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Temporal variability of marine archaea across the water column at SPOT

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Temporal variability of marine archaea across the water column at SPOT

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Temporal variability of marine archaea across the water column at SPOT

Author
Alma E. Parada

Advisor
Jed A. Fuhrman

A Dissertation Presented to the
FACULTY of the USC GRADUATE SCHOOL
University of Southern California
Department of Biological Sciences
Marine Biology/ Biological Oceanography
In partial fulfillment of the Requirements for the Degree
DOCOTR OF PHILOSOPHY

December 2015

Dissertation Committee
Dr. Jed A. Fuhrman, Chair
Dr. John F. Heidelberg
Dr. James W. Moffett
Dr. Douglas E. Hammond

i

Dedication
This work is dedicated to Mario Pamplona for his love, patience, humor and for all those
little nudges that got me through this adventure. This is also dedicated to my family for
unconditionally believing in me.

ii

Acknowledgements
I would like to thank my advisor, Jed Fuhrman, for his guidance and support throughout
my entire graduate school experience. I would also like to thank my dissertation committee,
John Heidelberg, Jim Moffett, and Doug Hammond, for their insightful advice and mentorship.
The research presented in this dissertation could not have been possible without all the
help from the multitude of people involved in gathering and processing samples from the San
Pedro Ocean Time-Series station. These include past and present members (graduate students,
undergraduate students, technicians and volunteers) of the Fuhrman and Caron laboratories, as
well as the crews of the R/V Seawatch and Yellowfin. I would also like to thank Linda Bazillian,
Don Bingham, and the rest of the staff responsible for all the administrative work of getting me
through graduate school.
This research was supported by NSF Microbial Observatories and Dimensions of
Biodiversity grants 1031743 and 1136818 and Gordon and Betty Moore Foundation grant
GMBF3779. I was additionally funded by the NSF Graduate Research Fellowship Program,
Wrigley Institute Summer fellowships, and Centers of Excellence in Genomic Science Research
Assistantship.

iii

Table of Contents

Dedication ........................................................................................................................................ i

Acknowledgements ........................................................................................................................ ii

Table of Contents .......................................................................................................................... iii

Synopsis .................................................................................................................................... iv-xii

Chapter 1 .................................................................................................................................. 1-13
“Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock
communities, time series and global field samples.”

Chapter 2 ................................................................................................................................ 14-44
“Changes in the marine archaeal community and its interaction with the microbial community
over time and depth at SPOT.”
Chapter 2 Supplementary Information ...................................................................... 45-61

Chapter 3 ................................................................................................................................ 62-86
“Strain variation of marine Thaumarchaea over time and depth via 16S rDNA tag and
metagenomics sequencing.”
Chapter 3 Supplementary Information ................................................................... 87-106

iv

Synopsis

The existence of a group of single-celled organisms now called archaea is historically a
new concept. A series of studies published in 1977 by Carl Woese and colleagues described how
cultures of extremophilic “bacteria” were in fact so genetically distinct from all other bacteria
that these organisms deserved to be considered a separate taxonomic entity called
archaebacteria (Balch et al. 1977; Fox et al. 1977; Woese and Fox 1977). This led to the official
1990 proposal (though the concept was introduced earlier) of categorizing all living organisms
into three domains: Archaea (formerly archaebacteria), Bacteria, Eukarya (Woese et al. 1990),
where the Archaea were comprised of two phyla, the Crenarchaeota and Euryarchaeota. At
this point however, all archaea in culture were extremophiles, such as the thermophilic
crenarchaeote Pyrodictium occultum and the halophilic euryarchaeote Halobacterium volcanii
(Woese 1987). In 1992, Fuhrman and colleagues changed this “extremophilic” perception when
they reported the existence of possible abundant organisms distantly related to the
Crenarchaea in the Pacific Ocean at 100m and 500m. A few months later DeLong (1992)showed
that with archaeal specific primers two groups of archaeal organisms, Marine Group I
(Crenarchaeal related) and Marine Group II (Euryarchaeal related),could be identified in near-
surface seawater in the Pacific and Atlantic Oceans. These two studies thus brought to light the
existence of archaea in relatively moderate environments.
Shortly after the discovery of archaea in the mesophilic ocean, several culture
independent studies found that these two groups could be found throughout the world’s
oceans (reviewed in DeLong, 2003, 2007; Allers and Mevarech, 2005; Francis et al., 2005;
Schleper et al., 2005). Most studies showed that the Euryarchaeota Marine Group II were most
v

abundant in surface waters, with the Crenarchaeota Marine Group I most abundant below
100m. Additionally, with the isolation of a Marine Group I (MGI) organism, Nitrosopumilus
maritimus and through genomic surveys, it became clear that most, if not all, MGI taxa are
capable of autotrophic ammonia oxidation (e.g. Venter et al., 2004; Könneke et al., 2005;
Francis et al., 2007; Beman et al., 2010). Due to their abundance and ubiquity the MGI, and not
the ammonia-oxidizing bacteria, appear to be the dominant organisms responsible for this first
step in nitrification in the oceans. Additionally, phylogenetic studies of the Crenarchaea Marine
Group I led to a formal proposal to place all mesophilic Crenarchaea into a new and separate
phylum called the Thaumarchaeota, in which the ammonia-oxidizing organisms were placed as
Marine Group I (Brochier-Armanet et al. 2008). The activity of the Euryarchaea Marine Group II
(MGII) has been more elusive, especially since to date no MGII organisms have been cultured.
However, a MGII genome obtained from metagenomic sampling and sequencing, has indicated
that, at least in the surface ocean, these organisms are likely heterotrophic (Iverson et al. 2012).
Furthermore, several studies have shown that the MGI and MGII communities have different
phylogenetic populations that can be found at different times of the year and at different
depths in the water column (e.g. (Massana et al. 1997, 2000; Martin-Cuadrado et al. 2008,
2015; Beman et al. 2010; Hugoni et al. 2013; Orsi et al. 2015), suggesting even greater diversity
within the archaea than initially presumed.
Previous studies on the MGI and MGII archaea have shown that these organisms are, at
the very least, numerically important members of the microbial community and show that their
community composition is temporally and vertically variable. These concepts have thus led to
the questions addressed in this thesis:
vi

1) How variable and predictable are MGI and MGII communities and how do the
communities change across depth?
2) How diverse are the MGI organisms? And is their ecological significance above
conventional species-level diversity indicative of microdiversity?
3) If MGI and MGII communities are variable and diverse, how can this impact our
understanding of the role they play I the microbial community and in
biogeochemical cycles?
The following chapters focus on addressing these questions through time-series
sampling of the microbial community at the San Pedro Ocean Time-series (SPOT) Station,
located in the San Pedro Channel off the coast of California (33.55°N, 118.4°W). The primary method to
characterize the archaeal community at SPOT was through 16S rRNA gene tag sequencing of samples
collected over 5 years (Feb 2009-Dec 2013) at 5 depths spanning the entire water column (total depth
900m). Initially >120 samples were sent to the Earth Microbiome Project (EMP) for sequencing using
their primers of choice, 515F/806R (Caporaso et al. 2012; Gilbert et al. 2014). However, we found that
these primers underestimated the globally important SAR11 in our samples by ~5-fold, as compared to
previous studies at SPOT (Chapter 1). Additionally, by amplifying mock communities created from
marine clones, we found that the 515F/806R primers inaccurately represented the abundance of most
clones (Chapter 1: Table 1 and Figure 1). However, an alternative primer pair, 515F/926R including a
single base modification to the forward primer, more accurately represented the mock community clone
abundances. Furthermore, the alternative primers produced a longer PCR product that allowed for
higher phylogenetic resolution allowing for distinction of highly related taxa with different ecological
patterns (Chapter 1: Figure 4). The modification to the forward primer also increased the detection of
total Thaumarchaea Marine Group I taxa; however, since the cumulative increase in a given sample
vii

rarely exceeded ~2% (Chapter 1: Table S4) and a majority of the samples used to characterize the
archaeal community were sequenced without this modification, we proceeded with original 515F. The
analyses of these primers clarified the need to evaluate primers using in silico analyses followed by
amplification of mock communities and environmental samples to more appropriately evaluate the use
of any given pair of primers.
Through 16S rRNA tag sequencing using the alternative pair of primers, we found that the MGI
and MGII communities at SPOT are seasonal at all depths and thus predictable over time (Chapter 2:
Table 1). Interestingly, seasonality occurs at all depths despite having different amounts of variability
over time and distinct community compositions at each depth (Chapter 2: Figure S1, Figure S2, and
Figure 3). This may indicate that seasonality is a trait likely inherent to these archaeal communities
influenced by the surface seasonality of the physical and biological environment (Figure 3). Furthermore,
correlation networks show that within the same depth most MGI and MGII taxa have distinct
correlations to other microbial members and environmental parameters (Chapter 2). For example, the
Thaumarchaea MGI are typically perceived as a group of organisms that oxidize ammonia (the first step
in nitrification), however results shown in Chapter 2 demonstrates that different MGI taxa are
correlated to different Nitrospina (presumed nitrite oxidizers performing the second step in
nitrification), and that these appear to form different nitrifying consortia with distinct ecological
patterns. These differences likely reflect distinct ecological roles within the microbial communities, due
to different activities, substrate affinities, fitness or controls selecting for groups and individuals at
each depth and time of the year.
Through the analyses in Chapter 2 and previous knowledge on the diversity of MGI and MGII
taxa, it is clear that the archaea are much more diverse than was assumed when they were first found in
the mesophilic ocean. However, conventional 97% 16S rRNA gene similarity cut-offs to delineate species
likely lumps together ecologically distinct taxa. In order to determine if the archaea can have
viii

ecologically relevant diversity more resolved than the typical species cut-off (Chapter 3), we focused on
evaluating the diversity contained in Thaumarchaeal Marine Group I 99% operational taxonomic units
(OTUs). Using a relatively new method that aims to separate sequences into clusters (called oligotypes)
based on single base positions of high variation, we found that several 99% MGI OTUs could be broken
down into different oligotypes with distinct ecological patterns over 12 months and across depths
(Chapter 3: Figures 1-3). Additionally, some OTUs demonstrated greater “microdiversity” and contained
many oligotypes, suggesting that some 99% taxa contain different genetic diversity. These results
suggest that the MGI community contains much more ecologically meaningful genetic diversity than has
been reported previously in studies that used low-resolution methods (e.g. DNA fingerprinting,
quantitative PCR, or oligonucleotide probes) or that used 97% similarity cutoffs when performing high-
throughput sequencing (e.g. Massana et al., 2000; Francis et al., 2005; Beman et al., 2010, 2011; Galand
et al., 2010; Santoro et al., 2010; Hugoni et al., 2013).
As the 16S rRNA gene is highly conserved, it does not always reflect the potential genetic
diversity contained across the genome. In order to determine how well the genomes of the MGI
community at SPOT are represented by existing complete and partial genomes, we recruiting
metagenomics reads from samples spanning 12 months and a depth profile to a variety of MGI
references. We found that most genomes recruit relatively few sequences that have percent identities
(%ID) >95% and fewer still that have >99%ID (Chapter 3: Figures 4 and 6), suggesting that the genomic
diversity near the species level of the natural community at SPOT is not well-represented by known
genomes. Additionally, analysis of the variability of reads recruiting above 95%ID, showed that even
within this high similarity cutoff, reads could be further broken down into distinct populations with
different abundances and in some cases ecological patterns (Chapter 3: Figures 4-6). Thus, oligotyping
and metagenomic fragment recruitment demonstrates that there is ecologically relevant diversity within
high similarity cut-offs. Additionally, these results suggest that the MGI communities contain an
ix

immense amount of genomic diversity leading to a wide range of activity, fitness, viral and grazing
susceptibility, and other adaptations within highly similar populations.
In conclusion, the results presented in Chapter 2 and 3 indicate that the marine archaea are
dynamic and that just a few base changes in the genetic code of these organisms drive this variability
allowing for selection of different taxa that would appear identical if examined at coarse phylogenetic
resolution. This concept should be taken into account when evaluating the role of these organisms in
biogeochemical cycles. This implies that the genetic diversity observed by oligotyping and
fragment recruitment allows for selection of different groups of organisms over time, and likely
depth, despite performing similar activities. However, further studies may show that the
presence of different consortia may actually lead to variability in the rates of activity or have
different impacts in the environment. Thus the presence of large amounts of diversity and
variability within the archaea, and thus within the microbial community, should be taken into
account when evaluating how changing environmental conditions could affect biogeochemical
cycles.

x

Literature Cited
Allers, T., and M. Mevarech. 2005. Archaeal genetics - the third way. Nat. Rev. Genet. 6: 58–73.
Balch, W. E., J. Magrum, Linda, G. E. Fox, R. S. Wolfe, and C. R. Woese. 1977. An ancient
divergence among the bacteria. J. Mol. Evol. 9: 305–311.
Beman, J. M., R. Sachdeva, and J. A. Fuhrman. 2010. Population ecology of nitrifying Archaea
and Bacteria in the Southern California Bight. Environ. Microbiol. 12: 1282–1292.
Beman, J. M., J. A. Steele, and J. A. Fuhrman. 2011. Co-occurrence patterns for abundant
marine archaeal and bacterial lineages in the deep chlorophyll maximum of coastal
California. ISME J. 5: 1077–1085.
Brochier-Armanet, C., B. Boussau, S. Gribaldo, and P. Forterre. 2008. Mesophilic Crenarchaeota:
proposal for a third archaeal phylum, the Thaumarchaeota. Nat. Rev. Microbiol. 6: 245–
252.
Caporaso, J. G., C. L. Lauber, W. A. Walters, D. Berg-Lyons, J. Huntley, N. Fierer, S. M. Owens, J.
Betley, L. Fraser, M. Bauer, N. Gormley, J. A. Gilbert, G. Smith, and R. Knight. 2012. Ultra-
high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.
ISME J. 6: 1621–1624.
DeLong, E. 2007. Microbial Domains in the Ocean: A Lesson from the Archaea. Oceanography
20: 124–129.
DeLong, E. F. 1992. Archaea in coastal marine environments. Proc. Natl. Acad. Sci. U. S. A. 89:
5685–5689.
DeLong, E. F. 2003. Oceans of Archaea. ASM News-American Soc. Microbiol. 69: 503–503.
Fox, G. E., L. J. Magrum, W. E. Balch, R. S. Wolfe, and C. R. Woese. 1977. Classification of
methanogenic bacteria by 16S ribosomal RNA characterization. Proc. Natl. Acad. Sci. U. S.
A. 74: 4537–4541.
Francis, C. A., J. M. Beman, and M. M. Kuypers. 2007. New processes and players in the
nitrogen cycle: the microbial ecology of anaerobic and archaeal ammonia oxidation. ISME
J. 1: 19–27.
Francis, C. A., K. J. Roberts, J. M. Beman, A. E. Santoro, and B. B. Oakley. 2005. Ubiquity and
diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean.
Proc. Natl. Acad. Sci. U. S. A. 102: 14683–14688.

xi

Fuhrman, J. A., K. McCallum, and A. A. Davis. 1992. Novel major archaebacterial group from
marine plankton. Nature 356: 148–149.
Galand, P. E., C. Gutiérrez-Provecho, R. Massana, J. M. Gasol, and E. O. Casamayor. 2010. Inter-
annual recurrence of archaeal assemblages in the coastal NW Mediterranean Sea (Blanes
Bay Microbial Observatory). Limnol. Oceanogr. 55: 2117–2125.
Gilbert, J. A., J. K. Jansson, and R. Knight. 2014. The Earth Microbiome project : successes and
aspirations. BMC Biol. 12: 69.
Hugoni, M., N. Taib, D. Debroas, I. Domaizon, I. J. Dufournel, G. Bronner, I. Salter, H. Agogué, I.
Mary, and P. E. Galand. 2013. Structure of the rare archaeal biosphere and seasonal
dynamics of active ecotypes in surface coastal waters. PNAS 110: 6004–6009.
Iverson, V., R. M. Morris, C. D. Frazar, C. T. Berthiaume, R. L. Morales, and E. V. Armbrust. 2012.
Untangling genomes from metagenomes: revealing an uncultured class of marine
Euryarchaeota. Science 335: 587–590.
Könneke, M., A. E. Bernhard, J. R. de la Torré, C. B. Walker, J. B. Waterbury, and D. A. Stahl.
2005. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437: 543–
546.
Martin-Cuadrado, A.-B., I. Garcia-Heredia, A. G. Moltó, R. López-Úbeda, N. Kimes, P. López-
García, D. Moreira, and F. Rodriguez-Valera. 2015. A new class of marine Euryarchaeota
group II from the mediterranean deep chlorophyll maximum. ISME J. 9: 1619–1634.
Martin-Cuadrado, A.-B., F. Rodríguez-Valera, D. Moreira, J. C. Alba, E. Ivars-Martínez, M. R.
Henn, E. Talla, and P. López-García. 2008. Hindsight in the relative abundance, metabolic
potential and genome dynamics of uncultivated marine archaea from comparative
metagenomic analyses of bathypelagic plankton of different oceanic regions. ISME J. 2:
865–886.
Massana, R., E. F. DeLong, and C. Pedrós-Alió. 2000. A few cosmopolitan phylotypes dominate
planktonic archaeal assemblages in widely different oceanic provinces. Appl. Environ.
Microbiol. 66: 1777–1787.
Massana, R., A. E. Murray, C. M. Preston, and E. F. DeLong. 1997. Vertical distribution and
phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel.
Appl. Environ. Microbiol. 63: 50–56.
Orsi, W. D., J. M. Smith, H. M. Wilcox, J. E. Swalwell, P. Carini, A. Z. Worden, and A. E. Santoro.
2015. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic
matter. ISME J. 9: 1747–1763.
xii

Santoro, A. E., K. L. Casciotti, and C. A. Francis. 2010. Activity, abundance and diversity of
nitrifying archaea and bacteria in the central California Current. Environ. Microbiol. 12:
1989–2006.
Schleper, C., G. Jurgens, and M. Jonuscheit. 2005. Genomic studies of uncultivated archaea.
Nat. Rev. Microbiol. 3: 479–488.
Venter, J. C., K. Remington, J. F. Heidelberg, A. L. Halpern, D. B. Rusch, J. A. Eisen, D. Wu, I. T.
Paulsen, K. E. Nelson, W. C. Nelson, D. E. Fouts, S. Levy, A. H. Knap, M. W. Lomas, K.
Nealson, O. White, J. Peterson, J. M. Hoffman, R. Parsons, H. Baden-Tillson, C. Pfannkoch,
Y.-H. Rogers, and H. O. Smith. 2004. Environmental genome shotgun sequencing of the
Sargasso Sea. Science (80-. ). 304: 66–74.
Woese, C. R. 1987. Bacterial Evolution. Microbiol. Rev. 51: 221–271.
Woese, C. R., and G. E. Fox. 1977. Phylogenetic structure of the prokaryotic domain: the
primary kingdoms. Proc. Natl. Acad. Sci. U. S. A. 74: 5088–5090.
Woese, C. R., O. Kandler, and M. L. Wheelis. 1990. Towards a natural system of organisms:
Proposal for the domains Archaea, Bacteria, and Eucarya. Proc. Natl. Acad. Sci. 87: 4576–
4579.
1

Chapter 1:
Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock
communities, time series and global field samples
Originally published in Environmental Microbiology

Please Cite: Parada, A.E., D.M. Needham, and J.A. Fuhrman. 2015. Every base matters: assessing
small subunit rRNA primers for marine microbiomes with mock communities, time series and
global field samples. Environmental Microbiology. doi: 10.1111/1462-2920.13023

Every base matters: assessing small subunit rRNA
primers for marine microbiomes with mock
communities, time series and global field samples
Alma E. Parada, David M. Needham and
Jed A. Fuhrman*
University of Southern California, Los Angeles, CA, USA
Summary
Microbial community analysis via high-throughput
sequencing of amplified 16S rRNA genes is an
essential microbiology tool. We found the popular
primer pair 515F (515F-C) and 806R greatly under-
estimated (e.g. SAR11) or overestimated (e.g.
Gammaproteobacteria) common marine taxa. We
evaluated marine samples and mock communities
(containing11or27marine16Sclones),showingalter-
native primers 515F-Y (5′-GTGYCAGCMGCCGCGG
TAA) and 926R (5′-CCGYCAATTYMTTTRAGTTT) yield
more accurate estimates of mock community abun-
dances,producelongerampliconsthatcandifferenti-
ate taxa unresolvable with 515F-C/806R, and amplify
eukaryotic 18S rRNA. Mock communities amplified
with515F-Y/926Ryieldedcloserobservedcommunity
composition versus expected (r
2
= 0.95) compared
with 515F-Y/806R (r
2
∼ 0.5). Unexpectedly, biases with
515F-Y/806R against SAR11 in field samples (∼4–10-
fold) were stronger than in mock communities (∼2-
fold). Correcting a mismatch to Thaumarchaea in the
515F-C increased their apparent abundance in field
samples, but not as much as using 926R rather than
806R. With plankton samples rich in eukaryotic DNA
(> 1 μm size fraction), 18S sequences averaged ∼17%
ofallsequences.Asinglemismatchcanstronglybias
amplification,butevenperfectlymatchedprimerscan
exhibit preferential amplification. We show that
beyond in silico predictions, testing with mock com-
munities and field samples is important in primer
selection.
Introduction
Next-generationsequencingcontinuestomakeanalysisof
microbial diversity easier and less expensive. Therefore,
the choice of primers to amplify 16S genes becomes
crucial to take advantage of the sequence length and
coverage made possible by improved sequencing tech-
nologies. In 2010, the Earth Microbiome Project (EMP)
wasestablishedtocreateacatalogueofmicrobialdiversity
fromhabitatsacrosstheworld(Gilbertet al.,2010)withthe
goalofcreatingadatabaseofmicrobialsamplesanalysed
exactlythesamewaytofacilitateglobalcomparisons.The
EMP proposed standard primers and protocols to permit
comparisons of diversity across samples. The primers
515F/806R were chosen to maximize the global coverage
of Bacteria and Archaea while also providing polymerase
chain reaction (PCR) products of suitable length for
sequencing with available Illumina platforms (Caporaso
et al.,2011;2012).Sinceitiscommonlyassumedthatone
mismatch in the middle of a primer will still allow binding
and amplification of target templates, these primers
appeared to have comprehensive coverage in silico.At
around the same time, reviews of various group-specific
and universal primers, such as Klindworth and colleagues
(2013), performed mostly in silico analysis of hundreds of
primers. Although Klindworth and colleagues (2013) did
not examine the exact reverse primer used by EMP, they
reported on similar primers that also had high apparent
coverageifonemismatchisallowed.Thus,this515F/806R
primer pair seemed a reasonable choice.
We submitted marine plankton samples from several
(5–890 m) depths to the EMP and were surprised to find
the SAR11 cluster, relating to the Candidate genus
Pelagibacter, was poorly represented in the results (typi-
cally ∼3%).Otherstudiesofthesesamplestakenfromthe
San Pedro Ocean Time Series (SPOT) analysed via the
Automated Ribosomal Intergenic SpacerAnalysis (Fisher
and Triplett, 1999; Beman et al., 2011; Chow et al., 2013;
Cram et al., 2015), as well as prior studies at this location
by FISH (Ouverney, 1999), indicated the SAR11 clade is
typically 20–40% of the bacterial community. This agrees
with many analyses of marine plankton samples from
around the world (Morris et al., 2002; Venter et al., 2004;
Carlson et al., 2009; Brown et al., 2012; Gómez-Pereira
et al., 2013; Logares et al., 2013; Needham et al., 2013;
Vergin et al.,2013;Salter et al.,2015;Apprill et al.,2015).
This suggested that the EMP PCR amplification was
strongly biased against SAR11. A recent publication by
Received 20 June, 2015; revised 31 July, 2015; accepted 12August,
2015. *For correspondence. E-mail: fuhrman@usc.edu; Tel.
(+1) 213 740 5759; Fax (+1) 213 740 8123.
bs_bs_banner
Environmental Microbiology (2015) doi:10.1111/1462-2920.13023
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd
2
Apprill and colleagues (2015) reports a mismatch in the
806R primer, which when corrected greatly increases the
SAR11 abundances to more closely resemble FISH
results.
Criteria for selecting PCR primers for small subunit
rRNA amplicon sequencing include sequencing depth,
high coverage of the taxa of interest (here all Bacteria
and Archaea), the ability to compare results with prior
studies, accuracy in relative abundances and also the
phylogenetic resolution of the sequenced PCR products.
Reducingprimerbiasesisespeciallyimportantinthecase
of applications such as association networks or predicting
functional processes using programs like PICRUSt
(Langille et al., 2013). While comparing primers to the
16S rRNAdatabase, we noted that the EMP 515F primer
has a single mismatch to a majority of the globally impor-
tant Thaumarchaea and Crenarchaea, which can be
corrected with a single degeneracy, as noted by Quince
and colleagues (2011). To take advantage of longer
sequences now available, we considered utilizing an
alternate reverse (926R) primer used by Quince and
colleagues (2011) that has very high coverage of bacteria
andarchaea.The515F-Y/926Rprimerpairencompasses
the V4 and V5 hypervariable regions, while 515F-Y/806R
encompasses only the V4. Therefore, the 515F-Y/926R
primer pair yields a more informative product of suitable
length (given current sequencing capabilities,
> 2 × 250 bp) that overlaps with the product of the EMP
primers, facilitating comparisons.
In this report, we analysed mock communities made of
marinebacterialandarchaeal16SrRNAclonesaswellas
natural marine samples. We found the primer pair 515F-
Y/926Rhadbettercoverageofextremelycommonmarine
taxa missed by the original EMP primers (515F-C/806R),
more accurately represented expected mock community
abundances, and the added length allowed for better
identification of the taxa present.
Results
In silico primer comparisons
Comparisons of the 515F-C (as used by the EMP) and
515F-Y (modified for this study from Quince et al., 2011)
primers showed an increase from 57% to 93% in the
perfect matches to all known Archaeal taxa with the Y
degeneracy, driven mainly by an increased detection of
Thaumarchaea Marine Group I (MGI) taxa in the data-
base (from 0.4% to 96.4%, Table S1). The primers 515F-
Y/926R increased the percentage of perfectly matched
SAR11 taxa from 3% to 96%, and matched all three
domains. The perfect matches to individual SAR11
subclades increased when using 515F-Y/926R
(Table S2). One mismatch was required with 515F-Y/
806R to match Deep 1, Surface 2 and Surface 3
subclades. However, perfect matches to Surface 4 were
similar between primer pairs.
Mock community comparisons
Clone abundances in the even mock community samples
amplified with 515F-Y/926R were more similar to the
expected than with 515F-Y/806R (Fig. 1A). Even though
only SAR11 and SAR116_a clones have a mismatch to
the 806R primer, seven clones had abundances < 2/3 of
the expected 9.1% (Fig. 1A). Additionally, SAR86_a and
Marine Group A were overrepresented > 2-fold in 515F-
Y/806R-amplified samples.
AmplificationfromthestaggeredmockcommunityDNA
exhibited greater primer bias. The observed community
composition was more similar to the expected with 515F-
Y/926R than with 515F-Y/806R (r
2
= 0.95 versus 0.53;
Fig. 1BandC).Additionally,thedeviationfromexpectation
(Observed ÷ Expected = 1) was low with 515F-Y/926R,
but included significant under- and overestimates with
515F-Y/806R (top inset, Fig. 1A). Several templates were
responsibleforthedeviationsobservedwith515F-Y/806R,
suchasSAR11,SAR116_aandallGammaproteobacteria
clones (Fig. 1B, Table 1). SAR116_a reads were almost
absent in the 515F-Y/806R samples, but this discrepancy
was likely due to a 3′ mismatch to that clone, as clones
SAR116_b and c were overrepresented. Removing the
SAR11 and SAR116_a clones from the analysis and
rescaling the abundances of the remaining operational
taxonomicunits(OTUs)increasedthe515F-Y/806Rr
2
only
to 0.69, and actually decreased it when only the SAR11
OTUswereremoved(r
2
= 0.48).Thelargestdeviationfrom
expected abundances in the 515F-Y/926R staggered
mock community dataset was underrepresentation of
SAR202_b (Fig. 1C, Table 1).
The mock communities, when clustered together
‘blindly’withthefieldsamples,alsoallowedustoevaluate
severalclusteringmethods(UCLUST,USEARCH,mothur,
SWARM;SupplementaryInformation).Thoughnomethod
canbeperfect,somemethodsinaccuratelyclusteredmore
than 5% of the sequences, and we found the average-
neighbour algorithm in mothur, with pre-clustering at 2
base similarity, produced mock community compositions
closest to those expected compared with UCLUST and
USEARCH (Table S3). We also found SWARM (Mahé
et al., 2014) in QIIME to give mock community composi-
tions similar to the expected without pre-clustering.
MGI
Several field samples from SPOT and other marine sites
(seeExperimentalProcedures)wereusedtocomparethe
abundance of taxa when amplifying with different combi-
nations of the primers. Amplification with 515F-Y/806R
2 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
3
Fig. 1. Comparisons of even mock community (A) and staggered mock community clones (A–C) show tag abundances are closer to expected
with 515F-Y/926R. The inset in (A) gives the observed ÷ expected ratio for the staggered mock clones for each reverse. Note that SAR116_a
is virtually undetected with the 806R primer (A), and the difference in detection of the SAR11 clone between reverse primers is greater in the
staggered community (A). Observed mock community profiles with different primer pairs (B and C); community profiles with 515F-Y/806R (in
B) and 515F-Y/926R (in C), plotted against the expected staggered mock community profile. One-to-one line (solid black line) indicates the
theoretical perfect match of observed and expected communities. Regression lines and 95% confidence intervals for each curve are included.
The mean of four replicates is given as the abundance of each clone, with the error bars representing the standard error of the mean.
Primers for marine microbiome studies 3
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
4
produced a statistically significant increase in MGI abun-
dance from samples taken at a minimum depth of 150 m,
in contrast to amplification with 515F-C/806R (Table S4).
However, these samples showed no difference in MGI
community composition (Bray–Curtis similarity = 83%,
indistinguishable from technical replicates, Table S4). No
statisticallysignificantdifferencewasobservedintheMGI
abundance or community composition when samples
were amplified with 515F-C/926R or 515F-Y/926R
(Table S4). The most significant difference in MGI abun-
dance was a 1.91-fold increase ± 0.0493 SEM when
amplifying with 515F-C/926R compared with 515F-C/
806R(P < 0.001).Therewasalsoastatisticallysignificant
1.55-fold increase ± 0.103 SEM in MGI abundance when
amplifying with 515F-Y/926R compared with 515F-Y/
806R (P = 0.0318); however, 5 of the 19 samples showed
higher abundance with the 806R primer. Comparison of
the MGI community composition of those five samples to
the others did not indicate an obvious reason for the
difference observed.
Field comparisons
Comparing SPOT samples from 5 and 890 m in October
2013 showed several differences between primer pairs
similartothoseobservedwithmockcommunities,buttoa
greaterextent(Fig. 2).Forexample,theSAR11cladewas
∼6-fold higher in abundance at 5 m with the 515F-Y/926R
primer and ∼4-fold higher at 890 m compared with
amplification with 515F-Y/806R. Gammaproteobacteria
were higher in abundance in both samples amplified with
515F-Y/806R, similar to that seen with the SAR86,
PseudospirillumandSAR92clonesinthestaggeredmock
communities (Fig. 1 and Table 1), with some severalfold
higher and one group (Other Oceanospirillales) only
slightly so. Many SAR116 taxa were detected with 515F-
Y/806R in field samples, even though SAR116_a was
nearlyabsentinthemockcommunities.Theabundanceof
Gammaproteobacteria with 515F-Y/806R was still higher
than with 515F-Y/926R even when rescaling the abun-
dances after removing SAR11 and SAR116 OTUs (1.32-
fold ± 0.0291 SEM, P < 0.001, t-test).
The 515F-Y/926R matches 86% of eukaryotic 18S
rRNA (0 mismatches, Table S1). However, our analytical
pipeline that removes non-overlapping paired-end reads
discards eukaryotic 18S sequences because they are
typically 160–180bp longer.As a result, we found no 18S
sequences in our merged reads. When we removed the
requirement of overlapping paired ends, we found an
average of ∼1.5% (range 0.5–3.8%) 18S sequences of
Table 1. Staggered mock community clone names, per cent expected and observed per cent abundance.
Clone name
Staggered
per cent
expected
515F-Y/806R
observed abundance
(mean ± SEM)
806R ratio
difference
(Obs ÷ Exp)
515F-Y/926R
observed abundance
(mean ± SEM)
926R ratio
difference
(Obs ÷ Exp)
SAR11 Surface 1 (Alpha) 31.5 14.5 ± 2.21 0.460 24.7 ± 0.62 0.784
OCS155_a (Actino) 15.8 23.0 ± 0.68 1.46 14.7 ± 0.27 0.930
OCS155_b (Actino) 9.01 18.6 ± 3.2 2.06 10.5 ± 0.34 1.17
Thaumarchaea MGI_a 9.01 5.70 ± 0.62 0.633 13.7 ± 1.5 1.52
Prochlorococcus 6.76 2.94 ± 0.36 0.435 5.41 ± 0.15 0.800
SAR86_a (Gamma) 4.5 8.60 ± 0.96 1.91 3.16 ± 0.12 0.702
AEGEAN-169 (Alpha) 2.25 4.34 ± 0.60 1.93 2.57 ± 0.18 1.14
SAR116_a (Alpha) 2.25 0.0294 ± 0.0037 0.0131 2.37 ± 0.080 1.05
Euryarchaea MGII 1.8 1.74 ± 0.13 0.967 2.23 ± 0.27 1.24
Flavobacteria 1.8 0.674 ± 0.062 0.374 2.74 ± 0.082 1.52
Planctomyces 1.8 0.859 ± 0.13 0.477 2.55 ± 0.067 1.42
SAR116_b (Alpha) 1.8 4.05 ± 0.64 2.25 2.29 ± 0.11 1.27
SAR202_a (Chloroflexi) 1.8 0.631 ± 0.048 0.351 2.61 ± 0.16 1.45
Marine Group A (aka SAR406) 1.35 2.64 ± 0.12 1.96 1.40 ± 0.077 1.04
Flavobacteria_Formosa 0.901 0.334 ± 0.031 0.371 1.65 ± 0.071 1.83
Flavobacteria_NS9 0.901 2.13 ± 0.044 2.36 1.17 ± 0.033 1.30
Pseudospirillum (Gamma) 0.901 1.68 ± 0.22 1.86 0.831 ± 0.086 0.922
SAR86_b (Gamma) 0.901 1.34 ± 0.057 1.49 0.560 ± 0.030 0.621
SAR92 (Gamma) 0.901 1.41 ± 0.12 1.56 0.645 ± 0.020 0.716
Thaumarchaea MGI_b 0.901 0.322 ± 0.056 0.357 0.823 ± 0.077 0.913
Verrucomicrobia 0.901 0.189 ± 0.017 0.210 0.830 ± 0.025 0.921
Rhodobacteriaceae (Alpha) 0.676 0.915 ± 0.094 1.35 0.493 ± 0.0073 0.729
SAR86_c (Gamma) 0.676 1.25 ± 0.096 1.85 0.449 ± 0.010 0.664
Flavobacteria_NS5 0.45 0.925 ± 0.11 2.06 0.565 ± 0.037 1.26
SAR86_d (Gamma) 0.22 0.337 ± 0.011 1.53 0.137 ± 0.016 0.623
SAR116_c (Alpha) 0.113 0.168 ± 0.030 1.49 0.0940 ± 0.0090 0.832
SAR202_b (Chloroflexi) 0.113 0.116 ± 0.048 1.03 0.0394 ± 0.014 0.349
Clones also used in the even mock community, each at 9.1% expected abundance, are bolded (results shown in Fig. 1). Observed abundances
are given as the mean ± standard error of the mean (SEM).The ratios of the observed ÷ expected (Obs÷Exp) abundances are also given for each
reverse primer. Broad group names of clones are in parentheses (Alpha and Gamma refer to Proteobacteria, Actino to Actinobacteria).
4 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
5
picoeukaryotes in the 0.2–1 μm size fraction (Fig. S1,
Table S6A). Preliminary analysis of marine plankton DNA
from > 1 μm filters (some collected during a spring
bloom),highlyenrichedineukaryotes,yieldedanaverage
of 17% (range 8.6–35%) 18S sequences (Fig. S2,
Table S6B).
The SAR11 in field samples were significantly (t-test,
P < 0.001) and consistently higher (generally > 4×) with
515F-Y/926R compared with 515F-Y/806R, though differ-
ent between subclades (Fig. 3). Some OTUs from the
515F-Y/926R samples were classified as Surface 3,
though none were classified as such with 515F-Y/806R.
Differences in phylogenetic resolution
Theadditionalsequencelengthprovidedby515F-Y/926R
added sequence variation not evident with 515F-Y/806R,
often coinciding with apparent ecological differences. For
example, several representative SAR11 OTU sequences
from515F-Y/926R-amplifiedsampleswereidenticalwhen
trimmed to the 515F-Y/806R amplicon length, yet had
distinct temporal and depth patterns (Fig. 4). SAR11
OTUs 2 and 3 had different abundances (Fig. 4C) and
often varied inversely at 5 m. However, at the 515F-Y/
806R length the representative sequences from those
SAR11 OTUs were the same sequence (compare Fig. 4A
and B). SAR11 OTUs 35, 163 and 13 (from the 515F-Y/
926Rdataset)haddifferentpatternsateachdepth,some-
times varying inversely at 150 m (Fig. 4D), but all three
wouldhavebeenconsideredidenticalatthe515F-Y/806R
length (Fig. 4B). A similar situation occurs with SAR11
OTUs 4000 and 264 (Fig. 4E).
Discussion
Primers for evaluating microbial communities by 16S
rRNA gene amplification and sequencing are chosen to:
(i) optimize the coverage of desired organisms with
minimal biases in relative abundances, (ii) optimize the
phylogenetic resolution, (iii) yield a high-quality product
easily and inexpensively sequenced with the chosen
sequencing platform and (iv) provide results generally
comparable to other labs. Using these criteria, we evalu-
ated the primers used initially in the EMP and an alterna-
tive set by amplification of both mock communities and
diverse marine samples. Sequencing depth is also an
Euryarchaea MGII
OCS155
Flavobacteria
Cyanobacteria
Marine Group A
AEGEAN-169
SAR116
Other Alphaproteobacteria
SAR86
Other Gammaproteobacteria
Verrucomicrobia
Other Bacteria
Chloroplasts
Unassigned
% of Total Tags
0
5
10
15
515F-Y/806R
515F-Y/926R
A) 5m Oct 2013
Euryarchaea MGII
Thaumarchaea MGI
Other Archaea
OCS155
Flavobacteria
SAR202
Marine Group A
AEGEAN-169
SAR116
SAR11
Other Alphaproteobacteria
Nitrospina
SAR86
Other Gammaproteobacteria
Verrucomicrobia
Other Bacteria
Unassigned
% of Total Tags
0
4
8
12
16
515F-Y/806R
515F-Y/926R
B) 890m Oct 2013
SAR11
0
10
20
30
40
Other Oceanospirillales
0
5
10
15
20
25
30
Fig. 2. Comparisons of two SPOT field
samples amplified with the 806R or 926R
show major differences in the taxa comprising
80% of each community. OTUs in order of
decreasing abundance making up a total of
80% of the tags for each sample were
grouped by taxonomy for each reverse primer.
Data are from SPOT Oct 2013 samples at (A)
5 m and (B) 890 m.
Primers for marine microbiome studies 5
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
6
Fig. 3. Amplification with 515F-Y/926R yielded higher abundance of total SAR11 and most subclades in SPOT and global samples. Samples
from (A, B) 5 m, (C, D) 500 m, (E, F) 890 m, (G, H) Global Surface and (I, J) Global Deep are given as bars of different patterns in
chronological order, the same fill and order is used for the 806R and 926R panels. The per cent abundance of each clade in a sample is
given on the x-axis (note scale is the same between reverse primers). The abundance of SILVA subclades Chesapeake Delaware-Bay, LD12,
Surface 3 and Unassigned groups are combined at 5 m as Other. In deeper depths, Unassigned is shown separately, but Surface 2 was
combined into Other. The mean abundance and the standard error of the mean are given where technical replicates were available.
6 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
7
importantcriterionforselectingprimers,andnewerhigher
throughput platforms can allow greater depth than the
MiSeq 2 × 300 bp platform/chemistry we used, but there
is no expectation that greater depth will reduce the quan-
titative biases we observed. Polymerase chain reaction
optimization may reduce some biases inherent to each
primer set, but we report results using PCR conditions
similar to the EMP and published studies for the primers
used.WereducedthenumberofcyclesusedbytheEMP,
but studies suggest this is unlikely to alter biases (Acinas
et al., 2005; Sipos et al., 2007). Preliminary results similar
tothosepresentedinthisstudyweresuppliedtotheEMP,
and alternative primer pair (515F-Y/926R) information is
available on the EMP website.
Amplifying the staggered mock community demon-
strated that the 515F-Y/926R primer pair produced com-
munities much more similar (r
2
= 0.95) to the expected
distribution than 515F-Y/806R (r
2
= 0.53). This was the
result of significant overestimation of several clone taxa
(notably Gammaproteobacteria, Actinobacteria, Marine
Group A) and underestimation of several clones
including SAR11 and SAR116_a. The total abundance of
Gammaproteobacteria was also higher in field samples
amplifiedwith515F-Y/806Rcomparedwith515F-Y/926R.
Removing SAR11 and SAR116 OTUs from both still
showed greater total Gammaproteobacteria abundance
with 515F-Y/806R. This suggests that differences
observed were not due to missing SAR11 or SAR116
reads in the 515F-Y/806R dataset, but rather a bias for
Gammaproteobacteria.
The use of mock communities allowed us to compare
primer biases, but we also stress the importance of addi-
tionally comparing primers with field samples. For
example, we found a less than twofold relative apparent
bias between the two primer sets for SAR11 based on the
mock communities (Fig. 1, Table 1); however, with field
samples the SAR11 abundances with 515F-Y/926R were
about 4–10-fold higher than with 515F-Y/806R (compare
Figs 1–3). While in our study we did not have an absolute
measure of SAR11 field abundances, another study that
OTU2
OTU3
20
10
0
0.18
0.09
0.00
OTU2 Abundance
(% of Total Tags)
OTU3 Abundance
(% of Total Tags)
04/2011
08/2011
01/2012
07/2012
12/2012
04/2013
07/2013
10/2013
OTU35
OTU163
OTU13
0.0
0.5
1.0
04/2011
08/2011
01/2012
07/2012
12/2012
04/2013
07/2013
10/2013
04/2011
04/2013
10/2013
04/2011
08/2011
01/2012
07/2012
12/2012
04/2013
07/2013
10/2013
Abundance
(% of Total Tags)
0.00
0.08
0.12
Abundance
(% of Total Tags)
04/2011
08/2011
01/2012
07/2012
12/2012
04/2013
07/2013
10/2013
04/2011
04/2013
10/2013
04/2011
08/2011
01/2012
07/2012
12/2012
04/2013
07/2013
10/2013
OTU264
OTU4000
“Candidatus Pelagibacter ubique HTCC1062”
SAR11 OTU2
SAR11 OTU3
SAR11 OTU4
SAR11 OTU111
SAR11 OTU35
SAR11 OTU163
SAR11 OTU18
SAR11 OTU130
SAR11 OTU13
SAR11 OTU48
SAR11 OTU675
SAR11 OTU11
SAR11 OTU6045
SAR11 OTU41
SAR11 OTU811
SAR11 OTU672
SAR11 OTU4000
SAR11 OTU264
SAR11 OTU394
Rickettsia canadensis str. McKiel
97
90
77
53
88
75
59
0.01
A) 926R L ength
SAR11 OTU3
SAR11 OTU2
“Candidatus Pelagibacter ubique HTCC1062 ”
SAR11 OTU4
SAR11 OTU13
SAR11 OTU35
SAR11 OTU163
SAR11 OTU18
SAR11 OTU111
SAR11 OTU6045
SAR11 OTU11
SAR11 OTU130
SAR11 OTU675
SAR11 OTU48
SAR11 OTU41
SAR11 OTU811
SAR11 OTU672
SAR11 OTU394
SAR11 OTU4000
SAR11 OTU264
Rickettsia canadensis str. McKiel
99
64
88
88
86
55
68
62
70
0.01
B) 806R Length
5 m
150 m
150m
500 m
500 m 150 m 890 m
890 m
C)
D)
E)
Fig. 4. Improved phylogenetic resolution with 515F-Y/926R shows ecological variations. The length of the 515F-Y/926R amplicon (A) resolved
several SAR11 OTU representative sequences (closed symbols) with different ecological (time series) patterns (C, D, E), whereas the
515F-Y/806R length (B) of those amplicons classified them as identical sequences (open symbols). The time-series figures show OTUs
resolvable only with 515F-Y/926R sometimes varying inversely, implying niche differentiation (E). Only bootstrap values ≥ 50% out of 1000
replicates are displayed on the trees. The subclade assigned to each OTU is given as S1 (Surface 1), D1 (Deep 1) or Unclassified.
Primers for marine microbiome studies 7
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
8
compared SAR11 FISH counts to the 515F-C/806R
primers reported a > 10-fold bias against SAR11 in
marine samples (Apprill et al., 2015). This indicates the
importance of evaluating primers using field samples,
in addition to in silico tests and amplification of mock
communities.
Our focus in this study is on bacteria and archaea, so
ourstandardsampleswerepre-filteredtoremovethevast
majority of eukaryotes; furthermore, our standard pipeline
in practice removes 18S sequences. Modifying our pipe-
line to allow inclusion of 18S sequences showed that with
515F/926R < 1% of the amplicons were 18S (Fig. S1),
and even in > 1 μm marine samples where chloroplast
sequences greatly exceed those of bacteria and archaea,
the 18S sequences averaged < 20% (Fig. S2). So while
18S amplification did not impact our study, it should be
considered when these primers are used. A detailed
analysis of the efficacy of 515F/926R for eukaryotic
studies is beyond the scope of this report.
Our use of mock communities revealed apparent prob-
lems with typical OTU clustering protocols (Table S3).
Due to our analyses, we chose to use mothur’s average-
neighbour algorithm with pre-clustering. The pre-
clustering step may help explain the more congruent
results observed, though it may mask natural sequence
diversity by merging real variants. Other methods may
require further optimization of available options to
produce results closer to expectation, but this is beyond
the scope of this study, and several studies have already
evaluated many of these pipelines (Bonder et al., 2012;
Pylro et al., 2014; Schmidt et al., 2014).
Analysis of field samples indicated that replacement of
the 515F-C with 515F-Y results in a detectable, though
small, increase in Thaumarchaea coverage when using
806R, not seen with 926R (Table S4). Thus, the modifica-
tion of the 515F-Y may be more important when using
806R. Hugerth and colleagues (2014) also analysed
changes to the 515F-C primer using the program
DegePrime.ThoughtheyevaluatedchangingtheCtoaY,
they proceeded with using a B (C, G or T) and utilized
a slightly different (805R) reverse primer. Our results
suggest that this level of ambiguity may be unnecessary.
We replaced the ‘N’ in the 515F primer used by Quince
and colleagues (2011) with a ‘Y’ to reduce non-specificity
that can conflict with some barcodes, potentially forming
hairpin loops (W. Walters, pers. comm.). Therefore,
greater ambiguity should be included only if it significantly
increases detection of target organisms.
As Apprill and colleagues (2015) concluded, marine
studies that used the original 515F-C/806R primer pair
probably significantly underestimated the abundance of
SAR11 in those samples (e.g. Paver et al., 2013; Taylor
et al., 2014). Though the 806R modification presented by
Apprill and colleagues (2015) reduced the bias against
SAR11, it did not significantly alter proportions of other
taxa. The 926R primer as we report here not only
increases SAR11 coverage, but also appears to have
more accurate estimates of multiple taxa and produces a
longerampliconthatcanimprovephylogeneticresolution,
and thus ecological analysis (Claesson et al., 2010;
Schloss, 2010; Jeraldo et al., 2011; Kim et al., 2011;
Ghyselinck et al., 2013).
In ecological research, it is ideal to measure microbial
communities with high resolution and fidelity to the
natural abundances. We found that beyond the initial
in silico prediction of primer coverage, it is important to
test primers with mock communities and examine further
with field samples to fully evaluate the effectiveness of
primers. We show that, compared with the 515F/806R
primers, 515F-Y/926R gives an accurate and well-
resolved picture of marine bacterial and archaeal
communities.
Experimental procedures
Sampling sites and DNA extraction
Samples from the USC Microbial Observatory were collected
at the SPOT station (33°33′N, 118°24′W) in 2011, 2012 and
2013 at various depths spanning surface to seafloor: 5 m,
deep chlorophyll maximum layer, 150, 500 and 890 m
(Table S5). Samples collected previously from different loca-
tions (global samples) were also analysed (Table S5,
Fuhrman et al., 2008).
Water samples were filtered sequentially through a ∼1 μm
A/Efilter(Pall)and0.22 μmDuraporefilter(EDMillipore).For
this study, DNA from the 0.22 μm filter was analysed except
when noted. The DNA was extracted by SDS lysis and puri-
fied by phenol-chloroform, as previously described (Fuhrman
et al., 1988).
Primers and in silico primer coverage analysis
We compared the original 515F (515F-C) primer (5′-
GTGCCAGCMGCCGCGGTAA, Caporaso et al., 2012) with
one that replaces the C at the fourth position with a Y
(515F-Y, 5′-GTGYCAGCMGCCGCGGTAA, modified from
Quince et al., 2011). We used reverse primers 806R (5′-
GGACTACHVGGGTWTCTAAT, Caporaso et al., 2012) and
926R (5′-CCGYCAATTYMTTTRAGTTT, Quince et al., 2011)
to evaluate a subset of samples amplified with either the
515F-C or the 515F-Y. The 515F and 926R primers are
similar to those originally published by Lane and colleagues
(1985). Comparisons between reverse primers were
performed only with 515F-Y because preliminary results
demonstrated that samples amplified with 515F-C/806R or
515F-Y/806R gave similar results (data not shown). In silico
primer coverage for primer pairs was analysed with zero or
one mismatch using SILVA TestPrime 1.0 and individual
primers were analysed using SILVA TestProbe 3.0. Both
analyses used the SILVA Database SSU r123 (Quast et al.,
2013).
8 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
9
DNA amplification
Triplicate 25 μl reaction mixtures contained 1 ng of DNA,
1.25× 5Prime Hot Master Mix (5Prime), 0.2 μM barcoded
forward primer and 0.2 μM indexed reverse primer. Cycling
conditions with the 806R primer followed the EMP tempera-
ture and time protocol, with a 3 min heating step at 94°C
followedby25cyclesof94°Cfor60 s,50°Cfor60 s,72°Cfor
105 s, and a final extension of 72°C for 10 min. Cycling
conditionswiththe926Rprimerincludeda3 minheatingstep
at 95°C followed by 25 cycles of 95°C for 45 s, 50°C for 45 s,
68°C for 90 s, and a final extension of 68°C for 5 min. Tripli-
cate reactions were pooled, and 5 μl used to check for ampli-
fication on a 2% agarose gel. The remaining 70 μl was
cleaned and concentrated using 1× magnetic Agencourt
AMPure XP beads (Beckman Coulter). Technical replicates
for some samples and no template controls (blanks) were
amplified and included in all analyses. Concentrated DNA
was quantified by PicoGreen fluorescence assay (Life Tech-
nologies), pooled at equimolar concentrations then cleaned
and concentrated with 0.8× SPRIselect magnetic beads
(Beckman Coulter).
Sequencing and data processing
We used a combination of an inline (read on the first read)
5 bp barcode (at least 2 bases different) on the forward
primers and unique 6 bp index (at least 2 bases different) on
the reverse primer(readasanindependentindexread; Huse
et al., 2014). Amplicons were sequenced using MiSeq
Illumina 2 × 300 bp chemistry. Sequences were initially
de-multiplexed by their reverse index allowing for one mis-
match at the sequencing facility. The forward and reverse
reads were merged using USEARCH v7, three mismatches
were allowed across the overlapping region, choosing the
higher quality base when a mismatch existed (Edgar, 2010).
Sequences were then de-multiplexed by their forward
barcode in QIIME 1.8, discarding any sequences with a mis-
match to the barcode or primer (Caporaso et al., 2010).
Sequences were discarded if the average quality score
dropped below q33 across a 50 bp sliding window, if the
sequence did not include the reverse primer, or contained
any ambiguous bases. We also removed both the forward
and reverse priming regions, excluding any sequences that
did not contain the reverse primer. No mismatches to the
reverse primer were allowed.
Pooled sequences were processed following the MiSeq
SOP (Kozich et al., 2013) including alignment against the
SILVAv119 database, and trimming to include only the over-
lapping regions. Sequences were then clustered de novo
to form operational taxonomic units (OTUs) with mothur
1.34.4at99%similaritywiththeaverage-neighbouralgorithm
(Schloss et al., 2009), and pre-clustered at 2 (806R-
amplified) or 3 (926R-amplified) base similarity to reduce the
effects of sequencing errors. Chimera detection performed
with UCHIME (Edgar et al., 2011) and classified with the
default mothur classifier (Wang et al., 2007) using the SILVA
v119 database at an 80% confidence cut-off (Quast et al.,
2013). Samples with fewer than 10 000 sequences were not
included in the analyses (results ranged from 10 134 to
96 492 sequences per sample). The samples were normal-
izedbyanalysingtherelativeabundanceforeachOTUasthe
proportion of all sequences (tags) in a sample after all OTUs
with fewer than six sequences across all samples was dis-
carded. Other clustering approaches tested are detailed in
Supplemental Materials and Methods in Supplementary
Information.
All sequence data have been submitted to the EMBLdata-
base under accession number PRJEB10633.
Mock communities
Mock communities containing 11 or 27 clones were prepared
from marine 16S rRNA clones (see Supplemental Material
and Methods in Supporting Information). Suitably diluted
DNA was treated like a sample throughout the process, and
sequences clustered blindly with field samples.
Evaluation of phylogenetic resolution gained by 926R
To evaluate differences in phylogenetic resolution, a tree was
made from 926R-amplified SAR11 OTU representative
sequences and a separate tree made from trimming those
sequencestothe806Rlength.Themostabundantsequence
from each SAR11 OTU from the 515F-Y/926R pool (371 bp)
was aligned with ClustalW and a maximum-likelihood tree
based on the Tamura–Nei model was constructed in MEGA6
(Tamura and Nei, 1993; Thompson et al., 1994; Tamura
et al., 2013). Reference sequences ‘CandidatusPelagibacter
ubique HTCC1062’ and Rickettsia canadensis str. Mckiel
were also trimmed to the overlapping regions (Accession
numbers NR_074224.1 and NR_074485.1, respectively).
Sequences were trimmed to remove the 806R primer in
mothur 1.34.4 (final length 255 bp). Each representative
sequenceisgivenasSAR11OTU#inthetrees(onlyasubset
of the SAR11 OTUs were plotted).
Acknowledgements
We thank Jack Gilbert, Rob Knight, Tony Walters and Amy
Apprillforhelpfuldiscussions;TroyGundersonandthecrewof
theR/VSeawatchandR/VYellowfin.Fundingwasprovidedby
the Gordon and Betty Moore Foundation grant GMBF3779
and NSF Dimensions of Biodiversity grant 1136818. A.E.
Parada and D.M. Needham were supported by National
Science Foundation Graduate Research Fellowships.
References
Acinas, S.G., Sarma-rupavtarm, R., Klepac-Ceraj, V., and
Polz, M.F. (2005) PCR-induced sequence artifacts and
bias: insights from comparison of two 16S rRNA clone
libraries constructed from the same sample. Appl Environ
Microbiol 71: 8966–8969.
Apprill, A., McNally, S., Parsons, R., and Weber, L. (2015)
Minor revision to V4 region SSU rRNA 806R gene primer
greatly increases detection of SAR11 bacterioplankton.
Aquat Microb Ecol 75: 129–137.
Beman, J.M., Steele, J.A., and Fuhrman, J.A. (2011)
Co-occurrence patterns for abundant marine archaeal and
bacterial lineages in the deep chlorophyll maximum of
coastal California. ISME J 5: 1077–1085.
Primers for marine microbiome studies 9
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
10
Bonder, M.J., Abeln, S., Zaura, E., and Brandt, B.W. (2012)
Comparing clustering and pre-processing in taxonomy
analysis. Bioinformatics 28: 2891–2897.
Brown, M., V, Lauro, F.M., DeMaere, M.Z., Muir, L., Wilkins,
D., Thomas, T., et al. (2012) Global biogeography of
SAR11 marine bacteria. Mol Syst Biol 8: 595.
Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K.,
Bushman, F.D., Costello, E.K., et al. (2010) QIIME allows
analysis of high-throughput community sequencing data.
Nat Methods 7: 335–336.
Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D.,
Lozupone, C.A., Turnbaugh, P.J., et al. (2011) Global pat-
terns of 16S rRNA diversity at a depth of millions of
sequences per sample. PNAS 108: 4516–4522.
Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D.,
Huntley, J., Fierer, N., et al. (2012) Ultra-high-throughput
microbial community analysis on the Illumina HiSeq and
MiSeq platforms. ISME J 6: 1621–1624.
Carlson, C.A., Morris, R., Parsons, R., Treusch, A.H.,
Giovannoni, S.J., and Vergin, K. (2009) Seasonal dynam-
ics of SAR11 populations in the euphotic and mesopelagic
zones of the northwestern Sargasso Sea. ISME J 3: 283–
295.
Chow, C.-E.T., Sachdeva, R., Cram, J.A., Steele, J.A.,
Needham,D.M.,Patel,A., et al.(2013)Temporalvariability
and coherence of euphotic zone bacterial communities
over a decade in the Southern California Bight. ISME J 7:
2259–2273.
Claesson, M.J., Wang, Q., O’Sullivan, O., Greene-Diniz, R.,
Cole, J.R., Ross, R.P., and O’Toole, P.W. (2010) Compari-
son of two next-generation sequencing technologies for
resolving highly complex microbiota composition using
tandem variable 16S rRNA gene regions. Nucleic Acids
Res 38: e200.
Cram, J.A., Chow, C.T., Sachdeva, R., Needham, D.M.,
Parada, A.E., Steele, J.A., and Fuhrman, J.A. (2015)
Seasonal and interannual variability of the marine
bacterioplankton community throughout the water column
over ten years. ISME J 9: 563–580.
Edgar, R.C. (2010) Search and clustering orders of
magnitude faster than BLAST. Bioinformatics 26: 2460–
2461.
Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C., and
Knight, R. (2011) UCHIME improves sensitivity and speed
of chimera detection. Bioinformatics 27: 2194–2200.
Fisher, M.M., and Triplett, E.W. (1999) Automated approach
for ribosomal intergenic spacer analysis of microbial diver-
sityanditsapplicationtofreshwaterbacterialcommunities.
Appl Environ Microbiol 65: 4630–4636.
Fuhrman, J.A., Comeau, D.E., Hagström, Å., and Chan, A.M.
(1988) Extraction from natural planktonic microorganisms
of DNA suitable for molecular biological studies. Appl
Environ Microbiol 54: 1426–1429.
Fuhrman, J.A., Steele, J.A., Hewson, I., Schwalbach, M.S.,
Brown, M.V., Green, J.L., and Brown, J.H. (2008) A latitu-
dinal diversity gradient in planktonic marine bacteria. Proc
Natl Acad Sci USA 105: 7774–7778.
Ghyselinck, J., Pfeiffer, S., Heylen, K., Sessitsch, A., and
De Vos, P. (2013) The effect of primer choice and short
read sequences on the outcome of 16S rRNAgene based
diversity studies. PLoS ONE 8: e71360.
Gilbert, J.A., Meyer, F., Jansson, J.K., Gordon, J.I., Pace,
N.R., Tiedje, J.M., et al. (2010) The Earth Microbiome
Project: meeting report of the ‘1st EMP meeting on sample
selection and acquisition’ at Argonne National Laboratory.
Stand Genomic Sci 3: 249–253. October 6th 2010.
Gómez-Pereira, P.R., Hartmann, M., Grob, C., Tarran, G.A.,
Martin, A.P., Fuchs, B.M., et al. (2013) Comparable light
stimulation of organic nutrient uptake by SAR11 and
Prochlorococcus in the North Atlantic subtropical gyre.
ISME J 7: 603–614.
Hugerth, L.W., Wefer, H.A., Lundin, S., Jakobsson, H.E.,
Lindberg, M., Rodin, S., et al. (2014) DegePrime, a
program for degenerate primer design for broad-
taxonomic-range PCR in microbial ecology studies. Appl
Environ Microbiol 80: 5116–5123.
Huse,S.M.,Young,V.B.,Morrison,H.G.,Antonopoulos,D.A.,
Kwon, J., Dalal, S., et al. (2014) Comparison of brush and
biopsysamplingmethodsoftheilealpouchforassessment
of mucosa-associated microbiota of human subjects.
Microbiome 2: 5.
Jeraldo, P., Chia, N., and Goldenfeld, N. (2011) On the suit-
ability of short reads of 16S rRNA for phylogeny-based
analyses in environmental surveys. Environ Microbiol 13:
3000–3009.
Kim, M., Morrison, M., and Yu, Z. (2011) Evaluation of differ-
ent partial 16S rRNA gene sequence regions for
phylogeneticanalysisofmicrobiomes. J Microbiol Methods
84: 81–87.
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast,
C., Horn, M., and Glöckner, F.O. (2013) Evaluation
of general 16S ribosomal RNAgene PCR primers for clas-
sical and next-generation sequencing-based diversity
studies. Nucleic Acids Res 41: e1.
Kozich, J.J., Westcott, S.L., Baxter, N.T., Highlander, S.K.,
and Schloss, P.D. (2013) Development of a dual-index
sequencing strategy and curation pipeline for analyzing
ampliconsequencedataontheMiSeqIlluminasequencing
platform. Appl Environ Microbiol 79: 5112–5120.
Lane, D.J., Pace, B., Olsen, G.J., Stahl, D.A., Sogin, M.L.,
and Pace, N.R. (1985) Rapid determination of 16S riboso-
mal RNA sequences for phylogenetic analyses. Proc Natl
Acad Sci USA 82: 6955–6959.
Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D.,
Knights, D., Reyes, J.A., et al. (2013) Predictive functional
profiling of microbial communities using 16S rRNA marker
gene sequences. Nat Biotechnol 31: 814–821.
Logares, R., Sunagawa, S., Salazar, G., Cornejo-Castillo,
F.M., Ferrera, I., Sarmento, H., et al. (2013) Metagenomic
16S rDNA Illumina tags are a powerful alternative
to amplicon sequencing to explore diversity and structure
of microbial communities. Environ Microbiol 16: 2659–
2671.
Mahé, F., Rognes, T., Quince, C., de Vargas, C., and
Dunthorn, M. (2014) Swarm: robust and fast clustering
method for amplicon-based studies. PeerJ 2: e593.
Morris, R.M., Rappe, M.S., Connon, S.A., Vergin, K.L.,
Siebold, W.A., Carlson, C.A., and Giovannoni, S.J. (2002)
SAR11 clade dominates ocean surface bacterioplankton
communities. Nature 420: 806–810.
Needham, D.M., Chow, C.-E.T., Cram, J.A., Sachdeva, R.,
Parada, A.E., and Fuhrman, J.A. (2013) Short-term obser-
10 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
11
vationsofmarinebacterialandviralcommunities:patterns,
connections and resilience. ISME J 7: 1274–1285.
Ouverney, C.C. (1999) Dissecting the marine
bacterioplankton ‘Black Box’ by type and function through
FISH and STARFISH. Doctoral Dissertation. Los Angeles,
CA: University of Southern California.
Paver, S.F., Hayek, K.R., Gano, K.A., Fagen, J.R., Brown,
C.T., Davis-Richardson, A.G., et al. (2013) Interactions
between specific phytoplankton and bacteria affect lake
bacterial community succession. Environ Microbiol 15:
2489–2504.
Pylro, V.S., Roesch, L.F.W., Morais, D.K., Clark, I.M., Hirsch,
P.R., and Tótola, M.R. (2014) Data analysis for 16s micro-
bial profiling from different benchtop sequencing platforms.
J Microbiol Methods 107: 30–37.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T.,
Yarza, P., et al. (2013) The SILVA ribosomal RNA gene
database project: improved data processing and web-
based tools. Nucleic Acids Res 41: D590–D596.
Quince, C., Lanzen, A., Davenport, R.J., and Turnbaugh, P.J.
(2011) Removing noise from pyrosequenced amplicons.
BMC Bioinformatics 12: 38.
Salter, I., Galand, P.E., Fagervold, S.K., Lebaron, P.,
Obernosterer, I., Oliver, M.J., et al. (2015) Seasonal
dynamics of active SAR11 ecotypes in the oligotrophic
Northwest Mediterranean Sea. ISME J 9: 347–360.
Schloss, P.D. (2010) The effects of alignment quality, dis-
tancecalculationmethod,sequencefiltering,andregionon
the analysis of 16S rRNA gene-based studies. PLoS
Comput Biol 6: e1000844.
Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R.,
Hartmann, M., Hollister, E.B., et al. (2009) Introducing
mothur: open-source, platform-independent, community-
supported software for describing and comparing microbial
communities. Appl Environ Microbiol 75: 7537–7541.
Schmidt, T.S.B., Matias Rodrigues, J.F., and von Mering, C.
(2014) Ecological consistency of SSU rRNA-based opera-
tional taxonomic units at a global scale. PLoS Comput Biol
10: e1003594.
Sipos, R., Székely, A.J., Palatinszky, M., Révész, S.,
Márialigeti, K., and Nikolausz, M. (2007) Effect of primer
mismatch, annealing temperature and PCR cycle number
on 16S rRNA gene-targetting bacterial community analy-
sis. FEMS Microbiol Ecol 60: 341–350.
Tamura, K., and Nei, M. (1993) Estimation of the number
of nucleotide substitutions in the control region of
mitochondrial DNA in humans and chimpanzees. Mol Biol
Evol 10: 512–526.
Tamura, K., Stecher, G., Peterson, D., Filipski, A., and
Kumar, S. (2013) MEGA6: molecular evolutionary
genetics analysis version 6.0. Mol Biol Evol 30: 2725–
2729.
Taylor, J.D., Cottingham, S.D., Billinge, J., and Cunliffe, M.
(2014) Seasonal microbial community dynamics correlate
with phytoplankton-derived polysaccharides in surface
coastal waters. ISME J 8: 245–248.
Thompson, J.D., Higgins, D.G., and Gibson, T.J. (1994)
CLUSTAL W: improving the sensitivity of progressive mul-
tiple sequence alignment through sequence weighting,
position-specific gap penalties and weight matrix choice.
Nucleic Acids Res 22: 4673–4680.
Venter, J.C., Remington, K., Heidelberg, J.F., Halpern, A.L.,
Rusch, D.B., Eisen, J.A., et al. (2004) Environmental
genome shotgun sequencing of the Sargasso Sea.
Science 304: 66–74.
Vergin, K.L., Beszteri, B., Monier, A., Thrash, J.C.,
Temperton, B., Treusch, A.H., et al. (2013) High-resolution
SAR11 ecotype dynamics at the Bermuda Atlantic
Time-series Study site by phylogenetic placement of
pyrosequences. ISME J 7: 1322–1332.
Wang, Q., Garrity, G.M., Tiedje, J.M., and Cole, J.R. (2007)
Naive Bayesian classifier for rapid assignment of rRNA
sequences into the new bacterial taxonomy. Appl Environ
Microbiol 73: 5261–5267.
Supporting information
Additional Supporting Information may be found in the online
version of this article at the publisher’s web-site:
Fig. S1. Analysisof18Ssequencesfromthe0.2 μmto1 μm
size fraction from SPOT, with pipeline modified to allow
detection of 18S rRNA sequences. These have longer PCR
products than 16S so the paired ends do not overlap signifi-
cantly. In the original pipeline, the vast majority of 18S
sequences is undetectable because sequences are removed
when the paired ends do not overlap. This modified pipeline
removed that requirement and analysed only the sequences
adjacenttothe515Fprimer.(A)showsthedistributionoftotal
tags in sequences adjacent to the 515F primer where the
Eukarya range from 0.58% to 4.3% (mean 1.5%) of all
sequences. (B) shows the distribution of major eukaryotic
subdivisions via 18S, with most samples dominated by
Chloroplastida (primarily Mamiellophyceae), Alveolata (pri-
marilySyndiniales)andStramenopiles(primarilyMAST).The
March samples included a spring phytoplankton bloom. The
mean abundances for March 23 2011 replicate samples are
reported, and the standard error of the mean given as error
bars in Fig. S1A.
Fig. S2. Analysis of eukaryotes and attached or large bac-
teria, > 1 μm size fraction from SPOT, with pipeline modified
to allow detection of 18S rRNA sequences, that have longer
PCR products than 16S. This analysis uses the same modi-
fied pipeline as Fig. S1. Here we analysed separately the
merged sequences and the sequences adjacent to the 515F
primer. (A) shows the distribution of total tags where the
Eukarya range from 8.6% to 35% (mean 17%). The inset
shows the percent of Eukarya detected by the standard pipe-
line that includes merging the paired ends, and they were
only detectable in one of the six samples at extremely low
levels. (B) shows the distribution of major eukaryotic subdi-
visions via 18S, with most samples dominated by Metazoa,
RhizariaandAlveolata.TheMarchsamplesincludedaspring
phytoplankton bloom.
Table S1. In silico evaluation of coverage showing per cent
hits to various taxa by individual and pairs of primers,
analysed by SILVA TestProbe 3.0
a
or SILVA TestPrime 1.0
b
and SILVA dataset r123. Zero and one mismatch allowed as
shown. Absolute differences greater than 10% between
primer sets at zero mismatch are shown in bold.
Table S2. In silico primer coverage evaluation of SAR11
clades using SILVA TestPrime 1.0 and SILVA dataset r123.
Primers for marine microbiome studies 11
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
12
Per cent of matches (zero or one mismatch allowed, as
shown) to sequences in each SAR11 subgroup. Absolute
differences > 10% between primers shown in bold.
Table S3. Evaluation of several clustering methods shows
mothur’s average-neighbour algorithm with pre-clustering,
yields mock communities most similar to expected com-
pared with commonly used methods. Each column desig-
nates a different clustering method and each row is the
clone abundance as an average of four replicates, with a
separate column for the standard error of the mean. The
name of each clustering method is given in the column
headers, indicating if default settings (default) or modified
settings (mod) were used, as described in the Materials and
Methods. All sequences including the simulated even and
staggered fasta file were clustered together. When a taxon
appears below the clones it is a different OTU from the
OTUs that include the perfect sequences from the simulated
mock community files.
Table S4. The difference in total abundance of
Thaumarchaea Marine Group I (MGI) was statistically signifi-
cant between 515F C and Y primers only when amplifying
with the 806R primer. Due to low abundance of MGI at
shallower depths, only samples from depths ≥ 150 m were
evaluated.Bothforwardprimerswereusedwitheachreverse
primer. The mean ratio between primer combinations and
standard error as well as the P-values of the Sign Tests are
given. The mean and standard error of Bray–Curtis similarity
values between MGI communities are also given.
Table S5. Sampling sites and depths.
Table S6. Percentrelativeabundancepersampleoftop150
eukaryotic OTUs and associated taxonomy for (A) the bac-
terial (0.2–1 μm) and (B) eukaryotic (> 1 μm) size fraction.
Each number represents 100*(number of tags of each
taxon ÷ total number of tags).
12 A. Parada, D. M. Needham and J. A. Fuhrman
© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology
13
14

Chapter 2:
Changes in the marine archaeal community and its interaction with the microbial community over time
and depth at SPOT
Abstract
For about 20 years, marine archaea have been known to be abundant throughout the
water column, though few studies have examined their variability over time and fewer still their
temporal variability across the water column. We evaluated the monthly variability of the
archaeal community and differences in their interaction with the microbial community from
surface to seafloor (890m) over 5 years at the San Pedro Ocean Time-series by 16S rDNA tag
sequencing with universal primers, with the archaeal taxa viewed as 99% similarity operational
taxonomic units (OTUs). Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II
(MGII) communities have different OTU composition at different depths corresponding to
different physical, chemical and biological conditions at those depths. Mantel tests of
correlation between MGI or MGII communities and time of year revealed statistically significant
seasonality at every depth sampled, and PERMANOVA tests statistically separated several MGI
(5m, 150m) and MGII (5m, deep chlorophyll maximum layer, 150m, 500m) communities by
calendar season. Correlation networks over time also indicated that within a depth, different
MGI taxa (presumed dominant ammonia oxidizers) correlated to distinct Nitrospina (the
presumed dominant nitrite oxidizers) taxa, indicating different consortia may be responsible for
nitrification at a given time. This study shows remarkably high diversity of MGI and MGII
archaea with different members having different ecology.

15

Introduction
Several studies have demonstrated marine archaea are ubiquitous and the two most
abundant archaeal groups, Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine
Group II (MGII), can be numerically important members of the microbial community (e.g.
Massana et al., 1998; Murray et al., 1998, 1999; Karner et al., 2001; Alderkamp et al., 2006;
Santoro et al., 2010; Beman et al., 2010, 2011; Belmar et al., 2011; Aluwihare et al., 2012;
Amano-Sato et al., 2013). Additionally, the ability of all cultured MGI to oxidize ammonia to
nitrite, and the abundance of the archaeal ammonia monooxygenase genes has established the
importance of these archaea in global biogeochemical cycles (Könneke et al. 2005; Francis et al.
2005, 2007; Beman et al. 2008, 2012; Galand et al. 2009; Newell et al. 2011; Christman et al.
2011; Tolar et al. 2013; Santoro et al. 2015). Although evidence indicates heterotrophic activity
of MGII, and particle attachment, less is known about the role and contribution of this group in
microbial communities and biogeochemical cycles, especially below the photic zone (Alderkamp
et al. 2006; Iverson et al. 2012; Orsi et al. 2015; Martin-Cuadrado et al. 2015).
Time-series studies of archaeal communities have shown both temporal and seasonal
variability at some sites (Massana et al. 1998; Murray et al. 1998, 1999; Pernthaler et al. 2002;
Wuchter et al. 2006; Levipan et al. 2007; Mincer et al. 2007; Galand et al. 2010; Beman et al.
2010, 2011; Rich et al. 2010; Steele et al. 2011; Robidart et al. 2012). However, these studies
have used methods with low phylogenetic resolution (e.g. DNA fingerprinting techniques,
oligonucleotide probes, and quantitative PCR), which limit the interpretation of how archaeal
community composition changes over time. Recently, Hugoni et al. (2013) used
pyrosequencing of the 16S DNA and RNA to demonstrate dynamics between abundant and rare
16

archaeal taxa, as well as changes in the active members of the community over 3 years in the
surface waters (3m) of the Bay of Banyuls-sur-Mer in France. Though this study provided
thoughtful insight into the community dynamics of archaeal communities, it was limited to
surface waters so it may not provide insights below the euphotic zone. Studies at the San Pedro
Ocean Time-series (SPOT) station, ~10km off the coast of Los Angeles, California, have shown
that microbial communities can have different temporal patterns at different depths and that
correlations between community members change across depths (Beman et al. 2010, 2011;
Steele et al. 2011; Cram et al. 2015). However, published studies to date have been limited to
analysis of archaeal communities through quantitative PCR of certain 16S rRNA types and
ammonia monooxygenase (amoA) gene abundance, and did not cover the archaeal community
comprehensively.
This study evaluates the long-term (~5 years) temporal changes in archaeal community
at SPOT across the water column (0-890m, encompassing euphotic and aphotic zones) by 16S
rRNA gene analysis using universal primers that also amplify bacteria. This allows comparisons
of the temporal variability of the Thaumarchaea Marine Group I and Euryarchaea Marine Group
II in the context of the entire microbial community at different depths, to understand the
ecology of these organisms throughout the water column. Concurrent analysis of the bacterial
community and environmental measurements can provide greater insight into the controls on
archaeal diversity and abundance across the water column, and how those relationships change
over time.
17

Methods
Sample Collection and DNA Extraction
Seawater samples were collected at the USC Microbial Observatory at the San Pedro
Ocean Time-series (SPOT) station in the San Pedro Channel (33.55°N, 118.4°W). Samples of 10L
were collected at 5m (or by bucket at 0m when 5m samples were not obtainable) and deep
chlorophyll maximum layer (DCM) and 20L at 150m, 500m, and 890m as previously described
(Fuhrman et al. 2006; Beman et al. 2011; Chow et al. 2013; Cram et al. 2015). Samples analyzed
in this study were taken at a nearly monthly basis when possible between February 2009-
December 2013 resulting in 54 samples from 5m, 48 samples from the DCM and 890m, 49
samples from 150m, and 46 samples from 500m (Table S1).
Seawater was filtered sequentially through an 80 m nylon mesh, 1 m glass fiber A/E
filter (Pall, Port Washington, NY), and 0.22 m Durapore filters (ED Millipore, Billerica, MA); only
DNA from the 0.22 m filter was analyzed. DNA was extracted by SDS lysis and purified by
phenol-chloroform as previously described (Fuhrman et al. 1988).
Environmental Measurements
An in situ sensor (Sea-Bird Electronics, Bellevue, WA, USA) measured depth,
temperature, and salinity and an in situ oxygen electrode measured Oxygen (Sea-Bird, model
13). Since no Winkler titration data for oxygen measurements were available for the samples
over our study period and, as previously described, electrode oxygen values were shown to be
linearly related to Winkler values (R
2
=0.93; Cram et al., 2014), only electrode values were used
in all analyses. The mixed layer depth (MLD) was calculated from the derived density values
(SigmaTheta) as the depth at which SigmaTheta was 0.125kg/m
3
greater than at 10m.
18

Nitrite (NO 2
-
), nitrate(NO 3
-
), and phosphate (PO 4
3-
) were measured by colorimetric
methods (Parsons et al. 1984). Samples were stored at -20 C until measured. Total prokaryotic
cell abundance and viral particle abundance was measured by epi-fluorescence microscopy
with SYBR Green (Noble and Fuhrman 1998; Patel et al. 2007). Bacterial productivity was
measured by [
3
H]leucine (Leu) and [
3
H]thymidine (Thy) incorporation (Fuhrman et al. 2006),
and cell turnover time (days) calculated as [cell abundance]/ ([
3
H]leucine or [
3
H]thymidine
incorporation).
Satellite and meteorological data was gathered and processed as described previously
(Cram et al. 2015). Additionally, absorbance due to detritus and gelbstoff (light absorbing part
of dissolved organic matter, ABSDetGel) and absorbance due to phytoplankton (ABSPhyto) at
433nm was downloaded from the ocean color data site (Maritorena et al., 2002;
oceancolor.gsfc.nasa.gov). A list of measurements and abbreviations used in this study is given
in Supplementary Materials and Methods (Table S2).
Sequencing and Data Processing
The 16S rRNA gene was partially amplified and sequenced as described previously, using
universal primers 515F-C (5’- GTGCCAGCMGCCGCGGTAA) and 926R (5’-
CCGYCAATTYMTTTRAGTTT) (Parada et al. 2015). Cycling conditions included a 3-min heating
step at 94°C followed by 25 cycles of 94°C for 60s, 50°C for 60s, 72°C for 105s, and a final
extension of 72°C for 10-min. DNA was sequenced on a MiSeq (Illumina) using the 2 x 250bp or
2 x 300bp paired-end chemistry. A 6bp reverse index, read as a separate read, and 5bp forward
barcode were used to multiplex samples. Sequences were demultiplexed first by their reverse
index by the sequencing facility allowing 1 mismatch. Paired-end sequences were merged with
19

USEARCH v7 allowing a maximum of 3 mismatches across the overlapping region (Edgar 2010).
Merged sequences were demultiplexed by their forward barcode in QIIME allowing a maximum
of 0 ambiguous bases, 0 mismatches to the primers and barcodes, and a minimum average
quality score of 33 across a 50bp sliding window (Caporaso et al. 2010). Forward and reverse
primers were trimmed from the sequences. Samples with less than 10,000 sequences were
discarded (range 10,740-131,763).
Sequences were clustered de novo at 99% similarity with the mothur 1.34.4 tool using
the average neighbor algorithm after preclustering sequences at a 3-base similarity (Schloss et
al. 2009; Kozich et al. 2013). Sequences were assigned taxonomy using the default classifier
and 80% confidence cutoff against the Silva v119 reference database (Wang et al. 2007; Quast
et al. 2013). Any resulting operational taxonomic units (OTUs) with less than 6 total sequences
across all samples were discarded. The relative abundance of each OTU is given as the percent
proportion of all sequences (tags) in a sample.
Statistical Analyses
Differences in community composition were evaluated separately for Thaumarchaea
Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) communities, by calculating the
Bray-Curtis distances between pairs of samples in QIIME or with the vegan package in R (Bray
and Curtis, 1957; Caporaso et al., 2010; R Core Team, 2013). For all multivariate analyses, the
log of each OTU abundance value was taken prior to calculating Bray-Curtis distance.
Seasonality was determined as statistically significant correlations between the Bray-Curtis
distances of samples and the time of year by Mantel tests (5000 permutations), such that the
greatest distance between samples occurred when two samples were taken 6months apart,
20

and the shortest distance occurred when samples were taken 12 months apart (calculated as in
Cram et al., 2014). Correlations between distances and days between samples by Mantel tests
(5000 permutations) was used to detect temporal variability. Permutational multivariate
analysis of variance (PERMANOVA with 5000 permutations) detected differences between
communities due to the calendar season the samples were taken. Seasons were defined as
follows: Winter= December, January, February; Spring=March, April, May; Summer=June, July,
August; Fall=September, October, November. The above tests were performed only on
communities from the same depth.
Non-metric multidimensional scaling (NMDS) ordination plots were used to evaluate
differences in MGI or MGII communities at all depths. Additionally, environmental parameters
(from Table S2) were fitted to the ordination plot to detect measurements correlating to the
differences observed between MGI or MGII communities. Environmental measurements were
transformed as previously described to reduce the effect of differences in units (Chow et al.
2014). The function similarity percentages (SIMPER, 5000 permutations) detected OTUs
influential to differences or similarities between samples of different seasons (same depth) or
different depths (Clarke 1993).
Correlation networks were calculated with LS analysis (eLSA) and visualized in Cytoscape
v.2.8.3 (Cline et al. 2007; Xia et al. 2011). As previously described, we determined LS
correlations (ranked Pearson’s correlations) between variables using a linear interpolation for
missing values and a delay up to 1 month (Ruan et al. 2006; Steele et al. 2011; Xia et al. 2011;
Chow et al. 2014). Only OTUs or environmental variables occurring and measured in >37
months at a given depth were included in the LSA calculations. P-values were calculated
21

through theoretical approximation followed by 2000 permutation tests (Xia et al. 2013; Chow et
al. 2014). Only correlations with LS values >|0.6| with associated p-values<0.01, q-values (false
discovery rate) <0.01 and alignment length >48 were visualized.
Results and Discussion
Temporal Dynamics
Though seasonality has been shown for several marine bacterial taxa and to some
extent in MGI and MGII communities (e.g. Murray et al., 1998; Morris et al., 2005; Carlson et al.,
2009; Robidart et al., 2012; Chow et al., 2013; Cram et al., 2015; Xiaomin et al., 2015), it is
unknown how the predictability of MGI and MGII communities changes across the water
column. Mantel Tests of the correlation between the Bray-Curtis Distances of two samples and
the time of year indicated seasonality in the MGI and MGII communities at all depths (Table 1),
corresponding to a decrease in similarity at 6month intervals and rise at 12month intervals
(Figures S1 and S2). This is in contrast to previous studies at this location that found seasonality
in the bacterial community only in the surface depths and at 890m (Fuhrman et al. 2006;
Beman et al. 2011; Cram et al. 2015). This suggests that different factors control the variability
of the MGI and MGII archaeal communities at SPOT or that the methods used in this study are
more sensitive to seasonal variability. Surface seasonality is typically attributed to seasonal
changes in the physical environment, nutrient availability and primary productivity, which has
been shown at this location (Beman et al. 2011; Chow et al. 2013; Cram et al. 2015), and may
affect the archaeal community similarly as it does the bacteria.
In addition to seasonality, MGI and MGII communities demonstrated further temporal
variability at some depths, suggesting additional controls affecting these communities not fully
22

explained by seasonal changes. Mantel Tests showed statistically significant correlations
between the Bray-Curtis distances between samples and the time between samples of MGI
communities at 5m and 890m, and between the MGII communities at 890m (Table 2),
indicative of temporal variability. This may demonstrate that the MGI and MGII communities
were highly variable over the length of this study at these depths suggesting sensitivity of these
taxa to the physical and biological variability at these depths. Similar to other studies, the MGI
archaea in this study peaked in abundance during “Winter” months (January and February),
associated with increases in the mixed-layer depth and low primary productivity (i.e. Mincer et
al., 2007; Beman et al., 2011; Hugoni et al., 2013) . MGII communities at SPOT also displayed
seasonality in surface depths (Figure 2). As MGII taxa are presumed to be heterotrophs
(Iverson et al. 2012; Orsi et al. 2015), these shifts in the community compositions may be due to
changes in the composition and availability of organic substrates or top-down controls affecting
different members of the MGII communities. Additionally, the average Bray-Curtis Similarities
of Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) communities
over time at each depth demonstrate communities were more similar over time deeper in the
water column (Figure S1 and S2). At 5m and DCM both groups consistently had community
similarities <50% between all possible pairs of samples, though communities demonstrated an
increase in similarity every ~12months (Figures S1a-b and S2a-b). However, the average
similarities between samples at depths below 150m were typically >60-70% (Figures S1c-e and
S2c-e). Despite these differences in similarity at each depth, MGI and MGII communities at
every depth demonstrated seasonality suggesting that the factors that shape these
23

communities cause greater shifts in the community composition at surface depths than at
deeper depths, yet all change the communities in a seasonally predictable manner.
Recently, seasonality was shown to occur in the bacterial communities at 890m during
analysis of 10-years of the time-series data at SPOT, attributed primarily to seasonal changes in
particle flux from the surface (Cram et al. 2015). However, no seasonality was detected at
intermediate depths and the study focused on bacterial communities, as the DNA fingerprinting
method used cannot detect archaea. Since the environment in intermediary depths is typically
more stable throughout the year, the same flux of particulate matter causing seasonality in
bacterial communities at 890m may be responsible for seasonality in the archaeal communities
in overlying depths. Decomposition of sinking particulate matter could release substrates for
MGI ammonia oxidation or potential heterotrophy (Ouverney and Fuhrman 2000; Teira et al.
2006; Qin et al. 2014), leading to the apparent seasonality. Furthermore, MGII taxa have
recently been shown to strongly associate with particles (Orsi et al. 2015), thus seasonal
changes in the flux and composition of particles, as shown by Collins et al. (2011), may induce
the shifts observed in the community composition of MGII taxa.
In addition to seasonality and temporal variability, MGI communities also separated into
seasons at 5m and 150m, and MGII communities separated into seasons at 5m, DCM, 150m,
and 500m (Table 2). This indicates different groups of recurring MGI and MGII communities that
are selected at distinct times of the year. Differences in seasonal grouping between the MGI
and MGII communities may be due to the different activities of these organisms, such that the
environmental changes that coincide with the calendar seasons used in this study affect the
MGII throughout the water column. In contrast, the MGI communities may be better separated
24

into seasons dependent on other criteria such as time since the deepest mixing as used by
(Carlson et al. 2009), which would coincide with the seasonal peaks in MGI abundance at 5m
during the winter months where the deepest mixing occurs at SPOT.
Several OTUs contributed to the differences seen in MGI or MGII community
compositions between seasons (Figures 1 and 2). For example, MGI 464 and MGII 89 were most
influential in differences between Winter samples and all other seasons at 5m, and both have
peaks in abundance in Winter samples (Table S3, Figures1a and 2a). Additionally, MGII 904
appeared to be most abundant during Spring and Fall seasons at 5m, which coincided with
significant differences between Spring and Winter or Summer, but not to Fall (Figure 2a). At
150m, two separate communities appear to occur during Spring and Fall, with MGI 840, 1283
and 570 most influential during Spring, while MGI 464 and 495 most influential during Fall
(Figure 1c). The MGII community at 150m was mostly driven by changes to the abundant MGII
2869 OTU, where it only contributed to differences between Spring and Fall, when its
abundance decreased in Fall (Figure 2c). At 500m, MGII 56 and 582 mainly influenced
differences in the MGII communities during different seasons, where they had inverse patterns
during the Summer and Fall seasons, with MGII most abundant during Spring and Summer and
MGII 582 during Fall and Winter months. Collectively, these results demonstrate that there are
distinct controls on different MGI and MGII taxa, potentially reflecting genomic and metabolic
diversity of these organisms. Additionally, these shifts in dominant taxa suggest that grazers
and viruses are either varying seasonally as well as following their preferred prey/host or are
grazing/infecting different MGI and MGII taxa throughout the year.
25

Differences in community composition between depths
Changes in the community composition between depths may suggest niche partitioning
within the MGI and MGII communities, selecting for taxa within the community better
equipped to take advantage of the environmental conditions found at each depth. Shifts in the
dominant MGI and MGII OTUs and the increase in total abundance of MGI with depth are
evident in Figures 1 and 2 (also Figures S3 and S4). Additionally, samples of MGI and MGII
communities form clusters correlated to depth in non-metric multidimensional scaling (NMDS)
plots (Figure 3). MGI 5m and DCM clusters (Figure 3a) are more separated (though many
samples still overlap) than the MGII surface communities where the 5m and DCM samples are
almost indistinguishable (Figure 3b). The ordination toward Surface MGI and MGII clusters
correlated to variables associated with secondary productivity and organic matter, though MGI
clusters appear to be less associated with these parameters than MGII clusters (compare
Figures 3a and 3b and Table S2). Similarities between the 5m and DCM communities (Figure 3)
may represent the biological and chemical similarities between these depths and supports the
possibility of exchanges between these communities during deep mixing. The persistence of
MGI communities in the surface following deeper mixing may be halted by competition with
phytoplankton and stratification limiting the niche availability for MGI in the surface, possibly
explaining the seasonality observed in this study. Deeper samples were frequently correlated to
variables associated with abiotic parameters. For example, both MGI and MGII communities at
150m and below were associated with changes in upwelling and the mixed-layer depth,
particularly at 150m. However, in both MGI and MGII, ordination of deeper communities
statistically correlated with surface satellite primary productivity measurements (Table S2).This
26

correlation may further support the idea of seasonal changes in the particle flux affecting the
composition of MGI and MGII communities throughout the water column, but most strongly at
5m and 890m.
Similar differences distinguishing MGI and MGII taxa into photic and aphotic
phylogenetic clusters has been observed previously (Santoro et al. 2010; Iverson et al. 2012;
Auguet et al. 2012; Luo et al. 2014; Martin-Cuadrado et al. 2015). However, the extent of
separation in the communities between depths is different between MGI and MGII taxa (Figure
3). These differences may indicate that those MGI communities are more similar throughout
the water column, while the factors controlling MGII communities may strongly influence
segregation between depths.
Several OTUs are influential to differences observed between depths (Table S4). For
example, MGI 464 and 495 were the most influential taxa in differences between 5m and DCM,
and were part of the 6 most influential OTUs in differences between 5m and 150m. Though
both these OTUs are the most abundant MGI OTUs at 5m and DCM, they have their highest
abundances at 150m. Comparisons of deeper samples by SIMPER revealed that many of the
most influential OTUs did not show significant differences between samples, which may explain
the proximity of the 3 deeper depths in NMDS plots. Of the most influential OTUs, only MGI 7
and MGI 3347 contributed to differences between 150m and 890m. Thus, most differences
seen in NMDS plots were most probably due to less abundant taxa. In contrast, most of the
influential MGII taxa in all possible depth comparisons contributed to differences between
depths (Table S2). These results show that the MGII taxa appear to have varying controls that
27

have a greater influence on which taxa occur at different points in the water column, while the
factors controlling the MGI are relatively homogenous below the photic zone.
Correlation Networks
Through correlation analyses, we can examine which archaeal and bacterial OTUs are
highly correlated to evaluate the possible associations between different members of the
microbial community. Additionally, incorporating environmental measurements into these
correlation analyses can help determine which parameters were more likely to influence
specific taxa. A correlation network between archaea, bacteria and environmental parameters
at 5m (Figure 4) revealed that the seasonal and most influential MGI OTU (464) had positive
correlations to many bacterial OTUs from several phyla and was negatively correlated to
environmental parameters such as salinity, day length, and temperature. Additionally, this
network revealed that phylogenetically related OTUs, such as MGII 33 and 904, were correlated
to different archaeal OTUs, while more phylogenetically distant MGI 464,7 and 495 OTUs were
correlated to several of the same bacterial OTUs (compare Figure 4 and Figure S5). This may
mean that taxa with high 16S sequence similarity occupy different niches, suggesting that the
16S diversity may not always reflect the potential metabolic diversity and thus activity of MGI
and MGII taxa. In contrast, more distantly related taxa may have similar temporal patterns or
apparent relationships within the microbial community because their activities may be
dependent on similar organisms or are complimentary within the same consortia.
Archaeal OTUs that occur at several depths have different correlations with the
microbial community and environment at each depth. For example, networks show distinct
OTUs and environmental parameters correlated to MGI_840 and MGII_2869 at 150m, 500m,
28

and 890m, as well as different total correlations at each depth (Figures S6 and S7). MGI had the
most correlations at 150m, and many to bacterial taxa such as SAR406, Nitrospina, SAR11, and
different Gammaproteobacteria lineages. At 500m and 890m, most (all at 500m) correlations
were to other MGI OTUs. Only bacterial OTUs OCS116_clade_412 and SAR324_885 correlated
to MGI_840 at 150m and 890m, whereas several MGI OTUs correlated to MGI_840 at all 3
depths. Correlation networks of MGII_2869 also show different correlations at 150m, 500m,
and 890m, with the most at 890m. Unlike MGI_840, MGII_2869 correlated to bacterial OTUs at
all depths, and correlated to environmental parameters at 890m. The negative correlation of
MGII_2869 to days since the beginning of this study (ElapsedDays) reflects the overall decrease
in abundance over time seen in Figure 2e (though it is not clear why). MGII_2869 had positive
correlations to oxygen saturation (OxygenSaturation), surface particulate organic carbon (POC)
and density (SigmaTheta) with a one-month lag. Both MGI_840 and MGII_2869 show
correlations to several SAR406, SAR324, Verrucomicrobia and Nitrospina OTUs suggesting these
bacterial taxa are important members of the microbial communities at these depths that
commonly interact with the archaeal members of the community.
These correlation networks show that the MGI and MGII are highly interconnected with
bacteria and other archaea throughout the water column (Figures 4, 5, S6 and S7). Importantly,
the networks aid in showing that the MGI and MGII OTUs are correlated to different microbial
members even within a depth, suggestive of niche partitioning within these communities. This
reveals the importance of high phylogenetic resolution studies that enable observation of
different members of the archaeal communities. Previous correlation networks at SPOT only
included changes in the archaeal community by quantitative PCR of the 16S rRNA gene to
29

represent the archaea (Steele et al. 2011), thus the differences between taxa could not be
detected, as shown in this study.
Correlations and time-series plots of MGI and NItrospina OTUs (reported to be the
presumed marine midwater organisms to oxidize nitrite to nitrate, Mincer et al., 2007) indicate
that different consortia may be responsible for nitrification at different times of the year. At
150m, MGI 464 (found by SIMPER to be influential in Fall seasons) was positively correlated to
different Nitrospina taxa than were other MGI OTUs (Figure 5a). A time-series plot of MGI_464,
MGI_1283 (influential in Spring seasons by SIMPER), Nitrospina_1255, and Nitrospina_299 at
150m, show that the two MGI OTUs commonly had mirroring, or negatively correlated,
temporal patterns that were inverse to patterns between Nitrospina_1255 and Nitrospina_299
(Figure 5b). Deeper in the water column inverse patterns were not as obvious between MGI
OTUs, however, changes in the community composition of MGI taxa corresponded to changes
in the Nitrospina communities (Figure S8). For example, MGI_840 primarily dominated MGI
communities at 500m, but in August and September 2009 the community shifted and was
dominated by MGI_7 and MGI_840 (Figure S8a). On the same dates, Nitrospina communities
shifted from Nitrospina_21 dominated to Nitrospina_792, 2136, and 641 OTUs having their
highest abundance in the time-series. Similar corresponding shifts were observed July 2012. At
890m, changes in the dominant MGI OTUs coincided with changes to the community
composition of Nitrospina OTUs (Figure S8b). Prior to July 2012, MGI_840 and MGI_7 were the
most abundant OTUs. Afterward the clearly dominant OTU was MGI_7, coinciding with a shift
from a Nitrospina_2136/Nitrospina_792/Nitrospina_641 dominated community to primarily
Nitrospina_792/Nitrospina_641 community. Additionally, a spike in the abundance of MGI_840
30

and MGI_570 in February 2013 corresponded to a dip in total Nitrospina abundance, followed
by the highest abundance of cumulative Nitrospina OTUs, dominated by Nitrospina_792, of the
entire 890m time-series (Figure S8b).
MGI OTUs correlations to different Nitrospina suggests niches that are either competing
or are controlled by different factors, and that ongoing nitrification is not always performed by
the same consortia. In fact, seasonality by Mantel Tests was detected when examining the 100
most abundant MGI and 100 most abundant Nitrospina taxa (combined) at 150m, 500m and
890m (Table S5). This is in contrast to previous findings at SPOT where connections between
ammonia oxidizing archaea and nitrite oxidizing bacteria were not obvious (Beman et al. 2010).
However, in that study the archaea were analyzed by quantitative PCR, which would not be
able to distinguish the different MGI taxa (and perhaps also the nitrifying bacteria which were
analyzed by Automated Ribosomal Intergenic Spacer Analysis; Fisher and Triplett, 1999) at the
resolution obtained in this study. This also suggests that closely related organisms likely have
distinct ecological relationships with different members of the microbial community, requiring
high-resolution methods to study these interactions.
Conclusion
This study has shown that even though different communities of Thaumarchaea Marine
Group I and Euryarchaea Marine Group II communities exist throughout the water column, all
communities demonstrate temporal variability. This variability is likely controlled by multiple
interactions between the microbial community and the environment that differ for the MGI and
MGII. Seasonality seen in the archaeal community may thus reflect temporal variability of
31

consortia these organisms are a part of, suggesting changes in the taxa responsible for a specific
activity such as nitrification.
Acknowledgements
We thank all current and past members of the Fuhrman and Caron laboratories, Troy
Gunderson and the crew of the R/V Seawatch and R/V Yellowfin. Funding was provided by the
Gordon and Betty Moore Foundation grant GMBF3779 and NSF Dimensions of Biodiversity
grant 1136818. A.E. Parada was supported by National Science Foundation Graduate Research
Fellowship.

32

Literature Cited
Alderkamp, A., E. Sintes, and G. J. Herndl. 2006. Abundance and activity of major groups of
prokaryotic plankton in the coastal North Sea during spring and summer. Aquat. Microb.
Ecol. 45: 237–246.
Aluwihare, L. I., A. S. Waller, D. R. Mende, K. Bakker, H. Farnelid, P. L. Yager, C. Lovejoy, J.-É.
Tremblay, M. Potvin, F. Heinrich, M. Estrada, L. Riemann, P. Bork, C. Pedrós-Alió, and S.
Bertilsson. 2012. Role for urea in nitrification by polar marine Archaea. Proc. Natl. Acad.
Sci. U. S. A. 109: 17989–17994.
Amano-Sato, C., S. Akiyama, M. Uchida, K. Shimada, and M. Utsumi. 2013. Archaeal distribution
and abundance in water masses of the Arctic Ocean, Pacific sector. Aquat. Microb. Ecol.
69: 101–112.
Auguet, J.-C., X. Triadó-Margarit, N. Nomokonova, L. Camarero, and E. O. Casamayor. 2012.
Vertical segregation and phylogenetic characterization of ammonia-oxidizing Archaea in a
deep oligotrophic lake. ISME J. 6: 1786–1797.
Belmar, L., V. Molina, and O. Ulloa. 2011. Abundance and phylogenetic identity of
archaeoplankton in the permanent oxygen minimum zone of the eastern tropical South
Pacific. FEMS Microbiol. Ecol. 78: 314–26.
Beman, J. M. of ammonia oxidation rates and ammonia-oxidizing archaea and bacteria at high
resolution in the G. of C. and eastern tropical N. P. O., B. N. Popp, and S. E. Alford. 2012.
Quantification of ammonia oxidation rates and ammonia-oxidizing archaea and bacteria at
high resolution in the Gulf of California and eastern tropical North Pacific Ocean. Limnol.
Oceanogr. 57: 711–726.
Beman, J. M., B. N. Popp, and C. A. Francis. 2008. Molecular and biogeochemical evidence for
ammonia oxidation by marine Crenarchaeota in the Gulf of California. ISME J. 2: 429–441.
Beman, J. M., R. Sachdeva, and J. A. Fuhrman. 2010. Population ecology of nitrifying Archaea
and Bacteria in the Southern California Bight. Environ. Microbiol. 12: 1282–1292.
Beman, J. M., J. A. Steele, and J. A. Fuhrman. 2011. Co-occurrence patterns for abundant
marine archaeal and bacterial lineages in the deep chlorophyll maximum of coastal
California. ISME J. 5: 1077–1085.
Bray, J. R., and J. T. Curtis. 1957. An ordination of the upland forest communities of southern
Wisconsin. Ecol. Monogr. 27: 325–349.

33

Caporaso, J. G., J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello, N. Fierer,
A. G. Peña, J. K. Goodrich, J. I. Gordon, G. A. Huttley, S. T. Kelley, D. Knights, J. E. Koenig, R.
E. Ley, C. A. Lozupone, D. McDonald, B. D. Muegge, M. Pirrung, J. Reeder, J. R. Sevinsky, P.
J. Turnbaugh, W. A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld, and R. Knight. 2010.
QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7:
335–336.
Carlson, C. a, R. Morris, R. Parsons, A. H. Treusch, S. J. Giovannoni, and K. Vergin. 2009.
Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the
northwestern Sargasso Sea. ISME J. 3: 283–95.
Chow, C.-E. T., D. Y. Kim, R. Sachdeva, D. A. Caron, and J. A. Fuhrman. 2014. Top-down controls
on bacterial community structure: microbial network analysis of bacteria, T4-like viruses
and protists. ISME J. 8: 816–829.
Chow, C.-E. T., R. Sachdeva, J. A. Cram, J. A. Steele, D. M. Needham, A. Patel, A. E. Parada, and J.
A. Fuhrman. 2013. Temporal variability and coherence of euphotic zone bacterial
communities over a decade in the Southern California Bight. ISME J. 7: 2259–2273.
Christman, G. D., M. T. Cottrell, B. N. Popp, E. Gier, and D. L. Kirchman. 2011. Abundance,
Diversity, and Activity of Ammonia-Oxidizing Prokaryotes in the Coastal Arctic Ocean in
Summer and Winter. Appl. Environ. Microbiol. 77: 2026–2034.
Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community structure.
Aust. J. Ecol. 18: 117–143.
Cline, M. S., M. Smoot, E. Cerami, A. Kuchinsky, N. Landys, C. Workman, R. Christmas, I. Avila-
Campilo, M. Creech, B. Gross, K. Hanspers, R. Isserlin, R. Kelley, S. Killcoyne, S. Lotia, S.
Maere, J. Morris, K. Ono, V. Pavlovic, A. R. Pico, A. Vailaya, P.-L. Wang, A. Adler, B. R.
Conklin, L. Hood, M. Kuiper, C. Sander, I. Schmulevich, B. Schwikowski, G. J. Warner, T.
Ideker, and G. D. Bader. 2007. Integration of biological networks and gene expression data
using Cytoscape. Nat. Protoc. 2: 2366–2382.
Collins, L. E., W. Berelson, D. E. Hammond, A. Knapp, R. Schwartz, and D. G. Capone. 2011.
Particle fluxes in San Pedro Basin, California: A four-year record of sedimentation and
physical forcing. Deep Sea Res. Part I Oceanogr. Res. Pap. 58: 898–914.
Cram, J. A., C.-E. T. Chow, R. Sachdeva, D. M. Needham, A. E. Parada, J. A. Steele, and J. A.
Fuhrman. 2015. Seasonal and interannual variability of the marine bacterioplankton
community throughout the water column over ten years. ISME J. 9: 563–580.

34

Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics
26: 2460–2461.
Fisher, M. M., and E. W. Triplett. 1999. Automated approach for ribosomal intergenic spacer
analysis of microbial diversity and its application to freshwater bacterial communities.
Appl. Environ. Microbiol. 65: 4630–4636.
Francis, C. A., J. M. Beman, and M. M. Kuypers. 2007. New processes and players in the
nitrogen cycle: the microbial ecology of anaerobic and archaeal ammonia oxidation. ISME
J. 1: 19–27.
Francis, C. A., K. J. Roberts, J. M. Beman, A. E. Santoro, and B. B. Oakley. 2005. Ubiquity and
diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean.
Proc. Natl. Acad. Sci. U. S. A. 102: 14683–14688.
Fuhrman, J. A., D. E. C. Comeau, Å. Hagström, and A. M. Chan. 1988. Extraction from natural
planktonic microorganisms of DNA suitable for molecular biological studies. Appl. Environ.
Microbiol. 54: 1426–1429.
Fuhrman, J. A., I. Hewson, M. S. Schwalbach, J. A. Steele, M. V. Brown, and S. Naeem. 2006.
Annually reoccurring bacterial communities are predictable from ocean conditions. Proc.
Natl. Acad. Sci. U. S. A. 103: 13104–13109.
Galand, P. E., C. Gutiérrez-Provecho, R. Massana, J. M. Gasol, and E. O. Casamayor. 2010. Inter-
annual recurrence of archaeal assemblages in the coastal NW Mediterranean Sea (Blanes
Bay Microbial Observatory). Limnol. Oceanogr. 55: 2117–2125.
Galand, P. E., C. Lovejoy, A. K. Hamilton, R. G. Ingram, E. Pedneault, and E. C. Carmack. 2009.
Archaeal diversity and a gene for ammonia oxidation are coupled to oceanic circulation.
Environ. Microbiol. 11: 971–980.
Hugoni, M., N. Taib, D. Debroas, I. Domaizon, I. J. Dufournel, G. Bronner, I. Salter, H. Agogué, I.
Mary, and P. E. Galand. 2013. Structure of the rare archaeal biosphere and seasonal
dynamics of active ecotypes in surface coastal waters. PNAS 110: 6004–6009.
Iverson, V., R. M. Morris, C. D. Frazar, C. T. Berthiaume, R. L. Morales, and E. V. Armbrust. 2012.
Untangling genomes from metagenomes: revealing an uncultured class of marine
Euryarchaeota. Science 335: 587–590.

35

Karner, M. B., E. F. DeLong, and D. M. Karl. 2001. Archaeal dominance in the mesopelagic zone
of the Pacific Ocean. Nature 409: 507–510.

Könneke, M., A. E. Bernhard, J. R. de la Torré, C. B. Walker, J. B. Waterbury, and D. A. Stahl.
2005. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437: 543–
546.
Kozich, J. J., S. L. Westcott, N. T. Baxter, S. K. Highlander, and P. D. Schloss. 2013. Development
of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence
data on the miseq illumina sequencing platform. Appl. Environ. Microbiol. 79: 5112–5120.
Levipan, H., R. Quiñones, and H. Urrutia. 2007. A time series of prokaryote secondary
production in the oxygen minimum zone of the Humboldt current system , off central
Chile. Prog. Oceanogr. 75: 531–549.
Luo, H., B. B. Tolar, B. K. Swan, C. L. Zhang, R. Stepanauskas, M. Ann Moran, and J. T. Hollibaugh.
2014. Single-cell genomics shedding light on marine Thaumarchaeota diversification. ISME
J. 8: 732–736.
Maritorena, S., D. a Siegel, and A. R. Peterson. 2002. Optimization of a semianalytical ocean
color model for global-scale applications. Appl. Opt. 41: 2705–2714.
Martin-Cuadrado, A.-B., I. Garcia-Heredia, A. G. Moltó, R. López-Úbeda, N. Kimes, P. López-
García, D. Moreira, and F. Rodriguez-Valera. 2015. A new class of marine Euryarchaeota
group II from the mediterranean deep chlorophyll maximum. ISME J. 9: 1619–1634.
Massana, R., L. T. Taylor, A. E. Murray, K. Y. Wu, W. H. Jeffrey, and E. F. DeLong. 1998. Vertical
distribution and temporal variation of marine planktonic archaea in the Gerlache Strait,
Antarctica, during early spring. Limnol. Oceanogr. 43: 607–617.
Mincer, T. J., M. J. Church, L. T. Taylor, C. M. Preston, D. M. Karl, and E. F. DeLong. 2007.
Quantitative distribution of presumptive archaeal and bacterial nitrifiers in Monterey Bay
and the North Pacific Subtropical Gyre. Environ. Microbiol. 9: 1162–1175.
Morris, R. M., K. L. Vergin, J.-C. Cho, M. S. Rappé, C. A. Carlson, and S. J. Giovannoni. 2005.
Temporal and spatial response of bacterioplankton lineages to annual convective overturn
at the Bermuda Atlantic Time-series Study site. Limnol. Oceanogr. 50: 1687–1696.
Murray, A. E., A. Blakis, R. Massana, S. Strawzewski, U. Passow, A. Alldredge, and E. F. DeLong.
1999. A time series assessment of planktonic archaeal variability in the Santa Barbara
Channel. Aquat. Microb. Ecol. 20: 129–145.
36

Murray, A. E., C. M. Preston, R. Massana, L. T. Taylor, A. Blakis, K. Wu, and E. F. DeLong. 1998.
Seasonal and Spatial Variability of Bacterial and Archaeal Assemblages in the Coastal
Waters near Anvers Island , Antarctica. Appl. Environ. Microbiol. 64: 2585–2595.
Newell, S. E., A. R. Babbin, A. Jayakumar, and B. B. Ward. 2011. Ammonia oxidation rates and
nitrification in the Arabian Sea. Global Biogeochem. Cycles 25: GB4016.
Noble, R., and J. A. Fuhrman. 1998. Use of SYBR Green I for rapid epifluorescence counts of
marine viruses and bacteria. Aquat. Microb. Ecol. 14: 113–118.
Orsi, W. D., J. M. Smith, H. M. Wilcox, J. E. Swalwell, P. Carini, A. Z. Worden, and A. E. Santoro.
2015. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic
matter. ISME J. 9: 1747–1763.
Ouverney, C. C., and J. A. Fuhrman. 2000. Marine planktonic archaea take up amino acids. Appl.
Environ. Microbiol. 66: 4829–4833.
Parada, A., D. M. Needham, and J. A. Fuhrman. 2015. Every base matters: assessing small
subunit rRNA primers for marine microbiomes with mock communities, time-series and
global field samples. Environ. Microbiol. n/a–n/a.
Parsons, T. R., Y. Maita, and C. M. Lalli. 1984. A manual of chemical and biological methods for
seawater analysis, Pergamon Press Inc.
Patel, A., R. T. Noble, J. A. Steele, M. S. Schwalbach, I. Hewson, and J. A. Fuhrman. 2007. Virus
and prokaryote enumeration from planktonic aquatic environments by epifluorescence
microscopy with SYBR Green I. Nat. Protoc. 2: 269–276.
Pernthaler, A., C. M. Preston, J. Pernthaler, E. F. DeLong, and R. Amann. 2002. Comparison of
fluorescently labeled oligonucleotide and polynucleotide probes for the detection of
pelagic marine bacteria and archaea. Appl. Environ. Microbiol. 68: 661–667.
Qin, W., S. A. Amin, W. Martens-Habbena, C. B. Walker, H. Urakawa, A. H. Devol, A. E. Ingalls, J.
W. Moffett, E. V. Armbrust, and D. A. Stahl. 2014. Marine ammonia-oxidizing archaeal
isolates display obligate mixotrophy and wide ecotypic variation. Proc. Natl. Acad. Sci. 111:
12504–12509.
Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner.
2013. The SILVA ribosomal RNA gene database project: improved data processing and
web-based tools. Nucleic Acids Res. 41: D590–D596.

37

Rich, V. I., V. D. Pham, J. Eppley, Y. Shi, and E. F. DeLong. 2010. Time-series analyses of
Monterey Bay coastal microbial picoplankton using a “genome proxy” microarray. Environ.
Microbiol. 13: 116–134.
Robidart, J. C., C. M. Preston, R. W. Paerl, K. A. Turk, A. C. Mosier, C. A. Francis, C. A. Scholin,
and J. P. Zehr. 2012. Seasonal Synechococcus and Thaumarchaeal population dynamics
examined with high resolution with remote in situ instrumentation. ISME J. 6: 513–523.
Ruan, Q., D. Dutta, M. S. Schwalbach, J. A. Steele, J. A. Fuhrman, and F. Sun. 2006. Local
similarity analysis reveals unique associations among marine bacterioplankton species and
environmental factors. Bioinformatics 22: 2532–2538.
Santoro, A. E., K. L. Casciotti, and C. A. Francis. 2010. Activity, abundance and diversity of
nitrifying archaea and bacteria in the central California Current. Environ. Microbiol. 12:
1989–2006.
Santoro, A. E., C. L. Dupont, R. A. Richter, M. T. Craig, P. Carini, M. R. McIlvin, Y. Yang, W. D.
Orsi, D. M. Moran, and M. A. Saito. 2015. Genomic and proteomic characterization of “
Candidatus Nitrosopelagicus brevis”: An ammonia-oxidizing archaeon from the open
ocean. Proc. Natl. Acad. Sci. 112: 1173–1178.
Schloss, P. D., S. L. Westcott, T. Ryabin, J. R. Hall, M. Hartmann, E. B. Hollister, R. a Lesniewski,
B. B. Oakley, D. H. Parks, C. J. Robinson, J. W. Sahl, B. Stres, G. G. Thallinger, D. J. Van Horn,
and C. F. Weber. 2009. Introducing mothur: open-source, platform-independent,
community-supported software for describing and comparing microbial communities.
Appl. Environ. Microbiol. 75: 7537–7541.
Steele, J. A., P. D. Countway, L. C. Xia, P. D. Vigil, J. M. Beman, D. Y. Kim, C.-E. T. Chow, R.
Sachdeva, A. C. Jones, M. S. Schwalbach, J. M. Rose, I. Hewson, A. Patel, F. Sun, D. A.
Caron, and J. A. Fuhrman. 2011. Marine bacterial, archaeal and protistan association
networks reveal ecological linkages. ISME J. 5: 1414–1425.
Team, R. C. 2013. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria.
Teira, E., H. M. van Aken, C. Veth, and G. J. Herndl. 2006. Archaeal uptake of enantiomeric
amino acids in the meso- and bathypelagic waters of the North Atlantic. Limnol. Oceanogr.
51: 60–69.
Tolar, B. B., G. M. King, and J. T. Hollibaugh. 2013. An analysis of thaumarchaeota populations
from the northern gulf of Mexico. Front. Microbiol. 4: 72.

38

Wang, Q., G. M. Garrity, J. M. Tiedje, and J. R. Cole. 2007. Naive Bayesian classifier for rapid
assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol.
73: 5261–5267.
Wuchter, C., B. Abbas, M. J. L. Coolen, L. Herfort, J. van Bleijswijk, P. Timmers, M. Strous, E.
Teira, G. J. Herndl, J. J. Middelburg, S. Schouten, and J. S. Sinninghe Damsté. 2006.
Archaeal nitrification in the ocean. Proc. Natl. Acad. Sci. U. S. A. 103: 12317–22.
Xia, L. C., D. Ai, J. A. Cram, J. a. Fuhrman, and F. Sun. 2013. Efficient statistical significance
approximation for local similarity analysis of high-throughput time series data.
Bioinformatics 29: 230–237.
Xia, L. C., J. A. Steele, J. A. Cram, Z. G. Cardon, S. L. Simmons, J. J. Vallino, J. A. Fuhrman, and F.
Sun. 2011. Extended local similarity analysis (eLSA) of microbial community and other time
series data with replicates. BMC Syst. Biol. 5 Suppl 2: S15.
Xiaomin, X., G. Wang, and L. Hongbin. 2015. Dynamics of the bacterial and archaeal
communities in the northern South China Sea revealed by 454 pyrosequencing of the 16S
rRNA gene. Deep Sea Res. Part II Top. Stud. Oceanogr. 117: 97–107.

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Table 1 Mantel Tests of Seasonality and Temporal Variability in MGI or MGII archaeal
communities.
Taxa Depth Mantel R
(Seasonal)
p-value Mantel R
(Temporal)
p-value
MGI 5m 0.1491 + 0.0323 0.0002* 0.084 + 0.077 0.03678*
DCM 0.093 + 0.0344 0.0008* -0.00315 + 0.0793 0.505
150m 0.2595 + 0.0443 0.0002* -0.0485 + 0.0723 0.898
500m 0.1767 + 0.0384 0.0002* 0.0395+ 0.0842 0.195
890m 0.07354 + 0.0415 0.0078* 0.182 + 0.0735 0.0006*
MGII 5m 0.2018 + 0.0422 0.0002* 0.04005 + 0.062 0.132
DCM 0.1153 + 0.052 0.0022* -0.047 + 0.0597 0.94
150m 0.1994 + 0.0402 0.0002* -0.0476 + 0.0726 0.865
500m 0.261 + 0.04 0.0002* 0.0759 + 0.0813 0.0608
890m 0.1055 + 0.0481 0.0034* 0.191 + 0.072 0.0006*
DCM=Deep Chlorophyll Maximum, *=p<0.05
Mantel R value is given + the 95% confidence intervals. P-value given is based on 5000
permutation tests.

Table 2 PERMANOVA analysis of variability between samples of MGI or MGII archaeal
communities demonstrate several communities can be separated by the season the sample
was taken.
Taxa Depth F-statistic r
2
p-value
MGI 5m 2.5468 0.13489 0.0003999*
DCM 1.4414 0.08948 0.1318
150m 5.6543 0.27376 0.0002*
500m 0.9279 0.06216 0.4965
890m 1.4548 0.09024 0.1898
MGII 5m 4.3778 0.20803 2e-04**
DCM 2.1946 0.13016 0.02819*
150m 2.1102 0.12333 0.0224*
500m 1.9977 0.12487 0.04279*
890m 1.5297 0.09445 0.1548
DCM=Deep Chlorophyll Maximum, *=p<0.05
PERMANOVA r
2
indicates the correlation of samples to the categorical values of Winter,
Spring, Summer, and Fall. Winter=Dec,Jan,Feb; Spring=Mar,Apr,May; Summer=Jun,Jul,Aug;
Fall=Sep,Oct,Nov.

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Chapter 2: Supplementary Information
Materials and methods
MGI and MGII representative sequences from abundant OTUs were aligned by ClustalW and a
maximum-likelihood tree based on the Tamura-Nei model was constructed in MEGA6 (Tamura
and Nei 1993; Thompson et al. 1994; Tamura et al. 2013). Reference archaeal genomes
included Single Amplified Genomes, 16S clones, and 16S from complete genomes.
Literature Cited
Tamura, K., and M. Nei. 1993. Estimation of the Number of Nucleotide Substitutions in the
Control Region of Mitochondrial DNA in Humans and Chimpanzees. Mol. Biol. Evol. 10: 512 –
526.
Tamura, K., G. Stecher, D. Peterson, A. Filipski, and S. Kumar. 2013. MEGA6: Molecular
evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30: 2725 –2729.
Thompson, J. D., D. G. Higgins, and T. J. Gibson. 1994. CLUSTAL W: improving the sensitivity of
progressive multiple sequence alignment through sequence weighting, position-specific gap
penalties and weight matrix choice. Nucleic Acids Res. 22: 4673 –4680.

Table S1 Sample dates and depths
Date Depths(m)
18-Feb-2009 5, 16, 150, 500, 890
11-Mar-2009 18, 150, 500, 890
23-Apr-2009 5, 26, 150, 500, 890
13-May-2009 5, 29, 150, 500, 890
18-Jun-2009 5, 40.6, 150, 500, 890
9-Jul-2009 5, 22, 150, 500, 890
19-Aug-2009 5, 22, 150, 500, 890
24-Sep-2009 5, 35, 150, 500, 890
23-Dec-2009 5
28-Jan-2010 5, 42, 150, 500, 890
11-Feb-2010 5, 31, 150, 500, 890
24-Mar-2010 5, 15, 150, 890
27-Apr-2010 5, 45, 150, 890
17-May-2010 5, 17, 150, 500, 890
29-Jun-2010 5
21-Jul-2010 5, 28, 150, 500, 890
11-Aug-2010 5,na , 150, 500, 890

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15-Sep-2010 5
21-Oct-2010 5, 30, 150, 500, 890
23-Nov-2010 5, 27, 150, 500, 890
16-Dec-2010 5, 17.5, 150, 500, 890
11-Jan-2011 5, 5, 150, 500, 890
23-Feb-2011 5
15-Mar-2011 5, 10.8, 150, 500, 890
29-Apr-2011 5, 25.8, 150, 500, 890
24-May-2011 150, 500
22-Jun-2011 5, 17.7, 150, 500, 890
20-Jul-2011 5
17-Aug-2011 5, 27, 150, 500, 890
28-Sep-2011 5
25-Oct-2011 5, 26, 150, 500, 890
29-Nov-2011 5, 29.1, 150, 500, 890
20-Dec-2011 5, 34, 150, 500, 890
26-Jan-2012 5, 8.1, 150, 500, 890
16-Feb-2012 5, 22.4, 150, 500, 890
22-Mar-2012 5, 40.2, 150, 500, 890
3-May-2012 5
20-Jun-2012 5, 19.4, 150, 500, 890
9-Jul-2012 5, 31.9, 150, 500, 890
15-Aug-2012 5, 40.4, 150, 500, 890
28-Sep-2012 5, 45, 150, 500, 890
17-Oct-2012 5, 52, 150, 500, 890
28-Nov-2012 5, 44, 150, 500, 890
11-Dec-2012 5, 37, 150, 500, 890
16-Jan-2013 5, 25, 150, 500, 890
13-Feb-2013 5, 30.9, 150, 500, 890
13-Mar-2013 5, 17.8, 150, 500, 890
24-Apr-2013 5, 35, 150, 500, 890
22-May-2013 5, 24, 150, 500, 890
19-Jun-2013 5, 27.1, 150, 500, 890
18-Jul-2013 5, 33, 150, 500, 890
14-Aug-2013 5, 24, 150, 500, 890
18-Sep-2013 5, 38, 150, 500, 890
15-Oct-2013 5, 34, 150, 500, 890
13-Nov-2013 5, 45, 150, 500, 890
23-Dec-2013 5, 37.1, 150, 890

47

Table S2 Biotic and abiotic environmental variables measured and abbreviations used in eLSA
correlations and NMDS plots. Only measurements available for all 5 depths were fitted to
NMDS plots. Variables fitted to NMDS ordination plots in Figure 3 have corresponding NMDS
ordinates and associated r^2 and probability values. Values of environmental variables shown
in NMDS plots are bolded.

Table S3 Most influential OTUs by SIMPER and p-values when statistically different between
seasons at A)5m, B)DCM, C) 150m, and D)500m. Only showing depths and taxa that statistically
separated by season with PERMANOVA tests (Table 2).

Table S4 Most influential A) MGI and B) MGII OTUs by SIMPER and p-values when statistically
different between depths.

Table S5 Mantel Tests of Seasonality of MGI and Nitrospina communities.
Communities were taken as the 100 most abundant MGI and 100 most abundant
Nitrospina OTUs at each depth.
Depth Mantel R (Seasonal) p-value
150m 0.259 + 0.0429 0.0002*
500m 0.151 + 0.0333 0.0002*
890m 0.131 + 0.0451 0.0004*
Mantel R value is given + the 95% confidence intervals. P-value given is based on 5000
permutation tests.

Figure S1 Box plots of Bray-Curtis Similarities (y-axis) between any two pairs of samples
separated by the given time-lag (x-axis) between the samples indicates temporal variability in
MGI communities at all depths. Communities at the surface (A,B) were more different across
the time-series than communities deeper in the water column (C,D,E). However, decreases in
the average and range of similarities at every 6-month interval and increases in the similarities
at every 12-month interval suggests seasonality in the communities at all depths.

Figure S2 Box plots of Bray-Curtis Similarities between any two pairs of samples over time
show MGII communities displayed similar changes in similarities between samples across the
water column like MGI communities (Figure S1), but communities were on average more
similar in surface depths (A,B) than the MGI. Evidence of seasonality in the form of decreases
and increases in similarity over time are more evident at deeper depths (C,D,E).

Figure S3 A heat map of the dominant MGI OTU abundances over the entire time-series at all
depths show that the distribution of most MGI OTUs spanned multiple depths, but were in
general most abundant at one depth. Distributions of MGI OTUs also show shifts in the
dominant OTUs across the water column.
48

OTUs given as MGI_OTU#, and only OTUs with a maximum relative abundance >0.5% at any depth are
shown.

Figure S4 A heat map of the dominant MGII OTU abundances over the entire time-series at all
depths show that the MGII OTUs were relatively abundant at all depths, and many OTUs were
dominant at several depths, though some like MGII_56 and MGII_158 were only relatively
abundant at one depth. Distributions of MGII OTUs also show shifts in the dominant OTUs
across the water column.
OTUs given as MGII_OTU#, and only OTUs with a maximum relative abundance >0.5% at any depth are
shown.

Figure S5 Maximum-likelihood tree of the representative sequences of dominant MGI and
MGII OTUs across the water column with reference archaeal isolates or clones.

Figure S6 Networks of variables correlated to MGI_840 at a) 150m, b) 500m, and c) 890m
shows different total correlations between depths and differences in correlations to the
microbial community members at each depth.
Archaeal OTUs given as pink triangles and bacterial OTUs given as blue circles. Solid lines connecting
two OTUs represent a positive correlation and dashed lines represent a negative correlation, arrows
point to the lagging variable (1-month lag). All correlations are LS>|0.6|, p-value<0.01, q-value<0.01,
and alignment length >48.

Figure S7 Networks of variables correlated to MGII_2869 at a) 150m, b) 500m, and c) 890m
show correlations to archaeal and bacterial OTUs at all depths and correlations to
environmental variables at 890m.
Archaeal OTUs given as pink triangles, bacterial OTUs given as blue circles, biotic environmental
variables given as grey parallelograms, and abiotic variables as grey squares. Solid lines connecting two
OTUs represent a positive correlation and dashed lines represent a negative correlation, arrows point to
the lagging variable (1-month lag). All correlations are LS>|0.6|, p-value<0.01, q-value<0.01, and
alignment length >48. AirTempMax= maximum air temperature, Bact= total prokaryotic abundance,
Elapsed_Days=elapsed days since 18 Feb 2009, POC= satellite surface particulate organic carbon,
OxygenSaturation= CTD measured oxygen saturation potential, and SigTheta=density of seawater at 0
atmosphere.

Figure S8 Time-series plots at a) 500m and b) 890m of abundant MGI OTUs (top panels) and
NItrospina OTUs (bottom panels) show changes in dominant OTUs in both taxa over time,
where the shifts in community composition of MGI often coincided with shifts in the
composition of Nitrospina taxa.

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Measurement Units Abbreviation Thaumarchaea MGI NMDS NMDS1 NMDS2 r2 Pr(>r) Euryarchaea MGII NMDS NMDS1 NMDS2 r2 Pr(>r)
Absorbance due to detritus and gelbstoff at 433nm Au ABSDetGel -0.92821 -0.37206 0.0731 0.000999 -0.99582 -0.09131 0.0723 0.000999
Absorbance due to phytoplankton at 433nm Au ABSPhyto -0.9932 -0.11644 0.0499 0.002997 -0.94906 0.31509 0.062 0.001998
Maximum Air Temperature °C AirTempMax 0.83505 0.55017 0.5328 0.000999 0.99984 -0.0178 0.4206 0.000999
Minimum Air Temperature °C AirTempMin 0.83592 0.54884 0.5316 0.000999 0.99989 -0.01508 0.4205 0.000999
Average Wave Period s AvgWavePd 0.81049 0.58575 0.1485 0.000999 0.98526 -0.17109 0.1185 0.000999
Average Wind Speed m/s AvgWind 0.82074 0.5713 0.5135 0.000999 0.99952 -0.03113 0.3989 0.000999
Prokaryotic Cell Abundance cells/ml Bact 0.83143 0.55563 0.5353 0.000999 0.99958 -0.02912 0.421 0.000999
Chlorophyll-a Satellite Monthly average mg/m^3 Chl_A_Sat -0.38416 -0.92327 0.0925 0.000999 -0.77232 -0.63524 0.0249 0.048951
Chlorophyll-a Satellite 8-day average mg/m^3 ChlA_Sat8 -0.37333 -0.9277 0.094 0.000999 -0.80097 -0.59871 0.0211 0.082917
Depth of maximum chlorophyll fluorescence m Cmax_Depth 0.825 0.56513 0.5137 0.000999 0.9993 -0.03748 0.4052 0.000999
Day length h day.length 0.81904 -0.57374 0.0244 0.055944 0.99577 -0.0919 0.0229 0.052947
Dominant Wave Period s DomWavePd 0.83195 0.55486 0.2507 0.000999 0.99848 -0.05505 0.2091 0.000999
Days since February 18, 2009 d Elapsed_Days 0.83149 0.55554 0.5353 0.000999 0.99954 -0.03018 0.4213 0.000999
Bacterial productivity measured by leucine incorporation cells/ml/day Leu -0.68128 -0.73203 0.099 0.000999 -0.57611 0.81737 0.1021 0.000999
Multivariate ENSO Index Standard Departure MEI -0.89582 -0.44442 0.0011 0.88012 -0.19606 0.98059 0.0015 0.825175
depth at with SigmaTheta is 0.125kg/m
3
greater than density at 10m m MLD 0.81157 0.58425 0.5157 0.000999 0.99817 -0.06041 0.3979 0.000999
Nitrite concentration mM NO2 -0.99242 0.12287 0.0187 0.108891 -0.91114 -0.4121 0.0207 0.085914
Nitrate concdentration mM NO3
Oxygen concentration measured oxygen sensor on CTD ml/l OxygenCTD -0.99312 -0.11708 0.0092 0.325674 -0.97556 -0.21973 0.0057 0.492507
Maximum oxygen saturation potential ml/l OxygenSaturation -0.75954 -0.65046 0.0685 0.000999 -0.96834 -0.24962 0.0461 0.004995
Photosynthetically active radation, surface satellite mesurement Einstein/m^2/day PAR_SAT 0.83728 0.54677 0.5246 0.000999 0.99863 -0.05229 0.4175 0.000999
Phosphate concentration mM PO4
Particulate organic carbon, surface satellite mesurement mg/m^3 POC 0.82931 0.55879 0.5375 0.000999 0.99942 -0.03393 0.4211 0.000999
Precipitation mm PRCP 0.76694 0.64172 0.3921 0.000999 0.97855 -0.206 0.2921 0.000999
Primary Productivity Monthly average, surface satellite mesurement mg_C/m^2/day Prim_Prod 0.83188 0.55495 0.5352 0.000999 0.99952 -0.03102 0.4214 0.000999
Primary Productivity 8-day average, surface satellite mesurement mg_C/m^2/day Prim_Prod8 0.83182 0.55505 0.5352 0.000999 0.99951 -0.03125 0.4213 0.000999
Excess phosphate concentratio, [Phosphate]- [Nitrate + Nitrite]/16 mM Pstar
Salinity PSU SalinityCTD -0.62528 -0.7804 0.1237 0.000999 -0.92585 0.3779 0.0774 0.000999
SigmaTheta, density at p=0 kg/m^3 SigTheta -0.70969 -0.70451 0.049 0.001998 -0.86371 -0.50398 0.0335 0.012987
Sverdrup Transport Sv Svd 0.86548 0.50094 0.4995 0.000999 0.99851 -0.05453 0.4097 0.000999
Temperature °C TemperatureCTD 0.84312 0.53772 0.1086 0.000999 0.97069 0.24032 0.0832 0.000999
Bacterial productivity measured by thymidine incorporation cells/ml/day Thy
Cell turnover measured by cell abundance divided by Leucine incorporation day TurnoverLeu -0.90299 0.42966 0.0335 0.023976 -0.44054 -0.89773 0.0524 0.001998
Cell turnover measured by cell abundance divided by Thymidine
incorporation day TurnoverThy
Bakun Upwelling Index m^3/s/100m of Coast Upwelling 0.83489 0.55042 0.5321 0.000999 0.99935 -0.03595 0.4202 0.000999
Ratio of Virus particle abundance to prokaryotic cell abundance VBR 0.16958 -0.98552 0.0047 0.583417 0.9931 0.11726 0.003 0.702298
Vertical Velocity into the Eckman Layer Vert 0.86698 0.49835 0.4968 0.000999 0.99839 -0.05666 0.4082 0.000999
Virus particle abundance VLP/ml Vir 0.83178 0.55511 0.5353 0.000999 0.99952 -0.03084 0.4213 0.000999
Wave Height m WaveHeight 0.72225 0.69163 0.133 0.000999 0.92461 -0.38091 0.1013 0.000999
Maximum 2-minut Wind Gust speed m/s WindGust 0.82228 0.56908 0.5374 0.000999 0.99951 -0.03143 0.4181 0.000999
Table S2 Biotic and abiotic environmental variables measured and abbreviations used in eLSA correlations and NMDS plots. Only measurements available for all 5 depths were fitted to NMDS plots. Variables fitted to NMDS ordination plots in Figure 3 have corresponding NMDS
ordinates and associated r^2 and probability values. Values of environmental variables shown in NMDS plots are bolded.
57
A)5m B)DCM
Seasons MGI OTUs Cumulative Contributions p-value Seasons MGII OTUs Cumulative Contributions p-value Seasons MGII OTUs Cumulative Contributions p-value
Winter vs. Spring MGI_464 0.2962602 0.03739 Winter vs. Spring MGII_89 0.2749518 0.008598 Winter vs. Spring MGII_89 0.2625765
MGI_495 0.4162934 MGII_248 0.4638593 MGII_248 0.4696379
MGI_7 0.5041571 MGII_33 0.5873718 MGII_904 0.5716748 0.043791
MGI_840 0.5918089 MGII_904 0.6758795 0.015397 MGII_33 0.6503785 0.004399
MGI_570 0.675847 MGII_198 0.7439639 MGII_2521 0.7138093
MGI_283 0.7259567
Winter vs. Summer MGI_464 0.2859905 0.0002 Winter vs. Summer MGII_89 0.249825 0.0002 Winter vs. Summer MGII_89 0.299509 0.0291942
MGI_840 0.3794154 MGII_33 0.4078988 MGII_248 0.5053548
MGI_495 0.4656601 MGII_248 0.5555179 MGII_904 0.588781
MGI_7 0.5497844 MGII_4267 0.6459697 0.0002 MGII_3378 0.6593781
MGI_283 0.6209671 MGII_198 0.7086704 0.0012 MGII_198 0.7121613
MGI_1283 0.68217
MGI_570 0.7371769
Winter vs. Fall MGI_464 0.3124028 0.0004 Winter vs. Fall MGII_89 0.2752554 0.0002 Winter vs. Fall MGII_89 0.2490619
MGI_495 0.4086415 MGII_33 0.4914466 0.002999 MGII_248 0.4216009
MGI_283 0.5039016 MGII_248 0.6414051 MGII_582 0.5384953 0.0005999
MGI_7 0.5954044 MGII_4267 0.6966742 MGII_3378 0.6201796
MGI_840 0.6624421 MGII_198 0.7495414 MGII_904 0.689654
MGI_1283 0.7149992 MGII_198 0.7462627
Spring vs. Summer MGI_464 0.1703738 Spring vs. Summer MGII_248 0.2058516 Spring vs. Summer MGII_89 0.2774236 0.0167966
MGI_840 0.2779191 MGII_89 0.3772084 MGII_248 0.4928339 0.0073985
MGI_7 0.3800838 MGII_33 0.5341942 MGII_904 0.584199 0.0279944
MGI_495 0.482106 MGII_4267 0.63001 0.0006 MGII_33 0.656669 0.000599
MGI_570 0.5628017 MGII_904 0.7184492 0.002999 MGII_3378 0.7202024
MGI_283 0.6368347
MGI_1283 0.7033054
Spring vs. Fall MGI_464 0.1911222 Spring vs. Fall MGII_248 0.2031037 Spring vs. Fall MGII_248 0.2004281
MGI_495 0.3077466 MGII_33 0.3991301 MGII_89 0.3706649
MGI_7 0.4184635 MGII_89 0.5694096 MGII_582 0.4861093 0.0002
MGI_283 0.5124494 MGII_904 0.6547484 MGII_904 0.5762148
MGI_840 0.5932042 MGII_4267 0.7202424 MGII_33 0.6450117
MGI_570 0.6724676 MGII_3378 0.7093569
MGI_1283 0.7288427
Summer vs. Fall MGI_464 0.1365063 Summer vs. Fall MGII_33 0.2122685 Summer vs. Fall MGII_89 0.2834176
MGI_7 0.252112 MGII_248 0.4003644 MGII_248 0.4446381
MGI_840 0.355707 MGII_89 0.5503648 MGII_582 0.5494736 0.0002
MGI_283 0.4565302 0.04719 MGII_4267 0.6287743 MGII_904 0.6308659
MGI_495 0.5505049 MGII_904 0.6986901 MGII_3378 0.6951736
MGI_1283 0.6232757 0.04219 MGII_198 0.7553963 MGII_33 0.7421412
MGI_570 0.6831472
MGI_30 0.7418194
Table S3 Most influential OTUs by SIMPER and p-values when statistically different between seasons at A)5m, B)DCM, C) 150m, and D)500m. Only showing depths and taxa that statistically separated by
season with PERMANOVA tests (Table 2).
58
C)150m D)500m
Seasons MGI OTUs Cumulative Contributions p-value Seasons MGII OTUs Cumulative Contributions p-value Seasons MGII OTUs Cumulative Contributions
Winter vs. Spring MGI_840 0.1490616 Winter vs. Spring MGII_2869 0.267566 Winter vs. Spring MGII_56 0.258267
MGI_495 0.2972696 0.046191 MGII_158 0.4001282 MGII_1106 0.43172
MGI_464 0.4417222 MGII_582 0.4970512 MGII_2869 0.572281
MGI_283 0.5239907 MGII_357 0.5614134 MGII_582 0.637546
MGI_1283 0.5968703 MGII_248 0.6191311 MGII_3608 0.692618
MGI_570 0.6572308 MGII_3608 0.6722783 MGII_1952 0.730312
MGI_41 0.6882312 MGII_56 0.7184401
MGI_7 0.716498
Winter vs. Summer MGI_464 0.187289 Winter vs. Summer MGII_2869 0.2460597 Winter vs. Summer MGII_56 0.215685
MGI_495 0.3237587 MGII_158 0.4419237 MGII_1106 0.411188
MGI_840 0.4410948 MGII_582 0.5615443 MGII_2869 0.582633
MGI_283 0.5532008 MGII_357 0.6486619 MGII_158 0.660739
MGI_570 0.6086654 MGII_3608 0.7029218 MGII_582 0.718643 0.014997
MGI_1283 0.6565409
MGI_41 0.6886019
MGI_7 0.7141562
Winter vs. Fall MGI_464 0.1998073 Winter vs. Fall MGII_2869 0.2971822 Winter vs. Fall MGII_1106 0.213351
MGI_840 0.3334688 MGII_582 0.4271794 0.0311938 MGII_2869 0.419913
MGI_495 0.4519017 MGII_158 0.5459905 MGII_56 0.591607
MGI_283 0.5252417 MGII_357 0.6182967 MGII_3608 0.664378
MGI_570 0.5909956 MGII_89 0.677633 MGII_582 0.712666
MGI_1283 0.6507311 MGII_2521 0.7184241
MGI_7 0.693977
MGI_30 0.7281883
Spring vs. Summer MGI_464 0.2068753 0.017796 Spring vs. Summer MGII_2869 0.2500528 Spring vs. Summer MGII_56 0.194654
MGI_840 0.3638244 MGII_158 0.4536568 0.0002 MGII_2869 0.386032
MGI_283 0.4761409 0.0008 MGII_3608 0.5142476 0.0493901 MGII_1106 0.524359
MGI_495 0.559841 MGII_582 0.5703143 MGII_158 0.61195
MGI_1283 0.6324309 MGII_248 0.626299 MGII_3608 0.675073
MGI_570 0.7036118 0.037593 MGII_357 0.6776626 MGII_582 0.72002
MGII_56 0.7274204 0.0065987
Spring vs. Fall MGI_464 0.1725866 0.0012 Spring vs. Fall MGII_2869 0.2757093 0.0053989 Spring vs. Fall MGII_56 0.275843 0.0004
MGI_840 0.3340129 0.0002 MGII_158 0.4085245 MGII_1106 0.45409
MGI_495 0.4868535 0.0002 MGII_582 0.4978575 MGII_2869 0.595241
MGI_1283 0.568463 0.0002 MGII_357 0.5595403 MGII_582 0.6606 0.004599
MGI_570 0.6392953 0.0002 MGII_248 0.6079828 MGII_3608 0.721836
MGI_283 0.7066678 MGII_2521 0.6522259 0.0003999
MGII_3608 0.6958319
MGII_56 0.7394214 0.0095981
Summer vs. Fall MGI_464 0.1815018 Summer vs. Fall MGII_2869 0.2771445 Summer vs. Fall MGII_56 0.221796 0.0006
MGI_495 0.347991 MGII_158 0.4191718 MGII_1106 0.419001
MGI_840 0.4718359 MGII_582 0.5376266 MGII_2869 0.591548
MGI_283 0.5485203 MGII_357 0.6285919 0.0179964 MGII_158 0.668195
MGI_570 0.6070281 MGII_2521 0.6733864 MGII_582 0.729308 0.0002
MGI_1283 0.6624591 MGII_3608 0.7178094
MGI_7 0.7049227
59
A) MGI B) MGII
Depths MGI OTUs Cumulative Contribution p-value Depths MGII OTUs Cumulative Contribution p-value
5m vs. DCM MGI_464 0.4322919 0.0002 5m vs. DCM MGII_248 0.2349486 0.0002
MGI_495 0.6348602 0.0002 MGII_89 0.4421647 0.0002
MGI_7 0.7733352 MGII_33 0.5444107 0.0002
MGII_904 0.6360846 0.0002
MGII_198 0.7004722 0.0002
5m vs. 150m MGI_840 0.1719836 0.0002 5m vs. 150m MGII_2869 0.3445079 0.0002
MGI_464 0.3091827 0.0002 MGII_158 0.4709667 0.0002
MGI_495 0.4449508 0.0002 MGII_248 0.5773702 0.007798
MGI_283 0.5558617 0.0002 MGII_582 0.6367186 0.0002
MGI_570 0.6507927 0.0002 MGII_33 0.6956579 0.0002
MGI_1283 0.7194933 0.006599 MGII_89 0.7479584
5m vs. 500m MGI_840 0.3313894 0.0002 5m vs. 500m MGII_2869 0.3296209 0.0002
MGI_570 0.4898394 0.0002 MGII_56 0.4436261 0.0002
MGI_1283 0.6422406 0.0002 MGII_248 0.5546216 0.0006
MGI_7 0.6962586 MGII_33 0.6147662 0.0002
MGI_30 0.7390548 0.0002 MGII_89 0.6725117
MGII_582 0.7158434 0.0002
5m vs. 890m MGI_7 0.3297508 0.0002 5m vs. 890m MGII_1106 0.3326999 0.0002
MGI_840 0.5522852 0.0002 MGII_248 0.4446504 0.0004
MGI_570 0.6536997 0.0002 MGII_2869 0.5354573
MGI_1283 0.7289179 0.0002 MGII_33 0.5956535 0.0002
MGII_89 0.6541281
MGII_3608 0.7038152 0.0002
DCM vs. 150m MGI_840 0.1764542 0.001 DCM vs. 150m MGII_2869 0.3057669 0.0002
MGI_495 0.3071848 0.0002 MGII_248 0.4234362 0.0012
MGI_464 0.4349148 0.005799 MGII_158 0.5334833 0.0002
MGI_283 0.5482636 0.0002 MGII_89 0.6380328 0.0002
MGI_570 0.6457438 0.0002 MGII_904 0.6858424 0.0002
MGI_1283 0.7162857 0.033593 MGII_582 0.7336268 0.0002
Table S4 Most influential A) MGI and B) MGII OTUs by SIMPER and p-values when statistically different between depths.
60
DCM vs. 500m MGI_840 0.3301331 0.0002 DCM vs. 500m MGII_2869 0.2848446 0.0002
MGI_570 0.4880242 0.0002 MGII_248 0.4064923 0.0002
MGI_1283 0.6398436 0.0002 MGII_89 0.5184049 0.0002
MGI_7 0.6894281 MGII_56 0.6171567 0.0002
MGI_30 0.7320816 0.0002 MGII_904 0.6660624 0.0002
MGII_1106 0.7040783
DCM vs. 890m MGI_7 0.3172735 0.0002 DCM vs. 890m MGII_1106 0.2870911 0.0002
MGI_840 0.5356499 0.0002 MGII_248 0.4085705 0.0002
MGI_570 0.6351461 0.0002 MGII_89 0.5202032 0.0002
MGI_1283 0.7089481 0.0004 MGII_2869 0.5997127
MGII_904 0.648224 0.0002
MGII_3608 0.6913663 0.0002
MGII_582 0.7263659
150m vs. 500m MGI_840 0.1987318 150m vs. 500m MGII_2869 0.2767648
MGI_495 0.3146559 MGII_158 0.4638713 0.0002
MGI_464 0.4207757 MGII_56 0.6268266 0.0002
MGI_1283 0.5192203 MGII_1106 0.6969599
MGI_570 0.6048911 MGII_582 0.7481831
MGI_283 0.688956
MGI_7 0.7247987
150m vs. 890m MGI_7 0.2463553 0.0002 150m vs. 890m MGII_1106 0.319615 0.0002
MGI_464 0.3722942 MGII_2869 0.5875986 0.0002
MGI_495 0.4962063 MGII_158 0.7130235 0.0002
MGI_840 0.5883191
MGI_283 0.6412591
MGI_570 0.6819396
MGI_3347 0.7198348 0.0002
500m vs. 890m MGI_7 0.2462999 500m vs. 890m MGII_1106 0.3560939 0.0002
MGI_840 0.4495324 MGII_2869 0.6375265 0.008998
MGI_1283 0.5627357 MGII_56 0.7522241 0.0002
MGI_570 0.663369
MGI_283 0.7159115
61
62

Chapter 3:
Strain variation of marine Thaumarchaea over time and depth via 16S
rDNA tag and metagenomics sequencing
Abstract
Despite over 20 years of study, we still have a limited understanding of how
the genomic diversity of marine archaea varies over time and depth. We have
evaluated monthly Thaumarchaea Marine Group I (MGI) community variability and
microdiversity from surface to seafloor using 16S rRNA gene and metagenomics
analysis. Building upon variations in MGI diversity seen before, this study used
oligotyping of 16S rRNA sequences to divide the communities into subtypes with
distinctive single base variations. This method revealed distinct strains that, despite
sharing greater than 99% 16S rRNA gene similarity, had different temporal patterns
and distributions across depths. Beyond single marker genes, whole-genome
analysis via competitive metagenomic read recruitment to reference genomes,
single-cell amplified genomes, and assemblies from these metagenomses also shows
strain variation across depth and time that presumably relates to niche
specialization. The best recruitments of MGI in the top 50m was to a Thaumarchaeal
Group I strain (SPOT MGI enrichment) that we enriched when it unexpectedly took
over a “Candidatus Nitrosopelagicus brevis” enrichment culture, while being
maintained in 0.2um filtered surface water from the San Pedro Ocean Time-series
station. Examining the temporal variability of reads recruiting to each reference at
different levels of similarity revealed shifts in the community that occurred at
63

percent identities above 95%. These changes in microdiversity also occurred across
depths, suggesting each depth harbors a different community of highly related MGI
taxa with different temporal patterns. Thus, this study reveals 16S rRNA gene and
genomic strain level variation in the Thaumarchaea Marine Group I, showing the
existence ecologically significant strain-level microdiversity and niche partitioning
in the archaea.
Introduction
Several studies have shown the existence of strain-level diversity, or
microdiversity, in bacteria including marine bacteria such as Prochlorococcus and
Synechococcus taxa, where despite having >97% 16S rRNA sequence similarity
some strains in these taxa have different physiological and metabolic activity (Fox
et al. 1992; Stackebrandt and Goebel 1994; Rocap et al. 2003; Ahlgren and Rocap
2006; Kashtan et al. 2014). Thaumarchaea Marine Group I (MGI) may also be
divided into phylogenetic clusters with distinct ecological patterns (Pester et al.
2011; Tully et al. 2012; Auguet et al. 2012; Hugoni et al. 2013; Swan et al. 2014).
However, the 16S based studies have typically used the conventional 97% similarity
cut-off or clone sequences to examine differences in the MGI communities across the
water column and geographical locations. Eren et al. (2013, 2014) demonstrated
that several bacterial taxa sharing more than 99% 16S gene similarity had distinct
distributions across samples not obvious when clustering at 97% 16S gene
similarity. Additionally, in Chapter 2, MGI sequences clustered at 99% similarity
showed some taxa that were >97% similar had distinct interactions with the
microbial community and different temporal patterns. To follow up on these
64

observations, this study focuses on evaluating the ecological significance of
Thaumarchaea Marine Group I microdiversity occurring above the 99% 16S rRNA
gene OTU cut-off.
The 16S rRNA gene has proven valuable in determining the diversity of a
wide range of bacteria and archaea in the environment; however, 16S diversity may
not always reflect the genomic diversity or ecological diversity within a typical
phylogenetic group (Cohan 2006; Koeppel et al. 2008). Therefore, we also used
metagenomes to investigate the variability of the genomic diversity of reads
recruiting to reference MGI taxa. To focus on only sequences from organisms highly
similar to the references, we examined the variability of reads with percent
identities (using BLAST) to any reference greater than 95%. This study will
therefore evaluate the temporal dynamics and depth distribution of Thaumarchaea
MGI microdiversity using 16S rRNA genes and metagenomics at the San Pedro
Ocean Time-Series station.
Methods
Sample Site and Collection
Seawater samples were collected from the San Pedro Ocean Time-series
station located in the San Pedro Channel off the coast of California (33.55°N,
118.4°W). Samples for DNA extraction on collected on a nearly monthly basis at 5
depths spanning the 900m water column as described previously (Cram et al. 2015;
Parada et al. 2015). Samples presented in this study were collected on a nearly
monthly basis between July 2012 and June 2013 at 150m and at the 5m, deep
65

chlorophyll maximum depth (45m), 150m, 500m, and 890m in September 2012
(Table S1 and S2). Seawater was filtered sequentially through a 1 M A/E glass fiber
filter (Pall, Port Washington, NY) and 0.22M Durapore filter (ED Millipore,
Billerica, MA). Filters were stored at -80°C until processed. DNA from the 0.22M
Durapore filter was extracted by SDS lysis and purified by phenol chloroform as
previously described (Fuhrman et al. 1988).
16S rRNA gene sequencing
Only DNA from the 0.22 M filter was analyzed in this study. DNA was
amplified as described previously (Chapter 1 and 2), with the 515F-C (5’-
GTGCCAGCMGCCGCGGTAA) and 926R (5’- CCGYCAATTYMTTTRAGTTT) universal
primers and barcoding strategy detailed in Parada et al., (2015) and Chapter 2.
Amplicons were sequenced on the 2 x 300bp MiSeq Illumina platform. Paired-ends
were merged with USEARCH v7 allowing a maximum of 3 mismatches across the
overlapping region (Edgar 2010). Sequences were de-multiplexed by their forward
5bp barcode in QIIME v 1.8 allowing a maximum of 0 ambiguous bases, 0
mismatches to barcodes and primers, and an average quality score of q33 across a
sliding 50bp window (Caporaso et al. 2010). Chimeras were detected with UCHIME
and discarded with the mothur v1.34.4 tool (Schloss et al. 2009; Edgar et al. 2011).
Sequences were processed following the mothur MiSeq standard operating
procedure, including pre-clustering sequences at 3-base similarity and assigned
taxonomy with the default classifier against the Silva v119 reference database
(Wang et al. 2007; Kozich et al. 2013; Quast et al. 2013). Sequences were clustered
66

at 99% similarity to form operational taxonomic units (OTUs) with the average-
neighbor algorithm.
All sequences of the most abundant 99% Thaumarchaea Marine Group I
OTUs were analyzed for microdiversity with the unsupervised Minimum Entropy
Decomposition (MED) method (Eren et al. 2014). Briefly, the MED method
identifies individual nucleotide sites of high variation (or high Shannon entropy) to
separate sequences based-on information-rich sites, forming groups of sequences
named oligotypes. The sequences were analyzed allowing for 9 discriminant sites
and a minimum 0.1 entropy value, the default values were used for all other options.
The sequences from each individual OTU were analyzed separately by MED, such
that each oligotype form part of a single 99% OTU. The 16S rRNA gene sequences
analyzed in this study were from a 5 year dataset from SPOT (Chapter 2), however
only the results from samples overlapping with the metagenomic dataset described
below are shown in this study.
Metagenomes
The DNA (40ng per sample) was sheared on a Covaris using default settings
for a fragment size of ~500bp (Covaris, Woburn, MA). Sheared DNA was cleaned
and concentrated with 1x magnetic Agencourt AMPure XP beads (Beckman Coulter,
Brea, CA). Metagenomic libraries were prepared from sheared DNA using the
Ovation Ultralow System v2 1-16 library kit (NuGEN, San Carlos, CA). The DNA was
amplified as described in the kit using 11 PCR cycles. Amplified DNA was cleaned
and concentrated using 0.8X AMPure beads. The DNA was sequenced on the 2 x
150bp Hiseq Illumina platform.
67

Sequences were demultiplexed by their 6 bp barcode without allowing any
mismatches. Additionally, the sequences were filtered with the pipeline described
by Eren et al. (2013) using the Minoche method (Minoche et al. 2011). Sequences
from the 150m dataset were co-assembled (except August 2012) with the Ray
assembler v2.3.1 using a k-mer of 31 and minimum-contig-length of 1000 (Boisvert
et al. 2012). Metagenomes from the September 2012 500m and 890m samples were
also co-assembled separately. To determine the coverage of each scaffold, reads
from each metagenome were mapped to the scaffolds at 97% similarity with the
CLC assembler (CLC Bio, Waltham, MA). Scaffolds from the assemblies were further
analyzed with Anvi’o (Eren et al. 2015), which uses tetra nucleotide frequencies
(TNF) and differences in coverage between samples in an effort to bin scaffolds
belonging to the same genome. Visual representation of similarities in TNF and
coverage allow further refinement of genomic bins. Anvi’o also employs the Rapid
Annotation using Subsystem Technology (RAST) server for annotation of scaffolds
and determines the number of single-copy genes (Aziz et al. 2008; Overbeek et al.
2014; Brettin et al. 2015) present in a genomic bin to determine “completeness” of
bins and potential inclusion (“contamination”) of genes from other genomes.
Thaumarchaea Marine Group I (MGI) bins were identified as either those classified
by RAST as Nitrosopumilus maritimus regardless of completeness (Könneke et al.
2005) or with >4 genes annotated as archaeal, having a GC content <40% (other
sequenced Thaumarchaeal genomes have %GC<35, shown in Santoro et al., 2015)
and >40% completeness. Using these criteria, 8 genomic bins from the 150m co-
assembly and 6 bins from the 500m/890m assembly were obtained. Additionally,
68

the average nucleotide identity was calculated against other MGI references by
reciprocal best hits to determine similarity between the assemblies and known MGI
genomes (Goris et al. 2007).
For competitive fragment recruitment, reads from each metagenomic dataset
were aligned by blastn to several MGI reference genomes using a similar strategy as
that described in Dupont et al., (2012) and Santoro et al. (2015). References were
single amplified genomes (SAGs), a MGI genome obtained from an organism grown
in an enrichment culture in seawater obtained from SPOT, and the 14 genomic bins
(Table S1). Only the best hit was retained for each read, such that a single read
recruiting at a higher identity and e-value to Nitrosopumilus maritimus than to
“Candidatus Nitrosopelagicus brevis” str. CN25 would only be recorded as aligning
to N. maritimus. Alignments were required to have a minimum percent identity
(%ID) of 70% across a minimum alignment length of 50bases and minimum e-value
of 0.001. Additionally, recruitments were further filtered and separated by their
range of %ID and binned as follows: 95< %ID<97, 97< %ID<99, and %ID>99.
Results and Discussion
16S Microdiversity
The existence of ecologically relevant 16S microdiversity in the
Thaumarchaea Marine Group I taxa suggests that controls on these taxa distinguish
between organisms that have highly similar 16S rRNA genes. Though clustering
sequences at 99% similarity already forms OTUs that are above the commonly used
97% similarity “species” cut-off, some Thaumarchaea Marine Group I 99% 16S
69

OTUs could be further broken down into oligotypes, different by <6 bases, that have
different temporal patterns (Figure 1). For example, two oligotypes of OTU7, 1 and
4, were dominant in different months. Additionally, OTU283 was made up of several
oligotypes during the first 9 months of the time-series (only the first 5 oligotypes
are plotted in Figure 1b), but there is only one dominant oligotype in the last 3
months. The existence of oligotypes for some of the OTUs suggests that some 16S
clusters are comprised of ecologically distinct populations. These oligotypes may
indicate that these 16S based taxa have distinct physiology, viral susceptibility, or
other adaptations despite having highly similar 16S genes, comparable to that seen
in the high-light and low-light adapted Prochlorococcus strains (Moore et al. 1998)
Not all OTUs examined in this study showed micro-diversity (Figure 1b and
1d) while others had many dominant and minor oligotypes (Figure 1e and Figure 2),
indicating different constraints on the 16S gene diversity in MGI taxa. Hugoni et al.
(2013) found evidence of niche partitioning in MGI communities clustered into 97%
similarity OTUs in the northwestern Mediterranean Sea surface water. They found
temporal variability in the dominant and active MGI OTUs, as well as seasonal peaks
in abundance during winter months. However, we show that this variability exists
within 99% similar 16S OTUs, and occurs throughout the water column. This
suggests controls on the MGI communities occur at the strain level indicating these
highly related organisms have distinct ecological roles within the microbial
community.
A depth profile of MGI taxa demonstrated that examining only the 99% OTUs
masks the existence of changes in the dominant variants, observed as oligotypes,
70

across the water column (Figure 3). For example, OTU1283 had different dominant
oligotypes at 150m, 500m, and 890m in September 28, 2012 (Figure 3d). This
suggests that taxa with highly similar 16S rRNA gene sequences have different
adaptations and constraints that cause them to segregate by depth. The depth
profile of OTU464 also demonstrates that some OTUs have different numbers of
oligotypes at different depths, similar to that seen over time with the oligotypes of
OTUs 464 and 283 (compare Figures 1 and 3). This indicates differences in the
extent of microdiversity of certain taxa at different depths, and that this
microdiversity can be temporally variable. Recently, Qin et al. (2014) showed that
two newly isolated strains of the Thaumarchaeal Nitrosopumilus maritimus, HCA1
and PS0, and the previously isolated strain, SCM1, shared >99% of their 16S rRNA
gene sequence and >95% amoA gene, yet had distinct tolerances to pH, salinity, and
light intensity. This may be a strong indication that the diversity observed through
16S based studies of Thaumarchaeal Marine Group I taxa only show a glimpse into
the actual diversity within this dominant microbial group.
Metagenomic Diversity
The16S rRNA gene is most commonly used in studies analyzing diversity of
organisms. However several studies have shown that this gene does not always
reflect the physiological and metabolic diversity of closely related taxa (Scanlan and
West 2002; Kashtan et al. 2014; Biller et al. 2015). Therefore, to determine if the
microdiversity observed in the MGI oligotypes reflects genomic diversity, we
compared the competitive fragment recruitment of metagenomic reads to 86
references at different ranges of percent identity(%ID) (Table S1). To focus on
71

sequences from MGI organisms that are highly similar to a given reference we
evaluated the relative abundance of reads recruiting above 95 percent identity
(%ID). Additionally, we separated reads into 95<%ID<97%, 97<%ID<99% or
99<%ID<100% bins to investigate ecologically relevant differences in recruitment
within this high similarity range to determine the potential of subspecies or strains.
We used existing genomes from cultures and published SAGs to represent
previously described archaeal genomes (including our own enrichment culture;
“SPOT enrichment”) in the metagenomics recruitment. Additionally, we included
partial genomes determined from metagenomes to include additional genomic
diversity we could find in our samples. We used the program Anvi’o by Eren et al.
(2015) to separate scaffolds from assemblies of the metagenomics data by tetra
nucleotide frequency and differential coverage across samples into genomic bins.
The genomic bins identified as MGI were included in the competitive fragment
recruitments (Tables S1 and S2). Two of these bins, Group_90_l and Group_90_i,
demonstrated high recruitment in the time-series and depth profile (Figures 4 and
5). As mentioned previously, the genome of the “SPOT enrichment” obtained from
an enrichment culture that has 100% 16S rRNA gene similarity to the most
abundant MGI OTU (OTU464) in surface depths and 150m at SPOT was included as
a reference. The ANI between each genomic bin and the SPOT enrichment genome
demonstrates that these are different genomes despite having a similar origin
(Group_90_i v. SPOT enrichment= 89.2%, Group_90_l v. SPOT enrichment=79.2%,
Table S3). This further supports the existence of a genomically diverse MGI
72

community, reflected in the differences in the temporal patterns observed between
16S 99% OTUs and oligotypes.
Separating reads recruiting to the references at >95%ID into bins, revealed
differences in the abundances of each bin. These differences suggest that there are
populations of highly related organisms at SPOT. For example, we found that
fragment recruitment to the number of abundant references at 95-97%ID and 97-
99%ID over the 12-month time-series varied over time, with just three references
dominating in October and November (Figure 4a and 4b). However, at 99-100%ID,
recruitment was dominated by genomic bin Group_90_l (Figure 4c) and at a greater
relative abundance than it did at lower similarities. This suggests that many of the
organisms at SPOT were highly similar to the genome represented by Group_90_l,
but that there also existed other organisms that were similar but not identical to
Group_90_l and the other references. Focusing on the different similarity bins to a
few references over the 12 months (Figure 5) shows that there was distinct
distributions of reads recruiting to each reference. For example, maximum
recruitment to SAG AAA007-023 was from reads with 97-99%ID (Figure 5a), but
the “SPOT Enrichment” had relatives that are more distant in the natural community
with maximum recruitment occurring at 95-97%ID (Figure 5b). This suggests that
within the natural environment, there were organisms that were likely strains of
references SAG AAA007-O23 and the SPOT Enrichment. Additionally, Figure 5c
shows that reads recruiting above 99%ID have different patterns between July and
November from the other bins, indicating different ecological patterns between bins
likely indicative of different populations of highly related MGI taxa with different
73

controls on their abundances. Collectively, these results indicate a high level of
genomic diversity in the MGI communities at SPOT, and the total genomic diversity
is not static, but rather appears to be temporally variable. This variability may be
due to small genetic variations in key genes providing an advantage for the strains
that have them, leading to variability in the dominant strain over time.
The apparent genomic diversity of MGI communities at SPOT may thus
explain the seasonal patterns observed in these communities (Chapter 2), where
minor (single-base) variations in the genomes of related organisms may confer a
specific strain a competitive advantage when changes in the environment occur. For
example, small changes in the gene sequences of key enzymes may increase
efficiency of substrate acquisition and utilization or lead to a decrease in
susceptibility of viral infection during specific times of the year. These results also
indicate more diversity in the MGI community than has been previously shown with
16S rRNA, amoA, or nirK genes or metagenomics studies (i.e. Molina et al., 2010;
Santoro et al., 2010; Lund et al., 2012; Tully et al., 2012; Swan et al., 2014). Fragment
recruitment indicated that the MGI community at SPOT is not fully represented by
most, if any, of the references. Therefore, the natural diversity of these organisms
may be much higher than is captured by any known cultured MGI organisms or
sequenced genome.
Separating sequences recruiting above 95% into similarity bins allows for
detection of different populations that, although highly similar, may have different
distributions across the water column and thus, different niches. For example, the
reference SAG AAA008-M23 had maximal recruitment at 97-99%ID at 5m, 150m,
74

and 890m, but maximal recruitment of 99-100%ID reads at DCM and 500m (Figure
6 and Table S2). This suggests that different relatives to this reference occur
throughout the water column. Additionally, the reads recruiting at 95-97% ID
(Figure 6a) to genomic bin Group_7_depth had a different distribution across the
water column than the other reads recruiting at higher similarity (Figures 6b and
6c). These variations imply genomic diversity in the MGI populations across depths
and time that occurs within genetically similar organisms. This suggests that the
interactions of the MGI communities with the environment select for MGI taxa that
differ by only a few bases.
These results are consistent with the hypothesis that minor gene sequence
variations control metabolic diversity and fitness under varying conditions. This
genomic diversity may lead to subtle differences in resource utilization or
differential survival relating to resistance to certain grazers or viruses, selecting for
different strains of the same “species” dependent on the environment at each depth
and at different times of the year. Future studies using these data to follow up with
detailed evaluation of the specific differences in the close genomic variants may
allow us to determine which genes lead to differences in apparent fitness under
different conditions.
Conclusions
This study has shown the existence of strain-level diversity in the
Thaumarchaea Marine Group I, and distributions in time and depth that suggest at
least some of the strains have different niche characteristics (i.e. differences are not
neutral). Analysis of 16S rRNA genes and metagenomics recruitments indicates the
75

presence of microdiversity within genomic similarity levels commonly considered
to delineate species. The approaches used in this study also help demonstrate the
value of time-series studies, which allowed the detection of strain-level diversity
through observable changes in highly similar sequences over time. These results
suggest the potential of greater metabolic diversity in these globally important taxa
than previously observed and implies controls on MGI communities occur at the
sub-species level.

76

Literature Cited
Ahlgren, N. A., and G. Rocap. 2006. Culture Isolation and Culture-Independent Clone
Libraries Reveal New Marine Synechococcus Ecotypes with Distinctive Light
and N Physiologies. Appl. Environ. Microbiol. 72: 7193–7204.
Auguet, J.-C., X. Triadó-Margarit, N. Nomokonova, L. Camarero, and E. O. Casamayor.
2012. Vertical segregation and phylogenetic characterization of ammonia-
oxidizing Archaea in a deep oligotrophic lake. ISME J. 6: 1786–1797.
Aziz, R. K., D. Bartels, A. a Best, M. DeJongh, T. Disz, R. a Edwards, K. Formsma, S.
Gerdes, E. M. Glass, M. Kubal, F. Meyer, G. J. Olsen, R. Olson, A. L. Osterman, R. a
Overbeek, L. K. McNeil, D. Paarmann, T. Paczian, B. Parrello, G. D. Pusch, C.
Reich, R. Stevens, O. Vassieva, V. Vonstein, A. Wilke, and O. Zagnitko. 2008. The
RAST Server: rapid annotations using subsystems technology. BMC Genomics 9:
75.
Biller, S. J., P. M. Berube, D. Lindell, and S. W. Chisholm. 2015. Prochlorococcus: the
structure and function of collective diversity. Nat. Rev. Microbiol. 13: 13–27.
Boisvert, S., F. Raymond, E. Godzaridis, F. Laviolette, and J. Corbeil. 2012. Ray Meta:
scalable de novo metagenome assembly and profiling. Genome Biol. 13: R122.
Brettin, T., J. J. Davis, T. Disz, R. a. Edwards, S. Gerdes, G. J. Olsen, R. Olson, R.
Overbeek, B. Parrello, G. D. Pusch, M. Shukla, J. a. Thomason, R. Stevens, V.
Vonstein, A. R. Wattam, and F. Xia. 2015. RASTtk: A modular and extensible
implementation of the RAST algorithm for building custom annotation
pipelines and annotating batches of genomes. Sci. Rep. 5: 8365.
Caporaso, J. G., J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello,
N. Fierer, A. G. Peña, J. K. Goodrich, J. I. Gordon, G. A. Huttley, S. T. Kelley, D.
Knights, J. E. Koenig, R. E. Ley, C. A. Lozupone, D. McDonald, B. D. Muegge, M.
Pirrung, J. Reeder, J. R. Sevinsky, P. J. Turnbaugh, W. A. Walters, J. Widmann, T.
Yatsunenko, J. Zaneveld, and R. Knight. 2010. QIIME allows analysis of high-
throughput community sequencing data. Nat. Methods 7: 335–336.
Cohan, F. M. 2006. Towards a conceptual and operational union of bacterial
systematics, ecology, and evolution. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 361:
1985–1996.

77

Cram, J. A., C.-E. T. Chow, R. Sachdeva, D. M. Needham, A. E. Parada, J. A. Steele, and J.
A. Fuhrman. 2015. Seasonal and interannual variability of the marine
bacterioplankton community throughout the water column over ten years.
ISME J. 9: 563–580.
Dupont, C. L., D. B. Rusch, S. Yooseph, M.-J. Lombardo, R. A. Richter, R. Valas, M.
Novotny, J. Yee-Greenbaum, J. D. Selengut, D. H. Haft, A. L. Halpern, R. S. Lasken,
K. Nealson, R. Friedman, and J. C. Venter. 2012. Genomic insights to SAR86, an
abundant and uncultivated marine bacterial lineage. ISME J. 6: 1186–1199.
Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics 26: 2460–2461.
Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince, and R. Knight. 2011. UCHIME
improves sensitivity and speed of chimera detection. Bioinformatics 27: 2194–
2200.
Eren, A. M., O. C. Esen, C. Quince, J. H. Vineis, M. L. Sogin, and T. O. Delmont. 2015. An
advanced analysis and visualization platform for ’omics data. PeerJ Prepr. 3,
doi:https://dx.doi.org/10.7287/peerj.preprints.1275v1
Eren, A. M., L. Maignien, W. J. Sul, L. G. Murphy, S. L. Grim, H. G. Morrison, and M. L.
Sogin. 2013a. Oligotyping: Differentiating between closely related microbial
taxa using 16S rRNA gene data. Methods Ecol. Evol. 4: 1111–1119.
Eren, A. M., H. G. Morrison, P. J. Lescault, J. Reveillaud, J. H. Vineis, and M. L. Sogin.
2014. Minimum entropy decomposition: Unsupervised oligotyping for sensitive
partitioning of high-throughput marker gene sequences. ISME J. 9: 968–979.
Eren, A. M., J. H. Vineis, H. G. Morrison, and M. L. Sogin. 2013b. A Filtering Method to
Generate High Quality Short Reads Using Illumina Paired-End Technology. PLoS
One 8: 6–11.
Fox, G. E., J. D. Wisotzkey, and P. Jurtshuk Jr. 1992. How close is close: 16S rRNA
sequence identity may not be sufficient to guarantee species identity. Int. J. Syst.
Bacteriol. 42: 166–170.
Fuhrman, J. A., D. E. C. Comeau, Å. Hagström, and A. M. Chan. 1988. Extraction from
natural planktonic microorganisms of DNA suitable for molecular biological
studies. Appl. Environ. Microbiol. 54: 1426–1429.
Goris, J., K. T. Konstantinidis, J. a. Klappenbach, T. Coenye, P. Vandamme, and J. M.
Tiedje. 2007. DNA-DNA hybridization values and their relationship to whole-
genome sequence similarities. Int. J. Syst. Evol. Microbiol. 57: 81–91.
78

Hugoni, M., N. Taib, D. Debroas, I. Domaizon, I. J. Dufournel, G. Bronner, I. Salter, H.
Agogué, I. Mary, and P. E. Galand. 2013. Structure of the rare archaeal biosphere
and seasonal dynamics of active ecotypes in surface coastal waters. PNAS 110:
6004–6009.
Kashtan, N., S. E. Roggensack, S. Rodrigue, J. W. Thompson, S. J. Biller, A. Coe, H. Ding,
P. Marttinen, R. R. Malmstrom, R. Stocker, M. J. Follows, R. Stepanauskas, and S.
W. Chisholm. 2014. Single-Cell Genomics Reveals Hundreds of Coexisting
Subpopulations in Wild Prochlorococcus. Science (80-. ). 344: 416–420.
Koeppel, A., E. B. Perry, J. Sikorski, D. Krizanc, A. Warner, D. M. Ward, A. P. Rooney, E.
Brambilla, N. Connor, R. M. Ratcliff, E. Nevo, and F. M. Cohan. 2008. Identifying
the fundamental units of bacterial diversity : A paradigm shift to incorporate
ecology into bacterial systematics. Proc. Natl. Acad. Sci. 7: 2504–2509.
Könneke, M., A. E. Bernhard, J. R. de la Torré, C. B. Walker, J. B. Waterbury, and D. A.
Stahl. 2005. Isolation of an autotrophic ammonia-oxidizing marine archaeon.
Nature 437: 543–546.
Kozich, J. J., S. L. Westcott, N. T. Baxter, S. K. Highlander, and P. D. Schloss. 2013.
Development of a dual-index sequencing strategy and curation pipeline for
analyzing amplicon sequence data on the miseq illumina sequencing platform.
Appl. Environ. Microbiol. 79: 5112–5120.
Lund, M. B., J. M. Smith, and C. A. Francis. 2012. Diversity, abundance and expression
of nitrite reductase (nirK)-like genes in marine thaumarchaea. ISME J. 6: 1966–
1977.
Minoche, A. E., J. C. Dohm, and H. Himmelbauer. 2011. Evaluation of genomic high-
throughput sequencing data generated on Illumina HiSeq and genome analyzer
systems. Genome Biol. 12: R112.
Molina, V., L. Belmar, and O. Ulloa. 2010. High diversity of ammonia-oxidizing
archaea in permanent and seasonal oxygen-deficient waters of the eastern
South Pacific. Environ. Microbiol. 12: 2450–2465.
Moore, L. R., G. Rocap, and S. W. Chisholm. 1998. Physiology and molecular
phylogeny of coexisting Prochlorococcus ecotypes. Nature 393: 464–467.
Overbeek, R., R. Olson, G. D. Pusch, G. J. Olsen, J. J. Davis, T. Disz, R. a. Edwards, S.
Gerdes, B. Parrello, M. Shukla, V. Vonstein, A. R. Wattam, F. Xia, and R. Stevens.
2014. The SEED and the Rapid Annotation of microbial genomes using
Subsystems Technology (RAST). Nucleic Acids Res. 42: 206–214.
79

Parada, A., D. M. Needham, and J. A. Fuhrman. 2015. Every base matters: assessing
small subunit rRNA primers for marine microbiomes with mock communities,
time-series and global field samples. Environ. Microbiol. , doi:10.1111/1462-
2920.13023
Pester, M., C. Schleper, and M. Wagner. 2011. The Thaumarchaeota: An emerging
view of their phylogeny and ecophysiology. Curr. Opin. Microbiol. 14: 300–306.
Qin, W., S. A. Amin, W. Martens-Habbena, C. B. Walker, H. Urakawa, A. H. Devol, A. E.
Ingalls, J. W. Moffett, E. V. Armbrust, and D. A. Stahl. 2014. Marine ammonia-
oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic
variation. Proc. Natl. Acad. Sci. 111: 12504–12509.
Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O.
Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved
data processing and web-based tools. Nucleic Acids Res. 41: D590–D596.
Rocap, G., F. W. Larimer, J. Lamerdin, S. Malfatti, P. S. G. Chain, N. A. Ahlgren, A.
Arellano, M. Coleman, L. Hauser, W. R. Hess, Z. I. Johnson, M. Land, D. Lindell, A.
F. Post, W. Regala, M. Shah, S. L. Shaw, C. Steglich, M. B. Sullivan, C. S. Ting, A.
Tolonen, E. A. Webb, E. R. Zinser, and S. W. Chisholm. 2003. Genome divergence
in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature
424: 1042–1047.
Santoro, A. E., K. L. Casciotti, and C. A. Francis. 2010. Activity, abundance and
diversity of nitrifying archaea and bacteria in the central California Current.
Environ. Microbiol. 12: 1989–2006.
Santoro, A. E., C. L. Dupont, R. A. Richter, M. T. Craig, P. Carini, M. R. McIlvin, Y. Yang,
W. D. Orsi, D. M. Moran, and M. A. Saito. 2015. Genomic and proteomic
characterization of “ Candidatus Nitrosopelagicus brevis”: An ammonia-
oxidizing archaeon from the open ocean. Proc. Natl. Acad. Sci. 112: 1173–1178.
Scanlan, D. J., and N. J. West. 2002. Molecular ecology of the marine cyanobacterial
genera Prochlorococcus and Synechococcus. FEMS Microbiol. Ecol. 40: 1–12.
Schloss, P. D., S. L. Westcott, T. Ryabin, J. R. Hall, M. Hartmann, E. B. Hollister, R. a
Lesniewski, B. B. Oakley, D. H. Parks, C. J. Robinson, J. W. Sahl, B. Stres, G. G.
Thallinger, D. J. Van Horn, and C. F. Weber. 2009. Introducing mothur: open-
source, platform-independent, community-supported software for describing
and comparing microbial communities. Appl. Environ. Microbiol. 75: 7537–
7541.
80

Stackebrandt, E., and B. M. Goebel. 1994. Taxonomic Note: A Place for DNA-DNA
Reassociation and 16S rRNA Sequence Analysis in the Present Species
Definition in Bacteriology. Int. J. Syst. Bacteriol. 44: 846–849.
Swan, B. K., M. D. Chaffin, M. Martinez-Garcia, H. G. Morrison, E. K. Field, N. J. Poulton,
E. D. P. Masland, C. C. Harris, A. Sczyrba, P. S. G. Chain, S. Koren, T. Woyke, and R.
Stepanauskas. 2014. Genomic and metabolic diversity of marine group i
thaumarchaeota in the mesopelagic of two subtropical gyres. PLoS One 9:
e95380.
Tully, B. J., W. C. Nelson, and J. F. Heidelberg. 2012. Metagenomic analysis of a
complex marine planktonic thaumarchaeal community from the Gulf of Maine.
Environ. Microbiol. 14: 254–267.
Wang, Q., G. M. Garrity, J. M. Tiedje, and J. R. Cole. 2007. Naive Bayesian classifier for
rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl.
Environ. Microbiol. 73: 5261–5267.

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Full Name
Abbreviated Name
(References)
NCBI Accession
numbers or IMG
taxa_ID 7/9/2012 8/15/2012 9/28/2012 10/17/2012 11/28/2012 12/11/2012 1/16/2013 2/13/2013 3/13/2013 4/24/2013 5/22/2013 6/19/2013
Candidatus Nitrosopumilus sp. BD31 (assembly_A1_100119) A1-100119 2502545036 0.008 0.004 0.010 0.010 0.010 0.009 0.007 0.005 0.007 0.002 0.007 0.005
Marine Group I thaumarchaeote sp. SCGC AAA007-C21 SAG AAA007-C21 2524023106 0.038 0.022 0.015 0.009 0.032 0.038 0.049 0.035 0.057 0.013 0.058 0.069
Marine Group I thaumarchaeote sp. SCGC AAA007-E02 SAG AAA007-E02 2524023111 0.120 0.073 0.040 0.020 0.104 0.138 0.131 0.099 0.158 0.042 0.183 0.221
Marine Group I thaumarchaeote sp. SCGC AAA007-E15 SAG AAA007-E15 2524023107 0.106 0.060 0.037 0.019 0.092 0.114 0.134 0.095 0.151 0.039 0.166 0.194
Marine Group I thaumarchaeote sp. SCGC AAA007-G17 SAG AAA007-G17 2524023108 0.055 0.026 0.017 0.010 0.044 0.053 0.078 0.052 0.083 0.021 0.091 0.102
Marine Group I thaumarchaeote sp. SCGC AAA007-M20 SAG AAA007-M20 2524023109 0.042 0.020 0.014 0.009 0.034 0.040 0.050 0.036 0.056 0.014 0.060 0.069
Marine Group I thaumarchaeote sp. SCGC AAA007-N23 SAG AAA007-N23 2524023110 0.166 0.069 0.052 0.026 0.137 0.172 0.199 0.142 0.228 0.060 0.251 0.300
Thaumarchaeota archaeon SCGC AAA007-O23 (TG_002_783) SAG AAA007-O23 2527291500 0.310 0.166 0.103 0.054 0.267 0.338 0.365 0.270 0.428 0.112 0.477 0.574
Marine Group I thaumarchaeote sp. SCGC AAA008-E15 SAG AAA008-E15 2524023112 0.266 0.127 0.088 0.044 0.228 0.289 0.326 0.239 0.379 0.099 0.414 0.498
Marine Group I thaumarchaeote sp. SCGC AAA008-E17 SAG AAA008-E17 2524023113 0.057 0.024 0.019 0.011 0.046 0.051 0.083 0.058 0.093 0.023 0.095 0.107
Marine Group I thaumarchaeote sp. SCGC AAA008-G03 SAG AAA008-G03 2524023114 0.045 0.024 0.017 0.011 0.040 0.048 0.056 0.040 0.062 0.015 0.071 0.077
Marine Group I thaumarchaeote sp. SCGC AAA008-M21 SAG AAA008-M21 2524023115 0.124 0.051 0.038 0.019 0.093 0.106 0.175 0.128 0.204 0.043 0.224 0.229
Marine Group I thaumarchaeote sp. SCGC AAA008-M23 SAG AAA008-M23 2524023116 0.257 0.133 0.080 0.041 0.214 0.284 0.287 0.220 0.348 0.091 0.395 0.480
Marine Group I thaumarchaeote sp. SCGC AAA008-N07 SAG AAA008-N07 2524023117 0.142 0.085 0.046 0.022 0.122 0.153 0.180 0.128 0.208 0.055 0.223 0.266
Marine Group I thaumarchaeote sp. SCGC AAA008-O05 SAG AAA008-O05 2524023118 0.110 0.065 0.036 0.019 0.095 0.119 0.132 0.095 0.154 0.041 0.172 0.203
Marine Group I thaumarchaeote sp. SCGC AAA008-O18 SAG AAA008-O18 2524023119 0.105 0.061 0.038 0.019 0.091 0.115 0.133 0.095 0.149 0.038 0.163 0.195
Marine Group I thaumarchaeote sp. SCGC AAA008-P02 SAG AAA008-P02 2524023120 0.098 0.053 0.033 0.017 0.084 0.108 0.117 0.086 0.136 0.036 0.153 0.182
Marine Group I thaumarchaeote sp. SCGC AAA008-P23 SAG AAA008-P23 2524023121 0.201 0.106 0.073 0.039 0.177 0.221 0.235 0.176 0.274 0.066 0.307 0.329
Marine Group I thaumarchaeote SCGC AAA160-J20 SAG AAA160-J20 2529292698 0.159 0.090 0.235 0.240 0.214 0.180 0.129 0.079 0.122 0.043 0.126 0.067
Thaumarchaeota archaeon SCGC AAA282-K18 SAG AAA282-K18 2576861416 0.026 0.014 0.030 0.030 0.031 0.030 0.022 0.014 0.022 0.007 0.024 0.016
Marine Group I thaumarchaeote sp. SCGC AAA288-C17 SAG AAA288-C17 2524023122 0.277 0.152 0.107 0.058 0.251 0.314 0.306 0.232 0.359 0.087 0.421 0.459
Marine Group I thaumarchaeote sp. SCGC AAA288-D03 SAG AAA288-D03 2524023123 0.054 0.026 0.022 0.014 0.048 0.056 0.069 0.050 0.074 0.019 0.085 0.094
Marine Group I thaumarchaeote sp. SCGC AAA288-D22 SAG AAA288-D22 2524023124 0.137 0.081 0.047 0.025 0.119 0.146 0.168 0.121 0.194 0.051 0.210 0.250
Marine Group I thaumarchaeote sp. SCGC AAA288-E09 SAG AAA288-E09 2524023125 0.066 0.033 0.025 0.015 0.059 0.069 0.084 0.061 0.094 0.023 0.106 0.117
Marine Group I thaumarchaeote sp. SCGC AAA288-G05 SAG AAA288-G05 2524023126 0.101 0.044 0.037 0.022 0.088 0.104 0.122 0.091 0.141 0.035 0.155 0.175
Marine Group I thaumarchaeote sp. SCGC AAA288-K02 SAG AAA288-K02 2524023127 0.193 0.085 0.053 0.025 0.137 0.154 0.262 0.191 0.307 0.066 0.336 0.342
Marine Group I thaumarchaeote sp. SCGC AAA288-K05 SAG AAA288-K05 2524023128 0.054 0.024 0.018 0.010 0.045 0.051 0.076 0.049 0.084 0.021 0.089 0.099
Marine Group I thaumarchaeote sp. SCGC AAA288-K20 SAG AAA288-K20 2524023097 0.038 0.019 0.012 0.007 0.031 0.036 0.053 0.035 0.057 0.014 0.060 0.068
Marine Group I thaumarchaeote sp. SCGC AAA288-M04 SAG AAA288-M04 2524023098 0.035 0.019 0.013 0.009 0.032 0.037 0.044 0.032 0.050 0.013 0.055 0.063
Marine Group I thaumarchaeote sp. SCGC AAA288-M23 SAG AAA288-M23 2524023099 0.060 0.032 0.023 0.014 0.055 0.065 0.077 0.056 0.085 0.021 0.095 0.107
Marine Group I thaumarchaeote sp. SCGC AAA288-N15 SAG AAA288-N15 2524023100 0.061 0.033 0.020 0.012 0.052 0.064 0.076 0.055 0.086 0.022 0.093 0.114
Marine Group I thaumarchaeote sp. SCGC AAA288-N23 SAG AAA288-N23 2524023101 0.113 0.058 0.042 0.024 0.097 0.120 0.131 0.099 0.149 0.037 0.175 0.195
Marine Group I thaumarchaeote sp. SCGC AAA288-O17 SAG AAA288-O17 2524023102 0.017 0.011 0.008 0.005 0.022 0.022 0.041 0.028 0.047 0.010 0.045 0.047
Marine Group I thaumarchaeote sp. SCGC AAA288-O22 SAG AAA288-O22 2524023103 0.230 0.121 0.085 0.046 0.207 0.252 0.264 0.199 0.314 0.075 0.351 0.378
Marine Group I thaumarchaeote sp. SCGC AAA288-P02 SAG AAA288-P02 2524023104 0.211 0.092 0.064 0.033 0.155 0.175 0.293 0.210 0.338 0.073 0.368 0.373
Marine Group I thaumarchaeote sp. SCGC AAA288-P18 SAG AAA288-P18 2524023105 0.264 0.146 0.099 0.054 0.240 0.297 0.302 0.223 0.355 0.086 0.400 0.435
Marine Group I thaumarchaeote SCGC AAA799-E16 SAG AAA799-E16 2585428035 0.005 0.002 0.006 0.006 0.006 0.005 0.004 0.003 0.004 0.001 0.004 0.003
Marine Group I thaumarchaeote SCGC AAA799-N04 SAG AAA799-N04 2585428036 0.015 0.007 0.019 0.019 0.018 0.017 0.013 0.008 0.012 0.004 0.013 0.008
Thaumarchaeota archaeon SCGC AB-179-E04 (GBS-N_001_221) SAG AB-179-E04 2264867248 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Thaumarcheon SCGC AB-539-E09 SAG AB-539-E09 2517572171 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Marine Group I crenarchaeon SCGC AB-629-A13 SAG AB-629-A13 2264867040 0.089 0.051 0.039 0.027 0.080 0.089 0.117 0.079 0.125 0.032 0.133 0.144
Table S1 All genomes and genomic bins used for competitive fragment recruitment references and the percent of metagenomic reads recruiting to those references in each sample at 150m a)>70 percent identitiy (%ID), b) 95-97
%ID, c)97-99%ID, and d)>99%ID.
a) Competitive Fragment Recruitment >70 %ID
87
Marine Group I crenarchaeon SCGC AB-629-I23 SAG AB-629-I23 2264867027 0.276 0.167 0.111 0.067 0.242 0.297 0.336 0.245 0.378 0.099 0.412 0.490
Thaumarchaeota archaeon SCGC AB-661-I02 SAG AB-661-I02 2524023096 0.224 0.140 0.322 0.324 0.293 0.258 0.178 0.110 0.171 0.057 0.178 0.093
Thaumarchaeota archaeon SCGC AB-661-L21 SAG AB-661-L21 2576861411 0.065 0.042 0.093 0.094 0.087 0.078 0.053 0.032 0.051 0.016 0.053 0.030
Thaumarchaeota archaeon SCGC AB-661-M19 SAG AB-661-M19 2576861412 0.293 0.179 0.428 0.430 0.388 0.344 0.235 0.147 0.227 0.075 0.236 0.126
Thaumarchaeota archaeon SCGC AB-663-F14 SAG AB-663-F14 2576861413 0.237 0.135 0.087 0.046 0.208 0.263 0.268 0.199 0.314 0.077 0.359 0.391
Thaumarchaeota archaeon SCGC AB-663-G14 SAG AB-663-G14 2576861414 0.153 0.081 0.056 0.031 0.136 0.169 0.181 0.133 0.210 0.052 0.234 0.267
Thaumarchaeota archaeon SCGC AB-663-N18 SAG AB-663-N18 2576861415 0.135 0.074 0.050 0.028 0.117 0.146 0.149 0.110 0.178 0.043 0.200 0.218
Thaumarchaeota archaeon SCGC AB-663-O07 SAG AB-663-O07 2524023092 0.254 0.140 0.118 0.078 0.250 0.276 0.309 0.197 0.374 0.082 0.347 0.369
Thaumarchaeota archaeon SCGC AB-663-P07 SAG AB-663-P07 2524023094 0.113 0.069 0.040 0.021 0.101 0.126 0.138 0.101 0.159 0.041 0.177 0.207
Candidatus Nitrosopumilus salaria BD31 (Nitrosopumilus salaria BD31) BD31 AEXL02 0.049 0.049 0.049 0.056 0.047 0.045 0.041 0.040 0.041 0.043 0.043 0.036
Candidatus Nitrosoarchaeum limnia BG20 BG20 NZ_CM001158.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Thaumarchaeota archaeon BS1 BS1 2502957026 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Thaumarchaeota archaeon BS4 BS4 2519899514 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Candidatus Caldiarchaeum subterraneum Caldiarchaeum-subterraneum NC_022786.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Candidatus Nitrosopumilus sp. D3C D3C CP010868.1 0.012 0.006 0.016 0.016 0.015 0.014 0.011 0.007 0.011 0.003 0.011 0.008
Thaumarchaeota archaeon DS1 (Dragon_Thaumarchaeon) DS1 2263082000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrososphaera viennensis EN76 EN76 CP007536.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Candidatus Nitrososphaera evergladensis SR1 evergladensis CP007174.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Candidatus Nitrososphaera gargensis Ga9-2 Ga9-2 NC_018719.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Group_10_depth 0.001 0.002 0.003 0.003 0.002 0.002 0.002 0.014 0.002 0.098 0.001 0.001
Group_101 0.086 0.068 0.105 0.105 0.094 0.090 0.078 0.044 0.060 0.022 0.045 0.042
Group_13_c 0.059 0.055 0.072 0.052 0.103 0.118 0.096 0.082 0.081 0.029 0.088 0.113
Group_13_depth 0.129 0.121 0.188 0.192 0.212 0.184 0.115 0.060 0.127 0.027 0.100 0.080
Group_2_depth 0.028 0.021 0.035 0.024 0.044 0.047 0.028 0.020 0.025 0.016 0.024 0.039
Group_7_depth 0.147 0.081 0.091 0.064 0.119 0.139 0.128 0.079 0.135 0.036 0.148 0.100
Group_75_b 0.024 0.040 0.080 0.079 0.088 0.107 0.029 0.024 0.025 0.011 0.024 0.025
Group_82_d 0.082 0.055 0.068 0.062 0.106 0.102 0.089 0.049 0.107 0.024 0.088 0.098
Group_9_c_depth 0.003 0.002 0.006 0.013 0.004 0.003 0.003 0.018 0.002 0.015 0.002 0.002
Group_9_f_depth 0.008 0.010 0.069 0.084 0.028 0.014 0.010 0.093 0.006 0.111 0.004 0.003
Group_90_d 0.168 0.123 0.189 0.180 0.220 0.213 0.159 0.096 0.172 0.035 0.176 0.134
Group_90_h 0.124 0.094 0.160 0.164 0.178 0.163 0.120 0.069 0.129 0.027 0.122 0.097
Group_90_i 0.432 0.261 0.741 0.793 0.641 0.508 0.386 0.220 0.352 0.124 0.335 0.185
Group_90_l 1.116 0.921 1.593 1.680 1.849 1.586 1.011 0.521 1.086 0.243 0.874 0.698
Candidatus Nitrosopumilus koreensis AR1 koreensis-AR1 NC_018655.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Candidatus Nitrosoarchaeum koreensis MY1 koreensis-MY1 AFPU01 0.009 0.004 0.012 0.012 0.011 0.011 0.008 0.006 0.008 0.003 0.009 0.006
Thaumarchaeota archaeon MY2 MY2 AVSQ01 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.000 0.001 0.001
Candidatus Nitrosopelagicus brevis N.brevis CP007026.1 0.087 0.080 0.170 0.220 0.157 0.130 0.085 0.048 0.087 0.022 0.070 0.054
Candidatus Nitrosopumilus sp. NF5 NF5 CP011070.1 0.017 0.009 0.022 0.022 0.021 0.020 0.015 0.010 0.015 0.005 0.016 0.010
Candidatus Nitrosopumilus sp. AR2 Nitrosopumilus-AR2 NC_018656.1 0.021 0.010 0.027 0.027 0.027 0.024 0.018 0.011 0.018 0.006 0.019 0.012
SPOT Enrichment SPOT Enrichment 0.346 0.175 0.622 0.732 0.508 0.352 0.316 0.196 0.264 0.116 0.254 0.117
Aigarchaeota archaeon OS1 OS1 2545555825 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrosopumilus maritimus SCM1 SCM1 NC_010085.1 0.014 0.007 0.018 0.018 0.018 0.016 0.012 0.007 0.012 0.004 0.012 0.008
Candidatus Nitrosoarchaeum limnia SFB1 SFB1 NZ_CM001158.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrosopumilus sp. SJ SJ AJVI01 0.014 0.007 0.018 0.018 0.018 0.016 0.012 0.008 0.012 0.004 0.013 0.008
Cenarchaeum symbiosum A symbiosum-A NC_014820.1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
88
References 7/9/2012 8/15/2012 9/28/2012 10/17/2012 11/28/2012 12/11/2012 1/16/2013 2/13/2013 3/13/2013 4/24/2013 5/22/2013 6/19/2013
A1-100119 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA007-C21 0.006 0.003 0.002 0.001 0.005 0.006 0.008 0.006 0.009 0.002 0.009 0.011
SAG AAA007-E02 0.016 0.011 0.005 0.003 0.013 0.017 0.018 0.013 0.020 0.005 0.023 0.029
SAG AAA007-E15 0.019 0.011 0.006 0.003 0.016 0.019 0.024 0.017 0.026 0.006 0.028 0.033
SAG AAA007-G17 0.008 0.003 0.002 0.001 0.006 0.007 0.012 0.008 0.012 0.003 0.014 0.015
SAG AAA007-M20 0.007 0.003 0.002 0.001 0.005 0.007 0.008 0.006 0.009 0.002 0.009 0.011
SAG AAA007-N23 0.021 0.012 0.007 0.003 0.018 0.022 0.025 0.018 0.028 0.007 0.032 0.037
SAG AAA007-O23 0.058 0.035 0.019 0.009 0.050 0.063 0.069 0.051 0.079 0.021 0.088 0.106
SAG AAA008-E15 0.039 0.020 0.013 0.006 0.033 0.041 0.047 0.033 0.053 0.014 0.057 0.068
SAG AAA008-E17 0.012 0.004 0.003 0.002 0.008 0.009 0.018 0.012 0.020 0.005 0.019 0.022
SAG AAA008-G03 0.004 0.001 0.001 0.001 0.004 0.004 0.006 0.004 0.006 0.002 0.007 0.008
SAG AAA008-M21 0.024 0.010 0.007 0.003 0.018 0.021 0.034 0.025 0.041 0.008 0.044 0.044
SAG AAA008-M23 0.041 0.023 0.013 0.007 0.034 0.044 0.048 0.034 0.055 0.014 0.061 0.071
SAG AAA008-N07 0.025 0.017 0.008 0.004 0.022 0.027 0.032 0.022 0.036 0.010 0.037 0.046
SAG AAA008-O05 0.021 0.014 0.007 0.003 0.018 0.023 0.026 0.019 0.030 0.008 0.033 0.039
SAG AAA008-O18 0.015 0.011 0.005 0.002 0.013 0.017 0.019 0.013 0.021 0.005 0.023 0.026
SAG AAA008-P02 0.018 0.011 0.006 0.003 0.015 0.019 0.022 0.016 0.025 0.007 0.028 0.032
SAG AAA008-P23 0.050 0.028 0.018 0.009 0.043 0.054 0.057 0.043 0.065 0.016 0.073 0.080
SAG AAA160-J20 0.014 0.004 0.024 0.025 0.021 0.017 0.011 0.007 0.010 0.004 0.011 0.006
SAG AAA282-K18 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.001 0.000
SAG AAA288-C17 0.060 0.035 0.022 0.011 0.053 0.066 0.066 0.050 0.077 0.018 0.088 0.096
SAG AAA288-D03 0.008 0.004 0.002 0.001 0.007 0.008 0.010 0.007 0.010 0.003 0.011 0.013
SAG AAA288-D22 0.018 0.012 0.006 0.003 0.016 0.019 0.023 0.016 0.026 0.007 0.027 0.033
SAG AAA288-E09 0.005 0.002 0.002 0.001 0.005 0.006 0.008 0.005 0.009 0.002 0.009 0.010
SAG AAA288-G05 0.015 0.005 0.005 0.002 0.012 0.014 0.018 0.013 0.020 0.005 0.022 0.026
SAG AAA288-K02 0.044 0.020 0.011 0.005 0.030 0.033 0.059 0.043 0.069 0.015 0.076 0.075
SAG AAA288-K05 0.007 0.002 0.002 0.001 0.006 0.007 0.011 0.007 0.012 0.003 0.013 0.015
SAG AAA288-K20 0.005 0.002 0.001 0.001 0.004 0.005 0.008 0.005 0.008 0.002 0.008 0.010
SAG AAA288-M04 0.003 0.001 0.001 0.001 0.003 0.004 0.005 0.004 0.005 0.001 0.005 0.007
SAG AAA288-M23 0.007 0.003 0.002 0.001 0.006 0.007 0.009 0.007 0.010 0.003 0.011 0.012
SAG AAA288-N15 0.010 0.006 0.003 0.002 0.009 0.010 0.012 0.008 0.014 0.004 0.015 0.017
SAG AAA288-N23 0.021 0.011 0.007 0.003 0.018 0.021 0.025 0.018 0.028 0.007 0.033 0.037
SAG AAA288-O17 0.004 0.001 0.001 0.001 0.003 0.004 0.009 0.006 0.010 0.002 0.009 0.012
SAG AAA288-O22 0.057 0.031 0.020 0.011 0.050 0.063 0.066 0.048 0.076 0.018 0.086 0.090
SAG AAA288-P02 0.050 0.023 0.014 0.007 0.036 0.039 0.069 0.051 0.081 0.017 0.088 0.089
SAG AAA288-P18 0.059 0.035 0.021 0.011 0.053 0.066 0.067 0.049 0.078 0.019 0.087 0.095
SAG AAA799-E16 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA799-N04 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-179-E04 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-539-E09 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-629-A13 0.014 0.008 0.006 0.004 0.012 0.014 0.020 0.013 0.020 0.005 0.021 0.023
b) Competitive Fragment Recruitment 95<%ID<97
89
SAG AB-629-I23 0.046 0.029 0.017 0.010 0.039 0.048 0.055 0.040 0.061 0.016 0.067 0.079
SAG AB-661-I02 0.028 0.010 0.040 0.040 0.037 0.034 0.021 0.013 0.020 0.007 0.022 0.011
SAG AB-661-L21 0.006 0.002 0.008 0.008 0.008 0.007 0.004 0.003 0.004 0.002 0.005 0.003
SAG AB-661-M19 0.037 0.014 0.054 0.053 0.049 0.046 0.028 0.018 0.028 0.008 0.029 0.016
SAG AB-663-F14 0.061 0.038 0.021 0.011 0.054 0.068 0.068 0.051 0.081 0.020 0.091 0.101
SAG AB-663-G14 0.032 0.018 0.011 0.006 0.027 0.035 0.037 0.028 0.042 0.011 0.048 0.054
SAG AB-663-N18 0.035 0.020 0.012 0.007 0.031 0.038 0.038 0.029 0.046 0.011 0.053 0.056
SAG AB-663-O07 0.075 0.042 0.033 0.021 0.072 0.078 0.091 0.057 0.111 0.024 0.099 0.106
SAG AB-663-P07 0.021 0.014 0.007 0.004 0.018 0.023 0.025 0.019 0.029 0.007 0.032 0.038
BD31 0.002 0.001 0.002 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002
BG20 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Caldiarchaeum-subterraneum 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
D3C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
DS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
EN76 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
evergladensis 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Ga9-2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Group_10_depth 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.000 0.000
Group_101 0.009 0.006 0.009 0.009 0.009 0.008 0.010 0.005 0.007 0.002 0.005 0.005
Group_13_c 0.007 0.005 0.008 0.005 0.011 0.012 0.011 0.009 0.009 0.003 0.010 0.012
Group_13_depth 0.010 0.009 0.016 0.017 0.017 0.015 0.008 0.004 0.008 0.002 0.007 0.006
Group_2_depth 0.002 0.001 0.002 0.002 0.003 0.003 0.002 0.001 0.002 0.001 0.002 0.003
Group_7_depth 0.022 0.008 0.006 0.003 0.014 0.018 0.021 0.012 0.022 0.005 0.022 0.016
Group_75_b 0.001 0.001 0.004 0.004 0.004 0.004 0.001 0.001 0.001 0.000 0.001 0.001
Group_82_d 0.004 0.002 0.003 0.003 0.005 0.005 0.004 0.002 0.005 0.001 0.004 0.004
Group_9_c_depth 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.001 0.000 0.001 0.000 0.000
Group_9_f_depth 0.001 0.001 0.008 0.010 0.004 0.001 0.001 0.004 0.001 0.005 0.000 0.000
Group_90_d 0.020 0.016 0.021 0.019 0.024 0.028 0.016 0.012 0.019 0.004 0.023 0.019
Group_90_h 0.013 0.010 0.018 0.018 0.019 0.019 0.011 0.008 0.013 0.003 0.015 0.012
Group_90_i 0.069 0.032 0.116 0.127 0.099 0.078 0.059 0.034 0.053 0.019 0.052 0.027
Group_90_l 0.087 0.074 0.146 0.162 0.154 0.138 0.074 0.041 0.079 0.018 0.068 0.053
koreensis-AR1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
koreensis-MY1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
MY2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
N.brevis 0.005 0.003 0.013 0.019 0.011 0.008 0.006 0.003 0.005 0.001 0.004 0.003
NF5 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrosopumilus-AR2 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000
SPOT Enrichment 0.043 0.015 0.088 0.114 0.068 0.042 0.041 0.027 0.032 0.016 0.030 0.012
OS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SCM1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SFB1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SJ 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
symbiosum-A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
90
References 7/9/2012 8/15/2012 9/28/2012 10/17/2012 11/28/2012 12/11/2012 1/16/2013 2/13/2013 3/13/2013 4/24/2013 5/22/2013 6/19/2013
A1-100119 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA007-C21 0.008 0.004 0.002 0.001 0.006 0.008 0.011 0.008 0.012 0.003 0.012 0.015
SAG AAA007-E02 0.042 0.029 0.013 0.006 0.036 0.047 0.046 0.035 0.055 0.015 0.064 0.078
SAG AAA007-E15 0.039 0.023 0.012 0.006 0.034 0.042 0.049 0.034 0.056 0.014 0.061 0.073
SAG AAA007-G17 0.005 0.002 0.001 0.000 0.003 0.004 0.008 0.005 0.008 0.002 0.009 0.010
SAG AAA007-M20 0.009 0.004 0.002 0.001 0.006 0.009 0.011 0.008 0.012 0.003 0.013 0.016
SAG AAA007-N23 0.065 0.029 0.018 0.008 0.054 0.067 0.078 0.055 0.093 0.024 0.100 0.118
SAG AAA007-O23 0.111 0.061 0.034 0.016 0.093 0.121 0.129 0.096 0.153 0.040 0.171 0.210
SAG AAA008-E15 0.098 0.052 0.031 0.014 0.083 0.106 0.121 0.088 0.139 0.037 0.152 0.187
SAG AAA008-E17 0.011 0.003 0.002 0.001 0.007 0.008 0.018 0.012 0.019 0.005 0.019 0.023
SAG AAA008-G03 0.002 0.001 0.001 0.000 0.002 0.003 0.004 0.003 0.004 0.001 0.004 0.006
SAG AAA008-M21 0.036 0.015 0.008 0.003 0.024 0.028 0.050 0.038 0.058 0.013 0.066 0.067
SAG AAA008-M23 0.090 0.047 0.026 0.012 0.073 0.099 0.098 0.076 0.120 0.031 0.134 0.163
SAG AAA008-N07 0.051 0.031 0.016 0.006 0.042 0.054 0.064 0.045 0.074 0.020 0.078 0.096
SAG AAA008-O05 0.036 0.022 0.012 0.006 0.030 0.040 0.045 0.032 0.051 0.014 0.058 0.069
SAG AAA008-O18 0.037 0.023 0.013 0.006 0.032 0.040 0.050 0.035 0.054 0.014 0.058 0.071
SAG AAA008-P02 0.034 0.019 0.010 0.005 0.028 0.037 0.040 0.029 0.047 0.012 0.052 0.064
SAG AAA008-P23 0.065 0.033 0.022 0.011 0.057 0.073 0.076 0.057 0.091 0.022 0.102 0.111
SAG AAA160-J20 0.006 0.002 0.010 0.011 0.009 0.007 0.005 0.003 0.004 0.002 0.004 0.002
SAG AAA282-K18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA288-C17 0.098 0.055 0.036 0.018 0.088 0.113 0.107 0.082 0.126 0.031 0.148 0.164
SAG AAA288-D03 0.009 0.004 0.003 0.001 0.007 0.009 0.013 0.009 0.013 0.004 0.015 0.018
SAG AAA288-D22 0.048 0.031 0.015 0.007 0.041 0.051 0.059 0.042 0.067 0.018 0.073 0.088
SAG AAA288-E09 0.003 0.001 0.001 0.001 0.003 0.003 0.005 0.003 0.005 0.001 0.005 0.007
SAG AAA288-G05 0.019 0.007 0.006 0.003 0.015 0.019 0.024 0.018 0.027 0.007 0.029 0.034
SAG AAA288-K02 0.063 0.027 0.014 0.005 0.041 0.049 0.085 0.064 0.102 0.022 0.113 0.118
SAG AAA288-K05 0.003 0.001 0.001 0.000 0.002 0.002 0.006 0.004 0.006 0.002 0.006 0.007
SAG AAA288-K20 0.003 0.001 0.001 0.000 0.003 0.003 0.005 0.003 0.005 0.001 0.005 0.006
SAG AAA288-M04 0.002 0.001 0.001 0.000 0.002 0.002 0.003 0.003 0.004 0.001 0.004 0.005
SAG AAA288-M23 0.009 0.004 0.002 0.001 0.007 0.009 0.011 0.008 0.012 0.003 0.013 0.017
SAG AAA288-N15 0.022 0.013 0.006 0.003 0.018 0.022 0.028 0.020 0.030 0.008 0.032 0.041
SAG AAA288-N23 0.027 0.012 0.008 0.004 0.022 0.027 0.031 0.024 0.037 0.009 0.043 0.049
SAG AAA288-O17 0.003 0.001 0.001 0.001 0.004 0.003 0.008 0.005 0.011 0.002 0.007 0.008
SAG AAA288-O22 0.070 0.033 0.024 0.011 0.062 0.076 0.081 0.062 0.096 0.024 0.109 0.118
SAG AAA288-P02 0.061 0.024 0.015 0.006 0.042 0.049 0.085 0.062 0.100 0.022 0.111 0.113
SAG AAA288-P18 0.090 0.051 0.033 0.017 0.083 0.105 0.105 0.079 0.125 0.031 0.142 0.155
SAG AAA799-E16 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA799-N04 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-179-E04 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-539-E09 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-629-A13 0.009 0.004 0.004 0.003 0.008 0.009 0.012 0.009 0.013 0.003 0.014 0.015
c) Competitive Fragment Recruitment 97<%ID<99
91
SAG AB-629-I23 0.091 0.054 0.029 0.014 0.077 0.097 0.113 0.081 0.127 0.033 0.139 0.169
SAG AB-661-I02 0.006 0.002 0.010 0.010 0.009 0.007 0.005 0.003 0.005 0.001 0.005 0.003
SAG AB-661-L21 0.002 0.001 0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.000 0.001 0.001
SAG AB-661-M19 0.009 0.002 0.015 0.015 0.013 0.012 0.007 0.005 0.007 0.002 0.007 0.004
SAG AB-663-F14 0.071 0.038 0.025 0.012 0.062 0.080 0.080 0.061 0.096 0.023 0.110 0.120
SAG AB-663-G14 0.049 0.026 0.015 0.008 0.043 0.054 0.057 0.042 0.069 0.017 0.075 0.090
SAG AB-663-N18 0.036 0.018 0.012 0.006 0.031 0.040 0.041 0.030 0.049 0.012 0.055 0.063
SAG AB-663-O07 0.081 0.044 0.034 0.020 0.078 0.090 0.098 0.063 0.122 0.027 0.114 0.122
SAG AB-663-P07 0.035 0.022 0.011 0.005 0.031 0.039 0.044 0.032 0.051 0.013 0.056 0.068
BD31 0.001 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
BG20 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Caldiarchaeum-subterraneum 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
D3C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
DS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
EN76 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
evergladensis 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Ga9-2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Group_10_depth 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.007 0.000 0.000
Group_101 0.014 0.011 0.016 0.016 0.014 0.014 0.012 0.007 0.009 0.003 0.006 0.006
Group_13_c 0.012 0.012 0.013 0.008 0.019 0.023 0.018 0.016 0.016 0.005 0.017 0.024
Group_13_depth 0.025 0.024 0.035 0.036 0.040 0.034 0.021 0.011 0.023 0.005 0.017 0.014
Group_2_depth 0.001 0.001 0.001 0.001 0.002 0.002 0.001 0.001 0.001 0.000 0.001 0.002
Group_7_depth 0.028 0.005 0.008 0.003 0.014 0.011 0.032 0.016 0.031 0.008 0.024 0.016
Group_75_b 0.002 0.003 0.005 0.005 0.005 0.006 0.001 0.001 0.001 0.000 0.001 0.001
Group_82_d 0.005 0.003 0.003 0.003 0.006 0.006 0.005 0.003 0.006 0.001 0.005 0.006
Group_9_c_depth 0.000 0.000 0.001 0.002 0.000 0.000 0.000 0.001 0.000 0.002 0.000 0.000
Group_9_f_depth 0.001 0.001 0.009 0.011 0.003 0.002 0.001 0.012 0.001 0.015 0.000 0.000
Group_90_d 0.028 0.023 0.032 0.030 0.035 0.038 0.023 0.015 0.026 0.005 0.030 0.024
Group_90_h 0.016 0.012 0.021 0.021 0.023 0.021 0.015 0.008 0.016 0.003 0.016 0.012
Group_90_i 0.062 0.032 0.123 0.135 0.100 0.072 0.059 0.031 0.051 0.019 0.045 0.025
Group_90_l 0.187 0.166 0.264 0.274 0.303 0.260 0.159 0.081 0.167 0.036 0.135 0.109
koreensis-AR1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
koreensis-MY1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
MY2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
N.brevis 0.003 0.002 0.007 0.012 0.006 0.004 0.003 0.002 0.003 0.001 0.002 0.002
NF5 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrosopumilus-AR2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SPOT Enrichment 0.031 0.009 0.065 0.088 0.050 0.024 0.031 0.020 0.023 0.013 0.020 0.007
OS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SCM1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SFB1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SJ 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
symbiosum-A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
92
References 7/9/2012 8/15/2012 9/28/2012 10/17/2012 11/28/2012 12/11/2012 1/16/2013 2/13/2013 3/13/2013 4/24/2013 5/22/2013 6/19/2013
A1-100119 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA007-C21 0.002 0.001 0.001 0.000 0.002 0.003 0.004 0.003 0.004 0.001 0.004 0.006
SAG AAA007-E02 0.038 0.019 0.012 0.005 0.033 0.048 0.039 0.032 0.052 0.014 0.062 0.078
SAG AAA007-E15 0.021 0.008 0.006 0.003 0.018 0.023 0.028 0.020 0.032 0.009 0.035 0.042
SAG AAA007-G17 0.002 0.000 0.001 0.000 0.001 0.002 0.004 0.002 0.004 0.001 0.004 0.005
SAG AAA007-M20 0.003 0.001 0.001 0.000 0.003 0.003 0.005 0.004 0.005 0.001 0.005 0.007
SAG AAA007-N23 0.052 0.013 0.014 0.006 0.041 0.055 0.062 0.045 0.072 0.020 0.079 0.100
SAG AAA007-O23 0.058 0.021 0.017 0.008 0.049 0.067 0.068 0.053 0.085 0.023 0.094 0.121
SAG AAA008-E15 0.076 0.026 0.022 0.009 0.063 0.085 0.096 0.072 0.116 0.031 0.126 0.157
SAG AAA008-E17 0.003 0.001 0.001 0.000 0.002 0.002 0.006 0.004 0.007 0.002 0.007 0.009
SAG AAA008-G03 0.001 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.002 0.000 0.002 0.002
SAG AAA008-M21 0.018 0.005 0.004 0.002 0.013 0.015 0.025 0.019 0.032 0.007 0.035 0.038
SAG AAA008-M23 0.075 0.033 0.021 0.009 0.062 0.089 0.081 0.067 0.106 0.029 0.125 0.163
SAG AAA008-N07 0.029 0.011 0.008 0.003 0.024 0.031 0.036 0.027 0.044 0.012 0.047 0.058
SAG AAA008-O05 0.019 0.007 0.005 0.003 0.017 0.021 0.022 0.016 0.027 0.007 0.030 0.038
SAG AAA008-O18 0.029 0.013 0.009 0.004 0.025 0.032 0.037 0.027 0.045 0.012 0.048 0.060
SAG AAA008-P02 0.016 0.006 0.005 0.002 0.014 0.019 0.019 0.015 0.024 0.007 0.027 0.035
SAG AAA008-P23 0.023 0.006 0.007 0.003 0.019 0.026 0.029 0.022 0.035 0.008 0.038 0.041
SAG AAA160-J20 0.002 0.001 0.003 0.004 0.003 0.002 0.001 0.001 0.002 0.000 0.001 0.001
SAG AAA282-K18 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA288-C17 0.049 0.019 0.017 0.008 0.044 0.059 0.052 0.042 0.064 0.016 0.081 0.088
SAG AAA288-D03 0.003 0.001 0.001 0.000 0.002 0.003 0.005 0.004 0.005 0.002 0.006 0.007
SAG AAA288-D22 0.035 0.014 0.010 0.005 0.029 0.038 0.042 0.032 0.052 0.014 0.057 0.070
SAG AAA288-E09 0.001 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.000 0.001 0.002
SAG AAA288-G05 0.012 0.003 0.003 0.002 0.010 0.012 0.015 0.012 0.018 0.005 0.020 0.024
SAG AAA288-K02 0.025 0.007 0.005 0.002 0.016 0.020 0.034 0.026 0.043 0.009 0.049 0.052
SAG AAA288-K05 0.001 0.000 0.000 0.000 0.001 0.001 0.002 0.001 0.002 0.000 0.001 0.002
SAG AAA288-K20 0.001 0.000 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.002
SAG AAA288-M04 0.001 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.000 0.001 0.001
SAG AAA288-M23 0.003 0.001 0.001 0.000 0.003 0.004 0.005 0.004 0.006 0.001 0.006 0.007
SAG AAA288-N15 0.013 0.005 0.004 0.002 0.011 0.014 0.017 0.012 0.020 0.006 0.022 0.026
SAG AAA288-N23 0.011 0.003 0.003 0.001 0.008 0.012 0.012 0.010 0.014 0.003 0.018 0.021
SAG AAA288-O17 0.001 0.000 0.000 0.000 0.001 0.001 0.002 0.001 0.002 0.000 0.003 0.002
SAG AAA288-O22 0.028 0.008 0.009 0.004 0.024 0.030 0.030 0.024 0.038 0.009 0.042 0.048
SAG AAA288-P02 0.022 0.006 0.005 0.002 0.015 0.018 0.031 0.023 0.037 0.008 0.041 0.044
SAG AAA288-P18 0.037 0.012 0.013 0.006 0.033 0.041 0.041 0.031 0.049 0.012 0.057 0.062
SAG AAA799-E16 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AAA799-N04 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.000 0.001 0.000 0.000 0.000
SAG AB-179-E04 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-539-E09 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-629-A13 0.002 0.001 0.001 0.001 0.002 0.002 0.003 0.002 0.004 0.001 0.004 0.004
d) Competitive Fragment Recruitment %ID>99
93
SAG AB-629-I23 0.053 0.020 0.015 0.007 0.044 0.056 0.067 0.049 0.080 0.022 0.085 0.107
SAG AB-661-I02 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-661-L21 0.000 0.000 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SAG AB-661-M19 0.001 0.000 0.001 0.001 0.001 0.001 0.001 0.000 0.001 0.000 0.001 0.000
SAG AB-663-F14 0.019 0.006 0.006 0.003 0.017 0.022 0.021 0.017 0.026 0.006 0.030 0.034
SAG AB-663-G14 0.021 0.006 0.007 0.003 0.018 0.024 0.025 0.019 0.030 0.008 0.034 0.040
SAG AB-663-N18 0.010 0.003 0.003 0.002 0.008 0.010 0.011 0.009 0.014 0.004 0.015 0.017
SAG AB-663-O07 0.022 0.008 0.009 0.005 0.021 0.026 0.025 0.017 0.032 0.007 0.033 0.036
SAG AB-663-P07 0.014 0.005 0.004 0.002 0.012 0.016 0.018 0.013 0.022 0.006 0.024 0.029
BD31 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BG20 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
BS4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Caldiarchaeum-subterraneum 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
D3C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
DS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
EN76 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
evergladensis 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Ga9-2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Group_10_depth 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.010 0.000 0.028 0.000 0.000
Group_101 0.036 0.030 0.054 0.054 0.044 0.044 0.023 0.015 0.019 0.007 0.017 0.015
Group_13_c 0.020 0.018 0.021 0.013 0.035 0.045 0.031 0.029 0.029 0.010 0.034 0.045
Group_13_depth 0.069 0.056 0.090 0.085 0.111 0.093 0.063 0.031 0.070 0.015 0.054 0.043
Group_2_depth 0.001 0.002 0.005 0.001 0.002 0.002 0.002 0.001 0.001 0.000 0.001 0.001
Group_7_depth 0.016 0.008 0.014 0.003 0.008 0.007 0.016 0.008 0.017 0.004 0.013 0.007
Group_75_b 0.009 0.020 0.035 0.030 0.039 0.059 0.007 0.007 0.008 0.003 0.009 0.009
Group_82_d 0.024 0.015 0.010 0.005 0.030 0.034 0.025 0.013 0.037 0.008 0.027 0.036
Group_9_c_depth 0.000 0.000 0.002 0.007 0.001 0.001 0.000 0.001 0.000 0.007 0.000 0.000
Group_9_f_depth 0.001 0.003 0.015 0.020 0.004 0.003 0.001 0.052 0.001 0.068 0.000 0.000
Group_90_d 0.061 0.036 0.062 0.056 0.079 0.070 0.065 0.035 0.065 0.012 0.061 0.040
Group_90_h 0.039 0.023 0.046 0.045 0.055 0.046 0.042 0.022 0.042 0.008 0.037 0.026
Group_90_i 0.087 0.044 0.193 0.214 0.160 0.111 0.090 0.047 0.078 0.030 0.065 0.035
Group_90_l 0.606 0.435 0.761 0.754 0.985 0.821 0.567 0.276 0.615 0.136 0.473 0.385
koreensis-AR1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
koreensis-MY1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
MY2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
N.brevis 0.002 0.001 0.004 0.007 0.003 0.002 0.002 0.001 0.001 0.001 0.001 0.001
NF5 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Nitrosopumilus-AR2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SPOT Enrichment 0.022 0.003 0.039 0.052 0.032 0.011 0.023 0.012 0.016 0.009 0.013 0.004
OS1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SCM1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SFB1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
SJ 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
symbiosum-A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
94
Reference 5m 45m 150m 500m 890m
SAG AAA288-O17 0 0 0.007766512 0.047109946 0.005047796
SAG AB-539-E09 3.17E-06 0 2.56E-05 7.64E-05 1.23E-05
SAG AAA007-C21 9.58E-05 0.00011543 0.014191486 0.09032864 0.017485937
SAG AAA288-K20 0.000122929 0.000141695 0.011141454 0.093390841 0.018373846
SAG AAA288-N15 0.000137689 0.000265476 0.018731948 0.14190081 0.032268314
SAG AAA007-M20 0.000166771 0.000111648 0.013011229 0.085577655 0.015362192
SAG AAA799-E16 0.000174216 0.000397868 0.005310675 0.002930902 0.003052765
SAG AAA007-G17 0.000283756 0.000294776 0.016034905 0.132683712 0.025474648
SAG AAA008-P02 0.000291638 0.000303515 0.030542314 0.21207906 0.03950226
SAG AAA008-E17 0.000311886 0.000287405 0.017575196 0.133029695 0.017997816
SAG AAA008-O18 0.000317297 0.000336012 0.034723015 0.205604482 0.03663555
SAG AAA288-E09 0.000350501 0.000391654 0.023227422 0.152102408 0.039241232
SAG AAA288-K05 0.000354096 0.000317935 0.016149049 0.130522127 0.025960676
SAG AAA288-N23 0.000377353 0.000408485 0.038283698 0.196728528 0.038076242
SAG AAA008-G03 0.00038207 0.000302557 0.015941558 0.103890549 0.02686774
SAG AB-663-P07 0.000408029 0.000389259 0.036306789 0.241972459 0.057088718
SAG AAA288-K02 0.000411863 0.00070587 0.048690954 0.487118481 0.068544672
SAG AAA008-O05 0.000430908 0.000439379 0.033485922 0.222158931 0.045710691
SAG AAA288-D03 0.000466361 0.000453217 0.02033619 0.119140706 0.023679317
SAG AB-663-N18 0.000498353 0.000515758 0.045873823 0.230242074 0.043563834
SAG AAA008-M21 0.000538763 0.000841602 0.034935743 0.314458423 0.04544756
SAG AAA008-N07 0.000539798 0.000502647 0.042263885 0.321010291 0.06129447
SAG AAA288-D22 0.000547895 0.000537974 0.042839734 0.29993027 0.060081979
SAG AB-663-G14 0.000559764 0.000537979 0.051399533 0.294290447 0.062008485
SAG AB-629-A13 0.000574778 0.000443998 0.035936005 0.154788096 0.038993147
SAG AAA288-M04 0.000575469 0.000301508 0.012295063 0.087452288 0.024359163
SAG AB-179-E04 0.000579852 0.000106868 0.000157567 0.000222207 9.71E-05
SAG AAA007-N23 0.000589763 0.000769918 0.047620622 0.340567471 0.067759585
A1-100119 0.00059735 0.000713007 0.009083485 0.004973154 0.004547554
SAG AAA008-P23 0.000650427 0.000660422 0.067104163 0.367018916 0.069643195
SAG AAA288-O22 0.000664777 0.00085831 0.077596403 0.430477479 0.084750967
SAG AB-663-F14 0.000671741 0.000763105 0.079715513 0.417993169 0.078585605
SAG AAA799-N04 0.000717666 0.001082877 0.017355609 0.00879122 0.010059432
SAG AAA007-E02 0.000778878 0.000383256 0.03644521 0.20097867 0.03306678
SAG AAA288-P02 0.000787418 0.000831187 0.058383186 0.503810191 0.071292654
SAG AAA288-M23 0.000802077 0.000427674 0.021132821 0.126469139 0.025658827
SAG AAA288-C17 0.000851108 0.000994548 0.098120092 0.41226964 0.071931271
SAG AAA288-G05 0.000855938 0.000493871 0.033672061 0.212730943 0.044478286
SAG AAA288-P18 0.000966864 0.000778587 0.090667931 0.45261424 0.084133065
SAG AAA007-E15 0.00099423 0.000530455 0.033710252 0.237810399 0.05124917
SAG AB-663-O07 0.001116774 0.001019369 0.108723226 0.289828621 0.045665191
SAG AAA007-O23 0.001197228 0.001084752 0.094827326 0.656654721 0.119476745
SAG AAA282-K18 0.001280441 0.00161669 0.027939672 0.017476221 0.023088499
SAG AB-661-L21 0.001340974 0.003139402 0.085036644 0.028687006 0.028820589
SAG AAA008-E15 0.001365299 0.001093964 0.08104203 0.540589517 0.091138266
SAG AAA008-M23 0.001481836 0.001328635 0.073862825 0.529287412 0.084304911
SAG AAA160-J20 0.002184224 0.008647105 0.215538348 0.071578229 0.065748222
SAG AB-661-I02 0.00246817 0.012081808 0.29564928 0.097542405 0.089729109
Table S2 The percent of metagenomic reads recruiting to references at each depth in metagenomes from September 28, 2012
at a)>70 percent identitiy (%ID), b) 95-97 %ID, c)97-99%ID, and d)>99%ID. Full names for each reference given in Table S1.
a) Competitive Fragment Recruitment >70 %ID
95
SAG AB-661-M19 0.003438513 0.016574318 0.393119994 0.132049106 0.122228048
SAG AB-629-I23 0.007160824 0.004055609 0.102216999 0.535169847 0.110621216
BD31 0.033984641 0.018021656 0.044711396 0.037729752 0.037268602
BG20 0 0 0 0 0
BS1 4.02E-05 7.98E-05 3.30E-05 3.68E-05 2.18E-05
BS4 2.48E-05 9.97E-06 4.78E-05 3.05E-05 1.93E-05
Caldiarchaeum-subterraneum 8.28E-06 9.98E-06 9.02E-05 8.54E-05 2.29E-05
D3C 0.000603598 0.00081409 0.014366636 0.008000885 0.008851559
DS1 2.14E-05 4.19E-05 7.57E-05 6.82E-05 2.25E-05
EN76 0.000167546 7.87E-05 0.000253453 0.000331974 0.00016176
evergladensis 6.06E-06 0 1.48E-05 1.24E-05 4.35E-06
Ga9-2 2.81E-06 0 1.22E-05 2.09E-05 1.09E-05
Group_10_depth 0.043840927 0.239638576 0.002392386 0.001470562 0.004016846
Group_101 0.003765249 0.004772361 0.096674581 0.013383371 0.035966513
Group_13_c 0.006227348 0.005309761 0.066132771 0.089710161 0.07332968
Group_13_depth 0.001997515 0.003703239 0.172091626 0.010282071 0.000794922
Group_2_depth 0.005146571 0.004799844 0.032098346 0.021939952 0.232430049
Group_7_depth 0.001735094 0.003063608 0.083612936 0.205913808 0.726477778
Group_75_b 0.003520027 0.006715778 0.073264998 0.010391119 0.085427895
Group_82_d 0.001238115 0.000951192 0.062447688 0.044431211 0.008410026
Group_9_c_depth 0.009137617 0.308484172 0.005351081 0.0022978 0.007144747
Group_9_f_depth 0.006697639 0.448961329 0.063521616 0.00296727 0.032787415
Group_90_d 0.002330937 0.002490036 0.173332531 0.04410546 0.008508333
Group_90_h 0.002193248 0.004362542 0.147011575 0.028450794 0.004533954
Group_90_i 0.005981514 0.037249147 0.679723147 0.129121908 0.09296686
Group_90_l 0.01482771 0.035934646 1.461999292 0.088287475 0.008105363
koreensis-AR1 0 1.46E-06 0.000173639 5.15E-05 6.59E-05
koreensis-MY1 0.000505873 0.000737782 0.01059172 0.006120305 0.00525658
MY2 6.43E-05 6.66E-05 0.000586174 0.000653329 0.000141503
N.brevis 0.002485345 0.022936761 0.155667819 0.012566164 0.00301614
NF5 0.00075102 0.001388755 0.019992092 0.01138581 0.012717217
Nitrosopumilus-AR2 0.000834951 0.001283914 0.024336244 0.013521883 0.014986102
SPOT Enrichment 0.005606915 0.076052849 0.570501784 0.099470351 0.07594934
OS1 0.000104308 8.04E-05 9.68E-05 0.00032693 0.000115003
SCM1 0.000497949 0.000948142 0.016222044 0.008265645 0.009225806
SFB1 1.68E-05 9.46E-06 0.000125658 8.26E-05 9.72E-05
SJ 0.000409543 0.000866563 0.016184042 0.00924255 0.01012788
symbiosum-A 6.81E-06 1.17E-06 8.98E-05 0.000112916 5.78E-05
96
Reference 5m 45m 150m 500m 890m
SAG AAA288-O17 0 0 0.001040158 0.009609068 0.000931901
SAG AB-539-E09 0 0 0 0 0
SAG AAA007-C21 2.74E-05 1.65E-05 0.00197543 0.013972742 0.002796334
SAG AAA288-K20 1.07E-05 0 0.001228338 0.013988189 0.002530033
SAG AAA288-N15 1.38E-05 1.66E-05 0.003084888 0.021196397 0.004932849
SAG AAA007-M20 3.71E-05 0 0.001476603 0.014486006 0.002420564
SAG AAA799-E16 0 2.03E-05 0.00010709 8.36E-05 6.00E-05
SAG AAA007-G17 1.96E-05 0 0.001887124 0.019635804 0.003885105
SAG AAA008-P02 3.31E-05 0.000103834 0.005381265 0.036205415 0.006695153
SAG AAA008-E17 2.75E-05 0 0.002860339 0.022279876 0.002894837
SAG AAA008-O18 5.77E-05 8.11E-05 0.004852561 0.026158435 0.004514156
SAG AAA288-E09 1.91E-05 2.30E-05 0.001552911 0.019187665 0.005398657
SAG AAA288-K05 1.39E-05 1.67E-05 0.001715937 0.020344303 0.00362785
SAG AAA288-N23 4.48E-05 3.08E-05 0.006669917 0.035660952 0.007395212
SAG AAA008-G03 5.46E-06 4.60E-05 0.001229183 0.016534366 0.004948577
SAG AB-663-P07 5.83E-05 7.02E-05 0.00626898 0.043521884 0.008713773
SAG AAA288-K02 6.86E-05 0.000187497 0.010501349 0.10293125 0.013861637
SAG AAA008-O05 6.63E-05 6.99E-05 0.00611532 0.040523761 0.007534494
SAG AAA288-D03 7.52E-06 9.06E-05 0.002197803 0.017736881 0.003356353
SAG AB-663-N18 8.79E-05 5.65E-05 0.011463378 0.058920183 0.010684324
SAG AAA008-M21 3.99E-05 6.01E-05 0.006233587 0.058907084 0.007561689
SAG AAA008-N07 0.00013495 0.000103486 0.007126629 0.052383959 0.009171163
SAG AAA288-D22 6.09E-05 4.89E-05 0.00588812 0.035664587 0.006692543
SAG AB-663-G14 8.24E-05 6.21E-05 0.010045912 0.057058594 0.01088624
SAG AB-629-A13 3.93E-05 2.37E-05 0.005503304 0.026883369 0.006633346
SAG AAA288-M04 1.88E-05 3.02E-05 0.001069136 0.013624556 0.00424728
SAG AB-179-E04 0 0 0 0 0
SAG AAA007-N23 4.91E-05 2.96E-05 0.006640778 0.039638908 0.00718277
A1-100119 0 1.37E-06 5.63E-05 4.01E-05 5.42E-05
SAG AAA008-P23 0.000120449 0.000123375 0.016233532 0.086332306 0.015305769
SAG AAA288-O22 0.000150366 0.000219346 0.018699911 0.100580736 0.018987083
SAG AB-663-F14 0.000164437 0.000130697 0.019584423 0.105707282 0.019615858
SAG AAA799-N04 0 1.11E-05 0.000152304 8.04E-05 6.15E-05
SAG AAA007-E02 1.95E-05 6.26E-05 0.00471494 0.027852423 0.004591909
SAG AAA288-P02 9.77E-05 0.000132401 0.012724179 0.120757113 0.016270878
SAG AAA288-M23 1.42E-05 1.71E-05 0.002229685 0.016948332 0.003718005
SAG AAA288-C17 0.000206329 0.000130534 0.020224498 0.090570018 0.016525112
SAG AAA288-G05 4.71E-05 4.81E-05 0.004251417 0.033022195 0.007209779
SAG AAA288-P18 0.000187874 0.000143569 0.019522208 0.097187624 0.017684775
SAG AAA007-E15 4.55E-05 0.000100604 0.005320365 0.037565982 0.006645411
SAG AB-663-O07 0.000258358 0.000190818 0.030405158 0.074429732 0.009172921
SAG AAA007-O23 0.000178234 0.000149696 0.017496315 0.114458118 0.02026478
SAG AAA282-K18 3.82E-06 3.68E-05 0.000620195 0.000349146 0.000575915
SAG AB-661-L21 2.83E-05 0.00023363 0.007356159 0.002167971 0.001854506
SAG AAA008-E15 0.000121519 6.03E-05 0.011895851 0.070023552 0.01207411
SAG AAA008-M23 0.000211691 0.00011692 0.011571944 0.073588826 0.012731296
SAG AAA160-J20 0.000152223 0.000784937 0.021696927 0.005269225 0.00565453
SAG AB-661-I02 0.000402419 0.001219265 0.03669723 0.00969942 0.010845613
b) Competitive Fragment Recruitment 95<%ID<97
97
SAG AB-661-M19 0.000351227 0.001587173 0.049214533 0.013773457 0.015058757
SAG AB-629-I23 0.000513332 0.000433745 0.015610201 0.084544086 0.016939776
BD31 0.001403227 0.000897274 0.001705238 0.001697248 0.002081378
BG20 0 0 0 0 0
BS1 0 0 0 0 0
BS4 0 0 0 0 0
Caldiarchaeum-subterraneum 0 0 0 1.68E-06 0
D3C 3.48E-06 4.20E-06 0.00017293 8.71E-05 0.000181655
DS1 0 0 0 0 0
EN76 6.29E-05 1.99E-05 0.000108103 0.000135909 9.77E-05
evergladensis 0 0 0 0 0
Ga9-2 0 0 0 0 0
Group_10_depth 0.00115778 0.003945301 6.00E-05 4.71E-05 5.10E-05
Group_101 0.000315366 0.000344884 0.008038934 0.001450898 0.0041177
Group_13_c 0.000183521 0.000180749 0.007199996 0.011950692 0.014597467
Group_13_depth 0.000148167 0.000317421 0.014475115 0.000730548 2.13E-05
Group_2_depth 6.29E-05 0.000171191 0.001956304 0.001377773 0.007179639
Group_7_depth 3.33E-05 5.26E-05 0.005814463 0.017713649 0.020745745
Group_75_b 7.48E-05 0.000215346 0.003330882 0.000523823 0.010626416
Group_82_d 2.86E-05 1.92E-05 0.003106027 0.003074169 0.00103736
Group_9_c_depth 0.000292176 0.011096968 0.000412081 0.000318579 0.000812625
Group_9_f_depth 0.00042725 0.01543107 0.007673517 0.000195952 0.000749236
Group_90_d 0.00015145 0.000157332 0.019253809 0.007482396 0.0010672
Group_90_h 0.000141792 0.000403535 0.016065182 0.00387946 0.000468222
Group_90_i 0.000805204 0.005652444 0.106172608 0.014978395 0.009909963
Group_90_l 0.001146925 0.004152918 0.133571595 0.006629332 0.000361973
koreensis-AR1 0 0 2.38E-05 1.03E-05 1.57E-06
koreensis-MY1 1.24E-06 5.96E-06 7.43E-05 4.03E-05 2.56E-05
MY2 0 0 2.61E-06 6.39E-06 1.46E-06
N.brevis 0.000103287 0.002716861 0.011965702 0.000345124 4.38E-05
NF5 7.72E-06 1.86E-05 0.000252178 0.000221781 0.000268151
Nitrosopumilus-AR2 5.88E-06 3.26E-05 0.000622021 0.000334758 0.000562758
SPOT Enrichment 0.000679848 0.019188074 0.08113482 0.007381595 0.004748134
OS1 0 0 0 0 0
SCM1 0 1.31E-05 0.000167497 4.45E-05 4.38E-05
SFB1 0 0 0 4.77E-06 0
SJ 7.23E-06 8.71E-06 0.00017667 0.000143295 0.0001776
symbiosum-A 0 0 4.49E-06 1.38E-06 0
98
Reference 5m 45m 150m 500m 890m
SAG AAA288-O17 0 0 0.000970814 0.007568204 0.001087218
SAG AB-539-E09 0 0 0 0 0
SAG AAA007-C21 1.37E-05 4.95E-05 0.002244088 0.023197465 0.004566166
SAG AAA288-K20 0 1.29E-05 0.000660463 0.017772872 0.004099207
SAG AAA288-N15 2.75E-05 9.96E-05 0.005645027 0.051791748 0.011949249
SAG AAA007-M20 1.85E-05 4.47E-05 0.002140005 0.024090858 0.004577502
SAG AAA799-E16 1.40E-06 0 3.25E-05 1.99E-05 9.09E-06
SAG AAA007-G17 1.96E-05 0 0.001209115 0.022573553 0.005365748
SAG AAA008-P02 0.000159075 7.99E-05 0.009407645 0.076634951 0.013767498
SAG AAA008-E17 6.42E-05 3.32E-05 0.002086988 0.030178514 0.00401006
SAG AAA008-O18 0.000153841 6.95E-05 0.011837139 0.075043798 0.01382849
SAG AAA288-E09 1.91E-05 3.84E-05 0.00093469 0.017617273 0.007343822
SAG AAA288-K05 4.17E-05 2.51E-05 0.00064949 0.02340234 0.005765048
SAG AAA288-N23 6.40E-05 5.40E-05 0.007275602 0.052001403 0.010546863
SAG AAA008-G03 5.46E-06 3.29E-05 0.000680778 0.014841507 0.005146238
SAG AB-663-P07 8.99E-05 9.66E-05 0.010114515 0.081000305 0.01843588
SAG AAA288-K02 8.24E-05 0.000181982 0.012599504 0.172025197 0.023757734
SAG AAA008-O05 0.000107727 0.000139802 0.010766408 0.074674975 0.015797786
SAG AAA288-D03 7.52E-06 0 0.002475786 0.030072802 0.006070622
SAG AB-663-N18 9.38E-05 0.000169564 0.011375354 0.07128379 0.014445449
SAG AAA008-M21 2.00E-05 0.000156297 0.007650836 0.096577098 0.012903907
SAG AAA008-N07 0.00019629 0.000177405 0.014692473 0.114167494 0.022118688
SAG AAA288-D22 0.000126827 0.000152833 0.013768241 0.105377216 0.020097314
SAG AB-663-G14 0.000178575 0.000140702 0.014055551 0.102050842 0.021328325
SAG AB-629-A13 3.44E-05 2.37E-05 0.003716148 0.017664823 0.003710607
SAG AAA288-M04 0 1.51E-05 0.000859643 0.012986736 0.005363708
SAG AB-179-E04 0 0 0 0 0
SAG AAA007-N23 0.000221161 0.000148061 0.016573566 0.142372933 0.02593425
A1-100119 0 0 1.70E-05 1.61E-05 2.93E-06
SAG AAA008-P23 0.000150562 7.98E-05 0.020587513 0.132185149 0.026062647
SAG AAA288-O22 0.000166194 0.000228883 0.021661187 0.144773943 0.030041557
SAG AB-663-F14 0.000192426 0.000236099 0.022913816 0.136313538 0.026552676
SAG AAA799-N04 1.53E-06 5.54E-06 0.000113343 7.82E-05 0.000168583
SAG AAA007-E02 0.000110341 8.60E-05 0.011626187 0.074043322 0.012600466
SAG AAA288-P02 0.000134288 0.000272159 0.013848559 0.161672241 0.023135278
SAG AAA288-M23 1.42E-05 8.55E-06 0.002131316 0.023562806 0.00528783
SAG AAA288-C17 0.000252753 0.00016783 0.033211069 0.145658622 0.025237989
SAG AAA288-G05 5.80E-05 4.37E-05 0.005336261 0.046089307 0.010235353
SAG AAA288-P18 0.000302432 0.000165657 0.030280856 0.163407077 0.030580911
SAG AAA007-E15 0.000121433 0.000128041 0.011385756 0.089739165 0.019042978
SAG AB-663-O07 0.000220855 0.000135581 0.031329152 0.100296011 0.014557026
SAG AAA007-O23 0.000322261 0.000314578 0.031096244 0.24342397 0.044366898
SAG AAA282-K18 1.91E-06 2.30E-05 0.000271473 0.000208405 0.000229872
SAG AB-661-L21 3.64E-05 8.76E-05 0.002061777 0.000652107 0.000929865
SAG AAA008-E15 0.000357408 0.000344556 0.028224784 0.197993442 0.03539027
SAG AAA008-M23 0.000317536 0.000244469 0.023979188 0.17440964 0.028679641
SAG AAA160-J20 9.20E-05 0.000324213 0.00894942 0.002175874 0.002032884
SAG AB-661-I02 6.90E-05 0.000217066 0.008909845 0.002534767 0.002865066
c) Competitive Fragment Recruitment 97<%ID<99
99
SAG AB-661-M19 8.43E-05 0.000363992 0.013556002 0.003974356 0.004474472
SAG AB-629-I23 0.000387277 0.000387991 0.026996878 0.182356863 0.033307952
BD31 0.000667481 0.00048596 0.00079422 0.000782381 0.00092869
BG20 0 0 0 0 0
BS1 0 0 0 0 0
BS4 0 0 0 0 0
Caldiarchaeum-subterraneum 0 0 0 0 0
D3C 0 0 5.63E-05 3.12E-05 2.40E-05
DS1 0 0 0 0 0
EN76 1.18E-05 6.64E-06 1.54E-05 1.23E-05 3.05E-06
evergladensis 0 0 0 0 0
Ga9-2 0 0 8.10E-07 0 0
Group_10_depth 0.002928197 0.022520626 6.96E-05 5.74E-05 0.000163843
Group_101 0.000328126 0.000477785 0.014838924 0.001478005 0.002717234
Group_13_c 0.000176462 0.000148852 0.011587653 0.025050969 0.023489233
Group_13_depth 0.000351211 8.60E-05 0.031802157 0.00152327 4.26E-05
Group_2_depth 3.06E-05 3.03E-05 0.001073683 0.000998312 0.010451991
Group_7_depth 6.87E-05 9.03E-05 0.006888457 0.041703093 0.102450614
Group_75_b 0.000174547 0.000177785 0.004528367 0.00036491 0.005659897
Group_82_d 5.73E-05 3.84E-06 0.003069269 0.004606746 0.000938564
Group_9_c_depth 0.000484806 0.060284748 0.000770412 0.000247174 0.001553351
Group_9_f_depth 0.000498712 0.066048352 0.0081406 0.000262705 0.001443409
Group_90_d 0.000189748 7.55E-05 0.029617533 0.008120927 0.00126533
Group_90_h 0.000144809 0.000167231 0.019570186 0.003170219 0.000487732
Group_90_i 0.00088828 0.005082579 0.113176487 0.009901126 0.002785369
Group_90_l 0.002266412 0.001798716 0.242470852 0.011595503 0.000361973
koreensis-AR1 0 0 4.20E-06 0 0
koreensis-MY1 0 1.49E-06 1.00E-05 7.01E-06 3.20E-06
MY2 0 0 1.30E-06 3.19E-06 0
N.brevis 5.00E-05 0.001954506 0.006583 0.000217131 4.80E-05
NF5 0 7.97E-06 9.55E-05 7.50E-05 9.13E-05
Nitrosopumilus-AR2 3.53E-06 8.50E-06 0.000245821 0.000116582 0.000142971
SPOT Enrichment 0.000454694 0.013862066 0.059787373 0.003018896 0.001603512
OS1 0 0 0 0 0
SCM1 0 5.83E-06 5.02E-05 1.88E-05 3.28E-05
SFB1 0 0 0 0 0
SJ 0 1.45E-06 5.29E-05 6.31E-05 0.000140211
symbiosum-A 0 0 0 1.38E-06 0
100
Reference 5m 45m 150m 500m 890m
SAG AAA288-O17 0 0 0.000346719 0.003571512 0.00046595
SAG AB-539-E09 0 0 0 0 0
SAG AAA007-C21 2.74E-05 0 0.000663744 0.014437854 0.003451172
SAG AAA288-K20 0 2.58E-05 0.000191349 0.009809899 0.003884914
SAG AAA288-N15 8.26E-05 8.30E-05 0.003546031 0.040657302 0.010008164
SAG AAA007-M20 0 0 0.000920202 0.013200111 0.002875917
SAG AAA799-E16 0 0 3.25E-06 1.99E-06 0
SAG AAA007-G17 9.78E-06 2.36E-05 0.000463306 0.018180786 0.004340688
SAG AAA008-P02 1.99E-05 7.19E-05 0.004248367 0.047788894 0.009909855
SAG AAA008-E17 0 0 0.000667413 0.031997279 0.004413439
SAG AAA008-O18 4.81E-05 5.79E-05 0.008405923 0.068058229 0.012012879
SAG AAA288-E09 1.91E-05 1.54E-05 0.000404787 0.010667836 0.006610264
SAG AAA288-K05 0 3.35E-05 0.000264607 0.01406107 0.005217279
SAG AAA288-N23 1.28E-05 6.94E-05 0.002947173 0.026412836 0.005360288
SAG AAA008-G03 5.46E-06 1.97E-05 0.000365603 0.0064004 0.003211987
SAG AB-663-P07 5.83E-05 4.98E-05 0.003971756 0.042035956 0.012552356
SAG AAA288-K02 4.12E-05 0.000110292 0.004254447 0.100189787 0.015802977
SAG AAA008-O05 4.14E-05 0.000159774 0.004947763 0.046990193 0.012003745
SAG AAA288-D03 7.52E-06 4.53E-05 0.001007688 0.017172284 0.004737809
SAG AB-663-N18 3.52E-05 2.12E-05 0.002938633 0.021488928 0.004648326
SAG AAA008-M21 0 0.000120229 0.00403282 0.06454487 0.011832882
SAG AAA008-N07 7.36E-05 8.87E-05 0.006942442 0.082250634 0.016358943
SAG AAA288-D22 9.64E-05 0.00012838 0.009122192 0.09756032 0.022078831
SAG AB-663-G14 6.18E-05 5.79E-05 0.006175083 0.051198075 0.012356393
SAG AB-629-A13 0 5.92E-06 0.001060946 0.004529263 0.001181803
SAG AAA288-M04 6.26E-06 7.54E-06 0.000195045 0.007910747 0.004409081
SAG AB-179-E04 0 0 0 0 0
SAG AAA007-N23 0.000245734 0.00026651 0.01311128 0.118359899 0.027046626
A1-100119 1.13E-06 0 1.18E-05 3.21E-06 0
SAG AAA008-P23 7.83E-05 4.35E-05 0.006085836 0.057682808 0.012174512
SAG AAA288-O22 4.75E-05 9.54E-05 0.008024693 0.066710111 0.015046367
SAG AB-663-F14 3.15E-05 6.32E-05 0.005640573 0.040506095 0.008312418
SAG AAA799-N04 9.20E-06 7.76E-05 0.000749125 0.000380055 0.000577148
SAG AAA007-E02 7.14E-05 0.00010168 0.010696692 0.063987034 0.009586764
SAG AAA288-P02 2.14E-05 4.78E-05 0.004377682 0.071858652 0.010369625
SAG AAA288-M23 1.42E-05 8.55E-06 0.000729566 0.013369681 0.003488499
SAG AAA288-C17 0.000108323 9.95E-05 0.015554099 0.074323227 0.011921831
SAG AAA288-G05 3.26E-05 4.81E-05 0.003007406 0.037470355 0.00888909
SAG AAA288-P18 8.71E-05 9.94E-05 0.011674164 0.074247036 0.0150771
SAG AAA007-E15 3.79E-05 9.15E-05 0.005907621 0.066468678 0.01738408
SAG AB-663-O07 7.92E-05 5.02E-05 0.007969443 0.034016252 0.007550683
SAG AAA007-O23 0.000178234 0.000284205 0.015392183 0.157382143 0.028982617
SAG AAA282-K18 1.91E-06 1.61E-05 0.000260438 0.000129915 0.000202682
SAG AB-661-L21 4.04E-06 8.27E-05 0.000536435 0.000308893 0.001363454
SAG AAA008-E15 0.000200149 0.0003101 0.020258444 0.186068077 0.028502665
SAG AAA008-M23 0.000229332 0.000329502 0.019476723 0.198481218 0.027972347
SAG AAA160-J20 2.48E-05 0.000115181 0.002681964 0.000872355 0.000947763
SAG AB-661-I02 0 2.31E-05 0.000513434 0.000282244 0.000218102
d) Competitive Fragment Recruitment %ID>99
101
SAG AB-661-M19 3.51E-06 6.77E-05 0.001273664 0.000706331 0.000749531
SAG AB-629-I23 0.00034779 0.000280013 0.014050935 0.120915228 0.020854546
BD31 0.000608065 0.000274209 0.000308052 0.000390295 0.000232173
BG20 0 0 0 0 0
BS1 0 0 0 0 0
BS4 0 0 0 0 0
Caldiarchaeum-subterraneum 0 0 0 0 0
D3C 0 0 8.04E-06 0 0
DS1 0 0 0 0 0
EN76 0 0 0 0 0
evergladensis 0 0 0 0 0
Ga9-2 0 0 0 0 0
Group_10_depth 0.018258659 0.187671463 7.43E-05 9.56E-05 0.000979031
Group_101 0.000773831 0.001039046 0.049911499 0.002296394 0.001515993
Group_13_c 0.000315867 9.57E-05 0.019246183 0.028189838 0.009775008
Group_13_depth 0.000883516 9.92E-05 0.082762928 0.004103502 0.000149048
Group_2_depth 9.71E-05 4.12E-05 0.004197125 0.003514467 0.165580565
Group_7_depth 0.000210125 0.000188028 0.013154621 0.102286602 0.574885849
Group_75_b 0.000299223 0.000272938 0.032005741 0.000570908 0.001685069
Group_82_d 0.000159141 7.67E-06 0.009516573 0.014108724 0.000642175
Group_9_c_depth 0.001423389 0.220295762 0.001981274 0.000303932 0.001618562
Group_9_f_depth 0.001689234 0.313352233 0.01404236 0.00037037 0.002214276
Group_90_d 0.000428238 9.23E-05 0.056802456 0.005613646 0.000504331
Group_90_h 0.000289617 4.00E-05 0.042181992 0.002661788 0.000171682
Group_90_i 0.001508159 0.009656901 0.177185595 0.008760777 0.000942232
Group_90_l 0.006486437 0.000945649 0.698544714 0.036410812 0.001128505
koreensis-AR1 0 0 1.40E-06 0 0
koreensis-MY1 0 0 4.29E-06 3.50E-06 4.80E-06
MY2 0 0 1.30E-06 0 0
N.brevis 3.87E-05 0.001075465 0.003764909 0.000125707 8.35E-06
NF5 0 5.32E-06 0.000135004 7.34E-05 0.00010127
Nitrosopumilus-AR2 1.18E-06 0 4.75E-05 2.17E-05 3.04E-05
SPOT Enrichment 0.000298256 0.004792173 0.035799144 0.001053922 0.000423569
OS1 0 0 0 0 0
SCM1 0 0 1.40E-05 1.71E-06 6.25E-06
SFB1 0 0 0 0 0
SJ 0 5.81E-06 4.87E-05 3.24E-05 6.70E-05
symbiosum-A 0 0 0 1.38E-06 0
102
a) Count of reciprocal best hits
Group_10_a_depth Group_13_c_1_depth Group_2_c_2_depth Group_33_c Group_75_b Group_7_b_depth Group_82_d Group_90_d Group_90_h Group_90_i Group_90_l Group_9_c_depth Group_9_f_depth
Group_101 - 2 0 0 7 0 0 0 0 0 0 0 2 3
Group_10_a_depth 2 - 0 0 0 0 0 0 0 0 0 0 0 2
Group_13_c_1_depth 0 0 - 0 0 0 28 13 36 30 12 137.00 0 0
Group_2_c_2_depth 0 0 0 - 0 21 0 0 0 0 0 0 1 1
Group_33_c 7 0 0 0 - 0 0 0 0 0 0 0 0 0
Group_75_b 0 0 0 21 0 - 0 0 0 0 0 0 1 2
Group_7_b_depth 0 0 28 0 0 0 - 11 48 15 55.00 43 0 0
Group_82_d 0 0 13 0 0 0 11 - 40 26 6 19 0 0
Group_90_d 0 0 36 0 0 0 48 40 - 36 18 70.00 0 0
Group_90_h 0 0 30 0 0 0 15 26 36 - 7 51.00 0 0
Group_90_i 0 0 12 0 0 0 55.00 6 18 7 - 15 0 0
Group_90_l 0 0 137.00 0 0 0 43 19 70.00 51.00 15 - 0 0
Group_9_c_depth 2 0 0 1 0 1 0 0 0 0 0 0 - 7
Group_9_f_depth 3 2 0 1 0 2 0 0 0 0 0 0 7 -
Npelagicus 0 0 40 1 0 0 230.00 16 73.00 25 96.00 53.00 0 0
Nbr01 0 0 124.00 0 0 0 83.00 31 118.00 69.00 33 233.00 0 0
SCM1 0 0 31 0 0 0 248.00 8 59.00 26 52.00 44 0 0
Nspot_scaff1to4_6 0 0 40 1 0 0 229.00 16 73.00 25 97.00 53.00 0 0
AAA160-J20 0 0 10 0 0 0 66.00 2 20 6 29 15 0 0
AAA799-E16 0 0 32 0 0 0 227.00 10 53.00 10 43 43 0 0
AAA799-N04 0 0 23 0 0 0 182.00 9 38 8 29 35 0 0
AAA007-C21 0 0 0 0 0 0 1 1 3 3 0 0 0 0
AAA007-E02 0 0 3 0 0 0 4 1 9 3 1 8 0 0
AAA007-E15 0 0 3 0 0 0 8 3 7 3 0 8 0 0
AAA007-G17 0 0 1 0 0 0 1 3 7 1 1 2 0 0
AAA007-M20 0 0 1 0 0 0 1 0 5 0 0 2 0 0
AAA007-N23 0 0 0 0 0 0 0 1 2 0 0 0 0 0
AAA008-E15 0 0 4 0 0 0 8 4 10 4 2 8 0 0
AAA008-E17 0 0 2 0 0 0 5 6 7 0 2 4 0 0
AAA008-G03 0 0 1 0 0 0 5 5 6 4 2 5 0 0
AAA008-M21 0 0 3 0 0 0 1 2 6 1 1 4 0 0
AAA008-M23 0 0 1 0 0 0 5 1 3 5 3 5 0 0
AAA008-N07 0 0 2 0 0 0 3 1 2 2 0 2 0 0
AAA008-O05 0 0 1 0 0 0 1 5 2 1 1 5 0 0
AAA008-O18 0 0 3 0 0 0 5 3 3 1 0 4 0 0
AAA008-P02 0 0 3 0 0 0 7 3 11 3 3 4 0 0
AAA008-P23 0 0 2 0 0 0 11 4 4 5 3 4 0 0
AAA288-C17 0 0 5 0 0 0 6 5 10 10 5 9 0 0
AAA288-D03 0 0 2 0 0 0 1 2 8 7 0 3 0 0
AAA288-D22 0 0 4 0 0 0 8 4 11 1 4 8 0 0
AAA288-E09 0 0 1 0 0 0 5 3 7 3 1 2 0 0
Group_101
Table S3 Average Nucleotide Identity (ANI) by reciprocal best hits (RBHs) between each reference, including genomic bins. A) gives the number of RBHs between the two genomes and b) shows the ANI based on the RBHs between each genome. Green fills in a and b indicate 40+ RBH
between the pair of genomes indicating greater confidence in the calculated ANI. References abbreviated as given in Table S1.
103
AAA288-G05 0 0 4 0 0 0 7 9 11 6 2 13 0 0
AAA288-K02 0 0 4 0 0 0 9 2 8 5 4 11 0 0
AAA288-K05 0 0 1 0 0 0 3 4 5 3 1 7 0 0
AAA288-K20 0 0 4 0 0 0 11 1 10 5 0 6 0 0
AAA288-M04 0 0 1 0 0 0 5 1 8 1 2 3 0 0
AAA288-M23 0 0 4 0 0 0 3 8 6 3 1 6 0 0
AAA288-N15 0 0 0 0 0 0 0 0 4 1 1 0 0 0
AAA288-N23 0 0 6 0 0 0 6 2 5 2 2 7 0 0
AAA288-O17 0 0 0 0 0 0 0 0 0 0 0 0 0 0
AAA288-O22 0 0 5 0 0 0 7 0 5 5 2 7 0 0
AAA288-P02 0 0 5 0 0 0 14 9 21 5 5 15 0 0
AAA288-P18 0 0 8 0 0 0 9 3 9 11 3 10 0 0
BS1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
BS4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
DS1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
MY2 0 0 7 0 0 0 9 0 4 3 1 7 0 0
AAA007-O23 0 0 12 0 0 0 19 16 31 7 10 23 0 0
AAA282-K18 0 0 22 0 0 0 190.00 14 39 12 42 37 0 0
AB-179-E04 0 0 0 0 0 0 0 0 0 0 0 0 0 0
AB-661-I02 0 0 14 1 0 0 91.00 5 27 10 43 23 0 0
AB-661-L21 0 0 7 1 0 0 50 5 19 8 19 11 0 0
AB-661-M19 0 0 14 1 0 0 89.00 7 29 8 28 22 0 0
AB-663-F14 0 0 8 0 0 0 10 2 19 7 6 8 0 0
AB-663-G14 0 0 4 0 0 0 11 6 24 6 4 10 0 0
AB-663-N18 0 0 2 0 0 0 3 1 13 1 1 4 0 0
AB-663-O07 0 0 8 0 0 0 14 10 20 9 7 11 0 0
AB-663-P07 0 0 10 1 0 0 15 11 29 5 5 19 0 0
AB-539-E09 0 0 0 0 0 0 0 0 0 0 0 0 0 0
koreensis_MY1 0 0 27 0 0 0 167.00 13 61.00 11 41 45 0 0
BG20 0 0 27 0 0 0 167.00 9 55.00 13 44 36 0 0
koreensis_AR1 0 0 34 0 0 0 231.00 13 54.00 14 42 52.00 0 0
SFB1 0 0 27 0 0 0 155.00 7 49 17 43 39 0 0
salaria_BD31 0 0 18 0 0 0 223.00 10 46 11 44 33 0 0
Nitrosopumilus_sp._AR2 0 0 28 0 0 0 251.00 14 69.00 25 53.00 51.00 0 0
A1_100119 0 0 27 0 0 0 161.00 9 51.00 17 45 42 0 0
evergladensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ga9-2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
SCM1 0 0 31 0 0 0 248.00 8 59.00 26 52.00 44 0 0
Nitrosopumilus_sp._SJ 0 0 36 0 0 0 244.00 14 60.00 14 43 54.00 0 0
EN76 0 0 0 0 0 0 0 0 0 0 0 0 0 0
AB-629-I23 0 0 10 0 0 0 23 12 32 5 7 24 0 0
symbiosum_A 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Caldiarchaeum_subterraneum 0 0 0 0 0 0 0 0 0 0 0 0 0 0
AB-629-A13 0 0 7 0 0 0 5 3 8 2 3 5 0 0
Aigarchaeota_archaeon_OS1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D3C 0 0 27 0 0 0 235.00 13 61.00 12 54.00 44 0 0
NF5 0 0 26 0 0 0 232.00 13 61.00 20 50 42 0 0
104
b) Average Nucleotide Identity
Group_101 Group_10_a_depth Group_13_c_1_depth Group_2_c_2_depth Group_33_c Group_75_b Group_7_b_depth Group_82_d Group_90_d Group_90_h Group_90_i Group_90_l Group_9_c_depth Group_9_f_depth
Group_101 --- 76.67 0.00 0.00 85.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 71.98 82.71
Group_10_a_depth 76.66 --- 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 75.38
Group_13_c_1_depth 0.00 0.00 --- 0.00 0.00 0.00 78.56 80.40 83.26 87.64 87.58 99.72 0.00 0.00
Group_2_c_2_depth 0.00 0.00 0.00 --- 0.00 90.44 0.00 0.00 0.00 0.00 0.00 0.00 93.02 75.26
Group_33_c 85.58 0.00 0.00 0.00 --- 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Group_75_b 0.00 0.00 0.00 90.44 0.00 --- 0.00 0.00 0.00 0.00 0.00 0.00 72.66 78.42
Group_7_b_depth 0.00 0.00 78.56 0.00 0.00 0.00 --- 76.48 79.34 79.32 84.91 79.27 0.00 0.00
Group_82_d 0.00 0.00 80.40 0.00 0.00 0.00 76.49 --- 83.99 84.38 79.67 81.00 0.00 0.00
Group_90_d 0.00 0.00 83.25 0.00 0.00 0.00 79.34 83.99 --- 84.39 82.27 83.76 0.00 0.00
Group_90_h 0.00 0.00 87.64 0.00 0.00 0.00 79.32 84.38 84.39 --- 82.80 88.70 0.00 0.00
Group_90_i 0.00 0.00 87.58 0.00 0.00 0.00 84.91 79.67 82.28 82.82 --- 85.12 0.00 0.00
Group_90_l 0.00 0.00 99.72 0.00 0.00 0.00 79.26 80.99 83.76 88.70 85.12 --- 0.00 0.00
Group_9_c_depth 71.99 0.00 0.00 93.02 0.00 72.63 0.00 0.00 0.00 0.00 0.00 0.00 --- 78.52
Group_9_f_depth 82.71 75.38 0.00 75.27 0.00 78.43 0.00 0.00 0.00 0.00 0.00 0.00 78.51 ---
Npelagicus 0.00 0.00 79.68 71.54 0.00 0.00 84.07 78.66 80.90 79.35 89.22 79.21 0.00 0.00
Nbr01 0.00 0.00 87.99 0.00 0.00 0.00 78.20 80.25 82.17 86.04 82.21 88.08 0.00 0.00
SCM1 0.00 0.00 78.39 0.00 0.00 0.00 84.34 76.99 78.79 79.21 83.09 78.53 0.00 0.00
Nspot_scaff1to4_6 0.00 0.00 79.68 71.54 0.00 0.00 84.09 78.66 80.89 79.35 89.33 79.21 0.00 0.00
AAA160-J20 0.00 0.00 78.52 0.00 0.00 0.00 84.18 75.71 81.05 80.37 87.60 79.16 0.00 0.00
AAA799-E16 0.00 0.00 79.07 0.00 0.00 0.00 84.34 78.67 78.93 79.64 83.45 78.87 0.00 0.00
AAA799-N04 0.00 0.00 79.32 0.00 0.00 0.00 84.11 76.92 79.49 79.71 83.44 78.85 0.00 0.00
AAA007-C21 0.00 0.00 0.00 0.00 0.00 0.00 77.48 78.60 80.91 84.83 0.00 0.00 0.00 0.00
AAA007-E02 0.00 0.00 78.93 0.00 0.00 0.00 78.74 80.68 80.06 76.72 96.67 78.98 0.00 0.00
AAA007-E15 0.00 0.00 79.07 0.00 0.00 0.00 78.32 83.49 79.80 76.13 0.00 80.88 0.00 0.00
AAA007-G17 0.00 0.00 80.80 0.00 0.00 0.00 77.84 79.70 79.54 74.52 75.49 85.32 0.00 0.00
AAA007-M20 0.00 0.00 81.82 0.00 0.00 0.00 73.36 0.00 82.02 0.00 0.00 80.33 0.00 0.00
AAA007-N23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 83.19 81.53 0.00 0.00 0.00 0.00 0.00
AAA008-E15 0.00 0.00 78.13 0.00 0.00 0.00 79.38 74.82 79.21 77.77 86.39 80.22 0.00 0.00
AAA008-E17 0.00 0.00 74.25 0.00 0.00 0.00 76.73 80.30 78.11 0.00 74.49 78.45 0.00 0.00
AAA008-G03 0.00 0.00 77.85 0.00 0.00 0.00 74.95 77.77 78.31 80.76 85.57 78.73 0.00 0.00
AAA008-M21 0.00 0.00 79.33 0.00 0.00 0.00 79.39 80.12 79.10 76.25 78.46 79.03 0.00 0.00
AAA008-M23 0.00 0.00 77.27 0.00 0.00 0.00 78.06 83.62 78.08 80.83 76.82 80.94 0.00 0.00
AAA008-N07 0.00 0.00 83.24 0.00 0.00 0.00 76.77 79.73 77.84 77.70 0.00 83.26 0.00 0.00
AAA008-O05 0.00 0.00 71.53 0.00 0.00 0.00 80.11 81.12 79.70 74.97 77.31 76.01 0.00 0.00
AAA008-O18 0.00 0.00 80.00 0.00 0.00 0.00 78.86 75.46 81.59 76.43 0.00 81.11 0.00 0.00
AAA008-P02 0.00 0.00 77.24 0.00 0.00 0.00 78.17 77.30 78.79 86.57 76.76 83.19 0.00 0.00
AAA008-P23 0.00 0.00 80.42 0.00 0.00 0.00 77.00 80.10 81.46 81.86 78.76 83.61 0.00 0.00
AAA288-C17 0.00 0.00 78.47 0.00 0.00 0.00 77.79 76.95 79.49 83.36 75.88 80.71 0.00 0.00
AAA288-D03 0.00 0.00 78.69 0.00 0.00 0.00 74.41 79.12 80.45 79.60 0.00 79.09 0.00 0.00
AAA288-D22 0.00 0.00 82.47 0.00 0.00 0.00 77.58 79.43 82.68 75.14 81.31 81.88 0.00 0.00
AAA288-E09 0.00 0.00 76.09 0.00 0.00 0.00 78.21 90.88 79.07 80.41 78.90 84.73 0.00 0.00
105
AAA288-G05 0.00 0.00 78.11 0.00 0.00 0.00 76.34 79.63 79.68 78.19 76.58 79.94 0.00 0.00
AAA288-K02 0.00 0.00 77.32 0.00 0.00 0.00 77.75 85.63 80.29 83.86 77.38 81.81 0.00 0.00
AAA288-K05 0.00 0.00 70.79 0.00 0.00 0.00 78.69 79.84 77.18 76.91 75.49 81.08 0.00 0.00
AAA288-K20 0.00 0.00 77.31 0.00 0.00 0.00 77.77 79.72 78.39 78.51 0.00 80.06 0.00 0.00
AAA288-M04 0.00 0.00 78.89 0.00 0.00 0.00 77.16 74.44 77.93 82.37 72.59 81.32 0.00 0.00
AAA288-M23 0.00 0.00 77.44 0.00 0.00 0.00 76.92 78.31 80.36 77.57 76.40 77.62 0.00 0.00
AAA288-N15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 76.82 82.73 77.40 0.00 0.00 0.00
AAA288-N23 0.00 0.00 77.27 0.00 0.00 0.00 79.65 83.84 78.95 78.61 76.92 80.94 0.00 0.00
AAA288-O17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AAA288-O22 0.00 0.00 76.25 0.00 0.00 0.00 79.18 0.00 79.65 75.24 90.62 79.39 0.00 0.00
AAA288-P02 0.00 0.00 77.04 0.00 0.00 0.00 77.82 80.85 79.17 83.58 81.69 80.11 0.00 0.00
AAA288-P18 0.00 0.00 78.81 0.00 0.00 0.00 78.52 81.29 81.27 78.25 89.04 80.76 0.00 0.00
BS1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
BS4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
DS1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
MY2 0.00 0.00 75.74 0.00 0.00 0.00 76.41 0.00 76.44 72.29 78.05 75.18 0.00 0.00
AAA007-O23 0.00 0.00 78.69 0.00 0.00 0.00 76.59 81.23 79.62 81.88 78.05 79.88 0.00 0.00
AAA282-K18 0.00 0.00 79.69 0.00 0.00 0.00 84.68 77.76 79.15 78.20 83.09 78.66 0.00 0.00
AB-179-E04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AB-661-I02 0.00 0.00 81.46 73.00 0.00 0.00 84.67 75.68 81.27 79.83 88.96 80.98 0.00 0.00
AB-661-L21 0.00 0.00 80.87 73.51 0.00 0.00 83.75 76.75 80.04 78.28 89.12 79.26 0.00 0.00
AB-661-M19 0.00 0.00 80.35 73.26 0.00 0.00 83.80 74.95 80.67 76.99 89.11 79.71 0.00 0.00
AB-663-F14 0.00 0.00 77.84 0.00 0.00 0.00 75.57 82.50 77.93 79.97 81.32 79.32 0.00 0.00
AB-663-G14 0.00 0.00 77.91 0.00 0.00 0.00 78.62 78.49 78.16 79.26 83.03 80.10 0.00 0.00
AB-663-N18 0.00 0.00 79.53 0.00 0.00 0.00 75.98 78.33 78.23 73.62 74.68 78.90 0.00 0.00
AB-663-O07 0.00 0.00 79.51 0.00 0.00 0.00 77.55 77.81 80.21 79.22 85.23 80.40 0.00 0.00
AB-663-P07 0.00 0.00 78.89 98.69 0.00 0.00 75.92 81.12 79.35 81.13 80.79 79.44 0.00 0.00
AB-539-E09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
koreensis_MY1 0.00 0.00 78.36 0.00 0.00 0.00 82.09 77.89 79.07 78.22 81.36 78.48 0.00 0.00
BG20 0.00 0.00 77.61 0.00 0.00 0.00 82.21 76.75 79.02 79.69 81.23 78.52 0.00 0.00
koreensis_AR1 0.00 0.00 78.42 0.00 0.00 0.00 84.53 77.51 79.44 80.73 83.40 77.88 0.00 0.00
SFB1 0.00 0.00 77.31 0.00 0.00 0.00 82.26 77.80 78.66 78.68 81.38 78.62 0.00 0.00
salaria_BD31 0.00 0.00 80.09 0.00 0.00 0.00 84.49 77.53 79.23 79.33 84.03 78.21 0.00 0.00
Nitrosopumilus_sp._AR2 0.00 0.00 78.33 0.00 0.00 0.00 84.43 77.38 78.95 79.30 84.00 78.25 0.00 0.00
A1_100119 0.00 0.00 77.33 0.00 0.00 0.00 82.21 77.68 78.54 78.71 81.26 78.61 0.00 0.00
evergladensis 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Ga9-2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SCM1 0.00 0.00 78.39 0.00 0.00 0.00 84.34 76.99 78.79 79.21 83.09 78.53 0.00 0.00
Nitrosopumilus_sp._SJ 0.00 0.00 78.42 0.00 0.00 0.00 84.39 77.21 79.57 80.73 83.22 77.98 0.00 0.00
EN76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AB-629-I23 0.00 0.00 78.65 0.00 0.00 0.00 76.84 79.73 79.56 80.22 81.80 79.53 0.00 0.00
symbiosum_A 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Caldiarchaeum_subterraneum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AB-629-A13 0.00 0.00 78.99 0.00 0.00 0.00 76.55 80.04 79.59 80.10 82.68 77.79 0.00 0.00
Aigarchaeota_archaeon_OS1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
D3C 0.00 0.00 78.18 0.00 0.00 0.00 84.40 77.31 79.44 80.75 82.66 78.64 0.00 0.00
NF5 0.00 0.00 78.52 0.00 0.00 0.00 84.32 76.34 79.08 79.40 82.80 78.37 0.00 0.00
106

Abstract (if available)

Abstract The Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) are two of the most abundant marine archaeal taxa found throughout the world’s oceans. However, the variability of their community composition throughout the water column and the extent of their diversity has been relatively understudied. Though many studies have shown that the MGI taxa are capable of ammonia oxidation (the first step in nitrification), fewer studies have focused on the activity of the MGII taxa however these taxa are considered to be heterotrophic. Studying the dynamics and diversity of MGI and MGII taxa may help further our understanding of their controls, and can help supplement future studies on the activity of both these groups. We have taken a time-series approach to examine the dynamics of their community composition and their diversity over time at several depths spanning the entire water column at the San Pedro Ocean Time-series station. ❧ We performed 16S rRNA gene sequencing to evaluate changes in the relative abundance of the MGI and MGII Operational Taxonomic Units (OTUs). In order to do so, we examined the effectiveness of two different primer sets, the commonly used 515F/806R and an alternative pair 515F/926R. We found that the alternative pair more accurately represented the expected abundance of marine clones making up a simple and a more complex mock community. Additionally, the alternative pair produced an amplicon of greater length (~120bp longer), which allowed for higher phylogenetic resolution that was often ecologically relevant. The alternative primer pair was therefore used for all 16S rRNA gene sequencing and as these primers can also amplify bacteria, we were able to evaluate correlations between the archaea and the bacterial community. ❧ Analysis of MGI and MGII taxa showed that these communities demonstrated seasonality throughout the water column. This indicates that the controls on the archaeal community, selects for different OTUs at distinct times of the year, leading to distinct consortia found in different seasons. We found that the community composition of both the MGI and MGII archaea changed across the water column, selecting for different dominant OTUs at each depth, yet seasonality was still found at all depths, suggesting an inherent trait of the archaeal community at SPOT. The variability in the archaea may thus indicate that their contribution to the microbial community and biogeochemical cycles changes throughout the year dependent on differences in the activity of these consortia. In fact, we found many MGI OTUs at 150m correlated to different Nitrospina OTUs (presumed nitrite oxidizers, the second step in nitrification) forming nitrifying consortia that occurred at distinct times of the year. MGI and MGII communities are thus, dynamic members of the microbial community and their variability has the potential to affect not only the biological community, but also biogeochemical cycles. ❧ Further investigation of the 16S and genomic diversity of the MGI community revealed that these taxa demonstrated ecologically significant strain-level diversity. We evaluated the diversity found within a 99% MGI 16S OTU by oligotyping, which clusters sequences into oligotypes based on single base positions with high amount of sequence variation. This revealed that the dominant oligotype within an OTU changed over a 12-month period and across depths, with some OTUs displaying greater amount of strain-level diversity. Furthermore, we examined the genomic diversity of the MGI taxa by competitive fragment recruitment of metagenomic reads to 86 reference genomes. We focused on reads recruiting with 95-100 percent identity (%ID) to evaluate ecologically significant variability found within highly similar reads. This approach revealed that only one reference recruited reads that were mostly identical to it, while other references recruited reads between 95-97 %ID. Recruitment also showed shifts in the %ID of reads recruiting to a given reference across depths, indicating different controls selecting between organisms that had high genomic similarity. These results show that the MGI organisms at SPOT demonstrate ecologically relevant strain level diversity, indicating that controls on these organisms can select between genetically similar organisms, thus relatively few sequence variations likely confer a large range of metabolic diversity or fitness. ❧ Collectively, this dissertation showed that the marine archaea at SPOT are highly variable and genetically diverse, but the community composition at each depth is predictable over time. Additionally, controls on the community distinguish between genetically similar organisms, showing that closely related taxa likely have diverse fitness and metabolisms as well as interactions with their physical and biological environment allowing for selection between these organisms over time and depth.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Parada, Alma E. (author)

Core Title Temporal variability of marine archaea across the water column at SPOT

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Marine Biology and Biological Oceanography

Publication Date 10/29/2015

Defense Date 09/24/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag 16S,archaea,community dynamics,metagenomics,microbial ecology,OAI-PMH Harvest,time-series

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Fuhrman, Jed A. (committee chair), Hammond, Douglas E. (committee member), Heidelberg, John F. (committee member), Moffett, James W. (committee member)

Creator Email alma.parada@gmail.com,aparada@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-193706

Unique identifier UC11278438

Identifier etd-ParadaAlma-3999.pdf (filename),usctheses-c40-193706 (legacy record id)

Legacy Identifier etd-ParadaAlma-3999.pdf

Dmrecord 193706

Document Type Dissertation

Format application/pdf (imt)

Rights Parada, Alma E.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA