Diversity and dynamics of rare and of resident bacterial populations in coastal sands

12 Pages·2012·0.71 MB·English

by  Angélique Gobet

TheISMEJournal(2012)6,542–553
&2012InternationalSocietyforMicrobialEcology Allrightsreserved1751-7362/12
www.nature.com/ismej
ORIGINAL ARTICLE
Diversity and dynamics of rare and of resident
bacterial populations in coastal sands
Ange´lique Gobet1,2,7, Simone I Bo¨er1,8, Susan M Huse3, Justus EE van Beusekom4,
Christopher Quince5, Mitchell L Sogin3, Antje Boetius1,6 and Alban Ramette1
1HGF-MPG Group for Deep Sea Ecology and Technology, Max Planck Institute for Marine Microbiology,
Bremen, Germany; 2Jacobs University Bremen GmbH, Bremen, Germany; 3Josephine Bay Paul Center,
Marine Biological Laboratory, Woods Hole, MA, USA; 4Alfred Wegener Institute for Polar and Marine
Research, List/Sylt, Germany; 5School of Engineering, University of Glasgow, Glasgow, UK and
6HGF-MPG Group for Deep Sea Ecology and Technology, Alfred Wegener Institute for Polar and Marine
Research, Bremerhaven, Germany
Coastalsandsfilterandaccumulateorganicandinorganicmaterialsfromtheterrestrialandmarine
environment, and thus provide a high diversity of microbial niches. Sands of temperate climate
zones represent a temporally and spatially highly dynamic marine environment characterized by
strongphysicalmixingandseasonalvariation.Yetlittleisknownaboutthetemporalfluctuationsof
residentandraremembersofbacterialcommunitiesinthisenvironment.Bycombiningcommunity
fingerprinting via pyrosequencing of ribosomal genes with the characterization of multiple
environmentalparameters,wedisentangledtheeffectsofseasonality,environmentalheterogeneity,
sediment depth and biogeochemical gradients on the fluctuations of bacterial communities of
marinesands.Surprisingly,only3–5%ofallbacterialtypesofagivendepthzonewerepresentatall
times,but50–80%ofthembelongedtothemostabundanttypesinthedataset.About60–70%ofthe
bacterial types consisted of tag sequences occurring only once over a period of 1 year. Most
membersoftherarebiospheredidnotbecomeabundantatanytimeoratanysedimentdepth,but
varied significantly with environmental parameters associated with nutritional stress. Despite the
large proportion and turnover of rare organisms, the overall community patterns were driven by
deterministicrelationshipsassociatedwithseasonalfluctuationsinkeybiogeochemicalparameters
relatedtoprimaryproductivity.Themaintenanceofmajorbiogeochemicalfunctionsthroughoutthe
observationperiodsuggeststhatthesmallproportionofresidentbacterialtypesinsandsperform
thekeybiogeochemicalprocesses,withminimaleffectsfromtherarefractionofthecommunities.
The ISME Journal(2012)6, 542–553;doi:10.1038/ismej.2011.132;published online6October2011
Subject Category: microbial population and community ecology
Keywords: 454pyrosequencing;coastalseas;bacterialdiversity;multivariateanalysis;rarebiosphere
Introduction advected by currents and winds, including dis-
solved and particulate organic matter derived from
Marine coasts representhighly dynamic ecosystems
living and dead biomass of terrestrial or marine
where the atmosphere, continents and the ocean
origin (de Beer et al., 2005). Sandy sediments are
interact. Permeable sands constitute the dominant
also constantly subjected to biotic (e.g., bioturba-
sediment type on continental shelves (Emery, 1968;
tion) and abiotic disturbances (e.g., mixing by
Boudreau et al., 2001) and have a central role for
currents,seasonalandtidaltemperaturefluctuation,
global carbon and nutrient cycles. They act as
anoxia; Boudreau et al., 2001). They may also host
biocatalytic filters for various types of materials
human pathogens, depending on the human impact
by, for example, recreational use of beaches, and
temperature anomalies (Ruppert et al., 2004; Dins-
Correspondence: A Ramette, HGF-MPG Group for Deep Sea dale et al., 2008).
Ecology and Technology, Max Planck Institute for Marine The high diversity of niches provided by the
Microbiology,Celsiusstrasse1,28359Bremen,Germany.
sand habitat supports a rich community of bacteria
E-mail:[email protected]
7Currentaddress:EvolutionofPlanktonandPaleoOceans(EPPO) fluctuating with environmental variations (e.g.,
team,UMR7144CNRS–UPMC,StationBiologiquedeRoscoff, Hunter et al., 2006; Sørensen et al., 2007; Mills
Roscoff,France. et al., 2008; Rusch et al., 2009; Gaidos et al., 2011).
8Current address: Federal Institute of Hydrology, Koblenz,
Yet, the dynamics of environmental parameters in
Germany.
coastal sands could still be a challenge to many
Received2May2011;revised29July2011;accepted25August
2011;publishedonline6October2011 microbialpopulations,selectingforfew,buttolerant
Bacterialbiosphereofcoastalsands
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543
and well-adapted resident types (Gaidos et al., sands over six sampling dates within a year (2005–
2011). Some of these produce extracellular poly- 2006), and over different sediment depths (0–5cm,
meric substances to attach to sand grain surfaces. 5–10cm, 10–15cm). For this purpose, we combined
The resulting biofilm habitat can be considered as several data sets from the Long Term Ecological
an active bioreactor accumulating diverse microbial Research (LTER) Station, Sylt, Germany, including
populations, their extracellular enzymes, nutrients 454 massively parallel tag sequencing (MPTS) target-
and organic matter from various origins (Flemming ing the V6 region of the 16S rRNA gene (Gobet et al.,
and Wingender, 2010). The pore volume between 2010) and concurrent environmental surveys (e.g.,
the sand grains represents a place of constant nutrients, enzymatic activities, bacterial production,
particle exchange due to turbulent flow of currents weatherandwaterparameters;Bo¨eretal.,2009a,b),to
and pore-water advection, which may induce signi- obtain as complete a description as possible of the
ficant fluxes of particulate organic matter (Stoodley changes in the bacterial community in their environ-
et al., 2005). It remains yet unknown whether sand mentalcontext.Wefalsifiedthefollowinghypotheses:
grain-associated biofilms trap microbes from terres- (1) bacterial communities in coastal sands have the
trial sources and from the water column flushing same composition across sediment depth and in
through the sand, and if they are a source of comparison with the overlying water column, (2) as
diversity to the sea around them. As they represent a biofilm community, their temporal fluctuation is
atransitionzonebetweenlandandoceancharacter- low, and (3) the rare microbial biosphere in sands
izedbyhighmixingrates,thepresenceofmanyrare does not respond to environmental changes.
bacterial types could be expected. In coarse-grained
reef sediments, it has been shown that the typical
Materials and methods
patternsofraremicrobialtypesfoundinDNA-based
libraries (i.e., the characteristic long tail in rank-
Study site, sampling procedures and contextual
abundance curves; Pedro´s-Alio´, 2007) could not be
parameters
observedinRNA-basedlibraries,suggestingthatthe Detailed sample processing and environmental mea-
rare biosphere may be inactive (Gaidos et al., 2011). surementshavebeenpublishedelsewhere(Bo¨eretal.,
So far, few studies have examined the structure 2009a). Briefly, sediment samples were repeatedly
and ecology of microbial communities in coastal collected every 2–3 months on a shallow (0.5–2.5m),
sediments, and these were mostly based on low- subtidalsandflatoftheLTERsiteattheislandofSylt
resolution community fingerprinting approaches in the North Sea (551 000 47.700 N, 81 250 59.300 E)
(Urakawaetal.,2000;BerticsandZiebis,2009;Bo¨er betweenFebruary2005andMarch2006.Sandycores
et al., 2009b). Although those molecular techniques of 15cm were directly sectioned into three 5cm
have provided a first description of abundant layers. Subsamples were stored at (cid:2)201C for DNA
populations, they generally did not describe rare extraction or used for the measurement of contextual
populations that might represent considerable parameters (extracellular enzymatic activities, nutri-
diversity (Acinas et al., 2004). High-throughput ents, pigments, carbohydrates, bacterial cell counts;
sequencing strategies such as shotgun sequencing Bo¨er et al., 2009a). Water column data consisting of
(Venter et al., 2004) or pyrosequencing (Sogin et al., chlorophyll a, pH and water temperature, were
2006) allow a deeper exploration of microbial obtained from the Sylt LTER time series (van
diversity. Pyrosequencing of ribosomal genes has Beusekom et al., 2009), and data on wind speed was
already permitted the description of the rare micro- obtainedfromtheGermanWeatherService(Deutsche
bial biosphere in several types of ecosystems, such Wetterdienst; weather station of List at Sylt).
as surface and deep-sea water (Sogin et al., 2006), To compare the communities of sediments, pore
extremeenvironments(Huberetal.,2007;Brazelton water and overlying water, at one time, additional
et al., 2010), or the human hand surface (Fierer samplesweretakenatSyltKo¨nigshafeninApril2008
et al., 2008). Patterns of the rare biosphere in the (sediments551 20 2800 N,81 240 2600 E; seawater551 10
Arctic Ocean have been related to differences in 4100 N,81260 1000 E).Seawaterof1lwassuccessively
water masses and attributed to dispersal limitation filteredthrough10mmand0.2mmfilters(typeGTTP;
(Galand et al., 2009). Here we focus on temporal diameter 47mm) before DNA extraction. Pore water
fluctuations of resident and of rare microbial types oftheupper5cmofthesedimentwasseparatedfrom
in coastal sands, and on their link with fluctuations thesandgrains(withbacterialbiofilms)bylow-speed
in environmental parameters. centrifugation (10min at 1801g, at 41C) through
Including the rare microbial biosphere in ecolo- GF-C filters (47mm). The resulting ‘dry’ sediment
gical analyses is challenging when considering the (about 8g) and 2-ml pore water (filtered through
potentialfortechnicalnoiseinpyrosequencingdata 0.2-mm GTTP-type filters; diameter 47mm) were
(Quince et al., 2009; Kunin et al., 2010). Here we subsequently used for DNA extraction.
apply a new analytical method to assess the effects
of rarity on community structure and ecological
interpretation (Gobet et al., 2010). The main objec- DNA extraction and 454 MPTS
tive ofthis study was toexplore theputativefactors Forsamplestakenin2008,DNAwasextractedusing
structuring bacterial diversity in temperate coastal an UltraClean Soil DNA Isolation Kit (MoBio
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Laboratories Inc., Carlsbad, CA, USA) from (1) ca. Acinetobacter, Rickettsiella, Pseudomonas and
10g of homogenized wet sediment separated in the Ralstonia)) were standardized by Hellinger transfor-
dry sand and the pore water, and (2) 1l of water mation (Legendre and Gallagher, 2001). A forward
filtered through 0.2-mm filters (pore water and selection(basedonacanonicalredundancyanalysis
water column) cut into pieces with a sterilized algorithm and 999 Monte Carlo permutation tests)
cutter. DNAwas further stored in a final volume of oftheenvironmentalfactorswasdonetofindtheset
50–100ml of Tris-EDTA buffer. To study temporal of parameters that could best explain the variation
fluctuations and vertical community patterns in in the community table. The best-fitting models
sediments, DNA from samples collected between werechosenusingtheAkaikeInformationCriterion.
2005–2006 were similarly extracted from 1g of Canonical variation partitioning (Borcard et al.,
sediment, as described previously (Bo¨er et al., 1992; Ramette and Tiedje, 2007b) was then applied
2009b). on the community data to disentangle the specific
DNA was deposited within the International effects of environmental variables (pigments, nutri-
Census of Marine Microbes project (http://icomm. ents, extracellular enzymatic activities, cell abun-
mbl.edu/), where it was amplified using primers dance) selected previously and of their covaria-
targeting the V6 region of the bacterial 16S rRNA tion on microbial community structure. Statistical
gene, and which included 454 Life Science’s A or analyses were carried out using the R statistical
B sequencing adapter according to Sogin et al. environment(Rversion2.10.0,RDevelopment Core
(2006).PyrosequencingwasperformedonaGenome Team,2009),using thevegan(Oksanenetal.,2009),
Sequencer 20 system (Roche, Basel, Switzerland) gplots (Bolker et al., 2009) packages, and custom R
at 454 Life Sciences (Branford, CT, USA) by primer scripts.
extension (Margulies et al., 2005), yielding on
average 60-bp long fragments after removing primer
sequences. Results and discussion
Thebacterialcommunitycompositionofcoastalsands
Sequence analysis The three compartments, sand, pore water and the
Data from the 454 MPTS were retrieved from the overlyingwatercolumn, maybeexpected toshare a
publicly available ‘Visualization and Analysis of large proportion of the same microbial assemblages,
Microbial Populations Structure (VAMPS)’ website because the pore space of permeable sands is
(http://vamps.mbl.edu/; projects AB_SAND_Bv6 constantly flushed by the overlying water, trapping
and ICM_FIS_Bv6) in the framework of the Inter- detritus and living cells from the water column
national Census of Marine Microbes project. (Boudreau et al., 2001). We compared bacterial
Sequences were taxonomically assigned by an auto- communities from one large, well-mixed sample of
maticannotationpipeline(Sogin etal.,2006),using each compartment (sand, pore water and water
several known databases (Entrez Genome, RDP, column) at one time, to investigate the similarity
SILVA), allowing a taxonomic annotation of up to in community composition between those coastal
94% of the data set. In our study, analyses were compartments,whichareconstantlymixedbywind
basedonadefinitionofoperationaltaxonomicunits and waves. However, microscopic observations
(OTU) as unique (i.e. two sequences belong to two (Figure 1a) confirm that sand grains are covered by
different OTU , when they differ by at least one a biofilm consisting of cells embedded in extra-
unique
base) to keep a consistent definition throughout. cellular polymeric substances, and that there is
Otherclassificationsusedinthestudyareindicated potentially little exchange with cells flowing
as follows: OTU represents all, un-annotated through the sediment (Rusch et al., 2001). At any
all
sequences, PyroNoise and PyroNoise corre- time, the pore water contains less than 0.2% of the
0% 3%
spond to the PyroNoise-corrected data sets at 0% cellabundanceassociatedwiththesandgrains(this
and 3% sequence identity, respectively (Quince study and Rusch et al., 2003). Accordingly, at that
et al., 2009), OTU —x% rare, which represent the specific sampling time, we also found a rather low
all
OTU data set truncated of its x% rarer OTU proportion of shared bacterial populations between
all unique
according to Gobet et al. (2010). the sand and the water column, at the level of both
community composition and evenness (Figures
1b and c, Supplementary Figure S1). Only 2–3% of
Taxa-environment relationships all unique OTU (sequences from the original
unique
To investigate taxa-environment relationships, most OTU data set that have at least one nucleotide
all
of the parameters (except pH, water temperature, difference) were shared between the three compart-
wind speed and salinity) were log -transformed, ments, confirming previous findings (Epstein et al.,
10
whereas the community matrices (OTU data 1997; Llobet-Brossa et al., 1998).
all
set, and the resident single-sequence OTU rela- The Sylt (North Sea) water column was mainly
tive (SSO ) data sets or the (potential) patho- dominated by Bacteroidetes and by the Alpha- and
rel
gen sequence abundance matrix (including the Gamma-subdivisionsoftheProteobacteria,confirm-
genera Parachlamydia, Arcobacter, Francisella, ing previous results on the basis of fluorescence
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Figure 1 Microbial community distribution in the sand and the water column. (a) From top to bottom: acridine orange staining of
bacteriainthewatercolumn,theporewaterandonthesurfaceofasandgrain(scalebar¼50mm).(b)Relativenumberofsequencesin
differentcompartmentsforthetop5-cmsandlayerandintheoverlyingwatercolumninApril2008.Here,thephylumlevelwaschosen
for illustrative purposes. (c) Sequence distribution in the sand over time, in which eachbar represents an OTU (only OTU
unique unique
occurringmorethan100timesinthewholedatasetareshown).TheProteobacteriaphylumwasfurthersplitintoitscorresponding
classes,forexample,Alpha,Gamma,Delta;Cy,Cyanobacteria;Ba,Bacteroidetes;Aci,Acidobacteria;Others:Actinobacteria,NA,not
annotated Proteobacteria, Planctomycetes, Chloroflexi, Verrucomicrobia, WS3, Firmicutes, Lentisphaerae, Deferribacteres and
Gemmatimonadetes.
in situ hybridization and 16S rRNA gene-based and Spirochaetes (Scho¨ttner et al., 2011) in the
clone libraries (Glo¨ckner et al., 1999; Eilers et al., community. Acidobacteria sequences were abun-
2000; Zubkov et al., 2002). Sequences of the phyla dant in temperate (Figure 1b, Supplementary
Verrucomicrobia and Actinobacteria were also Figure S2) and tropical sands (Gaidos et al., 2011;
abundant (Figure 1b). The top 5cm of Sylt sands Scho¨ttneretal.,2011).Atthephylumlevel,thepore
were dominated by Bacteroidetes, Gammaproteo- water microbial composition resembled the sand
bacteria, Deltaproteobacteria and Planctomycetes, community with a dominance of Bacteroidetes,
also in accord with previous clone libraries Gammaproteobacteria, Deltaproteobacteria and
obtained from coastal sediments of the Wadden Acidobacteria (Figure 1b). At the OTU level,
unique
Sea(Llobet-Brossaetal.,1998;Musatetal.,2006),or substantial differences were detected between the
other permeable sediments from shelf sands pore water and the sand-associated bacterial com-
from South Atlantic Bight and coastal sediment in munity or the water column (Supplementary Figure
Hawaii(Hunteretal.,2006;Sørensenetal.,2007).In S1C). The further analysis of temporal fluctuations
contrast, tropical coral reef sands were less consis- was restricted to wet sands containing both the
tent, with a dominance of Proteobacteria, Actino- biofilm community, as well as the pore water types.
bacteria(Gaidosetal.,2011),orAlphaproteobacteria Certainly, temporal variation and the role of the
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environment in the exchange of microbial commu- shared between sediment layers or any two sam-
nities between land and sea, intertidal and subtidal pling dates. Only about 20% of sequences were
flats with their different physical compartments shared between the deeper layers and the upper
deserve further studies. layer(SupplementaryFigureS3A),thecommunities
The characterization of coastal sand communities of the two deeper layers were slightly more similar
oversixsamplingdates(Figure1)providedatotalof (Figure 2a). Moreover, resident OTUs seemed to be
197684 rRNA gene sequences, corresponding to characterizedbyahighandrelativelystablenumber
27630 OTU . Per sample, sequences ranged ofsequences(Figure 3b). About 70% of the resident
unique
from about 5000–19000, corresponding to 496– OTUs had abundances of more than 10 sequences
2993 OTU (i.e., OTU defined at 3% sequence persample(Table1,Figure3b),suggestingthatthey
3%
difference after denoising the V6 sequences by belongedtoarchetypalsandmicrobialcommunities.
PyroNoise; Supplementary Table S1). Noticeably, Whentimewasconsideredalone,only18–37%of
the high bacterial richness estimates for coastal OTU were found to be shared between anytwo
unique
sands were comparable to 454 MPTS-based esti- sampling times (Figure 2b, Supplementary Figure
matesreportedforcoarsecarbonatesandsfromcoral S3A). This indicates that a very large fraction of the
reefs(Gaidosetal.,2011),soils(Roeschetal.,2007), communitymaybeconstantlyreplaced.Yet,thefact
marine sediments and crusts (Sogin et al., 2006; thattheturnoverratedidnotincreasewithsampling
Huberetal.,2007;Brazeltonetal.,2010).Ahigh,yet time suggests that some populations vanished and
unexplored diversity in these habitats was already reappeared during the investigated time period
previously proposed based on clone libraries (Supplementary Figure S3). Some of the most
(Hunter et al., 2006; Mills et al., 2008; Rusch et al., sequence-abundant groups were found to be posi-
2009).Inaddition,herewefoundthattotalsequence tively correlated with time (Figure 4); for example,
and OTU numbers systematically increased with Gammaproteobacteria and Planctomycetes, which
depth from 0–15cm at most of the sampling dates followed the seasonal fluctuations in cell abun-
(Supplementary Table S1). A positive relationship dances observed in Sylt sediment (Musat et al.,
betweensedimentdepthandbacterialdiversitywas 2006). Remarkably, the fluctuation within a month
previously observed using different fingerprinting (beginning and end of March 2006) was almost as
techniques(Urakawaetal.,2000;Bo¨eretal.,2009b), high as within a year. This could be explained by
and explained by increasing physico-chemical the observed large environmental variations that
stability of the habitat. The Delta- and Beta- occurred in March 2006, starting with cold temp-
subdivisions of the Proteobacteria, Deferribacteres, eratures and high nutrient levels at the end of the
Spirochaetes and Nitrospira, all showed an increas- winter period, and ending with spring blooms and
ing OTU richness with depth. In contrast, Cyano- stormy conditions (Bo¨er et al., 2009b).
bacteria and Bacteroidetes decreased with depth Aninterestingaspectistheabundanceandroleof
(Supplementary Figure S2). the resident, potentially sand-biofilm associated
populations versus rare and transient populations
getting advected through the sands. Indeed, in our
Turnover of bacterial diversity with sediment depth data set, more than 50% of the OTU present at
unique
and time all times in the three depth layers were members of
The phylum to class levels are commonly used to the Gammaproteobacteria and Deltaproteobacteria,
describe the diversity of bacterial communities on two classes representing up to 23% and up to 10%,
the basis of whole-cell fluorescence in situ hybridi- respectively, of the total cell counts in such sands
zation or 16S-based clone libraries (Llobet-Brossa (Musat et al., 2006). But also, 40% of all OTU
unique
et al., 1998; Musat et al., 2006). At those levels, appearing only once in a given sample or in the
microbial community patterns in sands mostly whole OTU data set belonged to these classes.
all
remained unchanged over time and sediment depth Noticeably, the rare members of dominant classes
(Supplementary Figure S2). However, drastic never became abundant at any sampling time,
changes in bacterial community composition were similarly to observations made in Arctic waters
evidenced when finer taxonomic resolutions were (Galand et al., 2009).
used;overall,only152OTU (0.55%ofthetotal Observing a high turnover for a substantial
unique
number of 27630 OTU ) were present in all fraction ofthebacterialcommunityinmarinesands
unique
samples at all times, and their sequence abundance raises several questions about the ecological sig-
ranged from 54–6550. Also, only 3–5% of all nificance of these dynamics. Large turnover in
OTU within a sampling depth layer were community composition could be due to various
unique
present at all times (Table 1). The majority (77%) phenomena,whicharenotmutuallyexclusive,such
of these OTU , which we define as resident as: (1) migration of non-resident bacterial popula-
unique
OTU, consisted of the phyla Acidobacteria, Actino- tions into and out of an ecosystem (Sloan et al.,
bacteria, Bacteroidetes,Cyanobacteria,Alpha-,Delta- 2006). This could be due to the rapid physical
and Gammaproteobacteria and Verrucomicrobia. transport processes prevailing in marine coastal
For the OTU data set (i.e., data set with all sand ecosystems. (2) High adaptability of micro-
all
OTU ), a low percentage of OTU were bial communities to ever-changing and complex
unique unique
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Table1 TemporalpatternsofOTUvariationa
OTU SequencesimilaritythresholdsforPyroNoise-correcteddata
all
PyroN PyroN PyroN
0% 3% 5%
NumberofOTU(%)b
Total 19785(100) 9036(100) 7668(100) 5927(100)
SSO 13676(69) 5272(58) 3970(52) 2624(44)
abs
SSO 3735(19) 2136(24) 2268(30) 2087(35)
rel
DSO 1108(6) 697(8) 536(7) 386(6)
abs
TSO 236(1) 187(2) 170(2) 133(2)
abs
WithX5sequencesminimuminanysample 203(1) 124(1) 125(1) 135(2)
WithX10sequencesminimuminanysample 106(0.5) 67(0.7) 67(0.9) 69(1)
Temporalpatternsc
Feb Apr Jul Nov Mar1 Mar2
’ ’ ’ ’ ’ ’ 654(3) 394(4) 412(5) 443(7)
’ ’ ’ 29(0.2) 11(0.1) 12(0.2) 10(0.2)
’ ’ ’ 157(0.8) 110(1) 116(1) 106(2)
’ ’ ’ 34(0.2) 21(0.2) 23(0.3) 23(0.4)
’ ’ ’ 32(0.2) 18(0.2) 18(0.2) 19(0.3)
’ 1998(10/9)d 685(8/6) 515(7/5) 304(5/4)
’ 1371(7/6) 622(7/6) 492(6/5) 345(6/5)
’ 1807(9/8) 704(8/7) 554(7/6) 349(6/5)
’ 3657(18/17) 1571(17/15) 1171(15/13) 821(14/11)
’ 3539(18/16) 1706(19/15) 1301(17/13) 915(15/12)
’ 2833(14/13) 1018(11/9) 767(10/8) 501(8/7)
Typeoftemporalrelationshipse
Linear(total) 490(2) 287(3) 284(4) 261(4)
Linearincrease 375(2) 231(2) 230(3) 215(4)
Lineardecrease 115(0.6) 56(0.6) 54(0.7) 46(0.8)
Quadratic(total) 227(1) 113(1) 114(1) 103(2)
Quadraticpositive 208(1) 100(1) 97(1) 90(1)
Quadraticnegative 19(0.1) 13(0.1) 17(0.2) 13(0.2)
Abbreviations: DSO ,doublesequence OTU absolute; OTU, Operational Taxonomic Units; PyroN, PyroNoise; SSO , singlesequence OTU
abs abs
absolute, SSO , single sequence OTU relative; TSO , triple sequence OTU absolute; VAMPS, Visualization and Analysis of Microbial
rel abs
PopulationsStructure.
aSequences from the first top 10-cm sediment layers pooled were processed with the VAMPS pipeline and with the PyroNoise algorithm to
removepyrosequencingandPCRamplificationartifacts.Thresholdsfrom0–5%sequencesimilaritywereusedtodefineOTU.
bNumbersofsequencesaregivenforsingle-sequenceOTUoccurringinthewholedataset(SSO )oratleastinonesample(SSO ),double
abs rel
sequenceOTU(DSO ),ortriplesequenceOTU(TSO )inthewholedataset.Figuresinparenthesescorrespondtothepercentageofthetotal
abs abs
numberofsequencesinthefirsttop10-cmsedimentlayers.
cOccurrencepatternsweredefinedasthepresence(blacksquare)orabsenceofOTUatspecificsamplingdatesfromFebruary05(Feb),April05
(Apr),July05(Jul),November05(Nov),beginningofMarch06(1Mar),toendofMarch06(2Mar).
dThesecondvalueinparenthesesindicatestherespectivepercentageofSSO .
abs
eDifferent patterns of OTU abundance were examined by specifying linear or quadratic models of change with time. A positive quadratic
relationshipwithtimeimpliesaU-shaperelationship,whichisassociatedwithhighabundanceatthebeginningandattheendofthestudy,and
lowabundanceobservedatthethirdandfourthsamplingtimes.Anegativequadraticrelationshipwouldbeconverselydescribedbyaninverted
U-shapeabundancefunctionovertime.
environments, as previously observed in the labora- column and sanddescribed above mayindicate that
tory (Rosenzweig et al., 1994). (3) Emergence of the described bacterial community is typical of the
latent ‘rare’ prokaryotic stages that may become sand ecosystem, and that a large effect of extra-
dominant when appropriate conditions are met cellular DNA on our results is not likely. To further
(Pedro´s-Alio´, 2006). This fluctuation from rare to test hypotheses 1–3, we investigated the contribu-
dominanttypesmaybesupportedbythe‘seedbank’ tion of the rare biosphere to the overall community
hypothesis (Finlay, 2002). (4) Finally, the presence turnover and determined how the rare biosphere
of extracellular DNA could also have strongly depends on environmental conditions.
impactedourestimatesofcommunityturnover.This
could be possible because, first, sands are known to
act as natural filters that concentrate particles and Impact of the rare biosphere on community turnover
DNA in suspension (Naviaux et al., 2005). Second, The rare biosphere has been postulated to consist
our molecular approaches did not differentiate of low-abundance microbial organisms that would
between DNA from living cells and extracellular not be subjected to predation or viral lysis, and
DNAsettlingfromseawater(Corinaldesietal.,2005). would likely represent a huge proportion of micro-
However, the OTU dissimilarity between water bial communities, as generally indicated by long
unique
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Figure 2 Bacterial community turnover between two consecu-
tivedepthlayers(a)orsamplingtimes(b).ThepercentageofOTU
sharedbetweenasamplingdepth(ordate)andthepreviousone
wascalculatedafterPyroNoisecorrectionandOTUclusteringof
the454MPTSdatasetatseverallevelsofsequencedissimilarity.
Barscorrespondtos.d.calculatedover4–6samplingdates(a)or
three depth layers (b), except for July and November 2005 in
which two depth layers were considered. The first depth layer
andsamplingdate(February2005)areindicatedbythegreypoint
as100%ofOTU.OTU representstheoriginaldatasetwithall
all
OTU .Itisusedhereasareferencedatasettostudytheeffects
unique
of various correction levels on the observed dynamics of the
bacterialcommunitycomposition.
distributiontailsinrank-abundancecurves(Pedro´s-
Alio´,2007).Forexample,amongtherarebacteriain Figure3 Distributionofthe maximum abundanceof(a) SSO
rel
coastal sands were the potential human pathogens (i.e.,thoseOTU0%that,atleastinonesample,consistedofonly
onesequence)and(b)residentOTU (i.e.,OTU presentatall
(including Parachlamydia, Arcobacter, Francisella, 0% 0%
times) in the top 10-cm layer. In (a), panels 1, 2 and 3 are
Acinetobacter, Rickettsiella, Pseudomonas and
examplesofcases,inwhichparticularhighfluctuationsfromthe
Ralstonia) with a total of 16–88 sequences corre- single-sequence case (white arrow) to higher-sequence abun-
sponding to 2–54 OTU in the OTU data set dances were observed. In (b), panels 1, 2, 3 illustrate cases of
unique all
(0.22% of all sequences in OTU ). To understand particularlyhighfluctuationsinrelativeabundances(noabsolute
all abundanceswerecalculatedinthiscase).Alldatawereinitially
what fraction of the community may be associated
processed to remove pyrosequencing noise. Rel. abundance,
with the large diversity turnover observed in the relativeabundancetothetotalnumberofsequencespersampling
sands, wegradually removed increasing fractions of time: February 2005 (13285), April 2005 (7902), July 2005
the rare sequences in the data set starting from the (10910),November2005(21931),1March2006(17302),2March
2006(17897).
rarest ones (Gobet et al., 2010); the large turnover
previously described for the complete data set over
both sediment depth and time was no longer obser-
ved when up to 50% of the rare sequences were representing about 20% of all PyroNoise-corrected
removed (Supplementary Figure S3C), indicating OTU ), or as OTU appearing only once in the
0%
that most of the community turnover was due to whole data set (i.e., SSO absolute (SSO ), repre-
abs
changes in the rare tail of the data set. sentingabout58%oftheOTU ),ascomparedwith
0%
The rare biosphere could be identified as OTU the3–5%oftheOTU thatwereresident(Table1).
0%
appearing only once in a given sample (i.e., SSO , Moreover, when only the SSO fraction of the data
rel rel
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Bacterialbiosphereofcoastalsands
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Figure 4 Environmental factors associated with variations of the bacterial community structure at the phylum level. Pearson’s
r indicates correlations between phylum sequence abundance and several environmental parameters. For example, a red square
betweensedimentdepthandChloroflexiindicates a highernumberof sequenceswith increasingdepth;nonsignificantrelationships
between water temperature and sequence variation in any of the bacterial groups are indicated by white squares; a blue
square between chlorophyll a and Chloroflexi denotes a decrease in sequences as chlorophyll a concentration gets higher (the
latterbeingconcordantwiththerelationshipbetweenincreasingdepthandChloroflexisequencesnumber).TheProteobacteriaphylum
level was separated into its corresponding classes to obtain a higher resolution. NA-Proteobacteria are the Proteobacteria
with missing class annotation. The total number of sequences in each phylum is indicated in parentheses. SiO, silicate; PO,
2 4
phosphate; NO, nitrite; NO, nitrate; NH, ammonium; Chl a, chlorophyll a; Pheo, pheophytin; BCC, bacterial abundance; Bprod,
2 3 4
bacterial carbon production; Chit, chitinase; a-glu, a-glucosidase; b-glu, b-glucosidase; Lip, lipase; Amin, aminopeptidase; Phos,
phosphatase.
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set was retained, very similar fractions of the total Table S2), with all three parameters being highly
explained-biological variation were identified as for correlatedwithsedimentdepth.Cellabundancewas
the total data set (Supplementary Figure S4). This alsoidentifiedasanimportantfactorinfluencingthe
study therefore reports that not only a large fraction variation in the different OTU data sets (Supple-
of bacterial communities consists of rare types, as mentaryTableS2).Noticeably,communitydiversity
previously found in many 454 MPTS-based studies and composition varied much more than microbial
in other environmental samples (e.g., Gilbert et al., functions (e.g., biomass, benthic oxygen consump-
2009; Bolhuis and Stal, 2011), which may undergo tion and extracellular enzymatic activities). This
substantial replacement over few centimeters of may suggest that the few continuously abundant,
sedimentdepth,aspreviouslyobservedusinglower resident microbes perform these main microbial
resolution molecular techniques (Hewson et al., functions in sandy ecosystems, and that the rare
2007), but may also dramatically change over few types being replaced at high rates have little effects
months of time. In addition, rare bacterial types on bulk functions. Furthermore, with regard to
seemed to remain rare in the bacterial community heterotrophic degradation of organic matter, a sub-
over time and sediment depth; 94% of the SSO stantial level of functional redundancy may exist in
rel
had a maximum abundance of 10 sequences only thedominanttaxaoftheSyltsands,comprisingboth
(Figure 3a). In conclusion, both the presence of this resident and transient organisms.
large proportion of singleton OTU in sandy Our statistical analyses showed that the temporal
0%
sediments and the large turnover in community dynamics of bacterial communities in temperate
composition could be explained by the dispersal of sandy sediments were clearly distinct from the
OTU from other sand locations by advective trans- seasonallyreoccurring,cyclicbacterialpatternsthat
port and physical mixing (Boudreau et al., 2001). were observed in water column samples offshore
Further research would be needed to examine more Southern California (Fuhrman et al., 2006), in the
in detail the biogeography of rare and of resident Englishchannel(Gilbertetal.,2009),orintheBaltic
bacterial types in coastal sands. Sea (Andersson et al., 2010). Those patterns also
differed from the observed stability of the Arctic
bacterioplankton community through seasons
Ecological interpretation of overall microbial diversity (Kirchmanetal.,2010).Thisindicatesthatcontrast-
patterns ingpatternsoftemporalvariationmaybeassociated
To determine whether observed temporal patterns with different microbial habitats and ocean regions,
may be attributed to either deterministic (niche- thus inviting further studies on the main drivers
based) orstochasticprocesses, orboth(Rametteand of temporal fluctuation or stability in marine
Tiedje, 2007a), bacterial community structure was ecosystems.
interpreted in concert with previously published
contextual data (Bo¨er et al., 2009a) for the investi-
gated samples, to test the effects of time, sediment The rare biosphere responds to environmental drivers
depth,cellabundanceandbiogeochemicalgradients The biogeography (Galand et al., 2009) and the
(i.e., pigments,nutrientsandextracellularenzymes, temporal dynamics over thousands of years (Brazel-
Figure 4). A multivariate variation partitioning ton et al., 2010) of the rare microbial biosphere
approach (Borcard et al., 1992; Ramette and Tiedje, have previously been described in different marine
2007b) showed that biogeochemical gradients habitats, but not much is known about factors
(pigments, nutrients and extracellular enzymes), influencing the composition of the rare microbial
cell abundance and their covariation were directly biosphere.To better understand whether patternsof
relatedtothemajorchangesincommunitystructure the rare fraction were random or environmentally
(Supplementary Figure S4), as previously assessed driven, the proportion of SSO (Supplementary
rel
on the basis of a lower-resolution fingerprinting Table S3) in each sample was correlated with
technique for the most abundant OTUs (Bo¨er et al., environmental parameters. Notably, fluctuations in
2009b). This study, including also the rare types, SSO were not random (as determined by model
rel
showed that significant biogeochemical variables selectionusingMonteCarlopermutationtestandby
included in the most parsimonious multivariate selecting the lowest Akaike Information Criterion
models were qualitatively almost the same for the model values) and seemed to be negatively corre-
resident OTU, the phylum, OTU and SSO levels lated with pigments (chlorophyll a, pheopigments)
all rel
(SupplementaryFigureS4andSupplementaryTable and bacterial carbon production in the sediments
S2). Also, a greater amount of biological variation (Supplementary Figure S5). For OTU , the corre-
0%
couldbeexplainedwhen alowertaxonomicresolu- sponding Pearson correlation coefficients between
tion was used, leading to a less complex data set the proportions of SSO and chlorophyll a, pheo-
rel
(Gobet et al., 2010). pigments and bacterial carbon production were
Themainparameterssignificantlyinfluencingthe (cid:2)0.730, (cid:2)0.695 and (cid:2)0.626, respectively, and were
variation for several OTU definition levels were all highly significant. However, neither sediment
chlorophyll a, activity of the extracellular enzyme depthnorsamplingtimecouldsignificantlyexplain
phosphatase and cell abundance (Supplementary thevariationintheproportionofraretypes.Because
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Bacterialbiosphereofcoastalsands
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551
pigmentconcentrationandbacterialcarbonproduc- massiveparallel16SrRNAgenetagsequencing.ISME
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thoseparameterswouldindicatethatraritybecomes
6(7): 1149–1165.
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Acknowledgements
Huettel M, van Beusekom JEE et al. (2005). Transport
andmineralizationratesinNorthSeasandyintertidal
We acknowledge Martina Alisch, Marianne Jacob,
sediments, Sylt-Romo Basin, Wadden Sea. Limnol
Susanne Menger and Shalin Seebah for great assistance
Oceanogr50:113–127.
withSylt sampling andhelp withsample processing.We
CorinaldesiC,DanovaroR,Dell’AnnoA.(2005).Simulta-
thank Rafael Stiens for cell microscopy. This work was
neousrecoveryofextracellularandintracellularDNA
supported by the Marie Curie Early Stage Training
suitableformolecularstudiesfrommarinesediments.
fellowshipinMarineMicrobiology(MarMicESTcontract
Appl EnvironMicrobiol 71: 46–50.
MEST-CT-2004-007776 to AG) and by the International
Dinsdale EA, Pantos O, Smriga S, Edwards RA,
Max Planck Research School of Marine Microbiology
Angly F, Wegley L et al. (2008). Microbial ecology of
(AG), as well as by the Max Planck Society (AR, AB),
four coral atolls in the Northern Line Islands. PloS
Helmholtz Association (AB, JB) and Leibniz Program of
ONE3:e1584.
the DFG (AB). CQ is supported by an Engineering and
Eilers H, Pernthaler J, Glockner FO, Amann R. (2000).
Physical Sciences Research Council Career Acceleration
Culturabilityandinsituabundanceofpelagicbacteria
Fellowship(EP/H003851/1).Thesequencingandbioinfor-
from the North Sea. Appl Environ Microbiol 66:
matic infrastructure (VAMPS) were supported by grants
3044–3051.
from the Alfred P Sloan and the William M Keck
Emery K. (1968). Relict sediments on continental
Foundations to MLS. This is a contribution to the
shelves of the world. Am Assoc Pet Geol Bull 52:
InternationalCensus of Marine Microbes(ICoMM).
445–464.
EpsteinSS,AlexanderD,CosmanK,DompeA,Gallagher
S, Jarsobski J et al. (1997). Enumeration of sandy
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