Biogeochemical cycling of carbon (C) and nitrogen (N) in the ocean
depends on both the composition and activity of underlying biological
communities and on abiotic factors. The Southern Ocean is encircled by a
series of strong currents and fronts, providing a barrier to microbial
dispersion into adjacent oligotrophic gyres. Our study region straddles the
boundary between the nutrient-rich Southern Ocean and the adjacent
oligotrophic gyre of the southern Indian Ocean, providing an ideal region to
study changes in microbial productivity. Here, we measured the impact of C
and N uptake on microbial community diversity, contextualized by
hydrographic factors and local physico-chemical conditions across the
Southern Ocean and southern Indian Ocean. We observed that contrasting
physico-chemical characteristics led to unique microbial diversity patterns,
with significant correlations between microbial alpha diversity and primary
productivity (PP). However, we detected no link between specific PP (PP
normalized by chlorophyll-a concentration) and microbial alpha and beta
diversity. Prokaryotic alpha and beta diversity were correlated with
biological N2 fixation, which is itself a prokaryotic process, and we detected
measurable N2 fixation to 60∘ S. While regional water masses
have distinct microbial genetic fingerprints in both the eukaryotic and
prokaryotic fractions, PP and N2 fixation vary more gradually and
regionally. This suggests that microbial phylogenetic diversity is more
strongly bounded by physical oceanographic features, while microbial
activity responds more to chemical factors. We conclude that concomitant
assessments of microbial diversity and activity are central to understanding
the dynamics and complex responses of microorganisms to a changing ocean
environment.
Introduction
The Southern Ocean (SO), in particular its sub-Antarctic zone, is a
major sink for atmospheric CO2 (Constable et al., 2014). The SO is
separated from the Indian South Subtropical Gyre (ISSG) by the South Subtropical
Convergence province (SSTC), comprising the Subtropical Front (STF) and
the Subantarctic Front (SAF). The SSTC is a zone of deep mixing and thus
elevated nutrient concentrations (Longhurst, 2007). Further, the
SSTC has been shown to act as a transition zone both numerically and
taxonomically for dominant populations of marine bacterioplankton
(Baltar et al., 2016).
In this dynamic context, a key driver of microbial productivity is nutrient
availability, especially through tightly coupled carbon (C) and nitrogen (N)
cycles. The constant availability of nutrients through vertical mixing in
frontal zones, such as the STF, enhances primary productivity
(Le Fèvre, 1987) and chlorophyll-a (chl a) concentrations (Belkin and O'Reilly,
2009). Primary productivity (PP) and specific primary productivity (PB, meaning primary productivity per unit chl a) are reflected in the relative
abundance of different phytoplankton size classes whose productivity values are, in
turn, stimulated by nutrient injections via shallowing of mixed layer depth
(MLD) at the SO fronts (Strass et al.,
2002); decreasing the possibility of N limitation. However, N limitation can
also biologically be alleviated through N2 fixation mediated by
diazotrophs, significantly contributing to the N pool in oligotrophic
regions (Tang et al., 2019). In high-latitude regions,
biological N2 fixation could potentially have a large impact on
productivity (Sipler et al., 2017). However, large
disagreements exist between models of high-latitude N2 fixation and its
coupling to microbial diversity due to sparse sampling in these regions
(Tang et al., 2019).
Due to the dynamics of the region, conflicting observations, and
climate-driven changes, resolving the coupling of microbial productivity and
diversity is particularly important across the strong environmental
gradients crossing the ISSG, through the SSTC into the SO. Indeed, climate
variability has been shown to impact ocean productivity and thus influences
the provision of resources to sustain ocean life
(Behrenfeld et al., 2006). To date,
observations of climate-change-related effects in this region of the SO have
been synthesized only based on long-term nutrient concentration and physical
(temperature and salinity) changes (Lo Monaco et
al., 2010); however, these typically lack a microbial dimension. Microbial
composition, activity, and C export may all be impacted by climate-driven
changes in ocean dynamics (Evans
et al., 2011) such as MLD shallowing, eddy formation, and poleward shifts of
ocean fronts (Chapman et al., 2020). For a more
holistic ecosystem-based understanding of this region, concomitant
assessments of (1) steady-state biogeochemical processes through rate
measurements of key elements (such as C and N) and (2) the microbial
diversity that underpins it are essential enhancements to such long-term
investigations.
Here, we measure the impact of C and N uptake on microbial community
diversity, alongside the effects of hydrography (e.g., dispersal limitation)
and local physico-chemical conditions across the Southern Ocean and southern Indian Ocean. We focused our investigation on surface communities, aiming to
resolve horizontal surface variation. We used our observation to assess
whether the following relationships – previously observed in related systems
– hold in our study region:
Microbial diversity increases with increasing primary productivity (PP). Previous work has claimed that more resources support higher species
richness until intermediate rates of PP (Fig. 1;
Vallina et al., 2014) within ocean
provinces (Raes et al., 2018).
Frontal systems are discrete ecological transition zones between regions that provide perspectives on the findings of
Baltar et al. (2016; see above). These systems
often separate water masses with distinct trophic structures (e.g.,
Albuquerque et al., 2021).
Microbial alpha and beta diversity are impacted by N2 fixation,
which is itself correlated with the presence of other available sources of N and/or
temperature; this is to provide more evidence on the role of N2 fixation to the
N budget in high latitudes (see e.g., Shiozaki et
al., 2018; Sipler et al., 2017).
To our knowledge, there are no concomitant evaluations of how surface
gradients, microbial activity, and community composition relate to one
another in this region. Here, we provide perspectives on these key
relationships across the Indian South Subtropical Gyre (ISSG), the
Subtropical Front (STF), and Subantarctic Front (SAF), and the SO comprising
the Polar Front (PF) and Antarctic Zone (AZ).
(a) The MD206 transect and OISO stations. Stations are colored
according to water masses and encircled by sampling extent: black circles
indicate stations where only CTD (conductivity, temperature, depth) data are
provided, and stations encircled in red denote where additional samples for
C, N, and community composition were taken. (b) A plot of potential
temperature (in degrees Celsius (∘C) and salinity (in
practical salinity units)) using sea surface (7 m) data of the stations used
in further microbial and C/N analyses. The yellow circle highlights the
Indian South Subtropical Gyre (ISSG), light blue circle the Subtropical Front (STF),
blue circle the Subantarctic Front (SAF), dark green circle the Polar Front
Zone (PFZ) and the light green circle indicates the Antarctic Zone (AZ);
dashed lines indicate water masses clustered within ocean provinces: the blue
line marks the South Subtropical Convergence province (SSTC), and the green line marks the
Southern Ocean (SO); panels (c) and (d) show depth profiles of temperature, oxygen,
and salinity along two transects of the OISO stations. Colored bars indicate
water masses according to (b). Panel (c) shows the western transect covering OISO
stations 2, 3, 4, 5, 6, and 37 around 53±1∘ E longitude; panel (d) shows the eastern transect of OISO stations 10, 11, 12, 13, 14, 15, 16, and E
around 68±5∘ E.
Materials and methodsStudy region, background data, and sample collection
Our study region ranged from Réunion in the Indian South Subtropical Gyre (ISSG) to south of the Kerguelen Islands in the Southern Ocean
(56.5∘ S, 63.0∘ E; Fig. 1a) as part of a larger repeated
“OISO” sampling program – (Océan Indien Service d'Observations; Metzl and Lo Monaco, 1998; https://doi.org/10.17600/17009700). Samples were collected as part of the
VT153/OISO27 (MD206) cruise aboard the R/V Marion Dufresne from 6 January to 7 February 2017. Physical and biogeochemical data, as well as metadata, were
collected from a rosette equipped with Niskin bottles and a conductivity,
temperature, depth sensor (CTD) (Sea-Bird SBE32) equipped with a SBE43 O2 sensor
and a Chelsea Aqua tracker fluorometer. OISO long-term data, starting in
1998, were used as a backdrop to our data collected in 2018 and allowed us
to monitor changes in physical and chemical oceanographic properties over
time (Supplement File A).
Province delineation after Longhurst
We identified three main clusters (i.e., ocean provinces) and five
subclusters (i.e., water masses) on a temperature–salinity plot (Fig. 1b). As
an overview, we used CTD depth profiles to validate the vertical extent of
water masses in our samples (Fig. 1c, d) and checked the horizontal extent of
the identified clusters using remote sensing data of sea surface temperature
(Fig. S2 in the Supplement). Additionally, we checked the horizontal boundaries of these
clusters for matches in strong chl a concentration gradients as an
approximate for biological component of ocean provinces, following the
concept of Longhurst (2007). Satellite data were acquired from MODIS
(https://neo.sci.gsfc.nasa.gov/, last access: 16 June 2021), with images processed by NASA
Earth Observations (NEO) in collaboration with Gene Feldman and Norman
Kuring, i.e., NASA OceanColor Group (Fig. S3). We calculated the geodesic distance
between sites from latitude and longitude coordinates using the geodist package
in R (v0.0.4; Padgham et al., 2020).
Nutrient analysis
Dissolved inorganic nutrient concentrations, including phosphate
(PO43-), silicate (denoted Si), mono-nitrogen oxides (NOx), nitrite
(NO2-), and ammonium (NH4+), were assayed on a QuAAtro39
continuous segmented flow analyzer (Seal Analytical) following widely used
colorimetric methods (Armstrong,
1951; Murphy and Riley, 1962; Wood et al., 1967) with adaptations to
particular needs for Seal Analytical QuAAtro autoanalyzer. NH4+ was measured using the fluorometric method of
Kérouel and Aminot (1997). Detection
limits of these methods were 0.1 µmol L-1 for PO43-,
0.3 µmol L-1 for Si, 0.03 µmol L-1 for NOx, and
0.05 µmol L-1 for NH4+.
Dissolved inorganic nitrogen and carbon assimilation
At each CTD station, water samples to measure primary productivity (PP) and
N2 fixation were taken from the underway flow-through system (intake at
7 m). As the ship was moving during sampling, the distance between samples
of the same station can range up to ∼ 15 km. Incubations were
performed in acid-washed polycarbonate bottles on deck at ambient light
conditions. All polycarbonate incubation bottles were rinsed prior to
sampling with 10 % HCl (3×), deionized H2O (3×), and sampling water
(2×). In order to obtain the natural abundance of particulate nitrogen (PN)
and particulate organic carbon (POC), which we used as a t0 value to
calculate the assimilation rates, 4 L of water was filtered onto a 25 mm
pre-combusted GF/F filter for each station.
N2 fixation experiments were carried out in triplicate for each
station. We used the combination of the bubble approach
(Montoya et al., 1996) and the
dissolution method (Mohr
et al., 2010) proposed by Klawonn
et al. (2015). The 4.5 L bottles were filled up headspace free. All incubations
were initialized by adding a 15N2 gas bubble with a volume of 10 mL. We used 15N2-labeled gas provided by Cambridge Isotope
Laboratories (Tewksbury, MA). Bottles were gently rocked for 15 min.
Finally, the remaining bubble was removed to avoid further equilibration
between gas and the aqueous phase. After 24 h, a water subsample was
transferred to a 12 mL exetainer® and preserved with 100 µL HgCl2 solution for later determination of exact 15N–15N
concentration in solution. Natural 15N2 was determined using
membrane inlet mass spectrometry (MIMS; GAM200, IPI) for each station with
an average enrichment of 3.8±0.007 at. % 15N2 (mean ± SD; n=104). Primary productivity was measured by adding
Na13CO3 at a final 13C concentration of 200 µmol L-1.
Incubation bottles were incubated on board at ambient sea surface
temperature (SST; water intake at 7 m) using a continuous-flow-through
system. Temperature of both incubation bins was continuously measured. After
24 h, the C and N2 fixation experiments were terminated by
collecting the suspended particles from each bottle by gentle vacuum
filtration through a 25 mm pre-combusted GF/F filter (<10 kPa).
Filters were snap-frozen in liquid nitrogen and stored at -80∘C
while at sea. Filters with enriched (T24) and unenriched (T0) samples were
acidified and dried overnight at 60 ∘C. Analysis of 15N and
13C incorporated was carried out by the isotopic laboratory at the University of California, Davis, California campus, using an Elementar Vario EL Cube or MICRO cube
elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany).
Carbon assimilation rates were calculated according to
Knap et al. (1996), excluding the 14C–12C
conversion factor, and N2 fixation was calculated according to
Montoya et al. (1996).
The minimum quantifiable rate was calculated according to
Gradoville et al. (2017).
Pigment analysis
For pigment analyses, 4 L of seawater was filtered (< 10 kPa) on a 47 mm Whatman GF/F filter and stored at -80∘C until further
analysis. High-performance liquid chromatography (HPLC) was carried out as
described in Kilias
et al. (2013) with the following modifications: 150 µL of the internal
standard canthaxanthin was included to each sample. Samples were dissolved
in 4 mL acetone and disrupted with glass beads using a Precellys 24 tissue
homogenizer (Bertin Technologies, France) at 7200 rpm for 20 s. Detection of
the sample at 440 nm absorbance was performed using an HPLC analyzer (Varian Microsorb-MV 100-3 C8). We used chl a concentration to estimate phytoplankton biomass.
Pigment concentrations were calculated according to
Kilias et al. (2013) and quality controlled according to Aiken et
al. (2009) (Supplement File A).
HPLC output data were analyzed using diagnostic pigments for the different
taxa and phytoplankton functional types (PFTs) after
Hirata et al. (2011) (Supplement File A, Table S2). This
approach can be used to reveal dominant trends of the phytoplankton
community and size structure at the regional and seasonal scales
(Ras et al., 2008).
Furthermore, diagnostic pigments were used to delineate three different size
classes (pico-, nano-, and microplankton) according to
Vidussi et al. (2001). The relative proportion of each
phytoplankton size class (PSC) was calculated based on the linear regression
model proposed by Uitz et al. (2006). We investigated the patterns of PSCs with a second-order polynomial
fit (S1_code_archive/pigment_HPLC/diaganostic_ pigments.R L143:153).
DNA analysis
Two liters of seawater from the shipboard underway system from each station
were filtered through a 0.22 µm Sterivex® filter
cartridge for DNA isolation, snap-frozen in liquid nitrogen, and stored at
-80∘C. DNA was extracted using a DNeasy® Plant
Mini Kit (QIAGEN, Valencia, CA, USA, catalog no. or ID 69106) following the
manufacturer's instructions. Sterivex cartridges were gently cracked open,
and filters were removed and transferred into a new and sterile screw-cap
tube. Approximately 0.3 g of pre-combusted glass beads (diameter 0.1 mm;
11079101 Bio Spec Products) and 400 µL buffer AP1 were added to the
filter, followed by a bead beating step using a Precellys 24 tissue
homogenizer (Bertin Technologies, France), with two times at 5500 rpm for 20 s with 2 min on ice in between and a final bead beating step at
5000 rpm for 15 s. DNA concentrations were quantified by the
Quantus™ fluorometer and normalized to 2 ng µL-1.
Amplicon 16S and 18S rRNA gene PCR and sequencing
Amplicons of the bacterial 16S rRNA gene and eukaryotic 18S rRNA gene (using
primers from 27F–519R;
Parada et al., 2016,
TA-Reuk454FWD1 – TAReukREV3; Stoeck et al., 2010,
respectively) were generated following standard protocols of amplicon
library preparation (16S Metagenomic Sequencing Library Preparation,
Illumina, part no. 15044223 Rev. B; Supplement File B). The 16S and 18S rRNA gene
PCR products were sequenced using 250 bp paired-end sequencing with a MiSeq
sequencer (Illumina) at the European Molecular Biology Laboratory (EMBL) in
Heidelberg (Germany) and at the Leibniz Institute on Aging (FLI) in Jena
(Germany), respectively.
Amplicon sequence data analysis
For both 16S rRNA gene and 18S rRNA gene amplicon sequences, we used the
DADA2 R package, v1.15.1 (Callahan et al., 2016) to
construct Amplicon sequence variant (ASV) tables by the following steps:
prefiltering “filterandtrim” function with truncL = 50 and default parameters
(S1_code_archive/dada2). Primer sequences were
cut using the Cutadapt software implementation (v1.18) in the DADA2
pipeline, removing a fixed number of bases matching the 16S forward (515F-Y,
19 bp) and reverse (926R, 20 bp) primers and the 18S forward (TA-Reuk454FWD1, 20 bp)
and reverse (TAReukREV3, 21 bp) primers (S1_code_archive/dada2/dada2_16S.R L88:104;
S1_code_archive/dada2/dada2_18S.R L92:104). Primer-trimmed fastq files were quality trimmed with a
minimum sequence length of 50 bp and checked by inspection of the average
sequence length distribution (for both the 16S rRNA gene and 18S rRNA gene
sequences). Samples within forward and reverse fastq files were dereplicated
and merged with a minimum overlap of 20 bp. ASV tables were constructed, and
potential chimeras were identified de novo and removed using the
“removeBimeraDenovo” command. Sequencing statistics for removed reads and
sequences in each step can be found in Table S3. Taxonomic assignment was
performed using the SilvaNGS (v1.4; Quast et al., 2013)
pipeline for 16S rRNA gene data with the similarity threshold set to 1.
Reads were aligned using SINA v1.2.10 (Pruesse et al., 2012)
and classified using BLASTn (v2.2.30; Camacho et al., 2009)
with the Silva database (v132) as a reference database (Supplement File C).
For taxonomic assignment of 18S rRNA gene amplicons, we used the plugin
“feature-classifier” (from package “q2-feature-classifier”, v2019.7.0) in
QIIME2 (Bokulich et al., 2018) and the
pr2 database (v4.12; Guillou
et al., 2013). We removed ASVs annotated to mitochondria and chloroplasts
from 16S rRNA gene ASV tables and ASVs annotated as metazoans from 18S rRNA
gene ASV tables (S1_code_archive/import/import_16S.R L35:38; S1_code_archive/import/import_18S.R L29). ASV
tables of 16S rRNA gene amplicon (Table S4) and 18S rRNA gene amplicons
(Table S5) were used for further statistical analyses.
Ecological data and statistical analysis
A combination of temperature, salinity, dissolved oxygen concentrations, and
dissolved inorganic nutrient concentrations (NO3-, NO2-,
NH4+, Si, and PO43-) were used to characterize the
physical and biogeochemical environment of the study region.
All statistical tests were performed in R version 3.6.3 (R Core Team, 2020).
Statistical documentation, package citations, and scripts are available in
S1. Microbial alpha diversity was calculated with Hill numbers (richness,
Shannon entropy, inverse Simpson, q= 0–2;
Chao et al., 2014) using
the iNEXT package v2.0.20 in R with confidence set to 0.95 and bootstrap = 100 (S1_code_archive/alpha_diversity). Accordingly, rarefaction curves are shown in Fig. S6. Pearson
correlations between microbial richness (q=0), inverse Simpson diversity
(q=2), environmental parameters, and biological rates were calculated
and plotted (ggplot2) (Fig. S7). The p values were adjusted for multiple testing
using Holm adjustment (Holm, 1979), and residuals were checked for
normal distribution (Fig. S8). For comparability and statistical downstream
analyses, we performed the following transformations to the ASV table and
the environmental metadata: to account for the compositionality of
sequencing data (see Gloor et al., 2017), we
performed a centered log ratio (CLR)-transformation for redundancy analysis (RDA). We used
Hellinger transformation (decostand() function in vegan) of the ASV pseudocount data
(minimum pseudocount per ASV cutoff was 3) for PERMANOVA analyses.
Environmental data were z scored for comparable metadata analysis
(S1_code_archive/transformations). For
multivariate analyses of microbial beta diversity and environmental
parameters, we performed redundancy analyses (RDA) of the CLR-transformed
ASV tables (S1_code_archive/RDA). Differences
of microbial beta diversity (based on Hellinger-transformed ASV tables),
phytoplankton community composition (based on pigment concentrations), and
water masses were tested with permutational ANOVA (PERMANOVA;
Anderson, 2001) using the adonis2() function in
vegan along with a beta dispersion test to evaluate the homogeneity of
the dispersion (Fig. S9). To investigate where differences of environmental
variables have an impact on microbial community dissimilarity, we performed
a general dissimilarity model (GDM) of the community dissimilarity and
environmental variables, and we checked for the influence of geographic
distance based on spline magnitude (gdm package; S1_code_archive/GDM).
As differences in microbial beta diversity were significant in PERMANOVA
between provinces and water masses, we performed a similarity
percentage (SIMPER) analysis in R using the vegan package to assess which
ASVs contribute most to the observed variance of microbial community
composition (Table S6; S1_code_archive/taxonomy_analyses). To determine the number of ASVs
shared between provinces (or unique to certain provinces), we transformed
ASV pseudocount tables into binary tables and calculated shared and unique
ASVs using the upsetR package in R (v.4, Conway et
al., 2017; S1_code_archive/upsetR). We
calculated the percentage of all within-sample-observed ASVs within the
merged samples of a province (Table S7).
ResultsDelimitation of regional water masses
Through our analysis of temperature, salinity, oxygen, and dissolved
inorganic nutrient (N, P, Si) concentrations, we identified five distinct
water masses, fronts, and frontal zones: the ISSG, STF, SAF, PFZ, and AZ,
which broadly aligned with three oceanographic provinces (ISSG, SSTC, and SO;
Fig. 1a). Within the Southern Ocean (SO), we identified four water masses in
our transect including the Antarctic Zone (AZ) and three distinct frontal
systems: (1) the Polar Front (PF), (2) the Subantarctic Front (SAF), and (3) the Subtropical Front (STF; Fig. 1). In our analysis, stations 6, 7, and 9
were placed within the Polar Front Zone (PFZ), which is between the SAF and PF. Due
to the bathymetrically driven convergence of the STF and SAF around
Kerguelen island, we consider the SAF as part of the convergence zone
between the SO and Indian Ocean (IO), i.e., the South Subtropical Convergence province (SSTC),
rather than as a Southern Ocean frontal system. At 7 m depth, we noted clear
shifts in temperature (SST), salinity, and dissolved inorganic nutrient
(NO3-, PO43-, Si) concentrations when crossing the STF.
The STF is described as a circumpolar frontal zone creating the boundary
between our measurements of the warm (20–25 ∘C), saline
(>35), and oligotrophic (NO3-< 0.03 µM; PO43-: 0.04–0.21 µM) subtropical waters (STW) of the
Indian South Subtropical Gyre (ISSG) and the cold (3–6 ∘C)
macronutrient-rich SO (NO3-: 19.2–24.9 µM;
PO43-: 1.43–1.71 µM) (Figs. 1, 2, S3). In
the context of this study, STW and ISSG could be used interchangeably; we
henceforth refer to it as ISSG.
Nutrient concentrations (µmol L-1) and molar ratios of
particulate organic carbon (POC) to particulate nitrogen (PN) during the
MD206 expedition against sea surface temperature (∘C): (a) nitrate, (b) phosphate, (c) silicate, and (d) POC : PN ratio. Colored bars
indicate water masses according to their sea surface temperature: yellow bar
highlights the Indian South Subtropical Gyre (ISSG), light blue bar highlights the Subtropical Front
(STF), blue bar highlights the Subantarctic Front (SAF), dark green bar highlights the Polar
Front Zone (PFZ), and light green bar highlights the Antarctic Zone (AZ).
Primary productivity (PP)
Maximum primary productivity (PP) values within our dataset were measured near the
Kerguelen Plateau in the Polar Front Zone (PFZ) at station 9 (3236.8 and
3553.3 µmol C L-1 d-1, respectively) and station E (2212.4–2688.1 µmol C L-1 d-1, n=6). Comparing all PP
measurements across water masses, we found relatively high PP in other
stations of the PFZ (stations 6, 7; Fig. 3a; Table 1) and in the
Subantarctic Front (SAF) (stations 4, 15). Lowest PP values (190.4–642.6 µmol C L-1 d-1) were measured at the stations in the Indian South Subtropical Gyre (ISSG). While stations in the ISSG showed very little
variations within one station (e.g., 226.09–371.07 µmol C L-1 d-1, n=6, station 18), variation within SO stations was relatively
high (e.g., 587.42–1875.58 µmol C L-1 d-1, n=6,
station 37; Table 1).
Sampling stations visited during the MD206 cruise, including
chlorophyll-a concentrations, primary productivity (PP), specific primary
productivity (PB), and N2 fixation. Mixed layer depth (MLD) was
calculated using Δd=0.03 kg m-3 compared to a surface
reference depth of 7 m. NA indicates no data. Ranges and mean for sample
replicates of N2 fixation and PP are given (n=3 for stations 3, 9,
11, 15; n=6 for stations E, 37, 2, 4, 6, 7, 14, 16, 18).
Overall, the variation of specific primary productivity (PB) did not
show great variations between provinces, with maximum rates at station 11
(Table 1; Fig. 3b). We did not find a significant correlation between mixed
layer depth and PB (Pearson correlation: r=0.21, p=0.47, n=12).
Primary productivity (PP) and specific primary productivity
(PB) measured during the MD206 cruise. (a) PP in micromole carbon per
liter per day against sea surface temperature (SST) in degrees Celsius (∘C). (b)PB, normalized by chl a concentration. (c) Nitrogen fixation rates
against sea surface temperature (SST) in degrees Celsius measured during
the MD206 cruise. Rates are shown in nanomole nitrogen per liter per day.
Colored bars indicate water masses: yellow bar highlights the Indian South Subtropical Gyre (ISSG), light blue bar highlights the Subtropical Front (STF), dark blue bar highlights the
Subantarctic Front (SAF), dark green bar highlights the Polar Front Zone (PFZ), and
light green bar marks the Antarctic Zone (AZ).
N2 fixation
Di-nitrogen (N2) fixation was above the minimum quantifiable rate (MQR)
at all stations (Table 1). N2 fixation measurements did not show a
clear temperature-dependent trend (Fig. 3), and neither were they directly
associated with low dissolved inorganic nutrient (DIN) values (Fig. S10). N2 fixation in the warm
oligotrophic waters of the Indian South Subtropical Gyre (ISSG) was up to
7.93 nmol N L-1 d-1 (station 18; Fig. 3c; Table 1). Lowest
N2 fixations were measured in the productive zone of the STF and SAF
(0.24–2.01 nmol N L-1 d-1, n=3). In the AZ, N2 fixation
ranged between 0.89 and 1.97 nmol N L-1 d-1. The variation between
replicates was high; e.g., rates ranged between 0.9 and 7.9 nmol N L-1 d-1 at station 18 (Table 1). Across provinces, we did not find notable
differences in N2 fixation.
Phytoplankton pigment analyses
Photosynthetic pigment concentrations showed a clear separation between the
oligotrophic ISSG and the nutrient-rich SO (Fig. S5). Chlorophyll-a
concentrations were relatively low in the warmer water stations of the ISSG
than in the SSTC and SO (Table 1). The relative proportion of phytoplankton
biomass to the total organic matter was estimated by calculating the ratio
of PN : chl a and showed a strong increase in the ISSG (11.5–29.7 PN : chl a, n=4) in comparison to the SSTC (2.7–7.2 PN : chl a, n=3) and SO
(2.8–15.3 PN : chl a, n=6; Fig. S4).
The phytoplankton community composition was significantly and markedly
different across provinces (PERMANOVA; permutations = 999, R2=0.76, p<0.001; n=14) and water masses (PERMANOVA; permutations = 999, p=0.002; R2=0.81, n=14). The pigment concentration
of prokaryote-specific pigment zeaxanthin was high in the ISSG (0.03–0.06 mg m-3, n=4; Fig. S5a). Zeaxanthin still occurred in the STF and
SAF (0.03–0.04 mg m-3, n=3) but disappeared in the SO (<0.01 mg m-3, n=6). Prochlorococcus was distinctly identified through its
diagnostic pigment, divinyl chl a, and showed a relatively high pigment
concentration in the ISSG (0.02–0.03 mg m-3, n=4; Fig. S5a). We
found concentrations of diatom-specific fucoxanthin (except station 18)
ranging from 0.021 mg m-3 in the ISSG (station 16) to 0.34 mg m-3
in the SO (station 37; Fig. S5a). Across water masses, fucoxanthin
concentration was slightly higher in the AZ (0.06–0.5 mg m-3, n=4) than in all other water masses (0–0.13 mg m-3, n=10).
The distribution of potential phytoplankton size classes (PSCs; pico- nano-
and microplankton), calculated from diagnostic pigments (Supplement File A),
showed a clear pattern over temperature variations (Fig. S5b). The pigment
data suggested that picoplankton dominated warm water in the ISSG,
and picoplankton abundance sharply decreased (second-order polynomial fit:
R2=0.98, p<0.001, n=14) at lower values of SST.
Pigment data also suggested that microplankton showed a contrary trend to
the relative fraction of picoplankton, having high abundance in cold water
and decreasing at higher values of SST, with a minimum at 20 ∘C
SST and a slight increase (14 % microplankton of all phytoplankton size
classes) towards 25 ∘C SST (second-order polynomial fit: R2=0.77, p<0.001, n=14). Nanoplankton showed a maximum at
12 ∘C SST and decreased both towards warmer and colder waters
(second-order polynomial fit, R2=0.58, p<0.01, n=14).
Eukaryotic planktonic community composition
For each station, except station 4, the V4 region of the small subunit
ribosomal RNA gene (18S rRNA) was amplified and sequenced to determine the
community composition of micro-, nano-, and pico-eukaryotes in all three
oceanic provinces. We recovered a total of 2618 ASVs. After removing
sequences annotated to metazoans, 2501 ASVs remained (4.4 % of ASVs
removed).
We found a strong correlation between both eukaryotic richness and diversity
(inverse Simpson index) with SST (Pearson correlations: r=0.85, p<0.001 for richness and r=0.82, p=0.001 for inverse Simpson, n=12; Fig. S7a, c). Overall, eukaryotic diversity was
negatively correlated with PP (r=-0.66, p=0.02, n=12; Fig. S7e)
and significantly and positively associated with N2 fixation (r=0.74, p=0.01, n=12; Fig. S7g). However, a strong correlation between
rate measurements (PP, N2 fixation) and eukaryotic diversity was only
apparent in the ISSG, and no significant trend across other provinces (Pearson
correlation after removal of ISSG samples from dataset: for PP r=0.47, p=0.24 and for N2 fixation r=-0.48, p=0.23, n=8).
Our RDA constrained 81 % of the variance in the ASV table, with a p value
of 0.095 (permutations = 999, n=12). Sites were well separated between
Longhurst provinces along the first two RDA axes (capturing 52.67 %
constrained variance, Fig. 4a). Our PERMANOVA, which tested the
province-based separation, produced moderate but significant results
(permutations = 999, R2=0.54, p=0.001, n=12). An
additional PERMANOVA grouping sites by water masses produced similar results
(permutations = 999, R2=0.67, p=0.001, n=12; Fig. 4a). We
found that more ASVs only occurred in one province rather than in two or
more provinces (Fig. 4e). Sites within the ISSG province were associated
with SST and N2 fixation. Sites in the SSTC were associated with high
NH4+ concentrations. Sites belonging to the SO were associated
with dissolved inorganic nutrients (NO3-, PO43-, Si),
dissolved oxygen, and chl a concentrations as well as high PP. Linear
relationships between beta diversity and rates were only weak for PP
(PERMANOVA; permutations = 999, R2=0.27, p=0.004, n=12)
and both weak and insignificant between beta diversity and N2 fixation
(PERMANOVA; permutations = 999, R2=0.13, p=0.14, n=12).
(a) Eukaryotic and (b) prokaryotic community structures of
different water masses measured during the MD206 cruise. Redundancy analysis
(RDA) of 18S and 16S rRNA gene ASV tables as response variables and
environmental metadata as explanatory variables; environmental metadata are
represented as arrows. Constrained analyses were performed by water mass.
There were significant relationships between water masses and community
dissimilarities (PERMANOVA, 999 permutations; p<0.001, R2=0.67 for eukaryotes and p<0.001, R2=0.74 for prokaryotes). Colors indicate major water masses according to the legend:
yellow bar highlights the Indian South Subtropical Gyre (ISSG), light blue
bar highlights the Subtropical Front (STF), blue bar highlights the Subantarctic Front (SAF), dark
green bar highlights the Polar Front Zone (PFZ), and light green bar highlights the Antarctic
Zone (AZ). Eukaryotic (c) and prokaryotic (d) general dissimilarity model
(GDM) with (I) observed compositional dissimilarity against predicted
ecological distance, calculated from temperature + dissolved oxygen + NO3-+ NH4++ Si + chl a+ PP + N2
fixation; (II) observed compositional dissimilarity against predicted
compositional dissimilarity to test the model fit; and contribution of (III) PP and (IV) N2 fixation to community dissimilarity expressed as a
function of the environmental parameter (f(PP) and f(N2fix), respectively).
For all functional plots of environmental data of the GDM analysis, see Fig. S11. Eukaryotic (e) and prokaryotic (f) UpSet plots of ASV intersections
between Longhurst provinces. Analyses are based on binary tables (presence or absence) and the sum of all ASVs found across samples within one province.
Intersection size shows number of ASVs shared between provinces (black dots,
associated) and ASVs only found in one province (only black dot). Set size
shows number of ASVs found in a specific Longhurst province.
Investigating whether and at which magnitude environmental parameters have
an effect on microbial community dissimilarity, our general dissimilarity
model (GDM) showed the expected curvilinear relationship between the
predicted ecological distance and community dissimilarity (Fig. 4c I). Based
on I-spline magnitudes of all tested environmental variables, geographic
distance had little effect on community dissimilarity (Fig. S11a). Community
dissimilarity changed most notably in response to variability in low
magnitudes of PP (i.e., ISSG and STF; 17 % of total community
dissimilarity, n=12) and plateaued with PP above 1100 µmol C L-1 d-1 (Fig. 4c III). A community dissimilarity change occurred
most notably when N2 fixation rates were above 2 nmol N L-1 d-1 (∼ 19 % of change in total community dissimilarity
associated with changes in N2 fixation rates) (Fig. 4c IV). Among all
tested environmental parameters, our I-spline results showed that community
dissimilarity increased most in response to variability in MLD and
PO43- concentrations (49 % of change in total community
dissimilarity associated with MLD variability and 63 % with PO43-
variability, n=12; Fig. S11a).
Significant differences in community dissimilarity structure between
Longhurst provinces were associated with high-pseudocount taxa, dominated by
dinoflagellates (Dinophyceae) and diatoms (Bacillariophyta; SIMPER analysis;
Table S6). The pseudocount of ASVs belonging to the phylum Ochrophyta
(Bacillariophyta_X) contributed to differences between ocean
provinces (contributing to at least 9.51 % of the differences in community
dissimilarity between the SO and ISSG). Moreover, 4.79 % of the
differences in community dissimilarity between the SO and the SSTC were
associated with a higher ASV count of Bacillariophyta_X ASVs
in the SO. Further, we identified 10 ASVs belonging to the phylum
Dinophyceae, contributing 2.1 % to the community dissimilarity
structure between the SO and ISSG and 5.79 % to the community
dissimilarity structure between the SSTC and ISSG. This was further
supported by relatively high concentrations of the photosynthetic pigments
chl c3 and peridinin (both indicative pigments for dinoflagellates) in the
SO and SAF. We found a relatively high number of ASV94 and ASV23
(Chloroparvula pacifica) in the SSTC, contributing 3.07 % to the community dissimilarity between
the SSTC and the ISSG.
Prokaryotic community composition
From each of the 14 stations, a fragment of the small subunit ribosomal RNA gene
(16S rRNA) was amplified and sequenced to obtain insights into the diversity
and community composition of prokaryotes. A total of 1308 ASVs were recovered
from which we removed 267 ASVs annotated as chloroplasts and 68 ASVs
annotated as mitochondria. Prokaryotic richness increased with increasing
sea surface temperature (Pearson correlation: r=0.65, p value = 0.03, n=11; Fig. S7a). Maximum alpha diversity (inverse Simpson) estimate was
found in the SAF (81.92, station 15; Fig. S7d). Prokaryotic alpha diversity
(inverse Simpson) was positively (but not significantly) linked to primary
productivity (r=0.36, p=0.55, n=11; Fig. S7f) but showed a
significant negative correlation with N2 fixation (r=-0.7, p=0.05, n=11; Fig. S7h).
Our RDA of the prokaryotic ASV table captured 90 % of the total variance
with a p value of 0.06 (permutations = 999, n=11). Sites clustered
into Longhurst provinces along the first two RDA axes (62.48 % of variance
constrained; Fig. 4b). This was also shown in the PERMANOVA solution for
Longhurst provinces (permutations = 999, R2=0.62, p<0.001, n=11) and our PERMANOVA grouping into water masses (permutations = 999, R2=0.74, p<0.001, n=11; Fig. 4b). We found
more ASVs occurring in either the ISSG or the SO provinces rather than
across all provinces (Fig. 4f). Further, the ISSG and the SO shared the
least ASVs (Fig. 4f). In the RDA, sites within the ISSG province were
positively associated with SST and N2 fixation. Sites belonging to the
SO were positively associated with dissolved inorganic nutrients
(NO3-, PO43-, Si), dissolved oxygen, and chl a
concentrations as well as high PP (Fig. 4b). The community composition
within the SSTC (STF and SAF) was distinct from that of the ISSG and SO
along the second RDA axis (21.67 % variance constrained) and positively
associated with NH4+ concentrations (Fig. 4b). Linear
relationships between beta diversity and rates were weak for PP (PERMANOVA;
permutations = 999, R2=0.31, p=0.007, n=11) and N2
fixation (PERMANOVA; permutations = 999, R2=0.2, p=0.05, n=11).
Investigating whether and at which magnitude environmental parameters have
an effect on prokaryotic microbial community dissimilarity, our general
dissimilarity model (GDM) showed the expected curvilinear relationship (Fig. 4d I). Based on I-spline magnitude, geographic distance had little effect on
community dissimilarity. The largest magnitude in community dissimilarity
could be observed between 190–1200 µmol C L-1 d-1 (Fig. 4d III). Community dissimilarity changed most notably in response to
variability in low magnitudes of N2 fixation and did not change in
samples with highest average N2 fixation measurements (2.8 nmol N L-1 d-1 station 3, and 4.0 nmol N L-1 d-1 station 18). Largest magnitudes of community dissimilarity were associated
with dissolved oxygen concentrations (Fig. S11b).
Taxonomically, based on analysis of the CLR-transformed ASV table, the
prokaryotic community was dominated by Proteobacteria, Cyanobacteria, and
Bacteroidetes, which are all typical clades for surface water samples (e.g.,
Biers
et al., 2009). The greatest community differences occurred between stations
of the Southern Ocean (SO) and the Indian South Subtropical Gyre (ISSG)
provinces. Structure in community dissimilarity between the ISSG and SO were
mostly associated with the number of Flavobacteriaceae (11.52 % of total
community dissimilarity, SIMPER analysis, Table S6) and Planktomarina
(Alphaproteobacteria) (5.69 % of the total difference in community
dissimilarity, SIMPER analysis, Table S6). Further, the SO had distinct ASVs
belonging to the SUP-05 cluster, contributing 2.56 % (ASV_12) to the difference between SO and SSTC. The ISSG was characterized by a
high number of Cyanobacteria and some Actinobacteria. The cyanobacterial
fraction was dominated by Prochlorococcus and Synechococcus.
Within the class level, all stations were dominated by Alpha- and
Gammaproteobacteria, Bacteroidia, Oxyphotobacteria (Cyanobacteria), and
Verrucomicrobia. Within the Alphaproteobacteria, we found a great dominance
of ecotypes I, II, and IV of SAR11 clade throughout all samples (Table S4).
The relative number of pseudocounts of bacteria belonging to the phylum
Bacteroidetes decreased towards warmer SST in the ISSG, with significant
differences between the SO and ISSG (Welch two-sample t test t=4.58, p<0.001, n1= 341, n2= 151). The phylum Bacteroidetes was
largely dominated by the order Flavobacteriales (90.98 % of annotated
ASVs). Cyanobacteria mainly occurred in the SSTC and in the ISSG, which were
dominated by Prochlorococcus in the ISSG and Synechococcus in the SSTC. Cyanobacterial
pseudocounts were significantly lower in the SO in comparison to the SSTC
(Welch two-sample t test, t=-3.86, p value < 0.001, n1= 17,
n2= 31) and to the ISSG (Welch two-sample t test, t=-4.74, p<0.001, n1= 17, n2= 45). Atelocyanobacteria (UCYN-A) ASVs occurred in the SAF
(station 14) and ISSG (stations 2, 3).
Discussion
Each water mass in our study had a distinct microbial fingerprint, including
unique communities in frontal regions. We highlight clear relationships
between microbial diversity, primary productivity, and N2 fixation
(high linear and nonlinear covariability) in the southern Indian Ocean Gyre
(ISSG), the Southern Ocean (SO), and their frontal transition zone. Below,
we discuss how this clear provincialism of microbial diversity is
disconnected from regional gradients in primary productivity (PP) and
N2 fixation across our transect. This could suggest that microbial
phylogenetic diversity is more strongly bounded by physical oceanographic
boundaries, while microbial activity (and thus, perhaps, their functional
diversity, not assessed here) responds more to chemical properties that
changed more gradually between the low- and high-nutrient provinces we
sampled.
N2 fixation and associated microbial diversity display distinct regional variations
Overall, our N2 fixation (up to 4.4 ± 2.5 nmol N L-1 d-1) was comparable to N2 fixation measured by
González et al. (2014) above the Kerguelen Plateau
(up to 10.27 ± 7.5 nmol N L-1 d-1) and showed a similar
latitudinal trend as N2 fixation further east in the Indian Ocean,
although with around 10-fold lower absolute rates (0.8–7 vs. 34–113 nmol N L-1 d-1; Raes et al., 2014). We note that the localized rates reported by
González et al. (2014) are to date the only published
N2 fixation measurements in this region, likely to be close to the
annual maxima because of high irradiance; however, further investigations
across seasonal changes within the study area are needed to confirm our
observations. Our regional data are therefore important in closing the gaps
in N2 fixation measurements in the Southern Ocean, especially
considering that large disagreements exist between models of high-latitude
N2 fixation rates (Tang et al., 2019).
N2 fixation measurements often show high basin-wide variability as well
as high variability between samples at the same site, being sensitive to
details of experimental design, incubation, and sea-state conditions (Mohr
et al., 2010). In aggregate, these issues are best accounted for by
calculating the minimum quantifiable rate (MQR;
Gradoville et al., 2017). We observed high
heterogeneity of biological samples taken from the underway
flow-through system 5 min apart (separated by ∼ 15 km)
within the same water mass. Similar variability in absolute measurements of
N2 fixation (2.6–10.3 nmol N L-1 d-1± 7.5 nmol N L-1 d-1) were reported by González et al. (2014) close to our sampling site around Kerguelen island. This could imply
a sub-mesoscale variability or influence of other unmeasured parameters.
As oligotrophic gyres extend and displace southwards under climate change
(Yang et al., 2020), the biogeochemical and physical
characteristics of the SO are changing (Caldeira and Wickett, 2005;
Swart et al., 2018), and biological regional N2 fixation might become
an important N source for productivity. Our data showed maximal N2
fixation in the oligotrophic waters of the ISSG; however, notably,
measurable N2 fixation occurred well into the SO, to 56∘ S,
suggesting that N2 fixation contributes to the regional N pool, despite
other available sources of N (Shiozaki et al., 2018;
Sipler et al., 2017). Similarly, we found a negative N* in the SO, which
potentially indicates a P excess supporting N2 fixation
(Knapp, 2012). Noteworthy is a slight
increase in N2 fixation in the Antarctic Zone (AZ). High-latitude
measurements in northern polar regions (Bering Sea) reached 10–11 nmol N L-1 d-1 (Shiozaki et al., 2017),
substantially higher than our measurements of the SO (0.8–1.9 nmol N L-1 d-1), potentially supported by the close proximity to the
coast or other factors such as day length, seasonality, diazotroph community,
or trace metal concentrations.
Our results suggest that regional N2 fixation was not limited by the
presence of other sources of bioavailable N (Fig. S10); this is a conclusion also
reached in a number of studies including culture experiments
(Boatman et al., 2018;
Eichner et al., 2014; Knapp, 2012), as well as in situ measurements in the
South Pacific (Halm et al., 2012); off
the coast of Chile and Peru with rates up to 190 µmol N m-2 d-2 (Fernandez et al.,
2011); and across the eastern Indian Ocean (Raes et al.,
2015). This evidence counters the hypothesis of Breitbarth
et al. (2007) that N2 fixation occurs only when other sources of N are
limited. The contribution of N2 fixation to the N pool – and thus to
productivity – varies strongly with ecosystem structure: in the SO, despite
the local N2-fixation measurements, N2 fixation remains likely a
very minor contributor to the N required by the microbial community for
primary productivity.
Our results also strongly suggest that prokaryotic community structure and
composition (beta diversity) were strongly impacted by the presence of
biological N2 fixation, which is itself a prokaryotic process
(Karl et al., 2002).
For example, the N2-fixing Atelocyanobacteria (UCYN-A) occurred in the SAF and ISSG;
however, to gain a clear insight into the community and N2 fixation,
the diazotrophic community would need to be further resolved by amplicon
analysis of functional (nifH) genes (Luo et al., 2012) as shown
in other high-latitude studies (Fernández-Méndez et al.,
2016; Raes et al., 2020).
Total and specific primary productivity differentially affect microbial diversity
We found PP was highest in the PFZ and decreased towards higher latitudes in
the SO (Fig. 3a). Strass et al. (2002) showed that frontal maxima of PP are expected, and the observed
decrease was probably due to Fe limitation in the SO (Blain et al., 2008).
Primary productivity can also be limited by Si concentration and light
availability when the mixed layer deepens
(Boyd et al., 2000), but in our data Si
concentrations were high in the surface water samples, and light levels were
close to maximum in austral summer. The measured maximum PP above the
Kerguelen Plateau (station E) was likely stimulated by Fe inputs
(Blain et al., 2007).
Our results did not support prior observations that frontal regions (SAF and
STF) supported higher specific primary productivity (PB) (as reported
in the Antarctic Atlantic sector; Laubscher et al.,
1993). While phytoplankton community composition, phytoplankton size
distribution, and nutrient concentrations were strikingly different between
the ISSG and SO, we found little difference in PB, with some slightly
lower values observed within the SSTC (Fig. 3b). Differences in PB
usually arise from physiological changes due to variabilities in irradiance
(Geider, 1987), nutrient concentrations (Behrenfeld et al., 2008; Chalup
and Laws, 1990), or differences in phytoplankton community structure, where
cyanobacteria have the highest PP efficiency and diatoms the lowest
(Talaber et al., 2018). Thus, our observations
suggest that either (1) there is a lack of selective pressure on
photosynthetic efficiency between provinces or (2) mechanisms driving PB are different between provinces, and the sum of beneficial (e.g., increased
nutrient concentrations in the SO) and detrimental mechanisms (e.g., low
irradiance and photoinhibition through deep vertical mixing, reported from
the Antarctic circumpolar current (ACC); Alderkamp et al., 2011)
result in similar PB. The slight variation around the frontal system is
hard to interpret, as the complex interplay between factors may result in
stochasticity.
Primary productivity can be an important driver for (phylogenetic) microbial
alpha diversity (Vallina et al., 2014),
especially within ocean provinces (Raes et al., 2018). While
our observational study only has a small number of samples within and
between oceanic provinces (n=12, nISSG=4, nSSTC=3,
nSO=4), it did suggest that further validation of this assumption
is needed. We observed that PP changed gradually across the sampling region
and that local variability in PP was high between samples taken
∼ 15 km apart within the SSTC and SO (Fig. 3a). These local
variabilities can arise from complex physico-chemical interactions between
the STF, SAF, and SO (Mongin et al., 2008).
Counter to Vallina et al. (2014) and Raes et al. (2018), we found a significant negative
correlation between eukaryotic alpha diversity and PP within the ISSG.
Further, we found no correlation between eukaryotic diversity and PP within
the SSTC and SO and none between prokaryotic alpha diversity across all
provinces (Fig. S7).
In terms of beta diversity, we observed a structuring effect of PP for
pigment, 16S rRNA gene, and 18S rRNA gene-derived diversity profiles (Figs. 4a, b, S5). Pigment analysis revealed that photosynthetic prokaryotic
diversity is strongly impacted by the relative abundance of
Prochlorococcus, which does not generally occur in cold high-latitude waters (>40∘ S/N; Fig. S5) (Partensky et al., 1999) and,
if so, only in low abundance (reviewed in Wilkins et al., 2013). Our 16S rRNA gene
analyses confirm these observations showing that (1) picoplankton – and
specifically Prochlorococcus – had relatively high proportions in the ISSG but very low in
the SSTC, (2) Synechococcus dominated the Cyanobacterial fraction in the SSTC, and (3) both
Prochlorococcus and Synechococcus were not detected in the SO (Tables S4, S6). In the SSTC and SO,
phytoplankton communities had high proportions of dinoflagellates
(Dinophyceae) and diatoms (Bacillariophyta) (up to 74 % of diatom
diagnostic pigment concentrations), which are known as essential
contributors to marine PP and microbial diversity
(Malviya et al., 2016) and known to dominate the
phytoplankton fraction within the Polar Frontal Zone (PFZ), especially as
the blooming season progresses (Brown and
Landry, 2001).
Further, our results show that phytoplankton community structure appears to
be tightly coupled to the occurrence of specific heterotrophic organisms
(Table S6) and thus may mediate an indirect effect of PP through microbial
food webs (as also noted in, e.g., Sarmento and Gasol, 2012).
For example, in areas of relatively high diatom concentrations, we found
increased proportions of Flavobacteria. These bacteria specialize on
successive decomposition of algal-derived organic matter
(Teeling et al., 2012) and are known associates of
diatoms (Pinhassi et al., 2004). Further,
Planktomarina belonging to the Roseobacter clade affiliated (RCA) subgroup had relatively high proportions
in the SO and is generally suggested to occur in colder environments
(Giebel et al., 2009) and previously
detected in the Polar Front (Wilkins et al.,
2013b). The RCA subgroup is known for dimethylsulfoniopropionate (DMSP) degradation in phytoplankton
blooms (Han et al., 2020). In addition to
bacteria known to be associated with phytoplankton, we also observed those
which symbiose with other organisms (e.g.,
Georgieva et al., 2020), such as
the sulfur oxidizing Thioglobaceae (SUP-05 cluster), previously found in
symbiosis with Myctophidae fish near Kerguelen Islands
(Gallet et al., 2019). While beyond the scope of
this study, we encourage further investigations of such trans-kingdom
functional interactions as they themselves may offer regional insights.
Implications for microbial regionality
Microbial diversity was regionally constrained independent of geographical
distance (GDM analysis), but it was partitioned into ocean provinces as
repeatedly described for other ocean basins such as the Pacific
(Raes et al., 2018) and the Atlantic Ocean
(Milici et al., 2016). This supports the
classical concept of microbial biogeography (Martiny et al.,
2006). Further, we found that microbial beta diversity was even better
resolved by individual water masses, highlighting the importance of
including oceanographic boundaries that limit cross-front dispersal
(Hanson
et al., 2012; Hernando-Morales et al., 2017; Wilkins et al., 2013a).
Our beta diversity analysis confirmed the findings by Baltar
and Arístegui (2017), who found unique environmental sorting and/or
selection of microbial populations in the SAF and STF. Further, we were able
to link these communities to high NH4 concentrations. This suggests
high recycling of nitrogen sources within the microbial loop and
potentially favoring nitrification in this area (Sambrotto and
Mace, 2000). We also found increased dinoflagellate concentrations (PFT)
which have been described to grow well under NH4 conditions
(Townsend and Pettigrew, 1997). Despite our small sample size
within the SAF and STF, we were able to detect these characteristics,
supporting the call from Baltar et al. (2016) for
better integrating frontal zones in our understanding of microbial
biogeography.
Different trade-offs such as nutrient limitation and grazing can shape the
microbial seascape (Acevedo-Trejos et al., 2018). In our
study, the deviation between PN : chl a was large between the SO and IO with
high PN : chl a ratios in the ISSG (Fig. S4), which has been used as an
indicator of a relatively high abundance of heterotrophic microbes and
protists over autotrophic organisms (Crawford
et al., 2015; Hager et al., 1984; Waite et al., 2007). This would suggest
that grazers formed a higher fraction of total biomass in the ISSG than in
the SO. However, we did not measure zooplankton biomass or grazing rates, so
this remains speculative.
Conclusion and outlook
Our study leads us to conclude that simultaneous assessment of microbial
diversity, biogeochemical rates, and the physical partitioning of the ocean
(provincialism) is central to the understanding of microbial oceanography.
Each water mass in our study had a distinct microbial fingerprint, including
unique communities in frontal regions. Microbial alpha diversity and
community dissimilarity correlated with biogeochemical rate measurements;
however, mechanisms driving this association need further investigation
through high-resolution sampling across spatial and temporal scales. Our
results also indicate that high-latitude N2 fixation could meaningfully
contribute to the global and regional N pool (as reported for Arctic N2
fixation by Sipler et al., 2017), which may become
especially significant as global stratification (and concomitant
restrictions in deep water replenishment of nutrients) intensifies.
While our sampling is too limited to conclude the point, our observations
that phylogenetic diversity is constrained by hydrographic properties and
province boundaries but that biogeochemical rates and nutrient concentrations
are changing more gradually suggest that trans-province functional
redundancy is present despite strong biogeographic separation in
phylogenetic terms. As an outlook, we therefore encourage examining both
phylogenetic and functional diversity to assess how functional groups and
guilds contribute to the major biogeochemical (C, N) cycles across provinces
and other biogeographic regions. Coordinated studies across ocean provinces
are key to establishing the baselines we need to monitor the rapidly
changing properties of the southern high latitudes in the face of rising
temperature, acidification, and perturbations in regional currents.
Code availability
All code is available under S1_code_archive.zip in the Supplement and additionally publicly archived under 10.5281/zenodo.5000001 (Hörstmann, 2021).
Data availability
All HPLC data; environmental and rate measurement data, including PN, MIMS
data, PP, and N2 fixation; and minimum quantification rate calculations are
stored in the PANGAEA database (Hörstmann et al., 2018). All sequences
are archived in the European Nucleotide Archive (primary accession:
PRJEB29488).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-18-3733-2021-supplement.
Author contributions
CH did the post-voyage processing and analysis of all samples and wrote the
article. EJR conducted the fieldwork, designed the experiments, and
contributed to data analysis and writing of the article. PLB contributed to
data analysis, ecological interpretation, and writing of the article. CLM
provided the historic physical and chemical data and contributed to the
write-up. UJ helped with the DNA sequencing and writing of the article. AMW
contributed to the design of the experiments, data analysis, and writing of the
article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Nicolas Metzel as well as the captain and crew of the Marion Dufresne. We thank Gaute Lavik from the Max Planck Institute of Marine
Microbiology in Bremen for the guidance and for allowing us to use membrane
inlet mass spectrometry. We thank Stefan Neuhaus for his knowledge on the
bioinformatics pipeline. We thank Vladimir Benes and his team from the
Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg,
Germany, for their kind guidance and support with the 16S rRNA gene
sequencing. We thank the Leibniz Institute on Aging (FLI) in Jena (Germany)
for their support in 18S rRNA gene sequencing. We thank Allison Fong and
Matthias Ullrich for their comments on this study.
Financial support
The article processing charges for this open-access publication were covered by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI).
Review statement
This paper was edited by Koji Suzuki and reviewed by two anonymous referees.
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