Small algae in the world's oceans remove about as much carbon dioxide from the atmosphere as land plants. These algae do not grow continuously, but often surge in numbers during temporary blooms. Such blooms can be large enough to be seen from space by satellites. The lifespan of algae within such blooms is short, and when they die, marine bacteria feed on the remnants, which releases much of the stored carbon dioxide.

Much of an algal cell consists of different types of polysaccharides. These large molecules are essentially made from sugars linked together. Polysaccharides are varied molecules and can contain many different sugars that can be linked in a number of different ways. During algae blooms bacteria proliferate that are specialized in the degradation of these polysaccharides. In 2012, researchers reported how over the progression of an algae bloom different groups of marine bacteria bloomed in rapid succession. However, it remained unknown whether the same or different groups of bacteria respond to algae blooms at the same place from year to year, and whether or not these bacteria use the same enzymes to degrade the polysaccharides.

Teeling, Fuchs et al. – who include many of the researchers from the 2012 study – now report on the analysis of a series of algae blooms that occurred in the southern North Sea between 2009 and 2012. The analysis is based on samples collected every week during the spring seasons, and shows that certain groups of related bacteria, known as clades, became common during each bloom. Teeling, Fuchs et al. also found indications that the clades that repeatedly occurred had similar sets of genes for degrading algal polysaccharides, but that the sets were different between the clades.

These data suggest that there is a specialized bacterial community that together can degrade the complex mixture of algal polysaccharides during blooms. This community reappears each year with an unexpectedly low level of variation. Since different species of algae made up the blooms in each year, this finding suggests that the major polysaccharides in these algae are similar or even identical.

Future work will focus on the specific activities of bacterial enzymes that are needed to degrade polysaccharides during algae blooms. Study of these enzymes in the laboratory will help to resolve, which polysaccharides are attacked in which manner, and to ultimately help to identify the most abundant algal polysaccharides. This will improve our current understanding of the carbon cycle in the world’s oceans.

Pelagic zones of the world's oceans seemingly constitute rather homogenous habitats, however, they feature enough spatial and temporal variation to support a large number of species with distinct niches. This phenomenon has been termed 'paradox of the plankton' by G. Evelyn Hutchinson (Hutchinson, 1961). Interactions within planktonic microbial communities are manifold and complex (see Amin et al., (2012) and Worden et al. (2015) for reviews). Still, planktonic microbial communities are simple in comparison to benthic or terrestrial soil communities and thus particularly suitable for the study of microbial community composition dynamics. In recent years, continuous biodiversity studies at long-term sampling stations have started to reveal discernible deterministic patterns within marine microbial plankton communities (see Fuhrman et al. (2015) for a recent review). This is particularly true for less dynamic oligotrophic oceanic regions that are dominated by the members of the alphaproteobacterial Pelagibacteriaceae (SAR11 clade) and the cyanobacterial Prochlorococcaceae (Prochlorococcus marinus). By contrast, more dynamic eutrophic coastal regions are subject to frequent system perturbations and thus seldom in a state of equilibrium. This can lead to apparently stochastic changes in bacterioplankton community composition. To capture recurrence of biodiversity patterns in such coastal areas, sampling must occur at the order of weekly to sub-weekly time scales over multiple years. Owing to the lack of such intensively sampled long-term time series data, our current understanding of the extent and predictability of recurring microbial biodiversity patterns for such marine habitats is still limited.

A particularly important connection in the marine carbon cycle exists between marine microalgae as primary producers and heterotrophic bacteria that feed on algal biomass. Global photosynthetic carbon fixation is estimated to exceed 100 Gigatons yearly, of which marine algae contribute about half (Falkowski et al., 1998; Field et al., 1998; Sarmento and Gasol, 2012). Planktonic uni- to pluricellular algae such as diatoms, haptophytes, and autotrophic dinoflagellates are the most important marine primary producers. Diatoms alone are estimated to contribute 20–40% to global carbon fixation (Nelson et al., 1995; Mann, 1999; Armbrust, 2009).

Primary production by planktonic microalgae differs from primary production by sessile macroalgae or land plants as it is much less constant, but culminates in blooms that are often massive, as occurs worldwide during spring blooms from temperate to polar regions. These blooms are highly dynamic phenomena that are time-limited by nutrients, predator grazing and viral infections. Bloom termination results in a short-lived massive release of algal organic matter that is consumed by dedicated clades of heterotrophic bacterioplankton. This trophic connection leads to synchronized blooms of planktonic bacteria during phytoplankton blooms, as has been described in various studies (Bell and Kuparinen, 1984; Niu et al., 2011; Tada et al., 2011; Teeling et al., 2012; Yang et al., 2015; Tan et al., 2015).

The activities of these heterotrophic bacteria impact the proportion of algal biomass that is directly mineralized and released back into the atmosphere mostly as carbon dioxide, and the algae-derived biomass that sinks out to the bottom of the sea as carbonaceous particles. These are further remineralized by particle-associated bacteria while sinking and by benthic bacteria when reaching the sediment, even in the deep sea (e.g. Ruff et al., 2014). The remainder is buried for a long time as kerogen and forms the basis for future oil and gas reservoirs. The ratio between bacterial mineralization and burial of algae-derived organic matter thus has a profound influence on the atmospheric carbon dioxide concentration (Falkowski et al., 1998). However, the bulk of bacteria during phytoplankton blooms are free-living and not attached to particles or algae. These bacteria play a pivotal role in the mineralization of algae-derived non-particulate dissolved organic matter (DOM).

The bacterial clades that respond most to phytoplankton blooms belong to the classes Flavobacteriia (phylum Bacteroidetes) and Gammaproteobacteria, and the Roseobacter clade within class Alphaproteobacteria (Buchan et al., 2014). This response is typically not uniform, but consists of a series of distinct clades that bloom one after another. In the year 2009, we investigated the response of bacterioplankton to a diatom-dominated spring phytoplankton bloom in the German Bight (Teeling et al., 2012). Within the free-living bacteria (0.2 to 3 µm) we observed a swift succession of bacterial clades that were dominated by Flavobacteriia and Gammaproteobacteria, with consecutively blooming Ulvibacter (Flavobacteriia), Formosa (Flavobacteriia), Reinekea (Gammaproteobacteria), Polaribacter (Flavobacteriia) genera and SAR92 (Gammaproteobacteria) as prominent clades.

Using time-series metagenome and metaproteome analyses, we demonstrated that the substrate-spectra of some of these clades were notably distinct. The succession of bacterioplankton clades hence constituted a succession of distinct gene function repertoires, which suggests that changes in substrate availability over the course of the bloom were among the forces that shaped the bacterioplankton community. Dominance of bottom-up over top-down control is assumed to be characteristic for the initial phases of spring phytoplankton blooms. After winter, inorganic nutrients are aplenty, and the overall abundance of microbes is low. When suitable temperature and sunlight conditions are met in spring, algae and subsequently bacteria can enter an almost unrestricted proliferation. In contrast, predators such as flagellates, protists and zooplankton can only start proliferating when their food sources are available in larger numbers. Hence, top-down control by predation sets in only during later bloom phases. This situation is distinct from summer and fall phytoplankton blooms.

Pronounced differences between blooming clades were found in the gene frequencies and protein expression profiles of transporters and carbohydrate-active enzymes (CAZymes; [Cantarel et al., 2009; Lombard et al., 2014]), such as glycoside hydrolase (GH), polysaccharide lyase (PL), carbohydrate esterase (CE), or carbohydrate-binding module (CBM) containing genes. The latter indicates a pronounced niche partitioning with respect to algal polysaccharide degradation. Marine algae produce large quantities of distinct polysaccharides, for example,e.g. storage, cell matrix and cell wall constituents, or as part of extracellular transparent exopolymer particles (TEP). It has been recently shown that in particular Flavobacteriales and Rhodobacterales respond to TEP availability (Taylor et al., 2014). The diversity of algal polysaccharides is too high for a single bacterial species to harbor all the genes required for the complete degradation of all naturally occurring variants. Thus, polysaccharide-degrading bacteria specialize on dedicated subsets of polysaccharides, which is why the decomposition of algal polysaccharides during and after algal blooms is a concerted effort among distinct bacterial clades with distinct glycan niches (e.g. Xing et al., 2015).

In this study, we provide evidence that the succession of bacterioplankton clades that we reported for the 2009 North Sea spring phytoplankton bloom re-occurred during the spring blooms from 2010 to 2012. We tested whether the bacterioplankton clades and their associated CAZyme repertoires differ from year to year or exhibit recurrent patterns. We analyzed spring bacterioplankton community composition via 16S rRNA catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) and 16S rRNA gene tag sequencing, as well as gene function repertoires by deep metagenome sequencing. Our efforts have culminated into the as of yet highest resolved dataset capturing the response of planktonic bacteria to marine spring phytoplankton blooms and have allowed identification of recurring patterns that might ultimately lead to an explanatory model for bacterioplankton succession dynamics during spring algae blooms.

Nutrient-poor marine surface waters are dominated by clades such as SAR11 and Prochlorococcus. Both feature small, reduced genomes and can use sunlight and small organic molecules (e.g. Gómez-Pereira et al., 2013). The otherwise heterotrophic SAR11 use proteorhodopsin for supplemental phototrophy (Giovannoni et al., 2005) and phototrophic Prochlorococcus cyanobacteria are capable of supplemental uptake of amino acids (Zubkov et al., 2003) and glucose (Muñoz-Marín et al., 2013). Microbial communities in nutrient-rich 'green' surface oceans by contrast feature higher proportions of heterotrophic species that feed on more complex organic substrates. In particular during phytoplankton blooms, the release of algae-derived organic matter selects for fast growing species with genomic adaptations towards algal biomass remineralization. These are typically members of the Flavobacteriia and Gammaproteobacteria classes and the alphaproteobacterial Roseobacter clade. Similarly adapted species from these clades compete for substrates during phytoplankton blooms with variation in which species prevail. Despite this stochastic effect, the most well-adapted species will be successful more often and thus exhibit patterns of annual recurrence.

During spring phytoplankton blooms at Helgoland in the North Sea, we observed recurrent bloom-associated abundance peaks of in particular flavobacterial clades, namely Formosa, Polaribacter, the NS3a marine group, Tenacibaculum, Ulvibacter, and the Cryomorphaceae VIS6 clade. Within Gammaproteobacteria Alteromonadaceae/Colwelliaceae, Reinekea, and the SAR92 clade were clearly bloom-associated and recurrent as was Methylophilales within Betaproteobacteria, and 'Cd. Planktomarina temperata' from the DC5-80-3 lineage (a.k.a Roseobacter clade affiliated = RCA group) and the NAC11-7 lineage within the Roseobacter clade. It has already been shown that the abundant North Sea isolate 'Cd. Planktomarina temperata' RCA23^T is associated with decaying phytoplankton (Giebel et al., 2011) and high abundances and in particular high activity have been reported during a spring phytoplankton bloom event in the North Sea of 2010 (Voget et al., 2015; Wemheuer et al., 2015). High activities of members of the RCA and the SAR92 clades during North Sea spring phytoplankton blooms have also been reported in 2009 (Klindworth et al., 2014) and 2010 (Wemheuer et al., 2015), just as an increase of Bacteroidetes of the genera Marinoscillum and Polaribacter during 2010 (Wemheuer et al., 2015).

Many of the other clades we report here (including some low abundance groups) have been found during blooms of dinoflagellates, including AEGEAN-169, Alteromonadales, NS3a marine group, NS5 marine group, OM43, OM60 (NOR5), SAR116, SAR86, and ZD0405 (Yang et al., 2015) or Cryomorphaceae, Glaciecola and Sulfitobacter (Tan et al., 2015). The OM43 clade (order Methylophilales) comprises methylotrophs known to feed on algae C1 compounds (Halsey et al., 2012), and it has been reported that Sulfitobacter species SA11 shares a mutually beneficial exchange of compounds with the diatom species Pseudo-nitzschia multiseries (Amin et al., 2015).

There is of course unaddressed diversity in all these clades. Some of the genera might be dominated by a single species while others might be more diverse with considerable variation in competitive success between bloom events. Nevertheless, the high level of recurrence in particular of flavobacterial clades indicates a strong selection of few clades highly adapted for the manipulation and uptake of specific and complex polysaccharides (and likely other biopolymers) and disagrees with substantial levels of sloppy-feeding by these bacteria that would allow other less adapted clades to arbitrarily reach high abundances via cross-feeding. This might be attributed to the capability of Bacteroidetes for very efficient macromolecule uptake as it has been recently shown for uptake of α-mannan by the human gut bacterium Bacteroides thetaiotaomicron (Cuskin et al., 2015). This bacterium binds α-mannan macromolecules to its surface, followed by rapid cleavage into larger oligomers that are immediately imported via a TonB-dependent transporter (TBDT) into the periplasm without detectable loss. The bulk of the degradation into smaller molecules takes place in the periplasm where the substrate is secure from outside competitors before transport into the cytoplasm.

TonB-dependent transporters are not specific to Bacteroidetes, but it seems that only Bacteroidetes have evolved a functional coupling of SusC-like TBDT porins with SusD-like TonB-dependent receptors (TBDRs) that bind and guide the substrate to the porin. At least so far, only Bacteroidetes genomes feature characteristic susCD gene tandems. Within Bacteroidetes genomes such tandems are frequently found in so-called polysaccharide utilization loci (PULs; [Sonnenburg et al., 2010]). PULs are operons or regulons where one or more susCD gene tandems are co-located with CAZymes. Further frequent accessory genes in PULs encompass transcriptional regulators, proteases, transporter components and sulfatases. The latter are required for the desulfation of sulfated polysaccharides, which marine algae produce in large quantities. The diversity of PULs in marine Bacteroidetes genomes is high and largely unexplored and so far only few PULs have been linked to dedicated algal polysaccharides (e.g. Hehemann et al., 2012; Kabisch et al., 2014; Xing et al., 2015). The large, complex and efficient PUL uptake systems might explain why Flavobacteria consistently outcompeted Gammaproteobacteria during the onset of all blooms.

It is noteworthy that we observed a shift in bacterioplankton biodiversity alongside a shift in functional gene repertoires for the major clades in all four years of this study. The abundance of CAZymes and sulfatases increased from pre- to mid-bloom situations and leveled off post-bloom. Likewise, similar abundance patterns were observed for the most abundant CAZyme families in all studied years.

We did observe an increase in the abundance of TBDRs during bloom situations, and we have shown previously that TBDRs are among the most abundantly expressed proteins during the bacterial mineralization of algae biomass (Teeling et al., 2012). The relevance of TBDRs in nutrient-rich oceanic regions has been also supported by in situ metaproteome studies of samples from the South Atlantic Ocean (in particular at coastal upwelling zones; [Morris et al., 2010]) and from the Antarctic Southern Ocean (Williams et al., 2013).

The recurrent patterns in bacterioplankton diversity and functional repertoires during the four studied spring blooms are remarkable in view of the variation among algae taxa. For example, even though algal biomass of the 2012 spring bloom was dominated by silicoflatellate Chattonella spp. and not by diatoms as was true for the three preceding years, the respective bacterioplankton communities were strikingly similar. Likewise only few bacterioplankton taxa seemed to be weakly correlated with dedicated phytoplankton taxa, and no clear correlation was found between distinct bacterioplankton taxa and individual distinct diatom clades in statistical analyses (Supplementary files 6 and 7). It seems that phytoplankton community composition did not exert a strong effect on the composition of the free-living non-phycosphere bacterioplankton community. Instead, the dominating algae (in terms of biomass) seemed to produce similar or perhaps identical types of substrates for specifically adapted clades of heterotrophic bacterioplankton. It is therefore conceivable that recurrence is more pronounced on the functional level than on the taxonomic level, since species from different taxa with similar ecophysiological niches might functionally substitute each other in different years. This hypothesis is supported by the bacterioplankton communities' similar CAZyme gene repertoires in the 2009 to 2012 spring blooms and in particular the consistency on class level that was almost unaffected by distinct blooming clades, yet needs to be further tested by deep metatranscriptome sequencing and metaproteomics during multiple spring phytoplankton blooms in future studies. Considering the extent of recurrence, our combined metagenome data (>5 million predicted proteins) should provide sufficient search space for such an analysis.

The existence of recurrent key players during North Sea spring phytoplankton blooms suggests that the bacterioplankton community composition during and after such blooms is governed by deterministic effects. We have shown before that temperature exerts a strong effect on North Sea bacterioplankton as it selects for temperature-dependent guilds, for example when comparing spring and summer blooms (Lucas et al., 2015). Within short-lived spring blooms, however, the supply of algae-derived organic matter is among the main factors that shape the bacterioplankton composition. In particular the different types of structurally distinct polysaccharides that algae produce in large quantities seem to exert such substrate-induced forcing. Since Flavobacteriia are more specialized on polysaccharides than Gammaproteobacteria, this would also explain, why Flavobacteriia dominated the recurrent clades (Figure 3Q–T, Figure 4E–J) and Gammaproteobacteria clades exhibited more stochastic peaks (Figure 4Q–T).

At the beginning of a bloom, most available polysaccharides will be exopolysaccharides, but as the bloom commences and algae become senescent, more and more cellular algal substrates are released, culminating in the bloom's final die off phase. Bacteria will naturally consume the more degradable substrates such as storage polysaccharides (e.g. chrysolaminarin) first, and more recalcitrant substrates (e.g. branched and sulfated polysaccharides) later. TEP for example seems to undergo such selective feeding, as it has been suggested that in particular fucose-rich TEP is less readily degraded than mannose and galactose rich TEP (for review see Passow, 2002). Such selective feeding creates an additional change in substrate availability and leads to a succession of substrate niches for specifically adapted bacterioplankton clades to grow.

1. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2010/03/03

   Publically available at NCBI Sequence Read Archive (accession no. SRA212914).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212914
2. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2010/04/08

   Publically available at NCBI Sequence Read Archive (accession no. SRA212908).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212908
3. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2010/05/04

   Publically available at NCBI Sequence Read Archive (accession no. SRA212889).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212889
4. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2010/05/18

   Publically available at NCBI Sequence Read Archive (accession no. SRA212588).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212588
5. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2011/03/24

   Publically available at NCBI Sequence Read Archive (accession no. SRA212575).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212575
6. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2011/04/28

   Publically available at NCBI Sequence Read Archive (accession no. SRA212530).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212530
7. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2011/05/26

   Publically available at NCBI Sequence Read Archive (accession no. SRA212476).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212476
8. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2012/03/08

   Publically available at NCBI Sequence Read Archive (accession no. SRA212475).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212475
9. 1. this study

   (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2012/04/16

   Publically available at NCBI Sequence Read Archive (accession no. SRA212473).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA212473
10. 1. this study

    (2016) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2012/05/10

    Publically available at NCBI Sequence Read Archive (accession no. SRA212472).

    http://www.ncbi.nlm.nih.gov/sra/?term=SRA212472
11. 1. this study

    (2016) 16S rRNA V4 amplicons of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads from 2010 to 2012

    Publically available at NCBI Sequence Read Archive (accession no. SRA278189).

    http://www.ncbi.nlm.nih.gov/sra/?term=SRA278189

1. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
   8. Sixing Huang
   9. Alexander J. Mann
   10. Jost Waldmann
   11. Marc Weber
   12. Anna Klindworth
   13. Andreas Otto
   14. Jana Lange
   15. Jörg Bernhardt
   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/02/11

   Publically available at the European Nucleotide Archive (study accession no. PRJEB2888, experimental accession no. ERX069460 - ERX069464).

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888
2. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
   8. Sixing Huang
   9. Alexander J. Mann
   10. Jost Waldmann
   11. Marc Weber
   12. Anna Klindworth
   13. Andreas Otto
   14. Jana Lange
   15. Jörg Bernhardt
   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/09/01

   Publically available at the European Nucleotide Archive (study accession no. PRJEB2888, experimental accession no.ERX069492 - ERX069495).

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888
3. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
   8. Sixing Huang
   9. Alexander J. Mann
   10. Jost Waldmann
   11. Marc Weber
   12. Anna Klindworth
   13. Andreas Otto
   14. Jana Lange
   15. Jörg Bernhardt
   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/06/16

   Publically available at the European Nucleotide Archive (study accession no. PRJEB2888, experimental accession no.ERX069490 - ERX069491).

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888
4. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
   8. Sixing Huang
   9. Alexander J. Mann
   10. Jost Waldmann
   11. Marc Weber
   12. Anna Klindworth
   13. Andreas Otto
   14. Jana Lange
   15. Jörg Bernhardt
   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/04/14

   Publically available at the European Nucleotide Archive (study accession no. PRJEB2888, experimental accession no.ERX069474 - ERX069481).

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888
5. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
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   9. Alexander J. Mann
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   11. Marc Weber
   12. Anna Klindworth
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   14. Jana Lange
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   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/04/07

   Publically available at the European Nucleotide Archive (study accession no. PRJEB2888, experimental accession no.ERX069470 - ERX069473).

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888
6. 1. Hanno Teeling
   2. Bernhard M. Fuchs
   3. Dörte Becher
   4. Christine Klockow
   5. Antje Gardebrecht
   6. Christin M. Bennke
   7. Mariette Kassabgy
   8. Sixing Huang
   9. Alexander J. Mann
   10. Jost Waldmann
   11. Marc Weber
   12. Anna Klindworth
   13. Andreas Otto
   14. Jana Lange
   15. Jörg Bernhardt
   16. Christine Reinsch
   17. Michael Hecker
   18. Jörg Peplies
   19. Frank D. Bockelmann
   20. Ulrich Callies
   21. Gunnar Gerdts
   22. Antje Wichels
   23. Karen H. Wiltshire
   24. Frank Oliver Glöckner
   25. Thomas Schweder
   26. Rudolf Amann

   (2012) Metagenome of North Sea surface bacteriopankton (3 - 0.2 µm) from Helgoland Roads on 2009/03/31

   ERX069465 - ERX069469.

   http://www.ebi.ac.uk/ena/data/view/PRJEB2888

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Author details

1. Hanno Teeling

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   HT, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Bernhard M Fuchs

   For correspondence

   hteeling@mpi-bremen.de

   Competing interests

   The authors declare that no competing interests exist.

2. Bernhard M Fuchs

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   BMF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Hanno Teeling

   For correspondence

   bfuchs@mpi-bremen.de

   Competing interests

   The authors declare that no competing interests exist.

3. Christin M Bennke

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Present address

   Section Biology, Leibniz Institute for Baltic Sea Research, Warnemünde, Germany

   Contribution

   CMB, Sampling at Helgoland Island, Total cell count and CARD-FISH analyses, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Karen Krüger

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   KK, Metagenome analyses, CAZyme analyses, Bioinformatics, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Meghan Chafee

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   MC, Analyses of the complete 16S rRNA tag dataset, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Lennart Kappelmann

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   LK, Metagenome analyses, Selection and analyses of reference genomes for metagenome taxonomic classification, Bioinformatics, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

7. Greta Reintjes

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   GR, Sampling at Helgoland Island, CARD-FISH analyses, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

8. Jost Waldmann

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   JW, Taxonomic classification of metagenomes (programming of pipeline and compilation of reference datasets), Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

9. Christian Quast

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   CQ, Programming of the 16S rRNA tag data analyses pipeline, Assistance with 16S rRNA tag data analyses, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

10. Frank Oliver Glöckner

    Max Planck Institute for Marine Microbiology, Bremen, Germany

    Contribution

    FOG, Conception and design, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

11. Judith Lucas

    Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research, Helgoland, Germany

    Contribution

    JL, Sampling at Helgoland Island, Initial analyses of a part of the 16S rRNA tag data, Acquisition of data, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

12. Antje Wichels

    Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research, Helgoland, Germany

    Contribution

    AW, Provision of sampling logistics and lab space at Helgoland Island, Provision of physicochemical data, Provision of algae biodiversity data, Statistical analyses of 16S rRNA tag data, Acquisition of data, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

13. Gunnar Gerdts

    Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research, Helgoland, Germany

    Contribution

    GG, Provision of sampling logistics and lab space at Helgoland Island, Provision of physicochemical data, Provision of algae biodiversity data, Statistical analyses of 16S rRNA tag data, Acquisition of data, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

14. Karen H Wiltshire

    Alfred Wegener Institute for Polar and Marine Research, List auf Sylt, Germany

    Contribution

    KHW, Provision of sampling logistics and lab space at Helgoland Island, Provision and analyses of physicochemical data, Provision and analyses of algae biodiversity data, Acquisition of data, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

15. Rudolf I Amann

    Max Planck Institute for Marine Microbiology, Bremen, Germany

    Contribution

    RIA, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    ramann@mpi-bremen.de

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0002-0846-7372

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