Rank-abundance plot of the 38 isolated bacterial species.

<p>The orange triangles indicate the cultured isolates found in the 454-pyrosequencing (HTS) dataset and the white circles indicate the cultures that were not found in the HTS dataset. The isolated bacterial species are listed in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159195#pone.0159195.t002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159195#pone.0159195.t003" target="_blank">3</a>.</p

Summary of location and depth (m) of samples, total sequences before cleaning (Raw Reads) and after cleaning (Final Reads), observed richness (S_obs) computed as the total number of Operational Taxonomic Units (OTUs) clustered at 97% identity, and the percentage of singletons.

<p>Total richness (S) was estimated using the Chao1 lower bound estimator [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159195#pone.0159195.ref039" target="_blank">39</a>] and using the Sichel distribution fitted to the count frequency data by the Bayesian method of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159195#pone.0159195.ref033" target="_blank">33</a>] and selected from four alternative candidate models using the Deviance Information Criterion (DIC). Using the Sichel distribution, point estimates and 95% credible intervals (CIs) for S were obtained from the mean and (2.5%, 97.5%) quantiles of the posterior distribution sampled 150000 times by Markov Chain Monte Carlo (after a burn-in period of 100000 samples, see 33). The Required Sequencing Effort (RSE) to sequence 90% of the total richness was predicted by hierarchical simulation (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159195#sec002" target="_blank">Materials and methods</a>) and is quoted in terms of the number of final reads and as a multiple of the present sequencing effort. Point estimates and 95% prediction intervals (PIs) for RSE were calculated as the mean and (2.5%, 97.5%) quantiles from a set of 80 simulations using the Sichel distribution.</p

OTU collector’s curves of the surface (orange line) and bottom (green line) samples.

<p>Black dashed lines indicate the 95% confidence intervals (95% CI).</p

Rank-abundance plots of surface (A) and bottom (B) samples.

<p>The red line is the rank-abundance plot calculated with the actual data. The dark blue line shows the estimates of the sequencing effort necessary to retrieve 90% of the total richness calculated by simulation from the best-approximating Sichel distribution (posterior mean estimate). The vertical black line separates the real data (left) from the estimates (right). In (A) the percentage of cultured isolates found in the 454-pyrosequencing dataset is indicated at the left side of the black vertical line. The percentage of cultured isolates not found in the 454-pyrosequencing dataset, and that would presumably be found by increasing the sequencing effort, is indicated at the right of the black vertical line. Insert pictures show some of the bacterial cultures grown from the surface sample. Font size and pictures are scaled according to the percentage of cultured isolates found or not found in the 454-pyrosequencing dataset.</p

Cultured isolates with matching HTS sequences.

<p>Columns show the isolates’ closest relatives according to the BLAST results, the percentage of identity with the BLAST reference strain (identity BLAST), the GenBank accession number of the BLAST reference strain, the number of HTS reads matching the isolate sequences in the surface sample (Reads in Surface), the percentage of the total HTS reads in the surface sample represented by the isolate sequences (% Surface), and the number of isolates of each taxa sequenced. Abbreviations are: Actino (Actinobacteria), Bact (Bacteroidetes), Alpha-P (Alpha-Proteobacetria) and Gamma-P (Gamma-Proteobacteria).</p

DataSheet_1_Acclimation to various temperature and pCO2 levels does not impact the competitive ability of two strains of Skeletonema marinoi in natural communities.docx

Understanding the long-term response of key marine phytoplankton species to ongoing global changes is pivotal in determining how oceanic community composition will respond over the coming decades. To better understand the impact of ocean acidification and warming, we acclimated two strains of Skeletonema marinoi isolated from natural communities to three pCO2 (400 μatm, 600 μatm and 1000 μatm) for 8 months and five temperature conditions (7°C, 10°C, 13°C, 16°C and 19°C) for 11 months. These strains were then tested in natural microbial communities, exposed to three pCO2 treatments (400 μatm, 600 μatm and 1000 μatm). DNA metabarcoding of the 16S and 18S gene for prokaryotes and eukaryotes respectively was used to show differences in abundance and diversity between the three CO2 treatments. We found there were no significant differences in acclimated S. marinoi concentrations between the three pCO2 treatments, most likely due to the high variability these strains experience in their natural environment. There were significant compositional differences between the pCO2 treatments for prokaryotes suggesting that indirect changes to phytoplankton-bacteria interactions could be a possible driver of bacterial community composition. Yet, there were no differences for eukaryotic community composition, with all treatments dominated by diatoms (but not the acclimated S. marinoi) resulting in similar biodiversity. Furthermore, strain-specific differences in community composition suggests interactions between prokaryotic and eukaryotic taxa could play a role in determining future community composition.</p