Introduction
The Great Calcite Belt (GCB), defined as an elevated particulate inorganic
carbon (PIC) feature occurring alongside seasonally elevated chlorophyll a
in austral spring and summer in the Southern Ocean (Fig. 1; Balch et al.,
2005), plays an important role in climate fluctuations (Sarmiento et al.,
1998, 2004), accounting for over 60 % of the Southern Ocean area
(30–60∘ S; Balch et al., 2011). The region between
30 and 50∘ S has the highest uptake of anthropogenic carbon dioxide
(CO2) alongside the North Atlantic and North Pacific oceans (Sabine et
al., 2004). Our knowledge of the impact of interacting environmental
influences on phytoplankton distribution in the Southern Ocean is limited.
For example, we do not yet fully understand how light and iron availability
or temperature and pH interact to control phytoplankton biogeography (Boyd
et al., 2010, 2012; Charalampopoulou et al., 2016). Hence, if model
parameterizations are to improve (Boyd and Newton, 1999) to provide accurate
predictions of biogeochemical change, a multivariate understanding of the
full suite of environmental drivers is required.
The Southern Ocean has often been considered as a microplankton-dominated
(20–200 µm) system with phytoplankton blooms dominated
by large diatoms and Phaeocystis sp. (e.g., Bathmann et al., 1997;
Poulton et al., 2007; Boyd, 2002). However, since the identification of the
GCB as a consistent feature (Balch et al., 2005, 2016) and the recognition of
picoplankton (< 2 µm) and nanoplankton (2–20 µm) importance
in high-nutrient, low-chlorophyll (HNLC) waters (Barber and Hiscock, 2006),
the dynamics of small (bio)mineralizing plankton and their export need to be
acknowledged. The two dominant biomineralizing phytoplankton groups in the
GCB are coccolithophores and diatoms. Coccolithophores are generally found
north of the PF (e.g., Mohan et al., 2008), though Emiliania huxleyi
has been observed as far south as 58∘ S in the Scotia Sea (Holligan
et al., 2010), at 61∘ S across Drake Passage (Charalampopoulou et
al., 2016), and at 65∘ S south of Australia (Cubillos et al., 2007).
Diatoms are present throughout the GCB, with the Polar Front marking a strong
divide between different size fractions (Froneman et al., 1995). North of the
PF, small diatom species, such as Pseudo-nitzschia spp. and
Thalassiosira spp., tend to dominate numerically, whereas large
diatoms with higher silicic acid requirements (e.g., Fragilariopsis kerguelensis) are generally more abundant south of the PF (Froneman et al.,
1995). High abundances of nanoplankton (coccolithophores, small diatoms,
chrysophytes) have also been observed on the Patagonian Shelf (Poulton et
al., 2013) and in the Scotia Sea (Hinz et al., 2012). Currently, few studies
incorporate small biomineralizing phytoplankton to species level (e.g.,
Froneman et al., 1995; Bathmann et al., 1997; Poulton et al., 2007; Hinz et
al., 2012). Rather, the focus has often been on the larger and noncalcifying
species in the Southern Ocean due to sample preservation issues (i.e.,
acidified Lugol's solution dissolves calcite, and light microscopy restricts
accurate identification to cells > 10 µm; Hinz et al., 2012). In
the context of climate change and future ecosystem function, the distribution
of biomineralizing phytoplankton is important to define when considering
phytoplankton interactions with carbonate chemistry (e.g., Langer et al.,
2006; Tortell et al., 2008) and ocean biogeochemistry (e.g., Baines et al.,
2010; Assmy et al., 2013; Poulton et al., 2013).
The GCB spans the major Southern Ocean circumpolar fronts (Fig. 1a): the
Subantarctic Front (SAF), the Polar Front (PF), the Southern Antarctic
Circumpolar Current Front (SACCF), and occasionally the southern boundary of
the Antarctic Circumpolar Current (ACC; see Tsuchiya et al., 1994; Orsi et
al., 1995; Belkin and Gordon, 1996). The Subtropical Front (STF; at
approximately 10 ∘C) acts as the northern boundary of the GCB and is
associated with a sharp increase in PIC southwards (Balch et al., 2011).
These fronts divide distinct environmental and biogeochemical zones, making
the GCB an ideal study area to examine controls on phytoplankton communities
in the open ocean (Boyd, 2002; Boyd et al., 2010). A high PIC concentration
observed in the GCB (1 µmol PIC L-1) compared to the global
average (0.2 µmol PIC L-1) and significant quantities of
detached E. huxleyi coccoliths (in concentrations > 20 000
coccoliths mL-1; Balch et al., 2011) both characterize the GCB. The GCB
is clearly observed in satellite imagery (e.g., Balch et al., 2005; Fig. 1b;)
spanning from the Patagonian Shelf (Signorini et al., 2006; Painter et al.,
2010) across the Atlantic, Indian, and Pacific oceans and completing
Antarctic circumnavigation via the Drake Passage.
GCB waters are characterized as high nitrate, low silicate, and low chlorophyll
(HNLSiLC; e.g., Dugdale et al., 1995; Leblanc et al., 2005; Moore et al.,
2007; Le Moigne et al., 2013), in which dissolved iron (dFe) is considered an
important control on microplankton (> 20 µm) growth (e.g.,
Martin et al., 1990; Gall et al., 2001; Venables and Moore, 2010).
Sea surface temperature (SST) gradients are a driving factor behind
phytoplankton biogeography and community composition (Raven and Geider, 1988;
Boyd et al., 2010). The influence of environmental gradients on
biomineralizing phytoplankton in the Scotia Sea and the Drake Passage has also
been assessed (Hinz et al., 2012; Charalampopoulou et al., 2016). However,
the controls on the distribution of biomineralizing nanoplankton are yet to
be established for the wider Southern Ocean and GCB. Previous studies have
predominantly focused on a single environmental factor (e.g., Eynaud et al.,
1999) or combinations of temperature, light, macronutrients, and dFe (e.g.,
Poulton et al., 2007; Mohan et al., 2008; Balch et al., 2016) to explain
phytoplankton distribution. The inclusion of carbonate chemistry as an
influence on phytoplankton biogeography is a relatively recent development
(e.g., Charalampopoulou et al., 2011, 2016; Hinz et al., 2012; Poulton et
al., 2014; Marañón et al., 2016). Furthermore, natural variability in
ocean carbonate chemistry and the resulting impact on in situ phytoplankton
populations remains a significant issue when considering the impact of future
climate change.
An increasing concentration of dissolved CO2 in the oceans is resulting in
“ocean acidification” via a decrease in ocean pH (Caldeira and Wickett,
2003). In the high latitudes where colder waters enhance the solubility of
CO2 and reduce the saturation state of calcite, there may be potential
detrimental effects on calcifying phytoplankton (Doney et al., 2009).
However, this may be species specific (Langer et al., 2006) or even strain specific
(Langer et al., 2011), showing an optimum response when the opposing
influences of pH and bicarbonate are considered in a substrate-inhibitor
concept (Bach et al., 2015). The response of noncalcifiers (e.g., diatoms)
to ocean acidification is a greater unknown but is no less important given their
∼ 40 to 50 % contribution to global primary production (e.g.,
Tréguer et al., 1995; Sarthou et al., 2005). Tortell et al. (2008)
observed a switch from small to large diatom species with increasing
CO2, indicating a potential change in future community structure. Large
phytoplankton species (> 50 µm) may also have physiological
traits to withstand changes in ocean chemistry over smaller-celled
(< 50 µm) species (Flynn et al., 2012) and
potentially be less susceptible to grazing pressure (Assmy et al., 2013).
Alternatively, there may be a shift towards small phytoplankton groups due to
the expansion of low-nutrient subtropical regions (Bopp et al., 2001,
2005). The response of Southern Ocean phytoplankton biogeography to future
climate conditions, including ocean acidification, is complex (e.g.,
Charalampopoulou et al., 2016; Petrou et al., 2016; Deppeler and Davidson,
2017) and therefore understanding existing relationships between in situ
phytoplankton communities and ocean chemistry is an important stepping stone
for predicting future changes.
Here, we assess the distribution of coccolithophore and diatom species in
relation to the environmental conditions encountered across the GCB. Diatom
and coccolithophore cell abundances were obtained from analysis of scanning
electron microscopy (SEM) images, and their distribution was statistically
assessed in relation to SST, salinity, mixed layer average irradiance,
macronutrients, and carbonate chemistry. Herein, we examine the spatial
differences within the biomineralizing phytoplankton in the GCB, the main
environmental drivers behind their biogeographic variability, and the
potential effects of future carbonate chemistry perturbations.
Methods
Sampling area
Two cruises were undertaken in the GCB during 2011 and 2012
(http://www.bco-dmo.org/project/473206). The Atlantic sector of the
Southern Ocean (GCB1) was sampled from 11 January to 16 February 2011 onboard
the R/V Melville between Punta Arenas, Chile and Cape Town, South
Africa (Balch et al., 2016; Fig. 1). The Indian sector of the Southern Ocean
(GCB2) was sampled from 18 February to 20 March 2012 onboard the R/V
Revelle between Durban, South Africa and Fremantle, Australia
(Fig. 1). Water samples were taken at 27 stations across a latitudinal
gradient ranging from 38 to 60∘ S and a longitudinal gradient
ranging from 60∘ W to 120∘ E during the GCB cruises, which
enabled sampling of the major oceanographic features of this region.
Physiochemical environmental conditions
Water samples were collected from the upper 30 m of the water column using a
Niskin bottle rosette and CTD profiler for sea surface temperature, salinity,
chlorophyll a (Chl a), nitrate plus nitrite (NOx), ammonia (NH4),
phosphate (PO4), silicic acid (Si(OH4)), and carbonate chemistry.
Nutrient analyses of NOx, PO4, Si(OH4), and NH4 were run on
a Seal Analytical continuous-flow AutoAnalyzer 3, while salinity was
determined using a single Guildline Autosal 8400B stock salinometer (S/N
69-180). Chlorophyll a was sampled in triplicate following Joint Global
Ocean Flux Study (JGOFS; Knap et al., 1996) protocols. Mixed layer depths
were calculated from processed CTD data by applying a criteria of a
0.02 kg m-3 density change from the 5 m value (Arrigo et al., 1998).
Daily photosynthetically active radiation (PAR, mol PAR m-2 d-1)
was estimated from 8-day composite Aqua MODIS data from the closest time
and latitude–longitude point (averages were taken where necessary). Mixed
layer average irradiance (E‾MLD) was calculated from
daily PAR following Poulton et al. (2011).
Water samples were collected for total dissolved inorganic carbon
(CT) and total alkalinity (AT) following standardized
methods and analyzed using a Versatile Instrument for the Determination of
Titration Alkalinity (VINDTA) with a precision and accuracy of
±1 µmol kg-1 (Bates et al., 1996, 2012).
The remaining carbonate chemistry parameters were calculated from the
CT and AT values using CO2SYS (Lewis and Wallace,
1998) and CO2calc (Robbins et al., 2010) with the carbonic acid
dissociation constants of Mehrbach et al. (1973) refitted by Dickson and
Millero (1987). This includes computation of the saturation state (Ω)
for calcite (i.e., Ωcalcite).
Phytoplankton enumeration
Samples for biomineralizing phytoplankton community structure were taken from
the upper 30 m of the water column. One-liter seawater samples were
collected and prefiltered through a 200 µm mesh to remove any
large zooplankton. Seawater samples were gently filtered through a 25 mm,
0.8 µm Whatman® polycarbonate
filter placed over a 200 µm backing mesh to ensure an even
distribution of cells across the filter. Filters were rinsed with
∼ 5 mL of potassium tetraborate (0.02 M) buffer solution (pH = 8.5)
to prevent salt crystal growth and PIC dissolution, air-dried, and stored in
petri slides in the dark with a desiccant until further analysis.
To identify coccolithophores to the species level, each sample was imaged
using the SEM methodology of Charalampopoulou et al. (2011). A central
portion of each filter was cut out and gold coated, and 225 photographs were
taken at a magnification of 5000 × (equivalent to
∼ 1 mm2; GCB1) or 3000 × (∼ 2.5 mm2; GCB2)
using a Leo 1450VP SEM (Carl Zeiss, Germany). Detached coccoliths and whole
coccolithophore cells (coccospheres) were identified following Young et
al. (2003). Diatoms and other recognizable protists were identified following
Hasle and Syvertsen (1997) and Scott and Marchant (2005). Where a confident
species level identification was not possible, cells were assigned to the
level of genera (e.g., Chaetoceros spp. or Pappamonas sp.).
Each species identified was enumerated using the freeware ImageJ (v1.44o) for
all 225 images or until 300 cells (or coccoliths) were counted. A minimum of
10 random images was picked for enumeration when species were in high
abundance (> 1000 cells mL-1). The abundance of each species was
calculated following Eq. (1):
CellsmL-1=(C×F/A)/V,
where C is the total number of cells (or coccoliths) counted, A is the
area investigated (mm2), F is the total filter area (mm2), and V
is the volume filtered (mL).
Statistical analysis
Multivariate statistics (PRIMER-E v.6.1.6; Clarke and Gorley, 2006) were used
to examine spatial changes in coccolithophore and diatom abundance, species
distribution, and the influence of environmental variability on biogeography
(e.g., Charalampopoulou et al., 2011, 2016). Environmental data were initially
assessed for skewness, most likely due to strong chemical gradients across
fronts. Heavily left-skewed variables (NOx, silicic acid, and NH4)
were log(V+0.1) transformed to reduce skewness and stabilize variance.
Other environmental data, including SST, salinity,
E‾MLD, NOx, silicic acid, NH4, pH, pCO2,
and Ωcalcite, were then normalized to a mean of zero and a standard
deviation of 1, and Euclidean distance was then used to determine spatial
changes in these parameters. A principal component analysis (PCA) was used to
simplify environmental variability by combining the more closely correlated
variables and the relative influence of the environmental variables within
the data (Clarke, 1993; Clarke and Warwick, 2001; Clarke and Gorley, 2006).
Coccolithophore and diatom abundance and dominance information. The
area of the circles denotes abundance, while the shading denotes percentage
contribution of each phytoplankton group; red denotes coccolithophore
dominance and blue denotes diatom dominance. Fronts are defined as in Fig. 1.
Coccolithophore and diatom species diversity was assessed as the total number
of species (S) and Pielou's evenness index (J′), which assesses how
evenly the count data were distributed between the different species present
(before further statistical analysis). Species with cell counts of less than
1 cell mL-1 and/or consistently representing less than 1 % of the
total cell abundance were excluded from multivariate statistical analysis to
reduce the influence of rare species. Analysis of coccolithophore and diatom
community structure was carried out on standardized and square-root-transformed cell abundance (to reduce the influence of numerically abundant
species) using a Bray–Curtis similarity matrix. Bray–Curtis similarity
describes the percentage of similarity (or dissimilarity) between different
communities according to their relative species composition. To identify
which stations had a statistically similar biomineralizing phytoplankton
community across the GCB, a SIMPROF routine (1000 permutations, 5 %
significance level) was applied to the Bray–Curtis similarity matrix. SIMPROF
identifies, based on pairwise tests of the calculated Bray–Curtis percentage
similarity, whether the similarities between samples are smaller and/or
larger than those expected by chance and groups those that are statistically
distinct (Clarke et al., 2008). The phytoplankton species driving the
differences between the groups were identified through a SIMPER routine and
presented using nonmetric multidimensional scaling (nMDS; Clarke, 1993;
Clarke and Warwick, 2001; Clarke and Gorley, 2006). SIMPER allows for the statistical
identification of which species are primarily responsible for differences
between groups of samples and breaks down the Bray–Curtis similarity into
individual species contributions.
A BEST routine was applied to environmental and plankton data to determine
the combination of environmental variables that “best” described the
variability in coccolithophores and diatoms across the GCB. The BEST routine
statistically searches for relationships between the biotic and abiotic
patterns and to identify which environmental variable(s) explained most of
the variation in species distribution. Spearman's rank correlations were used
to further investigate the relationship between the key environmental variables
identified in the BEST routine and selected coccolithophore and diatom
species.
Details of Great Calcite Belt sampling stations including station
and cruise identifier, date of sample collection (DD.MM.YYYY), station
position decimal latitude (Lat) and longitude (Long), sea surface temperature
(SST), surface salinity (Sal), mixed layer average irradiance
(E‾MLD), surface macronutrient concentrations (nitrate
and nitrite, NOx; phosphate, PO4; silicate, Si(OH)4; ammonia,
NH4), and surface carbonate chemistry parameters (normalized total
alkalinity, AT; dissolved inorganic carbon, CT; pH;
partial pressure of carbon dioxide, pCO2; calcite saturation state,
Ωcalcite; surface chlorophyll a, Chl a, measured in mg
m-3).
Station
Date
Lat
Long
SST
Sal
E‾MLD
NOx
PO4
Si(OH4)
NH4
AT
CT
pH
pCO2
Ωcalc
Chl a
∘ S
∘ E
∘C
mol PAR
µM
µM
µM
µM
µmol kg-1
µmol kg-1
µatm
mg m-3
m-2 d-1
GCB1-6
14.01.2011
51.79
-56.11
8.6
34.0
17.8
14.2
1.05
1.7
0.64
2336
2138
8.09
367
3.3
0.84
GCB1-16
17.01.2011
46.26
-59.83
11.8
33.8
39.8
6.5
0.54
0.0
0.15
2333
2100
8.12
407
3.8
2.78
GCB1-25
20.01.2011
45.67
-48.95
16.1
35.1
25.5
0.0
0.23
0.2
0.16
2320
2047
8.12
390
4.6
0.73
GCB1-32
22.01.2011
40.95
-45.83
20.0
35.6
36.7
0.1
0.11
1.1
0.05
2307
2029
8.07
444
4.8
0.05
GCB1-46
26.01.2011
42.21
-41.21
18.3
34.9
16.0
0.2
0.19
0.3
0.00
2328
2050
8.09
356
4.7
0.09
GCB1-59
29.01.2011
51.36
-37.84
5.9
33.8
7.9
17.5
1.22
1.7
0.67
2368
2184
8.10
325
3.1
0.71
GCB1-70
01.02.2011
59.25
-33.15
1.1
34.0
9.7
22.3
1.74
78.5
1.54
2388
2235
8.10
407
2.6
0.13
GCB1-77
03.02.2011
57.28
-25.98
1.4
33.9
11.9
20.7
1.55
68.8
1.00
2386
2225
8.12
405
2.7
0.90
GCB1-85
05.02.2011
53.65
-17.75
4.1
33.9
8.9
19.1
1.33
0.7
0.30
2369
2191
8.12
363
3.0
1.11
GCB1-92
07.02.2011
50.40
-10.80
5.9
33.8
9.5
17.5
1.27
1.4
0.37
2362
2182
8.10
351
3.0
0.57
GCB1-101
09.02.2011
46.31
-3.21
11.0
34.0
17.1
12.5
0.95
0.6
0.16
2345
2134
8.08
400
3.5
0.46
GCB1-109
11.02.2011
42.63
3.34
15.1
34.4
20.0
5.3
0.56
0.8
0.00
2332
2098
8.07
359
4.0
0.39
GCB1-117
12.02.2011
39.00
9.49
18.8
35.0
19.4
0.0
0.20
0.7
0.06
2321
2047
8.08
299
4.7
0.32
GCB2-5
21.02.2012
37.09
39.48
21.0
35.5
11.2
0.0
0.05
1.1
0.07
2310
2005
8.10
340
5.2
0.12
GCB2-13
23.02.2012
40.36
43.50
18.4
35.3
13.7
0.1
0.17
0.2
0.02
2307
2032
8.09
351
4.7
0.19
GCB2-27
26.02.2012
45.82
51.05
7.7
33.7
5.8
20.1
1.35
2.9
0.14
2344
2194
8.00
425
2.6
0.47
GCB2-36
28.02.2012
46.74
57.48
8.1
33.7
8.7
18.9
1.40
1.7
0.49
2363
2175
8.08
355
3.1
0.21
GCB2-43
01.03.2012
47.52
64.04
6.5
33.7
5.9
21.7
1.53
0.5
0.38
2358
2197
8.04
387
2.8
0.34
GCB2-53
02.03.2012
49.30
71.32
5.1
33.7
8.5
23.8
1.66
7.1
0.17
2359
2210
8.03
396
2.6
0.41
GCB2-63
04.03.2012
54.40
74.56
3.5
33.8
3.0
25.3
1.70
10.5
0.21
2363
2210
8.07
360
2.6
0.26
GCB2-73
06.03.2012
59.71
77.75
1.1
33.9
4.3
28.0
1.91
40.4
0.34
2372
2233
8.07
360
2.4
0.29
GCB2-87
10.03.2012
54.25
88.14
3.4
33.9
4.3
24.2
1.69
9.0
0.45
2367
2216
8.06
367
2.6
0.28
GCB2-93
12.03.2012
49.81
94.13
7.8
34.0
5.9
17.5
1.27
1.5
0.26
2345
2149
8.10
333
3.3
0.18
GCB2-100
13.03.2012
44.62
100.50
13.0
34.8
4.7
6.4
0.55
0.2
0.15
2328
2083
8.11
326
4.1
0.33
GCB2-106
15.03.2012
40.13
105.38
17.0
35.4
12.8
0.1
0.14
0.3
0.03
2318
2029
8.13
313
4.9
0.24
GCB2-112
17.03.2012
40.26
109.60
15.8
34.9
11.1
3.6
0.43
0.2
0.00
2323
2060
8.11
332
4.4
0.36
GCB2-119
20.03.2012
42.08
113.40
13.8
34.8
11.2
5.3
0.55
0.2
0.01
2320
2080
8.10
342
4.1
0.27
Results
General oceanography
The GCB cruises crossed various biogeochemical gradients associated with the
Antarctic Circumpolar Current (ACC) fronts and subcurrents, with most parameters
following a recognizable latitudinal (or zonal) pattern. The position of
the oceanic fronts referred to in the text relates to those defined in Fig. 1
(see also Balch et al., 2016). Sea surface temperature decreased southwards
from 21 ∘C north of the STF to 1.1 ∘C close to
60∘ S (Table 1). The calcite saturation state (Ωcalcite)
decreased from 5.2 north of the subtropical front to 2.6 close to
60∘ S (Table 1). Macronutrient concentrations generally increased
southwards with a distinct divide across the SAF. NOx ranged from below
detection limits (< 0.1 µM) to as high as 28 µM, with
higher concentrations generally south of the Subantarctic Front
(> 12 µM) and lower concentrations (< 7 µM) north
of the Subantarctic Front (Table 1). PO4 followed a very similar
pattern with concentrations generally greater than 1 µM south of
the Subantarctic Front and < 0.6 µM to the north. Silicic acid
concentrations were divided by the PF, being generally less than
2 µM to the north and up to 78.5 µM to the south
(Table 1). E‾MLD was highest on the Patagonian Shelf
(∼ 40 mol PAR m-2 d-1) and generally less than
10 mol PAR m-2 d-1 south of the Subantarctic Front (Table 1).
There was no distinct latitudinal trend in pH or pCO2. Surface water
pH was generally greater than 8.06, ranging from 8.03 on the Kerguelen
Plateau to 8.13 in the Subtropical Front southwest of Australia (Table 1).
Surface water pCO2 ranged from 299 to 444 µatm with both
extremes in the vicinity of the Atlantic STF (Table 1). Chl a
concentrations were variable across the oceanic gradients, highest on the
Patagonian Shelf (2.78 mg m-3), and on average less than
1 mg m-3 in the South Atlantic compared with less than
0.5 mg m-3 in the southern Indian Ocean (Table 1).
Whole cell abundances of coccolithophores and diatoms in surface
samples of the Great Calcite Belt, the number of species in each group (S),
Pielou's evenness (J′; **** indicates that J′ was not calculated because only
one species was present), the dominant species, and its percentage
contribution to the total numerical abundance of coccolithophores (%Co) or
diatoms (%D). The + symbol denotes where one species had almost total numerical
dominance (> 99.8 %), with only one or two cells of a separate
species enumerated, and was therefore rounded up to 100 %.
Holococcolithophores are abbreviated as Holococco. “Position” denotes the
location relative to the Southern Ocean fronts and zones (Z; north of the
defined front) as defined by Orsi et al. (1995), and the letters after the front
abbreviation denote specific locations and proximity to landmasses:
Patagonian Shelf (PS), north of South Georgia (n SG), South Sandwich Islands
(SS), Crozet Islands (Cr), Kerguelen Island (K), and Heard Island (H).
Coccolithophores (Co)
Diatoms (D)
Station
Position
Cell mL-1
S
J′
Dominant species
% of Co
Cell mL-1
S
J′
Dominant species
% of D
GCB1-6
SAF, PS
243
2
0.02
E. huxleyi
100+
127
15
0.79
C. deblis
26
GCB1-16
SAF, PS
1636
2
0.00
E. huxleyi
100+
4610
5
0.11
F. pseudonana
96
GCB1-25
SAFZ
55
9
0.67
S. mollischi
38
28
10
0.84
Pseudo-nitzschia spp.
37
GCB1-32
STF
23
8
0.83
U. tenuis
31
19
8
0.70
Nitzschia spp.
55
GCB1-46
STF
3
1
****
Holococco
100
4
3
0.91
Chaetoceros spp.
56
GCB1-59
sPF, n SG
565
1
****
E. huxleyi
100
183
30
0.72
T. nitzschioides
29
GCB1-70
sPF
103
1
****
E. huxleyi
100
720
24
0.29
F. nana
81
GCB1-77
sPF, SS
2
1
****
E. huxleyi
100
6893
18
0.04
F. nana
98
GCB1-85
sPF
28
1
****
E. huxleyi
100
151
30
0.77
C. aequatorialis sp.
22
GCB1-92
PFZ
77
2
0.13
E. huxleyi
98
111
28
0.73
Pseudo-nitzschia spp.
32
GCB1-101
SAFZ
92
7
0.57
E. huxleyi
68
52
11
0.57
F. pseudonana
59
GCB1-109
SAFZ
39
9
0.90
E. huxleyi
25
129
17
0.55
Pseudo-nitzschia spp.
61
GCB1-117
STF
15
6
0.88
U. tenuis
35
209
9
0.13
C. closterium
95
GCB2-5
STFZ
37
15
0.69
E. huxleyi
46
6
8
0.76
Nanoneis hasleae
47
GCB2-13
STFZ
51
17
0.61
E. huxleyi
57
28
7
0.57
Nitzschia spp. < 20 µm
67
GCB2-27
SAF, Cr
478
6
0.04
E. huxleyi
99
375
24
0.28
F. pseudonana
83
GCB2-36
SAF
166
8
0.32
E. huxleyi
83
155
32
0.69
F. pseudonana
33
GCB2-43
PFZ
12
4
0.18
E. huxleyi
95
90
25
0.57
F. pseudonana
54
GCB2-53
sPF, K
51
3
0.90
E. huxleyi
56
512
28
0.39
F. pseudonana
47
GCB2-63
sPF, H
132
1
****
E. huxleyi
100
254
24
0.38
F. pseudonana
71
GCB2-73
sPF
0
0
****
n/a
n/a
538
24
0.55
F. pseudonana
56
GCB2-87
sPF
106
1
****
E. huxleyi
100
184
29
0.55
F. pseudonana
42
GCB2-93
PFZ
100
11
0.33
E. huxleyi
80
75
29
0.67
Pseudo-nitzschia spp.
37
GCB2-100
SAFZ
123
13
0.26
E. huxleyi
86
164
26
0.44
Pseudo-nitzschia spp.
67
GCB2-106
STF
90
19
0.77
E. huxleyi
29
80
22
0.58
Pseudo-nitzschia spp.
54
GCB2-112
STF
123
12
0.35
E. huxleyi
80
257
27
0.38
Pseudo-nitzschia spp.
74
GCB2-119
SAFZ
121
17
0.32
E. huxleyi
82
68
21
0.55
Pseudo-nitzschia spp.
47
Coccolithophores and diatoms
The most frequently occurring and abundant size group within the
coccolithophore and diatom counts were the nanoplankton (cells
2–20 µm). Large diatom species (cells > 20 µm) were
found in higher numbers (up to 50 cells mL-1) south of the PF.
Consideration of community biomass would potentially reduce the dominance of
the nanoplankton relative to microplankton in the GCB. However, converting
from cell size to biomass is not straightforward for diatoms, as highlighted
by Leblanc et al. (2012), and to avoid such issues we consider species
abundance only. Total cell abundances were less than 1000 cells mL-1
at most stations (Table 2), which are indicative of late summer, non-bloom
conditions. In the South Atlantic, the highest abundance of coccolithophores
was on the Patagonian Shelf (station GCB1-16; 1636 cells mL-1) and the
highest abundance of diatoms was east of the South Sandwich Islands (station
GCB1-77; 6893 cells mL-1; Table 2). In the southern Indian Ocean,
coccolithophore abundance was highest near the Crozet Islands (station
GCB2-27; 472 cells mL-1), and diatom abundance was highest at the most
southerly station (station GCB2-73; 538 cells mL-1; Table 2). There
were no stations in the southern Indian Ocean where coccolithophore and diatom
abundances were greater than 1000 cells mL-1 (Fig. 2, Table 2).
Additionally, the silicifying chrysophyte Tetraparma sp. was
particularly abundant east of the South Sandwich Islands (station GCB1-77)
at a cell density of 2000 cells mL-1, though they were present in low
numbers (< 5 cells mL-1) at three more stations in the South
Atlantic and absent throughout the rest of the GCB.
Coccolithophores dominated the biomineralizing community at 12 stations
in terms of abundance north of the PF (Fig. 2, Table 2). On average
coccolithophores contributed approximately 38 % to total (coccolithophore
and diatom) abundance in the GCB. Coccolithophores were greater than 75 %
of the total abundance at only one station to the north of South Georgia (station
GCB1-59) and never accounted for 100 % of total cell numbers.
Twenty-eight species of coccolithophores were identified as intact
coccospheres across the GCB. Coccolithophore diversity decreased south
towards 60∘ S, with the highest coccolithophore diversity
(19 species) found in the vicinity of the STF in the eastern part of the
southern Indian Ocean (station GCB2-106), while coccolithophore abundance was
more evenly distributed between the different species in the lower latitudes
(i.e., high J′; Table 2). Emiliania huxleyi was the most
numerically abundant coccolithophore at all but four stations and was encountered
in the mixed layer at all stations except one (station GCB2-73, the most
southerly station in the Indian Ocean). Other coccolithophore species (e.g.,
Syracosphaera spp. and Umbellosphaera spp.) were present
north of the PF throughout the GCB and were most abundant north of the STF.
At stations south of the SAF (50∘ S) only one (E. huxleyi)
or two species (E. huxleyi and Pappamonas sp.) were
observed as intact coccospheres.
Diatoms dominated 15 stations in terms of biomineralizing plankton abundance
across all environments sampled (Fig. 2, Table 2), being found in every
sample analyzed and contributing 62 % (on average) to total
(coccolithophores + diatoms) abundance. Diatoms made up 100 % of the
total cell counts at the most southerly station in the southern Indian Ocean
(station GCB2-73) and 99.7 % east of the South Sandwich Islands (station
GCB1-77; Fig. 2). Seventy-six species of diatom were identified as intact
cells across the entire GCB. The most frequently occurring species in the GCB
were small (< 5 µm in length) Fragilariopsis spp. The
highest abundance of diatoms in the South Atlantic Ocean
(6893 cells mL-1) was dominated by F. nana east of the South
Sandwich Islands (station GCB1-77). The highest diatom abundance in the
southern
Indian Ocean (538 cells mL-1) was dominated by F. pseudonana
at the most southerly station (station GCB2-73) sampled. Another frequently
dominant diatom was Pseudo-nitzschia spp., which was most abundant
north of the PF (Table 2).
Diatom species richness increased south towards 60∘ S with the
contribution of the different diatom species to the total biomineralizing
plankton abundance fairly even (J′ > 0.5, Table 2), except at stations
(stations GCB1-70, GCB1-77, GCB2-27, and GCB2-63) where Fragilariopsis spp. < 5 µm were dominant (> 70 % of the diatom
population, J′ < 0.5). The highest diatom species richness (32 species)
was found in the GCB south of the SAF (station GCB2-36) at a temperature of
8 ∘C, in HNLSiLC conditions (NOx 18.9 µM, silicic acid
1.7 µM, 0.21 mg Chl a m-3).
Statistical analysis
Three of the environmental variables were removed from the statistical
analysis following a Spearman's rank (rs) correlation analysis
(Table S1 in the Supplement). NOx and PO4 had a strong significant
positive correlation (rs=0.961, p<0.0001), so NOx was
deemed representative of the distribution of both nutrients. Sea surface
temperature displayed significant negative correlations with both
CT (rs=-0.981, p<0.0001) and AT
(rs=-0.953, p<0.0001), so sea surface temperature was
taken as being representative of these two variables of the carbonate
chemistry system.
Statistically significant groups of coccolithophore and diatom
communities in the Great Calcite Belt as identified by the SIMPROF routine.
The colors designate which statistical group defines the coccolithophore and
diatom assemblage at each station as shown in the group key. Fronts are
defined as in Fig. 1. See Table 4 for full group species descriptions.
The variation in environmental variables across the GCB was examined using a
principal component analysis (PCA), which simplifies environmental
variability by combining closely correlated variables into principal
components in order to account for the greatest variance in the data with the
fewest components. The first principal component (PC1) accounted for 58 %
of the variation in environmental variables, with an additional 17 % of
environmental variation described by PC2 (Table 3). PC1 describes the main
latitudinal gradients of environmental changes across the GCB (decreasing
SST, increasing macronutrients). PC1 is a predominantly linear combination of
SST, salinity, NOx, silicic acid, NH4, and Ωcalcite;
there is a significant positive correlation of PC1 with SST and
salinity and a significant negative correlation with all other variables
(Table 3). PC2 represented the environmental variation in the GCB occurring
independently of latitude and was driven predominantly by variation in
pCO2, with weaker influences from E‾MLD and pH
(Table 3). PC2 had significant positive correlations with pCO2 and
E‾MLD and a negative correlation with pH.
Principal component (PC) scores, percentage of variation described
(%V), and the Pearson's product moment correlation associated with each
variable and its significance level: p < 0.0001***,
p < 0.001**, p < 0.005*, p < 0.01, p < 0.05.
Variable
PC1 – EV 5 (58 %)
PC2 – EV 1.5 (17 %)
Temp
0.42
(0.97***)
0.08
(-0.10)
Salinity
0.36
(0.90***)
0
–
E‾MLD
0.24
(-0.55*)
0.5
(0.62**)
NOx
-0.4
(-0.91***)
-0.05
(-0.06)
Si(OH)4
-0.35
(-0.77***)
0.02
(-0.03)
NH4
-0.35
(-0.81***)
-0.07
(-0.09)
pH
0.18
(-0.39)
-0.42
(-0.50*)
pCO2
-0.15
(-0.33)
0.75
(0.89***)
Ωcalcite
0.43
(-0.99***)
-0.02
(-0.02)
The SIMPROF routine identified the stations in the GCB that had statistically
similar coccolithophore and diatom community composition through a comparison
of Bray–Curtis similarities. Six statistically significant groups (p<0.05)
were defined across the GCB (Fig. 3). Three of these groups (A, B,
C) were specific to the South Atlantic Ocean (Fig. 3). For example, groups A
and B represented individual stations GCB1-46 and GCB1-117, respectively, in
the subtropical region of the South Atlantic Ocean. The most southerly
stations in the South Atlantic Ocean (stations GCB1-70 and GCB1-77) defined
group C (Fig. 3). Groups D, E, and F included stations across the GCB in both
ocean regions. Here, group D was defined by eight stations sampled
predominantly north of the SAF, while group F was defined by 11 stations
predominantly sampled south of the SAF (Fig. 3). These statistically defined
similar community structures indicate that although the GCB covers a wide
expanse of ocean, the community structure is consistently latitudinally defined
across its longitudinal range.
Phytoplankton assemblage groups identified using the SIMPROF
routine at p<0.05 in the GCB (see also Fig. 3) from the South
Atlantic (GCB1) and the southern Indian (GCB2) oceans. Location is indicated as
in Fig. 2. Group average similarity (Group Av.Sim%) defines the percentage
of similarity of the community structure in all the stations within each group.
The defining species contributing > 50 % to the species
similarity for each group as identified through the SIMPER routine are
presented alongside the average similarity for each species in each group
(Average similarity); higher “Similarity SD” indicates more consistent
contribution to similarity within the group. The percentage of contribution per
species to the group similarity (Contribution%) was also calculated. Group averages not calculable (n/a) for single station groups A and B.
Group
Station
Location
Group
Defining
Average
Similarity
Contribution%
Av.Sim%
species
similarity
SD
A
GCB1-46
STF
n/a
Holococco
n/a
n/a
n/a
B
GCB1-117
STF
n/a
Cylindrotheca sp.
n/a
n/a
n/a
C
GCB1-70
SBDY
54.5
F. nana
53.3
n/a
97.8
GCB1-77
D
GCB1-25
N of PF
47.6
E. huxleyi
13.9
2.68
29.3
GCB1-109
Pseudo-nitzschia spp.
12.7
3.6
26.7
GCB2-36
GCB2-93
GCB2-100
GCB2-106
GCB2-112
GCB2-119
E
GCB1-32
N of SAF
42.3
E. huxleyi
18.9
3.8
44.8
GCB1-101
Holococco
8.45
4.01
20
GCB2-5
GCB2-13
F
GCB1-6
PS
40.6
E. huxleyi
15.1
1.51
37.3
GCB1-16
S of SAF
F. pseudonana
14.2
1.25
35
GCB1-59
GCB1-85
GCB1-92
GCB2-27
GCB2-43
GCB2-53
GCB2-63
GCB2-73
GCB2-87
A SIMPER routine statistically identified the species that define the
difference between (and the similarity within) the statistically different
community structures defined by the SIMPROF routine (Table 4). The abundance
and distribution of four phytoplankton species (E. huxleyi,
Pseudo-nitzschia spp., F. nana, and F. pseudonana;
Fig. 4) were identified as having the most significant contribution to
differences in community structure across the GCB (Table 4).
Emiliania huxleyi and F. pseudonana were the most
numerically dominant coccolithophore and diatom species, respectively, across
the GCB (Table 2). Fragilariopsis pseudonana was the numerically
dominant diatom (> 30 %) at seven stations in the southern Indian Ocean
(Table 2). The diatom with the highest abundance, F. nana
(6797 cells mL-1), was almost exclusively found in the South Atlantic
Ocean (Table 2) and the more frequently occurring Pseudo-nitzschia spp. was present at all but one station.
SEM images of the four phytoplankton species identified by the
SIMPER analysis as characterizing the significantly different community
structures: (a) E. huxleyi, (b) F. pseudonana, (c) F. nana, and (d) Pseudo-nitzschia spp.
Two-dimensional nonmetric multidimensional scaling (nMDS)
ordination of station groupings (a) as defined by the SIMPROF routine, with
group color identifiers as in Fig. 3; relative distances between
samples represent the similarity of species composition between
phytoplankton communities. Stations with statistically similar species
composition are clustered together, whereas stations with low statistical
similarity in terms of species composition are more widely spaced. Overlay
of bubble plots of the defining species abundance (cells mL-1)
characterizing the statistically significant groups in the GCB (see also
Table 4): (b) E. huxleyi abundance, (c) F. pseudonana abundance, (d) F. nana
abundance,
(e) Pseudo-nitzschia spp. abundance, and (f) Holococcolithophore abundance. The two-dimensional
stress of 0.15 gives a “reasonable” representation of the data in a 2-D
space (Clarke and Warwick, 2001).
The nonmetric multidimensional scaling (nMDS) plot of the Bray–Curtis
similarities (Fig. 5) shows the station distribution with respect to the
SIMPROF-defined groups (Fig. 5a), the four main species (Fig. 5b–e), and also
holococcolithophores (Fig. 5f). The more closely clustered the stations, the
more similar their biomineralizing species composition. Groups A and B were
defined by the absence of E. huxleyi (Fig. 5b) and the presence of
either holococcolithophores (group A; Fig. 5f) or the diatom
Cylindrotheca sp. (group B). Group C was defined by the dominance of
F. nana (Table 4; Fig. 5d) and low contributions from E. huxleyi and Pseudo-nitzschia spp. (Table 2; Fig. 5b, e), resulting
in a significant difference from the other groups. Group D had high total
species diversity overall (19–41 species; Table 2) and was defined by
similar relative abundances of E. huxleyi and
Pseudo-nitzschia spp., which were not found elsewhere (Fig. 5b, e).
Group E, composed of stations north of the SAF (Figs. 3, 5a), included
E. huxleyi, U. tenuis, and holococcolithophores (Table 4,
Fig. 5b, f). The low abundance and diversity (3–125 cells mL-1,
7–11 species; Table 2) of diatoms within group E separated it from the other
groups. The combination of E. huxleyi, F. pseudonana, and
Pseudo-nitzschia spp. that defined group F (Table 4, Fig. 5b, c, e)
represented stations on the Patagonian Shelf and south of the SAF (Figs. 3,
5a). The almost monospecific E. huxleyi coccolithophore community
(Table 2) in group F highlights its strong dissimilarity from the other
community structure groups identified (Fig. 5).
The influence of environmental variables on the biogeography of
coccolithophores and diatoms in the GCB was assessed using the BEST routine.
The strongest Spearman's rank correlation (rs=0.55, p<0.001)
between all possible environmental variables and the biogeographical patterns
observed came from a combination of five variables: (1) SST,
(2–4) macronutrients (NOx, silicic acid, NH4), and (5) pCO2.
This was followed by a correlation of rs=0.54 (p<0.001) that
included these parameters and Ωcalcite. Salinity was
included in the third-highest correlation, whereas E‾MLD
and pH did not rank as significant factors in the BEST analysis.
Discussion
Biogeography of coccolithophores and diatoms in the Great Calcite
Belt
Studies of Southern Ocean phytoplankton productivity have generally focused
on the microphytoplankton (Barber and Hiscock, 2006) as these species
contribute around 40 % to total oceanic primary production (Sarthou et
al., 2005; Uitz et al., 2010). However, nanoplankton and picoplankton are
becoming increasingly recognized as important contributors to total
phytoplankton biomass, productivity, and export in the Southern Ocean (e.g.,
Boyd, 2002; Froneman et al., 2004; Uitz et al., 2010; Hinz et al., 2012), as the dominant size
group in both post-bloom (Le Moigne et al., 2013) and non-bloom conditions (Barber
and Hiscock, 2006).
In this study, coccolithophores were generally numerically dominant at
stations sampled north of the PF, particularly around the Subantarctic
Front, whereas diatoms were dominant at stations south of the PF (Fig. 2).
There was also a significantly different species distribution (a priori
ANOSIM; R=0.227, p<0.01) north and south of the Subantarctic Front,
which has been previously identified as the divider between calcite- and opal-dominated
export in the Southern Ocean (e.g., Honjo et al., 2000; Balch et
al., 2016). Diatoms were more abundant (∼ 570 cells mL-1) than
coccolithophores (∼ 160 cells mL-1) on average in the entire
GCB. This is in contrast to Eynaud et al. (1999) for the South Atlantic Ocean at a
similar time of year, who reported a peak in coccolithophore cell abundance in
the vicinity of the PF (a feature that was not observed in this study). These
differences are likely due to the variability of Southern Ocean plankton on short
temporal scales (Mohan et al., 2008), including variability in the seasonal
progression of the spring bloom (Bathmann et al., 1997).
The coccolithophore E. huxleyi and diatoms F. pseudonana,
F. nana, and Pseudo-nitzschia spp. (Fig. 4) were all
identified as being central to defining the statistical similarities within,
and the differences between, the different biomineralizing phytoplankton
groups (Table 4, Fig. 5). Three of these species (E. huxleyi, F. nana,
and F. pseudonana) are part of the nanoplankton, whilst Pseudo-nitzschia spp. is at the lower end of the size range of the
microplankton (Pseudo-nitzschia spp. is > 20 µm in
length but < 5 µm in width) and contributes significantly to
biomass in Southern Ocean HNLC regions (Boyd, 2002). Emiliania huxleyi and Fragilariopsis spp. smaller than 10 µm have
been identified as two of the most abundant biomineralizing phytoplankton
further south in the Scotia Sea (Hinz et al., 2012). Our results further
highlight that nanoplankton have the potential to contribute a significant
proportion to GCB community composition alongside the larger phytoplankton
(including large diatoms) typical of HNLC regions.
Abundances of HNLC diatoms, such as F. kerguelensis
(< 10 cells mL-1), T. nitzschioides
(< 20 cells mL-1), and large Chaetoceros spp.
(< 10 cells mL-1), were lower than those observed in other studies
(e.g., Poulton et al., 2007; Armand et al., 2008; Korb et al., 2010, 2012).
Furthermore, the absence of the diatom Eucampia antarctica
(< 1 cell mL-1) in this study does not reflect the typical
assemblage (sometimes > 600 cells mL-1) found in previous studies
(e.g., Kopczynska et al., 1998; Eynaud et al., 1999; de Baar et al., 2005;
Poulton et al., 2007; Salter et al., 2007; Korb et al., 2010). Low abundances
of the large-celled diatoms in the silicic-acid-replete regions may partly
relate to the small filter area analyzed using SEM; in this study the area
imaged equates to a relatively small volume of water (2–6 mL depending on
magnification) relative to the larger volumes (10–50 mL) often examined for
light microscopy in other studies. Large, rare cells may not be enumerated
from such small sample volumes; however, the numerically abundant nanoplankton
groups were well represented in SEM images. Conversely, samples preserved in
acidic Lugol's solution for light microscopy analysis are biased towards
larger species since small diatoms (< 10 µm) are not clearly
visible and coccolithophores are not well preserved (Hinz et al., 2012). In
the future, a combination of both imaging techniques is recommended to fully
express the phytoplankton community structure of the Southern Ocean.
Emiliania huxleyi in the Great Calcite Belt
The importance of coccolithophores in the GCB was examined via species
composition and the abundance of intact cells, focusing on areas identified as
having high PIC reflectance from underway sampling and satellite observations
(Balch et al., 2014, 2016; Hopkins et al., 2015). Higher species diversity of
coccolithophores occurred north of the STF (i.e., 6–19 species; Table 2).
Coccolithophores are diverse in the stratified and low-nutrient waters
associated with lower latitudes (Winter et al., 1994; Poulton et al., 2017).
Only a few species are found in the colder waters south of the STF (Mohan et
al., 2008), the most successful being E. huxleyi, which was observed
at an abundance of 103 cells mL-1 at 1 ∘C in this study in
the South Atlantic (station GCB1-70). The 2 ∘C isotherm has been
previously assumed to represent the southern boundary of E. huxleyi
(e.g., Verbeek, 1989; Mohan et al., 2008), and interannual variability could
be influenced by the movement of the southern front of the Antarctic Circumpolar
Current (Holligan et al., 2010). The Southern Ocean E. huxleyi
morphotype (Cook et al., 2011; Poulton et al., 2011) may therefore have a
wider temperature tolerance than its Northern Hemisphere equivalent (Hinz et
al., 2012) and has been observed poleward of 60∘ S further east in
the Southern Ocean (Cubillos et al., 2007) and across the Drake Passage
(Charalampopoulou et al., 2016). There were three distinct E. huxleyi occurrences (the Patagonian Shelf, north of South Georgia, and north
of the Crozet Islands) within the GCB where E. huxleyi contributed
> 50 % of the total cell counts of biomineralizing phytoplankton.
Emiliania huxleyi was most abundant (1636 cells mL-1) on the
Patagonian Shelf and was the most frequently occurring coccolithophore across
the entire GCB. The main E. huxleyi occurrences are further discussed
below to examine why this species is so widely distributed in the
GCB.
Patagonian Shelf
The Patagonian Shelf is a well-known region for E. huxleyi blooms,
as observed in satellite imagery between November and January (Signorini et
al., 2006; Painter et al., 2010; Balch et al., 2011, 2014; Garcia et al.,
2011). The E. huxleyi cell abundance observed in this study
(∼ 1600 cells mL-1) was similar to that found by Poulton et
al. (2013; > 1000 cells mL-1). Using a value of
0.2 pg Chl a cell-1 (Haxo, 1985) and following the approach in Poulton
et al. (2013), such E. huxleyi abundance levels are equivalent to
estimated contributions of only ∼ 12 % to the total Chl a signal
(∼ 2.8 mg m-3). This estimate is similar to that estimated in an
identical way by Poulton et al. (2013) and highlights the significant
contribution of phytoplankton other than coccolithophores (flagellates,
diatoms) to phytoplankton biomass and production during coccolithophore
blooms. It should be noted that the cell Chl a content from Haxo (1985)
falls at the lower end of the current range of measurements for E. huxleyi cell Chl a content (e.g., 0.24–0.38 pg Chl a cell-1; Daniels et al.,
2014) and leads to conservative estimates of Chl a contribution from this
species. These data, combined with satellite observations, support the
hypothesis of a repeating phytoplankton structure on an interannual basis,
although the contribution of E. huxleyi to primary production may
vary. The optimum range for E. huxleyi blooms on the Patagonian
Shelf has been identified as between 5 and 15 ∘C at depleted silicic
acid levels relative to nitrate (Balch et al., 2014, 2016). During this
study, silicic acid was at almost undetectable levels on the Patagonian Shelf
(Table 1), with the source water for this region being Southern Ocean HNLSiLC
waters transported northwards via the Falklands current (Painter et al.,
2010; Poulton et al., 2013). The persistently low silicic acid availability and
residual nitrate (defined as [NO3-] – [Si(OH)4]) on the
Patagonian Shelf is therefore an ideal environment for E. huxleyi to
outgrow large, fast-growing diatoms (Balch et al., 2014).
South Georgia
South Georgia is renowned for intense diatom blooms of over
600 cells mL-1 with Chl a over 10 mg m-3 and integrated
primary production up to 2 g C m-2 d-1 (Korb et al., 2008).
However, E. huxleyi was the dominant species (> 75 % of total
cell numbers) within the diatom and coccolithophore population at the station
north of South Georgia (Table 2, Fig. 2). The associated calcite feature can
also be identified from the satellite composite in Fig. 1 (38∘ E,
51∘ S). Emiliania huxleyi contributed approximately
15 %, calculated by applying a value of 0.2 pg Chl a cell-1 (Haxo, 1985)
following Poulton et al. (2013), to the total Chl a signal
(0.71 mg m-3) around South Georgia. The high calcite feature at South
Georgia was found at an SST of 5.9 ∘C, which is below the considered
“optimum” growth conditions for E. huxleyi previously cultured
(Paasche, 2001). This population of E. huxleyi was most likely an
adapted cold-water morphotype (Cook et al., 2011, 2013; Poulton et al.,
2011). The dominant diatom species here were Actinocyclus sp. and
highly silicified Thalassionema nitzschioides with silicic acid
concentrations likely limiting (1.7 µmol Si L-1; Paasche
1973a, b), whereas NOx concentrations (17.5 µmol N L-1)
and PO4 concentrations (1.22 µmol P L-1) can be
considered replete. The low silicate concentrations could explain why
Eucampia antarctica was not observed in this study, though it has
been observed north of South Georgia (Korb et al., 2010, 2012). This
indicates that preceding diatom growth depleted silicic acid (and other
nutrients such as dissolved iron), allowing E. huxleyi to become
more dominant in the population with a similar residual nitrate environment
as found on the Patagonian Shelf (this study; Balch et al., 2014, 2016) and also in the North Atlantic (Leblanc et al., 2009).
Crozet Islands
The E. huxleyi feature north of the Crozet Islands with an abundance
of 472 cells mL-1 (the highest in the southern Indian Ocean) confirms the
presence of coccolithophores in this region. Coccolithophore abundances have
not previously been reported in this region, although elevated PIC had been
observed and attributed to E. huxleyi (Read et al., 2007; Salter et
al., 2007). Chl a was lowest (0.47 mg m-3) at Crozet out of all
three high PIC features, with E. huxleyi contributing
∼ 20 % of this signal, calculated by applying a value of
0.2 pg Chl a cell-1 (Haxo, 1985) following Poulton et al. (2013),
which is proportionally higher than on the Patagonian Shelf and near South Georgia.
Previous studies around the Crozet Islands and plateau (2004–2005) have
found evidence of coccolithophores in sediment trap samples (Salter et al.,
2007) and large (> 30 mmol C m-2 d-1) calcite fluxes (Le
Moigne et al., 2012), though surface cell counts were unavailable (Read et
al., 2007). The satellite-derived calcite signal was observed to increase
after the main Chl a event in this study (Fig. S1 in the Supplement) and
in previous years (Salter et al., 2007). An increase in coccolithophore
abundance following a diatom bloom is also observed in similar oceanic
regions from satellite-derived products (Hopkins et al., 2015) and is
associated with depletion of dissolved iron and/or silicic acid (Holligan et
al., 2010) in addition to a stable water column and increased irradiance
(Balch et al., 2014).
Summary of biogeochemical characterization of coccolithophore
occurrence and abundance
The Southern Ocean has been considered to have a biomineralizing
phytoplankton community dominated by diatoms. This study highlights the fact that
E. huxleyi can form distinct features within the GCB and contribute
up to 20 % towards total Chl a in these features compared to an
average of less than 5 % of Chl a across the rest of the GCB. Hence,
Emiliania huxleyi is likely to have a more important role in
the biogeochemical processes in the GCB than previously thought. This is
particularly important to consider when assessing the impact on calcium-carbonate-associated
export (e.g., Honjo et al., 2000; Balch et al., 2010, 2016) in the Southern Ocean. If E. huxleyi is
migrating poleward with time (Winter et al., 2013), then the dynamics of the
carbon system in the GCB may change, particularly south of the SAF where
silicic-acid-derived export has historically been dominant (Honjo et al.,
2000; Pondaven et al., 2000). Thus, it is essential to gain an understanding
of the environmental factors driving the distribution of E. huxleyi
(Winter et al., 2013; Charalampopoulou et al., 2016) amongst other
phytoplankton in the GCB to better understand the biogeochemistry of the Southern
Ocean.
Environmental controls on biogeography
The environmental variables that best describe coccolithophore and diatom
species distribution in this study were SST, macronutrients (NOx, silicic
acid, NH4), and pCO2 (Spearman's rank correlation = 0.55, p<0.001), with the second-highest correlation (Spearman's rank correlation = 0.54,
p<0.001) including the calcite saturation state
(Ωcalcite). The inclusion of pCO2 and
Ωcalcite as important factors indicates a potential
influence of carbonate chemistry on coccolithophore and diatom distribution
(and vice versa) in the GCB. However, Ωcalcite had a very
strong positive correlation (r=0.964, p<0.0001) with SST (Table S1),
and therefore separating the influences of the two variables was impossible
in this study due to the tight coupling between carbonate chemistry and
temperature (as also observed by Charalampopoulou et al., 2016).
Temperature
Temperature is recognized as a strong driving factor behind plankton
biogeography and community composition (Raven and Geider, 1988; Boyd et al.,
2010). The abundance of two of the dominant species, E. huxleyi and
F. pseudonana, did not significantly correlate (Pearson's product
moment correlation = 0.147, p=0.493 and r=-0.247, p=0.357
respectively) with SST, which does not agree with previous work (e.g., Mohan
et al., 2008) and implies that E. huxleyi distribution is not solely
determined by latitudinal variations in temperature. Nanoplankton are subject
to high grazing pressure (Schmoker et al., 2013), with the growth and
mortality of a species both directly influencing cell abundances (Poulton et
al., 2010), which could result in nanoplankton patchiness in addition to the
influence of temperature and/or other environmental gradients. In contrast,
the negative correlation of F. nana (Pearson's product moment
correlation = -0.976, p<0.05, n=4) versus the positive
correlation of Pseudo-nitzschia spp. (Pearson's product moment
correlation = 0.544, p<0.05, n=19) with SST indicates that these
two species have distinctly different physiological tolerances. Southern
Ocean diatoms are often observed to have negative relationships with
temperature (e.g., Eynaud et al., 1999; Boyd, 2002). Pseudo-nitzschia spp. was predominantly found in waters north of the PF in this study, as seen
by Kopczynska et al. (1986), and is likely to be outcompeted by other diatom
species (e.g., Chaetoceros spp. and Dactyliosolen spp.)
further south due to different nutrient affinities and requirements
(Kopczynska et al., 1986), particularly for dissolved iron and silicic acid.
Nutrients
Macronutrient gradients, particularly silicic acid, are considered one of the
key driving factors between the differences in community structure in the
Southern Ocean (Nelson and Tréguer, 1992). NOx (and PO4 by
association) was identified in the BEST test as an important factor in the
variability of biomineralizing species distribution, but it did not
significantly correlate with the four statistically dominant species (Fig. 4)
contributing over 50 % to changes in species composition in the GCB.
Nitrate drawdown by Southern Ocean diatoms is limited by dissolved iron (dFe)
availability south of the STF (Sedwick et al., 2002), which may explain the
dominance of the nanoplankton (with lower dFe and macronutrient requirements;
Ho et al., 2003) in this study as they are not affected by low dFe
concentrations as severely as the microplankton. The low silicic acid
concentrations in the region between the SAF and the PF indicate that there
was sufficient dFe to allow silicification and diatom growth, but either one
or both of the macronutrients were then depleted to limiting concentrations
(Assmy et al., 2013). As an essential nutrient for diatoms, silicic acid
concentrations less than 2 µM were most common in the GCB, a level
which is considered limiting for most diatom species (Paasche, 1973a, b; Egge
and Asknes, 1992). However, even at stations with greater than 5 µM
of silicic acid, the small diatom species (< 10 µm) were still
dominant and represented over 40 % of the total coccolithophore and
diatom assemblage (numerically). A significant positive correlation occurred
between silicic acid and the small (< 5 µm) diatom F. nana (Pearson's product moment correlation = 0.986, p<0.05, n=4).
Fragilariopsis nana may have a low cellular silicate requirement
similar to F. pseudonana (Poulton et al., 2013) and relative to larger
diatom species, so the high abundance of F. nana in the high-silicic-acid waters could be indicative of a seasonal progression driven by light
and/or temperature rather than silicic acid dependence.
Fragilariopsis spp. have been observed at high abundances near the
Ross Sea ice shelf (Grigorov and Rigual-Hernandez, 2014), and high abundances
of large diatoms in silicic-acid-replete (and dFe-replete) waters may occur further
south than we sampled. In the South Atlantic and the South Pacific Ocean,
silicic acid depletion moves southwards as spring to summer progresses, with
maximum diatom biomass observed in late January at 65∘ S (Sigmon et
al., 2002; Le Moigne et al., 2013).
A significant negative correlation between E. huxleyi and silicic
acid (Pearson's product moment correlation = -0.410, p<0.05, n=24) in this study has also been identified in the Scotia Sea (Hinz et al.,
2012) and the Patagonian Shelf (Balch et al., 2014) in the Southern Ocean, as
well as in the North Atlantic (Leblanc et al., 2009). Low silicic acid may be
considered a positive selection pressure for coccolithophores (Holligan et
al., 2010), especially when other macronutrients (and dFe) are replete.
However, a few non-blooming coccolithophore species are now recognized as
having silicic acid requirements, though this requirement is absent in
E. huxleyi (Durak et al., 2016). Therefore, low silicic acid in
the surface waters of the GCB may negatively impact coccolithophore species that
have a silicic acid requirement, such as Calcidiscus leptoporus, and
favor bloom-forming species that have no silicic acid requirement (e.g.,
E. huxleyi). To the south of the PF, silicic acid increased (from
< 1 to > 3 µM) with five stations between the SAF and PF (and
one south of the PF, station GCB1-59) all numerically dominated by
E. huxleyi, while other stations to the south of the PF were
dominated by diatoms (Fig. 2).
Schematic of the potential seasonal progression occurring in the
Great Calcite Belt, allowing coccolithophores to develop after the main
diatom bloom. Note that phytoplankton example images are not to scale.
These results from the GCB indicate a progression of biomineralizing
phytoplankton southwards during spring as irradiance conditions become
optimal and macronutrients are depleted. Low silicic acid is often associated
with a high residual nitrate concentration (defined as [NO3-] –
[Si(OH)4]), as has been observed on the Patagonian Shelf (Balch et al.,
2014). The highest coccolithophore abundances in this study (excluding the
Patagonian Shelf) were observed in regions with “residual nitrate”
concentrations greater than 10 µM (Balch et al., 2016). As silicic
acid becomes depleted in the more northerly surface waters in spring, diatoms
progressively become more successful further south as irradiance conditions
allow, thereby producing a large HNLSiLC area between the Subantarctic Front
and the Polar Front; an ideal environment for late-summer E. huxleyi
communities to develop (Fig. 6).
Dissolved iron (dFe) acts as a strong control on phytoplankton growth,
community composition, and species biogeography (e.g., Boyd, 2002; Boyd et
al., 2015). In this study, dFe measurements were only made at a small number
of sampling stations (n=6; Twining, unpublished data; Balch et al.,
2016),
limiting their use in the multivariate statistical analysis of community
composition. For these stations, dFe showed a statistically significant
negative correlation (Pearson's product moment = -0.957,
p < 0.01) with PC2 from the environmental analysis (Fig. S2). PC2
described the environmental variables least related to latitude (pH,
pCO2 and E‾MLD), indicating that dFe was also
decoupled from the strong latitudinal gradient in environmental parameters
(i.e., SST, Ωcalcire, macronutrients) in austral spring–summer.
Interestingly, dFe concentrations positively correlated with
coccolithophore abundance (Pearson's product moment correlation = 0.858,
p<0.05) rather than diatom abundance (p=0.132, ns) (Fig. S2).
Overall, these data support the hypothesis that coccolithophores occupy a
niche unoccupied by large diatoms when dFe is replete and silicic acid is
depleted (Balch et al., 2014; Hopkins et al., 2015). The numerical dominance
of small diatoms less than 20 µm in the GCB during austral spring
and summer, alongside the coccolithophore E. huxleyi, is thus
potentially due to the reduced impact of nutrient limitation (dFe, silicic
acid) on small cells with high ratios of surface area to volume (e.g., Hinz
et al., 2012; Balch et al., 2014).
Relating the Great Calcite Belt to carbonate chemistry
Relating carbonate chemistry to phytoplankton distribution, growth, and
physiology is an important step when considering the potential effects of
climate change and ocean acidification on marine biogeochemistry. In this
study, no significant correlation (Spearman's r=0.259, p=0.164, n=27) occurred between pH and Chl a. The inclusion of pCO2 and
Ωcalcite as influential factors in the statistical results
describing GCB species biogeography highlights the importance of
understanding phytoplankton responses to carbonate chemistry as a whole
rather than as individual carbonate chemistry parameters (Bach et al., 2015).
Of the four major species driving the differences in biomineralizing plankton
community composition and biogeography across the GCB, only F. pseudonana abundance was positively correlated with pCO2 (Pearson's
product moment coefficient = 0.577, p<0.05, n=16).
The response of diatoms to increasing pCO2 is not straightforward
(e.g., Boyd et al., 2015), with some studies implying that large diatoms may
be more successful in future climate scenarios (e.g., Tortell et al., 2008;
Flynn et al., 2012), although changes in nutrient and light availability (via
stronger stratification) may prevent a permanent switch in phytoplankton
community structure (Bopp et al., 2005). The carbonate chemistry system is complex,
as biological activity also impacts the concentration of each of the
components. Organic matter production reduces total dissolved inorganic
carbon (CT), and hence pCO2 via photosynthesis, and
increases alkalinity (AT) through nutrient uptake, while
subsequent respiration and remineralization of organic matter has the
opposite impact. The simultaneous actions of biological and physical
processes result in seasonal and localized changes in the carbonate system,
which are often difficult to decouple.
In our study, there was no significant correlation between E. huxleyi and Ωcalcite (Pearson's product moment = 0.093).
However, the waters of the GCB remained oversaturated (Ωcalcite > 2) throughout, and the relationship between
coccolithophores, calcification, and carbonate chemistry is now recognized as
being complex and nonlinear (e.g., Beaufort et al., 2011; Smith et al.,
2012; Poulton et al., 2014; Rivero-Calle et al., 2015; Bach et al., 2015;
Charalampopoulou et al., 2016; Marañón et al., 2016). Hence,
significant gaps remain in our understanding of the in situ
coccolithophore response to increasing pCO2, reduced pH, or decreasing
Ωcalcite. Notably, a significant positive correlation between
Pseudo-nitzschia spp. and Ωcalcite also existed
(Pearson's product moment correlation = 0.5924, p<0.01, n=19)
across the GCB despite there being no presently known detrimental effect on
diatoms with low saturation states. However, due to the tight coupling of
temperature and Ωcalcite (and Pseudo-nitzschia spp. and
temperature), the correlation is more likely to be temperature driven.