A natural microbial assemblage from Prydz Bay, East Antarctica, was incubated in six 650 L polythene tanks (minicosms) and exposed to six CO2 treatments: ambient (343 µatm) as well as 506, 634, 953, 1140, and 1641 µatm. Before commencement of the experiment, all minicosms were acid-washed with 10 % vol : vol HCl (AR grade, Sigma Aldrich), rinsed thoroughly with Milli-Q water, and finally rinsed with seawater from the sampling site. Seawater to fill the minicosms was collected from amongst the decomposing fast ice in Prydz Bay at Davis Station, Antarctica (68∘35′ S, 77∘58′ E), on 19 November 2014. A 7000 L polypropylene reservoir tank was filled by helicopter, using multiple collections in a thoroughly rinsed 720 L Bambi Bucket. The seawater was then gravity-fed from the reservoir to the minicosms through a Teflon-lined hose fitted with a 200 µm pore-size Arkal filter to exclude metazooplankton that would significantly graze the microbial community. Microscopic analysis showed that very few metazooplankton and nauplii passed through the pre-filter, and they were seldom observed throughout the experiment see. Thus, it is unlikely that their grazing affected the CO2-induced trends in community composition in our study. All minicosms were filled simultaneously to ensure uniform distribution of microbes.

The six minicosms were housed in a temperature-controlled shipping container, with the water temperature in each minicosm maintained at 0.0±0.5 ∘C. The temperature in each minicosm was maintained by offsetting the cooling of the shipping container against warming of the tank water with two 300 W Fluval aquarium heaters connected via CAREL temperature controllers and a temperature control program. Each minicosm was sealed with an acrylic lid, and the water was gently mixed by a shielded high-density polyethylene auger, rotating at 15 rpm.

Minicosms were illuminated by two 150 W HQI-TS (Osram) metal halide lamps on a 19:5 h light : dark cycle. Low-intensity light (0.9±0.22 µmol photons m-2 s-1) was provided for the first 5 d to slow phytoplankton growth while the CO2 levels were gradually raised to the target concentration for each minicosm (see below). Following this 5 d CO2 acclimation period, light was progressively increased over 2 d to a final light intensity of 90.5 ± 21.5 µmol photons m-2 s-1. The microbial assemblages were then incubated for 10 d, with samples taken at regular intervals (see below) and no further addition of nutrients or seawater (except for the small volume required for carbonate chemistry modification; see below). For further details on minicosm setup see .

Carbonate chemistry was measured throughout the experiment, allowing the fugacity of CO2 (fCO2) to be manipulated to the desired values over the first 5 d of acclimation and then maintained for the remainder of the experiment. Samples were taken daily from each minicosm in 500 mL glass-stoppered bottles (Schott Duran) following the guidelines of , with subsamples for dissolved inorganic carbon (DIC; 50 mL glass-stoppered bottles) and pH on the total scale (pHT; 100 mL glass stoppered bottles) gently pressure-filtered (0.2 µm) following . For each minicosm, DIC was measured in triplicate by infrared absorption on an Apollo SciTech AS-C3 analyser equipped with a LI-COR LI-7000 detector calibrated with five prepared sodium carbonate standards (Merck Suprapur) and daily measurements of a certified reference material batch CRM127 . DIC measurements were converted to micromoles per kilogram using calculated density from known sample temperature and salinity.

Measurements of pHT were performed using the pH indicator dye m-cresol purple (Acros Organics) following and measured by a GBC UV–vis 916 spectrophotometer at 25 ∘C in a 10 cm thermostated cuvette. A syringe pump (Tecan Cavro XLP 6000) was used for sample delivery, dye addition, and mixing to minimise contact with air. An offset for dye impurities and instrument performance (+0.003 pH units) was determined through measurement of pHT of CRM127 and comparison with the calculated pHT from known DIC and total alkalinity (TA), including silicate and phosphate. Salinity was measured in situ using a WTW197 conductivity meter and used with measured DIC and pHT to calculate practical alkalinity (PA) at 25 ∘C, using the dissociation constants for carbonic acid determined by and . Total carbonate chemistry speciation was then calculated for in situ temperature conditions from measured DIC and calculated PA.

During the acclimation period, the fCO2 in each minicosm was adjusted daily in increments until the target level was reached, after which fCO2 was kept as constant as possible for the remainder of the experiment. Measurements of pH were performed twice daily – in the morning (before sampling) and the afternoon – using a portable, NBS-calibrated probe (METTLER TOLEDO) to determine the amount of DIC to be added to the minicosm. Adjustment of the fCO2 in each minicosm was performed by addition of a calculated volume of 0.2 µm filtered CO2-saturated natural seawater to 1000 mL infusion bags and drip-feeding into the minicosms at ∼50 mLmin-1. One minicosm was maintained close to the fCO2 of the initial (ambient) seawater (343 µatm) and was used as the control treatment, against which the effects of elevated fCO2 were measured. The mean fCO2 levels in the other five minicosms were 506, 634, 953, 1140, and 1641 µatm. For further details of the carbonate chemistry sampling methods, calculations, and manipulation see .

Concentrations of the macronutrients nitrate plus nitrite (NOx), soluble reactive phosphorus (SRP), and molybdate reactive silica (silicate) were measured in each minicosm during the experiment. Samples were taken on days 1, 3, and 5 during the CO2 acclimation period and every 2 d for the remainder of the experiment (days 8–18). Samples were obtained following the protocol of . Seawater samples were filtered through 0.45 µm Sartorius filters into 50 mL Falcon tubes and frozen at -80 ∘C for analysis in Australia. Determination of the concentration of NOx, SRP, and silicate was performed by Analytical Services Tasmania using flow injection analysis.

Flow cytometric analyses were performed daily to determine the abundance of small protists (HNFs, pico- and nanophytoplankton, and prokaryotes) in each minicosm during the experiment. Samples were pre-filtered through a 50 µm mesh (NITEX), stored in the dark at 4 ∘C, and analysed within 6 h of collection following . Samples were analysed using a Becton Dickinson FACScan flow cytometer until day 15, after which the instrument broke down, and analysis was performed on a Becton Dickinson FACSCalibur. Both instruments were fitted with a 488 nm laser, and Milli-Q water was used as sheath fluid for all analyses. PeakFlow Green 2.5 µm beads (Invitrogen) were added to samples as an internal fluorescence and size standard. Final cell numbers were calculated from event counts on bivariate scatter plots divided by the analysed volume.

The analysed volume for each flow cytometer was calibrated by measuring the weight change of 1 mL seawater run for 1, 2, 3, 4, 5, and 10 min at high and low flow settings on each instrument. This weight change was converted to millilitres by dividing by 1.027, the density of seawater at 4 ∘C with a salinity of 34.3 PSU (Table S1 in the Supplement). A linear regression was fitted to each data set, and the analysed sample volume was determined by entering the sample run time (x) into the equations (Table S2). Average flow rates in millilitres per minute for each instrument at both flow settings were determined by dividing each analysed volume by the run time. The standard deviation for all mean flow rates on both instruments was <0.004. Details of instrument flow rates and equations for flow cytometry counts can be found in Table S2.

Microbial-community growth in the minicosms was measured in six unreplicated CO2 treatments, and thus triplicate subsamples from individual minicosms represent within-treatment pseudo-replicates. Therefore, means and standard error of these pseudo-replicate samples only provide the within-treatment sampling variability for each procedure. For the purpose of analysis, we treated pseudo-replicates as independent to provide an informal assessment of the difference among treatments.

A generalised additive model (GAM) was fitted to each CO2 treatment over time to visually assess temporal changes in the abundance of each microbial group using the mgcv and ggplot2 packages in R . Taking into account the pseudo-replicated sampling method, further statistical analysis of these curves was not performed. For growth rate analysis, a linear regression model was fitted on natural log-transformed data for each CO2 treatment over the incubation period, during which each microbial group sustained steady-state logarithmic growth. Growth rates for each treatment were determined from the slope estimate of the linear model. An omnibus test of differences between the linear models for each CO2 treatment was assessed by ANOVA to determine significant differences between the growth trends for each microbial group. The lack of replication in our study and limited number of time points at which each minicosm was sampled mean that the trends within treatments are indicative, and the statistical differences among treatments should be interpreted conservatively. The significance level for all tests was set at <0.05.

The carbonate chemistry of the initial seawater was measured as a pHT and DIC of 8.08 and 2187 µmol kg-1, respectively, resulting in a calculated fCO2 of 356 µatm and a PA of 2317 µmol kg-1 (Figs. , S1 in the Supplement; Table S1). Measurements of carbonate chemistry during the acclimation period showed a stepwise increase in fCO2, after which the CO2 level remained largely constant, with treatments ranging from 343 to 1641 µatm and a pHT range from 8.10 to 7.45 (Fig. ; Table ). Some decline in fCO2 was observed in the high-CO2 treatments towards the end of the experiment, indicating that the addition of CO2-saturated seawater was insufficient to fully compensate for its out-gassing into the headspace and drawdown by phytoplankton photosynthesis.

There was little variance in nutrient concentrations among all treatments at the start of the experiment (Table S1). Concentrations of NOx fell from 26.2±0.74 µM on day 8 to below detection limits on day 18 (Fig. a), with the 1641 µatm treatment being drawn down the slowest. SRP concentrations were drawn down in a similar manner as NOx, falling from 1.74±0.02 to 0.13±0.03 µM on day 18 in all treatments (Fig. b). Silicate was replete throughout the experiment in all treatments, with initial concentrations of 60.0±0.91 µM falling to 43.6±2.45 µM (Fig. c). Silicate drawdown was highest in the 634 µatm and lowest in the 1641 µatm treatment.

Picophytoplankton abundance did not change during the CO2 acclimation period, remaining at ∼2.04±0.02×106 cellsL-1. Cell numbers increased in all treatments from day 8, with treatments ≤506 µatm peaking on day 12 and all higher-CO2 treatments continuing to grow until day 13 (Fig. a). Steady-state logarithmic growth rates were calculated between days 8 and 12 (Fig. S2) and are presented in Table . The omnibus test of trends in fCO2 treatment over time for picophytoplankton steady-state growth indicated that there was a significant difference between treatments (F5,78=2.85, p<0.01; Table S3). Examining the significance of the individual linear model terms indicated that only the 953 µatm growth rate was significantly different to the control (p<0.01; Table ), with a higher growth rate of 0.32 d-1 (Table ). Despite the similarity in growth rates among treatments, there was a difference between peak abundances. The highest were observed in the 953 and 1641 µatm treatments, which reached 8.11±0.05×106 cellsL-1 (Fig. a). The 634 and 1140 µatm treatments peaked slightly lower, at 7.06±0.03×106 cellsL-1, and following this, the control (343 µatm) and 506 µatm treatments peaked at 5.28±0.17×106 and 4.47±0.13×106 cellsL-1, respectively. After reaching their peak, cell numbers rapidly declined in all treatments until day 18, falling to 0.50±0.01×106 cellsL-1. The 506 µatm treatment was excluded from analysis on day 18 due to high background noise on the flow cytometer, which caused artificially elevated counts.

Nanophytoplankton abundance declined during the CO2 acclimation period in all treatments, falling from a mean initial abundance of 1.19±0.03×106 to 0.96±0.02×106 cellsL-1 on day 7. Following acclimation, nanophytoplankton abundance increased in all treatments until day 15, after which growth plateaued (Fig. b). Steady-state logarithmic growth rates were calculated between days 9 and 15 (Fig. S2) and are presented in Table . There was a significant difference between growth trends among CO2 treatments (F5,113=5.92, p<0.01; Table S4), with significance due to enhanced growth rates in treatments ≥634 µatm (Table ). In the 634 µatm treatment, cell numbers were substantially higher than all other treatments from day 12 through day 18, reaching a final abundance of 8.83±0.24×106 cellsL-1 (Fig. b). Enhanced growth rates in treatments ≥953 µatm also led to cell numbers exceeding the control by day 15, averaging 5.61±0.12×106 cellsL-1. Between days 15 and 18, abundance in treatments ≥953 µatm dipped and then recovered, with a final abundance of 6.64±0.06×106 cellsL-1. It is uncertain whether the large dip in abundance on day 16 was due to a reduction in cell numbers in the tanks or associated with the change in flow cytometer on this day. Growth rates in the control and 506 µatm treatments were the slowest (0.22–0.23; Table ), displaying less of a plateau in growth between days 15 and 18 and reaching a final abundance of 5.96±0.15×106 cellsL-1, only slightly less than the ≥953 µatm treatments.

HNF abundance was initially low (0.94±0.04×105 cellsL-1) and remained steady throughout the CO2 acclimation period (Fig. c), with a small dip in cell numbers observed in the treatments ≥953 µatm on day 7 (Fig. S2). From day 8, HNF abundance increased in all treatments until day 15, with cell numbers in the control and 506 µatm treatments consistently higher than all other treatments (≥634 µatm; Fig. c). From day 15 to 18, the control treatment and the 634, 953, and 1641 µatm treatments continued to rise, while abundance in the 506 and 1140 µatm treatments stabilised. Steady-state logarithmic growth rates were calculated between days 8 and 15 (Fig. S2) and are presented in Table . The omnibus test of trends in fCO2 treatment over time showed that there was a significant difference between the treatments (F5,131=5.40, p<0.01; Table S5) due to significant differences in growth trends of the 506 and 1641 µatm treatments (Table ). Examining the growth rates of each of these treatments revealed that the 506 µatm treatment was slower than the control (0.32, p=0.02), while the 1641 µatm treatment was faster (0.40, p=0.02; Tables , ). The slower growth rate of the 506 µatm treatment appears to be due to a higher initial abundance on day 8 (2.42±0.35×105 cellsL-1) than the control (1.86±0.05×105 cellsL-1). Despite a higher growth rate in the 1641 µatm treatment, cell numbers in the highest-CO2 treatments, 1140 and 1641 µatm, remained consistently lower than the control throughout the entire growth period (between days 8 and 18), reaching abundances on day 18 of only 2.12±0.02×106 and 2.62±0.11×106 cellsL-1, respectively (Fig. c). The 506 µatm treatment plateaued after day 16, with a final abundance similar to the 1641 µatm treatment, at 2.66±0.02 cellsL-1. In contrast, the 634 and 953 µatm treatments continued to rise, exceeding the control after day 16 and reaching 3.42±0.08×106 cellsL-1 on day 18, with the control treatment slightly lower, at 3.13±0.04×106 cellsL-1.

Prokaryote abundance was similar in all fCO2 treatments at the start of the acclimation period (2.10±0.04×108 cellsL-1) and increased after day 4 in treatments ≥634 µatm, while abundance in treatments ≤506 µatm remained unchanged (Fig. d). Due to the large fluctuation in cell numbers between days 4 and 7 in treatments ≥634 µatm, steady-state logarithmic growth was not observed (Fig. S2). However, prokaryote growth rates were calculated from linear regression between days 4 and 8 to assess differences in prokaryote growth among treatments during the CO2 acclimation period (Table ). There was a significant difference between growth trends among CO2 treatments (F5,77=3.59, p<0.01; Table S6). Treatments ≥953 µatm all displayed significant differences in growth trends and were faster than the control (Tables , ). Between days 7 and 11, prokaryote cell numbers remained steady in all treatments, with abundance higher than the control in treatments ≥634 µatm (Fig. d). During this time, abundance was highest in treatments ≥953 µatm, with an average abundance of 3.17±0.03×108 cellsL-1, followed by the 634 µatm treatment, at 2.53±0.05×108 cellsL-1. The control and 506 µatm treatments had similar abundances, averaging 2.12±0.03×108 cellsL-1. From day 12, prokaryote cell numbers declined rapidly in all treatments, falling to 0.58±0.05×107 cellsL-1 by day 18.

Although grazing experiments were not performed, interactions between HNFs and their phytoplankton and prokaryote prey were assessed visually. There appeared to be no correlation between HNF and nanophytoplankton abundance as nanophytoplankton only displayed higher cell numbers than the control in the 634 µatm treatment, which showed no relationship to the CO2-induced reduction in HNF abundance at levels ≥634 µatm. In contrast, the co-occurrence of slowed HNF growth with increased picophytoplankton abundance between days 8 and 13 in CO2 treatments ≥634 µatm suggested that the picophytoplankton communities may have been released from grazing pressure. This hypothesis of a reduction in grazing pressure by HNFs at increased CO2 was further supported by the observation that above a threshold HNF abundance there was a rapid decline in both the picophytoplankton and prokaryote abundance, irrespective of treatment and the duration of incubation. For picophytoplankton, cell numbers rapidly declined when HNF abundance reached 0.87±0.02×106 cellsL-1 (Fig. a), and for prokaryotes this occurred once HNF abundance reached 0.32±0.02×106 cellsL-1 (Fig. b). Interestingly, the rate of decline in picophytoplankton and prokaryote abundances in the fCO2 treatments ≥634 µatm was greater than the control and 506 µatm treatments. Despite this, only HNFs in the 634 and 953 µatm treatments reached abundances as high as the control at the conclusion of the experiment, suggesting that high CO2 (≥1140 µatm) continued to have a negative effect on HNF growth (Fig. c).

Our study indicates that HNF abundance is negatively affected by elevated CO2. HNF abundance was reduced when CO2 levels were ≥634 µatm and remained lower than the ambient treatment at levels ≥1140 µatm. These observations are consistent with complementary studies in Prydz Bay, East Antarctica, that reported a reduction in HNF abundance when CO2 was ≥750 µatm in both high- and low-nutrient conditions . These results contrast with those reported for a Baltic Sea mesocosm study by , who found that high CO2 concentration (1040 ppm) had little effect on the HNF community. Interestingly, they also demonstrated that HNF communities form complex food webs, and trophic interactions between species can change with environmental conditions and prey availability. We were unable to determine whether species-specific sensitivities led to the reduction in HNF abundance with high CO2. However, reported a CO2-related change in the relative abundances of choanoflagellate species at CO2 levels ≥634 µatm (see Sect. 4.4 below). Therefore, it is possible that other CO2-induced changes to the HNF community composition may have occurred. The consistency of reduced abundance of HNFs with increased CO2 over the austral summer and between years indicates that ocean acidification alone may significantly alter the HNF growth and community structure by 2050 (following RCP8.5 projections; ). However, it must be acknowledged that a number of environmental factors will influence microbial communities with the onset of climate change see, and the sequence and severity of these additional stressors will be important in determining the nature and magnitude of the effect of ocean acidification on this community.

Increased top-down control of the HNF community by heterotrophic dinoflagellates and ciliates may have led to the lower abundance of HNFs in the high-CO2 treatments. saw no effect of CO2 on the composition or abundance of the microheterotrophic community in their coincident study. However, they did acknowledge that microheterotroph abundance was low in all treatments (∼1 % of all cells), and therefore any CO2 response might not have been apparent. Low abundances of heterotrophic dinoflagellates and ciliates in all treatments would suggest that grazing pressure on HNFs was low, and thus any reduction in HNF abundance at higher CO2 levels was not likely caused by increased grazing from larger taxa. Few other studies have investigated the effect of ocean acidification on heterotrophic protists, and as yet there are no reports of direct effects of elevated CO2 on microheterotrophic grazing rates, abundance, or taxonomic composition . One study by did report an increase in microzooplankton abundance when a natural North Atlantic microbial community was exposed to high CO2 (690 ppm). However, this increased abundance was believed to be an indirect effect of CO2-induced promotion of phytoplankton abundance and a change in the phytoplankton community composition as opposed to a direct effect of ocean acidification on microzooplankton physiology. A shift in the dominant nanophytoplankton taxa was reported by , with a threshold in this change appearing between 634 and 953 µatm (see Sect. 4.2 below). The prymnesiophyte Phaeocystis antarctica was dominant in treatments ≤634 µatm, whilst in higher-CO2 treatments P. antarctica was considerably reduced, resulting in a shift in dominance to the diatom Fragilariopsis sp. (<20 µm size). Low microzooplankton grazing rates have been reported in Antarctic waters dominated by colonial P. antarctica , suggesting that a shift in dominance to more palatable small diatom species with increasing CO2 may lead to a concurrent increase in microzooplankton and subsequent increase in HNF grazing.

It is difficult to evaluate the potential reasons for reduced abundance of the HNF community in high-CO2 treatments as the mechanism(s) responsible for CO2 sensitivity in HNFs is unstudied . Heterotrophs do not require CO2 for growth; thus increased [H+] from lowered pH is likely the dominant driver of the effects observed . The CO2 sensitivity of heterotrophic flagellates may be governed by the effectiveness of the mechanism(s) they possess to regulate intracellular pH . However, little is known about the pH sensitivities of heterotrophic flagellates. Among the few studies on flagellates, a decline in pH influenced the swimming behaviour of a harmful algal bloom, causing raphidophyte , and an inability to control intracellular pH disrupted the growth of the autotrophic dinoflagellates Amphidinium carterae and Heterocapsa oceanica . Disruption of flagella motility has also been observed in marine invertebrate sperm due to inhibition of the internal pH gradients required to activate signalling pathways . Whilst these examples do not provide evidence for direct inhibition of HNF growth, they do highlight the diverse sensitivities of flagellates to changes in pH that require further investigation. Size may also play a part in CO2 sensitivity, with size-related declines in the external pH boundary layer meaning small cells are likely to be more affected by lower ocean pH . As heterotrophs respire CO2 and do not photosynthesise, it is likely that pH would be even lower at the cell surface than for autotrophs. This may explain why HNFs showed reduced growth rates in our study, while the larger microheterotrophs may have been unaffected see.

An increase in picophytoplankton abundance was observed in our study when CO2 levels were ≥634 µatm (Fig. a), agreeing with other ocean acidification studies globally that have reported an increase in abundance of picophytoplankton at elevated CO2 levels e.g.. However, studies on phytoplankton communities in other Antarctic regions have reported shifts towards larger diatom species (Ross Sea; ) or no change (Antarctic Peninsula; ). This variability in response among sites in Antarctic waters may be due to factors such as differences in microbial-community seasonal succession or study methods that excluded picophytoplankton analysis. The increase in picophytoplankton abundance at CO2 levels ≥634 µatm reported here is similar to the findings of in their complementary study at the same site, indicating that this response is consistent across seasons and between years. It has been suggested that increased abundance of picophytoplankton may be due to increases in productivity derived from more readily available CO2 at the cell surface, allowing for more passive diffusion of CO2 into the cell and thus reduced requirements for energy-intensive carbon concentrating mechanisms CCMs;. Downregulation of CCMs in the high-CO2 treatment (1641 µatm) in small cells (<10 µm) was reported in our coincident study . However, it is uncertain whether this resulted in increased primary productivity for this size group as primary productivity measurements were performed on the whole community. Instead, primary productivity was significantly reduced when CO2 levels were ≥1140 µatm, suggesting CCM downregulation did not have a significant positive effect on growth.

The larger ratio of cell surface area to volume in small cells, allowing increased nutrient utilisation in nutrient-limited environments, has also been invoked to explain the increased abundance of picophytoplankton with elevated CO2 . Size-related differences in growth rates may allow picophytoplankton to establish a bloom faster than larger phytoplankton species e.g.. However, this is not seen in nutrient-replete East Antarctic waters, where early summer blooms are dominated by large diatoms, such as Thalassiosira sp. and Fragilariopsis sp. (>20 µm) as well as the prymnesiophyte P. antarctica in its colonial life stage . It was also not observed in this study, where only the 953 µatm treatment displayed a significantly enhanced picophytoplankton growth rate (Table ). Increased rates of nutrient drawdown were observed in the 634–953 µatm CO2 treatments (Fig. ), suggesting that moderate increases in CO2 may stimulate phytoplankton growth, but further increases in CO2 (≥1140 µatm) led to significant reductions in primary productivity .

Nanophytoplankton abundance was highest in the 643 µatm treatment, with significantly increased growth rates in treatments ≥634 µatm (Fig. b; Table ). This was likely due to favourable conditions, including the inhibition of growth of larger phytoplankton species, that allowed nano-sized phytoplankton to thrive at higher CO2 levels . The initial decline in nanophytoplankton abundance in all treatments between days 1 and 7 may have been due to acclimation of the community to the minicosms or grazing by microzooplankton. Increasing light intensity had a temporary inhibitory effect on growth at CO2 levels ≥1140 µatm between days 8 and 9 (Fig. S2), suggesting that the significantly enhanced growth rates in these treatments between days 9 and 15 may have been caused by an increase in relative abundance of more tolerant species. Interestingly, whilst no negative effect of CO2 was observed on the overall nanophytoplankton abundance, there were very strong species-specific responses to increasing CO2, resulting in a significant change in community structure. In their coincident study, identified the most abundant nanophytoplankton species present in the minicosms as Fragilariopsis sp. (<20 µm) and P. antarctica in its colonial form. These species displayed a CO2-related dominance threshold around 634 µatm, with a shift from P. antarctica to Fragilariopsis sp. in the high-CO2 treatments. Thus, it is likely that the relative fitness of both of these species was increased with a moderate increase in CO2 level, explaining the higher abundance observed at 634 µatm CO2. Increased abundance of Fragilariopsis sp. with elevated CO2 has also been observed in other ocean acidification studies on natural Antarctic microbial communities . Therefore, increasing CO2 might not result in a change in total nanophytoplankton abundance but may instead result in a shift in the summer nanophytoplankton community composition, with increased abundance of small diatoms over P. antarctica colonies.

There is an increased understanding of the prevalence of mixotrophy in the marine microbial community . Therefore, it is possible that mixotrophic nanoflagellates were included in our nanophytoplankton counts due to the presence of chlorophyll in their cells. Mixotrophs are able to utilise both autotrophic and heterotrophic methods of energy production and consumption, although the range methods employed can be diverse . It is currently unknown how mixotrophic phytoflagellates will respond to ocean acidification. speculated that autotrophic energy production may be more efficient with increasing levels of CO2, owing to increased availability of dissolved inorganic carbon species, an essential substrate for photosynthesis, with lower pH. However, the simultaneous increase in [H+] may have negative effects on both heterotrophic and autotrophic cellular mechanisms, causing multiple stresses to mixotrophic physiology. As molecular methods allow for better identification of mixotrophic species, further research into how these species respond to increasing CO2 may now be possible .

The prokaryote community responded favourably to increasing CO2, displaying increased abundance when CO2 levels were ≥634 µatm (Fig. d). This increase in prokaryote abundance with elevated CO2 was also observed in complementary studies at Prydz Bay, which reported consistent increases in prokaryote abundance and production with CO2 levels ≥780 µatm in high- and low-nutrient conditions spanning early to late summer . An increase in prokaryote abundance with increasing CO2 has also been reported in Arctic mesocosms , although in other studies CO2 had no influence on the prokaryote community . Thus, it is anticipated that heterotrophic prokaryotes will tolerate increasing CO2 levels and, in some instances, may thrive reviewed in. Like HNFs, prokaryotes do not require CO2 for growth, although it appears they may be more resistant to large variations in pH. Despite this, there is evidence that CO2 may induce changes in community composition, selecting for more tolerant or rare species . This may be related to differential responses of phylogenetic groups to maintaining pH homeostasis in either acid or alkaline conditions . The mechanisms for transporting H+ out of the cell are energetically demanding and may reduce the energy available for growth. Whether these energy demands are increased or decreased with ocean acidification depends upon the different strategies for pH homeostasis employed by individual prokaryote species . In addition to this, significant increases in growth efficiency with elevated CO2 might not result in an increase in productivity or abundance . Instead, these changes may affect dissolved organic carbon consumption, with potential impacts on organic-matter cycles .

The coincidence of the increase in picophytoplankton and prokaryote abundances with reduced HNFs suggests that these communities were being released from grazing pressure at CO2 levels ≥634 µatm. Grazing rates in East Antarctica are on average 62 % of primary production per day and can reach a maximum of 220 % . In addition, >100 % of prokaryote production can be removed by micro- and nanoheterotrophs when chlorophyll a concentration and prokaryote abundance is high . The rapid decline in abundance we observed in picophytoplankton and prokaryotes after 12 d of incubation is entirely consistent with the rapid rates of grazing observed in other Antarctic marine microbial communities in this region. In relation to fCO2, it is reasonable to hypothesise that the lower abundances of these prey in the control and 506 µatm treatments may have been due to stronger top-down control of the community as opposed to a reduction in growth rate. Grazing control of the picophytoplankton community has been proposed in other mesocosm studies to explain both positive and negative changes in picophytoplankton abundance, although they were not confirmed by HNF counts. In our minicosm study, the rapid decline in prokaryote abundance coincided with a dramatic increase in choanoflagellate abundance – bacterivorous eukaryotes – between days 14 and 16 . Furthermore, both picophytoplankton and prokaryotes in all CO2 treatments declined after HNF abundance appeared to reach a critical threshold (Fig. ), suggesting that at this point their growth was unable to exceed the top-down control of grazing. and , in their complementary studies, also noted that higher numbers of prokaryotes coincided with reduced HNF abundance across differing microbial-community compositions and nutrient availabilities in Prydz Bay, suggesting that this response is likely to be consistent on both seasonal and temporal scales.

Species-specific differences in the sensitivity of HNFs to CO2 may lead to significant changes in the composition of the picophytoplankton and prokaryote communities. HNF food webs are complex, and successional changes in taxa occur during phytoplankton blooms . In their coincident study, observed species-specific differences in the CO2 tolerances of choanoflagellate species, where Bicosta antennigera displayed significant CO2 sensitivity at levels ≥634 µatm, while other choanoflagellate species (principally Diaphanoeca multiannulata) were unaffected. This change in HNF community composition with increased CO2 did not affect the total prokaryote abundance but may have implications for the prokaryotic community composition through selective grazing. Changes in prokaryote community composition have been observed in other mesocosm studies . There is also evidence that different prokaryote phylogenetic groups have preferences for organic substrates produced by different phytoplankton taxa , leading to the possibility that future changes in prokaryote community composition could impact organic-matter recycling.

As viral abundance was not determined in our study, we cannot exclude viral lysis as an explanation for the rapid decline in picophytoplankton and prokaryote abundance. Viral lysis can account for up to 25 % of daily production, although grazing by micro- and nanoheterotrophs can be twice as high . In an Arctic mesocosm study, the decline in a picophytoplankton bloom coincided with a large increase in viral abundance . However, later in the study, picophytoplankton were also heavily grazed by microzooplankton. Bacteriophages are the dominant viruses in the Prydz Bay area , with viral abundance displaying no correlation to picophytoplankton . This suggests that viral lysis was unlikely to be the main cause of the decline in picophytoplankton numbers but may have affected the prokaryotes.