Regional Studies in Marine Science 43 (2021) 101683

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Seasonal variability of the protist community and production in thesouthern Okhotsk Sea revealed by weekly monitoring∗

Takuma Matsumoto a , , Kohei Matsuno a,b , Seiji Katakura c , Hiromi Kasai d ,Atsushi Yamaguchi a,baLaboratory of Marine Biology, Graduate School of Fisheries Science, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido 041-0821, Japan
Arctic Research Centre, Hokkaido University, Kita-21 Nishi-11 Kita-ku, Sapporo, Hokkaido, 001-0021, Japanc
Kaiyo-Koryukan, Kaiyo-Koen 1, Mombetsu, Hokkaido 094-0031, Japand
Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 116 Katsurakoi, Kushiro, Hokkaido 085-0802, Japanb

articleinfo

Article history:
Received 14 November 2019
Received in revised form 20 October 2020
Accepted 16 February 2021
Available online 22 February 2021
Keywords:
ProtistDiatoms
Dinoflagellates
Ciliates
Seasonal change
Okhotsk Sea

a b s t r a c t
To evaluate seasonal changes in a protist community (diatoms, dinoflagellates and ciliates) and theirproduction in the southern Okhotsk Sea, water sampling was conducted approximately once a weekfrom July 2016 to July 2017 at Mombetsu Port. Diatoms were the predominant category of the threetaxa throughout the study period. The abundances of all taxa were high from March to September andwere low from October to February. Four groups (A–D) were identified by cluster analysis according
to abundance. Group A, of which Thalassiosira spp. were dominant, occurred in March. Group B
showed the highest abundance (7.7×104 cells L−1 ), with Chaetoceros spp. and Pseudo-nitzschia spp.
being dominant. In group C, Odontella spp. and pennate diatoms were dominant and occurred from
November to February. Group D was mainly observed in late October, and pennate diatoms were
dominant. Seasonal changes in the predominant species of diatoms were explicitly observed, and thesechanges were associated with water mass exchanges. For dinoflagellates, abundance was highest in July
2016, and biomass and production were highest in September. Large fluctuations in the ciliate biomass
in July 2016 might have been induced by the high growth rate, which reflects the high chlorophyll
a concentration and water temperature and a decrease in predation pressure. The estimated primary
production was lower than that reported in previous studies, which could reflect differences in thedepth of the research areas and the methods used for estimating production. In the coastal area of thesouthern Okhotsk Sea, where production is high, not only the species composition but also the protist
community varied clearly with season because of variations in the water mass.
© 2021 Elsevier B.V. All rights reserved.

1. Introduction
The coastal area in the southern Okhotsk Sea off Hokkaido
is an important region because it has high primary production
and abundant fishery resources, including Japanese scallop andsalmon. In this area, the warm and saline Soya Warm Current
and cold and less-saline East Sakhalin Current are exchanged
seasonally; the former can be observed from March to November,and the latter can be observed from November to March (Aota,
1975; Takizawa, 1982; Fukamachi et al., 2008; Mustapha et al.,2009). In addition, this area is covered by sea ice from February
to March, and this sea ice is transported from the northern
Okhotsk Sea. Because of the water mass exchanges and sea ice
coverage, marine ecosystems in the coastal area of the southern
Okhotsk Sea vary greatly with season (Taguchi et al., 2000; Kasai∗ Corresponding author.
E-mail address: foodfoot1995@eis.hokudai.ac.jp (T. Matsumoto).
https://doi.org/10.1016/j.rsma.2021.101683
2352-4855/© 2021 Elsevier B.V. All rights reserved.

et al., 2010; Hikichi et al., 2018). However, the information of
dynamics for primary producers (e.g., diatoms) and mechanisms
to high productivity is not sufficient for sustainable managementof fisheries resources.
Protists, which support marine ecosystems, consist of autotrophs, such as diatoms; heterotrophs, such as ciliates andradiolarians; and mixotrophs, such as some species of dinoflagellates. In the Okhotsk Sea, previous studies have reported thatdiatom species are different in warm and cold waters (Kasai and
Hirakawa, 2015), and spring diatom blooms are composed mainlyof Thalassiosira nordenskioeldii, Thalassiosira gravida, Thalassiosira
eccentrica and Chaetoceros subsecundus in the coastal surface
water (Sorokin and Sorokin, 1999). Dinoflagellate species are
represented by autotrophs, heterotrophs and mixotrophs (Gainesand Elbrächter, 1987). In addition, many studies have been conducted on Alexandrium tamarense, which causes paralytic shellfish
poison in the Okhotsk Sea (Shimada et al., 2010). Ciliates are heterotrophic protozooplankton and are able to feed on bacteria andT. Matsumoto, K. Matsuno, S. Katakura et al.

Regional Studies in Marine Science 43 (2021) 101683

the regression formula of Marañón (2008): log10 P = b log10 V+ a (P: production (pg C cell−1 day−1 ); V : cell volume (µm3 );a, b: constant for diatoms (a = −1.25 and b = 1.01) and for
dinoflagellates (a = −0.91 and b = 0.89)). Production of ciliates(mg C m−3 day−1 ) was estimated by multiplying the growthrate (d−1 ) calculated according to Montagnes et al. (1988) by
the carbon biomass, as described above. The growth rate wasdetermined by the following formula: ln r = 0.1488 t – 0.3285
ln (V×10−3 ) – 1.3815 (r, growth rate (d−1 ), t: water temperature(◦ C), and V : cell volume (µm3 )).
A cluster analysis was performed based on protist abundance
data using software (PRIMER7, PRIMER-E Ltd.). The abundance(X : cells L−1 ) for all of the taxa were fourth root transformed(X 1/4 ) prior to the analysis to reduce any bias in the abundance (Quinn and Keough, 2002). Similarities between the protist
samples were calculated using the Bray–Curtis similarity index.
To group the samples, the similarity indices were coupled withhierarchical agglomerative clustering using a complete linkage
method (Unweighted Pair Group Method using Arithmetic Mean:
UPGMA) (Field et al., 1982). Non-metric Multidimensional Scaling (NMDS) ordination was performed to distribute the groups
onto a two-dimensional map. Pearson regression analyses were
performed to clarify which environmental parameters (surface
temperature, surface salinity, Chl a concentration, nitrate + nitrite
concentration, phosphate concentration, silicate concentration,
air temperature and rainfall) had significant relationships withthe groups. Inter-group differences in the major taxa and environmental parameters were tested by using a one-way ANOVA,
followed by Tukey–Kramer post hoc test.
Structural Equation Modelling (SEM) was performed to evaluate the factors controlling the variations in protist parameters (abundance, biomass and production) (Stomp et al., 2011).
For SEM analysis, environmental parameters (rainfall, maximum
wind speed, surface temperature, surface salinity and nitrate +nitrite concentration) and protist parameters were transformed
into normalized values (average = 0, standard deviation = 1), andcorrelation coefficients between all parameters were calculated.
For the path analysis, the parameters were grouped into thefollowing three categories: (1) atmospheric parameters (rainfall,
maximum wind speed), (2) hydrographic parameters (surface
temperature, surface salinity and nitrate + nitrite), and (3) protist
parameters. Non-significant interactions (p>0.05) were deleted
from the final model. The goodness-of-fit index (GFI) and theadjusted goodness-of-fit index (AGFI) were used to evaluate thefitting of the models. The standardized path coefficients wereindependent variables, which indicate the relative contributionsof the different paths within the SEM (Stomp et al., 2011). The
SEM analysis was performed using add-in software for MS-Excel(http://www.ohmsha.co.jp/data/link/978-4-274-06925-3/).

small phytoplankton, such as pennate diatoms (Capriulo and Carpenter, 1983; Gast, 1985; Paranjape, 1987), and they also play animportant role in microbial loops (Pomeroy, 1974). Their growthrate is controlled not only by the quality and quantity of theirfood but also by the water temperature, especially under cold
conditions, such as in polar regions; their growth rate increases
in warmer temperatures (Hansen and Jensen, 2000). In spite of
their contribution to primary production and their importantrole in microbial loops, protist communities have not been wellstudied along the coastal area of the Okhotsk Sea. Additionally,
previous studies of phytoplankton mainly revealed aspects ofthe chlorophyll a (Chl a) concentration (Hamasaki et al., 1998;
Taguchi et al., 2000; Kasai et al., 2017). Although Sildever et al.
(2019) investigate the variability and the species composition of
dinoflagellates in the study area with a metagenomic approach,the compositions of the other protist communities still havenot been clarified in this region. It is reported that zooplankton
communities vary in accordance with the seasonal exchange ofwater masses (Hikichi et al., 2018), but a relationship between
the variability in protist communities and the exchange of water
masses is still unknown.
In this study, seasonal variability in the abundance and community structure of protists (diatoms, dinoflagellates and ciliates)
was investigated to clarify the relationship among the protists
and environmental factors. We also used cell volumes and watertemperature to estimate protist production so that we could
discuss seasonal changes in this production.
2. Materials and methods
Surface water samples were collected with a bucket at intervals of approximately one week from 6 July 2016 to 3 July 2017from a bridge that connects a pier and the Okhotsk Tower in
Mombetsu Port at 9 m depth (Fig. 1). Samples containing 500 mLof water were preserved with 1% glutaraldehyde. Approximately
100 mL of sea surface water was filtered through a GF/F filter tomeasure the Chl a concentration. The filter samples for Chl a were
immersed in N,N-dimethylformamide (DMF), and the Chl a concentration was measured by a fluorometer (Turner Designs, Inc.,
10-AU) according to Welschmeyer (1994). The sea water fromthe surface was frozen to analyse the nutrients. Nutrients were
measured by an autoanalyser (Bran + Luebbe, AACS-III) according
to Parsons et al. (1984). Water temperature and salinity were
measured by CTD (JFE Advantech Inc., ASTD102). Data on air temperature, rainfall, the maximum wind speed, and wind direction
were obtained from the web site of the Japanese Meteorological
Agency (https://www.data.jma.go.jp/obd/stats/etrn/index.php).
The fixed protist samples were stored on a flat table for morethan 1 day to allow the microprotist cells to settle to the bottomof the bottle. Then, the samples were concentrated down to 20
mL using a siphon. Subsamples (1 mL) were mounted on a glass
microscope slide with a micro pipet. Identification, counting andmeasuring of the cells were conducted with an inverted microscope (Nikon, Eclipse-TE200) with 40–600× magnification by thebright field observation. The genera of the diatoms were identified according to Hasle and Syvertsen (1997) and Hoppenrath
et al. (2009). Concerning dinoflagellates, species identification
was not conducted. Ciliates species were counted for two groups:
loricate and aloricate ciliates. Counting was conducted for cells
larger than 10 µm. The abundance of each taxon in the samples (cells L−1 ) was calculated using the number of cells in the
sub-samples and the concentration. The cell volumes of eachtaxon were (µm3 ) calculated from the cell sizes, which were
measured according to Sun and Liu (2003). Then, they wereconverted into carbon biomass using the conversion equationfrom Menden-Deuer and Lessard (2000). The production of diatoms and dinoflagellates (mg C m−3 day−1 ) was estimated by

3. Results
3.1. Hydrography
Soya Warm Current is driven by the sea level difference (SLD)
between Japan Sea and Okhotsk Sea (Aota, 1975; Fukamachi et al.,2008). When East Sakhalin Current prevails in the Okhotsk Sea,
this SLD becomes small, resulting in decrease in the volume ofSoya Warm Current Water into the Okhotsk Sea. The SLD started
to decline from November 2016 and showed the minimum in the
middle of January 2017 (Fig. 2f). It kept increasing thereafter untillate April 2017 and returned to the same level as that of when
it started to decrease. The surface sea temperature (SST) ranged
from −1.7 to 22.3 ◦ C, and it had the maximum in August 2016
and the minimum in February 2017. From January to March 2017,it was approximately 0 ◦ C, and from July to August, it showed2

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Regional Studies in Marine Science 43 (2021) 101683

Fig. 1. Location of the sampling station (Okhotsk Tower) in Mombetsu Port in north-eastern Hokkaido. Arrows indicate the approximate direction of the current
flows.

higher values (> 15 ◦ C) (Fig. 2a). The surface sea salinity (SSS)
ranged from 20.85 to 33.59, and it declined sporadically due torain fall through the study period. Low salinity in April 2017
might be attributed to melting of sea ice (Fig. 2a). The Chl a concentration ranged from 0.24 to 6.1 µg L−1 , and there were large
peaks in July 2016, September 2016 and March 2017 (Fig. 2b). The
total nitrate and nitrite concentration ranged from 0 to 16.2 µM.
It increased from November 2016 to March 2017 and decreased
sharply in late March 2017. It also increased rapidly in April andJune 2017 (Fig. 2b). The silicate concentration ranged from 1.4
to 117.5 µM and showed high values in September 2016 andfrom April to June 2017 (Fig. 2c). The phosphate concentration
ranged from 0.063 to 1.2 µM, and it increased from the end of
October 2016 to the end of January 2017 and declined thereafter
to April 2017 (Fig. 2c). The air temperature (average) ranged from
−10.2 to 27.4 ◦ C, and it was highest on 21 July 2016 and waslowest on 11 January 2017 (Fig. 2d). Daily rainfall ranged from 0
to 75.5 mm, and it was highest during August 2016 (Fig. 2d). The
daily maximum wind speed ranged 2.3 to 13.5 m s−1 , and the frequency of strong winds (> 5 m s−1 ) was higher in September andOctober 2016 than that in spring (March and April 2017) (Fig. 2e).
In 2017, sea ice could be observed from 26 January to 11 April at
Mombetsu, and the period of visible sea ice was 7 days longer
than that of normal years (cf. website of sea ice information of
the City of Mombetsu: http://okhotsk-mombetsu.jp/).
3.2. Seasonal variability of abundance
The abundance of the diatoms ranged from 1.55×103 to
5.36×105 cells L−1 , that of dinoflagellates ranged from N.D. (notdetected) to 5.28×103 cells L−1 , and that of ciliates ranged from
N.D. to 5.92×103 cells L−1 (Fig. 3a). The abundance of each3

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Regional Studies in Marine Science 43 (2021) 101683

Fig. 2. Seasonal changes in environmental parameters. (a) Daily mean sea surface temperature (SST) and salinity (SSS), (b) chlorophyll a and nitrate and nitrite (NO+3
and NO−2 ) at sea surface, (c) silicate and phosphate at the sea surface, (d) daily averaged air temperature and daily amount of rainfall, (e) daily maximum windspeed and direction at Mombetsu Port and (f) daily mean sea level difference (SLD) between Wakkanai and Abashiri (cf. Fig. 1) (black and red lines indicate SLD
observed and SLD calculated, respectively) from July 2016 to July 2017.

Fig. 3. Seasonal changes in protist abundance and the predominant water masses (a) and species composition (b) at Mombetsu Port from July 2016 to July 2017.
4

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Regional Studies in Marine Science 43 (2021) 101683

Table 1Comparisons of environmental variables (temperature, salinity, chlorophyll a,nitrate + nitrite, phosphate, silicic acid, air temperature and rainfall) and microplankton species for the four groups (A-D) at Mombetsu Port from July 2016
to July 2017. The four groups were identified from Bray-Curtis similarity basedon microplankton abundance (cf. Fig. 4a). Results are shown as mean. Differences
between groups were tested by one-way ANOVA with the Tukey-Kramer HSD
post-hoc test. Any groups not connected by underlines are significantly different(p < 0.05). Numbers in parentheses indicate the number of stations included ineach group. NS: not significant; *: p < 0.05; **: p < 0.01; ***: p < 0.001.

spp. in March 2017 (Fig. 4c, d). Group B was observed from Julyto October 2016 and from April to July 2017. This group had
the highest abundance (7.7×104 cells L−1 ), and Chaetoceros spp.
and Pseudo-nitzschia spp. were dominant. Group C was observedin winter (November 2016 to February 2017), with a predominance of Odontella spp. and other pennate diatoms. Group D was
observed at the end of October 2016 and February 2017. This
group showed the lowest abundance (1.7×103 cells L−1 ), andother pennate diatoms were dominant (Fig. 4c, d).
The environmental parameters that had an inter-group difference were surface temperature, Chl a, nitrate + nitrite, phosphate
and air temperature (Table 1). Group B was characterized byhigh air temperature, high sea surface temperature, low nutrient concentration and high Chl a concentration. In contrast togroup B, group C was characterized by low air temperature,
low surface temperature, high nutrient concentration and low
Chl a concentration. Groups A and D had the middle values inthe environmental parameters between groups B and C. Protist
abundance was high in groups A and B and was low in groups
C and D. In addition, group A was characterized by a predominance of Thalassiosira spp., and group B was characterized by
predominance of Chaetoceros spp.
3.4. Seasonal changes of biomass and production
Carbon biomass of diatoms, dinoflagellates and ciliates ranged
from 0.9 to 61 mg C m−3 , from N.D. to 21 mg C m−3 and from N.D.
to 33 mg C m−3 , respectively (Fig. 5). Diatoms were dominant inbiomass among the three taxa, which was the same as for theabundance. Seasonal changes occurred in diatom carbon biomass,
which peaked in August 2016 and March 2017. Carbon mass indinoflagellates peaked in September 2016 (Fig. 5a), and that ofciliates peaked in July 2016. The composition in carbon biomass
was dominated by mainly Chaetoceros spp. and Guinardia spp.
from July to August 2016, and ciliates were dominant at the endof July 2016 and the beginning of August 2016 (Fig. 5b). From
September 2016 to the middle of December 2016, dinoflagellates
and other centric diatoms dominated, and from the middle ofDecember 2016 to the beginning of February 2017, Odontella
spp. and other centric diatoms dominated. From the beginning of
February 2017 to the beginning of April 2017, Thalassiosira spp.
dominated, and from the middle of April 2017 to the beginning ofJuly 2017, Chaetoceros spp. and other pennate diatoms dominated.
The production of diatoms, dinoflagellates and ciliates ranged
from 1.5 to 69 mg C m−3 day−1 , from N.D. to 9.2 mg C m−3
day−1 and from N.D. to 41 mg C m−3 day−1 , respectively (Fig. 6).
The production of diatoms showed a pulse-like variation, with
dinoflagellates showing a peak in late September 2016 but witha low value compared to that of the other taxa. The productionof ciliates peaked in July 2016.

taxon was high from July to September 2016 and from March
to July 2017, and it was low from October 2016 to February
2017. In this study, diatoms were most dominant among the threetaxa. The species composition of diatoms showed seasonality,and had a summer–autumn growth period (July–October), theminimum during late autumn to winter (November–February),a spring bloom (March–April), and then recurrence of a summer
growth period (April/May–July). From July 2016 to the beginningof August 2016, Chaetoceros spp. mainly dominated (58.3–88.7%),
and Pseudo-nitzschia spp. were mainly dominant from the middleof August 2016 to the middle of October 2017 (39.0–87.7%),
although other centric diatoms (mainly Skeletonema spp.) were
temporarily dominant (76.7%) at the end of August 2016. Fromthe end of October 2016 to the beginning of December 2017,
other pennate diatoms (pennates excluding Pseudo-nitzschia spp.)
were mostly dominant (36.7–74.2%). Odontella spp. and otherpennate diatoms dominated from December 2016 to the beginning of February 2017 (3.7–58.2% and 33.3–77.5%, respectively),
and Thalassiosira spp. were mainly dominant from March 2017
to the beginning of April 2017 (64.9–95.0%). From the middle ofApril 2017 to the beginning of July 2017, Chaetoceros spp. were
mostly dominant again (29.3–97.7%). Dinoflagellates showed thehighest value in July 2016. Ciliates showed high values in July andAugust 2016. Both of loricate and aloricate ciliates showed theirhighest values in July 2016.

3.5. SEM Analysis
From the SEM analysis on the protist parameters (abundance,
biomass and production) of each taxon, high values were observed for GFI (0.94–0.95) and AGFI (0.87–0.90) (Fig. 7). The
abundance of diatoms had positive correlations with water temperature and rainfall, and both the biomass and production of
diatoms had a positive correlation with dinoflagellates. Regarding
diatom production, a positive correlation with the maximum
wind speed was observed. For dinoflagellates, ciliates had a positive effect on the abundance, and water temperature had apositive effect on the biomass and the production. Every parameter for the ciliates had a strong positive correlation with watertemperature (path coefficients: pc = 0.47 to 0.63).

3.3. Cluster analysis based on abundanceProtist communities were divided into 4 groups (A–D) at
45% and 52% similarity by cluster analysis based on abundance(Fig. 4a). The environmental parameters that had a significant
relationship with the NMDS of each group were Chl a, air temperature, sea surface temperature, and nitrate + nitrite (Fig. 4b).
Group A, which occurred in March and April 2017, had high abundance (6.3×104 cells L−1 ) with a predominance of Thalassiosira
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Regional Studies in Marine Science 43 (2021) 101683

Fig. 4. Results of cluster analysis based on protist abundance by Bray–Curtis similarity connected with UPGMA. Four groups (A–D) were identified with the dissimilarity
at 45 and 52%, respectively (dashed lines) (a). Numbers in the parentheses indicate the quantity of samples included in each group. Non-metric multi-dimensional
scaling plots of the four groups, with arrows indicating directions of environmental parameters (b). Air T: air temperature, Chl a: chlorophyll a, Nut: nitrate and nitrite,
SST: sea surface temperature. Abundance and species composition of each group (c). Seasonal changes of the occurrence for protist groups and the predominantwater masses (d).

4. Discussion

October (SWCW) and November to May (ESCW). Considering the
succession of these periods correspond to that of the major water
masses, seasonal changes of Chl a and hydrographic conditions
such as nutrients in the study area may be related to the exchangeof water masses. Actually, those periods were also observed inthis study. The peaks of Chl a concentration were observed inJuly and September 2016 and March and June 2017. When thespecies composition was compared at the time of these peaks,
Chaetoceros spp. were dominant at the peaks in July 2016 andJune 2017, and Pseudo-nitzschia spp. were dominant in September
2016. These species might contribute to the peak in the Chl aconcentration. In March 2017, when sea ice retreat might occur,
Thalassiosira spp. were dominant. Because these species showhigh dominance in the ice algae communities of the study area
(Kasai et al., 2014), the peak in March might be partly caused byThalassiosira spp., which are thought to be released from melting
sea ice. Nutrient concentration began to increase in November
2016, when high-nutrient East Sakhalin Current might arrive, andthis concentration decreased rapidly in March 2017, when theinfluence of East Sakhalin Current might weaken. This decrease
might also attribute to the consumption of nutrients by diatom
blooms because Chl a concentration increased at the same timing. Thus, major seasonal changes in the nutrient concentrationconsisted of the exchange of water masses observed in the area.
In addition, nutrient concentration sporadically increased when
the rainfall was high. This might be caused by the inflow offresh water from a river near the sampling station. In addition, in
the SEM analysis, rainfall had a positive effect on the abundanceof diatoms. This effect suggests that the rapid increase in riverwater from the rainfall induced the input of high nutrients, which

4.1. Seasonal change of Chl a concentration
In the coastal region of the southern Okhotsk Sea, two of the
major water masses, Soya Warm Current Water (SWCW) andEast Sakhalin Current Water (ESCW), are seasonally exchanged.
According to previous studies, salinity of SWCW and ESCW is> 33.6 and < 32.0, respectively (e.g., Itoh and Ohshima, 2000).
However, this definition is not appropriate to the classification ofwater masses in this study because due to the sampling location
where was very close to the shore, the hydrographic conditions
might be affected by terrestrial waters (e.g., river water inflow).
Actually, in this study, sea surface salinity (SSS) did not excess
33.6 (the maximum was 33.58). Thus, water masses should not
be defined strictly in this study by the definition reported in theprevious studies. However, considering the seasonal change of
the SLD and SSS, in the southern Okhotsk Sea, the effect of Soya
Warm Current might exist from July to October 2016 and from
April to July 2017, and that of East Sakhalin Current might exist
from November 2016 to April 2017 (cf. Fig. 2, Supplementary).
Many previous studies have reported on Chl a concentrationin the study area (Hamasaki et al., 1998; Taguchi et al., 2000;
Kasai et al., 2010, 2017), and the results of this study were within
the values of the previous studies. According to Hamasaki et al.
(1998), in Mombetsu Port, Chl a is common for three periods:
June to October (SWCW is dominant), November to February(ESCW is dominant)/April to May (the transition from ESCW
to SWCW), and March (sea ice period). They also report thathydrographic conditions oscillate between two periods: June to6T. Matsumoto, K. Matsuno, S. Katakura et al.

Regional Studies in Marine Science 43 (2021) 101683

Fig. 5. Seasonal changes in protist biomass and the predominant water masses (a) and species composition (b) at Mombetsu Port from July 2016 to July 2017.

resulted in an increase in diatom growth. However, the rapid
growth of diatoms caused by the inflow of freshwater was likely atemporal event, and its effect was limited relative to the seasonalchanges in abundance.
In the study area, the Chl a concentration is generally higher inspring than in autumn (Kasai et al., 1997; Shiomoto et al., 2002).
However, the Chl a concentration in autumn (September) of 2016was higher than that in spring (March) of 2017 in this study. Thiscan be explained by the high frequency of strong wind (i.e., > 5 m
s−1 events that prevented stratification in the spring (March andApril) of 2017. Spring plankton blooms begin with stratificationof the water column, and strong wind events prevent occurrencesof phytoplankton bloom (Niebauer et al., 1995; Hunt and Stabeno,2002). Therefore, the difference in the Chl a concentration between spring and autumn in this study is thought to be relative

to the difference in the frequency of strong winds. On the other
hand, predation by large zooplankton is one of the factors that
affects the Chl a concentration (Kasai et al., 1997; Shiomoto et al.,2002). From winter to spring, when the East Sakhalin Current
Water is distinguished, large zooplankton are dominant and theirproductivity is low, and from summer to autumn, smaller species
are dominant and their productivity is high (Hikichi et al., 2018).
In addition, one small copepod, Pseudocalanus newmani, which
occurs in the study area, has a higher ingestion rate than alarge copepod, Neocalanus plumchrus (Nakagawa et al., 2016), andtherefore, grazing impacts by copepods might be high in summer
due to smaller copepods being more dominant in summer thanin spring. However, as mentioned above, the Chl a concentrationin September 2016 was higher than that in March 2017. Hence,the effect of winds on the Chl a concentration might be greater
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Regional Studies in Marine Science 43 (2021) 101683

Fig. 6. Seasonal changes in protist production estimated from cell size and ambient temperature, and the predominant water masses at Mombetsu Port from July
2016 to July 2017.

than that of grazing by zooplankton. Therefore, bottom-up effects,
such as water mass exchange, nutrient concentrations and wind,
greatly influenced seasonal changes of the Chl a concentration in
the Okhotsk Sea off Hokkaido.

(C. debilis, C. compressus, C. subsecundus), Thalassiosira hyalina
occur, whereas in the coastal region and in the warm watermass, Chaetoceros species (C. affinis, C. contortus, C. didymus) arefrequently observed. Although identification was only conducted
to genus in this study and the distinguishment between neritic or
pelagic species was not performed, it is assumed that Chaetoceros
spp. and Odontella spp. might be derived from Soya Warm Current and East Sakhalin Current, respectively. However, Odontella
aurita, which can be observed in cold water mass of the Okhotsk
Sea is also common along the coast of Hokkaido in winter, so that
Odontella spp. in this study probably originated from neritic water
as well as ESCW. In this way, the seasonal succession of dominant
species might be mainly caused by the exchange of ESCW and
SWCW as mentioned above. Thus, the diatom communities in the
coast of the Okhotsk Sea off Hokkaido varied seasonally with thewater mass exchange and sea ice melting.

4.2. Seasonal changes of diatoms
In this study, diatoms were the most dominant taxon, andthe dominant species changed seasonally. Though there is noprevious data about abundance of diatoms in the study area forcomparison with this study’s data, seasonal changes have beenreported in the species composition. In the coastal surface watersof the Okhotsk Sea, spring diatom blooms were dominated by T.
nordenskioeldii, T. gravida, T. eccentrica, C. subsecundus and others(Sorokin and Sorokin, 1999). In this study, Thalassiosira spp. were
dominant during the diatom bloom in March 2017, and thenthe abundance of Thalassiosira spp. decreased remarkably fromearly April 2017, when sea ice retreated and Soya Warm Current
started to prevail. From July to October 2016, when warm SWCW
might prevail around the study area, the abundance of Chaetoceros spp. was high. Odontella spp. were dominant in abundancefrom December 2016 to the beginning of February 2017, when
low temperature ESCW was dominant in the coastal area of the
Okhotsk Sea off Hokkaido. The results of cluster analysis and
NMDS also suggest the effect of water mass exchange on theseasonal succession of protist communities. Protist communities
were divided into 4 groups in this study. Group A appeared fromMarch to early April 2017, when sea ice existed around the study
area., and in this group, Thalassiosira spp. were predominant. This
result implies that the occurrence of those species in the study
area is associated with sea ice, corresponding to the dominance ofThalassiosira in ice algae communities in Kasai et al. (2014). GroupB mostly appeared from July to late October 2016 and from the
middle of April to July 2017, and these periods corresponded tothe period when SWCW might exist around the coastal regions of
the southern Okhotsk Sea. This group was characterized by highsea surface temperature (SST) and low nutrients, which may berelated to the water of Soya Warm Current. In contrast, group
C appearing from November 2016 to February 2017 when East
Sakhalin Current might prevail was characterized by low SST andhigh nutrients associated with ESCW. In group B, Chaetoceros spp.,
and in group C, Odontella spp. was dominant. According to Kasaiand Hirakawa (2015), in the pelagic region and cold water massof the Okhotsk Sea, Odontella aurita, some Chaetoceros species

4.3. Seasonal changes in ciliates
In this study, the biomass of the ciliates was greatest among
the three taxa studied in July 2016. Ciliates play an importantrole in connecting microbial food webs, including bacteria to flagellates, with classical food chains (Levinsen and Nielsen, 2002).
They are preyed upon by mesozooplankton such as copepods(Campbell et al., 2009), and predation pressure by the copepods decreases when the phytoplankton concentration is high(Levinsen et al., 2000). Therefore, the high biomass of ciliates
observed in July 2016 possibly can be attributed to the low
predation pressure on ciliates by copepods, since they might
have grazed on diatoms actively because of their high Chl aconcentration in July 2016. This phenomenon is referred to as
regulation windows and is known to occur in Disco Bay along
southwestern Greenland (Levinsen and Nielsen, 2002). The SEM
analysis showed that the abundance, biomass and productionof ciliates had positive correlations with water temperature. In
other regions, the biomass of ciliates was greatest in summer
when the water temperature and Chl a concentration was high
(Godhantaraman, 2002), so that ciliates in this study were alsoable to grow actively in the period with high Chl a concentration
and water temperature. However, soon after this peak, ciliates
biomass decreased remarkably. When the decrease occurred, Chl
a concentration was low. Because copepods are known to havea great feeding pressure on ciliates (Fessenden and Cowles,
1994), the low Chl. a concentration led them to change their8

T. Matsumoto, K. Matsuno, S. Katakura et al.

Regional Studies in Marine Science 43 (2021) 101683

The sum of the production of diatoms and dinoflagellates
ranged from 1.6 to 67 mg C m−3 day−1 . Assuming that production
was the same through the whole column (9 m depth), the watercolumn production was converted to 14.4 to 603 mg C m−2 day−1 .
According to Sorokin and Sorokin (1999), primary productionin the summer (July to August), when the abundance of phytoplankton was at the seasonal minimum, was more than 60 mg C
m−3 day−1 in the middle part of the Okhotsk Sea. The estimatedvalues of production in July and August of 2016 ranged from
1.8 to 38.3 mg C m−3 day−1 . In addition, in a pelagic region of
the southern Okhotsk Sea, the monthly average production fromspring to autumn, except for a period of phytoplankton bloom inearly spring, was approximately 500 mg C m−2 day−1 (Kasai et al.,2009). Production in the same season was approximately 139 mg
C m−2 day−1 in this study. The primary production estimated inthis study showed lower values than shown by previous studies.
In the oligotrophic condition (e.g., summer in this study), theproduction by nanoflagellates (< 2 µm) is much higher than thatby micro-sized phytoplankton (Maita and Odate, 1988). Notingthat the nanoflagellates were not enumerated in this study, taking
no account of their production is one of the potential reasons forthe low productivity. In addition, the expression for estimatingproduction was determined under experimental incubations instable conditions. According to Marañón (2008), the number oftaxa was less in experimental incubations than in natural conditions, and these incubations did not reflect the variability ofthe taxa in natural conditions, so discrete differences may exist
between the estimated values and the in situ values. Therefore,
the production estimated by the method in this study could be
different from the actual values in the study area because of
the differences that existed between the estimated values andthe reported values. However, even though the estimated values
may differ from the actual ones, few studies have reported on
seasonal changes in primary production in the study area, and thecalculation of production values is important, even if the values
are estimates. Primary production estimates in this study also
showed relatively high values even during the winter, and thereliability of this method should be tested by following up withstudies in the future.
4.5. Conclusion

Fig. 7. Results of structural equation models (SEM) for protist parameters(abundance, biomass and production) with environmental factors. The values
along the pathways represent standardized path coefficients. Arrows with solidor dashed lines indicate positive or negative effects. Thickness of arrows varies
with path coefficient values. The overall fit of the model was evaluated using thegoodness-of-fit index (GFI) and the adjusted goodness-of-fit index (AGFI). Rain:rainfall, Max. Wind: the maximum wind strength, SST: sea surface temperature,
SSS: sea surface salinity, Nut: nitrate + nitrite.

Seasonal variability in the protist community at Mombetsu
Port was determined by frequent observations. Bottom-up effects
by the exchange of water masses, nutrients and winds seemed
to have great influence on the seasonal variability of Chl a concentration. In particular, the exchange of water masses had agreat effect on diatom communities. Ciliates showed a dramatic
variability governed by an enhancement of growth associatedwith high Chl a concentration, high water temperatures andfluctuations in predation pressure from mesozooplankton. Identifications of species were not performed in this study, and such
identification is needed to investigate more detailed seasonalchanges of the protist community and the species composition
and the factors controlling the changes. Determining the production values is also necessary, not only by estimating it fromcell sizes but also by using incubation methods such as the13
C method, to investigate the degree of difference between theestimated values based on cell sizes and the values obtained fromincubations. Though the production estimated from cell sizes
showed discrete differences from the reported values because of
the differences in the methods, investigating seasonal changes
in primary production is important for understanding the mechanism required to promote high primary production and richfishery resources in the study area.

main prey from phytoplankton to ciliates. In addition, according
to Campbell et al. (2009), copepods prefer microzooplankton tophytoplankton, when microzooplankton is abundant. Therefore, achange in the prey of mesozooplankton, such as when copepods
switch from phytoplankton to ciliates, may result in the rapid
decrease in the ciliate biomass.
4.4. Seasonal change in primary production
13
C or 14 C incubation methods are usually used (Sorokin andSorokin, 1999; Kasai et al., 2009) when measuring the photosynthesis rates of phytoplankton, whereas we estimated production
using a relational expression of production rates and cell sizes.
The advantage of this method is that we can estimate production
from only cell size data without incubations.

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Regional Studies in Marine Science 43 (2021) 101683

CRediT authorship contribution statement

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Takuma Matsumoto: Formal analysis, Investigation, Writing- original draft, Visualization. Kohei Matsuno: Conceptualization, Formal analysis, Writing - review & editing, Supervision,
Project administration. Seiji Katakura: Investigation, Resources.
Hiromi Kasai: Investigation, Writing - review & editing. Atsushi
Yamaguchi: Conceptualization, Project administration.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.
Acknowledgements
This study was financially supported by the Arctic Challenge
for Sustainability (ArCS) Project (Programme Grant Number
JPMXD1300000000) and Arctic Challenge for Sustainability II(ArCS II) (Program Grant Number JPMXD1420318865). Part of thisstudy was supported by a Grant-in-Aid for Challenging Research(Pioneering) 20K20573, and Scientific Research 20H03054 (B),
19H03037(B), 18K14506 (Early Career Scientists), and 17H01483(A) from the Japanese Society for the Promotion of Science (JSPS).
This study was supported partly by the Grant for Joint Research
Program of the Japan Arctic Research Network Center.
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