the Arctic

The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmars.2023.1233492/
full#supplementary-material

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