Microbial Fingerprinting of Marine Water Masses in an Antarctic and Hydrographically Complex Area

doi:10.21203/rs.3.rs-8039863/v1

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Microbial Fingerprinting of Marine Water Masses in an Antarctic and Hydrographically Complex Area

https://doi.org/10.21203/rs.3.rs-8039863/v1

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Microorganisms are ubiquitous components of marine ecosystems, yet the role of water masses in their distribution remains underexplored, particularly in remote and extreme environments such as Antarctica. This work studies the microbial communities of the Gerlache-Bismarck Strait, an area with complex hydrography. In this area, we measured microbial abundances and diversity (both prokaryotic and eukaryotic), and we recorded multiple oceanographic and biogeochemical variables. Water samples covered from 0 to 400 m depth, and we included 3 size fractions: pico- (0.2-3.0µm), nano- (3.0–20 µm) and microparticles (20–200 µm). The results revealed that the water mass was the main driver determining the assembly of microbial communities and was more relevant than location, depth, size fraction, and local environmental conditions. Water masses detected in this area included AASW, GMW and TBW (in surface waters), as well as TWW, and CDW (in intermediate and deep waters). To examine in detail the links between water masses and microbes, we defined the microbial fingerprint of water masses, which includes different descriptors, such as the core microbiome and tracer microbes. Each water mass had a characteristic microbial fingerprint, which revealed ecological roles of the water masses linked to biogeochemical cycles (e.g., the ammonia-oxidizing archaea Nitrosopumilaceae in the CDW) and food webs (e.g., the phototroph Chrysochromulina simplex in the GMW). This study provides direct evidence of the strong links between microbes and water masses, revealing that microbial communities found in seawater are not only shaped by direct local conditions but also by water masses characteristics and transport. We thereby develop the concept of “microbial fingerprinting of water masses”, where microbial communities emerge as ecological indicators, an approach that can be explored and applied in other marine systems.

Marine and Freshwater Ecology

Antarctica

Oceanography

Water Masses

Microorganisms

Marine microbes are key components of marine food webs, biogeochemical cycles, and carbon cycling processes. However, little is known about the diversity, distribution, and role of microorganisms in the Southern Ocean, the marine ecosystem surrounding Antarctica (Murray and Grzymski 2007; Cavicchioli et al., 2015; Liu et al., 2021; Castillo et al., 2022),

Coastal areas of the Southern Ocean are highly productive (Moore and Abbott, 2000), in contrast to their offshore counterparts, which are characterized as high-nutrient-low-chlorophyll regions (HNLC). In particular, the coastal waters of the Western Antarctic Peninsula (WAP) fuel massive phytoplankton blooms and sustain high levels of primary productivity (Smith et al., 1996, Arnaldo et al., 2018), supporting large stocks of marine mammals, birds and the Antarctic krill, Euphausia superba (Ducklow et al., 2007). Ecosystems in the Western Antarctic Peninsula (WAP) are undergoing significant changes due to human impacts and climate change: Antarctic human activities are concentrated in this area, which experiences the highest levels of ship traffic, fishery exploitation, tourism, and scientific activity in Antarctica (Fishery et al., 2021), and the surrounding maritime area is recognized as one of the regions most affected by climate change (Morley et al., 2020 and references therein). Climate change is altering the physical drivers of productivity in the Southern Ocean (Petrou et al., 2016; Henley et al., 2020; Pinkerton et al., 2021), which subsequently affects marine biota, from microbes to whales (Constable et al., 2014). In the WAP, the marine area is experiencing a rapid increase in the average annual temperature, increasing stratification of the water column during warmer months, loss of sea ice, retreat of glaciers and ice shelves, and increased runoff. These shifts are driving significant ecosystem changes (Clarke et al. 2007; Ducklow et al. 2013; Kerr et al. 2018a; Siegert et al. 2019), with observed impacts on phytoplankton and microbial dynamics due to warming surface waters and enhanced glacial meltwater input (Montes-Hugo et al., 2009; Obryk et al., 2016; Alcamán-Arias et al., 2018; Tonelli et al., 2021).

A marine protected area has been proposed for the WAP (Sykora-Bodie et al., 2021a), where the Gerlache Strait, in particular, has been presented as the zone that most requires the implementation of conservation actions (Delegations of Argentina and Chile, 2019; Sykora-Bodie et al., 2021b). The Gerlache Strait is a 200 km long strait that is connected in the north to the Bransfield Strait, and to the south with the Bismarck Strait. The Gerlache-Bismark Strait is a particularly productive area (Holm-Hansen et al., 1989; Rodríguez et al., 2002a; Varela et al., 2002) where krill and whales aggregate (Secchi et al., 2001; Dalla Rosa et al., 2008; Nowacek et al., 2011; Secchi et al., 2020). The hydrography of the Gerlache-Bismark Strait is complex because of the multiple geographic components (islands, channels, glaciers, and straits) and the confluence of different currents and water masses (Zhou et al., 2002; Moffat et al., 2018; Parra et al., 2020; Su et al., 2022). These physical dynamics, in turn, determine the distribution of nutrients (Garcia et al., 2002), phytoplankton (Rodríguez et al., 2002a; Kerr et al., 2018a; Ferreira et al., 2020), and zooplankton (Zhou et al., 2002). However, information concerning the diversity and distribution of microorganisms inhabiting the Gerlache-Bismark Strait is still scarce.

Microbes include two evolutionarily distinct groups: unicellular eukaryotes (protists) and prokaryotes (bacteria and archaea). In Antarctica, pelagic communities include eukaryotes (phytoplankton and other groups such as dinoflagellates, ciliates, and flagellates see e.g., Priddle et al., 1986; Liu et al., 2021), bacteria (dominated by Gammaproteobacteria, Alphaproteobacteria, and Bacteroidota) and archaea (which includes Thermoplasmatota, formerly within the Euryarchaeota; and Crenarchaeota, also known as Thermoproteota) (Murray and Grzymski, 2007; Wilkins et al., 2013c; Cao et al., 2020; Castillo et al., 2022). In the Gerlache Strait, different phytoplankton communities display contrasting distributions, where cryptophytes and Phaeocystis sp. tend to dominate while diatoms are the most relevant primary producers (Rodríguez et al., 2002a; Mendes et al., 2018; Mascioni et al., 2021; Mascioni et al., 2023), and Crenarchaeota dominates among other archaea (Massana et al., 1998; Murray et al., 1998). However, information on bacterial diversity remains scarce. Yet, all those studies in the Gerlache are based on traditional methodologies like microscopic taxonomic analysis. Technologies such as High-throughput DNA sequencing have the potential to unveil hidden microbial diversity that is normally underestimated by more traditional methodologies (Liu et al., 2021), and studies in the Gerlache-Bismarck Strait implementing High-throughput DNA sequencing are still lacking.

In this study, we investigated the diversity of microbial communities (both prokaryotic and eukaryotic, from 0.2 to 200 µm) present along the Gerlache-Bismark Strait during summer. We included contrasting areas, such as those influenced by freshwater inputs coming from melting glaciers and those that feature an inflow of seawater from the open ocean. We combined microbial distributions with an analysis of environmental variables and different water masses present in the Gerlache-Bismark Strait. The results indicate a significant connection between microbial communities and water masses, allowing the identification of specific microbial fingerprints (i.e., specific microbial assemblies) for each water mass in the region. The links between microbes and water masses has emerged as key dynamic features of the ecosystem functioning in the Gerlache-Bismarck Strait. This information will help to predict how changes in microbial diversity and distribution may alter ecosystem services in this critical region.

1. Oceanographic context

Seawater in the Gerlache-Bismark Strait (Fig. 1) originates mainly from the Bellingshausen Sea (entering mainly through the Gerlache and Bismarck straits, and Schollaert Channel) and the Weddell Sea (entering through the Bransfield Strait). The dominant current is the Gerlache Strait Current, which flows NE transporting most of the Gerlache Strait waters (including surface and deep waters) towards the Bransfield Strait (Zhou et al., 2002; Savidge et al., 2009; Su et al., 2022). The opposite current is the Bransfield Strait Current, which moves waters from the Bransfield Strait towards the Gerlache Strait (Kerr et al., 2018a; Moffat et al., 2018). Water currents in the Gerlache Strait are linked to the ACC/APCC (Antarctic Circumpolar Current/Antarctic Peninsula Coastal Current) transport system (Kerr et al., 2018a; Moffat et al., 2018).

During summer, there is a permanent isopycnal at approximately 100 m which separates surface and intermediate water layers (Parra et al., 2020). Two main fronts have been described in the Gerlache Strait: the surface water thermal front (STF) and the sub-pycnocline front (SPF). The STF is situated above the pycnocline, at approximately 64.5 °S, close to the Schollaert Channel. This surface front is permanent during the austral summer (Parra et al., 2020). The SPF is situated between 100 and 400 m depth (Parra et al., 2020) and has a variable location along the strait where it separates the relatively warmer intermediate waters entering from the Bismarck Strait from colder waters present in the northern part of the Gerlache Strait (Kerr et al., 2018a; Parra et al., 2020). In this region of Antarctica, water depths are traditionally categorized as surface waters (0 m-100 m), intermediate waters (100 m-300 m), and deep waters (below 300 m to the seafloor) (Parra et al., 2020). The bathymetry of the north and south ends of the Gerlache Strait is shallower (around 200–300 m deep) than that of the central basin with average bottom depths of around 800 m (Fig. 1).

Surface waters in the Gerlache Strait (from 0 to 100 m) have variable characteristics and are affected by multiple processes throughout the year, such as sea ice formation and melting, water mixing, wind forcing, and solar heating. In summer, surface water is continuously mixed (Parra et al., 2020). Surface waters include cold waters, such as Transitional Zonal Waters with Bellingshausen Sea influence (TBW) and Antarctic Surface Waters (AASW) (García et al., 2002). Surface waters also evidence Glacially Modified Waters (GMW), which originates from melting glaciers and typically forms a thin (20–30 m) layer at the surface extending from inner fjords and bays into the strait (Dierssen et al., 2001; Cape et al., 2019, Osińska and Herman, 2024).

Intermediate waters in the Gerlache-Bismarck Strait (from 100 to 300 m) are characterized by a mixing of distinct waters coming from the Bellingshausen and Weddell seas (Kerr et al., 2018b), which include Circumpolar Deep Waters (CDW), Transitional Zonal Waters with Weddell Sea Influence (TWW), and High Salinity Shelf Water (HSSW). Circumpolar Deep Water (CDW) is a relatively warm, salty, and low-oxygenated intermediate water mass (Smith et al., 1999; Kerr et al., 2018b), which transforms into modified mCDW (modified-CDW) when it enters the Gerlache-Bismarck Strait (García et al., 2002; Kerr et al., 2018b). CDW enters the area through the Bismarck Strait (Palmer Deep Canyon), Schollaert Channel, and north off Liége Island. CDW dominates the Bismarck Strait, which is the most important source path into the Gerlache Strait (Parra et al., 2020). CDW can be divided into Upper (UCDW) and Lower (LCDW, normally between 800 and 1000 m) CDW (Smith et al., 1999), and CDW flowing from the Bellingshausen Sea into the Gerlache-Bismark Strait has been described either as LCDW (García et al., 2002) or UCDW (Kerr et al., 2018b). TWW water is generated in the Weddell Sea and crosses the Bransfield Strait towards the northern end of the Gerlache Strait. TWW is found between depths of 300 and 700 m (García et al., 2002; Sangrà et al., 2011). The intermediate and deep-water masses in the Gerlache Strait from the Weddell Sea have been reported as a modified version of the HSSW (mHSSW in Parra et al., 2020; HSSW-derived in Kerr et al., 2020b). The mHSSW enters the Gerlache Strait as bottom water and is characterized by low temperature, high salinity, and high oxygen content (Kerr et al., 2018b). This water mass extends to the southernmost point of the WAP in the central area of the Gerlache Strait (around the Schollaert Channel), becoming scarce in the southern Gerlache Strait, and negligible in the Bismarck Strait. Generally, in the northern part of the Gerlache Strait, mCDW intrudes the TWW (García et al., 2002) and HSSW (Kerr et al., 2018b; Parra et al., 2020), forming the sub-pycnocline front (Parra et al., 2020), which separates the older waters coming from the Bellingshausen Sea from the younger ones coming from the Weddell Sea (Kerr et al., 2018b). Information about the thermohaline indices of the different water masses in the Gerlache-Bismark strait can be found in Supplementary Table 1.

2. Sampling

Sampling was carried out in February 2020 (summer in the Southern Hemisphere), onboard the R/V Karpuj. A transect was performed along the full Gerlache-Bismarck Strait (Fig. 2), from northeast off Brabant Island (64,32º S, 61,80º W) to southwest off Anvers Island (64,97º S, 64,52º W), including 13 oceanographic stations (Fig. 2). At each oceanographic station, a CTD (SBE 25 plus by Seabird®) was used to assess the oxygen concentration (mg L^− 1) and saturation percentage (% sat.), temperature (°C), potential temperature (°C), and salinity (practical salinity) (see details in Höfer et al., 2019). Additionally, seawater from the oceanographic stations was sampled at 0, 10, 100, 200, 240, and 400 m using 5 L Niskin and 10 L Go-Flo bottles in order to study the biogeochemistry and microbial communities (Fig. 2). The measured biogeochemical variables included chlorophyll concentration (mg m^− 3), in situ pH, dissolved silicate (µmol L^− 1), dissolved nitrate (µmol L^− 1), dissolved phosphate (µmol L^− 1), partial pressure of CO₂ (µatm), concentration of suspended inorganic matter (ISM, mg L^− 1), and concentration of suspended organic matter (OSM, mg L^− 1). The fraction of organic matter present in suspended matter was defined as %OSM. Biogeochemical variables were sampled and analyzed as per Höfer et al. (2019) except for ISM and OSM that were processed following Giesecke et al. (2019).

Microbial parameters included microbial abundance and diversity. Microbial abundance was sampled by fixing seawater (1,350 µL) with glutaraldehyde (150 µL of 1% glutaraldehyde), and samples were collected in triplicate. Prokaryotic and eukaryotic abundances were determined by flow cytometry using a FACSCalibur flow cytometer (Department of Oceanography, University of Concepcion, Chile). The microbial groups detected by flow cytometry included unicellular eukaryotes (composed by phototrophs < 6 µm, including picoeukaryotes and nanoeukaryotes; cells mL^− 1) and prokaryotes (including bacteria and archaea; cells mL^− 1). To determine microbial diversity (including microscopic prokaryotes and eukaryotes), 9 L of seawater were filtered using a peristaltic pump. To diminish the impact of clogging and dislodging particles from the filters, we filtered at a very low speed and pressure, and replaced the filters when needed. The filters were submerged in a nucleic acid preserving solution (RNAlater, Invitrogen), rapidly frozen at -20ºC onboard and stored at -80 ºC until DNA extraction. The filtration system included a 200 µm mesh followed by 3 polycarbonate filters (pore sizes: 20 µm, 3 µm, and 0.2 µm) arranged sequentially. Using this procedure, we obtained three size fractions corresponding to three sizes of plankton and marine particles: picoplankton and picoparticles (0.2–3 µm), which include free-living prokaryotes and eukaryotes; nanoplankton and nanoparticles (3–20 µm), which consist mainly of larger eukaryotic microorganisms and prokaryotes associated with particles or protists; and microplankton and microparticles (20–200 µm), which include larger eukaryotic microorganisms and prokaryotes associated with larger particles or protists.

3. Characterization of water masses mixing

The principal characteristics of the different water masses present in the Gerlache-Bismarck Strait (i.e., AASW, GMW, TBW, TWW and CDW) were obtained from: Smith et al., 1999; Garcia et al., 2002; Zhou et al., 2002; Sangrà et al., 2011; Kerr et al., 2018b; Moffat et al., 2018; Parra et al., 2020. The contribution of each water mass to the seawater properties at each station and depth was determined by applying the mixing triangle method (Mamayev, 1975) as described in Silva et al., (2009). The method involves analyzing the distribution of water masses using T-S (Temperature-Salinity) diagrams for the considered oceanographic stations. According to this approach, to calculate the mixing ratio percentages, it is essential to define the distinct thermohaline indices characteristic of each water mass within a given triangle (Supplementary Table 1). Finally, for each station and depth, the contribution (in %) of each water mass was obtained, and the dominant water mass at each station and depth was determined as the water mass contributing with a higher % to that particular mixing.

4. DNA extraction, Sequencing and Sequence processing

This work includes the study of prokaryotic (bacteria and archaea) and eukaryotic microbes through DNA analysis, using primers described by Parada et al., (2015). DNA extraction, library preparation, and sequencing were performed at the IMR (Integrated Microbiome Resource, Halifax, Canada), and protocols were carried out as originally described by Comeau et al., (2017) and updated as in Comeau and Kwawukume (2023). DNA extraction was performed using the QIAGEN PowerSoil Pro kit on QIAcubeHT, and sequencing was performed on an Illumina MiSeq platform with a v3 600 cycle kit (2x300bp). Computational analyses of demultiplexed reads were performed based on QIIME2 (Bolyen et al., 2019) and DADA2 (Callahan et al., 2016) along with a custom bioinformatic pipeline incorporating elements from McNichol et al., (2021), and Yeh et al., (2021). Briefly, primer sequences were removed with Cutadapt (Martin 2011), discarding any sequence pairs that did not contain the forward and reverse primers. Mixed amplicon sequences were then split into 16S and 18S rRNA genes pools using bbsplit.sh from the bbtools package (https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/) against curated 16S/18S databases (https://osf.io/e65rs/) derived from SILVA 132 (Quast et al., 2012) and PR2 (Guillou et al., 2012). Subsequently, parallel denoising of 16S and 18S sequences were conducted to generate ASV tables for subsequent analysis. For both 16S and 18S fractions, the forward read was truncated to 220bp and the reverse read to 180bp. 16S reads were merged prior to denoising, whereas for 18S the forward and reverse read were concatenated since sequence lengths are not sufficient for full overlap with 18S rRNA for this primer region. In this study, we used the terms ASVs and OTUs interchangeably.

5. Data analysis

Data analysis was performed using R software (v4.3.2) within RStudio interface (R Core Team, 2020), and environmental variables together with the geography were visualized using ODV software (Ocean Data View v5.6.3). To analyze microbial communities, the ASV/OTU table was first randomly subsampled (with the rrarefy function, vegan package) to the number of reads present in the sample with the lowest number of reads (n = 4041), reducing from 111 samples to 107 and a total of 13,548 ASVs.

The diversity of each sample was calculated with different diversity indices, those were: the total number of species per sample or richness (index S.Obs), the Shannon index (H), the Simpson index (D), and the Pielou index (J’) (evenness). The formulas used for calculating the indices were implemented in R, following Borcard (2011) and Kers and Saccenti (2022). The specific formulas used are shown in Supplementary Table 2. Subsequently, correlation analysis (Supplementary Fig. 1) was performed among the different indices using the cor and cor.test functions (base package), where we applied the Spearman method. Results were visualized using the ggpairs function (GGally package), revealing that all indices were generally correlated (R² ≥ 0.4; R > 0.5; p-value < 0.001). Among all indices, we chose the richness index (S.Obs) for downstream diversity analyses because of its conceptual simplicity. To determine whether diversity in terms of richness was statistically similar or different among the levels within each factor (fraction, depth, stations), a Kruskal-Wallis test was conducted using the kruskal.test function from the R base package.

Low abundant ASVs (also referred to as “rare”), were defined as those representing < 1% of the total abundance of all sequences (considering all samples). Taxonomic composition of microbial communities was visualized with barplots (tidyverse and ggplot2 packages), by selecting the most abundant taxa at the Phylum level. The other taxa, together with the rare, were represented as “Others”.

To assess the influence of different factors (such as water fronts, water masses, stations, depth, size fractions and zones) on microbial communities, a series of non-parametric permutational multivariate analysis of variance (PERMANOVA) tests with 999 permutations were conducted (adonis2 function, vegan package). The effects of the environment (explanatory variables) on microbial communities (response variables) were assessed by considering two types of explanatory variables: environmental variables (including physical and biogeochemical variables, as well as microbial abundances) and water masses mixing (i.e., the contribution, in percentage (%), of each water mass). A simple set of the explanatory variables explaining microbial data was obtained by first discarding redundancies (we applied Spearman correlation method and the thresholds: R > 0.9; p-value < 0.001) (Supplementary Fig. 2). Then the Bioenv procedure was applied (bioenv function, vegan package), which identifies the smallest subset of explanatory variables that correlates maximally with microbial community data. For this, distance matrices were constructed (for explanatory variables using Euclidean distances and for response variables using Bray-Curtis distances) and the Spearman-rank-based correlation method was applied. Finally, the effect of selected environmental variables on microbial communities were visualized in a distance-based redundancy analysis (dbRDA) (capscale function, vegan package). To assess the significance of the selected variables on microbial communities, a permutational ANOVA test was conducted (anova.cca function, package vegan), with 999 permutations.

To gain a deeper understanding of the structure of the microbial communities present in different water masses, we explored: co-occurrence networks, indicator (tracer) microbes and the core microbiome. Co-occurrence networks were explored with the igraph R package for network analysis and visualizations. Only those ASVs that appeared in at least three samples were considered. Hypothetical interactions among ASVs were detected through correlations. For this, a correlation matrix between the ASVs was generated by applying the Spearman correlation method and those correlations with a threshold of R > 0.65 were selected. Different co-ocurrence network parameters were calculated (igraph package) including: number of nodes and edges (gorder and gsize functions, respectively), average path length mean_distance function), modularity (cluster_walktrap and modularity function), average degree (mean function, base package), clustering coefficient (transitivity function), and density (edge_density function). Networks were visualized using the functions graph_from_adjacency_matrix, delete.vertices, and graph_from_data_frame. To characterize the indicator species (usually defined as those microbes that are abundant in a particular group of samples, and nearly absent in other groups of samples), we were restrictive in terms of their distribution (we contemplated only those microbes exclusively present in a group of samples, and not present in others) and we included low-abundant taxa, so we renamed the indicator species as tracer microbes. Tracer microbes (i.e., those microbes only present in a particular group of samples, and absent in other groups of samples) of each water mass were obtained with a presence-absence (binomial) ASVs table and applying the multipatt function (indicspecies R package) with 9999 permutations, and selecting r.g as species-site group association function. p-values were adjusted post hoc for multiple comparisons using the p.adjust function with the False Discovery Rate (FDR) method, to reduce the false positive rate (Benjamini and Hochberg, 1995). Tracer microbes were calculated at the ASV level and represented at the Phylum level in dotplots (option geom_point, ggplot function, ggplot2 package). Finally, the core microbiome (i.e., those microorganisms that appear consistently in samples from a given group, regardless of what microorganisms are in other groups) of the water masses was determined using the function core_members from the phyloseq package and visualized with ggplot2. The core microbiome was calculated at the ASV level, considering a minimum abundance of 1%, and a prevalence of 75% among samples of the same water mass. These thresholds align within the range commonly used in similar studies (Custer et al., 2023).

The oceanographic environment

The environmental variables exhibited high heterogeneity among different stations and depths (Supplementary Figs. 3–8). Yet, some generalizations and patterns could be observed. For surface measurements (i.e., 0 m depth), the levels of oxygen increased towards the south (from 8.17 to 8.96 mg mL^− 1) and the same trend was evident with chlorophyll (from 1.10 to 2.06 mg m^− 3), whereas salinity decreased (from 33.14 to 32.94 practical salinity). In the middle parts of the Gerlache-Bismarck Strait (stations 11 and 15), we found lower temperature (1.18 ºC) and chlorophyll (~ 1.4 mg m^− 3) and higher salinity (33.79 practical salinity), dissolved silicate (66.27 µM), dissolved nitrate (21–22 µM) and dissolved phosphate (1.9 µM). For the inshore fjords, Wilhelmina Bay presented lower levels of chlorophyll (0.09 mg m^− 3) and higher levels of in situ pH (8.07), whereas Andvord and Flandres Bay presented higher levels of dissolved nitrate (20.27 and 19.34 µM, respectively) and phosphate (1.61 and 1.63 µM, respectively). At 10 m, 100 m and intermediate and deep depths (200, 240 and 400 m) we observed, in general, similar patterns to those in surface waters. From the surface to the deeper waters, we observed a decreasing pattern in the mean values of oxygen (from 8.22 to 5.78 mg mL^− 1), temperature (from 1.57 to 0.18 ºC), chlorophyll (from 1.33 to 0.03 mg m^− 3) and in situ pH (from 8.04 to 7.93), whereas increases were observed for salinity (from 33.41 to 34.49 practical salinity), in situ pH, dissolved nitrate (from 20.35 to 27.25 µM), dissolved phosphate (from 1.77 to 2.27 µM) and dissolved silicate (from 57.68 to 71.98 µM).

Based on the physical properties of the seawater, we detected the summer pycnocline at a depth of approximately 100 m, the surface water thermal front in the northern part of the Gerlache-Bismarck Strait (around stations 31 and 34), and the sub-pycnocline front in the middle part of the strait (between stations 6 and 11) (Supplementary Fig. 4). Five water masses were detected: Antarctic Surface Waters (AASW), Transitional Zonal Waters with Bellingshausen Sea influence (TBW), Glacially Modified Waters (GMW), Transitional Zonal Waters with Weddell Sea Influence (TWW) and Circumpolar Deep Waters (CDW) (Fig. 3). Surface waters were dominated by AASW, TBW and GMW, where GMW was more evident in areas near fjords, particularly in Wilhelmina Bay. In the intermediate and deep waters, the most relevant water masses included TWW and CDW, where TWW dominated in the northern part and CDW dominated in the south.

Microbial abundances

The abundance of prokaryotes was 83 times higher (152,159 cells mL^− 1 on average) than that of eukaryotes (1,826 cells mL^− 1 on average). In general, the abundance of microbes peaked at 10 m depth and then decreased with increasing depth, with a mean of 175,606 cells mL^− 1 in surface waters (0 and 10 m), and 117,771 cells mL^− 1 in intermediate and deep waters (from 100 to 400 m) (Supplementary Fig. 9). The abundance of microbial eukaryotes decreased with depth, with a mean of 2738 cells mL^− 1 in surface waters, 490 cells mL^− 1 in intermediate waters, and nearly complete absence in deep waters (the method only detects phototrophs < 6 µm). Prokaryotes and eukaryotes presented similar distributions along the Gerlache-Bismarck Strait (Spearman correlation, R = 0.51; R² = 0.26; p-value < 0.001, Supplementary Fig. 2). However, local differences were noticeable between both groups, for example: although both were highly abundant in the Bismarck Strait, prokaryotic abundances peaked in Wilhelmina Bay, whereas eukaryotic abundances peaked in Andvord Bay (Supplementary Figs. 10, 11).

Microbial diversity (richness)

We observed high heterogeneity and contrasting values of richness among stations, depths, and size fractions (Supplementary Fig. 12). The highest microbial diversity values appeared in surface waters close to the Bismarck Strait (station 23), whereas the lowest values were found at the northernmost stations (stations 31 and 34). There was an increase in diversity close to the Schollaert Channel (stations 3, 6, and 11), which decreased towards the southern stations. Regarding the size fractions (Supplementary Fig. 13), we observed that, in general, diversity increased with size fraction at 0 m, decreased with size fraction at 10 and 100 m, while in intermediate waters, the size fraction 3–20 µm had the lowest diversity.

Microbial composition

In total, we obtained 13,148 ASVs from prokaryotes, comprising 13,055 bacterial and 93 archaeal ASVs, as well as 399 ASVs from eukaryotes, including 182 fungi. In terms of number of sequences, bacteria dominated community composition at all stations, fractions, and depths (98.32% of sequences), followed by eukaryotes (1.47%) and archaea (0.21%). Fungi (included within eukaryotes) corresponded to 0.95% of total sequences. The most dominant phylum was Bacteroidetes, followed by Proteobacteria (composed mainly by Alpha- and Gamma- proteobacteria), and Planctomycetota. The most abundant phylum of eukaryotes was Fungi, followed by Cryptophyta, Pseudofungi and Haptophyta, and the most abundant phylum of archaea was Crenarchaeota, followed by Nanoarchaeota and Thermoplasmatota. Certain groups were highly abundant in surface waters, such as Bacteroidota, and some became more dominant with depth, such as Acidobacteria and Planctomycetota. We also observed differences among size fractions; some microorganisms appeared more frequently in the smallest size fractions, such as specific groups of Alphaproteobacteria, whereas others were more common in the medium and large sized fractions, such as Bacillariophyta (Ochrophyta), Dinoflagellata, and Apicomplexa. Other microorganisms, such as Basidiomycota (phylum Fungi), appeared at a similar frequency across all three size fractions. In general, the microbial composition at the highest taxonomic levels varied among stations, water masses, depths, and size fractions (Supplementary Figs. 14–19). Those differences were tested, and we observed an overlap in the effect of depth and water masses on microbial communities: a PERMANOVA test revealed that, when the factors station, fraction, water mass and depth were tested together, the microbial communities were significantly different among stations (R² = 0.11939; p-value = 0.019), water masses (R² = 0.09958, p-value = 0.001), and size fractions (R² = 0.03881; p-value = 0.001), but not by depth (R² = 01906, p-value = 0.187).

Influence of the environment on microbial communities

The different zones (Gerlache Strait, fjords, and Bismarck Strait) were not relevant for explaining the variability in the microbial communities (PERMANOVA: R² = 0.02196, p-value = 0.171). However, microbial communities were different when comparing each side of the fronts (Surface Thermal Front, PERMANOVA: R² = 0.03367, p-value = 0.011; Sub-Pycnocline Front, PERMANOVA: R² = 0.03047, p-value = 0.005). The subset of environmental variables that better explained the variability in community composition (selected with the Bioenv procedure) were: temperature, oxygen, ISM, %OSM, nitrate, in situ pH, and eukaryotic abundance (Fig. 4A, Supplementary Fig. 2). Different combinations of these variables were relevant for each size fraction (Supplementary Fig. 20–21). The water masses that best explained the variability in community composition included AASW, GMW, TBW, TWW, and CDW (none were discarded with the Bioenv procedure) (Fig. 4B), even when the different size fractions were considered separately (Supplementary Fig. 22). Variability in microbial communities was better explained by the mixture of water masses (the variability explained by dbRDA ordination was 70%) than by the environmental variables (the variability explained by dbRDA ordination was 61.97%) (Fig. 4), revealing that water mass legacy had a greater influence on microbial communities than local environmental properties. A permutational ANOVA test confirmed the significant effect of the water masses on the structure of microbial communities (p-value < 0.05).

Altogether, some generalities can be described regarding the influence of the environment on microbial communities: above 100 m, microbes were linked to surface water masses (such as AASW, GMW, and TBW), and were influenced by high temperature, oxygen, abundance of eukaryotes, pH, and organic suspended matter. In contrast, below 100 m, microbial communities were linked to intermediate and deep-water masses (TWW and CDW) and were influenced by higher nitrate concentrations (Fig. 4).

Microbial fingerprinting of the water masses

Although each water mass harbored a specific community, the phylums Bacteroidota, Proteobacteria (mainly classes Alpha- and Gamma- proteobacteria), and Planctomycetota were dominant in all water masses (Fig. 5, Supplementary Fig. 23, 24, 25). Each water mass contained a specific list of tracer microbes (Fig. 6, and Supplementary Table 3). Tracer microbes were diverse, but each water mass harbored specific and exclusive phylum. The water mass with the highest number of tracer microbes was CDW (N = 79), followed by AASW (N = 29), suggesting that these water masses harbor multiple and very specific niches that are not present in the other water masses.

Each water mass was composed of a different core microbiome (Supplementary Table 4). The core microbiome of the different water masses was generally dominated by abundant groups, such as Bacteroidota and Proteobacteria, with the genus Methylobacterium being part of the core in all water masses. In surface water masses, the core microbiome was dominated by Proteobacteria, and the genera Polaribacter and Sulfitobacter (both Proteobacteria) were present in nearly all surface water masses (AASW, GMW, and TBW). The core microbiome of intermediate and deep waters was dominated by Proteobacteria, with the order Burkholderiales present in all deep-water masses (TWW, CDW). Microbial co-occurrence networks (Fig. 7) were composed principally of abundant taxa, such as Alpha- and Gammaproteobacteria, Bacteroidota, and Acidobacteriota (Fig. 5). Each water mass harbored a distinctive and, in general, highly cohesive microbial network (Fig. 7, Supplementary Table 5), where potential interactions within each water mass were higher than those among water masses (modularity values for each individual water mass were lower than those calculated for all samples, except for TWW). Co-occurrence network parameters (Supplementary Table 5) revealed that AASW and GMW had higher values of average degree, clustering, and density, and lower values of modularity and average distances than CDW, TBW and TWW water masses. The water mass with a less cohesive community was TWW. Together, these results indicate that microbial communities in AASW and GMW were more complex and interconnected than those in CDW, TBW and TWW, and that AASW and GMW may harbor more coupled and stable communities than CDW, TBW and TWW. In particular, the microbial fingerprint of each water mass can be summarized as:

AASW was dominated by the phyla Proteobacteria (mainly Alpha-) and Bacteroidota, and where Planctomycetota were highly abundant in larger size-fractions. The most abundant eukaryotic phylums were Fungi and Apicomplexa. The core microbiome of AASW consisted of 3 ASVs, which belong to the Classes Alphaproteobacteria (Rhizobial Methylobacterium and Rhodobacteral Sulfitobacter) and Gammaproteobacteria (Pseudomonadales), which together occupied a relevant part (15.72% of sequences) of the community. The tracer microbes of AASW included 28 ASVs, two of which belonged to higher-level taxa (phylums) not founded in other water masses: the eukaryotic phylum Cercozoa (Cryothecomonas aestivalis, Order Cryomonadida) and the prokaryotic phylum Fusobacteriota (Genus Fusobacterium, Order Fusobacteriales).

TBW was dominated by the phyla Bacteroidota and Proteobacteria (mainly Alpha-). The most abundant eukaryotic phylums were Cryptophyta and Fungi. The core of the TBW consisted of 11 ASVs, all of them Bacteroidota or Proteobacteria, which accounted for a substantial percentage (26.16% of sequences) of the community. The tracer microbes of TBW included 14 ASVs, one of which belonged to higher-level taxa (phylums) not found in other water masses: the candidate phylum Marinimicrobia (SAR406) and the phylum Cryptophyta (Geminigera cryophila, Order Cryptomonadales).

GMW was dominated by Bacteroidota, and Proteobacteria (mainly Alpha-and Gamma-), where Gammaproteobacteria were more dominant in the smaller size-fractions. The most abundant eukaryotic phylums were Haptophyta and Fungi. The core of the GMW consists of 6 ASVs, including multiple phyla (Bacteroidota, Firmicutes, Proteobacteria, Verrucomicrobiota, Actinobacteriota, and Acidobacteriota), accounting for a considerable percentage (26.03% of sequences) of the community. The tracer microbes of GMW included a total of 13 ASVs, two of which belong to higher-level taxa (phylums) not found in other water masses: the phylum Haptophyta (Chrysochromulina simplex, Order Prymnesiales) and the phylum Actinobacteriota (Mycobacterium sp., Order Corynebacteriales).

TWW water mass was dominated by the phyla Proteobacteria (mainly Alpha-), Bacteroidota, Planctomycetota, and Acidobacteriota, with the most abundant phylums of eukaryotes being Dinoflagellata and Fungi. The core microbiome consisted of 4 ASVs, all of which belong to the phylum Proteobacteria and cover 17.48% of the sequences. The tracer microbes of TWW water masses included a total of 3 ASVs, which all correspond to the phylum Proteobacteria and to the families Nitrosomonadaceae, Rhodanobacteraceae, and Oxalobacteraceae.

CDW water mass was dominated by the phyla Proteobacteria (mainly Alpha-) and Acidobacteria, with the most abundant eukaryotic phylum being Fungi. The core of the CDW consisted of 6 ASVs, which correspond to the phyla Proteobacteria and Firmicutes, accounting for an abundant part (20.46% of sequences) of the microbial community. The tracer microorganisms of CDW included 77 ASVs, all of which were prokaryotes, and included four phyla not found in the other water masses: two phyla of bacteria (Chloroflexi, Methylomirabilota), and two of archaea (Thermoplasmatota, Crenarchaeota).

Here, we analyzed the community composition and distribution of microorganisms (prokaryotes and eukaryotes) present in the Gerlache-Bismarck Strait during summer. The Gerlache-Bismark Strait is dominated by multiple surface and intermediate-deep marine water masses and currents with very different physical and chemical characteristics. We observed that these are key in shaping the diversity and assembly of microbial communities, revealing that each water mass has a specific microbial fingerprint.

The oceanographic context

Water mass distribution and variability in the Gerlache-Bismarck Strait regulate several processes, including those affecting phytoplankton communities and food webs (Kerr et al., 2018b; Mendes et al., 2018), carbonate system and ocean acidification (da Cunha et al., 2018; Kerr et al., 2018a; Lencina-Avila et al., 2018), and are key to understanding shelf-ocean interactions and the rates of tidewater glacier melting (Moffat et al., 2018).

Surface and intermediate water masses in the Gerlache-Bismarck Strait are separated by the summer pycnocline, which is located at a depth of ~ 100 m (Parra et al., 2020). In this study, we observed that in surface waters (0 and 10 m), AASW and TBW dominated along the strait, which is consistent with previous studies (García et al., 2002; Sangrà et al., 2011; Parra et al., 2020). The Surface Water Thermal front in the Gerlache-Bismarck Strait separates warmer surface waters in the northern part from the colder surface waters in the south and is regularly found close to the Schollaert Channel (front described in Parra et al., 2020). However, we detected this front northward near the entrance of the northern Gerlache Strait, indicating that the position of this front may vary interannually. We also detected the presence of GMW waters, extending from the fjords towards the center of the Gerlache Strait and in the Bismarck Strait. GMW originates from iceberg calving, glacial melt, and the basal melting of ice shelves (Dierssen et al., 2001; Cape et al., 2019, Osińska and Herman, 2024), mainly during summer months (Khim et al., 1997). Future increases in GMW are expected due to ongoing loss of ice shelves accelerated by climate change, a process observed over recent decades (Rignot et al., 2011). This influx, which is also composed of meteoric water, may enhance primary productivity in coastal regions by supplying organic matter and essential elements, such as iron (Fe) and manganese (Mn) (Gerringa et al., 2012; Krause et al., 2021; Forsch et al., 2021; Krause et al., 2024).

In contrast, in the intermediate and deep waters (from 100 to 400 m), we identified TWW and mCDW water masses. TWW waters enter from the northern part of Gerlache-Bismark Strait (e.g., Garcia et al., 2002; Sangrà et al., 2011), and mCDW water masses enter mainly from the southern part of the Gerlache-Bismark Strait (e.g., Smith et al., 1999; Parra et al., 2020). mCDW dominates the southern part of the Gerlache-Bismarck Strait (coinciding, e.g., with Parra et al., 2020), and its presence decreases northeastward and almost disappears in the middle part of the Gerlache Strait. In contrast, TWW dominates deep waters at the northern stations and decreases its influence on the Bismarck Strait. The deep waters of the Gerlache Strait are separated by the Sub-Pycnoclyne Front (described in Parra et al., 2020), whose position varies interannually from more northern to more southern locations (Parra et al., 2020). In our case, as the mCDW practically disappeared in the middle-south part of the Gerlache-Bismarck Strait, we located the Sub-Pycnoclyne Front in this area near the Schollaert Channel.

In the WAP, water masses and their physical properties are linked to specific biogeochemical characteristics (Bowman et al., 2018). In particular, in the Gerlache-Bismarck Strait, nutrient concentrations can delimitate deep-water masses (García et al., 2002). Silicate, nitrate, and phosphate were abundant in the southern part of the Gerlache Strait because of the inflow of the CDW (Garcia et al., 2002). Similarly, we found high levels of silicate and nitrate linked to CDW in the southern part of the strait, and the high values recorded in February 2020 (silicate > 80 mmol kg^− 1; nitrate > 25 mmol kg^− 1) coincided with previous records in the same area by Garcia et al., (2002) and Castro et al., (2002) during December and January 1995–1996. Chlorophyll in the surface waters of the Gerlache Strait has been described as being high in particular, varying, locations, such as the northern part (Varela et al., 2002; Mendes et al., 2018; da Cunha et al., 2018), middle part (Burkholder et al., 1961), or in the southern part (Varela et al., 2002). We observed high values linked to different entrances to the Gerlache-Bismarck Strait, and the highest values in the south. These contrasting results may be linked to temporal changes (intra-annual, inter-annual, or long-term changes) in phytoplankton dynamics (Smith et al., 1996; Montes-Hugo et al., 2009; Schofield et al., 2017; Ferreira et al., 2020) and physical properties (Su et al., 2022) that occur in the WAP and the Gerlache-Bismark Strait. The spatial distribution of phytoplankton groups in the Gerlache-Bismark Strait and the links between phytoplankton and the environment will be discussed below. The average chlorophyll concentration in the surface waters of the strait was 0.81 (mg m^− 3), with maxima values up to 2.5 mg m^− 3. These values were comparable to those recorded by Lorenzo et al. (2002) in the same area during the same season (December 1995 and early January 1996) but lower than those recorded by Burkholder et al. (1961) (December 1958 and January 1959) and Holm-Hansen and Mitchell (1991) (from December 1986 to March 1987), reaching 25 mg Chl a m^− 3 in the northern part of the Gerlache Strait. The potential for phytoplankton biomass in the waters of the Southern Ocean is in the range of 25–50 mg Chl a m^− 3 (discussed in Holm-Hansen et al., 1989), due to high macronutrient availability. As the values we obtained were lower, the biomass obtained in our stations may have been constrained by another limiting factor, potentially light, as both macronutrients and micronutrients were found in high concentrations.

Distribution of eukaryotes in the Gerlache-Bismark Strait.

Eukaryotic organisms in the Gerlache-Bismarck Strait have primarily been studied in summer and by microscopy, focusing mainly on phytoplankton (e.g., Burkholder et al., 1961; Rodríguez et al., 2002a, 2002b; Varela et al., 2002; Mendes et al., 2013; Mascioni et al., 2019; Pan et al., 2020; Mascioni et al., 2023). Additional studies have included nanoflagellates (Vaqué et al., 2002) and planktonic protists (Calbet et al., 2005). These studies indicate that the abundance of eukaryotes decrease with depth, coinciding with in our results. The same studies revealed that phytoplankton in the strait exhibit a basic assemblage (composed of flagellates, cryptophytes, dinoflagellates, prasinophytes, and haptophytes), particular blooms (which may be comprised of diatoms, cryptophytes, prasinophytes, or dinoflagellates), and north-south distributions (where cryptophytes dominate in the north and large diatoms in the south). However, due to global warming, diatoms seem to be decreasing in abundance, while cryptophytes seem to be increasing and moving southward, thus altering the efficiency of carbon sequestration and food webs (Arnaldo et al., 2018; Ferreira et al., 2020; Mendes et al., 2023). The success of cryptophytes over diatoms is attributed to their capacity to survive in warm, stratified and low-salinity environments with high radiation (Mendes et al., 2018, Mendes et al., 2023), conditions that are becoming more frequent in the WAP.

This is, as far as we know, the first study which incorporates high-throughput DNA sequencing to study microbial eukaryotes in the Gerlache-Bismarck Strait, allowing for increased detection and identification of the different species present in the strait, including those that cannot be easily detected under a microscope, such as the picoeukaryotes. In surface waters, we recorded a heterogeneous distribution of Bacillariophyta (diatoms) and Cryptophyta all along the strait, suggesting that cryptophytes may be expanding their presence in southern waters, reaching farther south than previously recorded, while diatoms may be experiencing a reduction in their favorable habitats as, at least in February 2020, Bacillariophyta were principally restricted to areas with glacial influence (e.g., Andvord and Flandres Bays).

Information on eukaryotes in the deep waters of the Gerlache-Bismarck Strait is still scarce, as most studies have focused on phytoplankton in surface waters. In this study, we observed that the diversity of eukaryotes decreased with depth, where diatoms and cryptophytes decreased their presence in deep waters, while other microorganisms, such as dinoflagellates and fungi, increased their presence. We found that dinoflagellates were more abundant at northern stations, coinciding with the results obtained in Vaqué et al. (2002). In contrast, fungi were especially abundant in the southern stations. Despite limited information on marine fungi in polar systems, previous studies have reported high fungal abundance in Arctic marine waters (Comeau et al., 2016), which aligns with our observations in Antarctica. We also observed that fungi were highly diverse, consistent with previous studies on cultivable Antarctic fungi (Gonçalves et al., 2017). In Antarctica, Fungi have been detected in deep waters, including meso- and bathypelagic waters (López-García et al., 2001; Zoccarato et al., 2016). The higher dominance of fungi in deep waters that we observed in the southern part of the Gerlache-Bismarck Strait may be linked to processes of decomposition that dominates in marine deep waters, and to the “fungal shunt” (Klawonn et al., 2021).

Distribution of prokaryotes in the Gerlache-Bismark Strait

We observed that prokaryotic abundances decreased with depth, coinciding with previous measurements in the Gerlache Strait (Pedrós-Alio et al., 2002; Vaqué et al., 2002), and in other Antarctic marine waters (Delille et al., 1996; Church et al., 2003; Davidson et al., 2004; Richert et al., 2015). To the best of our knowledge, this is the first comprehensive description of bacterial diversity in the Gerlache-Bismarck Strait. Our results revealed that, during summer, surface waters (0 and 10 m) were dominated by the phylums Proteobacteria (mainly Alpha- and Gamma-), Bacteroidota (mainly Flavobacteria), and Planctomycetota, whereas intermediate and deep waters (100–400 m) were dominated by Proteobacteria (mainly Alphaproteobacteria) and Acidobacteriota. Similar groups were found to be dominant in waters close to the Gerlache Strait (in the Bransfield Strait and Bismarck Strait) (Luria et al., 2014; Signori et al., 2014; Luria et al., 2016; Signori et al., 2018) and in open waters from the Southern Ocean (reviewed in Murray and Grzymski, 2007; Alonso-Sáez et al., 2011; Wilkins et al., 2013c; Cavicchioli, 2015; Milici et al., 2017; Cao et al., 2020; Castillo et al., 2022; Doytchinov et al., 2022).

In our samples, we observed a higher presence of Planctomycetota than in previously mentioned studies. Planctomycetes are more dominant in larger size fractions (Mestre et al., 2017). As in this study we incorporated three size fractions ranging from 0.2 to 200 µm, our approach contributed to the detection of this group compared to previous studies in Antarctica, which in general did not consider different size fractions. Among archaea, we found that the most abundant group was Crenarchaeota, coinciding with previous works in the same area (Massana et al., 1998; Murray et al., 1998). In our study, Crenarchaeota were primarily found below 100 m and were mainly associated with the CDW water mass, as was observed in previous studies (Massana et al., 1998; Alonso-Sáez et al., 2011).

Links between microbial communities and the environmental variables.

Multiple studies have shown that marine microbial communities in Antarctica are structured by location, depth, and physical and biogeochemical variables (Wilkins et al., 2013c; Cavicchioli et al., 2015; Obryk et al., 2016; Cao et al., 2020; Liu et al., 2021; Doytchinov et al., 2022; Castillo et al., 2022; Dutta et al., 2023). In particular, among the physical and biogeochemical variables explored (see e.g., Piquet et al., 2011; Signori et al., 2014; Luria et al., 2014; Torstensson et al., 2015; Yu et al., 2015; Moreno-Pino et al., 2016; Luria et al., 2016; Liu et al., 2024), temperature is a major driver of Southern Ocean microbial communities (reviewed in Wilkins et al., 2013c; Cavicchioli et al., 2015). In this study, we observed that, in line with previous studies, temperature is a major variable determining microbial communities. Yet, we also observed that other variables, such as oxygen, pH, and nitrate, also influenced microbial communities in the Gerlache-Bismark Strait. The same variables were relevant for microbial communities in the nearby area, the Bransfield Strait (Signori et al., 2014; Gonçalves-Araujo et al., 2015; Trefault et al., 2021; Mukhanov et al., 2022), where nitrate was key in determining microbial communities below the photic layers, as we observed in the Gerlache-Bismarck Strait.

Eukaryotes and prokaryotes respond differently to environmental factors (Moreno-Pino et al., 2016). An example of this are the different microbes found in two contrasting bays in the Gerlache Strait: Wilhelmina and Andvord Bays. In Wilhelmina Bay, more than half of the glaciers are in retreat and phytoplankton are recurrently dominated by cryptophytes (Mascioni et al., 2023). In contrast, in Andvord Bay, none of the glaciers are currently retreating (reviewed in Mascioni et al., 2023) and diatoms tend to dominate (Mascioni et al., 2021). We observed that, in Wilhelmina, prokaryotes were highly abundant, and eukaryotes had low abundances. Low abundance of eukaryotes may be explained by the high levels of inorganic suspended matter (i.e., sediments) in this area (Rodrigo et al., 2016) which can attenuate light availability, thereby limiting the growth of eukaryotic phototrophs (Schloss et al., 2002). In turn, the higher temperatures that we have recorded in Wilhelmina (> 3ºC) would enhance the dominant role of microorganisms in the carbon cycle, as has been previously theorized (Sarmento et al., 2010). Contrarily, in Andvord Bay, eukaryotes were highly abundant, and prokaryotes had low abundances. We observe the presence of diatoms (Bacillariophyta), similarly to what was observed in previous surveys in the same bay (Pan et al., 2019; Pan et al., 2020; Mascioni et al., 2021; Mascioni et al., 2023).

Beside environmental factors, in this study we tested the effect of water masses in structuring Gerlache Strait marine communities. Despite the fact that environmental variables measured in this study explained a large part of the distribution of microbes in the Gerlache-Bismarck Strait, water masses were a more prominent driver of the distribution and assembly of microbial communities. Water masses (as explanatory variables) encompasses not only the effect of local biogeochemical and physical parameters on selecting microbial communities, but also the legacy effects of water masses, including their role as vectors for microbial dispersion (Vass and Langenheder 2017). Dispersion and selection are two relevant process shaping microbial communities (Vellend 2010), and oceans are dynamic fluids with constant motion where water masses, together with fronts and currents, determine their structure and function. Altogether, our results indicates that microbial communities may be shaped not only by local environmental conditions but also by shifts occurring during their transport through water masses, as has been predicted by the Microbial Conveyor Belt (Mestre and Höfer 2021).

The Microbial fingerprinting of the water masses from the Gerlache Strait

In each of the five water masses, we identified a characteristic microbial community (the microbial fingerprint) defined by a particular community assembly, a characteristic network configuration, specific dominant taxa, exclusive microbes (i.e., tracer microbes), and a characteristic core microbiome. The microbial fingerprint of each water mass is detailed in the Results section. Microbial fingerprints of water masses have been characterized from samples collected during the summer season in a specific area, and their spatial variability might be explored further in future studies. Among tracer microbes, eukaryotes may be more relevant for defining the microbial fingerprint, as many (generally phytoplankton) are cultivable (so there is more information available about them) and are more sensitive to dispersal limitation and environmental selection than prokaryotes (i.e., they disperse less and are more influenced by the characteristics of the environment, and thus the water mass, than prokaryotes), as has been described in Antarctica (Moreno-Pinto et al., 2016) and elsewhere (Logares et al., 2020; Junger et al., 2023).

AASW waters, found in surface waters in the central part of the Gerlache-Bismarck Strait, include the eukaryotic phylum Cercozoa (Cryothecomonas aestivalis, Order Cryomonadida) as a tracer microbe. This nanoflagellate, which feeds on diatoms (Drebes et al., 1996), exhibits a bipolar distribution. Yet, information about this species in Antarctica is scarce. C. aestivalis has been detected in the Antarctic Peninsula (Piquet et al., 2011; Bachy et al., 2011; Luo et al., 2016) and is abundant in the Bransfield Strait (Luo et al., 2016), but there are no records of its presence in the Gerlache Strait, likely due to biases in the microscopy-based methods applied in prior surveys. No previous studies have linked this species to AASW waters, but this specie has been associated with sea ice and glacial melt water mixing in seawater from both polar regions (Piquet et al., 2011; Bachy et al., 2011), which explains its presence in AASW waters.

In TBW waters, situated in surface waters throughout the Gerlache-Bismarck Strait, the Cryptophyta species Geminigera cryophila is a tracer microbe. G. cryophila is a fully described cryptophyte species isolated from Antarctic waters and probably has an Antarctic circumpolar distribution (van den Hoff et al., 2020). G. cryophila is a mixotrophic nanoflagellate, and molecular analysis revealed that this species, in particular, dominates over other Cryptophyta species, at least during summer in the Bransfield Strait (Luo et al., 2016) and along the West Antarctic Peninsula (Brown et al., 2021). Cryptophyta have been reported to be highly abundant in TBW waters from the Bransfield Strait (Mukhanov et al., 2022), which is consistent with our observations. Members of the phylum Cryptophyta have increasing abundances in the WAP due to climate change (Mendes et al. 2023).

GMW is associated with melt water coming from glaciers and we found this water mass throughout the Gerlache Strait, with the Haptophyta Chrysochromulina simplex as a tracer microbe. Haptophytes are part of the basic assemblage of phytoplankton in the Gerlache Strait, as described by Mascioni et al., (2023). The haptophyta Chrysochromulina simplex is a nanoplankton phototrophic species, and some studies have suggested that it might be a mixotroph (Birkhead and Pienaar, 1995). It has mainly been described in the Arctic and, in Antarctica, C. simplex has been found in coastal surface waters from the Bransfield Strait (Abele et al., 2017), and reaching high abundances (Luo et al., 2016). There is little information about the most favorable environments for C. simplex, but in the Arctic, this species appears to be tolerant to a wide range of temperature and salinity conditions (Egge et al., 2015). This feature probably contributes to the adaptation of this species to the meltwater pulses that constitute the GMW water mass.

In TWW waters, found between 100 m and 400 m, tracer microbes correspond to the phylum Proteobacteria and to the families Nitrosomonadaceae, Rhodanobacteraceae, and Oxalobacteraceae. Information about these families in the marine waters of Antarctica is sparse, but it is known, for example, that Rhodanobacteraceae are found abundantly in terrestrial environments from the Bransfield Strait in the soil, penguin colonies, and volcanoes (Bendia et al., 2018; Ramírez-Fernández et al., 2019; Cui et al., 2023). Rhodanobacteraceae may have been transferred from land to sea, becoming tracers of the passage of TWW through the Bransfield Strait.

The CDW water mass, situated below 100 m and extending from Bismarck towards Gerlache Strait includes archaea as tracer microbes. In particular, Crenarchaeota from the class Nitrososphaeria and genera Nitrosopumilaceae (also known as Thaumarchaeota or Marine Group I archaea). Nitrosopumilaceae are aerobic marine ammonia-oxidizing archaea (Qin et al., 2017) that have been linked to seawater with high levels of nitrite (reviewed in DeLong, 2021). In Antarctica, Nitrosopumilaceae has been found to be abundant in the deep waters of the Antarctic Peninsula (Signori et al., 2014), including the Gerlache Strait (Massana et al., 1998). CDW water masses from Antarctica are enriched in nitrate (Garcia et al., 2002) and harbor highly diverse archaeal communities (Alonso-Sáez et al., 2011). We observed that Nitrosopumila species were characteristic of CDW waters, where they most likely play a key role in the remineralization of organic matter.

The relevance of the microbial fingerprint of water masses.

Links between microorganisms and water masses have been evident for decades: early studies proposed microbes as indicators of hydrographic phenomena by measuring variations in microbial abundances through colony-forming unit counts (Kriss, 1959a). The method contributed to mapping the circulation and distribution of water masses in the Indian, Pacific, and Atlantic Oceans (Kriss et al., 1959b, 1959c, 1960). Contemporary studies, boosted by advances in molecular technology and high-throughput sequencing, have shown that microbial diversity is determined by oceanic structures, such as water masses and fronts, in the Arctic (Lovejoy et al., 2002; Hamilton et al., 2008; Monier et al., 2013; Monier et al., 2015) and elsewhere (Agogué et al., 2011; Djurhuus et al., 2017). These structures directly influence processes such as dispersion and selection, and thus, the biogeography of microorganisms and their legacy (Martiny et al., 2006; Vellend 2010; Vass and Langenheder 2017; Mestre and Höfer, 2021).

In Antarctica, multiple studies have linked microbial communities to specific water masses (e.g., Alonso-Sáez et al., 2011; Richert et al., 2015; Zoccarato et al., 2016; Sow et al., 2022, Zhang et al., 2024), investigated their transport through water masses advection (Wilkins et al., 2013b), and revealed that bacterial community composition is more strongly influenced by dispersal mechanisms through water masses than biogeochemical parameters (Hernando-Morales et al., 2017). In this study, we found that the most crucial factor determining the microbial community composition was the water mass to which the microorganisms belong, thus revealing the strong link between microbes and water masses and the role of water masses in transporting and shaping microbial communities over local conditions. To gain deeper insight into on the links between water masses and microbial diversity and distribution, we have defined the microbial fingerprint characteristics of each water mass. The fingerprint we describe corresponds to a specific season and area, and its extrapolation to other areas and seasons might be tested further. The fingerprint we described might be applied in multiple ways, for example, to unveil the microbial composition in other regions of the Southern Ocean, determine the ecosystem contribution of water masses (e.g., in biogeochemical cycles or food webs), and predict or anticipate changes in the microbial communities expected due to global change in current and future scenarios (Holland et al., 2022). In climatically sensitive environments, such as the Gerlache-Bismarck Strait, understanding microbial fingerprints may provide critical insights into the present and future functioning of the ecosystem. For example, Cryothecomonas aestivalis, a key species in surface water masses (AASW), feeds on diatoms and thus directly shapes the food web. Another example is the archaea Nitrosopumilaceae, found exclusively in deeper water masses (CDW), which plays a key role in nitrogen cycling and fertilization of the Gerlache-Bismarck Strait. Any changes in water masses affecting those species, such as those triggered by climate change (Cavicchioli et al., 2019), may drastically change Gerlache-Bismark Strait ecosystem dynamics.

The Gerlache-Bismark Strait is a productive area that is an important feeding ground for krill, penguins, and whales. The productivity of the Gerlache-Bismark Strait is linked to its hydrological heterogeneity, which includes multiple water masses, each with contrasting biogeochemical characteristics that define the distribution of primary producers and associated food webs. In this study, we observed that each water mass had a specific microbial community structure, which we named the “microbial fingerprint of water masses”. The microbial fingerprint of each water mass in the Gerlache-Bismark strait is inherently linked to the ecology of this area, contributing to the biogeochemistry and trophic web. Furthermore, the information that emerges from this fingerprint might be extrapolated to other regions of Antarctica that contain the same water masses. Regarding protective measures in the Gerlache Strait and other areas of Antarctica, it is essential to consider not only the macroscopic diversity but also microbes and the hydrography they are linked to, along with the changes in seawater hydrographic properties due to global change (such as rising temperatures, ocean acidification, freshening as well as changes in currents and fronts), as these will directly affect microbial communities and, in turn, the entire ecosystem functioning.

ACKNOWLEDGEMENTS:

We would like to thank the Karpuj R/V crew, especially the captain and first officer, for their interest and involvement in the expedition. We also thank INACH (Instituto Antártico Chileno, Chile) for their logistical support during the Chilean Antarctic Expedition 56 (ECA56).

FUNDING INFORMATION:

This work was funded by the projects: INACH RG_49-19 (Chile), FONDAP IDEAL 15150003 (Chile), COPAS Coastal ANID FB210021 (Chile), FONDECYT regular 1211338 (Chile). MM was supported by FONDECYT-Postdoctorado 3190369 (Chile) and by a “Ramón y Cajal” fellowship (RYC2021-031946-I) funded by MCIN/AEI/10.13039/501100011033 and the European Union «NextGenerationEU»/PRTR. AP was funded by a JAE-Intro CSIC Fellowship (JAEINT23_EX_0329), and by a FPU fellowship (FPU23/03480), Spain. DR was funded by a JAE-Intro CSIC Fellowship (JAEINT_23_00500), Spain. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 872690” (Project CoastCarb).

COMPETING INTERESTS:

The authors have declared that no competing interests exist.

SUPPLEMENTAL MATERIAL

All supplementary material is included.

DATA AVAILABILITY STATEMENT

The DNA sequencing data, which includes 16S and 18S amplicon sequences, is available in the NCBI repository (https://www.ncbi.nlm.nih.gov/). The data can be accessed using the following accession ID: PRJNAXXXXXXX.

Environmental and oceanographic data have been deposited in the PANGAEA repository. The data is accessible via the DOI: https://doi.org/10.1594/PANGAEA.XXXXXX.

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The authors declare no competing interests.

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Microbial Fingerprinting of Marine Water Masses in an Antarctic and Hydrographically Complex Area

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Abstract

Figures

Introduction

Materials and methods

Results

The oceanographic environment

Microbial abundances

Microbial diversity (richness)

Microbial composition

Influence of the environment on microbial communities

Microbial fingerprinting of the water masses

Discussion

The oceanographic context

Distribution of prokaryotes in the Gerlache-Bismark Strait

The Microbial fingerprinting of the water masses from the Gerlache Strait

Conclusions

Declarations

References

Additional Declarations

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