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
   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 2009/04/07

   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

1. 1. Altschul SF
   2. Gish W
   3. Miller W
   4. Myers EW
   5. Lipman DJ

   (1990) Basic local alignment search tool

   Journal of Molecular Biology 215:403–410.

   https://doi.org/10.1016/S0022-2836(05)80360-2

   • Google Scholar
2. 1. Amin SA
   2. Parker MS
   3. Armbrust EV

   (2012) Interactions between diatoms and bacteria

   Microbiology and Molecular Biology Reviews 76:667–684.

   https://doi.org/10.1128/MMBR.00007-12

   • Google Scholar
3. 1. Amin SA
   2. Hmelo LR
   3. van Tol HM
   4. Durham BP
   5. Carlson LT
   6. Heal KR
   7. Morales RL
   8. Berthiaume CT
   9. Parker MS
   10. Djunaedi B
   11. Ingalls AE
   12. Parsek MR
   13. Moran MA
   14. Armbrust EV

   (2015) Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria

   Nature 522:98–101.

   https://doi.org/10.1038/nature14488

   • Google Scholar
4. 1. Apprill A
   2. McNally S
   3. Parsons R
   4. Weber L

   (2015) Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton

   Aquatic Microbial Ecology 75:129–137.

   https://doi.org/10.3354/ame01753

   • Google Scholar
5. 1. Armbrust EV

   (2009) The life of diatoms in the world's oceans

   Nature 459:185–192.

   https://doi.org/10.1038/nature08057

   • Google Scholar
6. 1. Bell RT
   2. Kuparinen J

   (1984)

   Assessing phytoplankton and bacterioplankton production during early spring in Lake Erken, Sweden

   Applied and Environmental Microbiology 48:1221–1230.

   • Google Scholar
7. 1. Buchan A
   2. LeCleir GR
   3. Gulvik CA
   4. González JM

   (2014) Master recyclers: features and functions of bacteria associated with phytoplankton blooms

   Nature Reviews. Microbiology 12:686–698.

   https://doi.org/10.1038/nrmicro3326

   • Google Scholar
8. 1. Cantarel BL
   2. Coutinho PM
   3. Rancurel C
   4. Bernard T
   5. Lombard V
   6. Henrissat B

   (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics

   Nucleic Acids Research 37:D233–D238.

   https://doi.org/10.1093/nar/gkn663

   • Google Scholar
9. 1. Caporaso JG
   2. Lauber CL
   3. Walters WA
   4. Berg-Lyons D
   5. Lozupone CA
   6. Turnbaugh PJ
   7. Fierer N
   8. Knight R

   (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample

   Proceedings of the National Academy of Sciences of the United States of America 108 Suppl 1:4516–4522.

   https://doi.org/10.1073/pnas.1000080107

   • Google Scholar
10. 1. Cuskin F
    2. Lowe EC
    3. Temple MJ
    4. Zhu Y
    5. Cameron EA
    6. Pudlo NA
    7. Porter NT
    8. Urs K
    9. Thompson AJ
    10. Cartmell A
    11. Rogowski A
    12. Hamilton BS
    13. Chen R
    14. Tolbert TJ
    15. Piens K
    16. Bracke D
    17. Vervecken W
    18. Hakki Z
    19. Speciale G
    20. Munōz-Munōz JL
    21. Day A
    22. Peña MJ
    23. McLean R
    24. Suits MD
    25. Boraston AB
    26. Atherly T
    27. Ziemer CJ
    28. Williams SJ
    29. Davies GJ
    30. Abbott DW
    31. Martens EC
    32. Gilbert HJ

    (2015) Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism

    Nature 517:165–169.

    https://doi.org/10.1038/nature13995

    • Google Scholar
11. 1. Edgar RC

    (2010) Search and clustering orders of magnitude faster than BLAST

    Bioinformatics 26:2460–2461.

    https://doi.org/10.1093/bioinformatics/btq461

    • Google Scholar
12. 1. Eilers H
    2. Pernthaler J
    3. Glöckner FO
    4. Amann R

    (2000)

    Culturability and in situ abundance of pelagic bacteria from the North Sea

    Applied and Environmental Microbiology 66:3044–3051.

    • Google Scholar
13. 1. Eilers H
    2. Pernthaler J
    3. Peplies J
    4. Glöckner FO
    5. Gerdts G
    6. Amann R

    (2001) Isolation of novel pelagic bacteria from the German bight and their seasonal contributions to surface picoplankton

    Applied and Environmental Microbiology 67:5134–5142.

    https://doi.org/10.1128/AEM.67.11.5134-5142.2001

    • Google Scholar
14. 1. Falkowski PG
    2. Barber RT
    3. Smetacek V

    (1998) Biogeochemical Controls and Feedbacks on Ocean Primary Production

    Science 281:200–207.

    https://doi.org/10.1126/science.281.5374.200

    • Google Scholar
15. 1. Field CB
    2. Behrenfeld MJ
    3. Randerson JT
    4. Falkowski P

    (1998) Primary production of the biosphere: integrating terrestrial and oceanic components

    Science 281:237–240.

    https://doi.org/10.1126/science.281.5374.237

    • Google Scholar
16. 1. Finn RD
    2. Bateman A
    3. Clements J
    4. Coggill P
    5. Eberhardt RY
    6. Eddy SR
    7. Heger A
    8. Hetherington K
    9. Holm L
    10. Mistry J
    11. Sonnhammer EL
    12. Tate J
    13. Punta M

    (2014) Pfam: the protein families database

    Nucleic Acids Research 42:D222–D230.

    https://doi.org/10.1093/nar/gkt1223

    • Google Scholar
17. 1. Fuhrman JA
    2. Cram JA
    3. Needham DM

    (2015) Marine microbial community dynamics and their ecological interpretation

    Nature Reviews. Microbiology 13:133–146.

    https://doi.org/10.1038/nrmicro3417

    • Google Scholar
18. 1. Gerlach W
    2. Stoye J

    (2011) Taxonomic classification of metagenomic shotgun sequences with CARMA3

    Nucleic Acids Research 39:e91.

    https://doi.org/10.1093/nar/gkr225

    • Google Scholar
19. 1. Giebel HA
    2. Kalhoefer D
    3. Lemke A
    4. Thole S
    5. Gahl-Janssen R
    6. Simon M
    7. Brinkhoff T

    (2011) Distribution of Roseobacter RCA and SAR11 lineages in the North Sea and characteristics of an abundant RCA isolate

    The ISME Journal 5:8–19.

    https://doi.org/10.1038/ismej.2010.87

    • Google Scholar
20. 1. Giovannoni SJ
    2. Tripp HJ
    3. Givan S
    4. Podar M
    5. Vergin KL
    6. Baptista D
    7. Bibbs L
    8. Eads J
    9. Richardson TH
    10. Noordewier M
    11. Rappé MS
    12. Short JM
    13. Carrington JC
    14. Mathur EJ

    (2005) Genome streamlining in a cosmopolitan oceanic bacterium

    Science 309:1242–1245.

    https://doi.org/10.1126/science.1114057

    • Google Scholar
21. 1. Gómez-Pereira PR
    2. Fuchs BM
    3. Alonso C
    4. Oliver MJ
    5. van Beusekom JE
    6. Amann R

    (2010) Distinct flavobacterial communities in contrasting water masses of the north Atlantic Ocean

    The ISME Journal 4:472–487.

    https://doi.org/10.1038/ismej.2009.142

    • Google Scholar
22. 1. Gómez-Pereira PR
    2. Schüler M
    3. Fuchs BM
    4. Bennke C
    5. Teeling H
    6. Waldmann J
    7. Richter M
    8. Barbe V
    9. Bataille E
    10. Glöckner FO
    11. Amann R

    (2012) Genomic content of uncultured Bacteroidetes from contrasting oceanic provinces in the North Atlantic Ocean

    Environmental Microbiology 14:52–66.

    https://doi.org/10.1111/j.1462-2920.2011.02555.x

    • Google Scholar
23. 1. Gómez-Pereira PR
    2. Hartmann M
    3. Grob C
    4. Tarran GA
    5. Martin AP
    6. Fuchs BM
    7. Scanlan DJ
    8. Zubkov MV

    (2013) Comparable light stimulation of organic nutrient uptake by SAR11 and Prochlorococcus in the North Atlantic subtropical gyre

    The ISME Journal 7:603–614.

    https://doi.org/10.1038/ismej.2012.126

    • Google Scholar
24. 1. Halsey KH
    2. Carter AE
    3. Giovannoni SJ

    (2012) Synergistic metabolism of a broad range of C1 compounds in the marine methylotrophic bacterium HTCC2181

    Environmental Microbiology 14:630–640.

    https://doi.org/10.1111/j.1462-2920.2011.02605.x

    • Google Scholar
25. 1. Hehemann JH
    2. Kelly AG
    3. Pudlo NA
    4. Martens EC
    5. Boraston AB

    (2012) Bacteria of the human gut microbiome catabolize red seaweed glycans with carbohydrate-active enzyme updates from extrinsic microbes

    Proceedings of the National Academy of Sciences of the United States of America 109:19786–19791.

    https://doi.org/10.1073/pnas.1211002109

    • Google Scholar
26. 1. Herlemann DP
    2. Labrenz M
    3. Jürgens K
    4. Bertilsson S
    5. Waniek JJ
    6. Andersson AF

    (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea

    The ISME Journal 5:1571–1579.

    https://doi.org/10.1038/ismej.2011.41

    • Google Scholar
27. 1. Huntemann M
    2. Ivanova NN
    3. Mavromatis K
    4. Tripp HJ
    5. Paez-Espino D
    6. Palaniappan K
    7. Szeto E
    8. Pillay M
    9. Chen I-MA
    10. Pati A
    11. Nielsen T
    12. Markowitz VM
    13. Kyrpides NC

    (2015) The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

    Standards in Genomic Sciences 10:86.

    https://doi.org/10.1186/s40793-015-0077-y

    • Google Scholar
28. 1. Hutchinson GE

    (1961) The Paradox of the Plankton

    The American Naturalist 95:137–145.

    https://doi.org/10.1086/282171

    • Google Scholar
29. 1. Kabisch A
    2. Otto A
    3. König S
    4. Becher D
    5. Albrecht D
    6. Schüler M
    7. Teeling H
    8. Amann RI
    9. Schweder T

    (2014) Functional characterization of polysaccharide utilization loci in the marine Bacteroidetes 'Gramella forsetii' KT0803

    The ISME Journal 8:1492–1502.

    https://doi.org/10.1038/ismej.2014.4

    • Google Scholar
30. 1. Klindworth A
    2. Mann AJ
    3. Huang S
    4. Wichels A
    5. Quast C
    6. Waldmann J
    7. Teeling H
    8. Glöckner FO

    (2014) Diversity and activity of marine bacterioplankton during a diatom bloom in the North Sea assessed by total RNA and pyrotag sequencing

    Marine Genomics 18:185–192.

    https://doi.org/10.1016/j.margen.2014.08.007

    • Google Scholar
31. 1. Krogh A
    2. Larsson B
    3. von Heijne G
    4. Sonnhammer ELL

    (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes

    Journal of Molecular Biology 305:567–580.

    https://doi.org/10.1006/jmbi.2000.4315

    • Google Scholar
32. 1. Loebl M
    2. van Beusekom JEE
    3. Philippart CJM

    (2013) No microzooplankton grazing during a Mediopyxis helysia dominated diatom bloom

    Journal of Sea Research 82:80–85.

    https://doi.org/10.1016/j.seares.2012.09.010

    • Google Scholar
33. 1. Lombard V
    2. Golaconda Ramulu H
    3. Drula E
    4. Coutinho PM
    5. Henrissat B

    (2014) The carbohydrate-active enzymes database (CAZy) in 2013

    Nucleic Acids Research 42:D490–D495.

    https://doi.org/10.1093/nar/gkt1178

    • Google Scholar
34. 1. Lucas J
    2. Wichels A
    3. Teeling H
    4. Chafee M
    5. Scharfe M
    6. Gerdts G

    (2015) Annual dynamics of North Sea bacterioplankton: seasonal variability superimposes short-term variation

    FEMS Microbiology Ecology 91:fiv099.

    https://doi.org/10.1093/femsec/fiv099

    • Google Scholar
35. 1. Luo R
    2. Liu B
    3. Xie Y
    4. Li Z
    5. Huang W
    6. Yuan J
    7. He G
    8. Chen Y
    9. Pan Q
    10. Liu Y
    11. Tang J
    12. Wu G
    13. Zhang H
    14. Shi Y
    15. Liu Y
    16. Yu C
    17. Wang B
    18. Lu Y
    19. Han C
    20. Cheung DW
    21. Yiu SM
    22. Peng S
    23. Xiaoqian Z
    24. Liu G
    25. Liao X
    26. Li Y
    27. Yang H
    28. Wang J
    29. Lam TW
    30. Wang J

    (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler

    GigaScience 1:18.

    https://doi.org/10.1186/2047-217X-1-18

    • Google Scholar
36. 1. Malmstrom RR
    2. Straza TRA
    3. Cottrell MT
    4. Kirchman DL

    (2007) Diversity, abundance, and biomass production of bacterial groups in the western Arctic Ocean

    Aquatic Microbial Ecology 47:45–55.

    https://doi.org/10.3354/ame047045

    • Google Scholar
37. 1. Mann DG

    (1999) The species concept in diatoms

    Phycologia 38:437–495.

    https://doi.org/10.2216/i0031-8884-38-6-437.1

    • Google Scholar
38. 1. Manz W
    2. Amann R
    3. Ludwig W
    4. Wagner M
    5. Schleifer K-H

    (1992) Phylogenetic Oligodeoxynucleotide Probes for the Major Subclasses of Proteobacteria: Problems and Solutions

    Systematic and Applied Microbiology 15:593–600.

    https://doi.org/10.1016/S0723-2020(11)80121-9

    • Google Scholar
39. 1. Manz W
    2. Amann R
    3. Ludwig W
    4. Vancanneyt M
    5. Schleifer KH

    (1996) Application of a suite of 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the phylum cytophaga-flavobacter-bacteroides in the natural environment

    Microbiology 142:1097–1106.

    https://doi.org/10.1099/13500872-142-5-1097

    • Google Scholar
40. 1. Markowitz VM
    2. Chen IM
    3. Chu K
    4. Szeto E
    5. Palaniappan K
    6. Pillay M
    7. Ratner A
    8. Huang J
    9. Pagani I
    10. Tringe S
    11. Huntemann M
    12. Billis K
    13. Varghese N
    14. Tennessen K
    15. Mavromatis K
    16. Pati A
    17. Ivanova NN
    18. Kyrpides NC

    (2014) IMG/M 4 version of the integrated metagenome comparative analysis system

    Nucleic Acids Research 42:D568–D573.

    https://doi.org/10.1093/nar/gkt919

    • Google Scholar
41. 1. Martinez-Garcia M
    2. Brazel DM
    3. Swan BK
    4. Arnosti C
    5. Chain PS
    6. Reitenga KG
    7. Xie G
    8. Poulton NJ
    9. Lluesma Gomez M
    10. Masland DE
    11. Thompson B
    12. Bellows WK
    13. Ziervogel K
    14. Lo CC
    15. Ahmed S
    16. Gleasner CD
    17. Detter CJ
    18. Stepanauskas R

    (2012) Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia

    PloS One 7:e35314.

    https://doi.org/10.1371/journal.pone.0035314

    • Google Scholar
42. 1. Meyer F
    2. Goesmann A
    3. McHardy AC
    4. Bartels D
    5. Bekel T
    6. Clausen J
    7. Kalinowski J
    8. Linke B
    9. Rupp O
    10. Giegerich R
    11. Pühler A

    (2003)

    GenDB--an open source genome annotation system for prokaryote genomes

    Nucleic Acids Research 31:2187–2195.

    • Google Scholar
43. 1. Morris RM
    2. Nunn BL
    3. Frazar C
    4. Goodlett DR
    5. Ting YS
    6. Rocap G

    (2010) Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction

    The ISME Journal 4:673–685.

    https://doi.org/10.1038/ismej.2010.4

    • Google Scholar
44. 1. Muñoz-Marín MC
    2. Luque I
    3. Zubkov MV
    4. Hill PG
    5. Diez J
    6. García-Fernández JM

    (2013) Prochlorococcus can use the Pro1404 transporter to take up glucose at nanomolar concentrations in the Atlantic Ocean

    Proceedings of the National Academy of Sciences of the United States of America 110:8597–8602.

    https://doi.org/10.1073/pnas.1221775110

    • Google Scholar
45. 1. Nelson DM
    2. Tréguer P
    3. Brzezinski MA
    4. Leynaert A
    5. Quéguiner B

    (1995) Production and dissolution of biogenic silica in the ocean: Revised global estimates, comparison with regional data and relationship to biogenic sedimentation

    Global Biogeochemical Cycles 9:359–372.

    https://doi.org/10.1029/95GB01070

    • Google Scholar
46. 1. Nielsen H
    2. Brunak S
    3. von Heijne G

    (1999) Machine learning approaches for the prediction of signal peptides and other protein sorting signals

    Protein Engineering 12:3–9.

    https://doi.org/10.1093/protein/12.1.3

    • Google Scholar
47. 1. Niu Y
    2. Shen H
    3. Chen J
    4. Xie P
    5. Yang X
    6. Tao M
    7. Ma Z
    8. Qi M

    (2011) Phytoplankton community succession shaping bacterioplankton community composition in Lake Taihu, China

    Water Research 45:4169–4182.

    https://doi.org/10.1016/j.watres.2011.05.022

    • Google Scholar
48. 1. Olenina I

    (2006)

    Biovolumes and size-classes of phytoplankton in the Baltic Sea

    HELCOM BaltSea Environ Proc 106:144.

    • Google Scholar
49. 1. Passow U

    (2002) Transparent exopolymer particles (TEP) in aquatic environments

    Progress in Oceanography 55:287–333.

    https://doi.org/10.1016/S0079-6611(02)00138-6

    • Google Scholar
50. 1. Punta M
    2. Coggill PC
    3. Eberhardt RY
    4. Mistry J
    5. Tate J
    6. Boursnell C
    7. Pang N
    8. Forslund K
    9. Ceric G
    10. Clements J
    11. Heger A
    12. Holm L
    13. Sonnhammer EL
    14. Eddy SR
    15. Bateman A
    16. Finn RD

    (2012) The Pfam protein families database

    Nucleic Acids Research 40:D290–D301.

    https://doi.org/10.1093/nar/gkr1065

    • Google Scholar
51. 1. Quast C
    2. Pruesse E
    3. Yilmaz P
    4. Gerken J
    5. Schweer T
    6. Yarza P
    7. Peplies J
    8. Glöckner FO

    (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

    Nucleic Acids Research 41:D590–D596.

    https://doi.org/10.1093/nar/gks1219

    • Google Scholar
52. 1. Rappé MS
    2. Connon SA
    3. Vergin KL
    4. Giovannoni SJ

    (2002) Cultivation of the ubiquitous SAR11 marine bacterioplankton clade

    Nature 418:630–633.

    https://doi.org/10.1038/nature00917

    • Google Scholar
53. 1. Reddy TB
    2. Thomas AD
    3. Stamatis D
    4. Bertsch J
    5. Isbandi M
    6. Jansson J
    7. Mallajosyula J
    8. Pagani I
    9. Lobos EA
    10. Kyrpides NC

    (2015) The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

    Nucleic Acids Research 43:D1099–D1106.

    https://doi.org/10.1093/nar/gku950

    • Google Scholar
54. 1. Ruff SE
    2. Probandt D
    3. Zinkann AC
    4. Iversen MH
    5. Klaas C
    6. Würzberg L
    7. Krombholz N
    8. Wolf-Gladrow D
    9. Amann R
    10. Knittel K

    (2014)

    Indications for algae-degrading benthic microbial communities in deep-sea sediments along the Antarctic Polar Front

    Deep Sea Research Part II: Topical Studies in Oceanography.

    • Google Scholar
55. 1. Sapp M
    2. Wichels A
    3. Wiltshire KH
    4. Gerdts G

    (2007) Bacterial community dynamics during the winter-spring transition in the North Sea

    FEMS Microbiology Ecology 59:622–637.

    https://doi.org/10.1111/j.1574-6941.2006.00238.x

    • Google Scholar
56. 1. Sarmento H
    2. Gasol JM

    (2012) Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton

    Environmental Microbiology 14:2348–2360.

    https://doi.org/10.1111/j.1462-2920.2012.02787.x

    • Google Scholar
57. 1. Schattenhofer M
    2. Fuchs BM
    3. Amann R
    4. Zubkov MV
    5. Tarran GA
    6. Pernthaler J

    (2009) Latitudinal distribution of prokaryotic picoplankton populations in the Atlantic Ocean

    Environmental Microbiology 11:2078–2093.

    https://doi.org/10.1111/j.1462-2920.2009.01929.x

    • Google Scholar
58. 1. Sonnenburg ED
    2. Zheng H
    3. Joglekar P
    4. Higginbottom SK
    5. Firbank SJ
    6. Bolam DN
    7. Sonnenburg JL

    (2010) Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations

    Cell 141:1241–1252.

    https://doi.org/10.1016/j.cell.2010.05.005

    • Google Scholar
59. 1. Stingl U
    2. Desiderio RA
    3. Cho JC
    4. Vergin KL
    5. Giovannoni SJ

    (2007) The SAR92 clade: an abundant coastal clade of culturable marine bacteria possessing proteorhodopsin

    Applied and Environmental Microbiology 73:2290–2296.

    https://doi.org/10.1128/AEM.02559-06

    • Google Scholar
60. 1. Tada Y
    2. Taniguchi A
    3. Nagao I
    4. Miki T
    5. Uematsu M
    6. Tsuda A
    7. Hamasaki K

    (2011) Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean

    Applied and Environmental Microbiology 77:4055–4065.

    https://doi.org/10.1128/AEM.02952-10

    • Google Scholar
61. 1. Tan SL
    2. Xiaoshan Zhu JS
    3. Yu S
    4. Zhan W
    5. Wang B
    6. Cai Z

    (2015) An association network analysis among microeukaryotes and bacterioplantkon reveals algal bloom dynamics

    Journal of Phycology 5:120–132.

    https://doi.org/10.1111/jpy.12259

    • Google Scholar
62. 1. Taylor JD
    2. Cottingham SD
    3. Billinge J
    4. Cunliffe M

    (2014) Seasonal microbial community dynamics correlate with phytoplankton-derived polysaccharides in surface coastal waters

    The ISME Journal 8:245–248.

    https://doi.org/10.1038/ismej.2013.178

    • Google Scholar
63. 1. Teeling H
    2. Fuchs BM
    3. Becher D
    4. Klockow C
    5. Gardebrecht A
    6. Bennke CM
    7. Kassabgy M
    8. Huang S
    9. Mann AJ
    10. Waldmann J
    11. Weber M
    12. Klindworth A
    13. Otto A
    14. Lange J
    15. Bernhardt J
    16. Reinsch C
    17. Hecker M
    18. Peplies J
    19. Bockelmann FD
    20. Callies U
    21. Gerdts G
    22. Wichels A
    23. Wiltshire KH
    24. Glöckner FO
    25. Schweder T
    26. Amann R

    (2012) Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom

    Science 336:608–611.

    https://doi.org/10.1126/science.1218344

    • Google Scholar
64. 1. Thiele S
    2. Fuchs BM
    3. Amann RI

    (2011)

    Treatise on Water Science

    171–189, Identification of microorganisms using the ribosomal RNA approach and fluorescence in situ hybridization., Treatise on Water Science, vol 3, 10.1016/B978-0-444-53199-5.00056-7.

    • Google Scholar
65. 1. Voget S
    2. Wemheuer B
    3. Brinkhoff T
    4. Vollmers J
    5. Dietrich S
    6. Giebel HA
    7. Beardsley C
    8. Sardemann C
    9. Bakenhus I
    10. Billerbeck S
    11. Daniel R
    12. Simon M

    (2015) Adaptation of an abundant Roseobacter RCA organism to pelagic systems revealed by genomic and transcriptomic analyses

    The ISME Journal 9:371–384.

    https://doi.org/10.1038/ismej.2014.134

    • Google Scholar
66. 1. Wemheuer B
    2. Wemheuer F
    3. Hollensteiner J
    4. Meyer FD
    5. Voget S
    6. Daniel R

    (2015) The green impact: bacterioplankton response toward a phytoplankton spring bloom in the southern North Sea assessed by comparative metagenomic and metatranscriptomic approaches

    Frontiers in Microbiology 6:805.

    https://doi.org/10.3389/fmicb.2015.00805

    • Google Scholar
67. 1. Wilhelm LJ
    2. Tripp HJ
    3. Givan SA
    4. Smith DP
    5. Giovannoni SJ

    (2007) Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data

    Biology Direct 2:27.

    https://doi.org/10.1186/1745-6150-2-27

    • Google Scholar
68. 1. Williams TJ
    2. Wilkins D
    3. Long E
    4. Evans F
    5. DeMaere MZ
    6. Raftery MJ
    7. Cavicchioli R

    (2013) The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics

    Environmental Microbiology 15:1302–1317.

    https://doi.org/10.1111/1462-2920.12017

    • Google Scholar
69. 1. Wiltshire KH
    2. Kraberg A
    3. Bartsch I
    4. Boersma M
    5. Franke H-D
    6. Freund J
    7. Gebühr C
    8. Gerdts G
    9. Stockmann K
    10. Wichels A

    (2010) Helgoland Roads, North Sea: 45 Years of Change

    Estuaries and Coasts 33:295–310.

    https://doi.org/10.1007/s12237-009-9228-y

    • Google Scholar
70. 1. Worden AZ
    2. Follows MJ
    3. Giovannoni SJ
    4. Wilken S
    5. Zimmerman AE
    6. Keeling PJ

    (2015) Environmental science. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes

    Science 347:1257594.

    https://doi.org/10.1126/science.1257594

    • Google Scholar
71. 1. Xing P
    2. Hahnke RL
    3. Unfried F
    4. Markert S
    5. Huang S
    6. Barbeyron T
    7. Harder J
    8. Becher D
    9. Schweder T
    10. Glöckner FO
    11. Amann RI
    12. Teeling H

    (2015) Niches of two polysaccharide-degrading Polaribacter isolates from the North Sea during a spring diatom bloom

    The ISME Journal 9:1410–1422.

    https://doi.org/10.1038/ismej.2014.225

    • Google Scholar
72. 1. Yang C
    2. Li Y
    3. Zhou B
    4. Zhou Y
    5. Zheng W
    6. Tian Y
    7. Van Nostrand JD
    8. Wu L
    9. He Z
    10. Zhou J
    11. Zheng T

    (2015) Illumina sequencing-based analysis of free-living bacterial community dynamics during an Akashiwo sanguine bloom in Xiamen sea, China

    Scientific Reports 5:8476.

    https://doi.org/10.1038/srep08476

    • Google Scholar
73. 1. Yin Y
    2. Mao X
    3. Yang J
    4. Chen X
    5. Mao F
    6. Xu Y

    (2012) dbCAN: a web resource for automated carbohydrate-active enzyme annotation

    Nucleic Acids Research 40:W445–W451.

    https://doi.org/10.1093/nar/gks479

    • Google Scholar
74. 1. Yool A
    2. Tyrrell T

    (2003) Role of diatoms in regulating the ocean's silicon cycle

    Global Biogeochemical Cycles 17:n/a–24.

    https://doi.org/10.1029/2002GB002018

    • Google Scholar
75. 1. Zubkov MV
    2. Fuchs BM
    3. Tarran GA
    4. Burkill PH
    5. Amann R

    (2003) High rate of uptake of organic nitrogen compounds by Prochlorococcus cyanobacteria as a key to their dominance in oligotrophic oceanic waters

    Applied and Environmental Microbiology 69:1299–1304.

    https://doi.org/10.1128/AEM.69.2.1299-1304.2003

    • Google Scholar

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

• 6,603

  views

• 1,149

  downloads

• 344

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.