Introduction
The sea surface microlayer (SML), the < 1000 μm-thick water layer at the boundary between the ocean and the atmosphere, is an extensive habitat covering 70% of the Earth’s surface (Liss & Duce Reference Liss and Duce1997). The SML has distinct physicochemical and biological properties compared to the underlying waters (Cunliffe et al. Reference Cunliffe, Salter, Mann, Whiteley, Upstill-Goddard and Murrell2009). It is considered to be a fundamental environment for biogeochemical cycling and has a potential influence on air-sea exchange processes such as gas transfer (via volatilization) and sea spray aerosol formation by bubble bursting associated with breaking waves, mechanical tearing and spilling of wave crests (Cunliffe et al. Reference Cunliffe, Engel, Frka, Gasparovic, Guitart and Murrell2013, Engel et al. Reference Engel, Bange, Cunliffe, Burrows, Friedrichs and Galgani2017, Sellegri et al. Reference Sellegri, Nicosia, Freney, Uitz, Thyssen and Grégori2021).
In the SML, inorganic nutrients and organic components (transparent exopolymers, dimethylsulphoniopropionate (DMSP), lipids, carbohydrates, amines, etc.) accumulate together with microorganisms such as viruses, prokaryotes and protists (phototrophic and heterotrophic; Cunliffe et al. Reference Cunliffe, Engel, Frka, Gasparovic, Guitart and Murrell2013). This accumulation favours the development of SML-associated microbial communities (Hardy Reference Hardy1982, Kuznetsova & Lee Reference Kuznetsova and lee2001, Rahlff Reference Rahlff2019, Martínez-Varela et al. Reference Martinez-Varela, Casas, Piña, Dachs and Vila-Costa2020), whose composition can be more variable than that in the subsurface waters (SSW) due to exposure to greater solar ultraviolet (UV) radiation, changes in atmospheric temperature and wind speed, salinity gradients, toxic organic substances and heavy metals (Hardy Reference Hardy1982, Tovar-Sanchez et al. Reference Tovar- Sánchez, González-Ortegón and Duarte2019).
Microorganisms and viruses play a pivotal role in ocean biogeochemistry, particularly within the SML, where their interactions are likely to be more pronounced due to the reduced space (Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021). This feature has been extensively investigated in temperate systems (Nakajima et al. Reference Nakajima, Tsuchiya, Nakatomi, Yoshida, Tada and Konno2013, Rahlff et al. Reference Rahlff2019, Zäncker et al. Reference Zäncker, Cunliffe and Engel2021), yet they remain poorly studied in polar systems. Nevertheless, the available information in Antarctic and Arctic systems (e.g. Tovar et al. Reference Tovar- Sánchez, González-Ortegón and Duarte2019, Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021) suggests that the SML harbours high microbial production and high viral activity. The interactions among microorganisms and the subsequent biogeochemical processes can be sources of metabolites that act as direct or indirect precursors of aerosols (Matrai et al. Reference Matrai, Tranvik, Leck and Knulst2008, Dall’Osto et al. Reference Dall'Osto, Ovadnevaite, Paglione, Beddows, Ceburnis and Cree2017). Thus, the interaction between viruses and their specific photosynthetic eukaryotic hosts may be a contributing factor in the release of aerosol precursors from cells, their enrichment in the SML and their subsequent transformation and emission to the atmosphere (Hill et al. Reference Hill, White, Cottrell and Dacey1998).
During the summer in the Southern Ocean, significant phytoplankton blooms occur (Tréguer et al. Reference Tréguer and Jacques1992), generally followed by the proliferation of prokaryotes, heterotrophic protists and viruses (Gowing et al. Reference Gowing, Garrison, Gibson, Krupp, Jeffires and Fritsen2004, Sotomayor-Garcia et al. Reference Sotomayor-Garcia, Sala, Ferrera, Estrada, Vázquez-Domínguez and Emelianov2020). The SML can contain great abundances and high levels of activity and diversity of these microorganisms (Obernosterer et al. Reference Obernosterer, Catala, Lami, Caparros, Ras and Bricaud2008, Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021). The grazing of protists and/or zooplankton (through sloppy feeding; i.e. incomplete ingestion and digestion of prey) and viral lysis on prokaryotes and phytoplankton (as observed by Engel et al. Reference Engel, Bange, Cunliffe, Burrows, Friedrichs and Galgani2017 and references therein) promote the leaching of organic matter and secondary metabolites. A notable secondary metabolite is DMSP, an algal osmolyte that is produced in high intracellular concentrations by numerous phytoplankton taxa, including haptophytes, cryptophytes and small dinoflagellates (Simó Reference Simó2001, Steiner et al. Reference Steiner, Sintes, Simó, de Corte, Pfannkuchen and Ivancic2019). DMSP is released from cells primarily through senescence or exudation during the late phases of phytoplankton blooms (Matrai & Keller Reference Matrai and Keller1994, Laroche et al. Reference Laroche, Vezina, Levasseur, Gosselin, Stefels and Keller1999), as well as through grazing (Archer et al. Reference Archer, Stelfox-Widdicombe, Burkill and Malin2001, Simó et al. Reference Simó, Saló, Almeda, Movilla, Trepat, Saiz and Calbet2018) and viral attack (Bratbak et al. Reference Bratbak, Levasseur, Michaud, Cantin, Fernandez, Heimdal and Heldal1995, Hill et al. Reference Hill, White, Cottrell and Dacey1998, Malin et al. Reference Malin, Wilson, Bratbak, Liss and Mann1998). Once released, DMSP is partially transformed into dimethylsulphide (DMS) through the action of enzymatic lyase activity (Stefels et al. Reference Stefels, Steinke, Turners, Malin and Belviso2007). As a volatile, part of the DMS escapes into the atmosphere, where it evolves by oxidation into secondary aerosols that eventually contribute to cloud condensation nuclei and cloud brightness, thereby impacting climate (Vogt & Liss Reference Vogt, Liss, Le Quére and Saltzman2009).
There is still only limited knowledge regarding the physicochemical properties and microbial composition of the SML in Antarctic sea waters, particularly regarding the abundance, diversity and ecological roles of DMSP-producing phototrophic phytoplankton (e.g. Phaeocystis antarctica) and their associated viruses (e.g. haptophyte viruses or giant viruses). With the aim to fill this knowledge gap, we sampled viral and microbial communities in the SML and SSW across three different areas along the western Antarctic Peninsula during two cruises: PEGASO (2015) and PI-ICE (2019). We assessed the abundances of viruses, prokaryotes and heterotrophic and phototrophic nanoflagellates, paying particular attention to the abundance of phototrophic nanoflagellates with high DMSP contents (e.g. Phaeocystis-like species) and their potential viruses. These results were compared with measurements of seawater temperature, salinity, dissolved organic carbon (DOC), DMSP and inorganic nutrient concentrations, as well as atmospheric UV radiation and wind speed, which all have the potential to affect the microbial communities in the SML.
Materials and methods
Sampling regions and strategy
The PEGASO and PI-ICE cruises were conducted on board the research vessel (R/V) BIO-Hespérides and at the Spanish Antarctic Base (BAE). PEGASO was carried out between 8 January and 13 February 2015, whereas PI-ICE took place between late January and mid-March 2019. The latter included sampling in both open ocean waters (21 January 2019–5 February 2019) and coastal waters (6 February 2019–13 March 2019) near Livingston Island, where BAE is located. Altogether, a total of 20 samplings were carried out in three different regions (Bellingshausen Sea, Gerlache Passage and the Bransfield Strait coastal area; Fig. 1 & Table I). The SML was sampled under calm sea conditions from a rubber boat at ≥ 1 nautical mile from the research vessel or from the shore to avoid contamination. The SML samples were obtained using a glass plate sampler (Fig. S1; Stortini et al. Reference Stortini, Cincinelli, Degli Innocenti, Tovar-Sánchez, Knulst and Pawliszyn2012), which had been previously cleaned with acid over 12 h and rinsed thoroughly with ultrapure water (Milli-Q water). To ascertain the extent of any procedural contamination, field SML blanks were collected by rinsing and collecting 0.5 l of ultrapure water. A glass plate of 975 cm2 surface area was used, and ~100 dips were required to collect 500 ml of the SML water. The SSW samples were collected just underneath the microlayer at between 0.1 and 0.5 m either manually with a clean jar (PI-ICE cruise) or with a 10 cm tube attached to a floating device and siphoned with a syringe (PEGASO cruise). Both SML and SSW samples were stored in acid-cleaned plastic carboys until they were processed either onboard the vessel or at the BAE laboratory. For chemical and microbiological measurements, between 600 ml and 1 l were collected from the SML, and 2 l were collected from the SSW. To determine whether higher values for all variables were observed in the SML compared to the SSW, we calculated the enrichment factor (EF) for both chemical and biological parameters. The EF is defined as the ratio of the concentration or abundance in the SML to that in the SSW. An EF of > 1.0 indicated an enrichment in the SML relative to the SSW, whereas an EF of < 1.0 indicated a depletion in the SML relative to the SSW.
Figure 1. a. Map with the location of the study area. b. Sampling sites along the western Antarctic Peninsula: Bellingshausen Sea, Gerlache Passage and Bransfield Strait. c. Zoomed-in window of the stations of the Bransfield Strait coastal area.
Table I. Date, location, zone, Station (St) and environmental conditions of the sampled stations during the PEGASO (PG) and PI-ICE (PI) cruises. Temperature (Temp) and salinity were collected at 10 cm depth. Irradiation and wind speed were measured from the corresponding sensors located in the research vessel BIO-Hespérides and in the Spanish Antarctic Base (BAE) (see ‘Materials and methods’ section).
Bel S = Bellingshausen Sea; Br S = Bransfield Strait; GP = Gerlache Passage.
Environmental variables
Temperature was only measured in the SSW immediately after sampling on the rubber boat using a calibrated thermometer. Samples for salinity were collected for both layers when feasible and stored refrigerated in a cool box for later analysis in the R/V BIO-Hespérides laboratory using a Portasal Guildline 8410-A salinometer, which determines practical salinity based on conductivity ratios according to the Practical Salinity Scale 1978 (PSS-78). Therefore, as practical salinity is a dimensionless quantity under the PSS-78 scale, no units are required (UNESCO 1981). Duplicate 10 ml samples for the determination of the dissolved inorganic nutrients nitrate (NO3 −), nitrite (NO2 −), ammonia (NH4 +), silicate (SiO4-4 ) and phosphate (PO4 3−) were kept frozen at -20°C (Becker et al. Reference Becker, Aoyama, Woodward, Bakker, Coverly, Mahaffey and Tanhua2020) until analysis on a Bran-Luebbe AA3 HR autoanalyser (3 months after sampling back to the laboratory of the Institute of Marine Sciences (ICM-CSIC)) according to standard spectrophotometric methods (Hansen & Koroleff Reference Hansen, Koroleff, Grasshoff, Kremling and Ehrhardt1999). Samples for DOC analyses were filtered through a glass fibre filter (GF/F), and 30 ml aliquots were transferred to duplicate glass ampoules pre-combusted at 450°C for 5 h, sealed under flame and stored until analysis in the ICM-CSIC laboratory. The DOC analyses were conducted using a Shimadzu total organic carbon TOC-5000 or TOC-Vcsh instrument, employing high-temperature catalytic oxidation techniques as described by Spyres et al. (Reference Spyres, Worsfold, Miller, Mimmo and Miller2000). Standards provided by D.A. Hansell and W. Chen (University of Miami) of 2 and 44 μmol l-1 DOC were employed to assess the accuracy of the estimates. For the analysis of DMSP, two pellets of sodium hydroxide (NaOH) were added to 30 ml of unfiltered seawater samples in glass vials for 24 h hydrolysis to DMS. Aliquots of between 0.1 and 1.0 ml were injected into a purge flask containing high-purity water, purged for a period of 4–6 min with ultrapure helium and analysed for evolved DMS using a gas chromatography system with a flame photometric detector. The concentration of DMSP was calculated by subtracting the endogenous DMS. PEGASO samples were analysed onboard, whereas PI-ICE samples were stored after the addition of NaOH and analysed at the ICM-CSIC laboratory in Barcelona within 4 months of the cruise. The measurements of wind speed and UV radiation were obtained from a Aanderaa Scanning Unit 3010 Automatic Weather Station, which was situated in the upper part of the BIO-Hespérides, and the automatic meteorological station (EMA) WMO 89064, sited in Livingston Island, not far from the BAE. Wind speed was transformed to a standard height of 10 m by means of a power law with an exponent of 0.11 (Hsu et al. Reference Hsu, Meindl and Gilhousen1994). The values of UV radiation (290–390 nm) were obtained at 10 min intervals using a UV Davis 6490 sensor. The UV data were displayed in terms of irradiance and a UV index. The UV index is based on erythemal exposure, which is defined as the level of UV radiation that causes reddening of the skin. The UV index was calculated by dividing the erythemal-weighted global irradiance by 25 mW m-2 (Kerr & Fioletov Reference Kerr and Fioletov2007).
Viral and microbial abundances
Subsamples (2 ml) were collected from the SML and the SSW for the enumeration of viruses and prokaryotes (heterotrophic bacteria and archaea) through flow cytometry (Brussaard Reference Brussaard2004, Gasol & Del Giorgio Reference Gasol and del Giorgio2000, respectively). The samples were previously fixed with glutaraldehyde (0.5% final concentration) for viruses and with P+G (paraformaldehyde (P) 1% + glutaraldehyde (G) 0.05%) for prokaryotes. Both were maintained at 4°C for 15–30 min in the dark and then flash-frozen in liquid nitrogen and stored at −80°C until analysis. The enumeration was conducted using a FACSCalibur flow cytometer at the ICM-CSIC laboratory up to 2 months after sampling. Samples for viral abundance (VA) were diluted with TE buffer (10:1 mM Tris:EDTA), stained with SYBR Green I and analysed at a medium flow speed (Brussaard Reference Brussaard2004), with a flow rate of 58–64 μl min-1. The presence of viruses was determined through the use of bivariate scatter plots, which employed the green fluorescence of stained nucleic acids in conjunction with the side scatter (Brussaard Reference Brussaard2004). Depending on their green fluorescence and side scatter signals, we identified four distinct virus populations (V1–V4). Presumably, the V1 and V2 populations are dominated by bacteriophages (Biggs et al. Reference Biggs, Huisman and Brussaard2021). The V3 and V4 fractions typically comprise eukaryotic viruses (Fig. S2; Evans et al. Reference Evans, Pearce and Brussard2009). In particular, the V4 fraction has been found to correspond to viruses infecting members of the Haptophyceae, such as Phaeocystis species (Brussaard et al. Reference Brussaard, Thyrhaug, Marie and Bratbak1999, Reference Brussaard, Kuipers and Veldhuis2005). Samples for prokaryotic abundance (PA) were stained with SYTO13 (SYTO™ 13, ThermoFisher) and enumerated by flow cytometry in accordance with the methodology outlined by Gasol & Del Giorgio (Reference Gasol and del Giorgio2000). Prokaryotic cells were identified based on their distinctive signatures in a plot of 90° light scatter (SSC) vs green fluorescence (FL1; Fig. S2; Gasol et al. Reference Gasol, Zweifel, Peters, Fuhrman and Hagström1999). Subsamples (30 ml) for the enumeration of nanoflagellates were fixed with glutaraldehyde (1% final concentration), filtered through 0.6 μm black polycarbonate filters and stained with 4,6-diamidino-2-phenylindole (DAPI) at a final concentration of 5 μg ml-1 (Sieracki et al. Reference Sieracki, Johnson and Sieburth1985). Counts of heterotrophic nanoflagellates (HNFs) and phototrophic nanoflagellates (PNFs) were conducted using epifluorescence microscopy (Olympus BX40-102/E at 1000×), with a blue wavelength excitation filter (bandpass (BP) 460–490 nm) and barrier (emission) filter (BA; 510–550 nm), and with an UV excitation filter (BP 360–370 nm) and barrier filter (BA 420–460 nm). PNFs and HNFs were distinguished under blue light, where the presence of plastidic structures in the PNFs could be observed as red fluorescence. A minimum of 20–100 cells of each type of nanoflagellate were counted per sample and separated into size classes of ≤ 2, 2–5, 5–10 and 10–20 μm. Phaeocystis-like species (Fig. S3) were predominantly observed in the 2–5 μm PNF size class.
Data analyses
The Shapiro-Wilk W-test was used to test the normality of the data, and the Levene’s test was used to test for homogeneity of variance from means. Data were logarithmically transformed prior to analysis, if necessary. Pearson correlation and linear regression analyses were applied to evaluate the relationships between the different biotic and environmental parameters. One-way analyses of variance (ANOVAs) were conducted to investigate the differences in physicochemical and biological variables between the SML and the SSW for the entire region and individual zones, as well as the differences in SML and SSW values among the various zones. These analyses were conducted using Kaleidagraph 5.01 (copyright 1986–2021 by Synergy Software) and the PAST4 app 1.06. Multivariate statistical analyses were conducted using R version 4.3.2 (R Core Team 2023). For principal component analysis (PCA), tidyverse version 1.3 (Wickham & RStudio Reference Wickham2023) and dplyr (Wickham et al. Reference Wickham, François, Henry, Müller and Vaughan2023a) were employed for data handling and manipulation. The prcomp function from the stats package, version 3.6.2 (Bolar Reference Bolar2019), was employed to conduct the PCA, which yielded the principal components and explained variances. To facilitate the interpretation of the results of the PCA, the packages factoextra version 1.0.7 (Kassambara & Mundt Reference Kassambara and Mundut2020) and ggplot2 version 3.4.4 (Wickham et al. Reference Wickham, Chang, Henry, Pedersen, Takahashi and Wilke2023b) were used together to create detailed and customized plots. The PCA included variables with enough coincident data in order not to introduce noise into the analyses. Thus, nutrient concentrations and some microbial abundances and VAs were not included.
Results
Environmental and physicochemical parameters
During both cruises, UV radiation varied between 2 and 18 UV index, with the lowest value recorded in the Bransfield Strait and the highest in the Gerlache Passage (Table I). A similar pattern was observed for wind speed, with minimum (1.2 m s-1) and maximum (13.3 m s-1) values recorded in the same areas as for the UV index (Table I). The two variables were positively correlated (r = 0.599, P < 0.01, Tables S1 & S2). The temperature and salinity values recorded in the SSW ranged from the coldest (−0.8°C) in the Bellingshausen Sea to the warmest (4.3°C) in the Bransfield Strait. Salinity was also lowest (29.9) in the Bellingshausen Sea and highest (33.4) in the Gerlache Passage and Bransfield Strait, with both these areas being close to the coast (Table I).
Almost all inorganic and organic compounds, except silicate (SiO4 4−), showed a significant positive correlation between the SML and SSW layers (Table S3). Regarding the inorganic nutrients, there were no significant differences between the layers, except for SiO4 4− (Table S4). When comparing the SML of the three sampled areas, SiO4 4− showed significantly higher values in the Gerlache Passage than in the Bransfield Strait. For NO3 −, NO2 − and NH4 +, their concentrations exhibited the opposite trend to that of SiO4 4−, being higher in the Bransfield Strait than in the Gerlache Passage (Tables II & S4). In the case of SSW, all inorganic nutrients showed lower values in the Bellingshausen Sea than in the other two areas (Tables II & S4). A similar pattern of high values for NO2 −, NO3 − and PO4 3− in the Bransfield Strait for both layers is reflected in the strong positive correlations among them in both the SML and SSW (Tables S1 & S2). Furthermore, the occurrence of a high DOC concentration was observed exclusively within the Bransfield Strait (Table II). Finally, DMSP concentrations were higher in the SML than in the SSW only in some sites of the Gerlache Passage and Bransfield Strait. Over the entire cruise, DMSP was on average 113.7 ± 21.3 nM (range of 24.5–206.7 nM) in the SML and 111.6 ± 23.1 nM (range of 22.6–268.9 nM) in the SSW. The highest and the lowest concentrations were observed in the Bransfield Strait, and the cruise average for the EF was ~1 (Table II).
Table II. Averages, minimum and maximum values and enrichment factors (EFs) of physicochemical variables registered in the Bellingshausen Sea, Gerlache Passage and Bransfield Strait at the surface microlayer (SML) and subsurface water (SSW) layers, and the EF average (EFt) for all pooled areas. Concentrations of nitrate (NO3 −), nitrite (NO2 −), ammonia (NH4 +), silicate (SiO4 4−), phosphate (PO4 3−), dissolved organic carbon (DOC) and dimethylsulphoniopropionate (DMSP) are shown. EF and EFt averages in bold correspond to values ≥ 1.0.
SE = standard error.
Viral and microbial variables
Viral and microbial abundances in both layers were significantly and positively correlated, meaning that they followed a similar pattern (Table S3) and did not show significant differences between the SML and the SSW (Table S4). However, in most cases, the averaged EF was ≥ 1, indicating a tendency to have higher values in the SML than in the SSW (Table III). PAs in the SML ranged from 1.6 × 105 to 9.7× 105 cells ml-1, with a mean of 5.0 × 105 ± 5.7 × 104 cells ml-1, whereas in the SSW they averaged 4.3 × 105 ± 5.2 × 104 (range of 1.2 × 105–9.0 × 105 cells ml-1), with the highest mean EF of 1.70 ± 0.41 recorded in the Bransfield Strait (Fig. 2a & Table III). Significantly higher abundances were observed in the SML of the Bellingshausen Sea and Bransfield Strait than in the SML of the Gerlache Passage (Table S4).
Table III. Averages, minimum and maximum values and enrichment factors (EFs) registered in the three sampled areas at the surface microlayer (SML) and subsurface water (SSW) layers of the Bellingshausen Sea, Gerlache Passage and Bransfield Strait and the EF average (EFt) for all pooled areas. Abundances of prokaryotes (PA), total viruses (VA), viral populations (V1–V4), heterotrophic nanoflagellates (HNFs), size fractions of HNFs (2–20 μm), phototrophic nanoflagellates (PNFs) and size fractions of PNFs (2–20 μm) are shown. EF and EFt averages in bold corresponded to values ≥ 1.0.
The mean total VA was higher in the SML (mean of 3.5 × 106 ± 5.5 × 105 viruses ml-1) than in the SSW (mean of 3.0 × 106 ± 4.8 × 105 viruses ml-1), but this difference was not significant (Table III). A total of 14 cases out of 19 showed higher VAs in the SML than in the SSW (Fig. 2b). Furthermore, the mean EFs in the three regions were similar (Table III). The lowest abundances were observed in the Bransfield Strait for both the SML and the SSW (0.7 × 106 and 0.8 × 106 viruses ml-1, respectively), whereas the highest abundances for both the SML and SSW were recorded in the Gerlache Passage (1.4 × 107 and 1.3 × 107 viruses ml-1, respectively; Fig. 2b & Table III). Similarly, three of the virus populations (V1 and V2 mainly being bacteriophages and V3 mainly infecting pico- and nanoeukaryotes) exhibited the highest abundance values in the Gerlache Passage and the lowest abundance values in the Bransfield Strait (Table III), whereas the opposite was observed for the V4 population abundance, with the lowest abundance in the Gerlache Passage (Table III). Additionally, V4 was the only group that displayed average EF values ≤ 1, especially in the Bellingshausen Sea and Gerlache Passage. These different spatial dynamics of the V4 population were reflected in the significant correlation between VAs with V1, V2 and V3 but not with V4 (Tables S1 & S2). In particular, the V4 population was identified as a potential specific virus of Phaeocystis-like species, which were abundantly present in the SML and SSW samples, mainly in the Bransfield Strait, coinciding with the highest abundance of PNFs within the 2–5 μm size class (Table III).
Figure 2. Abundances of a. prokaryotes, b. viruses and c. heterotrophic nanoflagellates (HNFs) for the sampling stations in the three areas at the surface microlayer (SML) and subsurface waters (SSW) of the sampling stations. Station numbers in blue are from PEGASO and those in red are from PI-ICE cruises.
The mean abundance of HNFs in the SML was 1.2 × 103 ± 0.2 × 103 cells ml-1 (range of 0.4 × 103–3.2 × 103 cells ml-1), and in the SSW this value was 1.5 × 103 ± 0.2 × 103 (range of 0.6 × 103–2.8 × 103 cells m-1; Fig. 2c & Table III). The EF values displayed considerable variability (Table III): in 9 out of 17 cases, the abundance of HNFs exhibited higher values in the SSW than in the SML, and average EF values only exceeded 1 in the Bransfield Strait (Table III). The HNF 2–5 μm size fraction is considered to primarily contain bacterivores, representing a substantial proportion of the total HNF abundance (up to 76%, ranging from 14% to 75% in the SML and from 25% to 76% in the SSW). This is also reflected in the significant correlation between the abundance of HNFs and the HNF 2–5 μm size fraction in both layers, as well as in the SML (r = 0.888, P < 0.005) and in the SSW (r = 0.947, P < 0.005; Tables S1 & S2). In the SML, this size class exhibited both the lowest (0.08 × 103 cells ml-1) and the highest abundances in the Bransfield Strait (2.4 × 103 cells ml-1). In the SSW, the abundance ranged from 0.15 × 103 cells ml-1 in the Bellingshausen Sea to 1.5 × 103 cells ml-1 in the Bransfield Strait. The abundance of the HNF 10–20 μm size fraction was relatively low (Table III), yet it was the sole size class that exhibited significant interlayer differences in the Bellingshausen Sea (Table S4). Furthermore, the abundance of this fraction was significantly higher in the SML of this region than in the SML of the Gerlache Passage and Bransfield Strait (Table S4). However, the HNF ≤ 2 μm size fraction abundance was higher in the SSW of the Bransfield Strait than in the SSW of the Gerlache Passage and Bellingshausen Sea (Table S4).
Potential virus-PNF interaction and its relationship with DMSP concentration
The abundance of PNFs, similarly to the other microbial variables, did not present significant differences between the SML and SSW layers (Table S4). The mean abundance of PNFs in the SML was 3.8 × 103 ± 1.4 × 103 cells ml-1, which is slightly higher than that observed in the SSW (3.6× 103 ± 1.3 × 103 cells ml-1; Fig. 3a), and the average value of EFt (pooling the three areas) was 1.20 ± 0.13 (Table III). In both SML and SSW layers, the lowest and the highest values were observed in the Bransfield Strait (0.5 × 103–22.7 × 103 cells ml-1 and 0.5 × 103–21.5 × 103 cells ml-1, respectively). Regarding the existence of significant differences in both layers between the sampled areas, only the abundance of the PNF ≤ 2 μm size fraction in the SML showed lower values in the Bellingshausen Sea than in the Bransfield Strait and Gerlache Passage (Tables III & S4). The PNF 2–5 μm size fraction includes Phaeocystis-like species, which belong to small haptophytes known for their high intracellular DMSP content. These organisms represent a substantial portion of the total PNF abundance in both the SML and SSW. In the SML, they accounted for an average of 74% ± 5.4%, with values ranging from 32% in the Bellingshausen Sea to 99.4% in the Bransfield Strait. Similar proportions were observed in the SSW, where they represented an average of 74% ± 5%, ranging from 27% to 99% in the same regions. Cell concentrations of these protists averaged 3.3 × 103 ± 1.3 × 103 cells ml-1 in the SML (ranging from 0.4 × 103 to 21.7 × 103 cells ml-1) and 3.0 × 103 ± 1.3 × 103 cells ml-1 in the SSW (ranging from 0.3 × 103 to 20.2 × 103 cells ml-1; Fig. 3a,b). Furthermore, the concentration of DMSP showed a similar distribution in both layers and in the three areas (Fig. 3c). However, the abundance of the viral (V4) population (supposed to include PNF viruses) followed the concentration of PNFs and the PNF 2–5 μm size fraction only in the SML (Fig. 3a,b,d). As expected, we observed that the DMSP concentration and the abundances of PNFs and the PNF 2–5 μm size fraction (e.g. Phaeocystis-like species) were significantly and positively related, both in the SML and in the SSW, with similar slopes (Fig. 4a,b). In contrast, the V4 population abundance was only significantly related to PNFs and the PNF 2–5 μm size fraction in the SML, explaining 45% and 57% of the variance, respectively, and not in the SSW (Fig. 4c,d).
Figure 3. Abundances of a. total phototrophic nanoflagellates (PNFs) and b. phototrophic nanoflagellates (2–5 μm; PNF2–5), c. dimethylsulphoniopropionate (DMSP) concentration and d. V4 population abundance at the surface microlayer (SML) and subsurface waters (SSW) of the sampling stations. Station numbers in blue are from PEGASO and those in red are from PI-ICE cruises.
Figure 4. Linear regression analyses between dimethylsulphoniopropionate (DMSP) concentration and a. phototrophic nanoflagellates (PNFs) and b. the PNF 2–5 μm size fraction abundances, as well as between V4 abundance and c. PNF and d. the PNF 2–5 μm size fraction abundances in the sampling stations and from the surface microlayer (SML) and the subsurface waters (SSW).
Relationships among all microbial variables in the three areas
To gain an overview of the relationships among all coincident viral (VA), microbial (PA, HNF, PNF, including the size classes) and environmental variables (UV, wind speed, salinity and temperature) analysed across all areas and layers, we performed a multivariate analysis (PCA; Fig. 5a). We selected PC1 and PC2 for visualization because they explain the largest proportions of variance in the dataset (34.3% and 28.1%, respectively). Although PC3 explained 10.5% of the variance, it did not contribute to additional biological or ecological separation, so we excluded it for clarity. Overall, the samples did not show any clustering concerning sampling zones (Bellingshausen Sea, Gerlache Passage and Bransfield Strait) or water layers (SML and SSW). When we examined the contribution of the most relevant variables to the first principal component (PC1; Fig. 5b), we found that the total abundance of PNFs, the different size classes (PNF 2–5 μm and PNF 5–10 μm), the concentration of DMSP and the abundance of V4 were the most significant contributors (> 13%). In addition, Fig. 5a shows that all of these variables have high-quality scores (cos2 > 0.8) and point in the same direction along the PC1 axis. This reflects that these four variables are well correlated (Tables S1 & S2). As can be seen in Fig. 4d and Table S1, the correlation is especially high between V4 and the PNF 2–5 μm size fraction in the SML. On the other hand, UV radiation, total VA, V2 and V3 and PA appear to explain most of the variance along the PC2 axis (Fig. 5c).
Figure 5. a. Principal component analysis biplot of the distribution of the samples and variables in the first two principal components (PCs). The colour of each arrows indicates the quality of the representation (cos2) of the variable on the PCs. The size of each arrows represents the contribution of the variable to the PCs. The axes represent the percentage of variance explained by each component: the first component (PC1) explains 34.3% of the variance, whereas the second component (PC2) explains 28.1%. b. Contribution of the variables to PC1. c. Contribution of the variables to the PC2. The dashed red lines represent the average contribution thresholds. DMSP = dimethylsulphoniopropionate; HNF = heterotrophic nanoflagellate; PA = prokaryotic abundance; PNF = phototrophic nanoflagellate; UV = ultraviolet; VA = viral abundance.
Discussion
In the present study, we aimed to test the hypothesis that the DMSP concentration in the SML would be largely determined by the abundance of Phaeocystis-like species cells, and that the interaction of these cells with their specific viruses (V4) would be stronger in the SML than in the SSW. Despite the weak enrichment of microorganisms in the sampled SMLs, we observed a potential interaction of V4 and Phaeocystis-like species mainly in this layer, which is consistent with the release of DMSP as a major precursor of DMS, an important aerosol compound that contributes to cloud formation.
Overall, most microbial variables were slightly enriched in the SML (EF > 1; Table III). This enrichment could be due to the upwards transport of microorganisms attached to buoyant particles or bubble scavenging, as reported in other studies (Joux et al. Reference Joux, Agogué, Obernosterer, Dupuy, Reinthaler, Herndl and Lebaron2006, Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021). The averaged EF values for PA (1.0–1.6) and total VA (1.1–1.2) were comparable to those previously reported by Joux et al. (Reference Joux, Agogué, Obernosterer, Dupuy, Reinthaler, Herndl and Lebaron2006) and Zäncker et al. (Reference Zäncker, Cunliffe and Engel2021) in the Mediterranean, and they were also slightly lower than those observed by Vaqué et al. (Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021) in polar systems. Even at some stations in the Bellingshausen Sea (PI2, PI4 and PI5) and in the Bransfield Strait (PI16 and PI17), the EF was less than 1 for PA, whereas for VA it was less than 1 only in the Bransfield Strait (PI12 and PI21). These lower abundances in the SML than in the SSW are in agreement with previous reports, where similar low EFs were detected in some locations of the polar systems (Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021), the Mediterranean Sea (Joux et al. Reference Joux, Agogué, Obernosterer, Dupuy, Reinthaler, Herndl and Lebaron2006) and Halong Bay (Pradeep Ram et al. Reference Pradeep Ram, Mari, Brune, Torreton, Chu and Raimbault2018). In addition, EFs < 1 have been recorded for PA in the subtropical Atlantic Gyre, in the western Mediterranean (Reinthaler et al. Reference Reinthaler, Sintes and Herndl2008) and in Raunafjorden, Norway (Cunliffe et al. Reference Cunliffe, Salter, Mann, Whiteley, Upstill-Goddard and Murrell2009). Furthermore, the SML was weakly enriched in HNFs and PNFs (mean EFt = 1.0 ± 0.2 and 1.2 ± 0.1, respectively) and diminished (mean EF < 1) mainly in the Gerlache Passage (Table III). Depletion of nanoflagellates in the SML has already been observed in the Arctic Ocean (Vaqué et al. Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021). In contrast, Joux et al. (Reference Joux, Agogué, Obernosterer, Dupuy, Reinthaler, Herndl and Lebaron2006) and Zäncker et al. (Reference Zäncker, Cunliffe and Engel2021) both found nanoflagellate enrichment in the Mediterranean Sea. However, Zäncker et al. (Reference Zäncker, Cunliffe and Engel2021) observed larger differences in the diversity of picoeukaryotic communities between the sampling sites than between SML and SSW. In our study, even though we do not have taxonomic indicators of diversity, we can take the size classes as an approximate indicator of the composition of the nanoflagellate assemblages. In the SML, the abundances of the PNF ≤ 2 μm size fraction were significantly lower in the Bellingshausen Sea compared to in the Gerlache Passage and Bransfield Strait, whereas the PNF 10–20 μm size fraction exhibited significantly higher abundances in the Bellingshausen Sea. No differences in the abundances of PNF size classes were observed between the SSW of the different sampled areas (Table S4). Unlike microbial variables, no enrichment in the EF was observed for physicochemical variables, except for ammonium and DOC in the Bransfield Strait (Table II).
The lack of clear differences between the layers could be due to the mixing of the SML and the SSW caused by the wind (Wurl et al. Reference Wurl, Wurl, Miller, Johnson and Vagle2011). Even though we did not observe strong winds during the study (range of 1.2–13.3 m s-1), except for during 1 day in the Gerlache Passage (Table I), there was a slight decrease in EF for microbial abundances, VA, V4, PNFs and DMSP concentration at wind speeds > 4 m s-1 (Table IV). These results are consistent with reports for marine waters elsewhere (Engel et al. Reference Engel, Bange, Cunliffe, Burrows, Friedrichs and Galgani2017, Rahlff et al. Reference Rahlff, Stolle, Giebel, Brinkhoff, Ribas-Ribas, Hodapp and Wurl2017). In the SML, wind speed was significantly negatively correlated with V4 abundance and marginally negatively correlated with DMSP concentration (Table S1). Furthermore, in the PCAs, wind speed showed an opposite influence on these variables and on total PNFs and for the different PNF size classes (Fig. 5). In contrast, although high levels of UV radiation could potentially cause cell damage or photoinhibition or be involved in the decrease of the EF for viruses and microorganisms in the SML (Buma et al. Reference Buma, Helbing, de Boer and Villafañe2001, Bigg et al. Reference Bigg, Leck and Tranvik2004), in our study we did not detect any significant statistical relationship of UV with PNFs, even when high radiation was observed (Fig. 5 & Table IV). Zäncker et al. (Reference Zäncker, Cunliffe and Engel2021) showed that phytoplankton abundance was greater in the SML despite high radiation levels, and Vaqué et al. (Reference Vaqué, Boras, Arrieta, Agustí, Duarte and Sala2021) observed that VA and viral activity were not affected by UV. However, HNF abundances showed low EF values (Table III) and were negatively correlated with UV (Table S1), suggesting that this radiation can be detrimental to HNFs. This is consistent with the study of Ochs (Reference Ochs1996), in which increasing doses of UV radiation were shown to cause a decrease in the HNF abundance and in grazing rates.
Table IV. Enrichment factors (EFs) of abundances of prokaryotes (PA), viruses (VA), V4 viral population (V4), phototrophic nanoflagellates (PNFs), Phaeocystis-like species (PNF 2–5 μm) and dimethylsulphoniopropionate (DMSP) concentration under different wind speed and ultraviolet (UV) index conditions. Figures within parentheses indicate ranges for wind speed and UV index irradiation.
The measured DMSP concentrations (23–269 nM) were within the range of previous observations in productive polar regions in summer, representing the waters and season with the highest DMSP concentrations in the global surface ocean (Galí et al. Reference Galí, Devred, Levasseur, Royer and Babin2015). DMSP enrichment in the SML only occurred in 5 out of 14 sampling sites (Fig. 3c); overall, DMSP had an EF that was not significantly different from 1. Another recent study in the northern Southern Ocean also did not find enrichment in the SML (Saint-Macary et al. Reference Saint-Macary, Marriner, Barthelmeß, Deppeler, Safi and Costa Santana2023). However, several previous studies had reported such enrichments (Yang & Tsunogai, Reference Yang and Tsunogai2005, Zemmelink et al. Reference Zemmelink, Houghton, Frew and Dacey2006), particularly in productive waters where dinoflagellates were dominant among the phytoplankton (Yang Reference Yang1999, Matrai et al. Reference Matrai, Tranvik, Leck and Knulst2008, Yang et al. Reference Yang, Levasseur, Michaud, Merzouk, Lizotte and Scarratt2009). Dinoflagellates were not the most abundant group in our studied waters, with their highest concentration being recorded in the SSW of the Bellingshausen Sea at 87 cells ml-1 (M. Delgado, personal observation 2021). Instead, the waters were dominated by PNFs, as shown in Table III.
Despite the weak or no enrichment of all parameters in the SML, the abundances of PNFs (total PNFs and Phaeocystis-like species) and DMSP concentration were significantly correlated, showing similar slopes in both SML and SSW (Fig. 4a). Indeed, many phytoplankton taxa included in the PNF abundances as haptophytes (e.g. Phaeocystis-like species) are recognized as major DMSP producers (Keller Reference Keller1989, Stefels et al. Reference Stefels, Steinke, Turners, Malin and Belviso2007). Subsequently, we found that the putative specific viruses (V4) of Phaeocystis-like species, with an average EF of 1.2 ± 0.2, showed a highly significant positive relationship with these PNFs exclusively in the SML (Fig. 4c,d & Table III). This result suggests that Phaeocystis-like species and their viruses were more strongly associated in this layer than in the SSW. The lysed cells would then release DMSP into the upper layer, promoting DMS production through microbial activity (Brussaard et al. Reference Brussaard, Bratbak, Baudoux and Ruardij2006).
Conclusions
Our results expand our understanding of the spatial variability of physicochemical, viral and microbial parameters in the SML and SSW across three regions of the Antarctic Peninsula. Almost all variables showed significant correlations between the SML and SSW, indicating a strong exchange between the two layers. Overall, there was low enrichment in the SML for each of the assessed parameters across the different areas, which may be partly explained by wind speed disrupting the stability of the microlayer and promoting mixing with the underlying water. Nevertheless, a significant relationship was observed between the abundances of PNFs and Phaeocystis-like species with DMSP concentration, both in the SML and in the SSW. Additionally, a significant correlation was found exclusively in the SML between Phaeocystis-like species and the abundances of their putative specific viruses (V4). Although the abundances of V4, Phaeocystis-like species, PNFs and DMSP are not markedly higher in the SML than in the SSW, their stronger correlations in the SML than in the SSW could reflect a more cohesive environment, where biogeochemical and ecological processes are more tightly interconnected. This suggests that the release of DMSP from those photosynthetic microorganisms may occur predominantly in this upper layer, where it is subsequently transformed into volatile DMS and emitted into the atmosphere through the air-sea interface.
Supplementary material
To view supplementary material for this article, please visit http://doi.org/10.1017/S0954102025100205.
Acknowledgements
We thank our colleagues S. Zeppenfeld, M. van Pinxteren, D.C.S. Beddows, J. Brean and C. Garcia-Botín for their invaluable assistance with sampling and laboratory work, and particularly to Dr M. Delgado, for the phytoplankton observations using the inverted microscope. DOC analyses were carried out by M. Abad (ICM-CSIC). We also thank the crew of the R/V BIO-Hespérides for enabling the field study, for their hospitality and for their logistical support. We are especially indebted to the Unidad de Tecnología Marina, and in particular to M. Ojeda and J. Riba, for their technical and logistical support at the Spanish Antarctic Base (BAE). We thank the TREC expedition (Tara Oceans) for their visit to the ICM, during which they provided confocal images of our Antarctic samples. This study was conducted as part of the POLARCSIC platform activities and was also supported by the institutional framework of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).
Financial support
The study was supported by the Spanish Ministry of Economy through projects PEGASO (CTM2012-37615) to R. Simó, BIO-NUC (CGL2013-49020-R) to M. Dall’Osto and PI-ICE (CTM2017-89117-R) to E. Berdalet and M. Dall’Osto.
Competing interests
The authors declare none.
Authors contribution
Conceptualization: DV, MMS. Fieldwork, sample and data analyses: EB, ME, AS-G, MC-B, MM-N, AR, XL-A, MV, CM. Writing - original draft: DV, EB, MMS. Revisions of the final draft: ME, RS, MD’O. Funding acquisition: EB, RS, MD’O. All authors approved the final version of the manuscript.
Dedication
This study is dedicated to the memory of our colleague and friend Dr Andrés Barbosa.
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