Spatiotemporal Dynamics of Marine Bacterial and Archaeal Communities in Surface Waters Off the Northern Antarctic Peninsula

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Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula

Camila N. Signori, Vivian H. Pellizari, Alex Enrich Prast and Stefan M. Sievert

The self-archived postprint version of this journal article is available at Linköping University Institutional Repository (DiVA): http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149885

N.B.: When citing this work, cite the original publication. Signori, C. N., Pellizari, V. H., Enrich Prast, A., Sievert, S. M., (2018), Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula, Deep-sea research. Part II, Topical studies in oceanography, 149, 150-160. https://doi.org/10.1016/j.dsr2.2017.12.017

Original publication available at: https://doi.org/10.1016/j.dsr2.2017.12.017 Copyright: Elsevier http://www.elsevier.com/

Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula

Camila N. Signori1*, Vivian H. Pellizari1, Alex Enrich-Prast2,3, Stefan M. Sievert4*

1 Departamento de Oceanografia Biológica, Instituto Oceanográfico, Universidade de São Paulo (USP). Praça do Oceanográfico, 191. CEP: 05508-900 São Paulo, SP, Brazil. 2 Department of Thematic Studies - Environmental Change, Linköping University. 581 83 Linköping, Sweden 3 Departamento de Botânica, Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ). Av. Carlos Chagas Filho, 373. CEP: 21941-902. Rio de Janeiro, Brazil 4 Biology Department, Woods Hole Oceanographic Institution (WHOI). 266 Woods Hole Road, Woods Hole, MA 02543, United States.

*Corresponding authors: Camila Negrão Signori Address: Departamento de Oceanografia Biológica, Instituto Oceanográfico, Universidade de São Paulo, São Paulo, Brazil. Praça do Oceanográfico, 191. CEP: 05508-900 São Paulo, SP, Brazil. Email: [email protected] Phone number: +55 11 3091-6503

Stefan M. Sievert Address: Biology Department, Woods Hole Oceanographic Institution (WHOI). 266 Woods Hole Road, Woods Hole, MA 02543, United States. Email: [email protected] Phone number: +1 (508) 289-2305

Submitted to: Deep-Sea Research II – Special Issue: Oceanographic processes and biological responses around northern Antarctic Peninsula (NAP): a 15-year contribution of the Brazilian High Latitudes Oceanographic Group

1 Highlights • Environmental conditions related to seasonal variation drive community composition. • Identification of a core microbiome across seasons. • Lack of spatial variability.

Abstract Seasonal changes in taxonomic and functional diversity of microbial communities in polar regions are commonly observed, requiring strategies of microbes to adapt to the corresponding changes in environmental conditions. These natural fluctuations form the backdrop for changes induced by anthropogenic impacts. The main goal of this study was to assess the seasonal and temporal changes in bacterial and archaeal diversity and community structure off the northern Antarctic Peninsula over several seasons (spring, summer, autumn) from 2013 to 2015. Ten monitoring stations were selected across the Gerlache and Bransfield Straits and nearby Elephant Island, and archaeal and bacterial communities examined by amplicon sequencing of 16S rRNA genes. Alpha-diversity indices were higher in spring and correlated significantly with temperature. Spring was characterized by the presence of SAR11, and microbial communities remaining from winter, including representatives of Thaumarchaeota (Nitrosopumilus), Euryarchaeota, members of Oceanospirillales, SAR324. Summer and autumn were characterized by a high prevalence of Flavobacteria (NS5 marine group and Polaribacter), Alphaproteobacteria (Rhodobacterales and SAR11 clade) and Gammaproteobacteria (Oceanospirillales/Balneatrix and Cellvibrionales), generally known to be associated with organic matter degradation. Relatively higher abundance of phytoplankton groups occurred in spring, mainly characterized by the presence of the haptophyte Phaeocystis and the diatom Corethron, influencing the succession of heterotrophic bacterial communities. Microbial diversity and community structure varied significantly over time, but not over space, i.e., were similar between monitoring stations for the same time. In addition, the observed interannual variability in microbial community structure could be attributed to an increase in sea surface temperature. Environmental conditions related to seasonal variation, including temperature and most likely phytoplankton derived organic matter, appear to have triggered the observed shifts in microbial communities in the waters off the northern Antarctic Peninsula.

Keywords: Microbial Oceanography, phytoplankton, interannual variability, seasonal changes, spatial changes, temperature, organic matter

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1. Introduction

The Southern Ocean is characterized by strong seasonality in environmental conditions, such as ice cover, mixed layer depths, light levels, and temperature, which have direct implications on microbial diversity and community structure (Smetacek and Nicol, 2005; Doney et al., 2012; Grzymski et al., 2012; Fuhrman et al., 2015 Bunse and Pinhassi, 2017).

Studies on the structure of microbial communities in the Southern Ocean have suggested that sampling with seasonal frequency, i.e. monthly and yearly, is fundamental to understand the taxonomic and functional diversity of microorganisms and their strategies to adapt to changing environmental conditions (e.g. Manganelli et al., 2009; Ducklow et al., 2012; Ghiglione & Murray, 2012; Cavicchioli, 2015; Luria et al., 2016; Schofield et al., 2017). In spring, short- lived phytoplankton blooms occur in shallow surface layers following the ice retreat, supplying organic carbon and nutrients to the food web and providing varied ecological niches for heterotrophic bacteria and archaea (Rousseau et al., 2000; Ducklow, 2003; Croft et al., 2005; Sher et al., 2011; Mendes et al., 2012; Delmont et al., 2014; Luria et al., 2016; Mendes et al., this issue). Consequently, the spring and summer communities are mainly composed of eukaryotic phototrophs and prokaryotic photoheterotrophs, chemoheterotrophs and aerobic anoxygenic phototrophs, whereas the winter community harbors relatively higher proportion of archaeal and bacterial chemolitoautotrophs (Ghiglione and Murray, 2012; Grzymski et al., 2012; Luria et al., 2016; Bunse and Pinhassi, 2017).

Surveys using temporal and spatial approaches are of equal importance in assessing the variation of marine microbial communities (e.g. Gilbert et al., 2012, Jones et al., 2012), although the use of temporal approaches may offer unique ecological information on community stability and its response to disturbances that cannot be obtained any other way (Faust et al., 2015; Fuhrman et al., 2015). Understanding seasonal shifts of microbial communities, as well as the parameters that influence their distribution, is essential to reveal the microbial response to perturbations that are predicted due to climate change. It is expected that polar regions will be – and already are – affected rapidly by climate change. The region off the northwestern Antarctic Peninsula is characterized by decreasing sea-ice extent and increasing sea surface temperatures, particularly during the summer, leading to changing wind

3 patterns and ocean circulation with impacts on the local, regional, and even global scale (Stammerjohn et al., 2008; Doney et al., 2012; Jones et al., 2016). These changes may also reflect the natural internal variability of the regional atmospheric circulation on the Antarctic Peninsula (Turner et al. 2016).

With the growing evidence of increasing sea surface temperatures in the northern and western Antarctic Peninsula (Vaughan et al., 2003; Meredith and King, 2005; Turner et al., 2005), it is urgent to understand the actual impact of physical changes on biological communities through temporal and spatial surveys to elucidate trends and relationships between environmental forcing and biological variables. It is expected that global warming will cause shifts in the cell size of plankton, spatial range and seasonal abundance of populations, as well as a stimulation of microbial activity and thus decreased food availability for organisms at higher trophic levels (Moline et al., 2004; Kirchman et al., 2009; Montes-Hugo et al., 2009; Schofield et al., 2010; Doney et al., 2012). However, predicting microbial responses to climate change represents a formidable challenge, as bacteria and archaea tend to be more resilient (e.g. faster response to environmental change) than larger organisms due to their fast growth rates, greater dispersal capability, high metabolic flexibility, and rapid evolution, not to mention their metabolic versatility and the fact that even very closely related taxa can differ in their function (Shade et al., 2012; Kashtan et al., 2014; Luria et al., 2014; Yawata et al., 2014; Martiny et al., 2017).

Seasonal sampling over multiple years has the potential to unveil the environmental conditions regulating taxonomic distribution, and how microbial interactions and metabolic capabilities help microbes to thrive in polar regions and respond to climate change (Bunse and Pinhassi, 2017). These natural fluctuations between seasons form the backdrop for changes induced by anthropogenic impacts, and taking advantage of the current time-series in the Bransfield and Gerlache Straits by the Brazilian research initiative, represent a unique opportunity to study the microbes in a changing ocean. Here, we used 16S rRNA gene based analysis to assess the seasonal and spatial changes in bacterial and archaeal diversity, and community structure in surface waters off the northern Antarctic Peninsula (NAP). Results from this study expand our current knowledge about the seasonal distribution of archaeal and bacterial communities in surface waters, reinforce the important role of phytoplankton structuring microbial communities, and emphasize the role of temperature in shaping the community structure in a rapidly changing NAP region.

4 2. Material and methods

2.1 The study area The NAP region comprises the Bransfield and Gerlache Straits (Fig.1). The circulation patterns in this area change seasonally, and the currents and eddies provide favorable physical conditions for plankton growth and dispersion. These straits are highly productive areas, all the way from phytoplankton and zooplankton to whales (Zhou et al., 2002), and exhibit a high export production of organic matter (Doval et al., 2002; Kim et al., 2005). The Bransfield Strait is a highly dynamic area, influenced both by the cold and salty waters from the Weddell Sea (called Transitional Zonal Water with Weddell Sea influence), and the relatively warm and fresh surface waters from the Bellingshausen Sea (called Transitional Zonal Water with Bellingshausen influence, TBW) (Tokarczyk, 1987; Garcia et al., 1994; Sangrà et al., 2011). Located south of Bransfield Strait, the Gerlache Strait is a relatively confined waterway that separates the Antarctic Peninsula from Brabant and Anvers Islands (see Kerr et al., in this issue). The main surface circulation pattern is driven by the Gerlache Strait Current, which flows northeastward along the strait and carries Gerlache waters into the Bransfield Strait (Zhou et al., 2002; Zhou et al., 2006).

2.2 Sampling strategy The research cruises were conducted with the Brazilian Navy polar vessel Almirante Maximiano (H41) in spring (November 2013, 2014), late austral summer (February 2014, 2015) and autumn (March 2014, 2015). Ten oceanographic stations were sampled (M1, M2, M3, M4, M5, M6, M7, M8, M9, M10) in the NAP region, comprising the Gerlache Strait, the Bransfield Strait and around Elephant Island (Fig. 1), to evaluate seasonal changes in the microbial community along an approximately 950 km long transect, which additionally allowed to assess the spatial variability of the microbial communities. Seawater and physical data (temperature and salinity) were collected at 5 m depth using a combined Sea-Bird CTD/Carrousel 911 system equipped with 24 5-l Niskin bottles. For microbial diversity, 3 l of seawater were filtered onto Sterivex filters (Millipore) with a pore size of 0.2 μm by using a peristaltic pump. Filters were immediately frozen onboard at -80oC until further analyses in the laboratory.

2.3 DNA extraction, 16S rRNA gene amplification and sequencing A protocol for low biomass samples developed by Boström et al. (2004) and Manganelli et al.

5 (2009), with slight modifications (Signori et al., 2014) was used for DNA extraction. The microbial diversity was assessed by amplicon sequencing of the V4 region of the 16S rRNA gene with the Illumina platform and the universal primers 515F-806R, targeting both Archaea and Bacteria (Caporaso et al., 2011), with overall coverage of 85% and 86%, respectively (SILVA TestPrime 1.0, http://www.arb-silva.de/search/testprime/, tested in March 2017, allowing for 1 mismatch with no mismatches in the last 4 bases of the 3’end). A single-step 30 cycles PCR using HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) was used on 5 ng of DNA under the following conditions: 94◦C for 3min, followed by 28 cycles of 94C for 30 s, 53C for 40s and 72C for 1min, after which a final elongation step at 72C for 5min was performed. Thereafter, all barcoded amplicon products from different samples were quantified using Qubit (Life Technologies), mixed in equal concentrations and purified using Ampure beads (Agencourt Bioscience Corporation, MA, USA). After these steps, samples were sequenced using Illumina MiSeq with 2x250 bp reads configuration, following the manufacturer’s guidelines. The PCR and sequencing were carried out at Molecular Research Lab (Shallowater, Texas, USA). All sequence data have been deposited in the National Center for Biotechnology Information Sequence Read Archives (SRA) under BioProject ID BioProject ID PRJNA383940.

2.4 Sequencing data and statistical analyses Raw sequencing reads were filtered for length (250-300 bp), quality score (mean, >50), maximum number of ambiguous bases=0, maximum homopolymer length=6 and minimum expected error of 1.0, using Quantitative Insights Into Microbial Ecology (QIIME) 1.8.0 pipeline (Caporaso et al., 2010). Chimeras were checked using uchime2 algorithm (Edgar, 2016). Sequences were clustered at 97% similarity using usearch and uclust pipelines (Edgar, 2010), and taxonomy was assigned to each Operational Taxonomic Unit (OTU) using SILVA database version 128 (Yilmaz et al., 2014). The OTU table was normalized with the cumulative sum scaling (CSS) method, which corrects the bias in the assessment of differential abundance introduced by total-sum normalization, using the metagenomeSeq Bioconductor package (Paulson et al., 2013). The CCS normalized reads were used for all downstream analyses. For each sample, alpha-diversity indices (number of observed OTUs, Chao1, Simpson and Shannon’s diversity) were calculated using QIIME 1.8.0 (Caporaso et al., 2010), and differences in alpha-diversity estimates between groups of samples were tested using Student`s t-test in R (version 3.3.2). The number of shared OTUs between samples were visualized using

6 ggplot2 package in R (Wickham, 2009). Beta-diversity between samples was visualized with non-metric multidimensional scaling (nMDS) based on weighted UniFrac distance (Lozupone and Knight, 2005), with fitting of the environmental gradients (temperature, salinity, season, year, longitude and latitude) applying the envfit function from the vegan R package (Oksanen et al., 2016). Differences in the relative abundances of taxa between groups of samples (spring vs. autumn, spring vs. summer, summer vs. autumn, spring vs. summer-autumn, within sampling periods, within monitoring stations and within sampling areas) were evaluated with a permutational multivariate analysis of variance (PERMANOVA, adonis function with 999 permutations, based on weighted UniFrac distance) using the vegan R package (Anderson, 2001; Oksanen et al. 2016). Mixed-effects models (lmer function) using lme4 R package (Bates et al., 2015) were performed to test and decouple the relations between alpha-diversity indices and the values of temperature and salinity over years. Thereafter, only the significant relations between alpha-diversity indices and temperature were retained for linear regressions. In addition, differences in temperature between groups of samples were tested using Student`s t- test in R (version 3.3.2).

3. Results

3.1. Thermohaline characteristics of Antarctic surface waters Seawater temperatures varied from -1.54 to 1.91C, and salinities ranged from 33.23 to 34.86 in the study area (Fig. 2). Significant differences (p<0.05) in thermohaline characteristics of surface waters were registered between austral spring, summer and autumn. Temperatures below zero were only found in spring, varying from -1.54 to -0.47C. In summer and autumn, temperatures were relatively higher and varied from 0.18 to 1.91C, and 0.35 to 0.95C, respectively. Salinities were more similar, ranging from 33.24 to 34.86 in spring, 33.62 to 34.38 in summer, and 33.23 to 33.89 in autumn.

3.2. Bacterial and archaeal community composition After quality checking and data filtering, a total of 4,432,018 sequences (range of 5,214- 109,883 reads per sample) were obtained from 73 surface water samples, and clustering of these reads at 97% similarity threshold resulted in 894 OTUs. Removing chloroplasts and mitochondria-related sequences, we found 2,927,955 sequences (range of 3,019-72,968 reads per sample) and 783 OTUs. Considering all sequences of bacteria and archaea retrieved in the

7 present study (Fig. 3a), sequences belonging to Bacteroidetes (37.9%) and Alphaproteobacteria (29.9%) accounted for the largest fraction, followed by Gammaproteobacteria (26.1%). Bacteroidetes was almost exclusively represented by the order Flavobacteriales (37.6%) and the taxa Polaribacter, Ulvibacter, Cryomorphaceae and NS9. Alphaproteobacteria was mainly represented by the orders Rhodobacterales (17.4%), SAR11 clade (9.9%) and SAR116 clade (0.9%), whereas Gammaproteobacteria was represented by Oceanospirillales (19.1%), Cellvibrionales (5.1%) and Alteromonadales (1.3%). The classes Betaproteobacteria (1.3%) and Deltaproteobacteria (0.3%) were also identified, and mainly represented by the order Methylophilales and SAR324 clade, respectively. The archaeal phylum Thaumarchaeota represented 2.4% of all sequences, and Euryarchaeota was also identified, although relatively less abundant (0.5%). In general terms, Thaumarchaeota/ Nitrosopumilus, Alphaproteobacteria/ SAR11 and Gammaproteobacteria/ Oceanospirillales represented a larger fraction of the microbial community in spring, with 6.2%, 14.5%, 26.3%, respectively (Fig. 3b). In summer and autumn, other taxa presented higher relative abundance: Flavobacteria/ Flavobacteriales (43.7% and 44.1%), Alphaproteobacteria/ Rhodobacterales (19.8% and 18.6%) and Gammaproteobacteria/ Cellvibrionales (5.9% and 6.3%) (Fig. 3b).

At various taxonomic levels for archaea and bacteria, samples from November 2013 and 2014, although with differences in relative abundance, were more enriched in the Thaumarchaeota/ Nitrosopumilus (6.7-5.7%), Euryarchaeota (1.6-0.8%), SAR11 clade (18.1-11.2%), Rhodobacterales (12.2-15.9%), Oceanospirillales (30.4-22.3%), Cellvibrionales (3.2-3.3%), Flavobacteria/Flavobacteriales (16.8-32.5%), and SAR324 (1.2-0.4%) (Fig. 4a and Supplementary Table S1). Samples from February 2014 and 2015 showed higher contribution of Flavobacteria/ Flavobacteriales (42.6-45.2%), Rhodobacterales (17.4-23%), SAR11 clade (7.8-6.7%), Oceanospirillales (18.6-16.3%) and Cellvibrionales (7.5-3.1%). Samples from March 2014 and 2015 presented higher relative contribution of Flavobacteria/Flavobacteriales (44.1-43.5%), followed by representatives of Rhodobacterales (19.8-16.1%) and SAR11 clade (8.2-7.3%), and gammaproteobacterial orders Oceanospirillales (13.6-11.8%), Cellvibrionales (6.3-6%) and Alteromonadales (0.4-5.4%).

3.3. Phytoplankton community composition based on 16S chloroplast sequences The use of the utilized primer pair also resulted in the amplification of 16S rRNA genes from chloroplasts, allowing an assessment of phytoplankton diversity (Fig. 4b). Spring samples were characterized by relatively higher abundance of the haptophyte Phaeocystis (35.2-

8 28.5%), the pennate diatom Corethron (29.9%-12.2%), and the dinoflagellate Dinophysis (9.2%-13.3%), totaling 74.23% and 53.93% of all chloroplast sequences in November 2013 and 2014, respectively (Fig. 4b). In summer, the phytoplankton community was dominated by the polar centric diatom Thalassiossira (39.5%-27.7%), the haptophyte Phaeocystis (21.0%- 27.7%), and the pennate diatom Fragilariopsis (17.0%-8.7%), making up 77.4% and 64.2% of all chloroplast sequences in February 2014 and 2015, respectively. In autumn, we identified higher relative abundance of Fragilariopsis (29.8%-5.9%), Thalassiossira (22.5%-34.5%) and Phaeocystis (17.6%-20.8%), totaling 69.8% and 61.2% of all chloroplast sequences in March 2014 and 2015, respectively.

3.4. Alpha-diversity estimates for Bacteria and Archaea The number of observed OTUs per sample varied from 150 to 395, both registered in November 2014 (Supplementary Table S2). Almost all the extreme values, maximum and minimum, for all indices were found in November 2014 and February 2015, respectively. Means of all alpha-diversity indices, except for Chao1, were significantly higher in samples originating from spring as compared to summer-autumn, with p-values equal to 0.01 (number of OTUs), 0.20 (Chao1), 0.0004 (Shannon), and 0.01 (Simpson).

After performing multiple-effects models, considering the relationships of alpha-diversity indices with both temperature and salinity over time, only temperature was found to be a significant parameter and therefore, was chosen for linear regression analysis (Supplementary Table S3). Although with low r2 values, the relations between temperature and different alpha- diversity indices were significant for the number of OTUs (r2=0.13, p=0.005), Shannon (r2=0.32, p<0.001), and Simpson (r2=0.19, p<0.001), suggesting higher richness and diversity in cooler compared to warmer surface waters (Fig. 5a-d).

3.5. Bacterial and archaeal community structure across space and time Beta-diversity was determined after analysis of the relative abundance of the different taxa in relation to the sampling locations and the thermohaline characteristics of the surface seawater by using nMDS (Fig. 6). The results showed a clear segregation between samples collected in spring versus summer and autumn, which was supported by PERMANOVA tests with significant differences between spring and summer-autumn (r2=0.34 and p=0.001), spring and autumn (r2=0.37 and p=0.001), spring and summer (r2=0.30 and p=0.001), and within sampling periods (month/year) (r2=0.51 and p=0.001). Also, significant differences were found in

9 microbial community structure between years, when we contrasted November 2013 vs. 2014 (r2=0.31 and p=0.001), February 2014 vs. 2015 (r2=0.19 and p=0.002), and March 2014 vs. 2015 (r2=0.14 and p=0.006). However, no significant differences were found among monitoring stations (r2=0.08, p=0.97), nor among sampling areas (r2=0.02, p=0.57) over all the expeditions (Supplementary Table S4). In addition, when plotting the environmental parameters, the influence of temperature (r2=0.64 and p=0.001) on the microbial community structure was found to be more important than salinity (r2=0.07 and p=0.15), and time of sampling (r2=0.64 and p=0.001 for all samples, r2=0.54 and p=0.001 for yearly comparisons, r2=0.55 and p=0.001 for seasonal comparisons) were more relevant than geographical location (r2=0.10 and p=0.08 for latitude, r2=0.07 and p=0.18 for longitude) (Supplementary Table S5).

A total of 96, 87 and 123 OTUs were present in all samples obtained in spring, summer and autumn, respectively (Fig. 7a). Spring and summer shared 70 OTUs, spring and autumn shared 80 OTUs, and summer and autumn shared 85 OTUs. The overall core microbiome of Antarctic surface waters consisted of 68 OTUs, belonging to Alphaproteobacteria (33.82%), Gammaproteobacteria (32.35%), Bacteroidetes (25%), Betaproteobacteria (5.88%), Deltaproteobacteria (1.47%), and Thaumarchaeota (1.47%) (Fig. 7b, Supplementary Table S6). Thus, even though the core microbiome of bacteria and archaea only consisted of 68 OTUs out of a total of 783, the main phylogenetic groups were still represented.

4. Discussion 4.1. Temporal dynamics Bacterioplankton communities are known to greatly change at different time scales (Fuhrman et al., 2006, 2015), although few studies have investigated the temporal dynamics of microbial communities in polar regions (e.g. Ducklow et al., 2012; Ghiglione & Murray, 2012; Grzymski et al., 2012; Bowman et al., 2016; Luria et al., 2016; Schofield et al. 2017), largely due to the logistical challenges that are inherent in studying these systems. In the present study, the alpha- diversity indices that varied most significantly over time were the community diversity indices including evenness (Shannon, Simpson), suggesting that the relative abundance of different taxonomic groups is driving the difference between season and not necessarily species richness itself. This is also supported by the absence of a significant correlation with Chao1, and is also consistent with the fact that the core microbiome across all seasons contains representatives of all major phylogenetic groups. This is in agreement with other studies showing that estimators

10 of alpha-diversity are higher during the Antarctic winter and spring, and that a significant decrease in richness and diversity occurs during summer (January and February), especially during phytoplankton blooms (e.g. Ghiglione and Murray, 2012; Grzymski et al., 2012; Hernández et al., 2014; Luria et al., 2016). However, our summer samples were likely taken after the summer phytoplankton bloom, that usually occurs in this area around January (Luria et al., 2016), explaining why the difference between spring and summer was not as pronounced in our case.

The presence of the archaeal phyla Thaumarchaeota (Nitrosopumilus) and Euryarchaeota in spring, and their very low abundance in summer-autumn are consistent with previous observations by Kalanetra et al. (2009), who found archaea to be almost completely absent during the summer season in an area where phytoplankton blooms occur. However, our seasonal patterns differ from Hérnandez et al. (2014), who found the presence of archaea throughout the entire year in an area not influenced by spring blooms. Possibly our results reflect the increase of phytoplankton occurrence in our study area in spring, followed by an increase in bacterial groups able to take advantage of the phytoplankton organic matter input (substrate availability) in summer and autumn, therefore, gaining importance in relation to archaeal groups. Other taxa present in spring, such as potentially chemoautotrophic groups SAR324 and Oceanospirillales (Sheik et al., 2014, Swan et al., 2011), have been previously registered in Antarctic deep waters in summer (Signori et al., 2014), and as part of the Antarctic surface winter community (Grzymski et al., 2012; Williams et al., 2012; Luria et al., 2014). Further, a combination of metagenomic and metaproteomic surveys showed that 18-37% of the bacterial and archaeal winter community were found to have the potential to fix CO2 by performing chemolithoautotrophy (Grzymski et al., 2012; Williams et al., 2012), with an important contribution by ammonia-oxidizing Thaumarchaeota (Tolar et al., 2016). Apparently, three major taxonomic groups found in spring (i.e. Nitrosopumilus, SAR324, Oceanospirillales) remain from the previous winter. The identification of Thaumarchaeota and the SAR324 clade as part of the core microbiome provides evidence that these groups might contribute to chemosynthetic production not only in deep-waters and during the winter at the surface, but also in spring and even during summer, when any potentially present chemoautotrophs are overshadowed by the abundant heterotrophic bacteria that recycle organic matter during or after phytoplankton blooms.

11 For phytoplankton, the spring season represents the beginning of significant growth in response to increased irradiance and water column stabilization caused by favorable winds, warming, and/or freshening due to input of melt water from sea ice or glaciers (Prézelin et al., 2000; Ducklow et al., 2008; Mendes et al., 2012; Venables et al., 2013, Rozema et al., 2017a). In the present study, we observed the succession of different bacterial groups in response to the occurrence of different types of phytoplankton species. Our data based on chloroplast 16S rRNA gene sequences allowed a limited assessment of phytoplankton diversity, which was mainly characterized by haptophytes, diatoms and dinoflagellates. Previous studies have already shown high abundances of the haptophyte Phaeocystis and pennate diatoms (such as Corethron) in winter and early spring in the southern Antarctic Peninsula (Rozema et al. 2017a), which are more often associated with spring phytoplankton communities and sometimes with sea-ice melting (Annett et al., 2010; Rozema et al., 2017b). The presence of Corethron in spring followed by polar centric diatoms (such as Thalassiossira) from summer to autumn was also noticed by Rozema et al. (2017b), and the latter were previously reported as important components of the phytoplankton communities in coastal and open ocean regions of the Southern Ocean (Díez et al., 2004; Garibotti et al., 2005; Pike et al., 2009; Annett et al., 2010; Piquet et al., 2011; Ducklow et al., 2012). Similar results to the present study were found by Mendes et al. (2012, this issue), who used a different technique (HPLC analysis) to assess the spatiotemporal variability in the composition and biomass of phytoplankton in NAP. However, Mendes et al. (this issue) not only found Phaeocystis antarctica and small diatoms, but also cryptophytes as a major component of the phytoplankton community in the shallow mixed-layers (< 25 m). The lack to identify cryptophytes in our study is most likely due to the use of a primer set targeting Bacteria and Archaea instead of Eukarya, and possibly also the use of an DNA extraction procedure not tailored for phytoplankton.

Concomitant with a larger fraction of 16S chloroplast sequences in spring, indicative of a higher contribution of phytoplankton, SAR11 and Oceanospirillales exhibited their highest relative abundance, decreasing in summer and autumn. Sequences belonging to the SAR11 clade represent the most abundant bacterioplankton in the global ocean (Giovannoni et al., 2017), are characterized as aerobic and free-living chemoheterotrophs, and have previously been detected in high relative abundance during phytoplankton blooms (Landa et al., 2016). Among Oceanospirillales sequences, the genus Balneatrix was relatively dominant in our study, confirming previous observations of Balneatrix as an important member of coastal Antarctic bacterial communities (Nikrad et al., 2013; Moreno-Pino et al. 2016). These taxa

12 might benefit from exudates released by actively growing phytoplankton. In summer and autumn, we observed a 1.5-2.5 times increase of Flavobacteria and Cellvibrionales, indicating degradation of phytoplankton biomass. Among Flavobacteria/Flavobacteriales, two taxa were the most abundant, the NS5 marine group and the genus Polaribacter, which have previously been identified in the Bransfield Strait as free-living bacteria (Milici et al., 2017) and frequently found in polar waters (Wilkins et al., 2013; Signori et al., 2014; Luria et al., 2016), respectively. Although relatively less abundant, Cellvibrionales were also present in late summer, particularly in February and March 2014. Cellvibrionales was recently proposed as a novel order within the Gammaproteobacteria, showing high seasonal abundances in coastal environments (Spring et al., 2015). Similarly, as Flavobacteria, members of this order have the capability to degrade complex carbohydrates and can occupy distinct nutrient ecological niches, like marine snow (Williams et al., 2013, Spring et al. 2015). In addition, some representatives of Rhodobacterales also increased in summer, which are known as dominant and primary colonizers of particulate organic matter (Dang et al., 2008), targeting common constituents of algal exudates, such as taurine, polyamines and glycolate, and forming commensal associations with phytoplankton (Buchan et al., 2005). Overall, the observed shifts in bacterial taxonomic groups may be intimately related to the differentiated use of organic matter that can shape the heterotrophic bacterial communities (Ducklow et al., 2012; Teeling et al., 2012; Kim and Ducklow, 2016; Landa et al., 2016; Luria et al., 2016).

Another important, but even less studied, component of the temporal variation in bacterial and archaeal communities in the Southern Ocean is the interannual variability. We found differences in microbial community composition between years, especially for spring (November 2013 and 2014) and summer months (February 2014 and 2015), that were corroborated by the statistics for beta-diversity (Supplementary Table S4). It is known from long-term studies on phytoplankton in the western Antarctic Peninsula that significant interannual variability of community composition and bloom magnitude are normally linked to winter sea ice cover and summer stratification strength, and changing temperatures (e.g. Rozema et al., 2017a; Schofield et al., 2017). A significant (p<0.05) increase in in situ temperature was observed in spring and summer between years, suggesting that the increase in temperature could play a role on the observed differences in microbial community structure.

4.2. Spatial variation

13 As part of our sampling design, we were also able to address the spatial variation of the microbial communities at a given time. Based on the literature, it is assumed that 2-20 km in horizontal extent corresponds to the size of a typical microbiologically coherent parcel of water, harboring a consistent microbial community composition (Hewson et al., 2006; Lie et al., 2013; Fuhrman et al., 2015). Surprisingly, in a transect of ca. 950 km length from the Gerlache and Bransfield Straits to the surrounding area of Elephant Island, we observed neither significant differences in microbial community structure among monitoring stations M1-M10, nor significant correlations between geographical distance and beta-diversity. This observation is consistent with Luria et al. (2014), who did not find a relationship with geographical distance, and observed that similar environmental conditions in summer lead to similar surface communities. Further, this reinforces previous findings of Signori et al. (2014), who found that transitions between seasons (summer to autumn) could have led to differences in bacterial and archaeal communities in a similar sampling region. Based on our dataset, the geographical distance appears to be less relevant than the seasonal variation, as the microbial diversity and community structure in surface waters were very similar over space, while significant differences were found between seasons and years.

4.3. The influence of environmental factors In a recent study, Moreno-Pino and collaborators (2016) determined that environmental factors control the spatial variation of bacterial communities in Fildes Bay, King George Island, Antarctica. Our results confirm this hypothesis on a larger scale, showing strong relationships between community composition and environmental conditions related to seasonal variation, but no spatial differences in microbial community structure. Our data suggest that an interplay of environmental conditions related to the seasonal variation in polar marine ecosystems is shaping the bacterial and archaeal communities, with temperature and organic matter being the most relevant regulatory factors (e.g. Kirchman et al., 2009; Ducklow et al., 2012; Signori et al., 2014; Torstensson et al., 2015; Bowman et al., 2016; Kim and Ducklow, 2016; Landa et al., 2016; Luria et al., 2016; Bunse and Pinhassi, 2017). Showing significant correlations with alpha- and beta-diversity, our study confirmed previous findings identifying seawater temperature as a key factor in determining the distribution of microorganisms in polar ecosystems (Fuhrman et al., 2006; Fuhrman et al., 2008; Wilkins et al., 2013; Yung et al., 2015; Schofield et al., 2017), affecting the microbiome’s diversity, activity and biogeochemical potential (Ward et al., 2017). In addition, climate-change induced sea-surface warming, together with other corresponding changes in oceanographic conditions, can further affect the

14 structure and function of marine ecosystems, impacting the microbial composition, biomass and production, with possible cascading effects on higher trophic levels (e.g. Moline et al., 2004; Kirchman et al., 2009; Montes-Hugo et al., 2009; Doney et al., 2012; Ducklow et al., 2013; Mendes et al., this issue; Bowman et al., 2017; Schofield et al., 2017; Ward et al., 2017).

4.4. Final remarks In conclusion, our study contributes to a better understanding of the shifts in marine microbial communities that occur every year between spring, summer and autumn in Antarctic surface waters. Interestingly, despite these shifts the main phylogenetic groups were part of the core microbiome, suggesting that the main functions of the community are retained throughout the year. Moreover, the relatively homogeneity of bacterial and archaeal communities found in Antarctic coastal waters across a 950 km transect suggests that a simple monitoring system of only a couple of stations may adequately cover the entire region. In addition, the observed tendency of interannual variability in community structure warrants further investigation. The observed changes in bacterial and archaeal community structure in response to environmental conditions related to seasonal variation and the lack of spatial variation provide critical knowledge to assess the ecosystem’s response in the rapidly changing NAP region.

Acknowledgements

Thanks to the captains of Npo. Almirante Maximiano (H41) and their respective crews of the Brazilian Antarctic Expeditions “OPERANTAR XXXI, XXXII, XXXIII, XXXIV”. We are thankful to our colleagues onboard for their help in sampling, and in particular to the project leader Eduardo Secchi, Rodrigo Kerr, Carlos Rafael Mendes, and Márcio de Souza from the Universidade Federal do Rio Grande (FURG, Brazil) for the very productive discussions. Thanks to Diego Franco, Natascha Bergo and Francielli Peres (Laboratório de Ecologia Microbiana, IOUSP) for helping with bioinformatics and statistics, and to Marcos Tonelli (Laboratório de Oceanografia Física, Clima e Criosfera, IOUSP) for helpful discussions on temperature data. We further thank the Laboratório de Estudos dos Oceanos e Clima (LEOC) at FURG for sharing their CTD data. This research was supported by the Project INTERBIOTA (CNPq grant n 407889/2013-2) and the INCT-MAR-COI (CNPq). AE-P is a research fellow from CNPq and Cientista do Estado from FAPERJ, and uses financial resources from these two agencies for this research. SMS received funding from the Investment in Science Funds at

15 WHOI. CNS was supported by the Scientific Committee on Antarctic Research (SCAR) Fellowship 2014-2015 and a Post Doc fellowship (FAPESP 2016/16183-5) from the São Paulo Research Foundation.

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24 Figure 1

Fig 1. Sampling map of ten selected monitoring stations (M1-M10, red dots) in the northern Antarctic Peninsula (NAP) to determine the spatiotemporal variation of surface microbial communities. The samples were collected in spring (November 2013, 2014), summer (February 2014, 2015) and autumn (March 2014, 2015) during the Brazilian Antarctic Expeditions. Basic scheme of surface circulation is represented: Black dashed arrows indicate the Antarctic Circumpolar Current, red dashed arrows indicate relatively warm and less saline water masses originating from the Bellingshausen Sea, blue dashed arrows indicate relatively cold and saline Weddell Sea water (modified from Bahk et al. 2003). The color scale bar on the right represents bathymetry.

Figure 2

)

(

e T

Salinity

Fig 2. Temperature and salinity distribution in surface waters in the northern Antarctic Peninsula (see little map on the top). Symbols in blue represent samples collected in spring, red symbols represent samples from summer, and yellow symbols, samples from autumn. Triangles represent samples from 2013, circles represent samples from 2014, and squares are samples from 2015.

Figure 3

26 (a) Others Thaumarchaeota 2% 3%

Gammaproteobacteria 26%

Bacteroidetes 38%

Betaproteobacteria 1%

Alphaproteobacteria 30%

27 (b) 100%

90%

80%

70%

)

% (

: 60%

d n

u 50%

t a

l 40%

e R

30%

20%

10%

0% spring summer autumn Thaumarchaeota;Nitrosopumilus Flavobacteria;Flavobacteriales Alphaproteobacteria;Rhodobacterales Alphaproteobacteria;Rhodospirillales Alphaproteobacteria;SAR116 Alphaproteobacteria;SAR11 Betaproteobacteria;Methylophilales Gammaproteobacteria;Alteromonadales Gammaproteobacteria;Cellvibrionales Gammaproteobacteria;Oceanospirillales Others Fig 3. Taxonomic composition of all sequences retrieved during this study (n=4,432,018 sequences) in a total of 73 samples. The external pie chart shows the relative abundance (%) of the main archaeal and bacterial classes besides chloroplast sequences, and the internal pie chart represents the relative abundance of the main orders and genera identified for each group according to the color scales. The legend on the right corresponds to the internal pie chart, and the category ‘Others’ comprises taxa accounting for less than 1% of the total (a). Relative abundance (%) of microbial taxa identified in spring, summer and autumn (b). Color scales represent the main groups: pink shades for Alphaproteobacteria, blue shades for Gammaproteobacteria, grey for Bacteroidetes, green shades for chloroplast sequences, dark blue for Thaumarchaeota, brown for ‘Others’.

28 Figure 4

Thaumarchaeota Flavobacteria Alphaproteobacteria Betaproteobacteria Deltaproteobacteria Gammaproteobacteria

Aureococcus Corethron Cymbomonas Dinophysis Fragilariopsis Haslea Phaeocystis Phalacroma Prymnesium Thalassiosira Triparma

2.0 1.5

1.0 ° 0.5 0.0 !0.5 !1.0 !1.5 !2.0

Fig 4. Relative abundance (%) of archaeal phyla and bacterial classes (a), 16S eukaryotic genera (b), across temperature variation in Antarctic surface seawater between November 2013 and March 2015, with spring, summer and autumn represented by blue, red and yellow, respectively (c)

Figure 5 (a) 453 (b) 703 y = -17.689x + 302.89 y = -12.798x + 458.41 r² = 0.13, p=0.0047 r² = 0.04, p=0.14 403 603

353 503

303

s 403

U o

253 a

h O C 303 203

153 203

103 103 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Temperature (°C) Temperature (°C)

0.94 5 0.92

4.5 0.9

n p

n 0.88

m i

h 4 S S 0.86

0.84 3.5 0.82

3 0.8 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Temperature (°C) Temperature (°C)

30 Fig 5. Relationships between temperature (C) and alpha-diversity indices: Number of OTUs (a), Chao 1 (b), Shannon (c) and Simpson (d), with the appropriate statistics information on the top right, and confidence intervals in light-colors lines. Symbols in blue represent samples collected in spring, red symbols represent samples from summer, and yellow symbols, samples from autumn. Triangles represent samples from 2013, circles represent samples from 2014, and squares are samples from 2015.

31 Figure 6

Fig 6. Non-metric multidimensional scaling (nMDS) ordination based on weighted UniFrac distance. Symbols in blue represent samples collected in spring, red symbols represent samples from summer, and yellow symbols, samples from autumn. Triangles represent samples from 2013, circles represent samples from 2014, and squares are samples from 2015. Each arrow shows one gradient (Year, Season, T=temperature, S=salinity). The arrow points to the direction of the most rapid change in the environment (direction of the gradient) and its length is proportional to the correlation between ordination and environmental variable (strength of the gradient).

32 Figure 7

(a) spring summer

96 87 70 68

80 85

123

autumn

(b) Core%Microbiome Alphaproteobacteria 33.82% Betaproteobacteria 5.88% Gammaproteobacteria 32.35% Deltaproteobacteria 1.47% Bacteroidetes 25.00% Thaumarchaeota 1.47%

Fig 7. Venn diagram of the core microbiome of Antarctic surface waters. Each circle (blue, red and yellow) contains numbers of OTUs present in 100% of samples within each group (spring, summer and autumn). Numbers of OTUs in the overlapping regions were shared by two or three groups (a). Relative abundance (%) of the taxonomic composition of 68 OTUs of Bacteria and Archaea shared between spring, summer and autumn (b).

33 Supplementary Material

Supplementary Table S1. Relative abundance (%) of 16S rRNA sequences of Archaea and Bacteria in November 2013, February, March and November 2014, and February and March 2015.

Nov Feb Mar Nov Feb Mar Taxonomy 2013 2014 2014 2014 2015 2015 Archaea;Euryarchaeota;Thermoplasmata;Thermoplasmatales;Marine;Marine_unclassified 1.60% 0.10% 0.10% 0.80% 0.10% 0.10% Archaea;Thaumarchaeota;Marine;Marine_unclassified;Marine_unclassified;Marine_unclassified 6.70% 0.80% 0.40% 5.70% 0.30% 0.20% Bacteria;Actinobacteria;Actinobacteria;Micrococcales;Microbacteriaceae;Candidatus 0.00% 0.00% 0.00% 0.00% 0.20% 0.20% Bacteria;Actinobacteria;Actinobacteria;PeM15;uncultured;uncultured_unclassified 0.00% 0.00% 0.10% 0.10% 0.10% 0.30% Bacteria;Bacteroidetes;Cytophagia;Cytophagales;Flammeovirgaceae;Marinoscillum 0.40% 0.20% 0.40% 0.20% 0.20% 0.30% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Cryomorphaceae;uncultured 1.70% 7.20% 8.00% 4.80% 7.60% 7.30% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;NS4 0.40% 0.20% 0.20% 0.60% 0.10% 0.20% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;NS5 8.00% 5.20% 8.00% 9.90% 6.00% 9.80% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Polaribacter 3.60% 22.40% 10.60% 7.00% 22.60% 9.90% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Ulvibacter 0.30% 4.50% 12.90% 2.80% 6.80% 8.30% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;uncultured 0.00% 0.00% 0.20% 0.30% 0.40% 2.50% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;NS7;NS7_unclassified 0.10% 0.10% 0.80% 0.20% 0.40% 1.50% Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;NS9;NS9_unclassified 2.70% 3.00% 3.40% 6.90% 1.30% 4.00% Bacteria;Marinimicrobia;Marinimicrobia_unclassified;Marinimicrobia_unclassified;Marinimicrobia_unclassified; 0.50% 0.10% 0.00% 0.20% 0.10% 0.10% Marinimicrobia_unclassified Bacteria;Proteobacteria;Alphaproteobacteria;Alphaproteobacteria_unclassified;Alphaproteobacteria_unclassified; 0.40% 0.10% 0.10% 0.40% 0.10% 0.10% Alphaproteobacteria_unclassified Bacteria;Proteobacteria;Alphaproteobacteria;Emcibacterales;Emcibacteraceae;Emcibacter 0.10% 0.10% 0.30% 0.10% 0.00% 0.20% Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;PS1;PS1_unclassified 0.20% 0.00% 0.00% 0.10% 0.00% 0.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Amylibacter 0.50% 1.90% 1.10% 0.30% 0.60% 0.50% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Ascidiaceihabitans 0.80% 1.10% 2.70% 1.00% 0.90% 1.70% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Halocynthiibacter 0.40% 0.10% 0.20% 0.30% 0.10% 0.10%

34 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Lentibacter 1.70% 0.90% 1.20% 2.20% 0.90% 0.90% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Octadecabacter 0.10% 0.10% 0.10% 0.00% 0.00% 0.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Planktomarina 8.10% 8.60% 9.40% 10.60% 5.60% 5.70% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Rhodobacteraceae_unclassified 0.10% 0.70% 0.70% 0.50% 2.20% 1.20% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;Sulfitobacter 0.30% 3.90% 4.30% 0.80% 12.70% 6.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae;uncultured 0.20% 0.10% 0.10% 0.20% 0.00% 0.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;AEGEAN-169 0.30% 0.00% 0.00% 0.10% 0.00% 0.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Magnetospira 0.70% 0.40% 0.90% 0.40% 0.30% 0.40% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;OM75 0.20% 0.00% 0.00% 0.10% 0.00% 0.00% Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;uncultured 0.50% 0.10% 0.20% 0.20% 0.10% 0.10% Bacteria;Proteobacteria;Alphaproteobacteria;SAR116;SAR116_unclassified;SAR116_unclassified 1.70% 0.60% 1.00% 0.90% 0.40% 0.40% Bacteria;Proteobacteria;Alphaproteobacteria;SAR11;SAR11_unclassified;SAR11_unclassified 18.10% 7.80% 8.20% 11.20% 6.70% 7.30% Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Erythrobacteraceae;Erythrobacter 0.00% 0.00% 0.10% 0.30% 0.30% 1.20% Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingomonas 0.20% 0.10% 0.10% 0.30% 0.30% 0.70% Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Acidovorax 0.00% 0.00% 0.00% 0.00% 0.10% 0.40% Bacteria;Proteobacteria;Betaproteobacteria;Methylophilales;Methylophilaceae;Methylotenera 0.00% 0.20% 0.10% 0.00% 0.00% 0.00% Bacteria;Proteobacteria;Betaproteobacteria;Methylophilales;Methylophilaceae;OM43 1.40% 1.20% 1.00% 1.00% 0.70% 0.70% Bacteria;Proteobacteria;Betaproteobacteria;Nitrosomonadales;Nitrosomonadaceae;Nitrosomonas 0.10% 0.00% 0.10% 0.10% 0.00% 0.10% Bacteria;Proteobacteria;Deltaproteobacteria;SAR324;SAR324_unclassified;SAR324_unclassified 1.20% 0.10% 0.10% 0.40% 0.10% 0.10% Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Marinobacter 0.10% 0.00% 0.00% 0.00% 0.00% 0.20% Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Paraglaciecola 0.10% 0.10% 0.00% 0.00% 0.10% 0.00% Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Colwelliaceae;Colwellia 0.10% 0.10% 0.10% 0.20% 0.00% 0.10% Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Pseudoalteromonadaceae;Pseudoalteromonas 1.00% 0.10% 0.30% 1.00% 0.10% 5.10% Bacteria;Proteobacteria;Gammaproteobacteria;BD7-8;BD7-8_unclassified;BD7-8_unclassified 0.00% 0.20% 0.10% 0.00% 0.00% 0.00% Bacteria;Proteobacteria;Gammaproteobacteria;Cellvibrionales;Halieaceae;OM60(NOR5) 0.10% 0.10% 0.60% 0.50% 0.40% 2.20% Bacteria;Proteobacteria;Gammaproteobacteria;Cellvibrionales;Porticoccaceae;SAR92 3.10% 7.40% 5.70% 2.80% 2.70% 3.80% Bacteria;Proteobacteria;Gammaproteobacteria;Gammaproteobacteria;Gammaproteobacteria_unclassified;Gamma 0.20% 0.00% 0.00% 0.10% 0.00% 0.00% proteobacteria_unclassified

35 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Halomonadaceae;Halomonas 0.00% 0.10% 0.10% 0.10% 0.10% 0.20% Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Oceanospirillaceae;Balneatrix 14.80% 13.70% 8.80% 11.10% 12.40% 7.40% Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Oceanospirillaceae;Pseudohongiella 1.30% 0.50% 0.50% 0.90% 0.30% 0.40% Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;Oceanospirillales_unclassified;Oceanospirillales 11.20% 2.20% 1.90% 8.40% 2.60% 2.20% _unclassified Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;OM182;OM182_unclassified 0.40% 0.40% 0.60% 0.20% 0.10% 0.30% Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86;SAR86_unclassified 2.70% 1.70% 1.70% 1.60% 0.80% 1.30% Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Psychrobacter 0.00% 0.00% 0.10% 0.00% 0.10% 0.60% Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas 0.00% 0.00% 0.00% 0.10% 0.20% 0.10% Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Piscirickettsiaceae;uncultured 0.10% 0.30% 0.10% 0.10% 0.10% 0.10% Bacteria;Proteobacteria;Gammaproteobacteria;Thiotrichales;Thiotrichaceae;Thiothrix 0.10% 0.10% 0.10% 0.10% 0.00% 0.10% Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Lentimonas 0.00% 0.00% 0.40% 0.00% 0.00% 0.00% Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Verrucomicrobiaceae;Roseibacillus 0.10% 0.10% 0.60% 0.10% 0.20% 1.60%

36 Supplementary Table S2. Number of reads, geographical locations and values of alpha- diversity estimates (Number of OTUs, Chao1, Shannon and Simpson) for each sample. Results of Student`s t-test, testing the null hypothesis of equality of means in “spring” vs. “summer- autumn” samples.

Latitude Longitude Monitoring Month/ Samples OTUs Chao1 Shannon Simpson (S) (W) Station Year #47 60.92 54.82 M1 13-Nov 318 413.92 4.87 0.93 #49 61.47 55.00 M2 13-Nov 285 382.68 4.73 0.92 #50 62.14 56.82 M3 13-Nov 300 424.09 4.83 0.93 #51 62.08 57.55 M4 13-Nov 296 448.00 4.84 0.93 #52 62.6 58.88 M5 13-Nov 281 448.44 4.20 0.89 #53 63.28 59.27 M6 13-Nov 363 530.17 4.92 0.93 #54 63.02 60.51 M7 13-Nov 268 361.72 4.79 0.92 #55 63.16 61.49 M8 13-Nov 252 325.03 4.53 0.91 #56 63.72 61.39 M9 13-Nov 272 437.37 4.30 0.89 #57 62.14 56.82 M3 13-Nov 279 348.38 4.66 0.92 #58 62.08 57.55 M4 13-Nov 323 405.27 4.79 0.93 #59 62.6 58.88 M5 13-Nov 367 466.13 5.04 0.94 #60 63.28 59.27 M6 13-Nov 366 460.32 4.52 0.90 #61 63.02 60.51 M7 13-Nov 338 463.08 4.94 0.93 #62 63.16 61.49 M8 13-Nov 339 434.00 4.88 0.93 #63 63.72 61.39 M9 13-Nov 324 452.46 4.62 0.92 #64 64.57 62.51 M10 13-Nov 347 491.56 4.33 0.90 #65 60.92 54.82 M1 14-Feb 395 514.90 4.80 0.92 #67 61.47 55.00 M2 14-Feb 307 416.69 4.85 0.93 #69 62.14 56.82 M3 14-Feb 392 507.02 5.03 0.94 #71 62.08 57.55 M4 14-Feb 352 455.33 4.95 0.93 #73 62.6 58.88 M5 14-Feb 372 596.08 4.59 0.92 #75 63.28 59.27 M6 14-Feb 351 446.64 4.92 0.94 #77 63.02 60.51 M7 14-Feb 317 461.03 4.93 0.94 #79 63.16 61.49 M8 14-Feb 280 419.79 4.69 0.93 #81 63.72 61.39 M9 14-Feb 367 500.06 4.92 0.94 #83 64.57 62.51 M10 14-Feb 150 235.56 4.60 0.90 #109 60.92 54.82 M1 14-Mar 299 416.00 4.88 0.94 #110 61.47 55.00 M2 14-Mar 285 401.92 4.76 0.94 #111 62.14 56.82 M3 14-Mar 309 418.60 4.91 0.94 #112 62.08 57.55 M4 14-Mar 317 414.35 4.83 0.94 #113 62.6 58.88 M5 14-Mar 306 412.95 4.75 0.93 #114 63.28 59.27 M6 14-Mar 307 478.70 4.98 0.94 #115 63.02 60.51 M7 14-Mar 336 429.20 4.97 0.95 #116 63.16 61.49 M8 14-Mar 317 412.02 4.76 0.93 #117 63.72 61.39 M9 14-Mar 369 476.12 4.21 0.84 #118 64.57 62.51 M10 14-Mar 285 428.08 4.48 0.92

37 #119 61.47 55.00 M2 14-Nov 290 405.03 4.52 0.93 #120 62.14 56.82 M3 14-Nov 302 443.98 4.26 0.91 #121 62.08 57.55 M4 14-Nov 362 537.53 4.67 0.93 #122 62.6 58.88 M5 14-Nov 268 359.00 4.05 0.89 #123 63.28 59.27 M6 14-Nov 203 279.61 3.83 0.87 #124 63.02 60.51 M7 14-Nov 269 428.68 4.35 0.91 #125 63.16 61.49 M8 14-Nov 209 312.54 3.73 0.83 #126 63.72 61.39 M9 14-Nov 278 375.35 4.05 0.87 #127 64.57 62.51 M10 14-Nov 294 371.94 4.08 0.87 #128 60.92 54.82 M1 14-Nov 275 429.69 4.48 0.92 #129 61.47 55.00 M2 14-Nov 297 423.92 4.65 0.91 #130 62.14 56.82 M3 14-Nov 259 404.70 3.60 0.82 #131 62.08 57.55 M4 14-Nov 265 402.50 3.91 0.85 #132 62.6 58.88 M5 14-Nov 150 197.53 3.88 0.84 #133 63.28 59.27 M6 14-Nov 252 376.70 4.62 0.92 #134 63.02 60.51 M7 14-Nov 275 438.50 4.43 0.91 #135 63.16 61.49 M8 14-Nov 275 445.63 4.39 0.91 #136 63.72 61.39 M9 14-Nov 291 450.18 4.11 0.87 #137 64.57 62.51 M10 14-Nov 305 499.24 3.34 0.81 #138 60.92 54.82 M1 15-Feb 329 412.57 4.87 0.94 #140 61.47 55.00 M2 15-Feb 333 448.03 4.86 0.94 #142 62.14 56.82 M3 15-Feb 304 385.02 4.81 0.94 #144 62.08 57.55 M4 15-Feb 371 488.02 4.80 0.93 #146 62.6 58.88 M5 15-Feb 266 503.39 4.85 0.94 #148 63.28 59.27 M6 15-Feb 320 472.63 4.58 0.93 #150 63.02 60.51 M7 15-Feb 360 509.53 4.56 0.91 #152 63.16 61.49 M8 15-Feb 320 419.00 4.51 0.91 #154 63.72 61.39 M9 15-Feb 302 397.14 4.68 0.93 #156 64.57 62.51 M10 15-Feb 361 518.50 4.71 0.93 #189 62.14 56.82 M3 15-Mar 323 447.56 5.03 0.95 #190 62.08 57.55 M4 15-Mar 354 494.88 5.04 0.95 #191 62.6 58.88 M5 15-Mar 285 451.29 4.90 0.95 #192 63.28 59.27 M6 15-Mar 334 454.73 5.02 0.95 #193 63.02 60.51 M7 15-Mar 306 444.24 5.06 0.95 #194 63.16 61.49 M8 15-Mar 318 421.06 4.71 0.93 #195 63.72 61.39 M9 15-Mar 284 384.74 4.85 0.94 T-TEST Average 318 436.27 4.75 0.92 "spring" Average 294 423.31 4.47 0.91 "summer-autumn" t-statistics 2.23 0.86 3.54 2.19 p-value 0.01 0.20 0.0004 0.01

38 Supplementary Table S3. Simplified outputs of the mixed-effects models using the lme4 R package testing the relationships between alpha-diversity indices (number of OTUs, Chao1, Shannon and Simpson) with temperature and salinity over years.

OTUs ~ temperature + salinity + (1 | Years) Estimate Std. Error df t value Pr (>|t|) (Intercept) -43.348 561.649 51.91 -0.077 0.9388 temperature -18.169 6.836 10.28 -2.658 0.0235 salinity 10.892 16.49 51.93 0.661 0.5118 Chao1 ~ temperature + salinity + (1 | Years) Estimate Std. Error df t value Pr (>|t|) (Intercept) -24.938 784.687 52.01 -0.032 0.975 temperature -13.997 9.431 52.01 -1.484 0.144 salinity 14.246 23.04 52.01 0.618 0.539 Shannon ~ temperature + salinity + (1 | Years) Estimate Std. Error df t value Pr (>|t|) (Intercept) 2.21283 3.24657 52.00000 0.682 0.499 temperature -0.1927 0.03902 52.00000 -4.938 0.000009 salinity 0.07134 0.09533 52.00000 0.748 0.458 Simpson ~ temperature + salinity + (1 | Years) Estimate Std. Error df t value Pr (>|t|) (Intercept) 0.738061 0.296434 51.85000 2.49 0.01603 temperature -0.012446 0.003725 9.06000 -3.342 0.00856 salinity 0.00519 0.008703 51.87000 0.596 0.55355

Supplementary Table S4. Outputs (r2 and p-values) of the permutational analysis of variance (PERMANOVA) using weighted UniFrac distance when comparing categories.

PERMANOVA, adonis function Categories r2 p spring vs. autumn 0.37 0.001 spring vs. summer 0.30 0.001 summer vs. autumn 0.05 0.054 spring vs. summer-autumn 0.34 0.001 within sampling periods (month/year) 0.51 0.001 November 2013 vs. 2014 0.31 0.001 February 2014 vs. 2015 0.19 0.002 March 2014 vs. 2015 0.14 0.006 within monitoring stations 0.08 0.97 within sampling areas 0.02 0.57

Supplementary Table S5. Squared correlation coefficient (r2) and p-values for environmental parameters obtained in non-metric multidimensional scaling (nMDS) using the envfit function, based on weighted UniFrac distance and 999 permutations.

Vectors nMDS1 nMDS2 r2 p temperature -0.99456 -0.10420 0.6454 0.001 *** salinity 0.96888 -0.24752 0.0712 0.153 - season -0.91066 -0.41317 0.5502 0.001 *** time (month/year) -0.99313 0.11701 0.6361 0.001 *** year -0.76198 0.64760 0.5440 0.001 *** longitude -0.19790 0.98022 0.0703 0.175 - latitude -0.07427 0.99724 0.1002 0.081 - ***high significance, *low significance, - no significance

40 Supplementary Table S6. List of 68 OTUs of Bacteria and Archaea present in all samples in spring, summer and autumn.

Core OTUs across all samples OTU 1 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 3 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Ulvibacter,uncultured OTU 4 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillales_unclassified,Oceanospirillales_unclassified OTU 5 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS5 OTU 6 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Polaribacter OTU 10 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Planktomarina,uncultured OTU 11 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Sulfitobacter,uncultured OTU 12 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,NS9,NS9_unclassified OTU 14 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,uncultured,uncultured OTU 16 Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,SAR92 OTU 20 Archaea,Thaumarchaeota,Marine,Marine_unclassified,Marine_unclassified,Marine_unclassified OTU 22 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,uncultured,marine OTU 23 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS5 OTU 26 Bacteria,Proteobacteria,Alphaproteobacteria,SAR116,SAR116_unclassified OTU 28 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,SAR86,SAR86_unclassified OTU 29 Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,Pseudoalteromonas,Pseudoalteromonas OTU 30 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Rhodobacteraceae_unclassified OTU 31 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Pseudohongiella,uncultured OTU 32 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,uncultured,marine OTU 33 Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,OM60(NOR5) OTU 35 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetospira,uncultured OTU 36 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 39 Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,Methylophilaceae,OM43 OTU 41 Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,Marinoscillum,uncultured OTU 42 Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,Methylophilaceae,OM43

41 OTU 44 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,NS7,NS7_unclassified OTU 46 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS4 OTU 48 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,NS9,NS9_unclassified OTU 49 Bacteria,Proteobacteria,Alphaproteobacteria,Alphaproteobacteria_unclassified,Alphaproteobacteria_unclassified,Alphaproteobacteria_unclassified OTU 51 Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Erythrobacter,bacterium OTU 52 Bacteria,Proteobacteria,Deltaproteobacteria,SAR324,SAR324_unclassified,SAR324_unclassified OTU 55 Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingomonas OTU 56 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,OM182,OM182_unclassified OTU 59 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,uncultured,uncultured OTU 62 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 63 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,OM182,OM182_unclassified OTU 64 Bacteria,Proteobacteria,Alphaproteobacteria,Emcibacterales,Emcibacteraceae,Emcibacter,uncultured OTU 65 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Balneatrix,uncultured OTU 67 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,uncultured,hydrothermal OTU 68 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,NS7,NS7_unclassified OTU 69 Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Moraxellaceae,Psychrobacter,endophytic OTU 72 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Pseudohongiella,uncultured OTU 74 Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Thiotrichaceae,Thiothrix,marine OTU 76 Bacteria,Proteobacteria,Gammaproteobacteria,Gammaproteobacteria,Gammaproteobacteria_unclassified,Gammaproteobacteria_unclassified OTU 79 Bacteria,Proteobacteria,Betaproteobacteria,Nitrosomonadales,Nitrosomonadaceae,Nitrosomonas,marine OTU 84 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas,uncultured OTU 85 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS5 OTU 88 Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Marimicrobium,uncultured OTU 98 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,uncultured OTU 100 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 103 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,SAR86,SAR86_unclassified OTU 106 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,SAR86,SAR86_unclassified OTU 116 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Oleispira,uncultured

42 OTU 122 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Amylibacter,uncultured OTU 137 Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas OTU 151 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Ascidiaceihabitans,uncultured OTU 171 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 221 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Lentibacter,uncultured OTU 234 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS5 OTU 328 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Planktomarina,uncultured OTU 355 Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,NS5 OTU 412 Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Balneatrix,uncultured OTU 436 Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,uncultured,uncultured OTU 508 Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,Methylophilaceae,OM43 OTU 648 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 762 Bacteria,Proteobacteria,Alphaproteobacteria,SAR11,SAR11_unclassified,SAR11_unclassified OTU 812 Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Halocynthiibacter,uncultured OTU 830 Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,SAR92