Deep–Sea Research II 174 (2020) 104788

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

Zooplankton community structure and dominant copepod population
structure on the southern Kerguelen Plateau during summer 2016
Kohei Matsuno a, b, c, *, Jake R. Wallis c, d, So Kawaguchi c, e, Sophie Bestley d, f,
Kerrie M. Swadling c, daGraduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido, 041-8611, Japan
Arctic Research Center, Hokkaido University, Kita-21 Nishi-11 Kita-ku, Sapporo, Hokkaido, 001-0021, Japan
Antarctic Climate & Ecosystems Cooperative Research Centre, 20 Castray Esplanade, Battery Point, Tasmania, 7004, AustraliadInstitute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Tasmania, 7004, AustraliaeAustralian Antarctic Division, 203 Channel Highway, Kingston, Tasmania, 7050, Australiaf
CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7001, Australiabc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Calanoides acutus
Calanus propinquus
Metridia gerlachei
Metridia lucens
Rhincalanus gigas
Fawn troughPrincess Elizabeth trough
BANZARE Bank

The influence of environmental factors on the horizontal community structure of zooplankton over the southern
Kerguelen Plateau was investigated during summer in 2016. Zooplankton abundance ranged from 1490 to
363,484 ind. 1000 m-3, with highest numbers observed in the eastern and central areas. Based on cluster analysis
the zooplankton were divided into six groups (A F), and these were only distinguished based on water masses
and frontal systems. Groups A to C had abundant zooplankton and were consistent with areas of high chlorophyll
a concentration. Group D represented low abundance near the southern Antarctic Circumpolar Current front,
while group E was clustered south of the Southern Boundary and group F comprised two stations to the east of
the Fawn Trough. Generalised linear model (GLM) highlighted both fronts and chlorophyll a concentration as
drivers of overall zooplankton distribution. However, the population structures of key species were more likely aresult of species-specific life cycles rather than water masses and frontal systems.

1. Introduction
The Kerguelen Plateau, and areas to the south, represents one of themost important regions for primary production in the Indian sector of
the Southern Ocean (Arrigo et al., 2008), with high stocks of toothfish
and krill found in the north and south, respectively (Duhamel et al.,2014; Nicol, 2006). However, the southern Kerguelen Plateau has neverbeen investigated as a single region to determine the distribution andabundances of key species, their habitat characteristics, and the transi­tion between the northern fish-based food web (Pruvost et al., 2005) andthe southern krill-based food web (Nicol et al., 2012). Zooplankton playa key role in both food webs, as conduits for transferring energy fromprimary producers to higher trophic levels. To date, the zooplankton in
the southern Kerguelen region have not been well described.
In the Southern Ocean, seawater temperature and variability in seaice extent are increasing (Bracegirdle et al., 2008; Turner et al., 2014),
although the magnitude and direction of these changes differ among
regions around Antarctica (Constable et al., 2014). Zooplankton

communities are influenced by the different frontal zones in theSouthern Ocean (Errhif et al., 1997; Hunt and Hosie, 2005; Ward et al.,2012; Tachibana et al., 2017), and shifts in the fronts are expected toinduce changes in zooplankton distributions (Constable et al., 2014). For
example, a modelled 1 � C temperature rise produced a poleward shift for
all zooplankton taxa (Atkinson et al., 2012). Food web structures are notwell understood for the East Antarctic, compared to, for example, the
Scotia Sea (Murphy et al., 2007), the Antarctic Peninsula (Ducklow
et al., 2006) and the Ross Sea (Smith et al., 2007). Long-term programs
such as the Southern Ocean CPR survey (McLeod et al., 2010) haveprovided information on the patterns of abundance and distribution of
zooplankton for much of East Antarctica; however, they do not cover thesouthern Kerguelen Plateau, the focus of the current study.
The Kerguelen Plateau is an area of significant ecological value, withhigh krill biomass to the south (Pauly et al., 2000), and seabirds, seals
and whales, using the plateau for migration and feeding (Patterson et al.,2016). The Kerguelen Axis (KAxis) voyage was designed as a synoptic
survey, with multiple transects between 57.6 � S and 65.5 � S and 73.3� E* Corresponding author. Graduate School of Fisheries Sciences, Hokkaido University, 3-1-1 Minato-cho, Hakodate, Hokkaido, 041-8611, Japan.
E-mail address: k.matsuno@fish.hokudai.ac.jp (K. Matsuno).
https://doi.org/10.1016/j.dsr2.2020.104788
Received 1 August 2019; Received in revised form 24 April 2020; Accepted 26 April 2020
Available online 4 May 20200967-0645/© 2020 Elsevier Ltd. All rights reserved.

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Deep-Sea Research Part II 174 (2020) 104788

and 93.6 � E (Fig. 1). The transects encompassed an oceanographicallycomplex region (Park et al., 2009), including the ice edge, the Southern
Boundary (SB) of the Antarctic Circumpolar Current (ACC) and theSouthern ACC Front (SAACF). The KAXIS research project aimed to
identify and spatially distinguish krill-based and copepod-fish-based
food webs and was designed to examine distribution of the food web
components from bacteria to mid-trophic levels (fish and squid). Thispart of the program aimed to describe the horizontal distribution of
zooplankton over the Southern Kerguelen Plateau. The population
structures of dominant large (>2 mm) copepods are also presented.
Finally, to evaluate the effects of environmental factors on zooplankton
distribution, and to determine whether distinct communities wereassociated with large-scale oceanographic features we applied general­ised linear modelling (GLM) and multivariate statistics. This approachhas the potential to provide powerful insights into the influence ofenvironmental factors on zooplankton distributions in the Southern
Ocean, particularly environmentally variable regions such as the Ker­guelen Plateau (Park et al., 2009).

Bestley et al., 2018 for details). Profiles of salinity, temperature andfluorescence were recorded at each station.
2.2. Samples and data analysis
In the laboratory, zooplankton samples were split with a Motoda box
splitter so that a minimum of 550 individuals was enumerated per
sample. Zooplankton were identified to species level where possible andcounted under a stereomicroscope (Leica M165C). Large-sized copepods(adults >2 mm; i.e. Calanoides acutus, Calanus propinquus, Calanus sim­
illimus, Metridia gerlachei, Metrida lucens and Rhincalanus gigas) wereidentified to copepodite stage. To identify the zooplankton, we referredto Razouls (1994) for copepods, Kirkwood (1982) for euphausiids, andBoltovskoy (1999) for other species. Abundance is reported as thenumber of individuals 1000 m-3.
The mean copepodid stage (MCS) of the six large copepods wascalculated for each station where they occurred using the following
equation:
P6i¼1 i � Ai
MCS ¼ P(1)
6i¼1 Ai2. Materials and methods
2.1. Field samplingwhere i is the copepodite stage (1–6 indicates copepodid stage 1
copepodid stage 6), and Ai is the abundance (ind. 1000 m 3) of the ith
copepodid stage (Marin, 1987).
Multivariate analyses, designed to explore relationships between
zooplankton and their environment, were performed with PRIMER v7(PRIMER-e). Abundance of each taxon was fourth-root transformed
prior to cluster analysis to reduce the effect of abundant species (Quinn
and Keough, 2002). A similarity matrix based on stations was con­structed using the Bray-Curtis index, which is useful for biological datawhen there are many zeros (Quinn and Keough, 2002). For grouping thesamples (Q-mode analysis), 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). Accompanying this analysis, similarity profile analysis(SIMPROF) was added to determine if groupings of the stations were
statistically significant (at 5% significance level). Similarity percentages(SIMPER) analysis was applied to determine which species contributed
to the top 50% of total abundance for each group. Non-metric multi-­
dimensional scaling (NMDS) with multiple regression analysis was un­dertaken to explore relationships between the sampling sites andenvironmental data.
Regional differences in mean abundance among groups were tested
by one-way ANOVA and the Tukey-Kramer post-hoc test was applied to
distinguish which groups were statistically different. Additionally, aMann-Whitney U test was performed on abundance between the clus­
tering groups (A-C vs D-F) using R (version 3.4.0; R Core Team, 2017).
To find potential indicator species in the groups that resulted fromthe cluster analysis, the program Indicator Value (IndVal) was applied(Dufr^ene and Legendre, 1997). IndVal was calculated as:

Thirty-seven sites were sampled over the southern Kerguelen
Plateau, including the BANZARE Bank and the Princess Elizabeth
Trough, from 23 January to 19 February 2016, onboard the RSV Aurora
Australis (Fig. 1, Supplementary 1). At each site zooplankton were
sampled using an RMT1þ8 net, which was deployed by standard double
oblique tows from the surface to 200 m. The RMT1 net had a mesh size of
315 μm and mouth area of 1 m2. The towing speed of the RMT1þ8 net
ranged between 0.8 and 1.5 m s-1 knots (mean ¼ 1.1 m s-1). A flow meter
was positioned in the mouth of the RMT8 net to calculate the volume ofwater filtered; these values were divided by a factor of 9.42 to calculate
the volume of water filtered by the RMT1, as per Ikeda et al. (1986). The
RMT was fitted with hard cod ends to ensure that the organisms
collected were in good condition. Upon retrieval, samples were imme­
diately preserved in 4% buffered formaldehyde solution.
A SeaBird SBE911 plus CTD mounted on a SeaBird rosette sampler
was deployed at each station to the full depth of the water column (seeIndValij ¼ Aij*Bij*100(2)where Aij ¼ Nindividualsij/Nindividualsi, and Bij ¼ Nsiteij/Nsitesj. Aij is ameasure of site specificity, where Nindividualsij is the mean number ofindividuals in species i across sites of group j, and Nindividualsi is thesum of the mean numbers of individuals of species i over all groups. Bij isa measure of group fidelity, where Nsiteij is the number of sites in group jwhere species i is present, while Nsitej is the total number of sites in thatgroup (Dufr^ene and Legendre, 1997).
To evaluate the effects of environmental drivers on zooplankton
community structure, we applied generalised linear models (GLMs, R
version 3.4.0; R Core Team, 2017). To run the GLMs for each taxon weused a negative binominal distribution based on count data, with filteredvolume applied as an offset. MCS of the copepods were tested based on
the gaussian distribution. We tested for overdispersion by calculating

Fig. 1. Sampling stations along the Kerguelen Axis in the Southern Ocean
during January and February 2016.
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Deep-Sea Research Part II 174 (2020) 104788

the dispersion parameter (Pearson’s chi-square statistic/degrees of
freedom in residual deviance) and found that values were close to 1
(0.89–1.34), indicating the models captured most of the variation. Only
radiolarians had a dispersion parameter (1.89) that justified theirremoval from further GLM. The following factors were included in the
GLM: depth (of the water column); chlorophyll a concentration (chl.a)
as a measure of food availability; density averaged over the top 10 dbar,
where low values can indicate recent sea ice melt; salinity200, which has
a direct effect on zooplankton physiology; MLD (mixed layer depth) as
shallower depths can enhance primary production leading to more foodfor zooplankton; Temp200, average temperature over the top 200 m as
zooplankton metabolism is coupled tightly to temperature; time sincemelt, with the ice edge a region of high productivity and enhanced foodsupply; SST (sea surface temperature); and PAR (photosynthetically
active radiation), as time when zooplankton are sampled can influence
their position in the water column. Further details for defining each ofthese environmental variables are shown in Table 1. To remove multi­
collinearity among the environmental parameters we calculated vari­ance inflation factors (VIF) for each parameter. If the VIF was higher
than 3, it was removed from the explanatory parameters (O’Brien,2007). To derive the final model, full models with all environmental
variables were first constructed. Then, model selection was applied by“stepAIC” in the “MASS” package to choose the final models. Compari­son between null and final models by ANOVA confirmed the goodness of
fit of the model. If the p value < 0.05, then the final model was deemed
to be better than the null model.

northeast area was warmest, while the southern area was cooler(Fig. 2b). The mean temperature averaged over the top 200 m of thewater column (Fig. 2c) had a smaller range (-1.65� to 1.76 � C) comparedwith the top 20 m, while mean salinity in the top 200 m ranged from 34
to 34.48, with the northeast area being slightly fresher (Fig. 2d). Mean
density in the upper SML ranged from 26.309 to 27.144 kg m-3, indicting
recent ice melt (Fig. 2e). Finally, Integrated chlorophyll a ranged from
19.7 to 132.8 mg m-2, with stations KX04-KX09, KX39 and KX47 having
concentrations higher than 100 mg m-2 (Fig. 2f).
3.2. Spatial changes in the zooplankton community
Total zooplankton abundance ranged from 1490 to 363,484 ind.
1000 m-3, with the lowest abundance at KX43, and the largest at KX15
(Fig. 3a). Higher abundances were observed in the eastern and central
areas. The zooplankton community was divided into 6 groups (A F) at
75.7 and 78.7% similarity by Q-mode cluster analysis with SIMPROF,
based on zooplankton abundances at each site (Fig. 4a). Based on oneway ANOVA and a Tukey-Kramer post-hoc test, the abundances ofgroups A and C were significantly higher than those of the remaining
four groups (B, D-F; p < 0.0001). Additionally, the mean abundance ofgroups A-C was about 10 times higher than that of groups D-F (67,230 �
79,473 vs 6193 � 4035 ind. 1000 m-3; Mann-Whitney U test, p < 0.01).
Copepods were the dominant taxon in all six groups (59–76%), whileforaminifera were the next most abundant group (4–31%; Fig. 4b).
NMDS showed clear separation among the groups, with low stress(0.11). Five environmental factors, namely latitude, longitude, tem­
perature, salinity and fluorescence, had correlations of R2 > 0.3 with thegroups based on associations among stations. The distribution of thegroups across the sampling region broadly corresponded to water mass
distribution (Fig. 5). Groups A, B and C were observed in the central andeastern areas, north of the subpolar zone and along the Kerguelen
Plateau. Group D occurred in the central region, mainly in the northernpart of the transect. Group E was observed from the Antarctic Slope
Current to the Southern Boundary front, while Group F was in the Fawn
Trough Current.
Based on the SIMPER analysis the copepods Calanoides acutus, Cal­
anus propinquus, Calanus simillimus, Ctenocalanus spp., Metridia gerlachei,
Metridia lucens, Oithona spp., Oncaea spp. and Rhincalanus gigas, thechaetognath Eukrohnia hamata, the euphausiid Thysanoessa macrura,radiolarians and foraminiferans were all important contributors to atleast one group (Table 2). Foraminifera was the dominant taxon in fourgroups (A-D), while C. acutus was the second dominant species in groups
A-C. Oithona spp. was the second dominant taxon in group D, andM. gerlachei was dominant in group E; Ctenocalanus spp. and C. simillimus
were the dominant species in group F.

3. Results
3.1. Spatial changes in hydrographic condition
The surface mixed layer (SML) varied among stations, with the
deepest at KX34 (68 m) and the shallowest at KX18-20 (13 m; Fig. 2a).
Across the sampling region, the mean temperature of the upper 20 m of
the water column (defined as SST) ranged from -1.68 to 3.75 � C; theTable 1
Physical and biological variables included as predictors in the generalised linear
models (GLMs). Oceanographic variables (temperature, salinity and density)
were all derived from in situ CTD measurements undertaken at each RMT sam­pling site.
Variable

Explanation and source

Depth

Bathymetric depth (m) at sampling stations (Weatherall et al.,2015). Values are log10 transformed.
Integrated estimate of water column chlorophyll-a (mg m-2;
Westwood and Pearce, 2018) obtained using High Performance
Liquid Chromatography following Wright et al. (2010), based on
six CTD sampling depths within the upper 150 dbar.
Mean potential density (kg m-3) calculated relative to the surface(averaged over the upper 10 dbar). Low values (e.g. <26.8 kg m-3)
are indicative of recent ice influence/melt.
Mean salinity over the depth between the surface and 200 m (thenet sampling depth).
Mixed layer depth (m) estimates based on a change in density
criterion of Δσθ ¼ 0.05 kg m-3 relative to 10 dbar, following deBoyer Mont�egut et al. (2004).
Mean temperature over the depth between the surface and 200 m(the net sampling depth).
The time since ice melted (days) calculated from daily passive
microwave estimates of sea ice concentration (%) obtained from the
National Snow and Ice Data Center SMMR-SSM/I polar productavailable for the Southern Hemisphere gridded at 25 km resolution(Cavalieri et al., 1996, updated yearly; Maslanik and Stroeve, 1999,
updated daily).
Mean near-surface water temperature (� C, averaged over the upper
20 dbar).
Ship-based measurement of PAR (photosynthetically active
radiation, Watts m-2) averaged from the port and starboard
underway data during the RMT sampling periods.

Chl.a

Density
Salinity200
MLD
Temp200
Time since
melt

SST
PAR

3.3. Spatial distribution of large-sized copepods
Calanoides acutus occurred at all stations along the transect, pre­dominantly around the central region, with MCS values falling between
CIII and CIV in the northwest and around CV towards the southeast(Fig. 6). Calanus propinquus also occurred at all stations, with younger
copepodid stages more prevalent than for the C. acutus population (3.05
� 0.54 vs 3.54 � 0.72, Mann-Whitney U test, p < 0.01). Calanus sim­
illimus were observed at only nine stations, most of them north of theSouthern Boundary and mainly to the east of plateau; MCS was >4.
Metridia gerlachei occurred at all stations and were abundant on theeastern-most transect and close to the continent; its average MCS was ~4. Metridia lucens had a more northerly distribution than M. gerlachei,
and its MCS ranged between 3.2 and 5. Rhincalanus gigas occurred at all
stations, though in lower numbers than C. acutus and C. propinquus (728
� 832 vs 6262 � 12,834 and 2989 � 4737 ind. 1000 m-3, respectively).
The mean MCS of R. gigas (4.5 � 0.67) was higher than those of C. acutus
and C. propinquus.
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Fig. 2. Hydrographic conditions along the Kerguelen Axis. (a) depth of surface mixed layer (SML); (b) mean temperature in upper layer of SML averaged over theupper 20 dbar; (c) average temperature over the top 200 m; (d) average salinity over the top 200 m; (e) density averaged over the top 10 dbar: (f) chlorophyll aconcentration averaged over the top 150 dbar.

3.4. Environmental drivers of the zooplankton communitymm) copepods (modelled separately), responded in a similar fashion to
the total copepod group, although M. gerlachei showed higher abun­dances with cooler temperatures in the top 200 m of the water column.
Calanoides acutus and M. lucens had increased abundance at stations
where sea ice had persisted for longer.
For the mean copepodid stages of the large-sized copepods, the ef­
fects of environmental drivers varied with species. Calanoides acutus
showed two positive (Density and Time since melt) and three negative
relations (Temp200, MLD, and chl.a), while C. propinquus exhibited one
positive (Density) and two negative relationships (Salinity200 and SST).

Summaries of the results from the GLM are shown in Tables 3 and 4.
For total zooplankton at each site, higher abundances were generally
associated with warmer temperatures, higher chlorophyll a, shallower
depths and decreased light intensity (night). When split into the maintaxonomic groups, the effects of environmental drivers were similar to
those shown for total zooplankton abundance. For euphausiacea, higher
abundance was observed at stations where the mixed layer depths wereshallow. The GLMs showed that abundances of five of the large-size (>24K. Matsuno et al.

Deep-Sea Research Part II 174 (2020) 104788

Fig. 3. Spatial distribution of total zooplankton abundance along the Kerguelen
Axis during January and February 2016.

Metridia gerlachei had a weak positive relationship with Temp200 and
M. lucens had one positive (SST) and one negative (Depth). Younger
stages in R. gigas occurred at stations were the MLD was shallower.
4. Discussion
4.1. Effects of water masses and frontal systems on zooplankton
community
Studies of zooplankton on the southern Kerguelen Plateau arelimited. Swadling et al. (2010) investigated the zooplankton community
based on RMT1 samples to the region west (BROKE-West; 30–80� E) of
the current study (71.2–93.6� E) during summer in 2006. In that survey,
foraminifera, small copepods and appendicularians dominated thezooplankton community, while euphausiids (Euphausia crystallorophias)
and the copepod Metridia gerlachei were highlighted as indicator species(Swadling et al., 2010). In the present study appendicularians were threeorders of magnitude lower than observed in the BROKE-West study,
while foraminiferans and radiolarians were much more abundant.
Appendicularians have a short generation time (e.g. 8 day at 15 � C),
which increases in colder temperatures (Deibel and Lowen, 2012). In
polar regions, they can develop quickly and time their reproduction to~ a et al., 1999); after repro­
the ice-edge phytoplankton bloom (Acunduction, their abundance decreases rapidly following the end of thephytoplankton bloom. One hypothesis is that the lower numbers ofappendicularians observed in the present study reflect some of the sites
being sampled after the end of the phytoplankton bloom.
High numbers of foraminifera are found in the sea ice of the Southern
Ocean, and are released into the water column with sea-ice melt (Ojima
et al., 2017). This suggests foraminifera might be abundant wheresea-ice cover persisted for the longest time and cells were released
during the ice melt. Given that days after melt were generally lower thanthose recorded in Swadling et al. (2010), this might explain why theabundances recorded in the present study were up to 20 times higher
than reported for BROKE-West. Despite this, we did not see a significant
relationship between the ice-melt indices (i.e. Density and Time sincemelt) and abundance of foraminiferans. This might be explained by thefact that foraminifera are known to be influenced by many environ­mental factors, including snow depth and chl.a in the water column

Fig. 4. (a) Results of Q-mode clustering based on abundance of zooplankton
community. Red lines mean station groupings are not significant, as tested by
SIMPROF. Labels show sampling stations. (b) Abundance and species compo­
sition of groups based on the cluster analysis at two similarity levels (from (a)).
Error bars indicate standard deviation around total abundance of each group.
(c) Non-metric multi-dimensional scaling (NMDS) plot with multiple regression
analysis showing six groups based on the cluster analysis. Vectors show sig­
nificant environmental factors. (For interpretation of the references to colour inthis figure legend, the reader is referred to the Web version of this article.)(Wallis et al., 2016). In our study, foraminifera were abundant in areas
with high primary production, indicating that feeding preferences offoraminiferans might be an important driver.
The zooplankton communities found across the Southern Kerguelen
Plateau in summer 2016 were split into two groups via multivariate
analyses (groups A C vs groups D F) based on abundance. The distri­bution of the high-abundance group (i.e. groups A C) was consistent
with the distribution of high chl. a, highlighting possible foodweb in­teractions where the zooplankton were tracking the higher concentra­
tions of phytoplankton. The GLM showed that total abundance ofzooplankton was positively influenced by water-mass indices (i.e.
Temp200), lower light intensity (i.e. PAR) and increased phytoplankton
biomasss (i.e. chl.a). The negative effect of light intensity on abundancesuggested most species performed diel vertical migration during sum­mer, whereby they were distributed deeper in the water column during
the day (Takahashi et al., 2017). According to Swadling et al. (2010), thezooplankton community between 30 � E and 80 � E was correlated withchlorophyll a concentration, proximity to the Antarctic Slope Current
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Table 2
Mean abundance (1000 m-3) for all species/taxon. Bold indicates IndVal of
greater than 25% for that group. * represents top 50% of species in each groupaccording to SIMPER analysis. Number in () represents N: number of samplingstations.
Species/taxon
Aetideopsis antarctica
Aetideopsis australis
Appendicularia
Calanoides acutus
Calanus nauplii
Calanus propinquus
Calanus simillimus
Calanus spp. C1
Candacia sp.
Clausocalanus
brevipes
Clausocalanus laticeps
Clio pyramidata
forma sulcata
Clione limacina
antarctica
Ctenocalanus spp.
Cyllopus magellanicus
Euchirella
rostromagna
Eukrohnia
bathypelagica
Eukrohnia hamata
Euphausia superba
Euphausia triacantha
Foraminifera
Gaedius tenuispinus
Haloptilus longicirrus
Heterorhabdus
austrinus
Hyperiella macronyx
Hyperiella sp.1(larva)
IsopodaLimacina helicina
Limacina retroversa
australis
MedusaMetridia gerlachei
Metridia lucens
Microcalanus
pygmaeus
Oithona spp.
Oncaea spp.
Ostracoda
Paraeuchaeta
antarctica
Paraeuchaeta barbata
Pleuromamma robusta
Polychaeta
Primno macropa
Pseudochirellamawsoni
Racovitzanus
antarcticus
Radiolaria
Rhincalanus gigas
Rhincalanus gigas
nauplii
Sagitta gazellae
Sagitta marri
Sagitta maxima
Salp
Scaphocalanus farrani
Scaphocalanus
vervoorti
Scina borealis
Scolecithricella minor
Scolecithricella ovata
Stephos longipes
Themisto gaudichaudii

Fig. 5. Station groupings along the Kerguelen Axis, as determined from cluster
analysis and NMDS. Positions of frontal systems based on Bestley et al. (2018).
ACC: Antarctic Circumpolar Current; ASF: Antarctic Slope Front; FTC: Fawn
Trough Current; SB: Southern Boundary; SACCF: Southern Antarctic Circum­polar Current Front.

and length of time without an ice cover. Thus, the zooplankton com­munity across the western side of the Indian sector appears to be gov­
erned by the interplay of frontal systems, their vertical migration andbottom-up factors affecting productivity around the Kerguelen Plateau
during summer.
Zooplankton assemblages are closely related to the different frontal
zones in the Southern Ocean (Hunt and Hosie, 2005, 2006a, b; Hosieet al., 2014; Tachibana et al., 2017). Around the Kerguelen Plateau,several currents and frontal zones are observed (Park et al., 2009;
Bestley et al., 2018). The ACC is disrupted by the plateau, which forces
the core of the ACC to pass along its northern escarpment (e.g. Parket al., 1993). Following that, the southern Antarctic Circumpolar Cur­rent front (sAACf) and the Southern Boundary (SB) extend northward on
the eastern edge of the Kerguelen Plateau. The Fawn Trough Current
flows southeasterly at the northeast of the Kerguelen Plateau (Roquet
et al., 2009). However, zonal homogeneity is broken by features such as
gyres (Kaiser et al., 2009). These features of complicated oceanographic
conditions potentially relate to zooplankton distribution. In this study
the Southern Boundary clearly divided the zooplankton assemblages inthe southeast region, while the central region was more homogeneous.
This implies that a stable frontal system was present in the southeast
region, with unstable system-induced gyres in the central region (Bestley
et al., 2018).
4.2. Environmental drivers of zooplankton abundance and population
structure of large-sized copepods
Population structure reflects growth and reproductive capacity andassists in our understanding of life cycles and the condition of a popu­
lation (e.g. Atkinson, 1989). Mean copepodid stage (MCS) of copepods isa useful index for evaluating their population structure, with the value
decreasing with input of new generations by reproduction andincreasing with ontogenetic development within the population.
Calanoides acutus and C. propinquus are distributed widely in theSouthern Ocean, from the Polar Front to the Antarctic coast; however,they are usually more abundant north of the SB-ACC and decrease to­wards the continent (e.g. Atkinson, 1996; Hosie et al., 2000; TanimuraGroupsA (4)B (14)

C (2)

D (8)

E (7)

F (2)

0
0
158
18223*
1383
6868*
36
13345*
34
383

1
0
25
5836*
109
3279*
3
2895*
0
205

0
9
62
34173*
444
14628*
1267
16408
9
639

0
0
25
607*
164
485*
0
468*
0
37

1
0
15
479
7
468*
0
268
0
38

0
0
30
282*
0
401*
442*
134*
1
13

62
223

26
59

9
18

5
28

0
1

35
0

0

18

0

1

4

0

6847*
13
79

2165*
5
24

6872*
0
9

285*
0
5

760*
1
5

458*
0
1

0

5

148

1

0

0

2690*
0
0
28765*
13
194
353

1809*
32
0
9032*
7
41
129

6289*
62
89
60372
0
0
35

355*
1
0
1072*
2
2
10

461*
21
0
1255
4
6
27

186
0
0
124
0
2
0

88
0
0
135
0

9
19
6
97
0

296
148
0
157
18

6
4
0
56
1

0
38
4
2
0

42
0
0
5
47

34
2523
2831
237

52
1031
627
121

157
183
9925*
0

1
92
181
2

37
1088*
100
3

0
12
204*
0

10186*
3311*
809
634

1589*
1524*
223
166

7458*
6621
1073
517

645*
251*
54
17

276*
308
139
168

123*
81
3
6

0
57
1549
72
13

0
7
406
92
3

9
248
2070
44
0

0
9
131
5
0

0
5
63
43
2

0
0
1
1
0

554

147

1931

31

67

0

1582
1714
2961*

931*
828
776

5293*
2406*
9352

150
104
72

404*
378*
18

209*
94
33

0
80
0
310
156
265

2
139
13
82
35
28

0
349
452
263
18
0

0
14
0
11
4
0

1
40
5
53
3
29

0
12
3
0
0
0

0
642
37
41
96

1
187
4
12
10

0
946
0
0
18

0
30
0
1
4

0
113
0
0
20

0
22
0
1
26

(continued on next page)6

K. Matsuno et al.

Deep-Sea Research Part II 174 (2020) 104788

drivers of abundances of C. acutus and C. propinquus were similar, thesea-ice melt indices (Density and Time since melt) showed negative
relationships only with C. acutus abundances and positive relationships
with their MCS. Thus, higher abundance of the younger stages was
associated with lower surface density, suggesting a shorter time sincesea-ice melt. Calanoides acutus reproduces from November to March in
the Weddell Sea (Hagen and Schnack-Schiel, 1996), and the appearanceof the cohort likely coincides with the high chlorophyll a concentrationin the summer period (Atkinson, 1998). Calanus simillimus occurred in
the northeast regions, particularly near the Fawn Trough Current. This
species might have been transported south from more northerly warmer
waters, because it is known to be distributed in subantarctic waters andnorthern parts of the ACC (Atkinson, 1998).
The two species of Metridia had different distributions: M. gerlachei
was dominant in the south, while M. lucens had higher abundances in thenorth, a common pattern in the Southern Ocean (e.g. Atkinson, 1989).
The GLM results indicated that abundance of M. gerlachei showed similar
responses to those of C. propinquus, except for the effects of temperature:higher temperatures in the lower mixed layer resulted in higher abun­dance of C. propinquus and lower abundance of M. gerlachei. Light in­tensity did not have a strong influence on Metridia species compared tothe other copepods. This was interesting because this genus is known toTable 2 (continued )Species/taxon

GroupsA (4)B (14)

C (2)

D (8)

E (7)

F (2)

Thysanoessa macrura
Undinella brevipes
Urchin larva
Vibilia antarctica
Vibilia armata

1642
13
0
25
0

945
0
26
2
0

4927
0
444
0
0

103
0
0
0
2

600*
0
2
1
0

60
0
0
0
5

Total abundance

11226435844

196864

5531

7832

3101

et al., 2008). In the present study, these two species were mainly foundnorth of the Southern Boundary, and their population structures weresimilar in terms of abundance and MCS. Wallis et al. (2016) reported apositive influence on C. propinquus abundance from chl.a in water andsea ice, snow depth, latitude and year. In the present study, C. propinquus
showed positive relationships with average temperature in the top 200
m and chlorophyll concentration, and negative relationships withaverage salinity, surface temperatures, water column depth and PAR.
This species migrates into the surface layer in spring and reproduces,
before descending into the deep layer for diapause during autumn(Schnack-Schiel et al., 1991; Atkinson, 1996). While the environmental

Fig. 6. Spatial distribution of dominant copepods along the Kerguelen Axis. Circle size and colour denotes the abundance and mean copepodite stage of each species.
(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)7

K. Matsuno et al.

Deep-Sea Research Part II 174 (2020) 104788

Table 3Result of GLM and “stepAIC” analysis for zooplankton abundance and mean copepodid stage along the Kerguelen Axis transect. If a variable is included in the model fora taxon it is shown as positive (up arrow) when higher abundances are associated with higher values of the variable and negative (down arrow) when higher
abundances are associated with lower values of the variable. Int.: intercept, remaining variables as per Table 1. If a variable is included in the model for a taxon it isshown with an arrow -: p *: p < 0.05; **: p < 0.01; ***: p < 0.001. MCS – mean copepodite stage.
Intercept
Total zooplankton
Amphipoda
Chaetognatha
Copepoda
Euphausiacea
Foraminifera
Copepods
C. acutus
C. propinquus
M. gerlachei
M. lucens
R. gigas
Mean copepodite stage
MCS_C. acutus
MCS_C. propinquus
MCS_M. gerlachei
MCS_M. lucens
MCS_R. gigas

Density

***↑
***↑
***↑
*↑
***↑

***↓

***↑
***↑
**↑
**↑
**↑

*↓

**↓
*↑

***↑
***↑

***↑
**↑

*↓

Salinity200

SST

Temp200

Depth

Chl.a

PAR

***↓

***↓

***↑

***↓

**↓

***↓

***↓

**↑
***↑

***↓

***↓

***↑

**↓
**↓
*↓
***↓

***↑
*↑
***↑
***↑

**↓
***↓
**↓
**↓

***↓

**↓

***↓
**↑

MLD

*↓

***↑
***↑
***↓
***↑
-↑
**↓
-↑

***↓
***↓
-↓
**↓
*↓
**↓
-↓

-↓

**↓

**↓
-↓

FeaturesTotal zooplankton
abundance

Generally, higher abundances were associated withwarmer temperatures, higher chlorophyll-a concentrationand shallower depth, which was consistent with highabundances observed over the southern Kerguelen plateau(Fig. 3). Lower abundances were associated with increased
daylight.
Relationships mainly consistent with those reported for
total zooplankton abundance. Additionally, chaetognaths
were associated with surface low-Density. Higher
abundances of euphausiids were associated with shallower
mixed layer depths.
Relationships mainly consistent with those reported for
total zooplankton abundance. However, M. gerlachei
showed an opposite relation with temperature below the
MLD. C. acutus and M. lucens were more abundant when
there was a shorter time since sea-ice melt.
The relationships varied with species.

Taxon abundances

Large copepods

Copepod stages

***↑
***↑
***↑
**↑
*↑

***
0.0605
**
***
*
***

**↓
**↓
-↓
**↓
***↓
***↓
*↓
**↓

***↓

p value (ANOVA, null vs final)

*↓
*↓
*↑

-↑

***
***
***
***
*
***
***
0.3302*****comparing the environmental drivers of developmental stages of co­
pepods using GLM. Thus, GLM is a powerful analytical tool capable ofdistinguishing structure within copepod populations even within com­plex oceanographic regions such as the south Kerguelen Plateau. Also,
GLM helped determine those conditions that are most suited to eachtaxonomic assemblage. This information is useful for identifying pro­ductive regions and understanding the response of zooplankton toenvironmental change. In future, detailed information (e.g. population
structure) for the main zooplankton species should be monitored andused in assessing the influence of climate change on key zooplankton
species.

Table 4
Summary of the important environmental dependencies evident in the models.
Model

***↑

Time since melt

CRediT authorship contribution statement
Kohei Matsuno: Investigation, Formal analysis, Visualization,
Writing - original draft. Jake R. Wallis: Methodology, Investigation. So
Kawaguchi: Conceptualization, Investigation, Supervision. Sophie
Bestley: Software, Validation. Kerrie M. Swadling: Conceptualization,
Writing - review & editing, Supervision.

undertake diel vertical migration rather than seasonal vertical migration(Atkinson and Peck, 1988; Huntley and Escritor, 1992; Schnack-Schieland Hagen, 1995). The MCS for the two Metridia species were high andmight not relate directly to chlorophyll distribution because these spe­
cies have a long reproductive period (i.e. there is no clear
reproductive-peak season) and distribute rather patchily without any
apparent link to the distribution of chlorophyll (Atkinson, 1989, 1998).
For Rhincalanus gigas, late copepodid stages were dominant, although
their nauplii were observed in almost all samples (data not shown). The
reproduction of this species is reported to occur mainly during summer(Atkinson, 1998), though with regional differences; e.g. reproduction
continues into late autumn around the Antarctic Peninsula (Martin and
Schnack-Schiel, 1993). Rhincalanus gigas was reproducing during theJanuary and February in the Indian sector. From the GLM, the re­lationships of R. gigas with environmental drivers was similar to theother copepods, although chlorophyll a concentration was not a strong
influence on this species.
Recently, research investigating environmental drivers ofzooplankton abundance in the Southern Ocean via statistical modelling(e.g. GLM, generalised additive models) has increased (e.g. Wallis et al.,2016; Kelly et al., this issue), but studies using aspects of population
structure (e.g. MCS) as a response variable in GLM are limited. In thisstudy, the discussion of population structure was augmented by

Acknowledgements
We thank the Science Technical Support Team of the Australian
Antarctic Division and the master and crew of the RV Aurora Australis fortheir considerable efforts to support this research. This study was sup­ported by AAS Grants 4331 and 4344. This project was also supported by
the Australian Government’s Business Cooperative Research Centres
Programme through the Antarctic Climate and Ecosystems Cooperative
Research Centre, and the Australia Research Council’s Special Research
Initiative for Antarctic Gateway Partnership (Project ID SR140300001).
Part of the present study was financially supported through Overseas
Research Fellowships and a Grant-in-Aid for Scientific Research
18K14506 (Early Career Scientists) from the Japanese Society for Pro­
motion of Science (JSPS). This project was also supported by theAustralian Governments Cooperative Research Centres Program,
through the Antarctic Climate and Ecosystems CRC. Three anonymous
reviewers substantially improved this manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.dsr2.2020.104788.

8

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Deep-Sea Research Part II 174 (2020) 104788

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