# Ocean acidification shows negligible impacts on high-latitude bacterial community structure in coastal pelagic mesocosms

A. -S. Roy, S. M. Gibbons, H. Schunck, S. Owens, J. G. Caporaso, M. Sperling, J. I. Nissimov, Sarah Romac, Lucie Bittner, M. Muehling, et al.

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► To cite this version:

A. -S. Roy, S. M. Gibbons, H. Schunck, S. Owens, J. G. Caporaso, et al.. Ocean acidification shows negligible impacts on high-latitude bacterial community structure in coastal pelagic mesocosms. Bio-geosciences, 2013, 10 (1), pp.555-566. 10.5194/bg-10-555-2013. hal-01258227

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# Ocean acidification shows negligible impacts on high-latitude bacterial community structure in coastal pelagic mesocosms

A.-S. Roy<fnref n="1" />, S. M. Gibbons<fnref n="2" />,<fnref n="3" />, H. Schunck<fnref n="1" />, S. Owens<fnref n="2" />,<fnref n="8" />, J. G. Caporaso<fnref n="2" />,<fnref n="4" />, M. Sperling<fnref n="1" />,<fnref n="9" />, J. I. Nissimov<fnref n="5" />, S. Romac<fnref n="6" />, L. Bittner<fnref n="6" />, M. Mühling<fnref n="10" />, U. Riebesell<fnref n="1" />, J. LaRoche<fnref n="1" />,*, and J. A. Gilbert<fnref n="2" />,<fnref n="7" />

Correspondence to: A.-S. Roy (sroy@geomar.de)

Received: 31 August 2012 – Published in Biogeosciences Discuss.: 26 September 2012

Revised: 19 December 2012 – Accepted: 2 January 2013 – Published: 29 January 2013

**Abstract.** The impact of ocean acidification and carbonation on microbial community structure was assessed during a large-scale in situ coastal pelagic mesocosm study, included as part of the EPOCA 2010 Arctic campaign. The mesocosm experiment included ambient conditions (fjord) and nine mesocosms with $pCO_2$ levels ranging from ~145 to ~1420 µatm. Samples for the present study were collected at ten time points ($t=1, t1, t5, t7, t12, t14, t18, t22, t26$ to $t28$) in seven treatments (ambient fjord (~145), $2 \times ~185$, ~270, ~685, ~820, ~1050 µatm) and were analysed for “small” and “large” size fraction microbial community composition using 16S rRNA (ribosomal ribonucleic acid) amplicon sequencing. This high-throughput sequencing analysis produced ~20 000 000 16S rRNA V4 reads, which comprised 7000 OTUs. The main variables structuring these communities were sample origins (fjord or mesocosms) and the community size fraction (small or large size fraction). The community was significantly different between the unenclosed fjord water and enclosed mesocosms (both control and elevated $CO_2$ treatments) after nutrients were added to the mesocosms, suggesting that the addition of nutrients is the

primary driver of the change in mesocosm community structure. The relative importance of each structuring variable depended greatly on the time at which the community was sampled in relation to the phytoplankton bloom. The sampling strategy of separating the small and large size fraction was the second most important factor for community structure. When the small and large size fraction bacteria were analysed separately at different time points, the only taxon $pCO_2$ was found to significantly affect were the Gammaproteobacteria after nutrient addition. Finally, $pCO_2$ treatment was found to be significantly correlated (non-linear) with 15 rare taxa, most of which increased in abundance with higher $CO_2$.

## 1 Introduction

The acidification of our oceans, caused predominantly by dissolution of anthropogenic carbon dioxide ($CO_2$) in seawater, has the potential to affect the physiology of marine microbes. Therefore, because marine microbes play a major role in global biogeochemical cycles, this increase may

<footnote n="1">¹GEOMAR Helmholtz Center for Ocean Research Kiel, Düsternbrooker Weg 20, 24105, Kiel, Germany</footnote>

<footnote n="2">²Argonne National Laboratory, Institute for Genomic and Systems Biology, 9700, S. Cass Ave. Lemont, IL 60439, USA</footnote>

<footnote n="3">³Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA</footnote>

<footnote n="4">⁴Department of Computer Science, Northern Arizona University, Flagstaff, AZ 86001, USA</footnote>

<footnote n="5">⁵Plymouth Marine Laboratory, Prospect Place, Plymouth, Devon, PL1 3DH, UK</footnote>

<footnote n="6">⁶CNRS, UMR7144 (Paris 6), Université Pierre et Marie Curie, Station Biologique de Roscoff, Place Georges Teissier, 29682, Roscoff, France</footnote>

<footnote n="7">⁷Department of Ecology and Evolution, University of Chicago, 5801 South Ellis Avenue, Chicago, IL 60637, USA</footnote>

<footnote n="8">⁸Computation Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA</footnote>

<footnote n="9">⁹Alfred-Wegener-Institute, Am Handelshafen 12, 27570, Bremerhaven, Germany</footnote>

<footnote n="10">¹⁰Institute of Biological Sciences, TU Bergakademie Freiberg, Leipzig Str. 29, 09599, Freiberg, Germany</footnote>

<footnote>*present address: Dalhousie University, 5850 University Avenue, Halifax, NS, B3K 6R8, Canada</footnote>
have unforeseen consequences on ocean biogeochemistry (Falkowski et al., 2008; Worden and Not, 2008). Experimental manipulation of the partial pressure of carbon dioxide ($pCO_2$) in marine mesocosms has demonstrated species-specific physiological responses to elevated dissolved $CO_2$ concentrations. For example, delayed or decreased coccolithophore calcification (Delille et al., 2005), a significant increase in photosynthetic capacity (Fu et al., 2008), higher $CO_2$ and $N_2$ fixation (Hutchins et al., 2007), and a decreased abundance of picoeukaryotes (Newbold et al., 2012) have been observed. However, the response of bacterial communities to elevated $pCO_2$ concentrations is less defined, with mixed reports of both significant increases in bacterial protein production (Grossart et al., 2006), and no significant changes in microbial community structure (Tanaka et al., 2008; Allgaier et al., 2008; Newbold et al., 2012). For example, during the 2008 PeECE III mesocosms study, elevated $pCO_2$ had no significant impact on bacterial abundance, diversity, or activity; however, the community structure of the small size fraction bacteria was significantly altered by the induced phytoplankton bloom (Allgaier et al., 2008; Arnosti et al., 2011; Riebesell et al., 2008).

While these existing studies have observed little impact of elevated $pCO_2$ on microbial community structure, they were all performed with molecular techniques that offered limited taxonomic resolution (e.g. High-Performance Liquid Chromatography, Denaturing Gradient Gel Electrophoresis, Terminal Restriction Fragment Length Polymorphism). To improve that resolution, this study employed high-throughput amplicon sequencing of 16S rRNA to characterize microbial taxonomic community dynamics. High-throughput amplicon sequencing provides an efficient method to obtain a deep molecular overview of microbial community structure, without having to cultivate environmental isolates (Agogué et al., 2011; Gilbert et al., 2009; Hubert et al., 2007; Huse et al., 2008; Margulies et al., 2005; Sogin et al., 2006). In this study, the variation of microbial assemblages was characterised through time, across a gradient of $pCO_2$, in a large-scale in situ pelagic mesocosm experiment in the coastal Arctic Ocean. In addition to characterizing the detailed response of the microbial community structure to elevated $pCO_2$, the analysis of the 16S rRNA database provided insight on the effect of isolating the water column in a mesocosm, and to investigate the community structure response to elevated $pCO_2$.

# 2 Methods

## 2.1 Location and carbonate system manipulation

The European Project on Ocean Acidification (EPOCA) supported a large mesocosm experiment in the Arctic which was conducted in the water of Kongsfjorden, Svalbard, Norway (78°56.2' N, 11°53.6' E) during the months of June and July

2010. Throughout the experiment, diverse environmental parameters were measured to explore the effect of ocean acidification (OA) on multiple biological processes. Briefly, nine mesocosms containing about 45 m³ of seawater and reaching down to 15 m depth were deployed from Ny-Ålesund and $pCO_2$ was manipulated by the addition of $CO_2$-saturated seawater in seven mesocosms, resulting in initial $pCO_2$ ranging from ~286 to ~1420 µatm. Two of the mesocosms were not manipulated and served as controls with starting $pCO_2$ of ~185 µatm. Additionally, samples were taken directly from the fjord (initial $pCO_2$ ~145 µatm) in which the mesocosms were suspended and from which the mesocosm water originated. These samples were used to monitor any natural changes in $pCO_2$ that may occur in the ambient water during the course of the experiment and were also important for detecting deviations in $pCO_2$ between the fjord and the untreated mesocosms with time. To promote phytoplankton growth, all nine mesocosms were subjected to nutrient additions (nitrate ($NO_3$), phosphate ($PO_4$) and silicate (Si)) on day (t) 13, creating pre-nutrient (t-1 to t12) and post-nutrient (t13 to t30) periods (Fig. 1). Detailed information about the experimental set-up, the mesocosms deployment, the carbonate chemistry, and the nutrients additions can be found in this issue in Riebesell et al. (2013), Czerny et al. (2013a, b), Bellerby et al. (2013), and Schulz et al. (2013), respectively.

## 2.2 Sampling, filtration and sample selection

A total of 10 L of water was collected using integrated water sampler (Hydrobios, Kiel, Germany) between 0 and 12 m water depth, from the fjord (~145 µatm), and six mesocosms (starting $pCO_2$ = 2× ~185, ~270, ~685, ~820, ~1050 µatm) on t-1, t1, t5, t7, t12, t14, t18, t22, t26 and t28 (Fig. 1). Only six of the mesocosms were chosen for this study due to time, personnel and equipment constraints. The collected water was first pre-filtered on a 20 µm sieve, and sequentially filtered through a 10 µm, a 3 µm filter to isolate associate-particle bacterial fraction (large size fraction) and through a 0.2 µm filter to isolate the small size fraction (Durapore® 47 mm, Millipore). To avoid nucleic acid degradation, processing of the samples from filtration to flash-freezing (in liquid nitrogen) was performed within 30 min of the sampling event. Samples were then stored at -80 °C until DNA/RNA extraction.

## 2.3 DNA extraction, PCR, and Sequencing

Total nucleic acid was extracted from the 0.2 and 3 µm filters using the “Total RNA and DNA purification – NucleoSpin® RNA II RNA/DNA buffer” kits from Macherey-Nagel (Macherey-Nagel GmbH & Co. KG, Düren, Germany). Standard protocol with minor modifications was followed. Changes to the protocol included making the filters brittle by immersing the samples in liquid nitrogen while still
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Chlorophyll *a* (µg L⁻¹) concentration measurements plotted against days, where arrows mark time points analysed in the present study. Figure derived from Schulz et al. (2013).</figcaption></figure>

in the cryovials to facilitate disruption and homogenization. The filters were crushed with RNase-free plastic pestles and lysozyme was directly added to the broken filter pieces while still in the cryovial. Both the RNA and DNA were isolated during the experiment. However, the RNA was kept for further purposes. DNA quality and quantity were assessed by micro-volume spectrophotometer nanodrop ND-1000 (PepLab GmbH, Erlangen, Germany) measurements. All samples were kept at -80 °C until further analysis.

Polymerase chain reaction (PCR) and sequencing were
performed following the Illumina HiSeq2000 and MiSeq V4-
16S rRNA protocol (Caporaso et al., 2012). Briefly, the V4
region of the 16S rRNA gene was amplified with region-
specific primers that included the Illumina paired-end flow-
cell adapter sequences (Illumina Inc., CA, USA). The bar-
code was read using the custom index sequencing primer
in an additional cycle (12 bp). Each sample was amplified
in triplicate, and was pooled afterwards. Each 25 µL PCR
reaction contained 12 µL of MoBio PCR Water (certified
DNA-free), 10 µL of 5 Prime HotMasterMix, 1 µL of For-
ward Primer (5 µM initial concentration), 1 µL Golay Bar-
code Tagged Reverse Primer (5 µM initial concentration),
and 1 µL of template DNA. The reactions were heated to
94 °C for 3 min for their initial denaturation, followed by 35
cycles in series of 94 °C for 45 s, 50 °C for 60 s, and 72 °C
for 90 s. The amplicons were quantified using Quant-it™
Picogreen® (Invitrogen by Life Technologies™, CA, USA),
and pooled in equal amounts (ng) into a 1.5 mL tube. Once
pooled, the entire amplicon pool was cleaned up with the
MO-BIO UltraClean® PCR Clean-Up Kit (MO-BIO Lab-
oratories, Inc., CA, USA). Finally, the pooled samples were

quantified using a Qubit® fluorometer (Invitrogen by Life
Technologies™, CA, USA), and the molarity was estimated
based on amplicon length. From this estimate, dilutions were
made down to 2 µM and the standard Illumina sample prepara-
ration for sequencing was followed. Pooled amplicons were
sequenced using custom sequencing primers, Read 1, Read
2, and Index. These sequencing primers were designed to
be complementary to the V4 amplification primers to avoid
sequencing of the primers. Amplicons were sequenced in
a paired-end, 100 bp × 100 bp cycle run on the Illumina
HiSeq2000, at a concentration of 4 pM with a 10% PhiX
spike. An entire control lane devoted to PhiX is also useful
when sequencing low base diversity samples, like amplicons,
and was included in the present analysis.

## 2.4 Sequence data analysis

All sequence analyses were performed using Quantitative In-
sights Into Microbial Ecology v. 1.5.0 (QIIME; Caporaso et
al., 2010). QIIME defaults were used for quality filtering of
raw Illumina data (including chimeras). Unique operational
taxonomic units (OTUs) were picked against the Greengenes
(McDonald et al., 2012) database and pre-clustered at 97 %
identity; sequences that did not hit the reference collection
were discarded. Representative sequences were aligned to
the Greengenes core set with PyNAST (Caporaso et al.,
2010). All sequences that failed to align were discarded. A
phylogenetic tree was built from the alignment, and taxon-
omy was assigned to each sequence using the Ribosomal
Database Project (RDP) classifier (Wang et al., 2007) re-
trained on Greengenes. Samples were rarefied to an even
depth of 81 181 sequences and only the OTUs that appeared
at least twice in the dataset were included in the further anal-
yses; 106 singleton OTUs were not included in this analysis.

2.5 Statistical analysis

Multivariate analysis of microbial community structure was
carried out in CANOCO 4.54 (ter Braak and Šmilauer, 2002),
where the count of each OTU (97 % similarity) was used as
a measure of abundance. All analyses had samples as scal-
ing focus, and all species data were Hellinger-transformed
using the program PrCoord 1.0 (Legendre and Gallagher,
2001; ter Braak and Šmilauer, 2002). Analysis of variance
(ANOVA) followed by a Tukey test was done to test for
significant differences between treatments (i.e. control vs.
fjord, fjord vs. mesocosm, control vs. mesocosm) within each
abundant phylum. Detrended correspondence analysis of the
transformed OTU abundance data showed axis lengths < 3.0,
suggesting a linear treatment of the data (Ramette, 2007).
Redundancy analysis (RDA), with manual forward selection
and Monte Carlo permutation tests (999 permutations), was
used to evaluate effects of environmental variables (salinity,
temperature, pH, chlorophyll *a*, etc.) on the microbial com-
munity composition. An indirect gradient analysis (PCoA)
was used to plot the distribution of samples in ordination space, with important environmental variables (as indicated by forward selection) overlaid as supplementary data. Microbial community composition differences were assessed by UniFrac (Lozupone and Knight, 2005) distance using QIIME (Caporaso et al., 2010).

In order to assess whether or not particular taxa were significantly influenced by pCO₂, a Bonferroni-corrected g-test was done using QIIME to remove significance due to chance. All analyses were considered to have a significant difference if *p* < 0.05 after Bonferroni correction.

Contour plots presenting mean abundance count plotted against pCO₂ and time (days) of the three most abundant genus of the OTUs significantly correlated to pCO₂ were created using Ocean data view (Bremen, Germany).

## 3 Results

The 256 sequenced samples generated ~20 000 000 16S rRNA V4 reads (~2 510 000 sequences per treatment); which clustered at 97% sequence identity into 6821 OTUs.

### 3.1 Experimental timeline

Phytoplanktonic bloom evolution was identified using the daily measured chlorophyll *a* (chl *a*) concentration (µg L⁻¹) (Fig. 1). The chl *a* protocol and patterns are presented in Schulz et al. (2013). Briefly, all treatments (fjord included) underwent a natural bloom between *t*0 and *t*11, with highest chl *a* concentrations on *t*6. Subsequently, a second and third strong phytoplankton bloom happened only in the mesocosms following nutrient addition on *t*13. The second bloom had its highest chl *a* concentration on *t*19 and the third one, which varied greatly between mesocosms, reached its highest concentration on *t*27. These 3 blooms were represented as four general phases in phytoplankton chlorophyll phases defined by Schulz et al. (2013): phase 0 occurred from the start of the experiment on *t*−4 until adjustment of CO₂ was completed on *t*4; phase 1 started with the end of CO₂ addition on *t*4 until the nutrient additions on *t*13; phase 2 included the end of the first bloom on *t*13 to the end of the second bloom on *t*22; and phase 3 started from the end of the second bloom on *t*22 and lasted until the end of the experiment, on *t*30 (the chl *a* minimum of the third bloom was not recorded) (Fig. 1). Detailed fluctuations of chl *a*, nutrient concentrations, pH and pCO₂ are presented in this issue in Schulz et al. (2013) and Bellerby et al. (2013).

### 3.2 Community-structuring variables

The significant structuring variables for the total community during the post-nutrient addition period (*t*13–*t*30) of the experiment were (in order of explanatory importance) “fjord vs. mesocosm origin” (i.e. whether the sample was from water contained in a mesocosm or from the open fjord), sampling

strategy (i.e. physical fractionation into small and large particle sizes), Si concentration, PO₄ concentration, mean primary production 14C-POC (PP), temperature (*T*), and pH (Fig. S1 and Table 1). The microbial community in the small size fraction (0.2–3 µm) from the fjord and all the analysed mesocosms was dominated by Proteobacteria (in order of abundance: Gamma (γ)-, Alpha (α)- and Beta (β)- proteobacteria) throughout the experiment. However, Proteobacteria began dropping in abundance gradually after *t*7, coincidentally with the increase in the abundance of Bacteroidetes (Fig. 2). In the large size fraction (3–12 µm) Bacteroidetes dominated consistently, while a fourth group comprised of the “Cyanobacteria and eukaryotic chloroplasts” (which included Chlorophyta, Haptophyceae, Rhodophyta and Stramenopiles) were also abundant (Fig. 2). The group classified as “others” in the small size fraction was composed predominately of Cyanobacteria at the beginning of the experiment, and of Actinobacteria towards the end (Fig. S2). In the large size fraction, the “others” group was extremely variable until *t*7. For example, at *t*−1 the fjord “others” group was dominated by the Verrucomicrobia while the mesocosms “others” groups was dominated by Actinobacteria; by *t*5 Firmicutes dominated in most mesocosms, while being almost absent from the fjord. At *t*7, the Actinobacteria was the dominant taxa in the “others” group in all treatments for the remainder of the experiment. At the end (*t*28), some Verrucomicrobia increased in the control, ~270, and ~685 µatm mesocosms (Fig. S2).

Once the community was analysed with regard to filter size fraction (small vs. large size fraction), the structuring community variables varied. The fjord had a significantly different assemblage from the mesocosms in the small and large size fraction before (origin 3%–4%) and after (origin 48%–12%) mesocosm nutrient addition (Table 2); however, the fjord and mesocosm communities were not significantly different until after *t*5. The microbial community in the fjord small size fraction was not significantly different from the mesocosms communities in the pre-nutrient addition phase and only the γ-proteobacterial abundance was significantly different (*p* < 0.05) between fjord and mesocosm in the post-nutrient addition phase. The fjord large size fraction microbial community was significantly different from the mesocosms during both the pre- and post-nutrient addition phases. In particular, the “Cyanobacteria and eukaryotic chloroplasts” group was significantly different between fjord and mesocosms pre- and post-nutrient addition; while the Bacteroidetes, α-proteobacteria and “others” were only significantly different post-nutrient addition (Fig. 3 and Table 3). Furthermore, the significant variables that correlated with community structure changes in the small size fraction were dimethyl sulphide (DMS-16%), bacterial production (bp-15%), density (d-12%) for the pre-nutrient period (t−4 to *t*12), and origin (48%), pCO₂ (10%), day (10%) for the post-nutrient period (*t*13–*t*30; Table 2). For the large size fraction, these variables were oxygen (O₂-7%), DMS (7%),
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Microbial community overview of the most abundant phyla in (a) the small (0.2–3 µm) and (b) the large (3–12 µm) size fraction during *t*−1, *t*5, *t*7, *t*12, *t*14, *t*18, *t*22, *t*26 and *t*28; x-axis represents percentage of total OTUs and y-axis represents *pCO*₂ in µm</figcaption></figure>

nitrate (NO₃-5 %) and origin (4 %) for the pre-nutrient period (*t*−4 to *t*12), and Si (27 %) and origin (12 %) for the post-nutrient addition period (*t*13–*t*30; Table 2). Therefore, the differences in the microbial community structure between the fjord and mesocosms were primarily due to the addition of nutrients to the mesocosms, and not to *pCO*₂ manipulation, as the control mesocosms were not significantly different from the elevated CO₂ mesocosms post-nutrient addition.

### 3.3 pCO₂ effect on microbial community

Although the *pCO*₂ treatment was not identified as a major community structuring variable, the relative abundances of 15 rare taxa (% abundance across time and treatment was <0.22%; Table 4) were significantly correlated to *pCO*₂ levels. From these 15 rare taxa in both small and the large size fractions, 12 showed a significant but slight increase with *pCO*₂, having their maximum abundances in either the medium (~685 and ~820 µm) or the high (~1050 µm) *pCO*₂ mesocosms. The remaining three decreased, with their highest abundances in the lowest (~185 µm) *pCO*₂ meso-

<table><caption>**Table 1.** Redundancy analysis showing the significant structuring variables for the whole bacterial community during the post-nutrient addition period (*t*13–*t*30). Significant values are *p* < 0.05.</caption><thead><tr><th>Variables</th><th>%</th><th>p</th><th>F</th></tr></thead><tbody><tr><td>Origin</td><td>25</td><td>0.001</td><td>24.84</td></tr><tr><td>Fraction</td><td>14</td><td>0.001</td><td>17.77</td></tr><tr><td>Si</td><td>8</td><td>0.001</td><td>11.32</td></tr><tr><td>PO<sub>4</sub></td><td>2</td><td>0.01</td><td>2.83</td></tr><tr><td>Primary production</td><td>2</td><td>0.026</td><td>2.31</td></tr><tr><td>Temperature</td><td>2</td><td>0.042</td><td>2.26</td></tr><tr><td>pH</td><td>1</td><td>0.029</td><td>2.36</td></tr></tbody></table>

<table><caption>**Table 2.** Results from RDA forward selection (with Monte Carlo permutation tests) showing only the significant (*p* < 0.05) structuring variables for the small (0.2–3 µm) and the large (3–12 µm) size fraction during the pre-nutrient period from *t*1 to *t*12 (a and c, respectively) and post-nutrient period from *t*13 to *t*30 (b and d, respectively).</caption><thead><tr><th>Variable</th><th>%</th><th>p</th><th>F</th></tr></thead><tbody><tr><td colspan="4"><strong>Small size fraction</strong></td></tr><tr><td>(a) Dimethyl Sulphide</td><td>16</td><td>0.001</td><td>9.79</td></tr><tr><td>Bacterial production</td><td>15</td><td>0.001</td><td>10.73</td></tr><tr><td>Density</td><td>12</td><td>0.001</td><td>9.65</td></tr><tr><td>NO<sub>2</sub></td><td>5</td><td>0.001</td><td>4.54</td></tr><tr><td>Day</td><td>2</td><td>0.024</td><td>2.14</td></tr><tr><td>Origin</td><td>3</td><td>0.014</td><td>2.53</td></tr><tr><td>(b) Origin</td><td>48</td><td>0.001</td><td>35.75</td></tr><tr><td>pCO<sub>2</sub></td><td>10</td><td>0.001</td><td>8.77</td></tr><tr><td>Day</td><td>10</td><td>0.001</td><td>11.43</td></tr><tr><td>CO<sub>2</sub></td><td>4</td><td>0.001</td><td>4</td></tr><tr><td>Mesocosm</td><td>2</td><td>0.002</td><td>3.27</td></tr><tr><td>Turbidity</td><td>3</td><td>0.001</td><td>3.91</td></tr><tr><td>Primary production 14C</td><td>1</td><td>0.007</td><td>2.3</td></tr><tr><td>NH<sub>4</sub></td><td>2</td><td>0.019</td><td>2.06</td></tr><tr><td>Density</td><td>1</td><td>0.032</td><td>1.99</td></tr><tr><td>Temperature</td><td>1</td><td>0.044</td><td>1.72</td></tr><tr><td>PO<sub>4</sub></td><td>1</td><td>0.033</td><td>1.92</td></tr><tr><td colspan="4"><strong>Large size fraction</strong></td></tr><tr><td>(c) O<sub>2</sub></td><td>7</td><td>0.002</td><td>3.81</td></tr><tr><td>Dimethyl sulphide</td><td>7</td><td>0.005</td><td>3.81</td></tr><tr><td>Origin</td><td>4</td><td>0.016</td><td>2.59</td></tr><tr><td>NO<sub>3</sub></td><td>5</td><td>0.014</td><td>2.95</td></tr><tr><td>(d) Si</td><td>27</td><td>0.001</td><td>13.36</td></tr><tr><td>Origin</td><td>12</td><td>0.001</td><td>7.11</td></tr><tr><td>PO<sub>4</sub></td><td>4</td><td>0.039</td><td>2.24</td></tr></tbody></table>

cosm, or before manipulation started (Fig. 4, Figs. S3 and S4). The three most abundant of these 15 taxa were Methylotenera (β-proteobacteria), Colwellia (γ-proteobacteria) and Fluviicola (Bacteroidetes). Methylotenera and Colwellia abundances were at their highest in, respectively, the ~686
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Mean abundance (±SE) of the main phyla of the bacterial community for the fjord (~145 µatm), the control (2 × ~185 µatm) and the manipulated mesocosms (~270, ~685, ~820, ~1050 µatm) of the small (a) and large (b) size fraction pre- (left) and post- (right) nutrient addition. Phyla with significantly different p values (<0.05) as a function of samples origin are marked with an *.</figcaption></figure>

and ~824 µatm mesocosms toward the end of the experiment (t22). Fluviicola was present from the beginning of the experiment, but decreased precipitously after CO2 was added and then recovered in abundance after t10, reaching its highest abundance in the 1050 µatm mesocosm between t12 and t22 (Fig. 4).

# 4 Discussion

## 4.1 Mesocosms and structuring effects

In this study, a large-scale mesocosm experiment was used to investigate the impacts of OA on the microbial community structure in a coastal, high latitude marine pelagic ecosystem. The experimental design provided the opportunity to test for the effects of four different pCO2 concentrations (~270, ~685, ~820, ~1050 µatm) against two negative controls (~185 µatm) over a six-week period. In addition, mesocosm-specific experimental artefacts were monitored by sampling the fjord microbial community throughout the course of the experiment. The microbial community structure post-nutrient-addition (t13) was significantly corre-

lated with seven variables, the most influential of which was sample origin (fjord or mesocosm). The overall community structure was not significantly different between mesocosms (including control versus elevated pCO2) over the course of the experiment. The significant effect of the mesocosm enclosures on microbial community structure could be due to the mesocosms themselves (isolating a microbial community from the surrounding fjord community) or since the effect was not significant before nutrient addition, more likely due to the addition of nutrients into the mesocosms at t13.

The sampling strategy separating the community into size fractions was the second most important variable in explaining differences in community structure. Before nutrient addition, the communities in the small size fraction were not significantly different between the fjord (ambient), control mesocosms, and the elevated pCO2 mesocosms. However, after the addition of nutrients, γ-proteobacterial abundances were significantly different between fjord and mesocosms, and probably reflected the utilization of metabolites released by decaying phytoplankton in the post-bloom system. In particular, the overall abundance of Bacteroidetes in the small and large size fractions increased in post-blooms conditions,
<table><caption>**Table 3.** Analysis of variance (ANOVA) showing the relationship in between each treatment pre- and post-bloom condition for (a) small and (b) large size fraction bacteria of phyla with significant differences. Significant values are *p* < 0.05.</caption><thead><tr><th>Time</th><th>Phylum</th><th>Treatment</th><th>p</th></tr></thead><tbody><tr><td rowspan="6">(a) Post-nutrient addition</td><td rowspan="3">Gamma-proteobacteria</td><td>fjord-control</td><td>0.001</td></tr><tr><td>mesocosm-control</td><td>0.140</td></tr><tr><td>mesocosm-fjord</td><td>0.038</td></tr><tr><td rowspan="3">"Cyanobacteria + euk.chloro"</td><td>fjord-control</td><td>0.317</td></tr><tr><td>mesocosm-control</td><td>0.289</td></tr><tr><td>mesocosm-fjord</td><td>0.020</td></tr><tr><td rowspan="12">(b) Post-nutrient addition</td><td rowspan="4">Bacteroidetes</td><td>fjord-control</td><td>0.001</td></tr><tr><td>mesocosm-control</td><td>0.864</td></tr><tr><td>mesocosm-fjord</td><td>0.002</td></tr><tr><td>fjord-control</td><td>0.002</td></tr><tr><td rowspan="3">Alpha-proteobacteria</td><td>mesocosm-control</td><td>0.787</td></tr><tr><td>mesocosm-fjord</td><td>0.006</td></tr><tr><td>fjord-control</td><td>0.000</td></tr><tr><td rowspan="2">"Cyanobacteria + euk.chloro"</td><td>mesocosm-control</td><td>0.839</td></tr><tr><td>mesocosm-fjord</td><td>0.001</td></tr><tr><td rowspan="3">"Others"</td><td>fjord-control</td><td>0.000</td></tr><tr><td>mesocosm-control</td><td>0.320</td></tr><tr><td>mesocosm-fjord</td><td>0.001</td></tr></tbody></table>

<table><caption>**Table 4.** Bonferroni-corrected g-test of significance (*p* < 0.05) listing 15 taxa significantly correlated with CO₂, for both small and large size fraction; bold highlights mark the taxa presented in Fig. 4. Greengenes OTU identifiers refer to prokMSA ids in the Greengenes database.</caption><thead><tr><th>Greengenes OTU Identifier</th><th>Taxa</th><th>Abundance</th><th>% total sequences (20 863 517)</th><th>Response to elevated pCO<sub>2</sub></th><th>p</th></tr></thead><tbody><tr><td>114 612</td><td>Methylotenera (genus)</td><td>2907</td><td>0.014</td><td>Highest in middle pCO<sub>2</sub></td><td>0.000</td></tr><tr><td>144 699</td><td>Oceanospirillaceae (family)</td><td>1182</td><td>0.006</td><td>Increased with pCO<sub>2</sub></td><td>0.000</td></tr><tr><td><b>105 727</b></td><td><b>Methylotenera (genus)</b></td><td><b>45 915</b></td><td><b>0.220</b></td><td><b>Highest in middle pCO<sub>2</sub></b></td><td><b>0.000</b></td></tr><tr><td>151 803</td><td>Flavobacteriaceae (family)</td><td>1841</td><td>0.009</td><td>Increased with pCO<sub>2</sub></td><td>0.000</td></tr><tr><td>522 744</td><td>Leucothrix (genus)</td><td>130</td><td>0.001</td><td>Decreased with pCO<sub>2</sub></td><td>0.000</td></tr><tr><td>419 525</td><td>Sphingobacteriales (order)</td><td>171</td><td>0.001</td><td>Increased with pCO<sub>2</sub></td><td>0.000</td></tr><tr><td>94 238</td><td>Oxalobacteraceae (family)</td><td>322</td><td>0.002</td><td>Highest in middle pCO<sub>2</sub></td><td>0.000</td></tr><tr><td><b>402 252</b></td><td><b>Fluviicola (genus)</b></td><td><b>20 950</b></td><td><b>0.100</b></td><td><b>Increased with pCO<sub>2</sub></b></td><td><b>0.001</b></td></tr><tr><td>592 739</td><td>Oleibacter (genus)</td><td>2976</td><td>0.014</td><td>Highest in middle pCO<sub>2</sub>/Increased</td><td>0.001</td></tr><tr><td>262 549</td><td>HTCC-1288 (genus)</td><td>25</td><td>0.001</td><td>Mixed, highest in high-middle pCO<sub>2</sub></td><td>0.001</td></tr><tr><td>140 859</td><td>Flavobacterium Succinicans (species)</td><td>344</td><td>0.0001</td><td>Decrease with pCO<sub>2</sub></td><td>0.004</td></tr><tr><td><b>235 556</b></td><td><b>Colwellia (genus)</b></td><td><b>32 153</b></td><td><b>0.154</b></td><td><b>Highest in high-middle pCO<sub>2</sub></b></td><td><b>0.008</b></td></tr><tr><td>591 187</td><td>Flavobacteria (class)</td><td>231</td><td>0.001</td><td>Decrease with pCO<sub>2</sub></td><td>0.010</td></tr><tr><td>243 032</td><td>Thioclava (genus)</td><td>59</td><td>0.0003</td><td>Mixed, highest in high pCO<sub>2</sub></td><td>0.011</td></tr><tr><td>554 148</td><td>SC3-41 (family)</td><td>571</td><td>0.003</td><td>Minimum increase</td><td>0.027</td></tr></tbody></table>

possibly also as a result of the dissolved organic carbon (DOC) released by a decaying algal bloom and aggregation of dying phytoplankton, respectively. The γ-proteobacteria and Bacteroidetes generally include many phytodetritus-assimilating organisms (Teske et al., 2011; Abell and Bowman, 2005; Pinhassi et al., 2004) and this would explain their increase in abundance during the demise of the bloom. Despite the observation that Bacteroidetes showed bloom-related dynamics, and contradictory to the findings of Zhang

et al. (2012), no significant difference in the Bacteroidetes abundance (in either fraction) was found between the control and elevated pCO₂ mesocosms, suggesting that elevated pCO₂ did not impact the relative abundance of Bacteroidetes. However, their abundance in the fjord was significantly lower than in the mesocosms, suggesting that the nutrient addition or influence of the mesocosm enclosure did have an impact.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Contour plots presenting the continuous interpolated mean abundance count of the three most abundant taxa that are significantly affected by pCO₂ levels (a) Methylotenera (b) Colwellia and (c) Fluviicola plotted against pCO₂ (μatm, y-axis) and time (days, x-axis). Left and right panel represent, respectively, the small (0.2–3 μm) and large (3–12 μm) size fraction.</figcaption></figure>

The large size fraction in the mesocosms also showed differences in the relative abundance of dominant phyla following nutrient addition (t13). It has previously been established that particle-associated assemblages were predominantly connected to phytoplankton development (Riemann et al., 2000; Allgaier et al., 2008). Furthermore, differences in the “Cyanobacteria and eukaryotic chloroplasts” group were measurable before nutrient addition. However these differences appear to be related to the natural phytoplankton bloom (which occurred in the fjord and mesocosms) that

reached its maximum on t7. The “post-nutrient addition” differences were significant between the fjord and mesocosms for almost every abundant phyla throughout the different phytoplankton phases; suggesting that nutrient addition influenced autotrophic and heterotrophic microbial community structure. However, no significant differences were found between the control and the elevated mesocosms, which suggests that high pCO₂ level was not an important community-structuring variable for the large size fraction in this experiment. Silica was the third most important structuring variable
and is potentially related to diatom abundance (de Kluijver et al., 2010). The recycling of Si from decaying diatoms, after a phytoplankton bloom, is carried out by a diverse fast growing bacteria related to cytophagales (from Flavobacteria; Riemann et al., 2000). Indeed, an increase in the abundance of Bacteroidetes, which contains the Flavobacteria, was observed in the post-nutrient addition phase.

However, no single environmental variable could account for the microbial community composition of the large and small size fractions for all of the phases of the mesocosm experiment (Fig. 1). Rather a shift was observed between pre- and post-nutrient addition with DMS concentration as the most influential variable for the small size fraction under pre-nutrient addition, while origin (Fjord vs. mesocosm) was most influential under post-nutrient addition conditions. Oxygen and Si were the most significant structuring variables for the large size fraction for the pre- and post-nutrient addition, respectively. Variables associated with phytoplankton bloom dynamics were most important for structuring the community, especially when looking at the taxonomic shifts between fjord, control mesocosms and elevated pCO₂ mesocosms. The differences were greater after *t*13 because of the two subsequent phytoplankton blooms that were triggered by the nutrient addition. The differences were most evident in the large size fraction, probably due to the association of the bacterial community with phytoplankton aggregates. Therefore, it is possible to state that nutrients, and the phytoplankton blooms, were the main drivers of microbial community structure in this experiment, which is in agreement with previous (Allgaier et al., 2008; de Kluijver et al., 2010) and present studies (Sperling et al., 2013).

## 4.2 Elevated pCO₂ effect

The effect of elevated pCO₂ on microbial community structure has also been investigated in previous (Newbold et al., 2012) or present mesocosms (Zhang et al., 2012), where no evidence of a major pCO₂ effect on the general bacterial community was found. However, other work suggests that only the community structure of the small size fraction bacteria is significantly affected by elevated pCO₂ (Allgaier et al., 2008). The extensive database of 16S rRNA sequence obtained in this study provided the high resolution necessary to study subtle but significant changes in community structure hinted at in prior studies. In agreement with Allgaier et al. (2008), the effect of elevated pCO₂ in this experiment was slight and only impacted the small size fraction bacteria after nutrient addition, which corresponded to post-nutrient addition and post-bloom conditions (after *t*13) in this study. This increased post-bloom CO₂ effect was previously observed in other mesocosms experiments (Arnosti et al., 2011; de Kluijver et al., 2010), confirming a possible increased CO₂ effect under nutrient (N, P, Si) limitation.

While pH was shown to be a weak driver of microbial community structure in our experiment, the direct impact of

pCO₂ was found to be non-significant, except for 15 rare taxa, which did show a response to elevated CO₂. Therefore, the level of taxonomic resolution afforded by this study suggests that, in this ecosystem, rare organisms may be disproportionately affected by acidification. The most abundant of these 15 rare taxa was Methylotenera (genus) and had its highest mean abundance in the medium pCO₂ mesocosms (~685 µatm). Species from this genus are generally aerobic, ubiquitous bacteria found in a wide range of O₂, salinity, temperature and pH. Methylotenera can colonize multiple pH range (5 to 8.5) but grows optimally at pH 7.5 (Kalyuzhnaya et al., 2006; Bosch et al., 2009), suggesting that pH may strongly influence for distribution of this taxa. Indeed, the pH close to this value from *t*5 until the end of the experiment in the mesocosms with a pCO₂ over ~685 µatm. The highest abundance was found from *t*22 until *t*28 where the pH was 7.9 and 7.94. A lower pH was found (pHT 7.57–7.80) in the ~1050 µatm mesocosm but this was not accompanied by an increase in Methyonera abundance, potentially because the pCO₂ concentration itself was toxic to this species at this stage or this could represent mesocosm variability, suggesting a need for improved replication. Functionally, the species included in this genus have been described as bacteria that require organic compounds containing no carbon-carbon bonds (C₁ compounds) like methylamine and/or methanol as energy sources (Lidstrom, 2006; Kalyuzhnaya et al., 2006, 2010). These organic compounds play an important role in the global carbon cycle because they are greenhouse gases whose emissions are on a scale similar to methane (Chistoserdova et al., 2009). Further investigation of the behaviour of these C₁-compound-degraders in response to elevated CO₂ are, therefore, important for understanding biotic influences on climate dynamics. The second most abundant group of the 15 pCO₂-correlated rare taxa was Colwellia, which is a versatile group with broad temperature range tolerance. For example, the psychrophilic Arctic marine strain *Colwellia psychrerythraea* grows at a range of temperature from -1 to 10 °C (optimal growth 8 °C), *Colwellia chuckchiensis* at a range from 0 to 30 °C and *Colwellia asteriadis* spp. at a range from 4 to 25 °C. These organisms are also capable of colonising a wide range of pH from 4 to 10 (Yu et al., 2011; Choi et al., 2010; Methé et al., 2005). *C. psychrerythrea* is considered a model organism for psychrophiles and shows multiple molecular adaptations to the cold, like enzymes for cryoprotection, for dissolving high-molecular-weight organic compounds (ex. carbon), for stability in extreme environments (extracellular polymeric substances) and for cold-active processes (Methé et al., 2005; Huston et al., 2004). These features make Colwellia spp. key participants in carbon and nutrient cycling in the cold marine environments. Since some methanogenic enzymes were previously found in Colwellia spp. (Methé, et al., 2005) one can speculate that these compounds were found in greater abundance toward the end of the experiment. This would also support the presence of the Methylotenera, which increased
in abundance towards the end of the experiment. Finally the
genus Fluviicola, the third most abundant OTU correlated
with pCO2, was dominant in the elevated CO2 mesocosms
(~1058 µatm). Interestingly, Fluviicola was present at the
beginning of the experiment but decreased shortly after CO2
treatment started. The abundance increased under elevated
pCO2, but stayed low in medium pCO2 mesocosms and ab-
sent in the controls, for both size fractions. Little is known
about this genus, making speculations about its ecological
role difficult.

5 Conclusions

In summary, multiple parameters were found to significantly
influence the structure of the bacterial community in Sval-
bard mesocosms. The most influential factors were the ori-
gin of the sample (fjord or mesocosms) and nutrient addi-
tion. Furthermore, the relative importance of sampling strat-
egy (small versus large size fraction), Si, PO4, primary pro-
duction, temperature, and pH in structuring the community
depended greatly on the time at which the community was
sampled in relation to the phytoplankton blooms. The di-
rect impact of pCO2 was found to be significant for only
15 rare taxa and should be further investigated as analy-
sis of low abundance community members is known to be
problematic in 16S surveys (Bokulich et al., 2013). If con-
firmed, this limited pCO2 effect could have evolutionary
consequences creating a shift in the taxa dominance and/or
diversity, profoundly affecting the structure of entire com-
munity in a high CO2 world. However, it should be noted
that the pCO2 conditions in which these organisms domi-
nated were super-elevated compared to predicted outcomes
for the surface ocean under current climate change scenar-
ios. Furthermore, the evolutionary response of the unicellu-
lar eukaryote Emiliania huxleyi to elevated CO2 was studied
by Lohbeck et al. (2012) and showed that only 500 asexual-
generations were necessary to permit evolution either via
adaptive changes from diverse genotype selection or via new
mutations. It would be interesting to investigate how the bac-
terial communities from the present mesocosms experiment
would evolve faced to extended elevated CO2 exposure, al-
lowing a longer population growth.

Future work should focus on exploring the
functional responses of the community (metage-
nomics/metatranscriptomics) to evaluate how elevated
pCO2 or OA influence these processes over a longer time
period.

**Supplementary material related to this article is**
**available online at:** http://www.biogeosciences.net/10/
555/2013/bg-10-555-2013-supplement.pdf.

*Acknowledgements.* This work is a contribution to the "European Project on Ocean Acidification" (EPOCA), which received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 211384. We gratefully acknowledge the logistical support of Greenpeace International for its assistance with the transport of the mesocosm facility from Kiel to Ny-Ålesund and back to Kiel. We also thank the captains and crews of M/V *ESPERANZA* of Greenpeace and R/V *Viking Explorer* of the University Centre in Svalbard (UNIS) for assistance during mesocosm transport and during deployment and recovery in Kongsfjorden. We thank the staff of the French-German Arctic Research Base at Ny-Ålesund, in particular Marcus Schumacher, for on-site logistical support. We would like to thank Mesoaqua for supporting J. I. N. in the field trip time to Svalbard on grant METAPOCA. We also want to thank Tina Baustian for her help during the RNA/DNA extraction. Finally, the funding for S. M. G. was provided by NIH Training Grant 5T-32EB-009412.

The service charges for this open access publication
have been covered by a Research Centre of the
Helmholtz Association.

Edited by: T. F. Thingstad

References

Abell, G. C. J. and Bowman, J. P.: Ecological and biogeographic relationships of class Flavobacteria in the Southern Ocean, FEMS Microbiol. Ecol., 51, 265–277, 2005.

Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L., and Herndl, G.
J.: Water mass-specificity of bacterial communities in the North
Atlantic revealed by massively parallel sequencing, Mol. Ecol.,
20, 258–274, 2011.

Allgaier, M., Riebesell, U., Vogt, M., Thyrhaug, R., and Grossart, H.-P.: Coupling of heterotrophic bacteria to phytoplankton bloom development at different pCO2 levels: a mesocosm study, Biogeosciences, 5, 1007–1022, doi:10.5194/bg-5-1007-2008, 2008.

Arnosti, C., Grossart, H. P., Mühling, M., Joint, I., and Passow, U.: Dynamics of extracellular enzyme activities in seawater under changed atmospheric pCO2: a mesocosm investigation, Aquat. Microb. Ecol., 64, 285–298, 2011.

Bellerby, R. G. J., Silyakova, A., Nondal, G., Slagstad, D., Czerny, J., De Lange, T., and Ludwig, A.: Marine carbonate system evolution during the EPOCA Arctic pelagic ecosystem experiment in the context of simulated future Arctic ocean acidification, Biogeosciences, in review, 2013.

Bokulich, N. A., Subramanian, S., Faith, J. J., Gevers, D., Gordon, J. I., Knight, R., Mills, D. A., and Caporaso, J. G.: Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing, Nat. Methods, 10, 57–59, doi:10.1038/nmeth.2276, 2013.

Bosch, G., Wang, T., Latypova, E., Kalyuzhnaya, M. G., Hack-
ett, M., and Chistoserdova, L.: Insights into the physiology of
Methylotenera mobilis as revealed by metagenome-based shot-
gun proteomic analysis, Microbiology, 155, 1103–1110, 2009.

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bush-
man, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich,
J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights,
D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J., and Knight, R.: QIIME allows analysis of high-throughput community sequencing data, Nat. Methods, 7, 335–336, 2010.

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S. M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J. A., Smith, G., and Knight, R.: Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms, ISME J., 6, 1621–1624, doi:10.1038/ismej.2012.8, 2012.

Chistoserdova, L., Kalyuzhnaya, M. G., and Lapidus, A.: The Expanding World of Methylotrophic Metabolism, Annu. Rev. Microbiol., 63, 477–499, doi:10.1146/annurev.micro.091208.073600, 2009.

Choi, E. J., Kwon, H. C., Koh, H. Y., Kim, Y. S., and Yang, H. O.: Colwellia asteriadis sp. nov., a marine bacterium isolated from the starfish Asterias amurensis, Int. J. Syst. Evol. Micro., 60, 1952–1957, doi:10.1099/ijs.0.016055-0, 2010.

Czerny, J., Schulz, K. G., Boxhammer, T., Bellerby, R. G. J., Büdenbender, J., Engel, A., Krug, S. A., Ludwig, A., Nachtigall, K., Nondal, G., Niehoff, B., Siljakova, A., and Riebesell, U.: Ellement budgets in an Arctic mesocosm CO₂ perturbation study, Biogeosciences, in review, 2013a.

Czerny, J., Schulz, K. G., Ludwig, A., and Riebesell, U.: A simple method for gas exchange measurements in mesocosms and its application for carbon budgeting, Biogeosciences, accepted, 2013b.

de Kluijver, A., Soetaert, K., Schulz, K. G., Riebesell, U., Bellerby, R. G. J., and Middelburg, J. J.: Phytoplankton-bacteria coupling under elevated CO₂ levels: a stable isotope labelling study, Biogeosciences, 7, 3783–3797, doi:10.5194/bg-7-3783-2010, 2010.

Delille, B., Harlay, J., Zondervan, I., Jacquet, S., Chou, L., Wollast, R., Bellerby, R. G. J., Frankignoulle, M., Borges, A. V., Riebesell, U., and Gattuso, J. P.: Response of primary production and calcification to changes of pCO₂ during experimental blooms of the coccolithophorid *Emiliania huxleyi*, Global Biogeochem. Cy., 19, GB2023, doi:10.1029/2004GB002318, 2005.

Falkowski, P. G., Fenchel, T., and Delong, E. F.: The microbial engines that drive Earth's biogeochemical cycles, Science, 320, 1034–1039, 2008.

Fu, F.-X., Mulholland, M. R., Garcia, N. S., Beck, A., Bernhardt, P. W., Warner, M. E., Sañudo-Wilhelmy, S. A., and Hutchins, D. A.: Interactions between changing pCO₂, N₂ fixation, and Fe limitation in the marine unicellular cyanobacterium Crocosphaera, Limnol. Oceanogr., 53, 2472–2484, 2008.

Gilbert, J. A., Field, D., Swift, P., Newbold, L., Oliver A., Smyth, T., Somerfield, P. J., Huse, S., and Joint, I.: The seasonal structure of microbial communities in the Western English Channel, Environ. Microbiol., 11, 3132–3139, 2009.

Grossart, H.-P., Allgaier, M., Passow, U., and Riebesell, U.: Testing the effect of CO₂ concentration on dynamics of marine heterotrophic bacterioplankton, Limnol. Oceanogr., 51, 1–11, 2006.

Hubert, J. A., Welch, D. M., Morrison, H. G., Huse, S. M., Neal, P. R., Butterfield, D. A., and Sogin, M. L.: Microbial population structures in the deep marine biosphere, Science, 318, 97–100, doi:10.1126/science.1146689, 2007.

Huse, S. M., Dethlefsen, L., Huber, J. A., Welch, D. M., Relman, D. A., and Sogin, M. L.: Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing, PLoS Genet, 4, e1000255, doi:10.1371/journal.pgen.1000255, 2008.

Huston, A. L., Methé, B., and Deming J. W.: Purification, Characterization, and Sequencing of an Extracellular Cold-Active Aminopeptidase Produced by Marine Psychrophile Colwellia psychrerythraea Strain 34H, Appl. Environ. Microbiol., 70, 3321–3328, doi:10.1128/AEM.70.6.3321-3328.2004, 2004.

Hutchins, D. A., Fu, F.-X., Zhang, Y., Warner, M. E., Feng, Y., Portune, K., Bernhardt, P. W., and Mulholland, M. R.: CO₂ control of Trichodesmium N₂ fixation, photosynthesis, growth rates, and elemental ratios: Implications for past, present, and future ocean biogeochemistry, Limnol. Oceanogr., 52, 1293–1304, 2007.

Kalyuzhnaya, M. G., Bowerman, S., Lara, J. C., Lidstrom, M. E., and Chistoserdova, L.: Methylotenera mobilis gen. nov., sp. nov., an obligately methylamine-utilizing bacterium within the family Methylophilaceae, Int. J. Syst. Evol. Microbiol., 56, 2819–2823, 2006.

Kalyuzhnaya, M. G., Beck, D. A. C., Suciu, D., Pozhitkov, A., Lidstrom, M. E. and Chistoserdova, L.: Functioning in situ: gene expression in Methylotenera mobilis in its native environment as assessed through transcriptomics, ISME J., 4, 388–398, 2010.

Legendre, P. and Gallagher, E. D.: Ecologically meaningful transformations for ordination of species data, Oecologia, 129, 271–280, 2001.

Lidstrom, M. E.: Aerobic methylotrophic prokaryotes, in: The Prokaryotes, edited by: Balows, A., Truper, H. G., Dworkin, M., Harder, W., and Schleifer, K.-H., Springer, New York, 618–634, 2006.

Lohbeck, K. T., Riebesell, U., and Reusch, T. B. H.: Adaptive evolution of a key phytoplankton species to ocean acidification, Nat. Geosci., 5, 346–351, doi:10.1038/NGEO1441, 2012.

Lozupone, C. and Knight, R.: UniFrac: a new phylogenetic method for comparing microbial communities, Appl. Environ. Microbiol., 71, 8228–8235, 2005.

Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y.-C., Chen, Z., Dewell, S. B., Du, L., Fierro, J. M., Gomes, X. V., Godwin, B. C., He, W., Helgesen, S., He Ho, C., Irzyk, G. P., Jando, S. C., Alenquer, M. L. I., Jarvie, T. P., Jirage, K. B., Kim, J.-B., Knight, J. R., Lanza, J. R., Leamon, J. H., Lefkowitz, S. M., Lei, M., Li, J., Lohman, K. L., Lu, H., Makhijani, V. B., McDade, K. E., McKenna, M. P., Myers, E. W., Nickerson, E., Nobile, J. R., Plant, R., Puc, B. P., Ronan, M. T., Roth, G. T., Sarkis, G. J., Simons, J. F., Simpson, J. W., Srinivasan, M., Tartaro, K. R., Tomasz, A., Vogt, K. A., Volkmer, G. A., Wang, S. H., Wang, Y., Weiner, M. P., Yu, P., Begley, R. F., and Rothberg, J. M.: Genome sequencing in microfabricated high-density picolitre reactors, Nature, 437, 376–380, 2005.

McDonald, D., Price, M. N., Goodrich, J., Nawrocki, E. P., DeSantis, T. Z., Probst, A., Andersen, G. L., Knight, R., and Hugenholtz, P.: An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea, ISME J., 6, 610–618, 2012.

Methé, B. A., Nelson, K. E., Deming, J. W., Momen, B., Melamud, E., Zhang, X., Moult, J., Madupu, R., Nelson, W. C., Dodson, R. J., Brinkac, L. M., Daugherty, S. C., Durkin, A. S., Deboy, R. T., Kolonay, J. F., Sullivan, S. A., Zhou, L., Davidsen, T. M., Wu, M.,
Huston, A. L., Lewis, M., Weaver, B., Weidman, J. F., Khouri, H., Utterback, T. R., Feldblyum, T. V., and Fraser, C. M.: The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses, *P. Natl. Acad. Sci.*, 102, 10913–10918, 2005.

Newbold, L. K., Oliver, A. E., Booth, T., Tiwari, B., DeSantis, T., Maguire, M., Andersen, G., van der Gast, C. J., and Whiteley, A. S.: The response of picoplankton to ocean acidification, *Environ. Microbiol.*, 14, 2293–2307, doi:10.1111/j.1462-2920.2012.02762.x, 2012.

Pinhassi, J., Sala, M. M., Havskum, H., Peters, F., Guadayol, O., Malits, A., and Marrase, C.: Changes in bacterioplankton composition under different phytoplankton regimens, *Appl. Environ. Microb.*, 70, 6753–6766, 2004.

Ramette, A.: Multivariate analyses in microbial ecology, *FEMS Microbiol. Ecol.*, 62, 142–160, 2007.

Riebesell, U., Bellerby, R. G. J., Grossart, H.-P., and Thingstad, F.: Mesocosm CO₂ perturbation studies: from organism to community level, *Biogeosciences*, 5, 1157–1164, doi:10.5194/bg-5-1157-2008, 2008.

Riebesell, U., Czerny, J., von Bröckel, K., Boxhammer, T., Büdenbender, J., Deckelnick, M., Fisher, M., Hoffmann, D., Krug, S.A., Lenz, U., Ludwig, A., and Schulz, K. G.: Technical Note: A mobile sea-going mesocosm system: New opportunities for ocean change research, *Biogeosciences*, in review, 2013.

Riemann, L., Steward, G. F., and Azam, F.: Dynamics of Bacterial Community Composition and Activity during a Mesocosm Diatom Bloom, *Appl. Environ. Microb.*, 66, 578–587, 2000.

Schulz, K. G., Bellerby, R. G. J., Brussaard, C. P. D., Büdenbender, J., Czerny, J., Fischer, M., Koch-Klavsen, S., Krug, S., Lischka, S., Ludwig, A. M. M., Nondal, G., Silyakova, A., Stuhr, A., and Riebesell, U.: Temporal biomass dynamics of an Arctic plankton bloom in response to increasing levels of atmospheric carbon dioxide, *Biogeosciences*, 10, 161–180, doi:10.5194/bg-10-161-2013, 2013.

Sogin, M. L., Morrison, H. G., Huber, J. A., Welch, D. M., Huse, S. M., Neal, P. R., Arrieta, J. M., and Herndl, G. J.: Microbial diversity in the deep sea and the underexplored “rare biosphere”, *P. Natl. Acad. Sci.*, 103, 12115–12120, doi:10.1073/pnas.0605127103, 2006.

Sperling, M., Piontek, J., Gerdts, G., Wichels, A., Schunck, H., Roy, A.-S., La Roche, J., Gilbert, J., Nissimov, J. I., Bittner, L., Romac, S., Riebesell, U., and Engel, A.: Effect of elevated CO₂ on the dynamics of particle-attached and free-living bacterioplankton communities in an Arctic fjord, *Biogeosciences*, 10, 181–191, doi:10.5194/bg-10-181-2013, 2013.

Tanaka, T., Thingstad, T. F., Lovdal, T., Grossart, H.-P., Larsen, A., Allgaier, M., Meyeröfer, M., Schulz, K. G., Wohlers, J., Zöllner, E., and Riebesell, U.: Availability of phosphate for phytoplankton and bacteria and of glucose for bacteria at different pCO₂ levels in a mesocosm study, *Biogeosciences*, 5, 669–678, doi:10.5194/bg-5-669-2008, 2008.

ter Braak, C. J. F. and Šmilauer, P.: CANOCO Reference Manual and Cano-Draw for Windows User's Guide, in: Software for Canonical Community Ordination Version 4.5 Microcomputer Power, Centrum voor biometrie, Plant research international Wageningen UR, Ithaca, NY, USA, p. 500, 2002.

Teske, A., Durbin, A., Ziervogel, K., Cox, C., and Arnosti, C.: Microbial community composition and function in permanently cold seawater and sediment from an Arctic fjord of Svalbard, *Appl. Environ. Microbiol.*, 77, 2008–2018, doi:10.1128/AEM.01507-10, 2011.

Wang Q., Garrity, G. M., Tiedje, J. M. and Cole, J. R.: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy, *Appl. Environ. Microbiol.*, 2007, 5261–5267, 2007.

Worden, A. Z., and Not, F.: Ecology and diversity of picoeukaryotes, in: Microbial Ecology of the Oceans, 2nd Edn., edited by: Kirchman, D. L., Hoboken, NJ, USA, John Wiley & Sons, 159–205, 2008.

Yu, Y., Li, H.-R., and Zeng, Y.-X.: Colwellia chukchiensis sp. nov., a psychrotolerant bacterium isolated from the Arctic Ocean, *Int. J. Syst. Evol. Micr.*, 61, 850–853, 2011.

Zhang, R., Xia, X., Lau, S. C. K., Motegi, C., Weinbauer, M. G., and Jiao, N.: Response of bacterioplankton community structure to an artificial gradient of pCO₂ in the Arctic Ocean, *Biogeosciences Discuss.*, 9, 10645–10668, doi:10.5194/bgd-9-10645-2012, 2012.