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Ecological implications of daily-to-weekly dynamics of marine bacteria, archaea, viruses, and phytoplankton

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Ecological implications of daily-to-weekly dynamics of marine bacteria, archaea, viruses, and phytoplankton

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Content University*of*Southern*California*
Department*of*Biological*Sciences*
Marine*Biology/Biological*Oceanography*
*
*
*
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Ecological*implications*of*daily>to>weekly*dynamics*of*marine*bacteria,*
archaea,*viruses,*and*phytoplankton*
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Author*
David*M.*Needham*
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*
*
Advisor*
Jed*A.*Fuhrman*
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* * * * * August*2015
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*
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Submitted*in*support*of*the*degree*of*
PhD*
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Dissertation*Committee*
Dr.*Jed*Fuhrman,*Chair*
Dr.*David*Caron*
Dr.*John*Heidelberg*
Dr.*Eric*Webb*
Dr.*Frank*Corsetti*
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*
*
*
Acknowledgements.
! I!am!grateful!for!the!opportunity!to!study!marine!bacteria!and!viruses,!which!
have!been!a!topic!of!great!interest!of!mine!since!the!first!time!I!observed!them!in!the!
microscope.!That!opportunity!was!made!possible!through!the!NSF’s!REU!program!in!
2007.!I!studied!that!summer!with!Judy!O’Neil!who!was!kind!and!supportive!enough!
to!allow!me!to!contribute!to!her!research!program!in!a!way!that,!at!least!I!felt,!
allowed!me!to!discover!what!I!thought!was!interesting!and!“go!for!it.”!After!that!I!
was!sure!what!I!wanted!to!study!in!graduate!school,!but!wanted!more!experience,!
and!I!thank!Joseph!Needoba,!Tawnya!Peterson,!and!the!NSF!funded!Center!of!
Coastal!Margin!and!Prediction!that!opportunity.!Joe!and!Tawnya!welcomed!me!to!
their!lab!and!were!kind!and!enthusiastic!and!continue!to!be!inspirations!to!me.!!
! I!thank!Jed!Fuhrman!for!allowing!me!to!grow!as!a!researcher!in!his!lab!during!
my!PhD!research.!His!support,!brilliance,!curiosity,!and!personal/professional!
consistency!encouraged!me!to!be!very!driven!and!comfortable!in!his!lab.!Jed’s!
motivation!to!address!relatively!‘big!questions’!while!also!being!detailToriented!is!
something!I!realized!and!appreciated!early!on.!I!also!thank!my!dissertation!
committee!members!Dave!Caron,!John!Heidelberg,!Eric!Webb,!and!Frank!Corsetti,!as!
well!as!Doug!Capone.!!
! I!thank!my!friends!and!family!who!have!been!supportive!throughout!my!time!
at!USC.!!
! I!thank!all!of!the!many!USC!students!and!colleagues!who!have!contributed!
helping!hands!and!ideas!throughout!my!time!at!USC,!especially!Rohan!Sachdeva,!
Jacob!Cram,!Cheryl!Chow,!Alma!Parada,!Joshua!Steele,!Anand!Patel,!Victor!
HernandoTMorales,!Laura!GomezTConsarnau,!Cathy!Roney,!Erin!Fichot,!Ella!
Sieradzki,!Elizabeth!Teel,!Jennifer!Chang,!Victoria!Trinh,!Victoria!Campbell,! Michael!
Morando,!Alle!Lie,!Troy!Gunderson,!Nathan!Ahlgren,!and!Lyria!Berdjeb,! !
! I!thank!the!NSF!and!the!Gordon!and!Betty!Moore!Foundation!for!financial!
support!and!the!Wrigley!Institute!of!Environmental!Studies!and!for!financial!
support!and!extensive!logistical!support!of!the!daily!timeTseries!on!which!I!report.!!!
Contents'
'
Introduction'(pp'114)'
'
Chapter'1:'Short1term'observations'of'marine'bacterial'and'viral'
communities:'patterns,'connections'and'resilience'(pp'6118)'
'
Chapter'2:'Time1series'of'phytoplankton,'Archaeal,'Bacterial'taxa'following'a'diatom'
bloom'reveals'pronounced'daily'succession'and'realized'niches'(pp'19155)'
'
Chapter'3:'Diversity,'dynamics,'and'co1occurrence'of'T41like1myoviruses'and'
microbial'taxa'using'single'nucleotide'resolution'and'ITS'sequencing'of'SAR11 '(pp'
56191)'
'
Summary/Conclusion'(pp'92196)'
Introduction*
! The!smallest!cellular!organisms!in!the!oceans,!bacteria!and!archaea,!dictate!
the!fate!of!about!half!of!the!organic!material!on!the!planet!as!they,!among!other!
functions,!remineralize!organic!material,!perform!25%!of!global!photosynthesis,!
transform!globablly!relevant!elements!like!N,!P,!Fe!into!their!various!forms!
(Kirchman!2000,!2008).!Unlike!terrestrial!ecosystems!where!biomass!has!a!long!
turnover!times,!(e.g.,!forests),!the!marine!environment!is!highly!dynamic!since!the!
majority!of!the!primary!production!is!performed!by!free!living!organisms!with!
generation!times!on!the!order!of!days.!The!biological!entities!composing!these!
complex!communities!span!an!extremely!large!size!range!and!abundances!from!viral!
pathogens!(around!0.05µm!diameter!at!10
5
L10
7
/mL)!in!surface!waters),!bacteria!
and!archaea!(around!1µm!at!10
5
L10
6
/mL)!to!chains!of!phytoplankton!cells!several!
millimeters!across.!These!organisms!influence!the!abundance!and!function!of!one!
another!constantly,!including!interactions!central!to!ecology!such!as!competition,!
grazing,!mutualism,!and!parasitism.!!Thus!the!abundance!and!function!of!particular!
microbial!populations!at!any!time!is!controlled!by!cumulative!outcome!of!these!
factors!in!addition!to!the!environmental!variation!caused!by!hydrological!and!
climatological!variation!which!mixes!water,!altering!its!chemical!and!physical!
properties.!!
! Marine!microbial!communities!harbor!vast!amounts!of!diversity,!with!
representatives!for!most!of!the!lineages!of!life,!including!the!eukaryotic!lineages.!
Broadly,!within!the!bacterial!and!archaeal!communities!of!the!surface,!communities!
are!dominated!by!many!phyla,!yet!even!at!low!levels!there!is!ecological!
differentiation!between!very!close!relatives!(within!a!typically!defined!species).!
Understanding!how!this!diversity!is!related!to!function!within!an!ecosystem!is!
challenging,!but!insights!may!be!gained!by!studying!the!distributions!of!taxa!in!the!
environment!over!spatial!gradients!and!timeLseries.!In!particular,!multiple!timeL
series!have!revealed!that!the!marine!bacterial!communities!at!particular!locations!
around!the!globe!are!often!seasonal!and!annually!repeating!(Fuhrman!et!al.!2006;!
Gilbert!et!al.!2012),!suggesting!that!the!bacterial!types!found!at!these!locations!are!
tied!to!seasonal!cycles!and!are!not!functionally!redundant.!TimeLseries!have!also!
1
revealed!some!organisms!tend!to!be!abundant!when!overall!community!turnover!
times!are!relatively!rapid,!and!others!when!it!is!slower!and!that!bacterial!lineages!
seem!to!be!more!correlated!to!one!another!than!they!are!to!environmental!
variables,!suggesting!interactions!are!likely!and!common.!Broad!interest!in!
understanding!the!longLterm!trends!of!the!marine!microbial!communities!and!
logistical!considerations!have!resulted!in!several!oceanic!timeLseries!being!sampled!
at!a!monthly!interval!for!many!years,!though!weeklyLtoLfortnightly!has!occasionally!
been!used(Sintes!et!al.!2013;!Lindh!et!al.!2015).!However,!given!that!turnover!times!
of!microbial!communities!in!the!surface!ocean!is!on!the!order!days,!a!shorter!timeL
scale!may!reveal!insights!not!apparent!from!by!more!coarse!timeLseries.!!
! Frequently!sampled!oceanic!timeLseries!have!revealed!that,!in!addition!to!
obvious!diel!variation!of!primary!productivity,!there!are!also!often!diel!trends!in!
bacterial!growth!rates,!viral!production,!without!much!variation!from!dayLtoLday!in!
general.!However,!few!studies!have!reported!on!the!microbial!community!and!
dynamics!over!daysLtoLweeks.!A!study!in!Danish!coastal!waters!showed!that!
bacterial!communities!from!consecutive!days!were!similar!between!days(Riemann!
and!Middelboe!2002).!Another!study,!showed!bacterial!communities!around!Santa!
Catalina!Island!were!quite!stable!from!dayLtoLday,!but!some!taxa!displayed!growthL
curve!like!increases(Steele!2010).!One!time!series!that!sampled!a!phytoplankton!
bloom!in!the!North!Sea!at!3!day!intervals!for!a!couple!of!months!reported!marked!
variation!in!the!abundances!in!the!dominant!lineages!present!and!their!function!
over!the!course!of!the!succession(Teeling!et!al.!2012).!Recently,!extended,!weekly!
sampled!timeLseries!over!one!year!found!that!periods!of!stability!were!often!broken!
up!by!periods!of!relatively!rapid!change!throughout!the!year(Sintes!et!al.!2013),!
suggesting!that!bacterial!communities!can!respond!quickly!to!environmental!
perturbations.!
! One!of!the!main!topLdown!controls!on!bacterial!communities!in!the!
environment!is!viral!infection.!This!infection!occurs!in!a!hostLspecific!way.!In!
general,!it!is!thought!that!viruses!infect!within!a!species,!but!there!are!examples!of!
viruses!which!can!infect!across!genera.!This!specificity,!in!addition!to!the!fact!that!
viral!proliferation!is!densityLdependent,!has!lead!to!the!“KillLtheLWinner!hypothesis!
2
(KtW)”.!Qualitative!descriptions!of!the!model!have!differed!in!their!details!over!time,!
but!the!original!model!suggested!that!the!“Winners”!are!the!taxa!that!would!grow!to!
high!abundance!(best!competitors)!if!not!for!viral!infection!whereby!they!are!
infected!and!reduced!to!a!lower!abundance!and!replaced!by!a!nearlyLasLcompetitive!
resource!competitor.!Recently,!variations!of!the!KtW!suggest!that!the!dynamics!may!
act!at!the!strain!level(Thingstad!et!al.!2014),!not!between!species.!At!least!one!study!
is!consistent!with!this,!where!it!was!found!that!species!abundances!were!stable!
geochemically!maintained!ponds,!but!strains!were!variable!over!time(RodriguezL
Brito!et!al.!2010).!However,!field!studies!that!investigate!the!dynamics!of!bacteria!
and!viruses!to!help!determine!how!each!of!these!communities!change!over!time!are!
rare.!!
! Given!the!rapid!turnover!times!or!a!few!days!in!most!surface!marine!
microbial!communities,!the!dailyLtoLweekly!timeLscale!provide!insight!into!how!
these!communities!are!structured!and!controlled,!but!this!timeLscale!has!hardly!
begun!to!be!explored!for!these!types!of!analysis.!Therefore,!we!set!out!to!perform!
extended!daily!timeLseries!where!we!would!explore!the!dynamics!of!microbial!
communities!and!their!viruses!to!address!the!following!simple!questions:!
! How!do!microbial!communities!vary!over!time?!
! How!do!viruses!influence!microbial!communities?!
! Exploration!of!various!specific!aspects!of!these!two!questions!follows!in!the!3!
chapters!of!this!dissertation.!!
! Chapter!1!entitled!“ShortLterm!observations!of!marine!bacterial!and!viral!
communities:!patterns,!connections!and!resilience”!explores!variation!using!DNA!
fingerprinting!over!about!80!days!with!38!days!of!near!daily!sampling!during!a!
period!of!relative!stability!during!the!summer!of!2010!about!1L2!km!offshore!of!
Santa!Catalina!Island.!
! Chapter!2!entitled!“TimeLseries!of!phytoplankton,!Archaea,!Bacteria!
following!a!diatom!bloom!reveals!pronounced!daily!succession!and!realized!niches!
of!taxa”!reports!the!microbial!variation!associated!with!a!massive!phytoplankton!
bloom!in!the!spring!of!2011!at!the!San!Pedro!Ocean!timeLseries,!and!followed!
through!until!the!late!summer.!This!study!used!16S!rDNA!ribosomal!subunit!gene!
3
sequencing!to!characterize!the!freeLliving!and!attached!bacteria!and!archaea,!in!
addition!to!applying!it!in!a!relatively!novel!way!to!characterization!of!the!eukaryotic!
phytoplankton!via!sequencing!16S!of!chloroplast.!!
! Chapter!3!entitled!“Diversity,!dynamics,!and!coLoccurrence!of!T4LlikeL
myoviruses!and!microbial!taxa!using!single!nucleotide!resolution!and!ITS!
sequencing!of!SAR11”!reports!how!viruses!responded!to!the!major!phytotoplankton!
bloom!which!was!described!in!Chapter!2.!We!also!explore!how!!‘classic’!arbitrarily!
defined!similarity!cutoffs!(e.g.,!99%!sequence!similarity)!may!be!decomposed!into!
finer!units!resolved!by!single!base!differences!in!marker!genes!and!the!implications!
it!has!on!ecological!interpretations.!Finally,!we!apply!a!novel!sequencing!assay!to!
explore!the!underlying!diversity!and!dynamics!of!perhaps!the!most!diverse!and!
abundant!lineage!on!the!planet,!SAR11.!!!
!
References*
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.!
Gilbert,!J.!A.,!J.!A.!Steele,!J.!G.!Caporaso,!L.!Steinbrück,!J.!Reeder,!B.!Temperton,!S.!
Huse,!A.!C.!McHardy,!R.!Knight,!I.!Joint,!P.!Somerfield,!J.!A.!Fuhrman,!and!D.!Field.!
2012.!Defining!seasonal!marine!microbial!community!dynamics.!ISME!J!6:!298–
308.!
Kirchman,!D.!L.,!ed.!2000.!Microbial!ecology!of!the!oceans,!WileyLLiss.!
Kirchman,!D.!L.,!ed.!2008.!Microbial!ecology!of!the!oceans,!2nd!ed.!WileyLBlackwell.!
Lindh,!M.!V,!J.!Sjöstedt,!A.!F.!Andersson,!F.!Baltar,!L.!W.!Hugerth,!D.!Lundin,!S.!
Muthusamy,!C.!Legrand,!and!J.!Pinhassi.!2015.!Disentangling!seasonal!
bacterioplankton!population!dynamics!by!highLfrequency!sampling.!Environ!
Microbiol!10.1111/14,!doi:10.1111/1462L2920.12720!
Riemann,!L.,!and!M.!Middelboe.!2002.!Stability!of!bacterial!and!viral!community!
compositions!in!Danish!coastal!waters!as!depicted!by!DNA!fingerprinting!
techniques.!Aquat!Microb!Ecol!27:!219–232.!
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RodriguezLBrito,!B.,!L.!Li,!L.!Wegley,!M.!Furlan,!F.!Angly,!M.!Breitbart,!J.!Buchanan,!C.!
Desnues,!E.!Dinsdale,!R.!A.!Edwards,!B.!Felts,!M.!Haynes,!H.!Liu,!D.!Lipson,!J.!
Mahaffy,!A.LB.!MartinLCuadrado,!A.!Mira,!J.!Nulton,!L.!Pašić,!S.!Rayhawk,!J.!
RodriguezLMueller,!F.!RodriguezLValera,!P.!Salamon,!S.!Srinagesh,!T.!F.!
Thingstad,!T.!Tran,!R.!V.!Thurber,!D.!Willner,!M.!Youle,!and!F.!Rohwer.!2010.!
Viral!and!microbial!community!dynamics!in!four!aquatic!environments.!ISME!J!
4:!739–751.!
Sintes,!E.,!H.!Witte,!K.!Stodderegger,!P.!Steiner,!and!G.!J.!Herndl.!2013.!Temporal!
dynamics!in!the!freeLliving!bacterial!community!composition!in!the!coastal!
North!Sea.!FEMS!Microbiol!Ecol!83:!413–24.!
Steele,!J.!A.!2010.!Marine!Bacterioplankton!Biogeography!of!over!short!to!medium!
spatioLtemporal!scales.!University!of!Southern!California.!
Teeling,!H.,!B.!M.!Fuchs,!D.!Becher,!C.!Klockow,!A.!Gardebrecht,!C.!M.!Bennke,!M.!
Kassabgy,!S.!Huang,!A.!J.!Mann,!J.!Waldmann,!M.!Weber,!A.!Klindworth,!A.!Otto,!J.!
Lange,!J.!Bernhardt,!C.!Reinsch,!M.!Hecker,!J.!Peplies,!F.!D.!Bockelmann,!U.!
Callies,!G.!Gerdts,!A.!Wichels,!K.!H.!Wiltshire,!F.!O.!Glöckner,!T.!Schweder,!and!R.!
Amann.!2012.!SubstrateLcontrolled!succession!of!marine!bacterioplankton!
populations!induced!by!a!phytoplankton!bloom.!Science!336:!608–611.!
Thingstad,!T.!F.,!S.!Våge,!J.!E.!Storesund,!R.LA.!Sandaa,!and!J.!Giske.!2014.!A!theoretical!
analysis!of!how!strainLspecific!viruses!can!control!microbial!species!diversity.!
Proc!Natl!Acad!Sci!U!S!A!111:!7813–8.!

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Chapter!1:!“Short&term)observations)of)marine)bacterial)and)viral)communities:)patterns,)connections)and)
resilience”)was)originally)published)in)The)International,Society,of,Microbial,Ecology.)
))
Please!cite:)Needham,)D.)M.,)C.&E.)T.)Chow,)J.)A.)Cram,)R.)Sachdeva,)A.)Parada,)and)J.)A.)Fuhrman.)2013.)
Short&term)observations)of)marine)bacterial)and)viral)communities:)patterns,)connections)and)resilience.)
ISME)J)7:)1274–85.")
)
)
6
ORIGINAL ARTICLE
Short-termobservationsofmarinebacterialandviral
communities: patterns, connections and resilience
David M Needham, Cheryl-Emiliane T Chow, Jacob A Cram, Rohan Sachdeva,
Alma Parada and Jed A Fuhrman
University of Southern California, Department of Biological Sciences, Los Angeles, CA, USA
Observation of short-term temporal variation in bacterial and viral communities is important for
understanding patterns of aquatic microbial diversity. We collected surface seawater once daily for
38 consecutive days with seven more samples interspersed over 40 more days at one location
B2km from Santa Catalina Island, California. Bacterial communities were analyzed by automated
ribosomal intergenic spacer analysis (ARISA) and viral communities were analyzed by terminal
restrictionfragmentlengthpolymorphism(TRFLP)oftheconservedT4-likemyoviralgeneencoding
the major capsid protein (g23). Common bacterial and viral taxa were consistently dominant, and
relatively few displayed dramatic increases/decreases or ‘boom/bust’ patterns that might be
expected from dynamic predator-prey interactions. Association network analysis showed most
significant covariations (associations) occurred among bacterial taxa or among viral taxa and there
were several modular (highly-interconnected) associations (Pp0.005). Associations observed
between bacteria and viruses (Pp0.005) occurred with a median time lag of 2 days. Regression of
allpairwiseBray-Curtissimilaritiesbetweensamplesindicatedarateofbacterialcommunitychange
thatslowsfrom2.1%–0.18%perdayoveraweekto2months;theratestaysaround0.4%perdayfor
viruses. Our interpretation is that, over the scale of days, individual bacterial and viral OTUs can be
dynamic and patterned; resulting in statistical associations regarded as potential ecological
interactions. However, over the scale of weeks, average bacterial community variation is slower,
suggesting that there is strong community-level ecological resilience, that is, a tendency to
converge towards a ‘mean’ microbial community set by longer-term controlling factors.
The ISME Journal (2013) 7, 1274–1285; doi:10.1038/ismej.2013.19; published online 28 February 2013
Subject Category: microbial population and community ecology
Keywords: ARISA; bacteria; marine; T4-like myovirus; network; time-series
Introduction
Marine bacterial and viral communities are diverse,
active, interconnected and critical components to
ecosystem production, nutrient recycling and bio-
geochemical pumps (Brussaard et al., 2008;
Fuhrman, 2009). Time-series studies aiming to
observe the temporal dynamics in marine microbial
communities typically do so at a monthly scale and
indicate that bacterial and viral communities are
seasonally dynamic and annually repeating (for
example, Brown et al., 2005; Fuhrman et al., 2006;
Carlson et al., 2009; Eiler et al., 2009; Campbell
et al., 2011; Eiler et al., 2011; Gilbert et al., 2011;
Parsons et al., 2011; Treusch et al., 2011; Chow and
Fuhrman, 2012; Giovannoni and Vergin, 2012).
Extended, daily sampling might reveal ecological
community dynamics—both subtle and dramatic—
in bacteria and virus communities missed by
monthly sampling, and would help contextualize
the longer-term monthly and intra-annual investiga-
tions. The few published reports so far using
molecular fingerprinting approaches for these
shorter time-scales have suggested that bacterial
and virus community composition is relatively
stable over days to weeks (Riemann and
Middelboe,2002;Hewsonetal.,2006;Steele,2010).
Turnover times of bacteria and virus communities
in the surface ocean is generally in the range of 3–5
days (Noble and Fuhrman, 2000) and can vary
slightly over a diel cycle (Fuhrman et al., 1985;
Shiah, 1999; Clokie et al., 2006). Over the course of
days, viral production can be steady (Winget and
Wommack,2009)ordynamic:astudywithextended
daily sampling of a phytoplankton bloom showed
veryclose couplingbetween anincrease in bacterial
and viral production following the bloom demise
(Matteson et al., 2011). This coupling had a 1-day
time lag such that increased virus production
preceded an increase in bacterial production; both
corresponded to a dramatic environmental nutrient
drawdown event.
In addition to short-term observations of produc-
tion and abundance of marine bacteria and viruses,
Correspondence: DM Needham, Department of Biological
Sciences, University of Southern California, 3616 Trousdale
Pkwy, AHF 230, Los Angeles, CA 90089, USA.
E-mail: dmneedha@usc.edu
Received 25 September 2012; revised 5 January 2013; accepted 9
January 2013; published online 28 February 2013
The ISME Journal (2013) 7, 1274–1285
& 2013 International Society for Microbial Ecology All rights reserved 1751-7362/13
www.nature.com/ismej
7
molecular community analyses suggest that the rate
at which the community composition changes is
variable and likely depends on location and season.
Usingvariouscommunitycompositionmetricssuch
as Bray-Curtis similarity or Sørensen’s index, bac-
terial community similarity is estimated to be
between 80–92% between adjacent days (Acinas
et al., 1997; Hewson et al., 2006). A study of virus
community composition showed undetectable
change over 48h (Riemann and Middelboe, 2002).
Aweek-long study of the bacterial community from
the same coastal Catalina Island location of this
study indicates that samples collected within a
week of one another are very similar, that rare taxa
are more variable than those that are more common
and that bacterial taxa could increase their propor-
tioninafashionresemblingagrowthcurvedespitea
stable ‘background’ community (Steele, 2010).
Therolethatviruseshaveinstructuringmicrobial
communitycompositionhasbeeninvestigatedsince
the seminal observations that marine viruses out-
numberandinfectbacterialhosts(Berghetal.,1989;
ProctorandFuhrman,1990).Anearlyandpersisting
hypothesis of how this influence might manifest is
often referred to as the ‘Kill-the-Winner’ (KtW)
hypothesis, which was initially a qualitative
description of changes over time (for example,
Fuhrman and Suttle, 1993) whereby abundant taxa
are more likely to be infected than more rare hosts
do primarily to potential encounter rates. This
description would lead to the demise of the
dominant taxa and open a niche for another to fill.
Later, the hypothesis was formally quantified by
ThingstadandLignell,(1997)andThingstad,(2000),
and shown to include steady-state solutions under
simplifyingassumptions,includingthatfastergrow-
ingbacteriaaremoresusceptibletoviruses.Ifanon-
steady-state boom-and-bust dynamic occurs, it may
have important implications on ecosystem robust-
ness or resilience, that is, the ecosystem’s ability to
sustain its functions and processes at a relatively
constant rate.
We aim here to observe short-term dynamics of
marine bacterial and virus communities through
daily sampling and molecular fingerprinting ana-
lyses. However, because all viruses do not share
universally conserved genes, PCR studies of viral
communities are more limited taxonomically than
cellular life. We target the T4-like-myovirus group
from which there have been many isolated represen-
tatives from the ocean infecting both cyanobacteria
(for example, Suttle and Chan, 1993; Waterbury and
Valois, 1993; Wilson et al., 1993; for a review see
Clokie et al., 2010) and heterotrophic bacteria (for
example, Wichels et al., 1998). Additionally, myo-
virusesmakeupaubiquitousandsignificantpercen-
tage (about 9–42%) of sequence matches in aquatic
viralmetagenomes(Breitbartetal.,2002;Benchetal.,
2007; Williamson et al., 2008). Ecologically, marine
cyano-myoviruses have been shown to have broader
host range compared with cyano-podoviruses and
cyano-siphoviruses (Sullivan et al., 2003). We stu-
died the T4-like-myovirus group via amplification of
major capsid gene, g23, which has been shown to
serve as a reasonable proxy for variation in globally
ubiquitous myovirus genomes (File ´e et al., 2005;
Comeau and Krisch, 2008). Here we study the
dynamics of this group in conjunction with the co-
occurring bacterial community at one location in
order to (a) investigate short-term dynamics of
distinct bacteria and T4-like myovirus operational
taxonomic units (OTUs) (b) detect statistical associa-
tions between distinct bacteria and viruses to under-
stand how viruses may influence the bacterial
community composition and determine potential
phage-host relationships (c) quantify the rates at
which the bacterial and myovirus community com-
positionchangesasawholeoverdaystomonthsand
(d) contextualize long-term observations of microbial
community composition.
Materials and methods
Sampling
Surface water (10l) was collected from the top 1m
depth at one geographic location (N 33
o
27
0
11
00
,W
118
o
29
0
2
00
) about 2km from USC Wrigley Marine
Science Center on Santa Catalina Island, CA, USA,
by bucket from a small boat between 1100 and 1700
hours. The sampling location has a total depth of
85mandisopentotheSanPedroChannel.Samples
were collected for 38 consecutive days (June 13,
2010—July 20, 2010) with seven additional samples
collected over the following 40 days. Samples were
processed within 2–3h. Seawater was prefiltered
through80mmnylonmeshandthenfilteredsequen-
tially through 47mm Type A/E glass fiber
filter (B1.0mm pore), 47mm 0.22mm Durapore
(Polyvinylidenefluoride,PVDF,Millipore,Billerica,
MA, USA), and25mm0.02mm Anotop(Aluminum-
oxide, Whatman) filters; all stored at !801C.
Ancillary parameters
Seawater temperature was measured daily from the
surface by a hand-held YSI 63 (Yellow Springs, OH,
USA). Bacteria and viruses were enumerated using
SYBR green epifluorescence microscropy (Noble
and Fuhrman, 1998; Patel et al., 2007). Chloro-
phyll-a estimates were downloaded from NOAA
CoastWatch browser using Science Quality 8-day
composites from the MODIS sensor on-board
NASA’s Aqua spacecraft.
Microbial community analyses
Bacterial and myoviral communities were analyzed
by observation of the variation of the length of the
intergenicspacerinbacteriaandofthemajorcapsid
protein gene of T4-like myoviruses (g23-terminal
restrictionfragmentlengthpolymorphism(TRFLP)).
Bacterial DNA was extracted from a crushed
Daily variation of marine microbial communities
DM Needham et al
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8
Durapore by phenol/chloroform chemical extraction
(Fuhrman et al., 1988) and viral DNAwas extracted
from the Anotop with Masterpure Epicentre kit
(Steward and Culley, 2010) and stored at !801C.
Uponthawing,bacterialandviralDNAintheextract
wasquantifiedwithPicoGreen(Invitrogen,Carlsbad,
CA, USA) and diluted to 2ngml
!1
and 2.5ngml
!1
,
respectively, for analysis by automated ribosomal
intergenic spacer analysis (ARISA) and TRFLP. Fifty
microliter ARISA PCR reactions contained 2ng of
bacterial DNA extract, 1"Thermopol PCR Buffer
(New England Biolabs, NEB, Ipswich, MA, USA),
10mM dNTPs (Promega, Madison, WI, USA),
20ngml
!1
BSA (Sigma-Aldrich, Catalog Number:
A7030, St Louis, MO, USA), 5U Thermopol Taq
polymerase(NEB).Primerconcentrations,sequences
and thermocycling for ARISA proceeded as
described elsewhere (Brown et al., 2005) except we
useda3-minuteinitialdenaturationand31cyclesof
amplification. g23-TRFLP is described elsewhere
(Chow and Fuhrman, 2012); only modifications are
mentionedhere.Weused2.5ngofviralDNAextract,
40ngml
!1
BSA and cycling conditions of 951C for
3min, 41 cycles of 951C for 30s, 551C for 45s and
721Cfor45swithafinalextensionat721Cfor5min.
HincII restriction digestion was carried out as in
Chow and Fuhrman, (2012). ARISA and digested
TRFLPPCRproductswerecleanedandconcentrated
using Zymo DNA Clean and Concentrate -5 kit and
then diluted to 5 and 7.5ngml
!1
, respectively.
Purified and diluted ARISA and TRFLP PCR
products (1ml) were loaded in duplicate onto an
ABI 377XL sequencer in non-adjacent lanes as in
Chow and Fuhrman, (2012). Only 5
0
TRFs were
analyzed in this study.
ABI 377 peak analysis
Analysis of electropherograms was carried out
similarly to previous studies (Steele et al., 2011)
with modifications (Chow and Fuhrman, 2012).
Bacterial ARISA peak relative abundances from
onePCR(machineduplicates)wereaveraged. These
averagedOTUswereplacedintobinscreatedfroma
database built from 410 years of dynamically
binned data from the nearby San Pedro Ocean
Time-Series (SPOT). Dynamic binning of the SPOT
OTUs was performed as in Ruan et al., (2006a). The
bin bounds were the lesser of either (a) the distance
between adjacent upper (larger) bound of observed
SPOT peaks or (b) the upper bound of a bin minus
the maximum bin size. Maximum bin widths for
fragments 390–450, 450–650, 650–900 and 900–
1400bp were 1, 2, 3 and 5bp, respectively. If a peak
did not fall intoapredeterminedbin fromthe SPOT
database, a bin was created. OTUs separated by less
than 0.2bp were manually merged. Myovirus TRFs
between 100 and 500bp were dynamically binned
with a 1bp maximum bin size as described else-
where (Ruan et al., 2006a; Chow and Fuhrman,
2012). After binning, OTUs (peaks) with relative
abundances (that is, peak area divided by cumula-
tiveareaofallpeaksinthatsample)lessthan0.05%
of the total area were removed and the data were
renormalized.
Bacterial OTU identification
Putative identification of bacterial OTUs was deter-
mined by comparison with various taxonomic and
ITS-length sources listed below in order of priority:
(1) 16S rRNA gene of clone libraries (16S-ITS-23S)
constructed from surface waters at the SPOT, (2)
ARISAfragmentsizesweredeterminedfrommarine
cyanobacteriabyin-silicoPCRofavailablegenomes,
(3) clone libraries (16S-ITS-23S) constructed from
environmental ARISA from various global ocean
locations: (a) Pacific Ocean, (b) Atlantic Ocean and
(c) Indian Ocean, (4) Global-Ocean Survey open
ocean rRNA-containing scaffolds (Yooseph et al.,
2007) and (5) whole-genomes from marine isolates
were queried by in-silico PCR using a custom
Python script and EMBOSS primersearch (Rice
et al., 2000) with a 14% mismatch. The primary
sourceofidentification,asnoted,weresurfacewater
clones from SPOT, which includes 184 clones from
four dates: October 2000, April 2001, August 2001
and December 2001. Putative IDs were determined
by searching within the taxonomy/ITS-length
sources in order of priority; thus, if a putative ID
was determined from the SPOT clones, the identi-
fication process was halted. If there were multiple
IDs associated with one fragment length within the
same level of priority, the most common ID was
used. If there were two equally common IDs, then
the length is identified as both. If there is no ID
associated with the fragment length, then it is
identified only as bacteria. 16S sequences were
classified via the Greengenes (McDonald et al.,
2012), Ribosomal Database Project (RDP) (Cole
et al., 2009) and SILVA 108 truncated SSU database
(Pruesse et al., 2007) using BLAST (word size 7).
Full taxonomy and associated 16s sequence (where
applicable) of reported bacterial OTUs are available
in Supplementary Table S1. The top hit (by e-value
with a minimum alignment length of 200bp and
minimum percent identity of 97%) was classified
with consistent hierarchical taxonomy by Green-
genes and the Silva identifier for the most discrimi-
nating taxonomy given before ‘uncultured’ or
‘unidentified.’ SAR11 clade designations came from
Ribosomal Database Project release 10. To identify
bacteria from ARISA fragment lengths, we prefer
empirical measurements of cloned ARISA fragment
lengths rather than simply counting base pair from
sequencing, because electrophoretic migration rates
vary with factors like GC content. 95 clones were
amplified using ARISA-PCR and run on the same
instrument (ABI 377) as environmental samples to
determine empirical (machine-estimated) ARISA
lengths.Forfragmentso800bptheempiricallength
corresponds well with sequence length; however,
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9
for longer fragments (to 1200bp) the empirically-
determined lengths tended to underestimate
sequence-length. Therefore, for clones or in-silico
PCR where the ARISA product size was not
explicitly determined, an adjusted length was used
tomake resultsdirectly comparable.Specifically,by
plotting the variation in empirical-length and
sequencelengthvssequencelengthofthe95ARISA
amplified clones, we determined that the best fit to
thesedatawasbyapplicationoflinearregressionsof
regions 400–800bp and 800–1200bp. To calculate
what the apparent ARISA length would have been
for the clones we did not run on the instrument,
lengths400–800and800–1200bpwereestimatedby
the equations y¼2.428—0.00227x and y¼29.46-
0.03683x, respectively, where y is the calculated
apparent length and x is ARISA sequence length.
The cloning and ARISA PCR for these steps can be
found in Brown et al., (2005).
Statistical analyses
Local similarity analysis. To observe correlations
between OTUs and environmental parameters over
the first 38 days, we used local similarity analysis
(Ruan et al., 2006b). Only OTUs observed at a
frequency greater than 10 times were analyzed. A
maximum time-lag of five steps (days) was allowed.
P- and Q-values were determined by 1000 random
permutation tests. Missing values were linearly
interpolated. The local similarity analysis output
data were input into visualization software, Cytos-
cape (Shannon et al., 2003), to visualize association
networks of microbial taxa. Only correlations with
P-valueandQ-valueslessthanorequalto0.005and
0.05 were examined, respectively. Modules were
detected by AllegroMCODE with the following
settings: Degree cutoff (2), Node Score Cutoff (0.4),
K-Core(2),Max.Depth(100)andthe‘haircut’option
was selected (Bader and Hogue, 2003).
Community similarity metrics. Group average
Bray-Curtis similarities of microbial communities
were calculated in Primer E- 6 as unweighted
entities. Time-dependent community similarity
‘decay’ values werecalculated by plotting similarity
values between all pairwise samples separated by
1–77 days and fitting linear and logarithmic regres-
sions in Sigma-Plot Version 11.0 (San Jose, CA,
USA).
Results
Environmental parameters
Over the first 40 days of the time-series, surface
temperature varied between 16.4 and 19.7 C and
peakedonday14(Figure1).Bacterialandviralcounts
varied less than fourfold reaching maxima on day 15
and 13 (Figure 1), respectively, and were positively
correlated from days 14–25 (r¼0.797, Po0.001;
LS¼0.43, Po0.05). Eight-day averages for satellite
chlorophyll varied between 0.3 and 0.45mgl
!1
.
Bacterial and T4-like-myovirus community overview
Over the 78 day time-series 286 bacterial and 153 T4-
like myoviral OTUs were detected at least twice with
an average richness of 123.7
±
14.6 and 60.3
±
14.2
OTUs each day. The majority (79.5 and 80% of
amplified DNA) of the total bacterial and T4-like
myovirus communities consisted of ‘common’
(detected 490% of days) OTUs (Figures 2a and c).
However, the majority of the bacteria and T4-like
myoviralOTUsobservedwererare(detectedo25%of
days) and totaled 16.8% and 11.6% of each commu-
nity (abundance), respectively (Figures 2b and d).
Individual bacterial/viral OTU fingerprint dynamics
As observed by ARISA, the relative abundances of
the top 10 (highest average relative abundance)
individual bacterial taxa showed different patterns,
including gentle variation around stable mean
abundance, ‘boom-bust’ dynamics and monotonic
increases/decreases (Figure 3a). For example, the
most abundant bacterial OTU (by average contribu-
tion over the 78 days), a putative Synechococcus
(Bacterial OTU 1056), increased from its lowest
detected relative abundance (2%) on day 1–20% by
day 10, and then oscillated about an average of
18.2% for the remainder of the time-series. The
secondmostabundantOTU,amemberoftheSAR11
clade (Bacterial OTU 666.4), was at its highest
relative abundance on day 1 and oscillated about a
mean relative abundance of 5% after day 7. In
contrast to both, the third most abundant OTU, an
Actinobacterium (Bacterial OTU 435; Acidimicro-
biales, clade OCS155) three times showed steady,
greater than sixfold increases in relative abundance
over the course of about 10 days followed by reduc-
tions of equal proportion over 4 days (Figure 3a).
Other common bacteria displayed variation about
their mean, but did not show obvious net increases
or decreases.
Temperature
Bacterial Abundance x 10
6
Virus Abundance x 10
7
Chlorophyll
Day
Temperature (C)
Bacterial or Viral Abundance
(cells or viral-like-particles/mL)
Chlorophyll (µg/L)
0.4
0.6
0.8
1.0
24
22
20
18
0 5 10 15 20 25 30 35 40
0
1
2
3
4
5
Figure 1 Environmental parameters over the first 38 days of the
time-series. Moving averages for environmental parameters were
calculated by smoothing with 0.1 sampling proportion and
polynomial degree 1, roughly corresponding to 3-day averages.
Daily variation of marine microbial communities
DM Needham et al
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The ISME Journal
10
By g23-TRFLP, the four most abundant T4-like
myovirus OTUs were at a maximum abundance on
day 1 and showed steady decreases in relative
abundance throughout the time-series (Figure 3b)
ofB0.01% per day. Notably, viral OTU 296 showed
no upward or downward trend overall, but twice
showed steady increases followed by steady decrea-
ses of 41% per day. In contrast, other dominant
T4-like myoviruses maintained stable abundances
throughout the time-series (Figure 3b).
Observations from association network analysis
We found tens to hundreds of significant correla-
tions (Pp0.005, Qo0.05) between taxa depending
on the correlation strength considered (Figure 4).
The majority of statistically significant positive
correlations between OTUs were intra-bacterial or
intra-viral associations with no time-lag (95.2%,
82.7%, 78.1% and69.4%for minimum LS values of
0.5, 0.45, 0.4 and 0.35, respectively) rather than
T4-like-myoviral-to-bacterial or intra-group with
time-lag (Figures 4a and b). In contrast to the
majority intra-group correlations that had no time-
lag, the bacteria-to-virus correlations had median
timelagsof1.5,2,2and2forLSof0.5,0.45,0.4and
0.35, respectively (Figure 4c).
Highly interconnected groupings of T4-like-myo-
viruses (Figure 5, Module 1) and bacteria (Figure 6)
had many connections outside of their core mem-
bers.Forexample,twomembersofthevirusmodule
correlated with a member of the SAR86 clade (OTU
402.4). In turn, this SAR86 OTU was positively
associated with four bacterial OTUs and negatively
associated with one T4-like-myovirus (384.3). The
bacterialmodule(Figure6)waslargelycomposedof
positively associated members of the SAR11 clade;
one SAR11 OTU was positively associated with the
mostabundantT4-like-myovirus(OTU382.3)witha
3-day delay. In addition to those associations
shown, there were other significant correlations
between OTUs, which were only associated for a
period of time shorter than the full 38 days or at
correlations of less strength. For example, Actino-
bacterium (OTU 435; Acidimicrobiales, clade
OCS155) and T4-like myovirus 296 were correlated
(LS¼0.249, P¼0.001) from days 10 to 31 with a
1-day time-lag (not shown, see Figure 3 for relative
abundances).
Microbial community similarity
Bacterial and T4-like myoviral Bray-Curtis similar-
ity was greater between communities with a closer
temporalproximity.Adjacentdayswere,onaverage,
74.7%
±
1.7 (
±
s.e.m.) and 81.2%
±
1.5 similar,
respectively (Figure 7). Notably, true duplicates,
collected on day 20 were 84% and 86% similar by
Bray-CurtissimilarityfortheARISAandg23TRFLP
while technical replicates (same PCR reaction,
measured more than once) during the time of study
averaged 84%±1.1 (n¼7) for ARISA and
84%
±
1.3(n¼14) for g23-TRFLP. Communities of
30 and 60 days apart were 62.9%
±
3.3 and
55.4%±2 similar for bacteria and 59.9%±2.5 and
53%
±
2.5 similar for T4-like myoviruses. Pairwise
comparison of all bacterial and T4-like myoviral
communities, plotted against the time lag between
them, resulted in a linear decrease in un-weighted
similarity of 0.18% (r¼0.296, Po0.001) and 0.4%
(r¼0.720,Po0.001)perday,respectively(Figure7).
The linear rate of bacterial community differentia-
tion over the first week (2.1%/day) was more rapid
than the change over the remainder of the time-
series.Thus,thefitofalogarithmicfunctionmaybe
more appropriate and has a higher correlation
coefficient for bacteria (0.357, Po0.001).
Discussion
Theresultsfromthisstudysuggestthat,overdays-to-
weeks, microbial communities exhibit characteristics
of subtle-to-dramatic short-term variation and resi-
lience. Individual bacterial and T4-like myoviral
OTUs of the surface ocean vary in their relative
abundance in connected ways over the extended
daily time-series. Over weeks to months the total
communitychanged,onaverage,relativelylittle.This
suggests that underlying the ephemeral, connected
variationofindividualtaxa,thereisarelativelystable
core, resilient community about which the variation
Figure2 Contributionofthemostcommontothemostraretaxato
total community composition and total OTU count. Percent of
the total summed (a) bacterial and (b) myoviral community (peak
area/total area) made up of OTUs which were detected on the
indicated percentage of days of the 78 day time-series. Number of
(c) bacterial and (d) myoviral OTUs, which were detected greater
than twice for the indicated percentage of days of the 78-day
time-series.
Daily variation of marine microbial communities
DM Needham et al
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The ISME Journal
11
Figure 3 Normalized relative abundances of distinct (a) bacterial and (b) myoviral OTUs over the 78-day time series. The plots are
organized from top to bottom from most abundant (on average) to 10th most abundant.
Daily variation of marine microbial communities
DM Needham et al
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The ISME Journal
12
occurs. A resilient community likely results in a
similar retention of function. The discussion first
focuses on variation and later on stability.
Spatial and error considerations
Sampling from one geographic location, likely
resulted in sampling of multiple regional water
masses, which mixed and advected past our sam-
pling location. Although most of this discussion is
framed within the temporal context of the study,
space and time are confounding variables. The
observation that variation between communities
from day-to-day can be as great as that observed a
month apart may be from sampling different water
masses. However, when averaging the points
LS>0.35 LS>0.35 LS>0.40 LS>0.40 LS>0.45 LS>0.45 LS>0.50 LS>0.50 LS>0.35 LS>0.45 LS>0.50 LS>0.40
Number of Associations
Bacteria-to-Bacteria
160
60
80
40
20
0
140
120
100
80
60
40
20
0
50
40
30
20
10
0
Myovirus-to-Myovirus Bacteria-to-Myovirus
Figure4 StatisticalpositiveassociationsbetweenbacteriaandT4-likemyovirusesasdeterminedbylocalsimilarityanalysis.Onlydata
forthefirst38dayswereusedinthisanalysisandonlythoseassociationsovertheentire38daysaredisplayed.(a–c)Stackedbargraphs
indicate the total number of positive associations (Pp0.005, qo0.05) between (a) Bacteria-to-bacteria, (b) myovirus-to-myovirus and
(c)bacteria-to-myovirus.Eachcolumnisbrokendownintosegmentsindicatingthenumberofassociationscorrespondingtodelaysof0–
5 days. Statistical associations are organized according to their corresponding local similarity scores (LS), a measure of correlation
strength similar to Pearson’s r, and corresponding time-delay of the optimal association as determined by local similarity analysis. Like
Pearson’s correlation coefficient, higher LS indicate a strongerassociationand is thus more discriminating. Each column is cumulative:
for example, correlations40.5 are also included within LS40.4 column.
Myo
372.2 SAR86
538.9
Myo
391.5
Myo
219.7
Delta
478.8
Myo
393.6
OTU
559
Myo
373.7
Myo
375.7
SAR11_S2
718.4
Prochl
828.8
Photob
857.1
Sar11_S1
(682)
SAR406
595.8
Roseob
1181.1
SAR11_S1
666.4
Myo
160.9
Myo
421.5
Myo
61.9
OTU
559.4
Myo
388.7
SAR86
618.3
Myo
221.1
Chloroplast
570
Myo
372.8
Myo
339.7
Myo
415.8
Myo
378.7
Myo
319.2
Myo
258.6
Flavo
646.9
Beta
836.8
Myo
415
Myo
420.7
SAR116/
Flavo
654.9
OTU
535
SAR116
657.6
SAR86
402.4
Myo
369.9
Myo
199.4
Myo
384.3
Rhodob/
Gamma
946.3
Myo
348.7
Actino
424.4
Beta
964.8
Roseob
987.8
SAR11_Aeg
653.1
Myo
323.3
SAR406
624.5
OTU
422.3
Thioba
840
Syn
1035
Myo
414.3
Prochl
813.5
OTU
628.6
Myo
296
Flavo
741.8
Prochl
1130.1
Flavo
969.6
Flavo
750.4
Flavo
773.1
SAR11_S1
670.5
SAR11_Aeg
662
Flavo
770.5
OTU
874
Myo
382.3
Prochl
816.5
Myo
287.7
Myo
246.3
OTU
490
Myo
212.5
Myo
398.2
OTU
755.4
Flavo
729.4
Myo
377.6
OTU
1040
Actino
435.5
SAR86
557.8
Flavo
726.4
SAR11_S2
716.8
SAR11_S1
686.9
Module 2
Module 1
Figure5 NetworkvisualizationofallstatisticalassociationstoOTUsassociatedwithT4-likeMyoviruses(|LS|40.45,Pp0.005,qo0.05)over
the full 38 days are shown. The network also displays correlations one-step beyond a connection to a T4-like Myovirus (LS40.45, Pp0.005,
qo0.05), but correlations (lines) between these taxa are not displayed for clarity. Circles indicate bacteria taxa; myoviruses by gray V-shapes.
Non-cyanobacterialbacterialOTUsaredarkgray,ProchlorococcusOTUslightgrayandaSynechoccoccusOTUlightgray.Abbreviated,putative
identitiesarefollowedbytheassociatedfragmentsize.ThesizeofeachnodecorrespondstotheOTU’saveragerelativeabundanceoverthetime-
series. Solid and dashed lines indicate positiveandnegativeassociationsbetweentaxa,respectively. The darkness of the line indicates the
lengthofdelayofcorrelations:blacklinesarenon-lagged,whereaslightershadesaredelayedcorrelations.Indelayedassociations,arrowspoint
at the OTU that was delayed, or trailed, in the association. Shaded gray areas indicate OTUs that made up highly-interconnected regions
(or modules) of the network. A full colour version of this figure is available at the International Society for Microbial Ecology journal online.
Daily variation of marine microbial communities
DM Needham et al
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together there is a robust pattern and the slow
variation observed at the community level over
the short-term is similar to that which has
shown community coherence over short spatial
scales (Hewson et al., 2006; Steele, 2010). Further,
co-occurrence patterns may be expected to be
consistent over space or time. That we find robust
associations over the time-series suggests that this
may be the case.
Due to the fact that this study is based on
relatively small variation between samples, it is
important to consider the implication of errors in
both the ARISA and g23-TRFLP methods. As noted,
both technical (same PCR reaction, run more than
once,induplicate)andtruereplicatesaverageabout
85% by un-weighted Bray–Curtis similarity. There-
fore, most differences between true replicates stem
from accumulation of slight errors in the fragment
analysis and binning procedures. Our ARISA repro-
ducibility is close to those reported previously
(Hewson et al., 2006). Calculated most easily via
Bray–Curtis Dissimilarity (1-similarity), our results
suggests that about 60% of the apparent day-to-day
change in bacterial community similarity (average
B25% Bray–Curtis Dissimilarity) is due to noise in
the measurements (average 15% Bray–Curtis Dis-
similarity between replicates), and for viruses about
84% of the apparent day-to-day change is due to
noise (16% and 19% Bray–Curtis Dissimilarity for
replicates and adjacent days, respectively). Regard-
less, this source of error should not influence the
rates of change over time, determined by the slopes
of regressions between all samples with respect to
number of days separating them.
Short-term microbial variation
As assessed by community fingerprints, the domi-
nant 10 bacterial and T4-like myoviral OTUs often
varied in observable patterns over the course of the
time-series, including subtle and dramatic oscilla-
tion, monotonic increases and decreases (Figure 2)
over the 78-day time-series. Relatively dramatic
oscillations were observed in particular taxa, for
example, an Actinobacterium (OTU 435; Acidimi-
crobiales,cladeOCS155);Actinobacteria,ingeneral,
are heterotrophic and prolific producers of
SAR11_S2
716.8
SAR11_S1
682
SAR11_S1
666.4
OTU
1040
SAR11_S2
718.4
SAR11_S1
670.5
SAR11_S1
686.9
SAR11_AEG
662
Photob
857.1
Actino
435.5
Figure 6 Network visualization of the largest highly-intercon-
nected nodes exceeding the threshold of modularity (see
Methods). All positive and negative correlations (|LS|40.45,
Pp0.005, qo0.05) are shown between members of the module.
The network shows seven SAR11 OTUs that are positively
correlatedwithnotime-lagandthreeOTUs,whicharenegatively
correlatedto theSAR11OTUs,one,anActinobacterial OTUwith
a 1-day time-lag.
0 20 40 60 80
0
20
40
60
80
100
0
20
40
60
80
100
f = y0+a*ln(x)
y0 = 72.064
a = -3.861
r = 0.357
f = y0+a*x
y0 = 65.783
a = -0.179
r = 0.296
f = y0+a*ln(x)
r = 0.696
y0 = 88.813
a = -7.415
f = y0+a*x
r = 0.720
y0 = 78.779
a = -0.436
Percent Bray-Curtis Similarity
Number of Days Separating Communities
Logarithmic Linear
Linear Logarithmic
a
b
Figure 7 Community similarity time-dependent decay. Commu-
nity composition was assessed by group average Bray–Curtis
similarity for all possible pairwise comparisons between days for
(a) T4-like myovirus and (b) bacteria. Both a linear and a
logarithmic function are shown (Po0.001).
Daily variation of marine microbial communities
DM Needham et al
1281
The ISME Journal
14
secondary compounds (Ward and Bora, 2006). Such
rapid fluctuation (either temporally or spatially)
maybeanimportanttoecosystemsasthefunctional
redundancy within the bacterial community is not
that well understood.
Likewise, particular T4-like myovirus OTUs were
found to exhibit dramatic oscillation, for example
T4-like myovirus OTU 296. This T4-like myovirus
OTU twice increased in relative abundance by about
2%perdayover5days.Wedonotmakeanattemptto
extrapolate to the absolute number of viruses pro-
duced by such variation, but future investigations
should attempt to estimate the ecosystem implica-
tions,forexample,thenumberofbacterialcellslysed.
Abetterunderstandingofthespatialextentofsuch
oscillations, T4-like myovirus community abundance
and decay rates would be useful to determine such
production values. Our results illustrate that estima-
tion of the productivity of individual viral OTUs over
the short-term may be informative.
Short-term microbial connectivity and modularity
The short-term variation of individual OTUs pro-
duced statistical association between taxa, includ-
ing highly interconnected groups of bacteria and
potential host-phage interactions. Local similarity
analysis, the method we employed to detect covar-
iation of variables and/or OTUs, can identify
correlations with time-delays, like those observed
between predators and prey. Among the many
significant positive and negative associations
between many OTUs (Figures 5 and 6), the bacterial
module of inter-connected bacteria (Figure 5) iden-
tified several OTUs from the SAR11 clade that were
correlated with no time-lag. Notably, some of the
SAR11 OTUs from Module 1 are 498% similar at
16Slevel(Brownetal.,2005),anditispossiblethey
respond similarly to abiotic and biotic forcing at the
daily time-scale, but interestingly many of these
same OTUs were not correlated when previously
examined monthly at SPOT over 3 years at the
Chlorophyll maximum depth (Fuhrman and Steele,
2008; Steele et al., 2011). Synchronous oscillations
of these OTUs, even when only distantly related,
couldbeduetoavarietyofnon-exclusivecauses,for
example, (a) similar positive or negative response to
environmental parameters such as varying inputs of
particular substrates, (b) tight ecological coupling
due to auxotrophy or cross-feeding, and (c) top-
down pressures by selective protists or viruses.
Many OTUs were negatively correlated to the
bacterialmodulewhichmightindicate,forexample,
inter-specific competitions for organic substrates or
nutrient regime shifts that favor bacteria not asso-
ciated with Module 1.
Similarly, intra-connected groups of viruses were
associated with zero time-delay (Figure 5b). The
ecological connectedness of individual viral OTUs
could be due to periods, in which a lineage of
bacteria became particularly susceptible to lytic
infection by multiple phage types (Comeau et al.,
2006; Holmfeldt et al., 2007). We hypothesize that
these intra-correlated groups of viruses may include
close relatives and infect ecologically similar hosts.
Inter-correlated groups of viruses might instead
result from environmentally influenced viral pro-
duction, for example, from pseudo-lysogens (for
example, Wilson et al., 1996). In this study, inter-
connected viruses were loosely associated with two
distinct, putative SAR86 OTUs (Figure 6), suggest-
ing that this group (and likely many others) may
experience pseudo-lysogeny in the environment.
Virus–bacteria associations. Most of the bacteria-
to-myovirus associations observed in this study had
atimelag.Time-laggedassociations,inwhichpeaks
in abundance of prey and predators are temporally
offset are described classically by the Lotka-Volterra
equations or the non-steady-state KtW hypothesis.
These predator-prey-like dynamics are often
depicted with an arbitrary time-scale along the
x-axis (Wommack and Colwell, 2000); the results
of this study suggest that a reasonable time-scale for
bacteria–virus dynamics can be short (days). How-
ever, most of the bacteria–virus correlations we
observed were not indicative of a boom-bust,
predator-prey-like dynamic. The correlations we
found were mostly among taxa that varied in their
relative abundance but remained relatively abun-
dant. For example, T4-like-myovirus OTU 382 was
significantly correlated to SAR11 (682.4) with a
3-day time-delay despite both remaining relatively
abundant throughout the time-series. These results
are in fact consistent with a modestly-changing
system where the steady-state KtW dynamics
(Thingstad 2000) may apply to a significant extent,
even though the system is not truly in steady state.
Given that the growth rates of specific bacterial
taxa influence the dynamics of their relationships
with viruses (Middelboe, 2000), it should be
expected that different hosts would have dissimilar
temporal dynamics with their viruses in the envir-
onment. Our median time-lag between bacteria and
virus correlations of 2 days was shorter than that
recently proposed byParsonsetal.,(2011)thatused
a range of average ocean values for marine bacterial
growthratesandvirusdecayrates.Thisimpliedthat
many host-phage relationships may be more closely
coupled than suggested by bulk rates and due to
bacteria which grow faster than the average com-
munity growth rate.
Short-term stability within microbial communities.
Characteristics of community stability was equally
notable. Generally, most common bacteria and
T4-like-myovirus OTUs were persistent; bacterial
and T4-like myoviral OTUs that were detected in at
least 90% of the samples made up 80% of the
cumulativecommunities(Figure1).Thus,asprevious
research has shown (Hewson et al., 2006; Steele,
2010), a microbial community from one location and
Daily variation of marine microbial communities
DM Needham et al
1282
The ISME Journal
15
one time provides a fairly good estimate of the
community of surrounding days at that location.
Althoughabout60%and84%oftheapparentday-
to-day change in the bacterial and T4-like-myovirus
community comes from analytical noise, the average
day-to-day change in the bacterial and T4-like-
myovirus communities was about 10% and 4%
greater than between replicates, respectively, with
some days that exhibited large changes and others
that showed no significant change (Figure 7). The
average rate of change of the bacterial community
over 1 week was about 2% per day (calculated from
the linear slope of days separated by 1–7 days,
Figure 7) which, if continued for a month, would
lead to substantially dissimilar communities of about
25%similarity (thatis,subtractionof therate2%per
day for 30 days, considering the initial similarity
between replicates of 85%). However, we observed
that this modest rate of change over a week was 12-
fold higher than the rate of change within the
bacterial community when averaged over 2 months
(the linear slope of the regression of all combinations
ofdaysseparatedby1–60daysis0.18%perday).We
interpret this decelerating rate of change in the
bacterial community to suggest that, although there
were significant fluctuations over days-to-weeks,
ecological processes forced the communities towards
a relatively steady and even predicable average
composition for a given time period of weeks to
months. This is consistent with the predictability of
bacterial communities at the nearby SPOT station
reported previously (Fuhrman et al., 2006) but from
that study we would have had no expectation of the
more rapid variation over the first week observed
here. The virus community change is relatively more
steady, 0.48%–0.37% per day over a week and 2
months, respectively. However, the rate of change we
observe in the T4-like-myovirus community (0.4%
perday)isabouttwicethatwhichwouldbeexpected
from the T4-like myovirus community similarity at 6
month intervals over 3 years based on data in Chow
andFuhrman, (2012). Thesetwodatasetsweretaken
from two locations roughly 10km apart and SPOTis
likely more open-ocean-like. Taken together with the
observed dynamics of individual taxa, the bacterial
(and to a smaller extent the myovirus) communities
decelerating rate of change is evidence that the
community retained a resilient background composi-
tion with significant, ephemeral variation over the
short-term, with a stable core bacterial community
likely providing ecosystem resilience that was regu-
latedbyoutsideforces.Ausefulanalogymightbethe
relatively ‘noisy’ or chaotic variations in weather, as
compared with longer term and much more predict-
able seasonal variations in climate.
Recognizing sensitivity of methods. Higher taxo-
nomic or genomic resolution would likely provide
more information. For example, studies investigat-
ing the source of resistance and infectivity in
picocyanobacteria and cyanophage reported that
successful infection could be controlled by single
genomicmutations:thislevelofinteractionbetween
host and phage helps to explain the ubiquity and
high abundance of picocyanobacterial populations
in the environment (Avrani et al., 2011; Marston
et al., 2012). Further, isolated marine bacteria can
vary in their susceptibility to viruses at a taxonomic
resolution finer than that observed by ARISA
(Holmfeldt et al., 2007) and metagenomic
approaches have illustrated that a greater degree of
temporal variation occurs at a finer-taxonomic
resolution for both bacterial and viral communities
(Rodriguez-Britoetal.,2010).Evenwiththeseissues
and possible future directions, our study clearly
demonstrated that, at daily resolution, variation
occurred within the bacterial and virus commu-
nities using DNA fingerprinting techniques.
Summary, implications, and the KtW hypothesis.
Given that marine bacterial communities turn over
onthescaleofdays,aresubjecttotop-downcontrols
from protists and viruses, horizontally transfer
genes, and can be functionally redundant, it might
be expected that over days-to-months distinct
bacterial taxa might fluctuate wildly or exhibit
unpredictable,boom-and-bustcycles. From samples
collected over three summer months including 38
consecutive days, we found that it was uncommon
forbacterialandviraltaxatoexhibitboomandbust-
type dynamics. In contrast, at our taxonomic
resolution, most dominant taxa retained their dom-
inance over the period of study. Further, most
common and abundant taxa persisted at stable
abundances. Thus, the steady-state solutions in
Thingstad’s formal KtW hypothesis may apply to a
significant extent. At the community level, the
relative rate of change over days-to-weeks is greater
than that over weeks-to-months showing that there
was variation about a mean community over days
that is smoothed over time, especially within the
bacterial community. This suggested that there are
barriers to fundamental change in the bacterial
community composition over the short term, and/
or strong external or community forcings promoting
particular bacterial and viral types. Our results also
indicated that bacterial and T4-like myoviral com-
munities were highly interconnected over days-to-
months potentially suggesting a steady, core com-
munity with relationships that sustained ecosystem
function over the short-term. In particular, the
observation of modularity within marine microbial
communities over the short-term warrants further
study to understand their implication on commu-
nity structure and to understand how microbial
communities respond to and influence ecosystem
perturbations.
Acknowledgements
We thank Wrigley Institute for Environmental Science
for logistical support and lab space throughout the
Daily variation of marine microbial communities
DM Needham et al
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The ISME Journal
16
fieldwork for this study. We thank Joshua Steele
for helpful comments and discussion on the manuscript
and Laura Go ´mez-Consaurnau for helpful discussions.
We thank Victoria Campbell, Jennifer Chang,
Ananias Chairez and Sherwin Abdoli for assistance with
fieldwork. This work was funded by NSF grant numbers
0703159, 1031743 and 1136818, NSF Graduate Research
Fellowship Program, and USC Wrigley Summer Fellow-
ship Program.
References
Acinas SG, Rodrı ´guez-Valera F, Pedro ´s-Alio ´ C. (1997).
Spatial and temporal variation in marine bacterio-
planktondiversityasshownbyRFLPfingerprintingof
PCR amplified 16S rDNA. FEMS Microbiol Ecol 24:
27–40.
Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. (2011).
Genomic island variability facilitates Prochloro-
coccus–virus coexistence. Nature 474: 604–608.
Bader GD, Hogue CWV. (2003). An automated method for
finding molecular complexes in large protein interac-
tion networks. BMC Bioinform 27: 1–27.
Bench SR, Hanson TE, Williamson KE, Ghosh D, Radoso-
vich M, Wang K et al. (2007). Metagenomic character-
ization of Chesapeake Bay virioplankton. Appl and
Environ Microbiol 73: 7629–7641.
Bergh O, Borsheim KY, Bratbak G, Heldal M. (1989). High
Abundance of viruses found in aquatic environments.
Nature 340: 467–468.
Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall
AM, Mead D et al. (2002). Genomic analysis of
uncultured marine viral communities. Proc NatlAcad
Sci USA 99: 14250–14255.
Brown MV, Schwalbach MS, Hewson I, Fuhrman JA.
(2005). Coupling 16S-ITS rDNA clone libraries and
automated ribosomal intergenic spacer analysis to
show marine microbial diversity: development
and application to a time series. Environ Microb 7:
1466–1479.
Brussaard CPD, Wilhelm SW, Thingstad TF, Weinbauer
MG, Bratbak G, Heldal M et al. (2008). Global-scale
processes with a nanoscale drive: the role of marine
viruses. ISME J 2: 575–578.
Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. (2011).
Activity of abundant and rare bacteria in a coastal
ocean. Proc Natl Acad Sci USA 108: 12776–12781.
Carlson CA, Morris RM, Parsons R, Treusch AH, Giovan-
noni SJ, Vergin K. (2009). Seasonal dynamics of
SAR11 populations in the euphotic and mesopelagic
zones of the northwestern Sargasso Sea. ISME J 3:
283–295.
ChowC-ET,FuhrmanJA.(2012).Seasonalityandmonthly
dynamics of marine myovirus communities. Environ
Microbiol 14: 2171–2183.
ClokieMRJ,MillardAD,MannNH.(2010).T4genesinthe
marine ecosystem: studies of the T4-like cyanophages
and their role in marine ecology. Virol J 7: 1–19.
Clokie MRJ, Millard AD, Mehta JY, Mann NH. (2006).
Virusisolationstudiessuggestshort-termvariationsin
abundance in natural cyanophage populations of the
Indian Ocean. J Bio Mar Biol Assoc UK 86: 499.
ColeJR,WangQ,CardenasE,FishJ,ChaiB,FarrisRJetal.
(2009). The Ribosomal Database Project: improved
alignments and new tools for rRNA analysis. Nucleic
Acids Res 37: D141–D145.
Comeau AM, Chan AM, Suttle CA. (2006). Genetic
richness of vibriophages isolated in a coastal environ-
ment. Environ Microbiol 8: 1164–1176.
Comeau AM, Krisch HM. (2008). The capsid of
the T4 phage superfamily: the evolution,
diversity, and structure of some of the most prevalent
proteins in the biosphere. Mol Biol Evol 25:
1321–1332.
Eiler A, Hayakawa DH, Church MJ, Karl DM, Rappe ´ MS.
(2009). Dynamics of the SAR11 bacterioplankton
lineage in relation to environmental conditions in
the oligotrophic North Pacific subtropical gyre.
Environ Microbiol 11: 2291–2300.
Eiler A, Hayakawa DH, Rappe ´ MS. (2011). Non-random
assembly of bacterioplankton communities in
the subtropical north pacific ocean. Front Microbiol
2: 1–12.
File ´e J, Tetart F, Suttle CA, Krisch HM. (2005). Marine
T4-type bacteriophages, a ubiquitous component of
the dark matter of the biosphere. Proc Natl Acad Sci
USA 102: 12471–12476.
Fuhrman JA. (2009). Microbial community structure and
its functional implications. Nature 459: 193–199.
Fuhrman JA, Comeau DE, Hagstrom A, Chan AM. (1988).
Extraction from natural Planktonic Microorganisms of
DNA Suitable for Molecular Biological Studies. Appl
Environ Microbiol 54: 1426–1429.
Fuhrman JA, Eppley RW, Hagstrom A, Azam F. (1985).
Diel variations in bacterioplankton, phytoplankton,
and related parameters in the Southern California
Bight. Mar Ecol Prog Ser 27: 9–20.
FuhrmanJA,HewsonI,SchwalbachMS,SteeleJA,Brown
MV, Naeem S. (2006). Annually reoccurring bacterial
communities are predictable from ocean conditions.
Proc Natl Acad Sci USA 103: 13104–13109.
Fuhrman JA, Steele JA. (2008). Community structure
of marine bacterioplankton: patterns, networks,
and relationships to function. Aquat Microbl Ecol 53:
69–81.
Fuhrman JA, Suttle CA. (1993). Viruses in marine
planktonic systems. Oceanography 6: 51–63.
Gilbert JA, Steele JA, Caporaso JG, Steinbru ¨ck L, Reeder J,
Temperton B et al. (2011). Defining seasonal
marine microbial community dynamics. ISME J 6:
298–308.
Giovannoni SJ, Vergin KL. (2012). Seasonality in ocean
microbial communities. Science 335: 671–676.
Hewson I, Steele JA, Capone DG, Fuhrman JA. (2006).
Temporal and spatial scales of variation in bacterio-
plankton assemblages of oligotrophic surface waters.
Mar Ecol Prog Ser 311: 67–77.
Holmfeldt K, Middelboe M, Nybroe O, Riemann L. (2007).
Large variabilities in host strain susceptibility and
phage host range govern interactions between lytic
marine phages and their Flavobacterium hosts. Appl
Environ Microbiol 73: 6730–6739.
Marston MF, Pierciey FJJ, Shepard A, Gearin G, Qi J,
Yandava C et al. (2012). Rapid diversification of
coevolving marine Synechococcus and a virus. Proc
Natl Acad Sci USA 109: 4544–4549.
Matteson AR, Loar SN, Pickmere S, Debruyn JM, Ellwood
MJ, Boyd PW et al. (2011). Production of viruses
during a spring phytoplankton bloom in the South
PacificOceannearofNewZealand.FEMSMicrobEcol
79: 709–719.
Daily variation of marine microbial communities
DM Needham et al
1284
The ISME Journal
17
McDonald D, Price MN, Goodrich J, Nawrocki EP,
DeSantis TZ, Probst A et al. (2012). An improved
Greengenes taxonomy with explicit ranks for ecologi-
cal and evolutionary analyses of bacteria and archaea.
ISME J 6: 610–618.
Middelboe M. (2000). Bacterial growth rate and marine
virus-host dynamics. Microb Ecol 40: 114–124.
Noble RT, Fuhrman JA. (1998). Use of SYBR Green I for
rapid epifluorescence counts of marine viruses and
bacteria. Aquat Microb Ecol 14: 113–118.
Noble RT, Fuhrman JA. (2000). Rapid virus production
and removal as measured with fluorescently labeled
viruses as tracers. Appl Environ Microbiol 66:
3790–3797.
Parsons R, Breitbart M, Lomas MW, Carlson CA. (2011).
Ocean time-series reveals recurring seasonal patterns
of virioplankton dynamics in the northwestern
Sargasso Sea. ISME J 6: 273–284.
Patel A, Noble RT, Steele JA, Schwalbach MS, Hewson I,
Fuhrman JA. (2007). Virus and prokaryote enumera-
tion from planktonic aquatic environments by epi-
fluorescence microscopy with SYBR Green I. Nature
Protocols 2: 269–276.
Proctor LM, Fuhrman JA. (1990). Viral mortality
of marine bacteria and cyanobacteria. Nature 343:
60–62.
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W,
Peplies J et al. (2007). SILVA: a comprehensive online
resource for quality checked and aligned ribosomal
RNA sequence data compatible with ARB. Nucleic
Acids Res 35: 7188–7196.
Rice Peter, Longden Ian, Bleasby A. (2000). The european
molecular biology open software suite embossa: the
european molecular biology open software suite.
Trends Genet 16: 276–277.
RiemannL,MiddelboeM.(2002).Stabilityofbacterialand
viral community compositions in Danish coastal
waters as depicted by DNA fingerprinting techniques.
Aquat Microb Ecol 27: 219–232.
Rodriguez-Brito B, Li L, Wegley L, Furlan M, Angly F,
Breitbart M et al. (2010). Viral and microbial commu-
nitydynamicsinfouraquaticenvironments.ISMEJ 4:
739–751.
RuanQ,DuttaD,Schwalbach MS,SteeleJA,Fuhrman JA,
Sun F. (2006b). Local similarity analysis reveals
unique associations among marine bacterioplankton
species and environmental factors. Bioinformatics 22:
2532–2538.
Ruan Q, Steele JA, Schwalbach MS, Fuhrman JA, Sun F.
(2006a). A dynamic programming algorithm for bin-
ningmicrobialcommunityprofiles.Bioinformatics22:
1508–1514.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT,
Ramage D et al. (2003). Cytoscape: a software
environment for integrated models of biomolecular
interaction networks. Genome Res 13: 2498–2504.
Shiah F-K. (1999). Diel cycles of heterotrophic bacterio-
plankton abundance and production in the ocean
surface waters. Aquat Microb Ecol 17: 239–246.
Steele JA. (2010). Marine Bacterioplankton Biogeography
over short-to-medium spatio-temporal scales. PhD
Dissertation, University of Southern California, UMI
Number 3403649.
Steele JA, Countway PD, Xia L, Vigil PD, Beman JM, Kim
DY et al. (2011). Marine bacterial, archaeal and
protistan association networks reveal ecological lin-
kages. ISME J 5: 1414–1425.
StewardGF,CulleyAI.(2010).ExtractionandPurification
of Nucleic Acids from Viruses In:American Society of
Limnology and Oceanography: Manual of Aquatic
Virology, Waco, TX, USA, pp 154–165.
Sullivan MB, Waterbury JohnB, Watson StanleyW, Valois
FredericaW, Franks DG, Chisholm SW. (2003). Cyano-
phages infecting the oceanic cyanobacterium Pro-
chlorococcus. Nature 424: 1047–1052.
Suttle CA, Chan AM. (1993). Marine cyanophages infect-
ing oceanic and coastal strains of Synechococcus:
abundance, morphology, cross-infectivity and growth
characteristics. Mar Ecol Prog Ser 92: 99–109.
Thingstad TF. (2000). Elements of a theory for the
mechanisms controlling abundance, diversity, and
biogeochemical role of lytic bacterial viruses in
aquatic systems. Limnol Oceanogr 45: 1320–1328.
Thingstad TF, Lignell R. (1997). Theoretical models
for the control of bacterial growth rate, abundance,
diversity and carbon demand. Aquat Microb Ecol 13:
19–27.
Treusch AH, Demir-Hilton E, Vergin KL, Worden AZ,
Carlson CA, Donatz MG et al. (2011). Phytoplankton
distributionpatternsinthenorthwesternSargassoSea
revealed by small subunit rRNA genes from plastids.
ISME J 6: 481–492.
Ward AC, Bora N. (2006). Diversity and biogeography
of marine actinobacteria. Curr Opin Microbiol 9:
279–286.
Waterbury JB, Valois FW. (1993). Resistance to
co-occurring phages enables marine synechococcus
communities to coexist with cyanophages
abundant in seawater. Appl Environ Microbiol 59:
3393–3399.
Wichels A, Biel SS, Gelderblom HR, Brinkhoff T, Muyzer
G, Schu ¨tt C. (1998). Bacteriophage diversity in the
North Sea. Appl Environ Microbiol 64: 4128–4133.
Williamson SJ, Rusch DB, Yooseph S, Halpern AL,
Heidelberg KB, Glass JI et al. (2008). The Sorcerer II
Global Ocean Sampling Expedition: metagenomic
characterization of viruses within aquatic microbial
samples. PloS One 3: e1456.
Wilson WH, Carr NG, Mann HN. (1996). The effect of
phosphate status on the kinetics of cyanophage
infection in the oceanic cyanobacterium Synechococ-
cus sp. WH7803. J Phycol 32: 506–516.
Wilson WH,Joint IR, CarrNG,Mann NH.(1993).Isolation
and molecular characterization of five marine cyano-
phages propagated on Synechococcus sp. strain
WH7803. Appl Environ Microbiol 59: 3736–3743.
Winget DM, Wommack KE. (2009). Diel and daily
fluctuations in virioplankton production in coastal
ecosystems. Environ Microbiol 11: 2904–2914.
Wommack KE, Colwell RR. (2000). Virioplankton: viruses
in aquatic ecosystems. Microb Molec Biol Rev 64:
69–114.
Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson
SJ, Remington K et al. (2007). The Sorcerer II Global
Ocean Sampling expedition: expanding the universe
of protein families. PLoS biology 5: e16.
Supplementary Information accompanies this paper on The ISME Journal website (http://www.nature.com/ismej)
Daily variation of marine microbial communities
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Chapter(2(
!
Time&series!of!phytoplankton,!Archaeal,!Bacterial!taxa!following!a!diatom!bloom!
reveals!pronounced!daily!succession!and!realized!niches!
(
Abstract!
Understanding!the!niches!of!microbial!lineages!is!central!to!microbial!ecology.!We!
observed!rapid!succession!of!photosynthetic!eukaryote!taxa!and!the!associated!
bacterial!and!archaeal!taxa,!by!frequent!sampling!following!a!large!diatom!bloom!
offshore!of!southern!California,!following!up!over!5!months!to!summer!stratified!
conditions.!Chloroplast!16S!rRNA!sequences!characterized!phytoplankton,!and!
bacterial/archaeal!16S!analysis!was!validated!with!mock!communities.!Initially,!
distinct!Pseudo'nitzschia!strains!peaked!on!successive!days,!followed!by!Tetraselmis,!
Heterosigma,!Ostreococcus!and!multiple!Prymnesiophytes.!The!rapid!bacterial!and!
archaeal!response!included!large!or!particle!attached!communities!responding!more!
than!the!small!and!free&living!communities.!Short&lived!peaks!of!Verrucomicrobia!
and!Flavobacteria!were!observed!in!the!large!fraction,!whereas!SAR92!and!
Roseobacter!peaked!in!the!smaller!fraction;!all!these!taxa!reached!maxima!on!
distinct!days.!After!the!initial!diatom!bloom!decline,!several!strains!of!MGII!Archaea!
bloomed!over!a!week,!comprising!about!40%!of!reads!in!both!the!small!and!large!
fraction,!strongly!implicating!this!poorly&known!group!with!a!boom/bust!lifestyle!
associated!with!phytoplankton!blooms.!The!spring&to&summer!bacterial!and!
archaeal!succession!was!better!explained!statistically!by!the!photosynthetic!
eukaryote!community!than!environmental!parameters.!!Network!analysis!identified!
tightly&correlated!individual!phytoplankton!and!bacterial!dynamics.!In!particular,!
the!nitrogen&fixing!cyanobacterium!(UCYN&A)!was!strongly!correlated!with!its!
known!symbiont,!Braarudosphaera,!and!also!other!potential!host!phytoplankton!and!
bacteria!including!Flavobacteria.!Overall,!the!description!of!ecological!niches!and!
apparent!interaction!between!organisms!in!this!high&temporally!resolved!time&
series!provides!novel,!thorough!description!and!allows!for!future!inspection!of!the!
mechanisms!controlling!these!dynamics.!
19
Introduction(
! Observing!the!dynamics!of!marine!microbial!communities!via!time&series!
analysis!in!the!field!helps!to!determine!the!niches!of!taxa!ultimately!leading!to!a!
better!understanding!of!how!they!influence!biogeochemical!cycles!(Fuhrman!et!al.!
2015).!Monthly!time&series!have!revealed!that!surface!communities!are!often!
seasonal!and!related!to!environmental!conditions(Steele!et!al.!2011;!Gilbert!et!al.!
2012;!Chow!et!al.!2013;!Cram!et!al.!2014)!and!short&term!time&series!(such!as!daily!
or!weekly)!have!revealed!that,!while!marine!microbial!communities!exhibit!
elements!of!stability!over!week&to&months,!occasionally,!rapid!and!steady!shifts!
occur!in!multiple!taxa!at!once(Sintes!et!al.!2013;!Needham!et!al.!2013;!Lindh!et!al.!
2015).!!
! Offshore!phytoplankton!blooms!disproportionally!contribute!to!particle!
export!in!a!given!location,!but!their!ephemeral!nature!results!in!them!mostly!being!
studied!by!manipulative!experiments!and!relatively!few!field!observations.!In!
general,!these!studies!find!a!few!taxa!are!often!found!to!be!associated!strongly!with!
phytoplankton!blooms,!such!as!Roseobacteria,!Flavobacteria,!and!
Gammaproteobacteria(Mayali!et!al.!2010;!Teeling!et!al.!2012;!Sintes!et!al.!2013;!
Buchan!et!al.!2014;!Lindh!et!al.!2015).!Further,!different!varieties!of!Flavobacteria!
have!been!shown!to!peak!over!short!time&scales!(Teeling!et!al.!2012).!Less!explored,!
however,!are!how!the!phytoplankton!communities!making!up!the!bloom!vary!over!
short&periods!of!time,!especially!as!analyzed!with!molecular!techniques!(here,!the!
16S!of!chloroplasts).!!The!diversity!and!dynamics!of!eukaryotic!organisms!are!
traditionally!assayed!in!field!environments!using!the!18S!rRNA!sequences!(López&
García!et!al.!2001;!Edgcomb!et!al.!2002;!Dawson!and!Pace!2002;!Amaral&Zettler!et!al.!
2009;!Caron!et!al.!2012),!but!can!also!be!explored!using!the!16S!gene!of!chloroplasts!
(Rappé!et!al.!1997;!Wilmotte!et!al.!2002;!Fuller!et!al.!2006;!Treusch!et!al.!2012).!
Considering!the!many!order!of!magnitude!variation!in!18S!copy!number!among!
algal!taxa!(from!a!1!copy!per!cell!to!>10,000!in!some!alveolates!(Zhu!et!al.!2005)),!it!
may!be!expected!that!chloroplast!16S!genes!may!more!closely!follow!the!
approximate!phytoplankton!biomass.!!
20
! Here,!we!describe!the!short&term!(day&to&day)!dynamics!of!photosynthetic!
eukaryotes!(PE),!archaea,!and!bacteria!during!and!after!a!spring!bloom!at!a!single!
location!20km!offshore!of!Southern!California!over!~3!weeks,!with!extended!
sampling!daily&to&weekly!for!5!months.!The!temporal!scale!at!which!we!observed!
the!succession!is!rare.!Additionally,!while!many!time&series!examine!the!bacterial!
associated!with!blooms,!few!examine!the!molecular!diversity!of!archaea!and!
eukaryotic!phytoplankton,!which!enabled!a!near&full!picture!of!the!cellular!
community!during!this!period.!We!also!assess!the!bias!and!shortcomings!of!the!
methods!via!analysis!of!our!16S!sequencing!pipeline!though!analysis!of!a!custom!
marine!mock!community.!!
Materials(and(Methods(
Location9and9Sampling9
! Water!samples!(10&20L)!were!collected!from!the!top!1m!of!the!water!column!
at!the!San!Pedro!Ocean!time&series!location!(Lat:!33°33’!N!,!Long:!118°24!W)!by!
multiple!bucket!casts!at!approximately!08:15!from!!the!USC!commuter!boat,!Miss!
Christie.!!Water!samples!were!stored!in!a!cool,!dark!location!until!arrival!at!the!
Wrigley!Institute!of!Environmental!Science,!Catalina!Island!around!08:45!where!
processing!began!immediately.!When!not!possible!to!collect!on!the!morning!
crossing,!water!was!collected!in!the!afternoon!and!filtered!at!the!University!of!
Southern!California!in!Los!Angeles!within!2&3!hrs.!Filtration!for!cellular!material!for!
molecular!analyses!was!via!in&line!peristaltic!pump:!whole!seawater!was!through!
80µm!mesh,!47mm!diameter!1µm!AE!Pall!glass!filter,!and!47mm!diameter!0.22µm!
Durapore!Filter.!The!AE!and!Durapore!filters!were!stored!at!&80C!until!extraction.!!
Environmental9measurements9
! Temperature,!salinity,!and!pH!were!determined!immediately!upon!sampling!
in!an!on&board!bucket!by!YSI!(Yellowsprings,!OH).!Chlorophyll!a9concentration!were!
determined!via!triplicate!0.5&1L!filtrations!onto!GF/F!filter,!frozen!at!&80C!and!
extracted!with!90%!acetone!and!analyzed!fluorometrically.!Bacterial!and!viral!
abundances!were!determined!via!SYBR!Green!epifluorescent!microscopy(Noble!and!
Fuhrman!1998).!Assessments!of!the!dominant!phytoplankters!were!made!from!the!
SYBR!green!microscopy!slides!by!scanning!vertically!once!across!a!full!filter!and!
21
identifications!were!made!visually!based!on!notable!morphologies.!Satellite!sea&
surface!chlorophyll!a9imagery!for!Southern!Californian!region!were!downloaded!
from!NOAA!Coast!Watch!using!a!8!day!averages.!Daily!averages!for!precipitation,!air!
temperature!,!and!wind!speed!were!downloaded!from!
http://www.wunderground.com/weather&forecast/US/CA/Catalina.html.!Wave!
height!and!water!temperature!were!collected!from!the!National!Buoy!Data!Center!
from!a!buoy!located!33°37’!N!118°!19’!W!which!is!approximately!11km!from!our!
sampling!location;!both!are!within!the!San!Pedro!Channel.!!!
DNA9extraction9
! DNA!was!extracted!via!the!47µm!Durapore!filter!via!SDS!and!
Phenol:Chloroform!(Fuhrman!et!al.!1988;!Needham!et!al.!2013).!DNA!was!extracted!
via!the!AE!filter!as!in!(Countway!et!al.!2005).!Briefly,!lysis!with!0.7M!NaCl,!1%!CTAB,!
and!0.5mm!zirconia/silica!beads.!Lysates!were!bead&beat!for!1!minute!and!heated!to!
70!C!for!5!minutes!(repeated!3x).!DNA!was!purified!from!lysates!via!2x!volume!of!
Phenol,!1:1!volumes!of!Phenol!and!Chloroform:Isoamyl!alcohol,!and!2x!volume!
(24:1)!Chloroform!Isoamyl!alcohol.!The!DNA!was!precipitated!with!2.2x!volume!of!
95%!ethanol!and!0.25x!of!10.5M!ammonium!acetate!and!left!overnight!at!&20C.!
Precipitate!was!collected!via!centrifugation!and!resuspended!in!TE!and!stored!at!&
80C.!!
Mock9Community9Generation9
! Custom!“even”!and!“staggered”!mock!communities!were!generated!by!
pooling!environmental!sequence!clones!intended!to!span!most!of!the!diversity!of!the!
surface!communities!at!the!SPOT!time&series.!Most!clones!were!16S&ITS&23S!
sequences!from!the!SPOT!time&series(Brown!et!al.!2005),!but!a!few!were!graciously!
provided!by!colleagues!(Table!1).!!The!even!and!staggered!communities!were!made!
of!10!OTUs!at!10%!relative!abundance!and!25!OTUs!at!35%!to!0.125%!relative!
abundances,!respectively!(Table!1).!Clones!were!plasmid!purified!with!the!Zymo!
Zyppy!Plasmid!Miniprep!kit!according!to!manufacturers!instructions,!diluted!to!
0.01ng/uL!and!amplified!with!M13F/R!primers,!and!cleaned/concentrated!with!
Zymo!Clean/Concentration!kit!according!to!manufacturers!instructions.!Plasmid!
inserts!were!diluted!to!0.01ng/uL!and!amplified!with!primers!intended!to!amplify!
22
near!full!length!16S!sequences!(Supplementary!Information)!and!then!
cleaned/concentrated!with!the!Zymo!kit.!Each!amplification!reaction!used!1x!HiFi!
Buffer!(Invitrogen),!10mM!dNTPS!(Promega),!2mM!MgSO4,!and!0.4U!Hifi!Taq!
Polymerase!(Invitrogen).!These!near&full&length!16S!sequences!were!then!re&
sequenced!individually!to!obtain!consensus!sequences!for!the!primary!region!of!
interest!(V4/V5)!and!pooled!to!their!respective!concentrations!with!consideration!
to!sequence!length!and!composition.!Mock!communities!were!amplified!in!parallel!
with!environmental!samples!and!the!input!template!concentration!was!adjusted!to!
be!similar!to!that!of!the!respective!genes!in!environmental!samples.!!
PCR,9barcoding,9sequencing9preparation99
QIIME!mapping!files!necessary!for!de&indexing!and!de&barcoding!are!provided!in!
Supplement!Information,!primer!documentation!(Supplementary!Information).!
Primers!were!ordered!salt&free!purified!from!Operon!(Huntsville,!AL).!Forward!
primer!construct!5’&3’:‘generic’!Illumina!flow!cell!adapter!and!sequencing!primer!4!
random!bases,!5!base!barcode,!and!16S!forward!primer!515F!
(GTGCCAGCMGCCGCGGTAA).!Reverse!primer!construct!5’&3’:!‘generic’!Illumina!flow!
cell!adapter,!6!base!index,!sequencing!primer,!and!16S!reverse!primer!926R!
(CCGYCAATTYMTTTRAGTTT).!See!Supplementary!Information,!primer!
documentation!for!construct!sequences,!barcode!and!index!sequences,!and!paired!
sample!names.!!We!ran!a!maximum!of!32!samples!in!triplicate!during!each!PCR!
setup.!Samples!order!was!randomized!to!decrease!the!chance!of!cross&
contamination!between!adjacent!days.!One!no!template!control!(NTC)!was!
randomly!placed!for!every!8!samples.!1!each!of!the!‘even’!mock!community!and!
‘staggered’!marine!mock!communities!were!added!to!their!own!tubes!once!per!PCR!
run.!25µl!triplicate!PCRs!were!performed!as!follows!on!1&2ng/µL!of!sample!extract!
or!representative!amounts!of!mock!community:!1x!Invitrogen!Platinum!tag!HiFi!
Buffer!(Cat.#:!11304&029),!0.2mM!dNTPs!(Promega!Cat.!#!U1515),!0.4µM!of!each!
primer,!2mM!MgSO4!(Invitrogen:!11304&029),!1U!Invitrogen!Platinum!HiFi!Taq!
(11304&029),!Molecular!grade!water!(VWR!Cat!#:!95043:414)!was!used!to!raise!to!
the!final!reach!final!reaction!volume.!Thermocycling!was!as!follows:!!Initial!
denaturation:!95!C,!120s,!25!cycles!of!95C,!45s;!50!C,!45s;!68!C,!90s;!68!C,!90s,!Final!
23
elongation!step!68C,!300s.!Triplicates!were!re&combined!and!were!run!on!agarose!
gel!to!confirm!expected!amplification!or!non&lification!(for!NTCs).!All!reactions!
were!then!cleaned!and!concentrated!with!1x!(vol:vol)!Ampure!XP!Magnetic!beads.!
Purified!products!were!then!pooled!in!equimolar!concentrations.!1µL!of!selected!
NTCs!(generally,!the!last!NTC!of!each!set!and!the!NTC!with!highest!quantification!
post&bead!cleanup.!All!sequences!were!sent!to!the!University!of!California!Davis!
Genome!Center!and!sequenced!with!generic!sequencing!primers!on!an!Illumina!
Miseq!(Davis,!CA).!0.22µm!and!1.0µm!size!fractions!were!analyzed!with!2x260!and!
2x300!sequencing,!respectively.!!
Sequence9Analysis9
! Supplementary!Information!includes!all!unix,!QIIME!version!1.8.0,!usearch7!
custom!scripts,!mapping!files!for!demultiplexing,!paired&end!merging,!quality!
filtering,!chimera!checking,!OTU!clustering,!taxonomy!assignment!of!sequences,!
along!with!parameters!used!for!each!command.!Paired!end!reads!were!merged!
using!usearch7!fastq_mergepairs9(Edgar!2013)9!command!where!reads!were!
truncated!at!the!first!base!with!a!qscore!below!5,!and!10%!mismatch!allowed!over!
the!expected!region!of!overlap!(about!100&200bp!depending!on!the!dataset).!For!
16S,!reads!merged!reads!shorter!than!336!or!longer!than!486!were!removed.!!Then!
QIIME!script!split_libraries_fastq.py!(Caporaso!et!al.!2010)!was!used!to!de&index!the!
sequence!files!allowing!no!mismatches!to!the!index.!Merged!reads!were!then!
demultiplexed;!if!sequences!contained!an!error!in!a!barcode!they!were!removed.!
Forward!and!reverse!primers!and!primers!were!removed!and!sequences!with!
average!q!identify_chimeric_seqs.py.!!Sequences!were!de!novo!clustered!into!99%!OTUs!by!
UCLUST!via!pick_otus.py9and!the!most!abundant!sequence!of!each!OTU!was!picked!
for!classification!via!pick_rep_set.py.!Taxonomy!was!assigned!to!all!sequences!with!
UCLUST!by!searching!against!Silva!(release!111)!(Quast!et!al.!2013)!and!Greengenes!
(release!13_8)(DeSantis!et!al.!2006)!databases!using!assign_taxnomy.py9(Database!
S2!and!Database!S3,!respectively).!16S!sequences!classified!as!mitochondria!or!
chloroplast!by!Greengenes!were!removed!from!bacterial/archaeal!OTU!table.!A!
separate!OTU!table!was!made!for!sequence!classified!as!chloroplast.!Since!
24
taxonomic!assignments!of!chloroplasts!were!usually!overly!broad!via!SILVA!and!
Greengenes,!we!performed!a!BLASTn!search!of!the!non&redundant!nucleotide!NCBI!
database,!excluding!sequences!classified!within!NCBI!as!from!!“environmental!
samples"!or!“metagenomes”.!An!environmental!sequence!(gi:!10719513)!was!
observed!to!have!been!carried!through!despite!attempts!to!remove,!thus!we!
manually!removed!it!(it!matched!one!of!the!abundant!diatom!OTU!sequences).!We!
used!the!best!BLASTn!match!to!this!database!to!classify!the!representative!OTU!
sequences!(Database!S4).!For!the!sequences!>0.4%!on!average,!we!examined!the!
classification!phylogenetically!by!aligning!the!best!BLASTn!match!sequence!to!the!
representative!sequences!set!for!OTUs!>0.4%!on!average.!Prior!to!alignment,!one!
NCBI!sequence!was!removed!because!it!was!did!not!cover!the!full!16S!region!of!
interest:!a!335bp!100%!match!to!denovo!OTU#64329,!Pyramimonas!gi:!658131508.!
Maximum!likelihood!phylogenetic!trees!were!generated!using!PHYML(Guindon!and!
Gascuel!2003)!with!the!HKY85!substitution!model!and!100!bootstraps!(Fig!1).!
Statistical9Analyses!
! R!code!are!available!for!all!analyses!in!Supplementary!Information.!!
9 Heatmaps!were!drawn!via!the!heatmap3!package!in!R(3.1.1).!The!
representative!sequences!for!the!phylogenetic!trees!were!aligned!in!Geneious!
(v6.1.6)(Kearse!et!al.!2012)!using!the!MAFFT!algorithm!G&INS&I!default!parameters!
(Katoh!et!al.!2002).!Maximum!likelihood!phylogenetic!trees!were!drawn!using!
PHYML!with!the!HKY85!Substitution!model!and!100!bootstraps!(Guindon!and!
Gascuel!2003).!
9 PCA9plots!were!generated!using!the!prcomp!function!(Vegan!(2.2&1)(Oksanen!
et!al.!2015)!on!the!square!root!of!the!rarified!data!matrices.!OTUs!with!principle!
component!scores!scores!were!scaled!relative!to!dates!by!1.5!for!visualization!purposes.!!
Community9Similarity!
! For!the!community!decay!in!similarity!and!Mantel!tests,!community!
similarity!was!derived!by!the!vegdist!function!in!Vegan(2.2&1)!using!the!Bray&Curtis!
method.!For!dates,!Euclidean!distance!matrices!were!calculated!via!the!R!base!dist!
function.!For!preparation!for!Mantel!tests,!communities!and!environmental!data!
25
with!missing!data!were!removed!to!generate!a!full!overlapping!dataset!(36!samples!
total).!Mantel!tests!were!performed!for!combinations!of!the!communities!and!
isolated!and!combined!environmental!variables!in!Vegan!using!mantel.!Partial!
mantel!tests!were!performed!via!the!mantel.partial!function!from!Vegan!where!the!
date’s!distance!matrix!controlled!for!auto&correlation!due!to!time&lapse!within!the!
communities!and!parameters.!!
LSA!
! Pairwise!correlation!matrices!were!generated!using!the!eLSA(Xia!et!al.!2011,!
2013)(Xia!et!al.!2011,!2013)!for!the!top!60!OTUs!that!were!present!in!over!¼!of!
samples,!this!corresponded!to!taxa!that!were!no!less!than!0.1%!on!average.!Linear!
interpolation!was!used!for!samples!with!missing!data!and!1,000!permutations!were!
performed!for!significance.!Q&values!were!performed!to!control!for!false!
positives(Storey!2003).For!the!March!time&series,!time&lagged!correlation!was!
examined!with!a!maximal!delay!of!3!days.!Correlation!matrices!were!imported!into!
Cytoscape!(v2.8.3)!(Shannon!et!al.!2003)!along!with!an!attribute!table!of!taxonomy!
and!abundance!information!for!mapping!onto!nodes!(Database!S5).!(
Results((
Environmental9Context9
! Phytoplankton!blooms!tend!to!occur!in!March!or!April!at!the!San!Pedro!
Ocean!time&series!location!(SPOT)!and!sampling!for!this!study!began!coincident!
with!the!largest!spring!phytoplankton!bloom!of!2011!(Fig!2),!with!chlorophyll!a!
concentration!of!10.3!µg/L!on!the!first!day!of!sampling!at!SPOT!(Fig!3).!Within!a!few!
days!the!concentration!of!chlorophyll!was!more!typical!for!spring!at!SPOT!(<2µg/L).!
The!primary!bloom!occurred!after!a!few!days!of!strong!winds!(>25km/hr!average)!
in!the!region!four!days!prior!to!the!commencement!of!sampling!(Fig3).!A!secondary!
bloom!in!chlorophyll!(up!to!3.1µg/L)!followed!a!second!storm!which!brought!2.5!cm!
rain!and!1!C!drop!in!surface!water!temperature.!Over!the!remainder!of!the!time&
series,!temperatures,!on!average,!increased!and!chlorophyll!concentrations!
remained!below!2.5µg/L!(average:!0.67µg/L)!(Fig!3).!Satellite!imagery!indicates!that!
the!bloom!conditions!observed!at!SPOT!were!region&wide!phenomena!(Fig!4).!
9
26
Photosynthetic9Eukaryotic9Dynamics9
! The!PE!community!during!primary!bloom!(3/12&3/18)!was!highly!dynamic!
with!a!different!OTU!becoming!the!dominant!taxon!on!each!day!sampled!(Fig!5a),!
including!3!OTUs!of!Pseudo'nitzschia9(19was9100%9match9to9Pseudo'nitzschia9
seriata),9Chaetoceros9socialis,9Tetraselmis9cordiformis9and9Heterosigma9akashiwo.!Via!
microscopy,!we!confirmed!that!Pseudo'nitzschia!and!Chaetoceros!appeared!to!be!the!
dominant!phytoplankter!on!3/12!and!3/18,!respectively.!Phaeocystis9globosa9and!
Ostreococcus!lucimarinus!(sp.!RCC356)!became!the!most!dominant!during!the!period!
prior!to!the!secondary!peak.!The!secondary!bloom!(3/23&3/28)!was!dominated!by!
Prymnesiophytes!(Imantonia!and!Chrysochromulina).!!
! The!rate!of!variation!slowed!after!March!(Fig!6).!Over!the!full!time!series!
(March!to!Augusta),!PE!dynamics!varied!both!broadly!and!subtly!between!and!
within!linages.!Broadly,!Stramenopiles!were!sparingly!represented!after!the!primary!
bloom!decline,!whereas!the!Mamiellaceae!were!more!abundant!after!the!primary!
bloom!(Fig!5b).!!The!Prymnesiophytes!(mostly!Chrysochromulina)!were!generally!
found!after!the!March!rain!event.!Despite!these!tendencies,!very!few!distinct!OTUs!
of!close!relatives!within!the!PE!showed!highly&correlated!dynamics!(Fig!5b,!Fig!7).!!
Bacterial9and9Archaeal9Dynamics9
! Similar!to!the!PE!community,!the!large!or!particle!attached!microbial!
community!(1µm)!was!highly!dynamic!during!the!first!6!days!of!sampling!with!a!
Verrucomicrobium!OTU!and!3!Flavobacteria!OTUs!becoming!becoming!dominant!
for!at!least!1!day!(Fig!8a).!A!single!MGII!Archaeal!OTU!peaked!between!the!primary!
and!secondary!blooms!(3/23),!reaching!an!estimated!relative!abundance!of!25%!of!
the!bacterial!and!archaeal!community;!the!top!4!MGII!OTUs!made!up!about!40%,!
cumulatively.!The!secondary!bloom!was!dominated,!initially,!by!the!same!
Verrucomicrobium!OTU!as!the!primary!chlorophyll!bloom;!SAR11!dominated!over!
the!remainder!of!March!(Fig!8a).!The!small,!free&living!fraction!was!less!variable,!
and!SAR92!was!the!only!OTU!to!dominate!in!this!fraction!post&bloom!and!not!in!the!
larger!fraction!(Fig!8b).!
! Over!the!full!time&series,!the!lineages!of!bacterial!and!archaeal!taxa!at!the!
class!level,!with!the!exception!of!the!cyanobacteria,!showed!considerable!within&
27
lineage!variability!in!their!ecological!(temporal)!dynamics!(Fig!8C,!Fig!9).!For!
example,!some!taxa!within!the!very!diverse!Flavobacteria!showed!increases!during!
bloom!conditions,!while!others!were!much!more!abundant!later.!Within!the!
Alphaproteobacteria,!SAR11!were!dominant!in!the!small/free&living!fraction!
throughout,!while!most!Roseobacteria!were!abundant!during!the!initial!bloom.!
However,!there!were!some!closely!related!OTUs!that!showed!remarkable!similar!
dynamics!to!each!other;!e.g.,!within!the!MGII!Archaea!and!SAR11!group!(Fig!9).!
! In!general,!the!dynamics!of!individual!bacterial!and!archaeal!OTUs!were!
correlated!between!both!the!0.22&1!µm!and!the!>1µm!size!fractions,!for!example!the!
cyanobacteria!and!MGII!Archaea.!However,!often!there!was!a!fraction!in!which!
lineages!were!found!to!be!more!abundant.!In!particular,!the!Verrucomicrobia!and!
Flavobacteria!were!more!abundant!in!the!1µm!size!fraction.!On!the!other!hand,!
Alphaproteobacteria,!especially!SAR11,!tended!to!be!more!abundant!in!the!0.22&
1µm!fraction.!!
Associations9of9communities/environmental9factors9
! In!order!to!understand!which!factors!most!influence!the!microbial!
community!structures!and!how!the!microbial!communities!influence!each!other,!we!
used!Mantel!and!partial!Mantel!tests.!Partial!Mantel!tests!factor!out!time!as!a!
variable!in!order!to!reduce!the!effect!of!autocorrelation.!Environmental!parameters!
were!more!correlated!to!the!PE!community!composition!than!either!size!fraction!of!
the!bacterial/archaeal!community!(Table!1).!The!bacterial/archaeal!communities!
were!more!highly!correlated!to!the!PE!community!than!they!were!to!environmental!
parameters!(Table!1).!!
Associations9with9Photosynthetic9Eukaryotes99
9 We!generated!pairwise!correlation!matrices!for!OTUs!and!environmental!
parameters!over!the!full!time&series!with!no!time&lag!(since!time!lag!analysis!
requires!approximately!even!temporal!distribution).!To!focus!on!PE&to&
bacteria/archaeal!correlations,!we!first!visualized!correlations,!via!network!
analysis,!to!5!different!PE!OTUs!that!had!distinct!temporal!dynamics!and!high!
relative!abundances:!Pseudo'nitzschia,!Micromonas,!Phaeocystis,!Chrysochromulina,!
and!Heterosigma.!The!analysis!reveals!that!many!different!genera!of!Flavobacteria!
28
were!correlated!with!each!of!the!PEs.!Notable!correlations!were!also!found!over!the!
full!time&series!between!MGII!Archaea!and!Phaeocystis!and!between!Synechoccocus!
and!Micromonas9(Fig!10).!Allowing!for!up!to!a!3!day!time&lag!for!the!same!5!PE!in!for!
the!March!time&series!revealed!many!several!consistent!Flavobacterial!correlations!
to!Pseudo'nitzschia!whereby!specific!Flavobacterial!OTUs!trailed!Pseudo'nitzschia9by!
1&2!days!(Fig!11).9!
Associations9with9MGII9Archaea9
! To!further!explore!the!associations!to!MGII!Archaea,!we!examined!the!
pairwise!correlations!to!abundant!MGII!archaeal!OTUs!(>0.4%!on!average)!of!the!
full!time&series.!Most!of!the!MGII!OTUs!bloomed!around!March!23,!but!one!MGII!
OTU!varied!much!less!(Fig!9).!The!“blooming&type”!MGII!OTUs!were!positively!
correlated!with!3!Prymnesiophytes!(including!Phaeocystis)!and!Flavobacterial!OTUs!
over!the!full!time&series!(Fig!12A)!and!were!negatively!associated!with!3!SAR11!
OTUs!and!an!AEGEAN&169!OTU!(a!close!relative!of!SAR11).!On!the!other!hand,!the!
“stable&type”!MGII!OTU!was!correlated!with!a!variety!of!bacterial!and!PEs!that!were!
common!throughout!the!middle!of!the!time&series!(April&June),!for!example!
Synechococcus!and!SAR86.!Restricting!the!analysis!to!March!and!allowing!for!time&
lag,!indicated!that!the!best!correlation!between!MGII!and!Phaeocystis!was!time&
lagged!such!that!Phaeocystis!trailed!by!1!day!(Fig!13),!despite!both!peaking!on!the!
same!day!(Fig!8).!
UCYN'A/Braarudosphaera9Associations9
! Recently,!it!was!found!that!a!wide&spread!marine!diazotrophic!and!aberrant!
cyanobacterium!(missing!photosystem!II)!called!UCYN&A!is!symbiotic!with!
Braarudosphaera9bigelowii(Thompson!et!al.!2012)!as!well!as!other!Prymnesiophytes!
and!possibly!alveolate!protists(Krupke!et!al.!2014).!Braarudosphaera9and!UCYN&A!
were!positively!correlated!over!the!full!time&series!(Spearman’s!correlation!=!0.67,!p!
correlation!values!(Fig!12B).!Positive!correlations!to!UCYN&A!and!Braarudosphaera!
included!Prochloroccocus,!several!Flavobacteria,!an!OTU!whose!closest!database!
match!is!90%!sequence!similarity!to!a!Rhodophyte!alga,!the!stramenopile!
Mesopedinella!arctica,!and!an!unassigned!bacterium!with!29
sequence!in!SILVA,!Greengenes,or!NCBI!databases.!These!taxa!were!positively!
correlated!with!water!temperature!(summer!conditions),!i.e.,!when!conditions!are!
oligotrophic.!!
Mock9Community9Analysis! ! 9
We!assessed!the!accuracy!and!precision!of!our!method!by!amplification!and!
sequencing!of!an!“even”!and!a!“staggered”!custom,!marine!mock!community!in!
parallel!with!samples.!The!“even”!community!contained!10!OTUs!at!10%!input!each!
and!the!“staggered”!contained!25!OTUs!ranging!from!0.1%!to!30%!input.!The!mean!
percentage!difference!between!the!expected!and!observed!OTU!relative!abundance!
was!35%!(+/&!30%)!and!37%!(+/&!37%!SD)!for!the!even!and!staggered,!respectively!
(Fig!14).!Differences!tended!to!occur!in!low!abundance!taxa,!with!2/10!and!10/25!of!
the!taxa!in!the!even!and!staggered!mock!communities!having!abundances!that!were!
off!by!more!than!50%!of!the!expected!abundances.!MGII!Archaea!was!the!most!over&
estimated!OTU!by,!on!average,!90%!(+/&19%!SD)!and!147%!(+/&21%!SD)!for!the!
even!and!staggered!communities,!respectively.!Overall!the!results!were!highly!
reproducible!with,!with!Bray&Curtis!similarity!between!replicates!of!the!even!and!
staggered!communities!of!91%!(+/&!6%!SD)!and!95%!(+/&!1%!SD).!During!analysis,!
apparently!spurious!OTUs!were!created,!cumulatively!making!up!6.5%!(+/&0.16%)!
and!5.4%!(+/&5.7%)!of!the!staggered!and!even!communities.!!
Discussion(
Considerations9for916S/Chloroplast9analysis!
! Classification!of!16S!chloroplast!sequences!was!successful:!all!chloroplast!
OTUs!averaging>!1%!of!the!total!chloroplast!sequences!matched!within!98%!
similarity!to!such!sequences!from!isolated!phytoplankton!taxa;!over!half!of!these!
OTUs!were!100%!matches!to!a!known!species.!Further,!phylogenetic!evaluation!of!
our!classifications!for!the!abundant!chloroplast!sequences!(>0.4%!on!average)!
indicates!that!the!classifications!are!correct,!with!the!exception!of!some!
Prymnesiophyte!sequences.!Prymnesiophytes!made!up!about!half!of!the!OTUs!that!
we!detected!that!were!>0.4%!on!average!and!had!a!remarkable!amount!of!variation!
in!their!dynamics.!However,!about!half!of!these!were!classified!as!either!
Chrysochromulina!or!Imantonia,!probably!the!result!of!significant!absences!of!
30
appropriate!cultured!representatives!for!this!group.!!
! Interestingly,!some!of!the!apparent!PE!dynamics!we!observed!may!show!
patterns!of!organisms!other!than!the!original!chloroplast!source!since!many!
protists,!including!ciliates(Stoecker!et!al.!1987)!and!dinoflagellates(Hackett!et!al.!
2003;!Gast!et!al.!2007),!are!capable!of!kleptoplasty.!Kleptoplasty!is!the!ability!to!
ingest!photosynthetic!prey,!partially!digest!the!prey!but!retain/utilize!the!prey’s!
chloroplast!for!days!to!weeks.!We!observed!at!least!2!PE!OTUs!classified!as!taxa!that!
have!previously!been!reported!to!participate!in!kleptoplastic!relationships.!First,!an!
OTU!classified!as!the!cryptophyte!Teleaulax9amphioxeia!chloroplasts.!Teleaulax!
chloroplasts!are!known!to!be!utilized!by!the!ciliate!Mesodinium9rubrum!and!
potentially!further!transferred!to!the!dinoflagellate!Dinophysis!(alternative!name:!
Phalacroma)!(Garcia&Cuetos!et!al.!2010;!Kim!et!al.!2012).!We!also!observed!an!OTU!
classified!as!Phalacroma9(alternative!name:!Dinophysis9mitra),!but!the!sequence!of!
the!OTU!appears!of!Prymnesiophyte!origin,!which!is!consistent!with!the!conclusions!
of!the!original!description!of!the!Phalacroma!species!chloroplast!sequence!we!
detected!(Koike!et!al.!2005).!Thus!the!extent!to!which!some!of!the!chloroplast!
sequences!(especially!those!of!Prymnesiophyte!origin)!that!we!observed!are!
associated!with!dinoflagellate!or!other!host!(versus!free&living)!is!unclear,!but!an!
exciting!area!of!future!research.!Finally,!we!may!be!underestimating!the!
contribution!of!dinoflagellates,!in!general,!due!to!their!permanent!chloroplasts!being!
very!aberrant,!including!truncated,!extended,!or!absent!ribosomal!subunit!
sequences(Zhang!et!al.!1999;!Koumandou!et!al.!2004;!Barbrook!et!al.!2006).!While!
there!are!unexplained!chlorophyll!peaks!(e.g.,!on!3/16)!that!may!be!due!to!
dinoflagellates,!no!dinoflagellates!blooms!were!obvious!via!microscopy,!though!
preliminary!analysis!of!18S!sequences!does!reveals!their!presence,!often!as!
significant!components!of!the!total!sequences.9
! A!major!strength!of!this!study!is!that!the!mock!community!analysis!allows!us!
to!know!the!extent!of!potential!over/under!estimation!of!the!major!taxa!in!our!
analysis,!while!virtually!all!prior!marine!studies!have!left!the!biases!of!primers!and!
clustering!methods!unknown.!We!consider!the!apparent!bias!associated!with!the!
method!encouraging!since!the!observed!bias!is!generally!less!than!potential!16S!
31
copy!number!issues,!though!16S!copy!number!variation!may!not!be!a!major!problem!
in!the!ocean!for!oligotrophic!bacteria!(Klappenbach!et!al.!2000;!Brown!et!al.!2005).!
Nevertheless,!our!analysis!of!variations!in!particular!taxa!over!time,!and!the!use!of!
rank!correlations!for!association!analysis,!reduces!effects!of!copy!number!biases!on!
our!interpretation.!
Ecological9Interpretation9and9Implications!
We!observed!very!rapid!succession&like!patterns!within!photosynthetic!
eukaryotic!community!during!a!large!phytoplankton!bloom.!The!bacterial!and!
archaeal!response!is!strong!and!rapid,!including!an!increase,!then!decrease!of!the!
MGII!archaea!over!just!a!few!days.!The!changes!within!the!communities!are!linked,!
and!we!find!that!the!dynamics!between!protists!and!microbes!are!stronger!to!one!
another!than!to!environmental!parameters,!suggesting!that!ecological!niches!of!the!
microbes!are!tied!strongly!to!the!presence!and!abundance!of!other!particular!
organisms.!!
The!initial!bloom!of!diatoms!is!unsurprising!since!annual!blooms!are!common!
off!of!southern!California(Schnetzer!et!al.!2007)!and,!3!days!prior!to!our!first!
sample,!a!biotoxin!produced!most!commonly!by!Pseudo'nitzschia,9domoic!acid,!
reached!the!highest!concentration!ever!recorded!in!the!San!Pedro!Channel(Stauffer!
et!al.!2012).!The!rapid!bloom!decline!that!we!observe!is!presumably!due!to!a!
combination!of!factors!including!grazing,!viral!lysis,!and!export!such!as!sinking!upon!
expiration!of!their!preferred!nutrient!conditions(Smetacek!1985).!Most!of!the!
bacteria!associated!with!the!Psuedo'nitzshchia9are!likely!capable!of!consuming!
carbohydrates!as!was!recently!found!for!some!Flavobacteria(Teeling!et!al.!2012;!
Xing!et!al.!2015)!and!SAR92(Sosa!et!al.!2015).!The!response!was!strongest!in!the!
1µm!size!fraction.!Functionally,!the!particle&attached!size!fraction!is!reportedly!
enriched!in!carbohydrate!transporters!further!suggesting!that!they!are!involved!in!
breakdown!of!fresh!phytoplankton!exudate(Smith!et!al.!2013).!It!is!possible!that!the!
bacteria!that!are!associated!with!the!diatoms!are!not!only!consuming!the!released!
organic!material!but!also!supply!important!substrates!for!growth,!e.g.,!vitamins!that!
the!diatoms!are!incapable!of!synthesizing(Croft!et!al.!2006;!Sañudo&Wilhelmy!et!al.!
2014).!!
32
The!rapid!increase!and!then!decrease!of!MGII!archaea!between!the!primary!and!
secondary!blooms!during!March!was!surprising.!The!mock!community!results!
indicate!our!method!overestimates!MGII!abundance!by!perhaps!double,!but!even!
correcting!for!that,!the!MGII!would!still!be!the!dominant!microbe!present.!It!has!
previously!been!suggested!that!these!organisms!may!be!associated!with!increases!in!
chlorophyll!(Murray!et!al.!1999)!and!that!they!may!be!boom/bust!type!organisms!
since!they!are!relatively!variable!in!their!spatial!distribution(Martin&Cuadrado!et!al.!
2014);!our!data!provide!direct!and!clear!support!for!these!conclusions.!Genomic!
evidence!suggests!that!the!MGII!archaea!are!probably!utilizing!lipids!and!proteins!
from!the!environment(Iverson!et!al.!2012;!Martin&Cuadrado!et!al.!2014;!Orsi!et!al.!
2015);!these!molecules!may!persist!longer!than!freshly!produced!carbohydrates!and!
may!explain!the!delayed!response!of!the!MGII.!An!additional!possible!explanation!
(but!not!mutually!exclusive)!is!that!these!organisms!are!associated!directly!with!
blooms!of!Phaeocystis9and/or!Ostreococcus!that!peaked!around!the!same!time!as!the!
MGII.!!The!MGII!Archaea!were!recently!shown!to!increase!their!abundance!when!
grown!with!Micromonas(Orsi!et!al.!2015),!suggesting!that!Micromonas!produces!a!
substrate!readily!utilized!by!MGII!archaea.!!Thus,!it!is!possible!that!Phaeocysis!or!
Ostreococcus!may!induce!a!similar!(or!even!bigger)!response!in!MGII!Archaea.!!Of!
particular!interest!to!us!is!not!only!the!rapid!increase!but!also!the!equally!rapid!
decline!in!MGII!Archaeal!abundance,!because!they!as!yet!have!no!known!viruses!or!
grazers.!A!slowing!of!growth!alone!would!not!make!them!disappear,!though!our!
observation!that!many!of!them!are!associated!with!the!larger!size!fraction!may!
mean!some!of!them!may!sink!out!of!surface!waters!or!be!grazed!along!with!larger!
particles!or!organisms!with!which!they!may!be!associated.!
Most!of!the!PE!taxa!that!increased!during!the!secondary!bloom!are!considered!
mixotrophs!and!possible!osmotrophs(Worden!et!al.!2015).!Therefore,!it!is!likely!that!
these!organisms!outcompete!other!PE!in!intermediate!nutrient!conditions!where!
inorganic!nutrients!are!limiting!and!they!can!supplement!their!growth!with!
consumption!of!bacteria!and!possibly!organic!material!remaining!from!the!initial!
bloom(Mitra!et!al.!2014).!Different!genera!of!Flavobacteria!are!associated!with!the!
second!bloom!compared!with!the!first,!in!addition!to!a!variety!of!other!bacteria.!This!
33
may!suggest!that!the!different!PE!significantly!alter!the!supply!of!organic!substrates!
present,!or!alternatively!(not!mutually!exclusively)!perhaps!the!earlier!blooming!
types!were!eventually!kept!in!check!by!viruses!and!grazers!stimulated!by!the!earlier!
bloom,!and!the!later!blooming!types!may!be!relatively!resistant!to!these.!Future!
work!should!allow!further!interrogation!of!these!hypotheses!regarding!which!
substrates!the!bacteria/archaea!are!likely!responding!to!during!these!two!blooms,!
and!which!grazers!and!viruses!are!present,!leading!to!a!more!mechanistic!
understanding!of!how!bottom!up!and!top!down!factors!work!together!to!control!
communities!during!these!important!and!dramatic!events.!!
Spatial9vs9temporal9variation9
! Community!dynamics!are!a!combination!of!growth!and!death!and!both!
horizontal!and!vertical!import!and!export.!Supporting!the!temporal!nature!of!the!
dynamics,!and!the!chlorophyll!dynamics!at!SPOT!were!region&wide!(Fig!4)!and!the!
temperature!measurements!made!at!SPOT!and!a!buoy!about!10km!away!are!
strongly!correlated,!suggesting!that!temporal!variability!is!dominant.!Additionally,!
the!protistan!community!around!SPOT!were!more!variable!over!short!time&periods!
than!they!were!over!short&distances!in!May(Lie!et!al.!2013)!and!most!of!the!
dynamics!of!individual!taxa!are!within!expectations!of!potential!growth!rates.!One!
potential!explanation!for!why!communities!are!more!variable!in!spring,!assuming!
that!spatial!variability!is!significant,!is!that!the!ocean!is!more!spatially!
heterogeneous!during!the!spring!bloom!and!that!shifts!in!community!structure!
could!be!because!of!different!water!masses.!Regardless,!the!extent!to!which!spatial!
variability!influences!the!dynamics!observed!during!this!time&series!is!an!open!
question!but!is!an!important!factor!for!interpretation!for!any!marine!time&
series(Fuhrman!et!al.!2015).!However,!the!statistical!analyses!presented!are!not!
dependent!on!the!time&series!being!temporal,!for!example,!ecological!distance!
metrics!and!co&occurrence!patterns!are!often!explored!within!a!completely!spatial!
framework(Faust!and!Raes!2012;!Lima&Mendez!et!al.!2015).!!
Conclusions9
The!frequency!of!our!sampling!over!an!extensive!period,!the!broad!coverage!of!
our!molecular!analyses,!and!our!use!of!16S!rRNA!sequences!to!characterize!
34
phytoplankton!with!high!phylogenetic!resolution,!allowed!us!to!demonstrate!rapid!
dynamics!of!PE,!Bacteria,!and!Archaea!not!previously!observed.!Notably,!the!
dominant!organisms!from!all!three!domains!changed!almost!on!a!daily!scale!over!a!
period!of!weeks.!We!show!that!short&term!changes!during!large!environmental!
shifts!can!be!very!informative!about!the!apparent!niches!and!successional!patterns!
of!these!communities.!To!what!extent!the!mechanisms!and!controls!of!these!
dynamics!are!bottom&up!vs!top&down!remains!to!be!more!fully!explored!and!will!
add!to!our!understanding!of!how!nutrients!and!energy!are!transformed!during!these!
common,!but!ephemeral!events.!
(
Acknowledgements!
! We!thank!the!USC!Wrigley!Institute!of!Environmental!Science!for!sampling!
opportunities!and!laboratory!space,!especially!Roberta!Marinelli,!Sean!Conner,!and!
Captain!Gordon!Boivin!and!the!crew!of!the!Miss9Christie.!We!thank!Jennifer!Chang,!
Cheryl!Chow,!Alle!Lie,!Sean!McCallister,!and!Elizabeth!Teel!for$sampling$assistance.$
We$thank$Dave$Caron,$Frank$Corsetti,$John$Heidelberg,$Eric$Webb$for$insightful$
discussion$of$the$manuscript.$We$thank$Rohan$Sachdeva$for$computational$support$
and$helpful$discussion$and$Jacob$Cram,$Laura$Gomez$Consarnau,$Erin$Fichot,$Alma$
Parada,$and$Ella$Sieradzki$for$helpful$discussion.$This$work$was$supported$by$NSF$
Grants$1031743$and$1136818,$grant$GBMF3779$from$the$Gordon$and$Betty$Moore$
Foundation$Marine$Microbiology$Initiative,$and$a$National$Science$Foundation$
Graduate$Student$Research$Fellowship$to$DMN.
References(
Amaral&Zettler,!L.!A.,!E.!A.!McCliment,!H.!W.!Ducklow,!and!S.!M.!Huse.!2009.!A!
method!for!studying!protistan!diversity!using!massively!parallel!sequencing!of!
V9!hypervariable!regions!of!small&subunit!ribosomal!RNA!genes.!PLoS!One!4:!
e6372.!
Barbrook,!A.!C.,!N.!Santucci,!L.!J.!Plenderleith,!R.!G.!Hiller,!and!C.!J.!Howe.!2006.!
Comparative!analysis!of!dinoflagellate!chloroplast!genomes!reveals!rRNA!and!
tRNA!genes.!BMC!Genomics!297:!1–15.!
35
Brown,!M.!V.,!M.!S.!Schwalbach,!I.!Hewson,!and!J.!A.!Fuhrman.!2005.!Coupling!16S&
ITS!rDNA!clone!libraries!and!automated!ribosomal!intergenic!spacer!analysis!to!
show!marine!microbial!diversity:!development!and!application!to!a!time!series.!
Environ!Microbiol!7:!1466–79.!
Buchan,!A.,!G.!R.!LeCleir,!C.!A.!Gulvik,!and!J.!M.!González.!2014.!Master!recyclers:!
features!and!functions!of!bacteria!associated!with!phytoplankton!blooms.!Nat!
Rev!Microbiol!12:!686–698.!
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,!and!W.!A.!Walters.!2010.!
QIIME!allows!analysis!of!high&throughput!community!sequencing!data.!Nat!
Methods!7:!2010.!
Caron,!D.!A.,!P.!D.!Countway,!A.!C.!Jones,!D.!Y.!Kim,!and!A.!Schnetzer.!2012.!Marine!
protistan!diversity.!Ann!Rev!Mar!Sci!4:!467–93.!
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.!
Countway,!P.!D.,!R.!J.!Gast,!P.!Savai,!and!D.!A.!Caron.!2005.!Protistan!Diversity!
Estimates!Based!on!18S!rDNA!from!Seawater!Incubations!in!the!Western!North!
Atlantic.!J!Eukaryot!Microbiol!52:!95–106.!
Cram,!J.!A.,!C.&E.!T.!Chow,!R.!Sachdeva,!D.!M.!Needham,!A.!E.!Parada,!J.!A.!Steele,!and!J.!
A.!Fuhrman.!2014.!Seasonal!and!interannual!variability!of!the!marine!
bacterioplankton!community!throughout!the!water!column!over!ten!years.!
ISME!J!1–18.!
Croft,!M.!T.,!M.!J.!Warren,!and!A.!G.!Smith.!2006.!Algae!need!their!vitamins.!Eukaryot!
Cell!5:!1175–83.!
Dawson,!S.!C.,!and!N.!R.!Pace.!2002.!Novel!kingdom&level!eukaryotic!diversity!in!
anoxic!environments.!Proc!Natl!Acad!Sci!U!S!A!99:!8324–9.!
DeSantis,!T.!Z.,!P.!Hugenholtz,!N.!Larsen,!M.!Rojas,!E.!L.!Brodie,!K.!Keller,!T.!Huber,!D.!
Dalevi,!P.!Hu,!and!G.!L.!Andersen.!2006.!Greengenes,!a!chimera&checked!16S!
rRNA!gene!database!and!workbench!compatible!with!ARB.!Appl!Environ!
Microbiol!72:!5069–72.!
Edgar,!R.!C.!2013.!UPARSE:!highly!accurate!OTU!sequences!from!microbial!amplicon!
reads.!Nat!Methods!10:!996–8.!
36
Edgcomb,!V.!P.,!D.!T.!Kysela,!A.!Teske,!A.!de!Vera!Gomez,!and!M.!L.!Sogin.!2002.!
Benthic!eukaryotic!diversity!in!the!Guaymas!Basin!hydrothermal!vent!
environment.!Proc!Natl!Acad!Sci!U!S!A!99:!7658–62.!
Faust,!K.,!and!J.!Raes.!2012.!Microbial!interactions:!from!networks!to!models.!Nat!
Rev!Microbiol!10:!538–50.!
Fuhrman,!J.!A.,!D.!E.!Comeau,!A.!Hagstrom,!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.,!J.!A.!Cram,!and!D.!M.!Needham.!2015.!Marine!microbial!community!
dynamics!and!their!ecological!interpretation.!Nat!Rev!Microbiol!13:!133–146.!
Fuller,!N.,!C.!Campbell,!D.!Allen,!F.!Pitt,!K.!Zwirglmaier,!F.!Le!Gall,!D.!Vaulot,!and!D.!
Scanlan.!2006.!Analysis!of!photosynthetic!picoeukaryote!diversity!at!open!
ocean!sites!in!the!Arabian!Sea!using!a!PCR!biased!towards!marine!algal!plastids.!
Aquat!Microb!Ecol!43:!79–93.!
Garcia&Cuetos,!L.,!Ø.!Moestrup,!P.!J.!Hansen,!and!N.!Daugbjerg.!2010.!The!toxic!
dinoflagellate!Dinophysis!acuminata!harbors!permanent!chloroplasts!of!
cryptomonad!origin,!not!kleptochloroplasts.!Harmful!Algae!9:!25–38.!
Gast,!R.!J.,!D.!M.!Moran,!M.!R.!Dennett,!and!D.!A.!Caron.!2007.!Kleptoplasty!in!an!
Antarctic!dinoflagellate:!caught!in!evolutionary!transition?!Environ!Microbiol!9:!
39–45.!
Gilbert,!J.!A.,!J.!A.!Steele,!J.!G.!Caporaso,!L.!Steinbrück,!J.!Reeder,!B.!Temperton,!S.!
Huse,!A.!C.!McHardy,!R.!Knight,!I.!Joint,!P.!Somerfield,!J.!A.!Fuhrman,!and!D.!Field.!
2012.!Defining!seasonal!marine!microbial!community!dynamics.!ISME!J!6:!298–
308.!
Guindon,!S.,!and!O.!Gascuel.!2003.!A!Simple,!Fast,!and!Accurate!Algorithm!to!
Estimate!Large!Phylogenies!by!Maximum!Likelihood.!Syst!Biol!52:!696–704.!
Hackett,!J.!D.,!L.!Maranda,!H.!S.!Yoon,!and!D.!Bhattacharya.!2003.!Phylogenetic!
evidence!for!the!cryptophyte!origin!of!the!plastid!of!Dinophysis!(Dinophysiales,!
Dinophyceae).!J!Phycol!39:!440–448.!
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!587.!
Katoh,!K.,!K.!Misawa,!K.!Kuma,!and!T.!Miyata.!2002.!MAFFT :!a!novel!method!for!
rapid!multiple!sequence!alignment!based!on!fast!Fourier!transform.!Nucleic!
Acids!Res!30:!3059–3066.!
37
Kearse,!M.,!R.!Moir,!A.!Wilson,!S.!Stones&Havas,!M.!Cheung,!S.!Sturrock,!S.!Buxton,!A.!
Cooper,!S.!Markowitz,!C.!Duran,!T.!Thierer,!B.!Ashton,!P.!Meintjes,!and!A.!
Drummond.!2012.!Geneious!Basic:!an!integrated!and!extendable!desktop!
software!platform!for!the!organization!and!analysis!of!sequence!data.!
Bioinformatics!28:!1647–9.!
Kim,!M.,!S.!W.!Nam,!W.!Shin,!D.!W.!Coats,!and!M.!G.!Park.!2012.!Dinophysis!Caudata!
(Dinophyceae)!Sequesters!and!Retains!Plastids!From!the!Mixotrophic!Ciliate!
Prey!Mesodinium!Rubrum.!J!Phycol!48:!569–579.!
Klappenbach,!J.!A.,!J.!M.!Dunbar,!and!T.!M.!Schmidt.!2000.!rRNA!Operon!Copy!
Number!Reflects!Ecological!Strategies!of!Bacteria.!Appl!Environ!Microbiol!66:!
1328–1333.!
Koike,!K.,!H.!Sekiguchi,!A.!Kobiyama,!K.!Takishita,!M.!Kawachi,!K.!Koike,!and!T.!Ogata.!
2005.!A!novel!type!of!kleptoplastidy!in!Dinophysis!(Dinophyceae):!presence!of!
haptophyte&type!plastid!in!Dinophysis!mitra.!Protist!156:!225–37.!
Koumandou,!V.!L.,!R.!E.!R.!Nisbet,!A.!C.!Barbrook,!and!C.!J.!Howe.!2004.!Dinoflagellate!
chloroplasts&&where!have!all!the!genes!gone?!Trends!Genet!20:!261–7.!
Krupke,!A.,!G.!Lavik,!H.!Halm,!B.!M.!Fuchs,!R.!I.!Amann,!and!M.!M.!M.!Kuypers.!2014.!
Distribution!of!a!consortium!between!unicellular!algae!and!the!N
2
!fixing!
cyanobacterium!UCYN&A!in!the!North!Atlantic!Ocean.!Environ!Microbiol!16:!
3153–67.!
Lie,!A.!A.!Y.,!D.!Y.!Kim,!A.!Schnetzer,!and!D.!A.!Caron.!2013.!Small&scale!temporal!and!
spatial!variations!in!protistan!community!composition!at!the!San!Pedro!Ocean!
Time&series!station!off!the!coast!of!southern!California.!Aquat!Microb!Ecol!70:!
93–110.!
Lima&Mendez,!G.,!K.!Faust,!N.!Henry,!J.!Decelle,!S.!Colin,!F.!Carcillo,!S.!Chaffron,!J.!C.!
Ignacio&Espinosa,!S.!Roux,!F.!Vincent,!L.!Bittner,!Y.!Darzi,!J.!Wang,!S.!Audic,!L.!
Berline,!G.!Bontempi,!A.!M.!Cabello,!L.!Coppola,!F.!M.!Cornejo&Castillo,!F.!
d’Ovidio,!L.!De!Meester,!I.!Ferrera,!M.&J.!Garet&Delmas,!L.!Guidi,!E.!Lara,!S.!Pesant,!
M.!Royo&Llonch,!G.!Salazar,!P.!Sánchez,!M.!Sebastian,!C.!Souffreau,!C.!Dimier,!M.!
Picheral,!S.!Searson,!S.!Kandels&Lewis,!G.!Gorsky,!F.!Not,!H.!Ogata,!S.!Speich,!L.!
Stemmann,!J.!Weissenbach,!P.!Wincker,!S.!G.!Acinas,!S.!Sunagawa,!P.!Bork,!M.!B.!
Sullivan,!E.!Karsenti,!C.!Bowler,!C.!de!Vargas,!and!J.!Raes.!2015.!Ocean!plankton.!
Determinants!of!community!structure!in!the!global!plankton!interactome.!
Science!348:!1262073.!
Lindh,!M.!V,!J.!Sjöstedt,!A.!F.!Andersson,!F.!Baltar,!L.!W.!Hugerth,!D.!Lundin,!S.!
Muthusamy,!C.!Legrand,!and!J.!Pinhassi.!2015.!Disentangling!seasonal!
bacterioplankton!population!dynamics!by!high&frequency!sampling.!Environ!
Microbiol!10.1111/14,!doi:10.1111/1462&2920.12720!
38
López&García,!P.,!F.!Rodríguez&Valera,!C.!Pedrós&Alió,!and!D.!Moreira.!2001.!
Unexpected!diversity!of!small!eukaryotes!in!deep&sea!Antarctic!plankton.!
Nature!409:!603–607.!
Martin&Cuadrado,!A.&B.,!I.!Garcia&Heredia,!A.!G.!Moltó,!R.!López&Úbeda,!N.!Kimes,!P.!
López&García,!D.!Moreira,!and!F.!Rodriguez&Valera.!2014.!A!new!class!of!marine!
Euryarchaeota!group!II!from!the!mediterranean!deep!chlorophyll!maximum.!
ISME!J!1–16.!
Mayali,!X.,!B.!Palenik,!and!R.!S.!Burton.!2010.!Dynamics!of!marine!bacterial!and!
phytoplankton!populations!using!multiplex!liquid!bead.!Environ!Microbiol!12:!
975–989.!
Mitra,!A.,!K.!J.!Flynn,!J.!M.!Burkholder,!T.!Berge,!A.!Calbet,!J.!A.!Raven,!E.!Granéli,!P.!M.!
Glibert,!P.!J.!Hansen,!D.!K.!Stoecker,!F.!Thingstad,!U.!Tillmann,!S.!Våge,!S.!Wilken,!
and!M.!V.!Zubkov.!2014.!The!role!of!mixotrophic!protists!in!the!biological!
carbon!pump.!Biogeosciences!11:!995–1005.!
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.!
Needham,!D.!M.,!C.&E.!T.!Chow,!J.!A.!Cram,!R.!Sachdeva,!A.!Parada,!and!J.!A.!Fuhrman.!
2013.!Short&term!observations!of!marine!bacterial!and!viral!communities:!
patterns,!connections!and!resilience.!ISME!J!7:!1274–85.!
Noble,!R.!T.,!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.!
Oksanen,!A.!J.,!F.!G.!Blanchet,!R.!Kindt,!P.!Legendre,!P.!R.!Minchin,!R.!B.!O.!Hara,!G.!L.!
Simpson,!P.!Solymos,!M.!H.!H.!Stevens,!and!H.!Wagner.!2015.!vegan:!Community!
Ecology!Package.!
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!1–17.!
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:!D590–6.!
Rappé,!M.!S.,!P.!F.!Kemp,!S.!J.!Giovannoni,!and!D.!Gordon.!1997.!Phylogenetic!
diversity!of!marine!coastal!picoplankton!16S!rRNA!genes!cloned!from!the!
continental!shelf!off!Cape!Hatteras!,!North!Carolina.!Limnol!Oceanogr!42:!811–
826.!
39
Sañudo&Wilhelmy,!S.!A.,!L.!Gómez&Consarnau,!C.!Suffridge,!and!E.!A.!Webb.!2014.!The!
role!of!B!vitamins!in!marine!biogeochemistry.!Ann!Rev!Mar!Sci!6:!339–67.!
Schnetzer,!A.,!P.!E.!Miller,!R.!A.!Schaffner,!B.!A.!Stauffer,!B.!H.!Jones,!S.!B.!Weisberg,!P.!
M.!DiGiacomo,!W.!M.!Berelson,!and!D.!A.!Caron.!2007.!Blooms!of!Pseudo'
nitzschia!and!domoic!acid!in!the!San!Pedro!Channel!and!Los!Angeles!harbor!
areas!of!the!Southern!California!Bight,!2003–2004.!Harmful!Algae!6:!372–387.!
Shannon,!P.,!A.!Markiel,!O.!Ozier,!N.!S.!Baliga,!J.!T.!Wang,!D.!Ramage,!N.!Amin,!B.!
Schwikowski,!and!T.!Ideker.!2003.!Cytoscape:!a!software!environment!for!
integrated!models!of!biomolecular!interaction!networks.!Genome!Res!13:!
2498–504.!
Sintes,!E.,!H.!Witte,!K.!Stodderegger,!P.!Steiner,!and!G.!J.!Herndl.!2013.!Temporal!
dynamics!in!the!free&living!bacterial!community!composition!in!the!coastal!
North!Sea.!FEMS!Microbiol!Ecol!83:!413–24.!
Smetacek,!V.!S.!1985.!Role!of!sinking!in!diatom!life&history!cycles:!ecological,!
evolutionary!and!geological!significance.!Mar!Biol!84:!239–251.!
Smith,!M.!W.,!L.!Zeigler!Allen,!A.!E.!Allen,!L.!Herfort,!and!H.!M.!Simon.!2013.!
Contrasting!genomic!properties!of!free&living!and!particle&attached!microbial!
assemblages!within!a!coastal!ecosystem.!Front!Microbiol!4:!1–20.!
Sosa,!O.!A.,!S.!M.!Gifford,!D.!J.!Repeta,!and!E.!F.!DeLong.!2015.!High!molecular!weight!
dissolved!organic!matter!enrichment!selects!for!methylotrophs!in!dilution!to!
extinction!cultures.!ISME!J!1–15.!
Stauffer,!B.!A.,!A.!G.!Gellene,!A.!Schnetzer,!E.!L.!Seubert,!C.!Oberg,!G.!S.!Sukhatme,!and!
D.!A.!Caron.!2012.!An!oceanographic,!meteorological,!and!biological!“perfect!
storm”!yields!a!massive!fish!kill.!Mar!Ecol!Prog!Ser!468:!231–243.!
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:!1414–25.!
Stoecker,!D.!K.,!A.!E.!Michaels,!and!L.!H.!Davis.!1987.!Large!proportion!of!marine!
planktonic!ciliates!found!to!contain!functional!chloroplasts.!Nature!326:!790–
792.!
Storey,!J.!D.!2003.!The!positive!false!discovery!rate:!A!bayesian!interpretation!and!
the!q&value.!Ann!Stat!31:!2013–2035.!
Teeling,!H.,!B.!M.!Fuchs,!D.!Becher,!C.!Klockow,!A.!Gardebrecht,!C.!M.!Bennke,!M.!
Kassabgy,!S.!Huang,!A.!J.!Mann,!J.!Waldmann,!M.!Weber,!A.!Klindworth,!A.!Otto,!J.!
40
Lange,!J.!Bernhardt,!C.!Reinsch,!M.!Hecker,!J.!Peplies,!F.!D.!Bockelmann,!U.!
Callies,!G.!Gerdts,!A.!Wichels,!K.!H.!Wiltshire,!F.!O.!Glöckner,!T.!Schweder,!and!R.!
Amann.!2012.!Substrate&controlled!succession!of!marine!bacterioplankton!
populations!induced!by!a!phytoplankton!bloom.!Science!336:!608–611.!
Thompson,!A.!W.,!R.!A.!Foster,!A.!Krupke,!B.!J.!Carter,!N.!Musat,!D.!Vaulot,!M.!M.!M.!
Kuypers,!and!J.!P.!Zehr.!2012.!Unicellular!Cyanobacterium!Symbiotic!with!a!
Single&Celled!Eukaryotic!Alga.!Science!337:!1546–1550.!
Treusch,!A.!H.,!E.!Demir&Hilton,!K.!L.!Vergin,!A.!Z.!Worden,!C.!A.!Carlson,!M.!G.!Donatz,!
R.!M.!Burton,!and!S.!J.!Giovannoni.!2012.!Phytoplankton!distribution!patterns!in!
the!northwestern!Sargasso!Sea!revealed!by!small!subunit!rRNA!genes!from!
plastids.!ISME!J!6:!481–92.!
Wilmotte,!A.,!C.!Demonceau,!A.!Goffart,!J.&H.!Hecq,!V.!Demoulin,!and!A.!C.!Crossley.!
2002.!Molecular!and!pigment!studies!of!the!picophytoplankton!in!a!region!of!
the!Southern!Ocean!(42–54°S,!141–144°E)!in!March!1998.!Deep!Sea!Res!Part!II!
Top!Stud!Oceanogr!49:!3351–3363.!
Worden,!A.!Z.,!M.!J.!Follows,!S.!J.!Giovannoni,!S.!Wilken,!A.!E.!Zimmerman,!and!P.!J.!
Keeling.!2015.!Rethinking!the!marine!carbon!cycle:!Factoring!in!the!
multifarious!lifestyles!of!microbes.!Science!347:!1257594–1257594.!
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:!230–7.!
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.!
Xing,!P.,!R.!L.!Hahnke,!F.!Unfried,!S.!Markert,!S.!Huang,!T.!Barbeyron,!J.!Harder,!D.!
Becher,!T.!Schweder,!F.!O.!Glöckner,!R.!I.!Amann,!and!H.!Teeling.!2015.!Niches!of!
two!polysaccharide°rading!Polaribacter!isolates!from!the!North!Sea!during!
a!spring!diatom!bloom.!ISME!J!9:!1410–1422.!
Zhang,!Z.,!B.!R.!Green,!and!T.!Cavalier&Smith.!1999.!Single!gene!circles!in!
dinoflagellate!chloroplast!genomes.!Nature!400:!155–159.!
Zhu,!F.,!R.!Massana,!F.!Not,!D.!Marie,!and!D.!Vaulot.!2005.!Mapping!of!picoeucaryotes!
in!marine!ecosystems!with!quantitative!PCR!of!the!18S!rRNA!gene.!FEMS!
Microbiol!Ecol!52:!79–92.!

41
Figure 1. Maximum likelihood phylogenetic tree of NCBI best BLASTn matches to photosynethetic eukaryotic OTU
chloroplast representative sequences > 0.4%.
0.2
gi|68051195|dbj|AB196967.1|_Chrysochromulina_33854_Chrysochromulina_63726_Chrysochromulina_65123_Chrysochromulina_71545_Chrysochromulina_141955
Micromonas_214081
Imantonia_70989
gi|387235408|gb|JN207204.1|_Virgulinella_132078____
Bolidomonas_mediterranea_67610
gi|197359082|gb|FJ002231.1|_Minidiscus_trioculatus_152534____
gi|21489333|emb|AJ319825.1|_Chaetoceros_225988____
Braarudosphaera_bigelowii_171775
Pseudo-nitzschia_241792
Plagioselmis_sp._TUC-1_12092
gi|514253615|gb|KC900889.1|_Phaeocystis_76808_Phaeocystis_216629_Phaeocystis_245699__
Imantonia_188241
gi|336286138|gb|JN022705.1|_Emiliania_huxleyi_235133____
Pyramimonas_64329
gi|71040611|dbj|AB199888.1|_Phalacroma_12408_Phalacroma_244782___
gi|68051194|dbj|AB196966.1|_Chrysochromulina_221725____
Chrysochromulina_2279
gi|197359028|gb|FJ002177.1|_Pseudo-nitzschia_241792____
gi|54125613|gb|AY702151.1|_Dictyochophyte_91331_Dictyochophyte_202752___
gi|239997260|gb|GQ231541.1|_Aureococcus_83487____
gi|197359093|gb|FJ002242.1|_Leyanella_56146____
gi|239997366|gb|GQ231542.1|_Aureoumbra_25710____
Chrysochromulina_141640
Chrysochromulina_71545
gi|75993399|gb|DQ187908.1|_Chrysochromulina_43307_Chrysochromulina_63404_Chrysochromulina_115346_Chrysochromulina_141640_
gi|197359079|gb|FJ002228.1|_Rhizosolenia_setigera_207408____
Chrysochromulina_32160
Leyanella_56146
Emiliania_huxleyi_235133
gi|68051193|dbj|AB196965.1|_Imantonia_70989_Imantonia_91314_Imantonia_188241_Imantonia_191666_
gi|549084991|dbj|AB847986.1|_Braarudosphaera_bigelowii_171775____
Virgulinella_132078
gi|10719513|gb|AF277518.1|_diatom_sp._SIC.928_16384_diatom_sp._SIC.928_159037___
Phaeocystis_76808
Ostreococcus_150770
Phaeocystis_216629
Phalacroma_244782
Chrysochromulina_221725
diatom_sp._SIC.928_16384
Aureoumbra_25710
Imantonia_91314
gi|371782133|emb|HE610165.1|_Tetraselmis_150170____
Aureococcus_83487
Ptilota_serrata_211821
Bathycoccus__47305
Mesopedinella_arctica_244783
Chrysochromulina_67228
gi|54125617|gb|AY702155.1|_Ostreococcus_150770____
Imantonia_191666
Chrysochromulina_63404
gi|54125601|gb|AY702139.1|_Pelagomonas_11236_Pelagomonas_214254___
Mesopedinella_arctica_83458
gi|290770787|emb|FN563099.1|_Bathycoccus__47305____
Cymbomonas__123424
Chrysochromulina_33854
Teleaulax_90882
gi|387235424|gb|JN207220.1|_Virgulinella_135474____
Micromonas_22405
gi|290770788|emb|FN563100.1|_Cymbomonas__123424____
gi|54125614|gb|AY702152.1|_Chrysochromulina_2279_Chrysochromulina_32160_Chrysochromulina_67228_Chrysochromulina_139973_
Chaetoceros_225988
Chrysochromulina_63726
Mesopedinella_arctica_151955
Chrysochromulina_139973
Chrysochromulina_115346
gi|157777862|gb|EU168191.1|_Heterosigma_222584____
Chrysochromulina_43307
Pelagomonas_11236
Phaeocystis_245699
gi|118138848|gb|EF051748.1|_Micromonas_22405____
Chrysochromulina_65123
Rhizosolenia_setigera_207408
Pelagomonas_214254
gi|54125606|gb|AY702144.1|_Bolidomonas_mediterranea_67610____
Minidiscus_trioculatus_152534
Dictyochophyte_91331
gi|68137879|gb|DQ026676.1|_Ptilota_serrata_211821____
gi|51870685|dbj|AB164406.1|_Plagioselmis_sp._TUC-1_12092____
gi|290770785|emb|FN563097.1|_Micromonas_214081____
Teleaulax_100255
gi|298916852|dbj|AB471793.1|_Teleaulax_90882_Teleaulax_100255___
Tetraselmis_150170
gi|54125620|gb|AY702158.1|_Mesopedinella_arctica_83458_Mesopedinella_arctica_151955_Mesopedinella_arctica_244783__
Phalacroma_12408
Chrysochromulina_141955
Virgulinella_135474
Dictyochophyte_202752
Heterosigma_222584
diatom_sp._SIC.928_159037
Substitutions per base
Mammiellales
Stramenopile
Pelagophyceae
Pyramimonadales
Cryptophyte
Prymnesiophyte
/Haptophyte
Fig 1
42
0
5
10
15
20
2002 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 2014
Satellite chlorophyll a (µg/L)
Figure 2. 8-day average chlorophyll concentrations taken from the MODIS satellite from 2002-2014 for the
San Pedro Ocean time-series location.
Fig 2
43
Chlorophyll a (µg/L)
10
12
14
16
18
20
22
Temperature
80
100
Average Wind Speed (km/hr)
3/05
3/15
3/25
4/05
4/15
4/25
5/05
5/15
5/25
6/05
6/15
6/25
7/05
7/15
7/25
8/05
8/15
10
8
6
4
2
0
40
60
20
0
Fig 3
Figure 1: Environmental variation over the full length of the
time-series where the open black circles are temperature
measured on-board, solid black line is temperature collected
by a nearby buoy, orange is average wind speed and blue is
precipitation.
44
34.0°
33.5°
34.0°
33.5°
34.0°
33.5°
34.0°
33.5°
34.0°
33.5°
34.0°
33.5°
118.0° 118.5° 118.0° 118.5°
118.0° 118.5°
118.0° 118.5°
2/18-2/25
2/26-3/5 3/6-3/13 3/14-3/21
3/22-3/29 3/30-4/6 4/7-4/14 4/15-4/22
4/23-4/30 5/1-5/8 5/9-5/16 5/17-5/24
5/25-6/1 6/2-6/9 6/10-6/17 6/18-6/25
6/26-7/3 7/4-7/11 7/12-7/19 7/20-7/27
7/28-8/4 8/5-8/12 8/13-8/20 8/21-8/28
San Pedro Ocean
Time-series
Santa Catalina Island
Los Angeles, CA
0.04 0.1 0.2 0.4 1 2 4 10 20 30
-118.6° -118.4° -118.2°
Figure 4. 8-day average chlorophyll concentrations
taken from the MODIS satellite from 2002-2014.
Fig 4
45
0.0 0.2 0.4 0.6
Micromonas 214081 1
Micromonas 22405 1
Ostreococcus 150770 1
Pelagomonas 11236 1
Aureococcus 83487 1
Virgulinella 135474 1
Chaetoceros 225988 1
Pseudo−nitzschia 16384 0.99
Pseudo−nitzschia 241792 1
Pseudo−nitzschia 159037 0.98
Leyanella 56146 0.99
Heterosigma 222584 1
Teleaulax 100255 1
Pyramimonas 64329 1
Phalacroma (klepto) 244782 1
Phaeocystis 216629 0.98
Phaeocystis 245699 1
Emiliania huxleyi 235133 1
Chrysochromulina 33854 1
Chrysochromulina 141640 0.98
Chrysochromulina 139973 0.98
Imantonia 191666 0.99
Imantonia 188241 0.98
0.2
Substitutions per site
Relative Abundance
Chlorophyll a (µg/L)
8
10
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Pseudo-nitszchia
Pseudo-nitszchia
Tetraselmis
Heterosigma
Chaetoceros
Phaeocystis
Imanatonia
Chrysochromulina
6
4
2
0
Ostreococcus
Pseudo-nitszchia
3/12
3/14
3/16
3/18
3/20
3/22
3/24
3/26
3/28
3/30
4/01
A.
B.
~Daily March
12-Apr 1
Weekly June
2-Aug 3
Weekly
April,
May 12
Daily May
16-May27
Prymnesiophyte
/Haptophyte
Crypto-
Raphido-
Stramenopile
Mammiellales
Pyramimon-
adales
Pelagophyte
Fig 5
Figure 5: Dynamics of photosynthetic eukaryote (PE) OTUs for March (A)
and over the full-time series (B). In (A), only those OTUs that became the
most represented sequence on at least a single day in March are shown.
The shaded background gray color indicates chlorophyll a concentra-
tion. The PE OTUs are represented as a fraction of the chloroplast
sequences. In (B), all PE OTUs that were >1%, on average, over the full
time-series are shown. The phylogenetic tree is a maximum likelihood
model; breaks indicate 0.2 substitutions per site. Relative abundances
are represented as fraction of chloroplast sequences. The numbers
following taxonomy information are (1) a unique OTU identifi er used
throughout the manuscript and (2) the fraction of sequence similarity
the OTU representative sequence had to the database sequence by
which it was classifi ed. The Phalacroma (aka Dinophysis) chloroplast is
reportedly a kleptochloroplast from a Prymnesiophyte (Reference).
Genera are used for brevity, species name can be found in Dataset S3.
46
0.0 0.2 0.4 0.6 0.8 1.0
All dates, Linear Model
y = −0.0024x + 0.5102
p−value = 4.41e−81
r2 = 0.359
0.0 0.2 0.4 0.6 0.8 1.0
March, All daily
y = −0.0147x + 0.5093
p-value =1.22e−08
r2 = 0.307
y = −0.0215x + 0.7181
p−value = 1.37e−15
r2 = 0.634
020406080 100 120 140
0.0 0.2 0.4 0.6 0.8 1.0
All dates, Linear Model
y = −0.0016x + 0.4716
p−value = 3.37e−38
r2 = 0.177
05 10 15 20
0.0 0.2 0.4 0.6 0.8 1.0
y = −0.0229x + 0.5413
p−value = 2.16e−20
r2 = 0.566
y = −0.0092x + 0.6163
p−value = 0.00949
r2 = 0.101
0.0 0.2 0.4 0.6 0.8 1.0
All dates, Linear Model
y = −0.0014x + 0.5935
p−value = 1.2e−23
r2 = 0.127
0.0 0.2 0.4 0.6 0.8 1.0
Bray-Curtis Similarity
March, 12 days apart
y = −0.036x + 0.7344
p−value = 1.82e−15
r2 = 0.507
March, All daily
y = −0.0169x + 0.6508
p−value = 8.21e−11
r2 = 0.302
May All daily (12 days)
y = −0.0274x + 0.7979
p-value = 4.98e−14
r2 = 0.815
A.
B.
C.
D.
E.
F.
Bray-Curtis Similarity Bray-Curtis Similarity
May All daily (12 days)
May All daily (12 days) March, All daily
y = −0.0308x + 0.5827
p−value = 9.31e−11
r2 = 0.463
March, 12 days apart
y = −0.0358x + 0.5987
p−value = 9.15e−17
r2 = 0.585
March, 12 days apart
Small/Free-living Small/Free-living
Large or Particle-Attached Large or Particle-Attached
Photosynthetic Eukaryotes Photosynthetic Eukaryotes
Figure 6. Rates of community change by Bray-Curtis similarity over daily time-scales (A-C) and full time-series
(D-F) for the small/free-living microbial communities (A, D), large or particle-attached (B,E), and photosynthetic
eukaryotic communities (C,F). Each point represents the Bray-Curtis similarity of samples separated by the
indicated number of days for March (blue) and May (black). Linear regressions were calculated to estimate the
percent change per day. Regressions for March were determined based on samples a maximum of 11 days apart,
but also for the full March time-series to allow for comparison with the 11 adjacent days sampled in May.
Number of days separating samples Number of days separating samples
Fig 6
47
Figure 7. Heatmap of photosynthetic eukaryotes >0.4% on average for the full
time-series, similar to Figure 5 except for 1% threshold.
0.0 0.2 0.4 0.6
Eukaryotic Phytoplanknton
Micromonas 22405
Micromonas 214081
Bathycoccus 47305
Ostreococcus 150770
Rhizosolenia setigera 207408
Bolidomonas mediterranea 67610
Virgulinella 132078
Minidiscus trioculatus 152534
Virgulinella 135474
Chaetoceros 225988
Pseudo-nitzschia 16384
Pseudo−nitzschia 241792
Pseudo-nitzschia 159037
Leyanella 56146
Mesopedinella arctica 83458
Dictyochophyte 91331
Dictyochophyte 202752
Mesopedinella arctica 151955
Mesopedinella arctica 244783
Heterosigma 222584
Ptilota serrata 211821
Tetraselmis 150170
Cymbomonas 123424
Pyramimonas 64329
Plagioselmis sp. TUC−1 12092
Teleaulax 100255
Teleaulax 90882
Chrysochromulina 115346
Chrysochromulina 63404
Chrysochromulina 141955
Chrysochromulina 141640
Chrysochromulina 32160
Chrysochromulina 63726
Chrysochromulina 43307
Imantonia 191666
Imantonia 188241
Imantonia 70989
Phaeocystis 76808
Phaeocystis 245699
Phaeocystis 216629
Phalacroma 12408
Phalacroma 244782
Chrysochromulina 33854
Chrysochromulina 65123
Imantonia 91314
Chrysochromulina 71545
Chrysochromulina 221725
Chrysochromulina 139973
Chrysochromulina 2279
Chrysochromulina 67228
Emiliania huxleyi 235133
Braarudosphaera bigelowii 171775
Aureoumbra 25710
Pelagomonas 214254
Pelagomonas 11236
Aureococcus 83487
0.1
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
5−M ay
12−M ay
16−M ay
17−M ay
18−M ay
19−M ay
20−M ay
21−M ay
22−M ay
23−May
24−May
25−May
26−May
27−M ay
2−Jun
9−Jun
16−Jun
22−Jun
1−Jul
7−Jul
14−Jul
26−Jul
3− Aug
~Daily March 12-Apr 1
Weekly June 2-Aug 3
Weekly
April, May
Daily May 16-May27
Prymnesiophyte
/Haptophyte
Cryptophyte
Raphido-
Stramenopile
Mammiellales
Pyramimon-
adales
Pelagophyte
Rhodophyte
Fig 7
48
0.00 0.10 0.20 0.30
MGII 23046
Persicirhabdus 65085
Roseibacillus 229167
Roseibacillus 90250
Puniceicoccaceae 48217
SAR86 26873
SAR92 49500
SAR116 28895
SAR11 S2 118472
SAR11 S1 142927
SAR11 S1 164672
Roseo DC5 72034
Rhodobacteraceae 32682
Roseob OCT 11664
Roseovarius 74339
OCS155 22417
NS9 130911
NS9 211105
Fluviicola 208385
Flavob 133459
Polaribacter 112640
Polaribacter 31534
NS4 122984
NS4 223562
NS2b 168292
Formosa 20586
Formosa 36344
Formosa 222693
Flavob 151540
Ulvibacter 115646
Croceitalea 98487
Pro 62609
Syn 74083
Syn 18078
0.5
Substitutions per site
~Daily March
12-Apr 1
Weekly June
2-Aug 3
Weekly
April,
May 12
Daily May
16-May27
Relative Abundance
10
0.0
0.1
0.2
0.3
Persicirhabdus 65085
Fluviicola 54289
Flavob 133459
Polaribacter 31534
MGII 23046
SAR11 S1 164672
Chlorophyll a (µg/L)
MGII 23046
SAR11 S1 164672
Flavob 133459
SAR92 49500
8
6
4
2
0
0.0
0.1
0.2
0.3
A. B.
Relative Abundance
C.
3/12
3/14
3/16
3/18
3/20
3/22
3/24
3/26
3/28
3/30
4/01
3/12
3/14
3/16
3/18
3/20
3/22
3/24
3/26
3/28
3/30
4/01
~Daily March
12-Apr 1
Weekly June
2-Aug 3
Weekly
April,
May 12
Daily May
16-May27
Cyano-
Archaea
Flavo-
Actino-
Alpha-
Gamma-
Verrucomicrobia
Fig 8
Figure 8: Dynamics of bacteria and archaeal OTUs for March (A) and over the full-time series
(B), as in Figure 2 except sequences are represented as a fraction of bacterial and archaeal
sequences. Breaks in phylogenetic tree indicate 0.5 substitutions per site. The details of
classifi cation, i.e., the database matches and percent similarity of the OTU representative
sequence to the database match are found in Dataset S3/S4.
49
A.
B.
Large or Attached Small and Free−Living
MGII 23046
MGII 233009
MGII 71038
MGII 152345
UCYN−A 198172
Syn 18078
Syn 74083
Pro 62609
OM190 103649
Puniceicoccaceae 48217
Persicirhabdus 65085
Roseibacillus 90250
Roseibacillus 164556
Roseibacillus 229167
Chitinophagaceae 130853
Saprospiraceae 102805
Saprospiraceae 35685
Owenweeksia 24947
NS9 130911
NS9 25185
NS7 169564
Fluviicola 208385
Fluviicola 54289
Flavob 133459
Polaribacter 112640
Polaribacter 31534
NS4 223562
NS5 234939
NS4 122984
Flavob 151540
Ulvibacter 115646
Croceitalea 98487
NS2b 168292
NS2b 76090
Formosa 36344
Formosa 222693
Formosa 250621
Formosa 20586
Formosa 23791
NS9 8970
NS9 211105
SAR116 28895
SAR116 58188
SAR116 68284
Rhodobium 99121
OCS116 211825
Roseo DC5 72034
Roseob OCT 193427
Rhodobacteraceae 32682
Roseob OCT 11664
Roseovarius 74339
AEGEAN−169 32683
SAR11 S2 118472
SAR11 S1 142927
SAR11 S1 164672
SAR11 S4 86652
SAR92 49500
SAR86 101668
SAR86 26873
Pseudospirillum 115328
Pseudospirillum 218981
OM60 NOR5 117897
OM60 NOR5 126170
Pseudoalteromonas 102741
OM43 22406
JL−ETNP−Y6 141902
OCS155 22417
0.5
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
27−Mar
28−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
12−M ay
16−M ay
17−M ay
20−M ay
21−M ay
22−M ay
23−M ay
24−M ay
25−M ay
27−M ay
2−Jun
9−Jun
16−Jun
22−Jun
1−Jul
7−Jul
20−Jul
26−Jul
3− Aug
22−M ay
23−M ay
24−M ay
25−M ay
26−M ay
27−M ay
2−Jun
9−Jun
16−Jun
22−Jun
1−Jul
7−Jul
14−Jul
26−Jul
3− Aug
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
5−M ay
12−M ay
16−M ay
17−M ay
18−M ay
19−M ay
20−M ay
21−M ay
~Daily March 12-Apr 1
Weekly June 2-Aug 3 Weekly
April, May
Daily May 16-May27 ~Daily March 12-Apr 1
Weekly June 2-Aug 3 Weekly
April, May
~Daily May 16-May27
Substitutions per site
0.00 0.10 0.20 0.30
Figure 9. Heatmap of bacteria and archaea >0.4% on average for the full time-series, similar to Figure 8 except
for 1% threshold.
Cyano-
Archaea
Flavo-
Actino-
Alpha-
Gamma-
Verrucomicrobia
Planctomycetes
Fig 9
50
Ulvibacter
115646
Micromonas
22405
KI89A
203648
AEGEAN-169
32683
Teleaulax
90882
Pseudo-nitzschia
241792
Owenweeksia
141805
Polaribacter
31534
OM60(NOR5)
117897
Syn
18078
Syn
18078
Micromonas
214081
Bathycoccus
47305
NS9
25185
Puniceicoccaceae
48217
Rhodobium
99121
Roseovarius
74339
Pyramimonas
64329
Balneola
178883
Roseovarius
74339
Imantonia
91314
OM43
22406
Chrysochromulina
33854
Braarudosphaera
bigelowii
171775
Chrysochromulina
56390
Pseudo-nitzschia
16384
Fluviicola
54289
Roseibacillus
164556
Chrysochromulina
32160
Chaetoceros
225988
Syn
74083
UCYN-A
198172
NS9
141622
NS2b
76090
Virgulinella
132078
DEV007
1844
Formosa
250621
Croceitalea
98487
Chrysochromulina
100897
Pseudo-nitzschia
159037
Unknown
211821
Pro
62609
Unassigned
150704
Formosa
23791
Water
temp
buoy
OCS155
170157
Water
temp
bottle
Chrysochromulina
71545
Chrysochromulina
63726
NS4
223562
NS4
223562
Syn
74083
Pseudospirillum
218981
Flavob
133459
OM60(NOR5)
126170 SAR92
49500
NS5
88814
AEGEAN-169
32683
Phaeocystis
216629
Phaeocystis
245699
Leyanella
56146
Outside
temp
Formosa
20586
NS2b
76090
Stramenopile
83458
SAR406
34574
NS11-12
40440
Pro
62609
Imantonia
70989
MGII
23046
Imantonia
191666
Chrysochromulina
67228
MGII
23046
Owenweeksia
24947
Polaribacter
31534
Flavob
133459
Polaribacter
112640
Psychrobacter
135853
MGII
233009
Roseob
OCT
11664
SAR86
101668
Polaribacter
112640
Persicirhabdus
65085
Heterosigma
222584
SAR116
194812
Puniceicoccaceae
48217
Fig 10
Figure 10: Correlation network between photosynthetic eukaryotic (PE) OTUs, microbial OTUs and environmental
parameters. Networks display correlations between distinctive PE over the full time-series (|Spearman’s
correlation| > 0.5, p & q < 0.001). For each network, the same 5 PE were chosen as centroids by which to determine
relationships. As such, only positive (solid lines) and negative correlations (dashed lines) to these 5 taxa are
shown. PE OTUs, bacterial OTUs, archaeal OTUs and environmental parameters are colored blue, red, purple and
yellow respectively. 1µm microbial OTUs are squares while 0.22µm are circular. Size of nodes is proportional to
average relative abundance over the full time-series. Node labels are abbreviated taxa names and numbers indi-
cate arbitrary, but unique, OTU IDs, full taxonomic information is available in Dataset S5.
51
Figure 11. Correlation network of photosynthetic eukaryotes for March 12- April 1 (Spearman’s correlation >
|0.85|, p & q < 0.001) and allowing a total time-lag of 3 days. Red edges with arrow-heads represent time-
lagged correlations. The node at which the arrow-head points is the taxon that is delayed. The darkness of
the arrow-heads indicates the time-lag. The darkness of the arrow-head indicates the length of time of the
delay, where the darkest arrow-heads are 1 day delay and the lightest are 3 day delays. The other network
visual properties are the same as in Figure 10.
Croceitalea
98487
Ulvibacter
115646
Water
temp
buoy
MGII
222983
SAR92
49500
Pelagomonad
25710
MGII
93647
Formosa
23791
Syn
74083
Bathycoccus
47305
Chitinophagaceae
130853
Teleaulax
90882
Micromonas
214081
JL-ETNP-Y6
141902
Stramenopile
91331
NS4
223562
Micromonas
22405
OCS116
131260
Syn
18078
Roseob
OCT
11664
Water
temp
bottle
Roseovarius
74339
MGII
23046
NS5
88814
Heterosigma
222584
MGII
23046
Flavob
133459
Flavob
133459
Pseudo-nitzschia
159037
Chrysochromulina
139973
OM60(NOR5)
126170
Owenweeksia
141805
Chrysochromulina
244781
Chrysochromulina
2279
Chrysochromulina
32160
Roseob
OCT
193427
Phalacroma
12408
Aureococcus
83487
OM190
103649
Pyramimonas
64329
Leyanella
56146
Roseovarius
74339
Chrysochromulina
221725
Chrysochromulina
141640
Phalacroma
244782
SAR116
68284 SAR86
26873
Puniceicoccaceae
48217
Chrysochromulina
71545
Prasinophyte
123424
NS2b
76090
Formosa
250621
Formosa
222693
Chrysochromulina
63404
Imantonia
191666 Roseob
OCT
193427
Phaeocystis
245699
Chrysochromulina
63726
Chrysochromulina
56390
Imantonia
188241
NS4
223562
NS9
141622
Chrysochromulina
33854 Chrysochromulina
65123
Chrysochromulina
144926
Polaribacter
31534
Pseudo-nitzschia
16384
Polaribacter
31534
Fig 11
52
A.
B.
Polaribacter
112640
Chaetoceros
225988
Polaribacter
112640
Polaribacter
31534
Pseudospirillum
218981
Owenweeksia
24947
KI89A
203648
NS2b
76090
Braarudosphaera
bigelowii
171775
Water
temp
bottle
Stramenopile
83458
Pro
62609
Formosa
23791
Unassigned
150704
NS9
141622
UCYN-A
198172
Pro
62609
Croceitalea
98487
Persicirhabdus
65085
Water
temp
buoy
Polaribacter
31534
NS11-12
40440
Unknown
211821
NS2b
76090
SAR11
S4
86652
Flavob
133459
MGII
23046
Imantonia
70989
MGII
233009
MGII
222983
MGII
23046
Chrysochromulina
67228
MGII
233009
Pseudo-nitzschia
159037
SAR406
34574
NS9
130911
MGII
152345
SAR116
194812
MGII
93647
AEGEAN-169
32683
Pelagomonad
25710
MGII
71038
Polaribacter
112640
NS5
88814
SAR11
S1
142927
SAR11
S1
84458
Cymbomonas
123424
SAR86
101668
Teleaulax
90882
MGII
152345
Bathycoccus
47305
Owenweeksia
141805
Pelagomonas
214254 SAR92
49500
NS2b
76090
Syn
74083
Syn
18078
SAR86
2046
DEV007
1844
NS2b
76090
Phaeocystis
245699
Roseob
OCT
11664
Chitinophagaceae
130853
Formosa
20586
Syn
18078
Syn
74083
Fig 12
Figure 12: Correlation networks for Marine Group II Archaea
(|Spearman’s correlation|> 0.6, p & q < 0.001) (A) and UCYN-
A/Braarudosphaera (|Spearman’s correlation| > 0.65, p and q < 0.001) (B)
for the full time-series. Network visual properties are the same as in
Figure 11.
53
Figure 13. Correlation network of MGII Archaea for March 12- April 1 (0.8 < Spearman’s correlation < -0.9, p &
q < 0.001) and allowing a total time-lag of 3 days. Network visual properties are the same as in Figure 11.
Formosa
23791
Phalacroma
12408
SAR406
34574
KI89A
203648
NS9
130911
AEGEAN-169
32683
Unassigned
150704
Formosa
20586
Roseibacillus
164556
Chrysochromulina
43307
MGII
222983
Flavob
133459
Heterosigma
222584
Chaetoceros
225988
SAR116
194812
Chrysochromulina
67228
Phaeocystis
216629
MGII
71038
NS5
88814
NS9
211105
OCS155
22417
MGII
233009
Phaeocystis
245699
Owenweeksia
24947
MGII
23046
Polaribacter
112640
Polaribacter
112640
Chrysochromulina
141640
Puniceicoccaceae
48217
NS7
169564
Puniceicoccaceae
48217
SAR116
28895
MGII
152345
Stramenopile
91331
Chitinophagaceae
130853
MGII
152345
Roseob
OCT
193427
OM190
103649
Flavob
133459
Micromonas
22405
Syn
18078
Croceitalea
98487
Roseob
OCT
11664
Chrysochromulina
221725
Syn
74083
Phaeocystis
76808
MGII
23046
SAR92
49500
MGII
233009
Imantonia
70989
Teleaulax
100255
Pyramimonas
64329
MGII
93647
Roseovarius
74339
NS4
223562
Chrysochromulina
65123
SAR116
68284
SAR11
S1
164672
SAR11
S2
118472
Fig 13
54
SAR11_S1
OCS155
OCS155
Pro
SAR86
SAR116
AEGEAN−169
MGII
Blastopirellula
SAR202
NS2b
SAR116
SAR406
Pseudospirillum
Formosa
MB11C04
SAR86
NS9
SAR92
Rhodobacteraceae
SAR86
NS5
SAR86
SAR116
SAR202
1e−04
5e−04
1e−03
5e−03
1e−02
5e−02
1e−01
5e−01
Expected (Staggered)
Observed (Staggered)
Puniceispirillum
AEGEAN−169
NS9
SAR92
SAR11_S1
Formosa
Formosa
SAR92
Formosa
JL−ETNP−Y6
SAR92
Formosa
Formosa
2e−04
5e−04
1e−03
2e−03
5e−03
1e−02
MGII
Blastopirellula
NS2b
SAR202
Pro
SAR406
OCS155
SAR116
SAR11_S1
SAR86
Even_Input
Even_Average
0.00
0.05
0.10
0.15
0.20
SAR11_S1
Syn
Syn
Surface_2
Roseob_OCT
SAR86
SAR116
Rhodobacteracea
AEGEAN−169
0.000
0.001
0.002
0.003
0.004
0.005
A. B.
Figure 14 Results of custom mock communities amplifi ed and analyzed in parallel with environmental samples.
Dark gray bars indicated the expected abundance each clone for the even (A) and staggered (B) communities and
the light gray bars indicates the average retrieved sequence abundance for the communities. Inset within each
fi gure are OTUs that were not intended to be found in the mock communities and are likely either physical
contamination or spurious OTUs generated from sequencing and/or clustering inaccuracies. Error bars indicate
standard deviations.
Fig 14
55
Chapter(3(
(
Diversity,*dynamics,*and*co1occurrence*of*T41like1myoviruses*and*microbial*taxa*
using*single*nucleotide*resolution*and*ITS*sequencing*of*SAR11*
*
Abstract(
* As*microbial*sequences*become*ever*cheaper*to*obtain,*a*fundamental*
question*in*microbial*ecology*moves*from*“what*level*of*microbial*phylogenetic*
resolution*can*we*obtain”*to*“what*level*of*phylogenetic*resolution*should*we*use”*
when*we*are*studying*ecological*patterns*such*as*dynamics,*distributions,*and*
interactions.**One*particular*microbial*interaction*in*the*ocean,*viral*infection,*is*
thought*to*influence*community*structure*by*density1dependent,*host1specific*
infection*processes,*in*some*cases,*dependent*on*single*base*mutations.*Therefore,*
observations*of*host/virus*dynamics*in*the*environment*should*benefit*from*highly*
resolving*methods.**Recently,*we*reported*a*daily1sampled*marine*time1series*
whereby*bacterial*and*archaeal*communities*showed*a*pronounced*successional*
pattern*related*to*a*phytoplankton*bloom.*With*the*goal*of*observing*how*viruses*
may*be*influencing*and/or*responding*to*the*microbial*successional*pattern*we*add*
additional*analyses.*We*couple*‘classic’*99%*OTU*clustering*&*minimum*entropy*
decomposition*(MED),*an*approach*that*enables*resolution*of*taxa*to*single*base*
differences*of*marker*genes,*to*this*time1series*to*resolve*taxa*by*the*16S*rRNA*
genes*of*bacteria,*archaea,*the*ITS*region*of*the*SAR11*clade,*and*the*major*capsid*
protein*gene*(g23)*of*T41like1myoviruses.*We*found*that*the*extent*that*the*
abundant*99%*OTU*clusters*could*be*resolved*into*“MED1types”*depended*on*the*
gene*and*taxon*(in*general,*~64%*were*decomposed*beyond*the*OTU*level);*most*
MED1types*had*significant*trends*over*the*5*month*study,*indicating*distinct*
ecological*niches*of*these*MED1types.*Mantel*tests*revealed*that*the*microbial*and*
viral*communities*were*significantly*correlated*at*both*the*OTU*and*MED1type*level.*
During*the*initial*bloom*decline,*a*daily1sampled*portion*of*the*time1series,*the*
correlation*between*the*microbial*community*and*virus*community*was*highest*
with*no*time1lag,*suggesting*that*if*there*is*a*lag*between*bacteria*and*virus*
56
communities*it*is*sub1daily.*The*highest*pairwise*correlations*were*found*between*
bacteria*and*viruses*that*coincided*with*the*bloom*or*summer*conditions,*but*there*
were*many*correlations*observed*between*SAR11*ITS*and*16S*subtypes*to*T41like1
virus*taxa,*including*a*putative*SAR11*phage*correlating*strongly*to*a*SAR11*ITS*
MED1type.*The*ecological*and*evolutionary*implications*of*the*varying*levels*of*
‘clonality’*we*find*between*lineages*with*the*highly*resolving*methods*is*probably*
important*to*understanding*the*controls*and*lifestyles*of*these*organisms*in*the*
environment.*We*found*that*the*correlations*between*bacteria*and*virus*taxa*were*
improved*with*the*high*resolution*methods*(higher*spearman’s*correlation*values),*
allowing*observation*of*many*associations*missed*by*OTU1based*analyses.*
Introduction*
* Marine*microbial*communities*are*complex*and*dynamic*with*a*variety*of*
controls*including*nutrient*supply,*viral*infection,*grazing,*syntrophy,*and*
competition*(Kirchman*2008).*Time1series*are*an*observational*tool*that*allow*for*
learning*how*these*factors*influence*the*abundance*and*dynamics*of*organisms*
(Fuhrman*et*al.*2015).**Studying*microbial*time1series*at*a*variety*of*temporal*
scales*and*taxonomic*resolutions*have*shown*that*these*communities*can*repeat*
annually*(Fuhrman*et*al.*2006),*are*resilient*over*the*short1term*but*vary*in*
patterned*and*connected*ways*(Needham*et*al.*2013),*and*that*rare*organisms*can*
periodically*become*more*abundant*(Vergin*et*al.*2013b).*Also,*via*time1series*we*
have*learned*that*microbial*taxa*are*most*often*more*correlated*to*one*another*than*
they*are*to*measured*environmental*parameters*(Steele*et*al.*2011)*and*clear*
delineation*of*ecological*niches*for*some*taxa,*e.g.,*SAR11,*SAR406,**have*been*
reported*(Vergin*et*al.*2013a;*Cram*et*al.*2014).*For*studying*the*niches*of*
microbes,*it*is*particularly*informative*to*study*a*time*series*when*disturbances*
happen,*because*following*the*dynamic*responses*of*the*microorganisms*allows*
observation*of*how*changes*in*some*organisms*affect*others.*We*recently*reported*
on*a*short1term*time1series*whereby*we*examined*the*decline*of*a*diatom*bloom*
and*continued*the*study*through*to*the*summer*(a*total*of*about*5*
months)(Needham*and*Fuhrman,*Chapter*2).*The*phytoplankton,*bacteria,*and*
archaea*associated*with*this*bloom*showed*pronounced*day1to1day*variation*
57
immediately*following*the*bloom,*with*multiple*taxa*becoming*the*dominant*
organisms*over*2.5*weeks.*The*shifts*in*community*structure*and*temporal*
resolution*offers*the*opportunity*to*make*strides*in*understanding*how*a*variety*of*
factors*may*be*influencing*the*community*dynamics*and*here*we*will*explore,*in*
particular,*how*the*influence*of*host1specific*pathogens,*viruses,*may*influence*(and*
be*influenced*by)*the*successional*pattern.** *
* Viruses*influence*marine*microbial*communities*by*re1directing*energy*and*
nutrients*via*mortality*and*lysis*of*hosts*(Fuhrman*1999;*Breitbart*et*al.*2008)*and*
are*thought*to*influence*community*composition*via*their*highly1host*specificity*
(Thingstad*and*Lignell*1997;*Rodriguez1Brito*et*al.*2010),*but*results*are*often*
variable*between*experiments*making*simple*conclusions*difficult*(Hewson*and*
Fuhrman*2006).*Regardless,*dynamics*between*cellular*organisms*and*viruses*in*
the*environment*have*been*shown*in*many*cases.*For*example,*viruses*have*been*
occasionally*implicated*in*termination*of*phytoplankton*blooms*(Brussaard*et*al.*
1996,*2008)*and*they*display*seasonal*peaks*in*abundance*in*the*North*Atlantic,*
coincident*with*Prochlorococcus*abundance*(Parsons*et*al.*2011).*In*order*to*assess*
virus*community*dynamics,*since*viruses*do*not*harbor*a*universally*conserved*
marker*genes*like*microbial*16S,*studies*aimed*at*characterizing*the*diversity*
and/or*dynamics*of*environmental*viral*communities*have*often*targeted*particular*
groups*of*viruses*(Short*et*al.*2010).*Among*them,*the*T41like1myoviruses*are*a*
very*diverse*group*of*viruses*with*a*broad*host*range,*which*includes*cultured*and*
sequenced*isolates*that*infect*the*ubiquitous*marine*cyanobacteria,*Prochlorococcus*
and*Synechococcus-(Sullivan*et*al.*2003,*2010;*Mann*et*al.*2005;*Weigele*et*al.*2007;*
Marston*and*Amrich*2009;*Clokie*et*al.*2010)-and-the*most*abundant*marine*
lineage,*SAR11*(Zhao*et*al.*2013);*other*known*hosts*include*include*Gamma1
Proteobacteria,*Aeromonas,*Vibrio,*Escherichia-coli-(Nolan*et*al.*2006;*Petrov*et*al.*
2010).**A*recent*report*also*suggests*that*archaea*may*be*infected*by*these*
myoviruses,*as*well*(Labonté*et*al.*2015),*thus*implicating*these*viruses*as*infection*
two*out*of*the*three*domains*of*life.*The*T41like*myovirus*group*in*the*environment*
has*been*assayed*via*amplification*of*the*g23*major*capsid*gene.*Such*studies*have*
revealed*the*group*to*be*very*diverse*(Filée*et*al.*2005;*Comeau*and*Krisch*2006)*
58
and*dynamic,*displaying*seasonality*(Chow*and*Fuhrman*2012)*and*often*
correlations*to*environmental*conditions*and*bacterial*communities*(Needham*et*
al.*2013;*Pagarete*et*al.*2013;*Chow*et*al.*2014) .**-
- Although*some*T41like*viruses*can*infect*two*different*genera*(Sullivan*et*al.*
2003),*viral*infection*is*thought*to*be*very*host1specific.*In*some*cases,*single*
mutations*can*yield*successful*infection*or*induce*susceptibility*(Avrani*et*al.*2011;*
Marston*et*al.*2012).*Therefore*the*more*highly*resolving*the*taxonomic*methods*
are*for*assessing*the*community,*the*more*likely*it*is*that*dynamics*between*
host/phage*will*be*observed.*Regardless,*it*is*much*more*acceptable*to*err*on*the*
side*of*too*resolving*(and*then*lump*categories*as*needed)*than*to*lack*sufficient*
resolution*and*be*completely*unable*to*separate*truly*different*categories.*Recent*
methodological*and*sequencing*technologies*have*made*it*possible*to*resolve*single*
nucleotide*variation*of*marker*genes*between*taxa*which*often*display*distinct*
ecological*dynamics*(Eren*et*al.*2013,*2014b;*Tikhonov*et*al.*2015) .*These*
approaches*have*already*revealed*dynamics*in*a*variety*of*environments*that*are*
sometimes*masked*by*standard*clustering*techniques*(e.g.,*Eren,*Borisy,*et*al.*2014;*
Turlapati*et*al.*2015;*Newton*et*al.*2015;*R eveillaud*et*al.*2014).*To*date,*these*
methods*have,*to*our*knowledge,*only*been*applied*to*16S*ribosomal*DNA.*
However,*given*the*highly*conserved*nature*of*16S,*even*the*most*discriminating*
methods*will*mask*diversity,*therefore,*a*more*phylogenetically*resolving*gene*may*
uncover*additional*important*variation.*One*genetic*region,*the*16S123S*intergenic*
spacer*(ITS),*is*classically*used*to*differentiate*ecotypes*of*the*well1studied*marine*
cyanobacteria*that*the*16S*does*not*distinguish*(Rocap*et*al.*2002;*Ahlgren*and*
Rocap*2012).*Further,*it*was*recently*found*that*the*ITS*is*a*good*proxy*for*genome*
diversity*based*sequencing*of*hundred*of*single*cells*of*Prochlorococcus-(Kashtan*et*
al.*2014).*Therefore,*we*supplement*the*single*nucleotide*resolution*of*16S*and*g23*
analysis*with*an*analysis*of*the*ITS*of*the*most*abundant,*and*perhaps*most*diverse,*
lineage*on*the*planet,*SAR11*clade,*to*explore*the*short 1term*dynamics*of*the*ITS*
types,*comparing*them*to*those*of*the*other*bacterial*and*viral*types.**
*
59
Methods*
Sampling-and-Processing-
* Seawater*from*the*top*meter*was*taken*by*bucket*from*a*boat*at*the*San*
Pedro*Ocean*time1series*(SPOT)*at*33’33N,*118’24’W*between*March*12*and*August*
3,*2011,*at*a*daily*or*weekly*resolution.*Filtration*was*performed*with*2*hours*at*the*
University*of*Southern*California*or*Wrigley*Institute*of*Environmental*Sciences*as*
described*in*Chapter*2.*Briefly,*seawater*was*filtered*through*80µm*mesh*and*then*
serially*collected*on*1µm*AE*filter,*a*0.22µm*Durapore*filter,*and*a*0.02µm*Anotop*
filter.*Physical*and*chemical*extractions*were*performed*on*the*AE*and*Durapore*
filters*as*described*in*Chapter*2.**Viral*DNA*was*extracted*from*Anotops*using*
Epicentre*Blood*and*Tissue*extraction*kit*as*previously*described*(Steward*and*
Culley*2010).*All*DNA*extractions*were*stored*at*180*until*further*analysis.*To*test*
the*reproducibility*of*the*viral*sequencing*full*processing*from*sample*to*analysis*
we*took*replicate*filtrations*of*a*0.02µm*size*fraction*water*sample*from*the*San*
Pedro*Channel*on*8/8/2012*and*8/25/2012.**
16S-Sequencing-
* Molecular*assays*describing*V4/V5*16S*PCR,*purification*and*sequencing*are*
described*in*Chapter*2.***
G23-Sequencing-Assay-
- Custom*g23*T41specific*primers*were*generated*from*previously*published*
T41specific*template*primers*(Chow*and*Fuhrman*2012)*and*Illumina*sequencing*
primers*with*a*5bp*barcode*on*the*forward*read*and*a*6bp*index*on*the*reverse*
read.*However,*due*to*poor*amplification*of*template*DNA*with*the*full*length*
primer,*a*first*round*of*PCR*was*performed*with*only*the*g231specific*primer*as*
follows:*~2*ng*of*0.02*µm*DNA*was*amplified*in*triplicate*in*25*µL*reactions*of*1x*
NEB*Buffer,*2*units*of*NEB*Taq*polymerase*(M0267)*,*0.48*µM*of*each*primer,*320*
ng/µL*(0.8*µL*(100*mg/10mL)*BSA,*0.2*µM*dNTPS*(Promega),*and*2.8*mM*MgCl2.*
Amplification*proceeded*as*follows:*Initial*Denaturation*180*s*at*95*C,*followed*by*
35*cycles*of*Denaturation*at*95*C*for*30*s,*Annealing*at*59*C*for*45*s,*and*Extension*
at*72*C*for*60*s.*A*final*extension*was*performed*at*72*C*for*300*s.**1*µL*of*PCR*
reactions*was*then*added*to*a*new*reaction*mix*which*was*the*same*as*above*
60
except*the*primer*construct*containing*the*g23*specific*and*Illumina*adapters*were*
added*to*the*mix*cycled*for*an*additional*5*rounds*of*amplification*with*the*g23.*
Positive*amplification*was*confirmed*by*gel*electrophoresis,*cleaned/concentrated*
with*Ampure*beads*as*previously*described*(Chapter*2)*and*sequenced*via*Illumina*
MiSeq*2x300*at*UC*Davis*Genome*Center.**
Mock-community-generation**
* 16S*Mock*community*generation*was*previously*described*in*Chapter*2.*
Similarly,*we*generated*T41like1myovirus*mock*communities*from*SPOT*clones.*We*
generated*an*even*mock*community*with*SPOT*g23*clones*added*in*equal*
proportions*(an*intended*10%*each).**The*SPOT*g23*clones*were*mini1prepped*
Zymo*Miniprep*as*per*manufacturers*instructions.*Plasmids*were*diluted*to*
0.01ng/uL*and*amplified*for*25*cycles*in*the*following*reaction*mixture*of*25uL:*
M13F/R*primer*0.5uL(100ng/uL),*1x*HiFi*Buffer,*0.16mM*dNTPs,*2mM* of*(50mM)*
MgS04,*0.4*units*HiFi*Platinum*Taq.*Amplified*products*were*clean*and*
concentrated*with*Ampure*beads*and*eluted*in*TE*and*at*equal*proportions.*After*
mock*community*generation,*clones*were*re1sequenced*via*Sanger*sequencing*to*
confirm*consensus*sequences*(Dataset*S1).*From*this*re1sequencing,*apparent*
contamination*in*a*single*clone*of*the*even*mock*community*was*detected.*In*the*
mock*community*sequencing*results,*2*spurious*OTUs*were*found*in*the*mock*
community*sequencing*results,*likely*from*this*contaminated*clone.*The*
contaminating*sequences*were*g23*sequences,*but*very*divergent*(<80%*similar)*to*
any*clones*that*were*intentionally*added.*Thus,*we*remove*them*and*the*
contaminated*clone*from*the*analysis*aimed*at*determining*the*accuracy*of*the*
method,*though,*since*they*are*legitimate*sequences,*we*use*them*for*assessment*of*
the*MED1technique.**
SAR11-ITS-
- To*design*primers*specific*for*SAR11*ITS,*potential*ITS*sequences*were*
mined*from*Sanger*metagenomic*reads*from*the*Global*Ocean*Survey*(GOS)*(Venter*
et*al.*2004;*Rusch*et*al.*2007)*and*the*Gulf*of*Maine*(GoMA)*(Tully*et*al.*
2011) .*Universal*SSU*rRNA*gene*primer*1492R*(TACGGCTACCTTGTTACGACTT)*was*
searched*against*the*GOS*and*GoMA*metagenomes*using*BLASTn*(word1size*=*7)*
61
and*only*those*reads*with*a*mismatch*<=*2*were*retained. *Sequences*that*were*then*
classified*by*BLASTn*search*against*the* Silva*111*database*and*sequences*classified*
as*SAR11*were*retained*and*added*to*an*existing *database*of*SPOT*SAR11*16S1ITS*
database*and*aligned*in*Geneious.*The*resulting*forward*primer*
(CCGTCCKCRYTTCTBTT)*is*located*about* 45*bases*within*the*ITS*sequence*
sequence*of*SAR11*(near*the*16S*end)*and*the*reverse*primer*
(WBWGTGCCDAGGCATYC)*is*about*45*bases*inside*the*23S*ribosomal* subunit*
resulting*in*an* in-silico-length*range*of*367 1447bp,*including*primers.*Triplicate*25 *
µL*PCR*reactions*proceeded*as*follows:*1x*HiFi*Buffer,*0.5 *µL*dNTPS*(Promega),*0.4*
µM*of*each*primer,*2 *mM*MgSO 4,*1*unit*of*HiFi*Platinum*Taq.*Amplification*proceed*
with*an*initial*denaturation*at*95*for*120,*followed*by,*foll owed*by*35*rounds*of*
denaturation*at*95 *C*for*30 *s,*annealing*at*59 *C*for*45s,*and*extension*at*72 *C*for*
60s.**A*final*extension*at*72 *C*for*300 *s*was*performed.*Positive*amplification*was*
confirmed*by*gel*electrophoresis,*cleaned/concentrated*with*Ampure *beads*and*
sequenced*via*Illumina* Miseq*2x300*at*UC*Davis. *
Sequence-Analysis* *
* g23*and*SAR11*ITS*Sequences*were*merged*in*USEARCH7*with*
fastq_mergepairs-(Edgar*2013),*truncating*at*quality*scores*<5,*with*a*maximum*of*
10*differences*in*overlap;*merged*sequences*were*removed*if*they*had*a*50bp*
window*with*an*average*quality*score*below*30*using*the* split_libraries.py*
command*in*QIIME*1.9.0 *(Caporaso*et*al.*2010) .*Merged*sequences*less*than*250*or*
greater*than*600*were*removed.*Sequences*without*a*perfect*match*to*a*barcode*
were*discarded.**g23*se quences*were*translated*in*the*3*forward*frames*via* transeq*
in*EMBOSS*6.6.0.0 *(Rice*et*al.*2000) *and*sequences*that*contained*a*stop*codon*(*)*
or*unidentified*residues*(X)*in*each*of*the*3*frames*were*removed.*Chi meras*were*
detected*de-novo*via*identify_chimeric-_seqs.py*within*QIIME*with*USEARCH61*
(settings:*usearch61_minh*0.05,*usearch*61_mindiffs*1,*usearch*61_xn*2)*and*
removed.*99%*OTUs*were*generated*with* pick_otus.py*in*QIIME*with*UCLUST. *
* 16S*sequence*analysis*was*described*in*Chapter*2,*but*was*similar*to*the*
above*with*the*following*exceptions:*merged*sequences*with*an*average*quality*
62
score*less*than*25*across*the*full*length*were*removed*and*chimera*checking*was*
supplemented*with*reference1based*detection*(as*described*in*Chapter*2).** *
* Sequence*alignments*for*heatmaps*were*generated*in*Geneious*6.1.6*(Kearse*
et*al.*2012)*by*alignment*with*the*MAFFT*G1INS1I*algorithm*with*gap*open*penalty*
of*2.84*and*offset*value*of*0.275*and*scoring*matrix*of*100PAM/k=2*(Katoh*et*al.*
2002).*Maximum*likelihood*phylogenetic*trees*were*drawn*using*PHYML*(Guindon*
and*Gascuel*2003)*with*the*HKY85*Substitution*model*and*100*bootstraps.*
Decomposition-of-99%-OTUs--
* We*performed*minimum*entropy*decomposition*(MED)*(Eren*et*al.*2014b)*
analysis*on*the*99%*OTUs*of*each*dataset*that*exceeded*0.5%*on*average*of*the*
total*community.*The*10
4
110
5
**sequences*from*each*99%*OTU*were*aligned*with*
MAFFT*v7.123b*fast*alignment*settings*(11 retree*1*11 maxiterate*0*11 nofft*–parttree).*
Shannon*Entropy*(metric*that*assigns*a*value*to*a*string*of*characters*based*on*the*
amount*of*variation*observed)*was*calculated*for*each*position*within*every*the*
alignments*for*every*OTU*independently*using*the*decompose-command*of*MED.*We*
set*the*decompose-command*to*only*decompose*OTUs*when*entropy*of*any*single*
base*was*greater*than*0.25*(assessed*via*mock*communities*Figure*3B/C).*
Decomposition*continued*until*all*alignment*positions*had*entropy*<0.25.*
Additionally,*it*was*required*that*the*most*abundant*sequence*of*each*MED1type*
exceed*50*sequences,*but*for*the*SAR11*16S,*the*value*was*lowered*to*10*since*for*
relatively*rare*16S*SAR11*types*only*were*made*up*of*a*few*thousand*sequences.*
Finally,*if*MED1types*did*not*exceed*5%*of*the*99%*OTUs*average*abundance,*they*
were*removed*from*further*analysis.*
* Validation*of*our*application*of*the*MED*method*was*performed*on*the16S*
and*g23*mock*communities.*For*the*16S*mock*communities,*we*aligned*sequences*
from*the*OTUs*that*matched*the*theoretical*input*(n=25)*that*we*obtained*from*
mock*communities*sequenced*in*parallel*with*samples.*This*analysis*was*
necessarily*performed*independently*of*the*environmental*samples*since,*by*
design,*the*software*would*split*the*mock*community*OTUs*based*on*variation*of*
bases*in*the*environmental*samples.*Entropy*values*for*each*position*in*the*
alignments*were*obtained*via*the*entropyManalysis*command*within*MED.*Similarly,*
63
validation*was*performed*on*the*g23*sequence*dataset;*for*this*analysis*we*used*the*
9*confirmed*input*OTUs*and*the*2*apparent*contaminating*OTUs.*
Sequence-Identification-
- For*16S*sequence*classification,*as*described*in*chapter*2,*the*most*abundant*
representative*sequence*from*each*OTU*was*searched*against*the*Greengenes*
(DeSantis*et*al.*2006)*and*SILVA115 (Quast*et*al.*2013)*databases*using*UCLUST,*
requiring*a*2/3*consensus*matches*at*>90%*similarity.**g23*sequences*were*
classified*using*BLASTx*against*viral*proteins*from*genomes*downloaded*from*NCBI*
in*March*2015*with*a*minimum*e1value*of*0.01,*minimum*match*length*of*50AA*and*
30%*similarity.*Sequences*obtained*from*the*SAR11*ITS*sequencing*assay*were*
classified*using*the*best*hit*from*a*BLASTn*search*against*NCBI*genomic*reference*
(refseq_genomic,*downloaded*March*2015)*sequences*with*an*e1value*cutoff*of*
0.0001,*minimum*length*match*of*100bp*and*70%*similarity.*We*also*classified*the*
SAR11*ITS*sequences*with*a*custom*ITS*database*from*clones*and*metagenomics*as*
described*in*(Brown*et*al.*2012).*We*augmented*the*ITS*database*with*SAR11*S4*
sequences*from*SILVA119.*Sequence*cutoffs*for*the*SAR11*ITS*database*search*was*
an*e1value*cutoff*of*0.0001,*minimum*length*of*match*of*300bp*and*95%*similarity.*
Statistical-Analyses-
* We*identified*monotonic*increases*and*decreases*of*OTUs*and*MED1types*
using*the*Mann1Kendall*test*within*the*“Kendall”*package*in*R.*Trends*with*p*<*0.05*
were*considered*significant.*Pairwise*correlations*between*variables*were*
determined*using*extended*local*similarity*analysis(Xia*et*al.*2011,*2013) .*We*
analyzed*the*full*time1series*which*was*made*up*of*temporally*unequally*
distributed*samples,*therefore*we*did*not*allow*time1lagged*correlations.*We,*
generally,*performed*LSA*on*the*most*abundant*taxa:*for*the*16S*OTUs*
(Chloroplasts*(1µm),*bacteria/archaea*(0.22µm*and*1µm)* and*g23*OTUs*we*used*
the*60*most*abundant*OTUs*that*were*present*over*half*of*the*time1series*(data*
from*Chapter*2).*We*supplemented*this*table*with*OTUs*and*MED1types*for*16S*and*
ITS*SAR11*OTUs*that*were*greater*than*0.5%*on*average.*All*MED 1types*for*16S,*
SAR11*ITS,*and*g23*that*exceeded*the*thresholds*described*previously*were*
included*in*the*LSA*analysis.*For*LSA,*in*order*to*estimate*the*relative*abundance*of*
64
sub1types*of*taxa,*we*multiplied*the*sub1types*by*the*estimated*relative*abundance*
of*the*parent*OTU.*For*example,*for*SAR11*ITS*OTUs*we*determined*an*estimated*
relative*abundance*by*multiplying*the*OTU*relative*abundance*by*cumulative*SAR11*
abundance*from*16S.*In*turn,*the*proportions*of*the*MED1types*of*SAR11*ITS*types*
were*multiplied*by*the*estimated*relative*abundance*of*the*SAR11*ITS*OTUs,*
providing*a*relative*abundance*estimate*for*each*MED1type.*
* Mantel*and*Partial*Mantel*tests*were*performed*in*R*via*the*Vegan*package*
(Oksanen*et*al.*2015)*on*Bray1Curtis*community*similarity*matrices*for*the*1*µm*
bacterial/archaeal,*0.22*bacterial/archaeal,*1*µm*Photosynthetic*Eukaryote,*T41like1
myoviruses*communities,*and*environmental*data.*We*performed*the*analysis*on*
the*most*abundant*60*OTUs*of*each*data*type*which*matches*the*LSA*analysis.*We*
also*performed*Mantel*tests*on*the*MED1type*data*by*substituting*decomposed*
OTUs*with*their*respective*MED1types.*-
Results((
Assessment-of-Molecular-Assays-
* To*assess*if*the*g23*and*SAR11*ITS*assays*were*amplifying*the*intended*
targets*we*searched*7,500*reads*from*each*SAR11*ITS*sample*and*g23*T41like1
myovirus*sample*against*known*bacterial*and*viral*genomes*and*found*that*the*vast*
majority*of*each*of*the*sequencing*assays*were*amplifying*the*target.*For*the*SAR11*
ITS*assay,*about*93%*of*the*sequences*matched*SAR11*sequenced*genomes*(90%*Nt*
ID,*>200bp)*and*96%*matched*a*custom*SAR11*ITS*database*(>97%*Nt*ID,*>*300bp)*
(SAR11*ITS*Figure1a/b).*For*the*g23*sequences,*94%*(40%*AA*ID,*>75AA)*and*98%*
(30%,*>50*AA)*matched*capsid*protein*like*genes*from*viral*genomes,*but*only*1%*
matched*at*>*80%*amino*acid*identity*over*75AA*to*sequenced*phage*(Figure*2).*
Most*matches*were*to*cyanophages,*but*about*10%*of*the*sequences*were*best*
matches*to*the*only*reported*SAR11*T41like*phage.* *
* Replication*of*marker*gene*PCR*results*was*excellent.**Bray1Curtis*
similarities*of*replicate*PCRs*(i.e.,*replicate*pooled,*triplicate*PCR*reactions)*of*the*
SAR11*ITS*assay*for*the*top*19*OTU*clusters*were*an*average*of*95%*(+/10.7%*SD,*
n=6)*similar.*Similarity*of*independent*filtrations*(from*split*water*samples)*for*the*
T41like1myovirus*(representing*the*total*error*in*the*method)*was*90%*(+/10.2%*
65
SD,*n=2)*for*the*top*60*OTUs.*We*used*these*abundance*cutoffs*due*to*our*analytical*
focus*on*the*most*abundant*taxa*(described*below).**
* We*generated*a*mock*community*from*environmental*g23*sequence*clones*
to*test*the*sequence*pipeline*from*PCR1analysis*for*the*g23*sequence*assay.*The*
nine*analyzed*clones*(i.e.,*after*removal*of*contaminating*OTUs,*see*methods)*in*the*
mock*community*were*retrieved*at*abundances*between*3117%*from*their*
expectation*of*11.1%*each*(Figure*3a).**Average*percentage*difference*between*the*
OTUs*and*their*theoretical*percentage*averaged*over*two*samples*was*43%,*+/1*18*
%*SD*.*The*average*difference*between*the*retrieval*of*each*OTU*between*two*mock*
communities*sequenced*in*parallel*with*this*study*was*0.6%*+/10.5%*SD*(Figure*
3a).**
* To*investigate*the*efficacy*of*the*MED*on*our*datasets,*we*interrogated*the*
mock*communities*using*comparable*parameters*to*those*used*for*the*
environmental*samples,*i.e.,*“MED1types”*needed*to*exceed*a*0.25*Shannon*Entropy*
threshold*within*the*mock*community*dataset*and*represent*>5%*of*the*sequences*
to*be*reported.*The*OTUs*within*the*16S*even*(n=10)*and*staggered*community**
(n=25)*displayed*no*spurious*MED1types*by*this*analysis.*In*fact,*no*OTUs*exceeded*
the*0.25*threshold(Figure*3B),*though*1*OTUs*had*two*instances*just*below*the*
threshold*(at*0.2499).*The*T41like*community*also*performed*well*with*only*1*OTU*
of*the*11*dominant*OTUs*being*split*by*the*procedure*(Figure*3A),*but*3*exceeded*
the*0.25*threshold*(Figure*3C).**
Ecological-Dynamics-and-diversity-within-and-between-the-communities-
Bacterial-and-Archaea-
* Previously,*using*99%*OTU*clustering*of*16S*sequence*data,*we*reported*
pronounced*successional*patterns*of*the*photosynthetic*eukaryotes,*archaea,*and*
bacteria*over*a*period*of*days1to1weeks.*We*found*there*to*be*day1to1day*shifts*
early*in*the*time1series*which*gave*way*to*a*more*stable*community*after*the*month*
of*March*(Chapter*2).*10*of*18*days*during*March*had*different*dominant*
eukarytotic*phytoplankton*taxa*including*PseudoM nitzschia,*Chaetoceros,*
Chrysochromulina,*and*Heterosigma*akashiwo.*Micromonas,*and*Ostrecococcus*
appeared*mostly*after*the*spring*bloom.*In*response,*various*OTUs*including*
66
Roseobacteria,*Verrucomicrobia,*Flavobacteria,*SAR92,*and*MGII*Archaea*
responded*in*the*1µm*(Figure*4A)*and*0.22µm*(Figure*4D)*size*fractions.*After*
March,*oligotrophic*like*organisms*such*as*SAR11,*Actinobacteria,*SAR86,*and*
cyanobacteria*were*dominant.**
Here,*we*applied*MED*to*the*16S*99%*bacteria/archaeal*OTUs*that*were*observed*
to*be*over*0.5%,*on*average,*during*this*time1series.*22*of*34*OTUs*were*
decomposed*into*at*least*2*MED1types*(Figure*4B/C)*and,*of*these*MED1types,*27*of*
49*(1µm*fraction)*and*26*of*49*(0.22µm*fraction)*had*significant*monotonic*trends*
over*the*full*time1period*(Figure*4B/C).*Only*2*OTUs*that*were*decomposed*were*
split*into*more*than*3*MED1types*(Figure*4).*The*top*5*highest*Mann1Kendall*values*
(similar*to*a*Spearman’s*correlation,*but*where*positive*values*indicate*positive*
increase*over*time)*were*a*Flavobacteria*OTU#122984*(MED1type*#2:*0.67,*1µm*
fraction;*0.69,*0.22µm*fraction),*MGII*Archaea*OTU#23046(MED1type*#5:*
0.53,0.53),*SAR92*(MED1type*#40,*0.5,*0.682),*and*Synechococcus*OTU#74083*
(MED1type#26,*0.66,0.56).Overall,*the*trends*in*each*MED1types*in*the*0.22µm*and*
1µm*size*fractions*were*similar*(Pearson’s*Correlation*of*Mann1Kendall*trends*=*
0.90).**
SAR11-dynamics-via-16S-&-ITS-sequencing-
* In*both*the*16S*and*ITS*analysis,*the*dominant*SAR11*OTU*decreased*over*
time*as*a*proportion*of*the*total*SAR11*community,*while*other*SAR11*S1*and*2*
increased*their*proportions*(SAR11*16S:*Figure*5A,*SAR11*ITS:*Figure*6A).*SAR11*
S4*peaked*in*April.*12*of*14*SAR11*16S*OTUs*(>0.5%*on*aver age)*were*decomposed*
(Figure*5B)*and*all*of*the*SAR11*ITS*OTUs*were*decomposed*into*significant*MED 1
types*(Figure*6B).*Unlike*most*of*the*16S*MED1types,*however,*there*were*often*
many*MED1types*associated*with*the*individual*SAR11*OTUs.*23*of*41*16S*SAR11*
MED1types*had*monotonic*trends*(Figure*5B)*and*61*of*84*SAR11*ITS*had*
monotonic*trends(Figure*6B).*The*16S*SAR11*OTU*that*had*the*strongest*trends*
within*its*MED1types*was*the*most*abundant*OTU.**The*2*MED1types*were*
negatively*correlated,*with*Mann1Kendally*values*10.59*and*0.5.*The*SAR11*ITS*
MED1type*with*the*strongest*trend*was*in*a*more*rare*OTU*(#160973).*The*OTU*
peaked*near*the*end*of*the*time1series*(June*118/3)*and*the*MED1type*(#1)*that*
67
increased*steadily*from*April*20
th
*onward*starting*from*an*average*of*5%*(March*
121April*20)*to*68%*over*the*last*month*of*the*time1series.**
T4MlikeMdynamics--
* The*T41like*myovirus*dynamics*were*very*similar*to*the*dynamics*of*the*
broad*cellular*succession,*with*nearly*every*OTU*displaying*distinct*ecological*
dynamics*(Figure*7A).**Also,*like*the*cellular*organisms,*many*different*OTUs*were*
dominant*over*the*course*of*the*primary*succession*in*March(Figure*7A).*Often*
close*relatives*had*very*dissimilar*temporal*dynamics*(Figure*7A)*including*two*
(denovo64629*and*denovo49922)*that*appear*nearly*completely*opposed*to*one*
another*(negatively*correlated).**The*g23*OTUs*were*decomposed*the*least*of*all.*
Only*9%*of*g23*OTUs*overall*were*split*into*more*than*2*MED1types,*whereas*the*
percentages*for*the*16S*and*SAR11*ITS*we re*26%*and*79%,*respectively(Figure*
7B);*however,*38%*of*the*MED1types*exhibited*monotonic*trends*over*the*time1
series.**
Correlations-within-and-between-the-communities-
* Change*in*the*virus*community*composition*was*most*correlated*with*the*
time*between*samples*(Mantel’s*rho=0.8).*However,*the*T41like1virus*community*
was*also*highly*correlated*to*both*the*0.2µm*and*1µm*size*fractions*of*the*
bacterial/archaeal*community,*and*the*Photosynthetic*Eukaryotic*community,*even*
when*time*was*factored*out*(Table*1).*The*Mantel*tests*results*were*similar*for*the*
communities*with*and*without*consideration*of*the*MED1types.*For*the*March*daily*
time1series,*correlations*were*strongest*between*the*communities*when*there*was*
no*time1shift*allowed*(Table*1).***
* To*investigate*which*particular*microbes*and*viruses*were*correlated*over*
time,*we*utilized*pairwise*correlation*analyses*and*network*visualizations*for*the*
full*length*time1series.*For*this*analysis,*we*obtained*estimated*relative*abundances*
of*each*sub1type*of*SAR11* ITS*and*MED1types*by*multiplying*them*by*the*relative*
abundance*of*the*OTU*from*which*they*were*decomposed*(their*“parent”*OTU).*
* Many*of*the*strongest*correlations*(i.e.,*r*>*|10.8|)*between*bacteria*and*
viruses*over*the*full*length*of*study*were*between*OTUs*that*were*relatively*
abundant*in*the*spring*bloom*environment*or*summer*warm*water*conditions,*with*
68
many*viruses*being*associated*with*each*community*(Figure*8).*Two*highly*
positively*intra1correlated*groupings*(“modules)*were*negatively*correlated*to*one*
another,*while*a*third*module*was*loosely*connected*to*each*(negatively*to*Spring*
bloom,*positively*to*summer)*and*included*many*Synechococcus*OTUs*and*MED1
types*(Figure*8).*The*bacteria*most*associated*with*the*“bloom”*module*peaked*
around*the*2
nd
*or*3
rd
*day*of*the*time1series*and*included*a*1*(of*3)*SAR92*MED1
types,*Persicirhabdus*(of*the*Verrucomicrobia),*and*several*Polaribacter*OTUs*and*
MED1types.**In*the*late*summer*module,*many*T41like*viruses*were*correlated*with*
a*SAR11*S1*ITS*MED1type,*including*a*T41virus*with*a*96%*AA*ID*to*the*only*
reported*SAR11*T41like1phage.*The*correlations*between*those*phage*and*SAR11*
were*the*highest*in*the*summer*module*(r=~0.86).*Flavobacteria,*Prochloroccous-,*
UCYN1A,*and*Synechococcus*MED1types*and/or*OTUs*were*also*correlated*with*the*
summer*bacteria1virus*module.*Finally,*there*were*some*highly*correlated*virus*
correlations*beyond*the*main*modules,*including*one*that*involved*a*
gammaproteobacterium*(denovo**OTU#*141902*JL1ETNP1Y6*of*the*
Oceanospirillum).**
* We*investigated*the*positive*pairwise*correlations*(r>0.7)*between*the*OTUs*
and*MED1types*of*the*SAR11*clade*to*viral*taxa*and*found*that*83%*were*to*SAR11*
ITS*MED1types.*Overall,*there*were*4*main*groupings:*group*1*peaked*in*March,*
groups*2*&*3*in*April,*and*Group*4*in*late*summer.*Each*grouping*contained*SAR11*
MED1types*from*at*least*2*phylotypes,*indicating*relatively*distant*relatives*can*
have*similar*ecological*patterns.**On*the*other*hand,*sequences*that*are*very*similar*
in*sequence*can*be*dissimilar*in*their*ecological*dynamics.*For*example,*g23*
OTU#297693*(and*its*MED1types)*were*associated*with*group*3*while,*two*
sequences*within*97%*of*this*OTU*were*associated*with*grouping*4.*A*more*
extreme*case*is*g23*OTU#206942*which*has*1*MED1type*representative*in*both*
Group*2*and*3.*-
Discussion* -
* This*study*shows*that*the*T41like1virus*community’s*response*to*a*large*
phytoplankton*bloom*and*succession*to*summer*conditions*is*similar*to*that*
observed*recently*in*the*microbial*community*Chapter*2)*in*that*there*is*
69
pronounced*variation*during*the*initial*decline*and*robust*statistical*correlation*
between*the*virus*community*and*the*microbial*communities.*This*dramatic*event,*
which*created*large*variation*in*the*microbial*communities,*aided*in*detection*of*co1
occurrence*between*particular*virus*and*bacterial*taxa,*providing*strong*evidence*
for*particular*viruses*being*associated*with*the*relatively*rich*spring*conditions*
versus*the*oligotrophic*summer*communities.*Our*analysis*clearly*shows*that*
relatively*closely1related*99%*viral*OTUs*can*have*profoundly*different*ecological*
dynamics,*and*even*within*the*finely1resolved*99%*OTUs,*there*is*an*extensive*
amount*of*variation*that*is*important*to*detecting*co1occurrence*in*many*taxa.*This*
is*highlighted*for*the*SAR11*lineage*for*which*we*detected*strong*co1occurrence*
between*strains*(detected*by*down*to*single*base*ITS*variations)*and*viral*types,*
several*involving*a*putative*SAR11*T41like*phage*(based*on*sequence*similarity).**
The*interrogation*of*the*OTUs*down*to*single*nucleotide*resolution*of*marker*genes*
revealed*in*some*cases*subtle,*but*often*pronounced*differences*in*the*ecological*
dynamics*of*microbes*and*viruses.**
* Similar*to*the*bacterial*communities,*very*few*of*the*abundant*viral*taxa*
(each*>0.5%*on*average)*remained*near*a*steady*relative*abundance*over*the*full*
time1series;*i.e.,*it*was*more*common*for*virus*types*to*be*relatively*abundant*for*
shorter*periods*of*time,*typically*weeks.*The*rapid*successional*pattern*of*viruses*
during*the*initial*phytoplankton*bloom*degradation*period*suggests*that*these*
viruses*respond*extremely*quickly,*since*nearly*all*peaks*of*bacteria*were*
associated*with*peaks*of*viruses*and*that*the*Mantel*tests*correlation*was*strongest*
without*a*time1lag*between*bacteria*and*viruses.*Our*lack*of*detection*of*time1lag*
between*the*two*communities*during*this*period*of*rapid*change,*suggests*that*the*
time1lag*may*be*sub1daily,*assuming*the*dominant*force*of*change*in*the*dynamics*
we*observed*is*temporal.*We*may*not*be*able*to*resolve,*even*with*daily*scale*
measurements,*to*what*extent*particular*viruses*are*the*cause*of*the*declines*in*
abundance*of*individual*taxa,*as*opposed*to*viruses*rising*and*falling*with*
abundance*controlled*primarily*by*other*factors.**
* Despite*the*pronounced*temporal*variation*we*observed*in*the*99%*OTUs*of*
bacteria,*archaea,*and*viruses,*most*of*the*underlying*sub1types*(i.e.,MED1types)*
70
appeared*more*stable.*However,*most*of*the*MED1types*displayed*simple*monotonic*
trends*over*the*time1series,*suggesting*they*have*distinct*ecological*niches*from*one*
another.*One*expectation*of*the*dynamics*within*taxa*separated*by*a*single*base*
variations*would*be*that*they*would*make*up*a*random,*but*consistent*proportion*
of*an*OTU*if*they*variation*is*neutral,*i.e.,*not*under*environmental*or*evolutionary*
selection*during*these*time1frames.*Despite*the*majority*of*MED1types*displaying*
temporal*trends,*for*the*16S*and*g23,*in*most*cases,*the*dominant*MED1type*
remained*the*dominant*MED1type*and*often*the*secondary*MED1type*made*up*a*
small*portion*of*the*overall*OTU.*However,*it*is*possible,*perhaps*likely,*that*the*
relatively*rare*MED1types*found*here*study*are*more*abundant*at*a*different*
location*or*different*year*through*either*random*or*non1random*processes.*The*
monotonic*trends*were*generally*downward*in*the*more*abundant*MED1type*which*
suggests*an*increase*in*diversity*in*the*summer.*While*studies*have*shown*diversity*
is*typically*highest*in*the*winter(Gilbert*et*al.*2012;*Cram*et*al.*2014),*spring*
months*can*be*among*the*least*diverse*at*SPOT.*This*may*be*driven*by*decreases*in*
diversity*associated*with*phytoplankton*blooms*(Wemheuer*et*al.*2014).*It*is*
possible*that*large*phytoplankton*blooms*are*important*enough*disturbances*that*
they*have*lasting*effects*on*the*microbial*community*for*months,*potentially*serving*
as*a*local*or*regional*“bottleneck”*for*taxa*from*which*the*community*diverges*the*
remainder*of*the*year,*with*various*other*disturbances*and*immigrations*taking*
place.**
* Our*study*reveals*that*taxa*have*different*levels*of*“clonality:”*i.e.,*some*taxa*
can*not*be*further*decomposed*using*marker*genes,*while*others*can*be*
decomposed*considerably.*Some*of*these*“clonal”*taxa,*such*as*the*Actinobacteria,*
are*also*known*to*be*made*up*of*comparatively*few*OTU*clusters,*compared*to*the*
SAR11*or*Flavobacterial*lineages,*for*example.*It*is*likely*that*the*different*levels*of*
‘clonality’*within*the*linages*is*important*to*understanding*the*lifestyle,*roles,*and*
diversity*of*function*of*these*organisms*in*the*environment.*One*possible*lifestyle*
inference*that*could*be*made*by*the*differences*in*‘clonality’*is*the*‘boom/bust’*
tendencies*of*an*organism.*It*would*be*our*assumption*that*if*an*organism*is*a*
‘bloom1forming’*copiotroph’*it*is*likely*that*a*single*“clone1like”*population*may*
71
make*up*a*substantial*proportion*of*that*lineages*abundance,*whereas,*for*example,*
the*SAR11*types*which*are*very*diverse*maintain*their*dominance,*or*near 1
dominance,*year*around*and*rarely*exceed*or*decrease*beyond*certain*thresholds,*
thus*experience*fewer*bottle*necks*with*more*time*for*drift*to*occur.*However,*
many*of*the*taxa*with*no*MED1types*are*quite*stable*in*abundance,*so*this*may*not*
be*the*primary*or*only*explantion.*It’s*possible*that*the*amount*of*diversity*is*
related*to*amount*of*viral*pressure*that*the*lineage*experiences*and*models*are*
beginning*to*consider*how*underlying*diversity*may*result*in*the*success*the*
species(Thingstad*et*al.*2014).****
* We*find*that*there*seems*to*be*ecological*convergence*within*some*lineages*
where*relatively*distantly*related*taxa*are*more*correlated*and*more*closely*related*
organisms*are*negatively*correlated*or*uncorrelated.*It*is*possible*that*these*
organisms*are*dissimilar*enough*that*they*are*not*negatively*influenced*by*the*same*
top1down*controls*as*one*another*but*are*otherwise*ecologically*very*similar.**
Whereas*within*a*99%*OTUs*there*may*be*cases*where*there*are*conditions*where*
one*MED1type*has*a*competitive*advantage*to*the*disadvantage*of*the*other*MED*
types*within*that*group.**
Limitations-and-Future-Prospects-
* With*time1series*in*the*ocean*there*are*always*spatial*effects*due*to,*at*least,*
mixing*with*surrounding*waters*by*diffusion*or*currents*causing*microbially1
variable*water*masses*to*pass*the*sample*site,*and*this*time1series*is*no*different.*It*
is*possible*that*the*co1occurrence*patterns*that*we*mostly*discuss*as*temporal*
patterns*are*at*least*somewhat*controlled*by*spatial*variation.*However,*co1
occurrence*can*be*determined*from*completely*spatial*studies*alone*and*generally*
draw*the*same*conclusions*(Faust*and*Raes*2012).*Therefore,*while*the*dynamics*
we*observe*may*not*be*a*purely*temporal*successional*pattern*(as*if*in*a*bottle),*and*
may*be*more*or*less*complex*than*if*they*were,*our*interpretations*are*likely*still*
valid.*Additionally,*we*have*addressed*the*bias*in*the*molecular*methods*elsewhere*
(Chapter*2)*and*these*need*to*be*considered,*especially*when*attempting*to*
extrapolate*to*estimated*abundances,*but*the*statistics*we*employ*rely*only*on*the*
72
dependence*that*the*bias*is*similar*across*samples*and*there*is*no*evidence*to*the*
contrary.**
* For*ecological*studies,*it*bears*repeating*that*it*is*better*to*be*too*
taxonomically*resolving*and*maintain*the*ability*to*post1hoc*merge*taxa*with*
ecological*similar*coherent*patterns*than*it*is*to*not*be*able*to*resolve*taxa*with*
different*ecologically*distinct*niches.*However,*marker*gene*studies,*for*some*taxa,*
may*never*be*‘too1resolving’*to*differentiate*some*ecologically*relevant*units.**
However,*we*reveal*examples*of*useful*observations*from*these*marker*gene*
studies,*and*they*remain*a*useful*way*to*assay*the*community*and*determine*what*
may*be*controlling*abundance*of*organisms.*However,*while*we*did*find*stronger*
correlations*at*a*fine*taxonomic*level*between*SAR11*and*viruses,*most*of*the*
SAR11*strains*detected*did*not*have*highly*significant*correlations*to*virus*taxa.*It*is*
likely*that*there*are*still*enormous*and*important*amounts*of*variation*beyond*that*
which*can*be*observed*even*with*relatively*highly*resolving*marker*gene*
approaches.*That*is,*we*are*likely*still*missing*many*microbial/viral*co1occurrence*
patterns*that*are*masked*by*the*lumping*of*multiple*populations.**
Conclusions* *
* Understanding*the*controls*of*microbial*communities*in*the*environment*is*a*
challenging*problem,*but*one*of*the*best*ways*to*do*this*is*through*studying*their*
natural*dynamics*as*they*relate*to*other*organisms*and*environmental*variation*
through*observational*time1series.*Here*we*studied*the*microbes*present*during*a*5*
month*interval*following*the*decline*of*a*phytoplankton*bloom*through*to*
oligotrophic*summer*conditions*at*a*temporal*resolution*of*days1to1weeks*and*at*
phylogenetic*resolution*of*single*nucleotides*of*different*marker*genes.*We*found*
that*the*virus*and*bacterial*communities*are*highly*correlated*over*a*51month*time1
series*with*many*statistical*correlations*to*the*most*abundant*organisms*present,*
including*a*putative*SAR11*phage*OTU.**We*also*examined*the*underlying*diversity*
of*each*of*the*abundant*populations*using*single*nucleotide*resolution*and*found*
that,*within*taxa*that*had*detectable*underlying*diversity,*the*decomposed*taxa*
most*commonly*had*distinct*ecological*dynamics.**However,*the*extent*of*the*
diversity*varies*from*taxon*to*taxon*which*may*have*very*important*implications*
73
and*may*yield*insight*into*their*varied*lifestyles.*Finally,*we*detected*many*stronger*
association*based*on*our*most*highly*resolving*analysis*(single*base*variation*of*
SAR11*ITS*sequences)*than*the*more*16S,*suggesting*that*association*between*these*
entities*in*the*environment*is*highly1specific;*however,*even*higher*resolving*
methods*would*likely*be*even*more*beneficial.**
References*
Ahlgren,*N.*A.,*and*G.*Rocap.*2012.*Diversity*and*Distribution*of*Marine*
Synechococcus:*Multiple*Gene*Phylogenies*for*Consensus*Classification*and*
Development*of*qPCR*Assays*for*Sensitive*Measurement*of*Clades*in*the*Ocean.*
Front*Microbiol*3:*1–24.*
Avrani,*S.,*O.*Wurtzel,*I.*Sharon,*R.*Sorek,*and*D.*Lindell.*2011.*Genomic*island*
variability*facilitates*Prochlorococcus–virus*coexistence.*Nature*474:*604–608.*
Breitbart,*M.,*M.*Middelboe,*and*F.*Rohwer.*2008.*Marine*viruses:*community*
dynamics,*diversity*and*impact*on*microbial*processes,*p.*443–479.*In*D.L.*
Kirchman*[ed.],*Microbial*Ecology*of*the*Oceans.*John*Wiley*&*Sons,*Inc.*
Brown,*M.*V.,*F.*M.*Lauro,*M.*Z.*DeMaere,*L.*Muir,*D.*Wilkins,*T.*Thomas,*M.*J.*Riddle,*
J.*A.*Fuhrman,*C.*Andrews1Pfannkoch,*J.*M.*Hoffman,*J.*B.*McQuaid,*A.*Allen,*S.*R.*
Rintoul,*and*R.*Cavicchioli.*2012.*Global*biogeography*of*SAR11*marine*
bacteria.*Mol*Syst*Biol*8:*595.*
Brussaard,*C.*P.*D.,*R.*S.*Kempers,*A.*J.*Kop,*R.*Riegman,*and*M.*Heldal.*1996.*Virus1
like*particles*in*a*summer*bloom*of*Emiliania-huxleyi*in*the*North*Sea.*Aquat*
Microb*Ecol*10:*105–113. *
Brussaard,*C.*P.*D.,*S.*W.*Wilhelm,*T.*F.*Thingstad,*M.*G.*Weinbauer,*G.*Bratbak,*M.*
Heldal,*S.*Kimmance,*M.*Middelboe,*K.*Nagasaki,*K.*E.*Wommack,*J.*H.*Paul,*D.*C.*
Schroeder,*C.*A.*Suttle,*and*D.*Vaque.*2008.*Global1scale*processes*witha*
nanoscale*drive:*the*role*of*marine*viruses.*ISME*J*2:*575–8.*
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,*and*W.*A.*Walters.*2010.*
74
QIIME*allows*analysis*of*high1throughput*community*sequencing*data.*Nat*
Methods*7:*2010.*
Chow,*C.1E.*T.,*and*J.*A.*Fuhrman.*2012.*Seasonality*and*monthly*dynamics*of*marine*
myovirus*communities.*Environ*Microbiol*14:*2171–83.*
Chow,*C.1E.*T.,*D.*Y.*Kim,*R.*Sachdeva,*D.*A.*Caron,*and*J.*A.*Fuhrman.*2014.*Top1
down*controls*on*bacterial*community*structure:*microbial*network*analysis*of*
bacteria,*T41like*viruses*and*protists.*ISME*J*8:*816–29.*
Clokie,*M.*R.*J.,*A.*D.*Millard,*and*N.*H.*Mann.*2010.*T4*genes*in*the*marine*
ecosystem:*studies*of*the*T41like*cyanophages*and*their*role*in*marine*ecology.*
Virol*J*7:*1–19.*
Comeau,*M.,*and*H.*M.*Krisch.*2006.*The*Capsid*of*the*T4*Phage*Superfamily :*The*
Evolution*,*Diversity*,*and*Structure*of*Some*of*the*Most*Prevalent*Proteins*in*
the*Biosphere.*Camera*,*doi:10.1093/molbev/msn080*
Cram,*J.*A.,*C.1E.*T.*Chow,*R.*Sachdeva,*D.*M.*Needham,*A.*E.*Parada,*J.*A.*Steele,*and*J.*
A.*Fuhrman.*2014.*Seasonal*and*interannual*variability*of*the*marine*
bacterioplankton*community*throughout*the*water*column*over*ten*years.*
ISME*J*1–18.*
DeSantis,*T.*Z.,*P.*Hugenholtz,*N.*Larsen,*M.*Rojas,*E.*L.*Brodie,*K.*Keller,*T.*Huber,*D.*
Dalevi,*P.*Hu,*and*G.*L.*Andersen.*2006.*Greengenes,*a*chimera1checked*16S*
rRNA*gene*database*and*workbench*compatible*with*ARB.*Appl*Environ*
Microbiol*72:*5069–72.*
Edgar,*R.*C.*2013.*UPARSE:*highly*accurate*OTU*sequences*from*microbial*amplicon*
reads.*Nat*Methods*10:*996–8.*
Eren,*A.*M.,*G.*G.*Borisy,*S.*M.*Huse,*and*J.*L.*Mark*Welch.*2014a.*Oligotyping*analysis*
of*the*human*oral*microbiome.*Proc*Natl*Acad*Sci*U*S*A*111:*E2875–84.*
Eren,*A.*M.,*L.*Maignien,*W.*J.*Sul,*L.*G.*Murphy,*S.*L.*Grim,*H.*G.*Morrison,*and*M.*L.*
Sogin.*2013.*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.*
2014b.*Minimum*entropy*decomposition:*Unsupervised*oligotyping*for*
75
sensitive*partitioning*of*high1throughput*marker*gene*sequences.*ISME*J*9:*
968–979.*
Faust,*K.,*and*J.*Raes.*2012.*Microbial*interactions:*from*networks*to*models.*Nat*
Rev*Microbiol*10:*538–50.*
Filée,*J.,*F.*Tetart,*C.*A.*Suttle,*and*H.*M.*Krisch.*2005.*Marine*T41type*bacteriophages*
,*a*ubiquitous*component*of*the*dark*matter*of*the*biosphere.*Proc*Natl*Acad*
Sci*U*S*A*102:*12471–12476.*
Fuhrman,*J.*A.*1999.*Marine*viruses*and*their*biogeochemical*and*ecological*effects.*
Nature*399:*541–8.*
Fuhrman,*J.*A.,*J.*A.*Cram,*and*D.*M.*Needham.*2015.*Marine*microbial*community*
dynamics*and*their*ecological*interpretation.*Nat*Rev*Microbiol*13:*133–146.*
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.*
Gilbert,*J.*A.,*J.*A.*Steele,*J.*G.*Caporaso,*L.*Steinbrück,*J.*Reeder,*B.*Temperton,*S.*
Huse,*A.*C.*McHardy,*R.*Knight,*I.*Joint,*P.*Somerfield,*J.*A.*Fuhrman,*and*D.*Field.*
2012.*Defining*seasonal*marine*microbial*community*dynamics.*ISME*J*6:*298–
308.*
Guindon,*S.,*and*O.*Gascuel.*2003.*A*Simple,*Fast,*and*Accurate*Algorithm*to*
Estimate*Large*Phylogenies*by*Maximum*Likelihood.*Syst*Biol*52:*696–704.*
Hewson,*I.,*and*J.*A.*Fuhrman.*2006.*Viral*impacts*upon*marine*bacterioplankton*
assemblage*structure.*J*Mar*Biol*Assoc*UK*86:*577–589.*
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.*Single1cell*genomics*reveals*hundreds*of*coexisting*
subpopulations*in*wild*Prochlorococcus.*Science*344:*416–20.*
Katoh,*K.,*K.*Misawa,*K.*Kuma,*and*T.*Miyata.*2002.*MAFFT :*a*novel*method*for*
rapid*multiple*sequence*alignment*based*on*fast*Fourier*transform.*Nucleic*
Acids*Res*30:*3059–3066.*
Kearse,*M.,*R.*Moir,*A.*Wilson,*S.*Stones1Havas,*M.*Cheung,*S.*Sturrock,*S.*Buxton,*A.*
Cooper,*S.*Markowitz,*C.*Duran,*T.*Thierer,*B.*Ashton,*P.*Meintjes,*and*A.*
76
Drummond.*2012.*Geneious*Basic:*an*integrated*and*extendable*desktop*
software*platform*for*the*organization*and*analysis*of*sequence*data.*
Bioinformatics*28:*1647–9.*
Kirchman,*D.*L.,*ed.*2008.*Microbial*ecology*of*the*oceans,*2nd*ed.*Wiley1Blackwell.*
Labonté,*J.*M.,*B.*K.*Swan,*B.*Poulos,*H.*Luo,*S.*Koren,*S.*J.*Hallam,*M.*B.*Sullivan,*T.*
Woyke,*K.*Eric*Wommack,*and*R.*Stepanauskas.*2015.*Single1cell*genomics1
based*analysis*of*virus1host*interactions*in*marine*surface*bacterioplankton.*
ISME*J*1–14.*
Mann,*N.*H.,*M.*R.*J.*Clokie,*A.*Millard,*A.*Cook,*W.*H.*Wilson,*P.*J.*Wheatley,*A.*
Letarov,*and*H.*M.*Krisch.*2005.*The*genome*of*S1PM2,*a*“photosynthetic”*T41
type*bacteriophage*that*infects*marine*Synechococcus*strains.*J*Bacteriol*187:*
3188–200.*
Marston,*M.*F.,*and*C.*G.*Amrich.*2009.*Recombination*and*microdiversity*in*coastal*
marine*cyanophages.*Environ*Microbiol*11:*2893–903.*
Marston,*M.*F.,*F.*J.*Pierciey,*A.*Shepard,*G.*Gearin,*J.*Qi,*C.*Yandava,*S.*C.*Schuster,*M.*
R.*Henn,*and*J.*B.*H.*Martiny.*2012.*Rapid*diversification*of*coevolving*marine*
Synechococcus*and*a*virus.*Proc*Natl*Acad*Sci*U*S*A*109:*4544–9.*
Needham,*D.*M.,*C.1E.*T.*Chow,*J.*A.*Cram,*R.*Sachdeva,*A.*Parada,*and*J.*A.*Fuhrman.*
2013.*Short1term*observations*of*marine*bacterial*and*viral*communities:*
patterns,*connections*and*resilience.*ISME*J*7:*1274–85.*
Newton,*R.*J.,*S.*L.*Mclellan,*D.*K.*Dila,*J.*H.*Vineis,*H.*G.*Morrison,*A.*M.*Eren,*and*L.*
Sogin.*2015.*Sewage*Reflects*the*Microbiomes*of*Human*Populations.*MBio*6:*
1–9.*
Nolan,*J.*M.,*V.*Petrov,*C.*Bertrand,*H.*M.*Krisch,*and*J.*D.*Karam.*2006.*Genetic*
diversity*among*five*T41like*bacteriophages.*Virol*J*3:*30.*
Oksanen,*A.*J.,*F.*G.*Blanchet,*R.*Kindt,*P.*Legendre,*P.*R.*Minchin,*R.*B.*O.*Hara,*G.*L.*
Simpson,*P.*Solymos,*M.*H.*H.*Stevens,*and*H.*Wagner.*2015.*vegan:*Community*
Ecology*Package.*
Pagarete,*A.,*C.1E.*T.*Chow,*T.*Johannessen,*J.*A.*Fuhrman,*T.*F.*Thingstad,*and*R.*a*
Sandaa.*2013.*Strong*seasonality*and*interannual*recurrence*in*marine*
myovirus*communities.*Appl*Environ*Microbiol*79:*6253–9.*
77
Parsons,*R.*J.,*M.*Breitbart,*M.*W.*Lomas,*and*C.*A.*Carlson.*2011.*Ocean*time 1series*
reveals*recurring*seasonal*patterns*of*virioplankton*dynamics*in*the*
northwestern*Sargasso*Sea.*ISME*J*1:*1–12.*
Petrov,*V.*M.,*S.*Ratnayaka,*J.*M.*Nolan,*E.*S.*Miller,*and*J.*D.*Karam.*2010.*Genomes*of*
the*T41related*bacteriophages*as*windows*on*microbial*genome*evolution.*
Virol*J*19.*
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*web1based*tools.*Nucleic*Acids*Res*41:*D590–6.*
Reveillaud,*J.,*L.*Maignien,*a*Murat*Eren,*J.*a*Huber,*A.*Apprill,*M.*L.*Sogin,*and*A.*
Vanreusel.*2014.*Host1specificity*among*abundant*and*rare*taxa*in*the*sponge*
microbiome.*ISME*J*8:*1198 –209.*
Rice,*P.,*I.*Longden,*and*A.*Bleasby.*2000.*EMBOSS:*The*European*Molecular*Biology*
Open*Software*Suite*(2000).*Trends*Genet*16:*276–277.*
Rocap,*G.,*D.*L.*Distel,*J.*B.*Waterbury,*and*S.*W.*Chisholm.*2002.*Resolution*of*
Prochlorococcus*and*Synechococcus*Ecotypes*by*Using*16S123S*Ribosomal*DNA*
Internal*Transcribed*Spacer*Sequences.*Appl*Enironmental*Microbiol*68:*
1180 –1191. *
Rodriguez1Brito,*B.,*L.*Li,*L.*Wegley,*M.*Furlan,*F.*Angly,*M.*Breitbart,*J.*Buchanan,*C.*
Desnues,*E.*Dinsdale,*R.*A.*Edwards,*B.*Felts,*M.*Haynes,*H.*Liu,*D.*Lipson,*J.*
Mahaffy,*A.1B.*Martin1Cuadrado,*A.*Mira,*J.*Nulton,*L.*Pašić,*S.*Rayhawk,*J.*
Rodriguez1Mueller,*F.*Rodriguez1Valera,*P.*Salamon,*S.*Srinagesh,*T.*F.*
Thingstad,*T.*Tran,*R.*V.*Thurber,*D.*Willner,*M.*Youle,*and*F.*Rohwer.*2010.*
Viral*and*microbial*community*dynamics*in*four*aquatic*environments.*ISME*J*
4:*739–751.*
Rusch,*D.*B.,*A.*L.*Halpern,*G.*Sutton,*K.*B.*Heidelberg,*S.*Williamson,*S.*Yooseph,*D.*
Wu,*J.*a*Eisen,*J.*M.*Hoffman,*K.*Remington,*K.*Beeson,*B.*Tran,*H.*Smith,*H.*
Baden1Tillson,*C.*Stewart,*J.*Thorpe,*J.*Freeman,*C.*Andrews1Pfannkoch,*J.*E.*
Venter,*K.*Li,*S.*Kravitz,*J.*F.*Heidelberg,*T.*Utterback,*Y.1H.*Rogers,*L.*I.*Falcón,*V.*
Souza,*G.*Bonilla1Rosso,*L.*E.*Eguiarte,*D.*M.*Karl,*S.*Sathyendranath,*T.*Platt,*E.*
Bermingham,*V.*Gallardo,*G.*Tamayo1Castillo,*M.*R.*Ferrari,*R.*L.*Strausberg,*K.*
78
Nealson,*R.*Friedman,*M.*Frazier,*and*J.*C.*Venter.*2007.*The*Sorcerer*II*Global*
Ocean*Sampling*expedition:*northwest*Atlantic*through*eastern*tropical*Pacific.*
PLoS*Biol*5:*e77.*
Short,*S.*M.,*F.*Chen,*and*S.*W.*Wilhelm.*2010.*The*construction*and*analysis*of*
marker*gene*libraries,*p.*82–91.*In*Wilhelm,*Steven*W.,*Weinbauer,*M.*G.,*and*
C.A.*Suttle*[eds.],*Manual*of*Aquatic*Viral*Ecology.*American*Society*of*
Limnology*and*Oceanography.*
Steele,*J.*A.,*P.*D.*Countway,*L.*C.*Xia,*P.*D.*Vigil,*J.*M.*Beman,*D.*Y.*Kim,*C.1E.*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 –25.*
Steward,*G.*F.,*and*A.*I.*Culley.*2010.*Extraction*and*Purification*of*Nucleic*Acids*
from*Viruses,*p.*154–165.*In*American*Society*of*Limnology*and*Oceanography:*
Manual*of*Aquatic*Virology.*
Sullivan,*M.*B.,*K.*H.*Huang,*J.*C.*Ignacio1Espinoza,*A.*M.*Berlin,*L.*Kelly,*P.*R.*Weigele,*
A.*S.*Defrancesco,*S.*E.*Kern,*L.*R.*Thompson,*S.*Young,*C.*Yandava,*R.*Fu,*B.*
Krastins,*M.*Chase,*D.*Sarracino,*M.*S.*Osburne,*M.*R.*Henn,*and*S.*W.*Chisholm.*
2010.*Genomic*analysis*of*oceanic*cyanobacterial*myoviruses*compared*with*
T41like*myoviruses*from*diverse*hosts*and*environments.*Environ*Microbiol*
12:*3035–3056.*
Sullivan,*M.*B.,*J.*B.*Waterbury,*and*S.*W.*Chisholm.*2003.*Cyanophages*infecting*the*
oceanic*cyanobacterium*Prochlorococcus.*Nature*424:*1047–52.*
Thingstad,*T.*F.,*and*R.*Lignell.*1997.*Theoretical*models*for*the*control*of*bacterial*
growth*rate,*abundance,*diversity*and*carbon*demand.*Aquat*Microb*Ecol*13:*
19–27.*
Thingstad,*T.*F.,*S.*Våge,*J.*E.*Storesund,*R.1A.*Sandaa,*and*J.*Giske.*2014.*A*theoretical*
analysis*of*how*strain1specific*viruses*can*control*microbial*species*diversity.*
Proc*Natl*Acad*Sci*U*S*A*111:*7813–8.*
Tikhonov,*M.,*R.*W.*Leach,*and*N.*S.*Wingreen.*2015.*Interpreting*16S*metagenomic*
data*without*clustering*to*achieve*sub1OTU*resolution.*ISME*J*9:*68–80.*
79
Tully,*B.*J.,*W.*C.*Nelson,*and*J.*F.*Heidelberg.*2011.*Metagenomic*analysis*of*a*
complex*marine*planktonic*thaumarchaeal*community*from*the*Gulf*of*Maine.*
Environ*Microbiol*,*doi:10.111 1/j.1462 12920.2011.02628.x *
Turlapati,*S.*a,*R.*Minocha,*S.*Long,*J.*Ramsdell,*and*S.*C.*Minocha.*2015.*Oligotyping*
reveals*stronger*relationship*of*organic*soil*bacterial*community*structure*with*
N1amendments*and*soil*chemistry*in*comparison*to*that*of*mineral*soil*at*
Harvard*Forest,*MA,*USA.*Front*Microbiol*6:*1–16.*
Venter,*J.*C.,*K.*Remington,*J.*F.*Heidelberg,*A.*L.*Halpern,*D.*Rusch,*J.*a*Eisen,*D.*Wu,*I.*
Paulsen,*K.*E.*Nelson,*W.*Nelson,*D.*E.*Fouts,*S.*Levy,*A.*H.*Knap,*M.*W.*Lomas,*K.*
Nealson,*O.*White,*J.*Peterson,*J.*Hoffman,*R.*J.*Parsons,*H.*Baden1Tillson,*C.*
Pfannkoch,*Y.1H.*Rogers,*and*H.*O.*Smith.*2004.*Environmental*genome*shotgun*
sequencing*of*the*Sargasso*Sea.*Science*304:*66–74.*
Vergin,*K.*L.,*B.*Beszteri,*A.*Monier,*J.*C.*Thrash,*B.*Temperton,*A.*H.*Treusch,*F.*
Kilpert,*A.*Z.*Worden,*and*S.*J.*Giovannoni.*2013a.*High1resolution*SAR11*
ecotype*dynamics*at*the*Bermuda*Atlantic*Time1series*Study*site*by*
phylogenetic*placement*of*pyrosequences.*ISME*J*7:*1322–32.*
Vergin,*K.*L.,*B.*Done,*C.*Carlson,*and*S.*J.*Giovannoni.*2013b.*Spatiotemporal*
distributions*of*rare*bacterioplankton*populations*indicate*adaptive*strategies*
in*the*oligotrophic*ocean.*Aquat*Microb*Ecol*71:*1–13.*
Weigele,*P.*R.,*W.*H.*Pope,*M.*L.*Pedulla,*J.*M.*Houtz,*A.*L.*Smith,*J.*F.*Conway,*J.*King,*
G.*F.*Hatfull,*J.*G.*Lawrence,*and*R.*W.*Hendrix.*2007.*Genomic*and*structural*
analysis*of*Syn9,*a*cyanophage*infecting*marine*Prochlorococcus*and*
Synechococcus.*Environ*Microbiol*9:*1675–95.*
Wemheuer,*B.,*S.*Güllert,*S.*Billerbeck,*H.1A.*Giebel,*S.*Voget,*M.*Simon,*and*R.*Daniel.*
2014.*Impact*of*a*phytoplankton*bloom*on*the*diversity*of*the*active*bacterial*
community*in*the*southern*North*Sea*as*revealed*by*metatranscriptomic*
approaches.*FEMS*Microbiol*Ecol*87:*378–89.*
Xia,*L.*C.,*D.*Ai,*J.*A.*Cram,*J.*A.*Fuhrman,*and*F.*Sun.*2013.*Efficient*statistical*
significance*approximation*for*local*similarity*analysis*of*high1throughput*time*
series*data.*Bioinformatics*29:*230–7.*
80
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.*
Zhao,*Y.,*B.*Temperton,*J.*C.*Thrash,*M.*S.*Schwalbach,*K.*L.*Vergin,*Z.*C.*Landry,*M.*
Ellisman,*T.*Deerinck,*M.*B.*Sullivan,*and*S.*J.*Giovannoni.*2013.*Abundant*
SAR11*viruses*in*the*ocean.*Nature*494:*357–60.*
*
81
0.0 0.2 0.4 0.6 0.8 1.0
Ca.P.ubique_HTCC9022
Ca.P.ubique_HTCC7217
Ca.P.sp._HTCC7211
Ca.P.ubique_HTCC1002
Ca.P.ubique_HTCC1040
Ca.P.ubique_HTCC8051
Ca.P.ubique_HIMB058
Erythrobacter_litoralis_HTCC2594,_complete
Ca.P.ubique_HIMB083
Alpha_proteobacterium_HIMB114_HIMB114
Ca.P.ubique_HTCC7214
Salinisphaera_shabanensis_E1L3A_Contig17,
Rhodanobacter_fulvus_Jip2_contig048,
Francisella_tularensis_subsp._tularensis
Oceanicaulis_sp._HTCC2633_scf
0.0 0.2 0.4 0.6 0.8 1.0
P1a.2
P1a.3
P1a.1
S4a
P2.1
S4b
P3.1
>99% Nt
>300bp
>97% Nt
>300bp
>95% Nt
>300bp
>95% Nt
>200bp
>90% Nt
>200bp
>80% Nt
>100bp
>70% Nt
>100bp
A. B.
Figure 1
Figure 1: Fraction of SAR11 ITS sequences that match by BLASTn search against NCBI genomic refer-
ence sequences (A.) and a custom SAR11 ITS database from genomic sequences, metagenomics
sequences, and environmental cloned sequences (B.). Sequence identifi ers/classifi cations in (B.) are as
defi ned Brown et al. 2012, whereby “P” stands for “Phylotype” . “S4” stands for SAR11 Surface 4, as
defi ned by SILVA119 which were left out of the SAR11 ITS database originally. The sequence thresh-
olds are shown at bottom.
82
0.0 0.2 0.4 0.6 0.8 1.0
major_head_protein_Cyanophage_P−RSM1
major_capsid_protein_Synechococcus_phage_S−CAM8
hypothetical_protein_CPXG_00096_Cyanophage_P−RSM6
major_capsid_Pelagibacter_phage_HTVC008M
major_capsid_protein_Synechococcus_phage_S−SKS1
precursor_of_major_head_subunit_Prochlorococcus_phage_Syn1
major_capsid_protein_Synechococcus_phage_S−CAM1
precursor_of_major_head_subunit_Synechococcus_phage_S−SM2
precursor_of_major_head_subunit_Synechococcus_phage_S−SM1
precursor_of_major_head_subunit_Prochlorococcus_phage_P−SSM2
major_capsid_protein_Synechococcus_phage_KBS−M−1A
major_capsid_protein_Synechococcus_phage_S−RSM4
major_capsid_protein_Prochlorococcus_phage_MED4−213
precursor_of_major_head_subunit_Synechococcus_phage_S−SSM7
precursor_of_major_head_subunit_Prochlorococcus_phage_P−SSM7
major_capsid_Synechococcus_phage_S−SSM4
precursor_of_major_head_subunit_Prochlorococcus_phage_P−HM2
precursor_of_major_head_subunit_Synechococcus_phage_S−PM2
head_vertex_subunit_gp24_precursor_Synechococcus_phage_metaG−MbCM1
precursor_of_major_head_subunit_Prochlorococcus_phage_P−HM1
0.00 0.01 0.02 0.03 0.04 0.05
>80% AA
>75 AA
>70% AA
>75 AA
>60% AA
>75 AA
>40% AA
>75 AA
>30% AA
>50 AA
Figure 2
Figure 2: Fraction of total sequences and sequence annotations of g23 T4-like-myovirus
sequence that match by viral reference genomic sequences in NCBI by BLASTx at the
given thresholds (at bottom).
83
0.0
0.1
0.2
0.3
0.4
0 100 200 300 400 500
Shannon Entropy
0.00
0.05
0.10
0.15
0.20
0.25
0 100 200 300 400
Alignment Location
Shannon Entropy
16S Entropy Analysis of Mock Community OTUs
T4-like-myovirus Entropy Analysis of Mock Community OTUs
Figure 3
A. B.
C.
denovo156436
denovo90146
denovo39947
denovo118017
denovo92343
denovo372370
denovo386592
denovo206942
denovo377165
MED-type 5th
MED-type 4th
MED-type 3rd
MED-type 2nd
Even_Main_Avg
0.00
0.05
0.10
0.15
0.20
Figure 3: Results of T4-like even mock community analysis showing the average
and standard deviation of the total 99% OTUs (full height of bar) retrieved from
PCR to sequence analysis pipeline (A.) Where bars are split, the Minimum
Entropy Decomposition split an OTU into the proportions shown. Red asterisk
identifi es the only spurious MED-type (besides the primer MED-types) that
exceeded the defi ned threshold for signifi cance for our analysis. Shannon
entropy values for each location along the alignments for all clones are shown
for the 16S (A.) and g23 (B.) mock communities. Low values (0-0.1) are likely
most due to background PCR and sequencing error, whereas those points that
exceed 0.25 are considered signifi cant single nucleotide polymorphisms.
84
1µm Size Fraction
0.22µm Size Fraction
0.00 0.10 0.20 0.30
MGII Archaea 23046
Persicirhabdus 65085
Roseibacillus 229167
Roseibacillus 90250
Puniceicoccaceae 48217
SAR86 26873
SAR92 49500
SAR116 28895
SAR11 S2 118472
SAR11 S1 142927
SAR11 S1 164672
Roseo DC5 72034
Rhodobacteraceae 32682
Roseob OCT 11664
Roseovarius 74339
OCS155 22417
NS9 130911
NS9 211105
Fluviicola 208385
Flavob 133459
Polaribacter 112640
Polaribacter 31534
NS4 122984
NS4 223562
NS2b 168292
Formosa 20586
Formosa 36344
Formosa 222693
Flavob 151540
Ulvibacter 115646
Croceitalea 98487
Prochlorococcus 62609
Synechococcus 18078
Synechococcus 74083
0.5
MGII Archaea 23046
Persicirhabdus 65085
Roseibacillus 229167
Roseibacillus 90250
Puniceicoccaceae 48217
SAR86 26873
SAR92 49500
SAR116 28895
SAR11 S2 118472
SAR11 S1 142927
SAR11 S1 164672
Roseo DC5 72034
Rhodobacteraceae 32682
Roseob OCT 11664
Roseovarius 74339
OCS155 22417
NS9 130911
NS9 211105
Fluviicola 208385
Flavob 133459
Polaribacter 112640
Polaribacter 31534
NS4 122984
NS4 223562
NS2b 168292
Formosa 20586
Formosa 36344
Formosa 222693
Flavob 151540
Ulvibacter 115646
Croceitalea 98487
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
27−Mar
28−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
5−M ay
12−M ay
16−M ay
17−M ay
18−M ay
19−M ay
20−M ay
21−M ay
22−M ay
23−M ay
24−M ay
25−M ay
26−M ay
27−M ay
2−Jun
9−Jun
16−Jun
22−Jun
1−Jul
7−Jul
14−Jul
20−Jul
26−Jul
3− Aug
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
22−Jun
1−Jul
7−Jul
14−Jul
20−Jul
26−Jul
3− Aug
12 −Mar
14 −Mar
15 −Mar
16 −Mar
17 −Mar
18 −Mar
22 −Mar
23 −Mar
24 −Mar
25 −Mar
26 −Mar
27 −Mar
Synechococcus 74083
Prochlorococcus 62609
Flavobacteria 151540
Formosa 36344
Formosa 20586
NS2b 168292
NS4 223562
NS4 122984
Polaribacter 31534
NS9 211105
Fluviicola208385
Flavobacteria 133459
Polaribacter 112640
RoseoDC5 72034
SAR92 49500
SAR11 S2 118472
SAR11 S1 142927
SAR11 S1 164672
SAR116 28895
MGII Archaea 23046
Persicirhabdus 65085
SAR86 26873
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
27−Mar
28−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
5−M ay
12−M ay
16−M ay
17−M ay
18−M ay
19−M ay
20−M ay
21−M ay
22−M ay
23−M ay
24−M ay
25−M ay
26−M ay
27−M ay
2−Jun
9−Jun
16−Jun
28 −Mar
29 −Mar
30 −Mar
1−Apr
6−Apr
15 −Apr
20 −Apr
29 −Apr
5−M ay
12 −M ay
16 −M ay
17 −M ay
18 −M ay
19 −M ay
20 −M ay
21 −M ay
22 −M ay
23 −M ay
24 −M ay
25 −M ay
26 −M ay
27 −M ay
2−Jun
9−Jun
16 −Jun
22 −Jun
1−Jul
7−Jul
14 −Jul
20 −Jul
26 −Jul
3− Aug
March 12-April 1 May 16-May 27 April 6-
May 12
June 2-August 3 March 12-April 1 May 16-May 27 April 6-
May 12
June 2-August 3
1
27
26
2
3
6
1
2
16
1
5
2
1
1
2
1
6
1
2
1
2
1
2
1
10
2
1
5
1
4
1
25
20
16
1
30
3
1
2
41
55
47
25
41
40
1
12
1
6
1
21
5
1
2
13
6
Substitutions per site
Figure 4
12−Mar
14−Mar
15−Mar
16−Mar
17−Mar
18−Mar
22−Mar
23−Mar
24−Mar
25−Mar
26−Mar
27−Mar
28−Mar
29−Mar
30−Mar
1−Apr
6−Apr
15−Apr
20−Apr
29−Apr
5−M ay
12−M ay
16−M ay
17−M ay
18−M ay
19−M ay
20−M ay
21−M ay
22−M ay
23−M ay
24−M ay
25−M ay
26−M ay
27−M ay
2−Jun
9−Jun
16−Jun
22−Jun
1−Jul
7−Jul
14−Jul
20−Jul
26−Jul
3− Aug
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
Proportion of Microbial
Community
0.00 0.10 0.20 0.30
Proportion of Microbial
Community
A. D.
B. C.
Prochlorococcus 62609
Synechococcus 18078
Synechococcus 74083
0.0 0.25 0.50 0.75 1.0
Proportion of parent OTU
Figure 4: Phylogeny and relative abundance of bacterial and archaeal 16S OTUs and MED-types over 0.5%
on average over the whole time-series. OTUs are identifi ed by the most resolving taxonomic descriptor
from uclust classifi cation against SILVA 115 and their denovo OTU which is unique but consistent within this
study (and Chapter 2). Relative abundances are proportions of the total bacterial/archaeal sequences for
each sample for the 1µm size fraction (A.). The relative abundance of 16S MED-types of the 99% OTUs that
were decomposed into at least 2 MED-types are shown for the 1µm size fraction as proportions of the
parent OTU (scaled to 1.0) (B.). Up/down arrows adjacent to the MED-type relative abundance heatmaps
indicate if the corresponding MED-type had a downward or upward trend (p<0.05) as determined by the
Mann-Kendall test, a dash indicates that the parent OTU was undetected for >5 samples of the time-series
and therefore, there is no trend reported overall. Similarly, MED-types (C.) and 16S OTUs (D.) and upwards
and downwards trends for the 0.22µm size fraction are reported.
85
SAR11 16S
S4-233883
S4-86652
SAR11-60205
SAR11-117761
S2-118472
S1-190925
S1-142927
S1-116932
S1-164672
S1-218524
S1-34348
S1-142808
S1-84458
S1-49766
0.1
0.0 0.1 0.2 0.3 0.4 0.5
3
8
49766
0.0 0.1
18
19
14
12
36
1
84458
25
1
164672
16
20
142927
11
12
7
28
13
34348
10
22
15
33
29
190925
16
9
5
10
22
117761
16
1
4
2
86652
30
32
1
118472
2
8
233883
1
12
2
60205
142808
1
13
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
Figure 5
Proportion of total SAR11
A. B.
Substitutions per site
0.0 0.25 0.50 0.75 1.0
Proportion of parent OTU
Figure 5: Phylogeny and relative abundance (as proportion of the total SAR11 population) of the SAR11
16S sequences greater than 0.5% on average of total SAR11 over the full time-series (A.). OTUs are
identifi ed by their classifi cation as obtained by UCLUST classifi cation against the SILVA119 database.
Arrows indicate upward or downward trend in relative abundance over the full time-series as deter-
mined by the Mann-Kendall test (p<0.05). SAR11 16S OTU MED-types are displayed (B.) as proportions
of their parent OTU (scaled to 1.0) and Mann-Kendall determined trend shown, where signifi cant as
described for Figure 4.
86
Figure 6
0.00 0.05 0.10 0.15 0.20 0.25
S4a-463671
S4b-64249
P3.1-475353
P1a.2-581679
P1a.2-544858
P1a.3-160973
P1a.3-303344
P1a.3-165712
P1a.3-318044
P1a.3-482885
P1a.3-375470
P1a.2-338041
P1a.2-214585
P1a.2-47135
P1a.2-567898
P1a.2-596176
P1a.2-503421
P1a.1-319810
P1a.1-396544
319810
7
2
475353
3
1
2
SAR11 ITS 99% OTUs
165712
396
95
214585
65
6
98
3
45
7
58
44
396544
596176
597
658
432
665
463671
96
5
9
1
2
482885
76
99
12
108
80
112
4
503421
611
568
526
549
64249
57
3
53
47135
161
105
82
185
8
1
581679
83
34
68
44
69
30
544858
56
12
69
9
22
2
303344
129
326
194
239
179
332
338041
546
437
318044
1
3
375470
22
9
24
4
26
1
160973
6
5
37
13
53
54
1
544858.txt000000002
567898
133
9
100
128
1
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
Proportion of total SAR11
A.
B.
Substitutions per site
0.4
0.0 0.25 0.50 0.75 1.0
Proportion of parent OTU
Figure 6: Phylogeny and relative abundance data for the SAR11 ITS sequencing assay where the
relative abundance of each OTU is shown as a proportion of the total SAR11 community (A.).
Upwards and downwards arrows indicate the direction of a trend (where applicable) over the full
time-series as indicated by Mann Kendall test (p<0.05). OTUs are identifi ed by their best match to a
custom SAR11 ITS database from genomic sequences, metagenomics sequences, and environmen-
tal cloned sequences (Brown et al. 2012), supplemented with SAR11 Surface 4 sequences from
SILVA119 and a unique and consistent OTU cluster name within this study. SAR11 ITS OTU MED-
types are displayed (B.) as described for Figure 4.
87
0.00 0.05 0.10 0.15
T4-like-myovirus 99% OTUs
177507
64629
49922
297693
223134
391957
256263
123332
276825
64630
387874
303277
39947
81323
92343
177500
401798
142810
330610
206942
235651
329392
225615
225619
122539
342369
412753
338983
241984
301435
305715
230635
288324
249286
303038
Putative Pelagiphage 200611
145862
189951
67845
247910
392445
351694
32
1
225619
2
5
206942
8
1
256263
1
13
297693
4
1
230635
2
5
189951
5
1
64629
3
1
305715
3
1
123332
30
16
1
303038
9
1
330610
9
1
142810
3
1
276825
4
3
2
1
329392
3
1
122539
4
21
2
1
391957
10
3
1
401798
8
1
392445
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
3/12
3/14
3/15
3/16
3/17
3/18
3/22
3/23
3/24
3/25
3/26
3/27
3/28
3/29
3/30
4/1
4/6
4/15
4/20
4/29
5/5
5/12
5/16
5/17
5/18
5/19
5/20
5/21
5/22
5/23
5/24
5/25
5/26
5/27
6/2
6/9
6/16
6/22
7/1
7/7
7/12
7/20
7/26
8/3
March 12-April 1 May 16-May 27
April 6-
May 12
June 2-August 3
0.5
Figure 7
Proportion of T4-like-myoviruses
A.
B.
Substitutions per site
0.0 0.25 0.50 0.75 1.0
0.0 0.25 0.50 0.75 1.0
Proportion of parent OTU
Figure 7. Phylogeny and relative abundance for the g23-based T4-like-myovirus OTUs where the
relative abundance is a proportion of the total T4-like-myovirus community (A.). OTUs are identi-
fi ed by unique OTU identifi ers (consistent within this study) and an OTU with a 96% amino acid
sequence match to the only reported SAR11 T4-like phage is indicated. g23 T4-like-myovirus
OTU MED-types are displayed (B.) as described for Figure 4.
88
g23
Figure 8
Polaribacter
112640-1
g23
49922
SAR11
S1
116932
g23
189951
SAR11_S_1
164672.25
S1
P1a.3
303344.326
g23
200611
g23
64629.5
g23
64629.1
g23
240558
NS9
141622
g23
64629
Roseo
DC5
72034-4
g23
401798.1
SAR92
49500-41
g23
230635
g23
230635.1
g23
189951.5
g23
49361
g23
295140
Water
temp
buoy
g23
288324
g23
92343
Pro
62609-6
g23
391957.21
UCYN-A
198172
NS2b
76090
g23
177507
Formosa
23791
g23
92152
Unknown
211821
NS2b
76090
Syn
74083-26
Formosa
20586-1
Polaribacter
31534
Polaribacter
31534-1
Polaribacter
31534-1
Chaetoceros
225988
SAR92
49500-41
Polaribacter
31534
Polaribacter
112640-1
g23
123332.1
Persicirhabdus
65085
g23
362719
g23
276825.3
g23
241984
g23
64630
Persicirhabdus
65085-1
Polaribacter
112640
g23
206942
g23
276825.1
g23
276825
g23
330610.1
g23
414451
g23
303038.16
g23
303038.1
g23
249286
g23
303038
g23
303038.30
g23
412753
g23
419365
g23
98708
g23
401798
S1
P1a.2
544858.2
g23
401798.3
g23
189951.2
g23
303277
NS9
211105-1
g23
Polaribacter
112640
g23
39947
g23
123332
g23
206942.5
g23
391957.1
g23
391957
g23
391957.2
g23
391957.4
g23
388214
Braarudosphaera
bigelowii
171775
S1
P1a.2
338041.546
g23
247910
Formosa
20586
Pseudospirillum
218981
g23
67845
g23
230635.4
g23
8916
g23
297693.13
g23
206942.2
g23
329392.1
g23
40211
g23
329392
Roseo
DC5
72034
g23
329392.4
g23
305715.3
g23
329392.2
S1
P1a.1
319810.7
g23
223134
g23
305715
g23
142810
g23
329392.3
g23
351694
392445.1
g23
392445
g23
392445.8
g23
305715.1
g23
225619.1
g23
225619.32
JL-ETNP-Y6
141902
g23
301435
g23
256263
g23
268968
g23
338983
Syn
74083
Syn
74083-1
g23
235651
g23
256263.1
Syn
18078
Water
temp
bottle
Unassigned
150704
g23
177500
Pro
62609
Pro
62609-2
Pro
62609-2
Pro
62609
S1
P1a.3
160973.1
Pro
62609-3
g23
401798.10
g23
420121
DEV007
1844
g23
122539.1
g23
122539
Owenweeksia
141805
g23
256263.8
g23
330610.9
Pseudo-nitzschia
159037
Syn
74083
g23
122539.3
Syn
74083-26
Leyanella
56146
g23
330610
g23
142810.1
g23
142810.9
Syn
74083-27
g23
297693.1
g23
225619
g23
297693
S1
P1a.2
567898.1
Bloom Associated Module
Summer Module
Synechococcus Module
g23
Figure 8. Pairwise Spearman’s orrelation network of all microbial OTUs and MED-types and environmen-
tal parameters to T4-like-myovirus OTUs and MED-types (p<0.001, |r|>0.8). Nodes (circles) represent
OTUs where light gray are T4-like MED-types, gray are T4-like-OTUs, light red are 16S MED-types, red are
16S OTUs, light blue are SAR11 ITS MED-types, green are eukaryotic phytoplankton, and yellow are
environmental parameters. Green and red edges (lines) connecting nodes indicate positive and nega-
tive correlation, respectively. Gray outline of nodes indicates OTU represents the abundance of the OTU
in the 1µm size fraction; the red outlined node represents the putative SAR11 T4-like phage.
89
Figure 9
S1
P1a.3
160973.1
g23
64629.1
S1
P1a.3
160973.37
S2
P3.1
475353.3
g23
64629
g23
92343
g23
177507
g23
391957.21
g23
92152
S2
P3.1
475353.1
S1
P1a.2
581679.34
g23
297693
S1
P1a.2
581679.83
g23
225619.32
g23
297693.13
g23
249286
g23
412753
g23
247910
g23
49361
S1
P1a.2
338041.546
S1
P1a.2
567898.1
S1
P1a.3
303344.179
g23
8916
S1
P1a.1
319810.7
S4
S4a
463671.96
g23
206942.2
SAR11_S_4
86652.4
S1
P1a.3
160973.53
g23
64629.5 SAR11
S1
116932
g23
288324
SAR11_S_1
164672.25
S2
P3.1
475353
S1
P1a.2
544858.2
S1
P1a.3
303344.326
g23
200611
g23
297693.1
g23
206942
g23
206942.5
g23
329392.1
S1
P1a.2
47135.105
S1
P1a.3
318044.1
S4
S4a
463671.5
Group 1 Group 2 Group 3 Group 4
Figure 9: Pairwise Spearman’s correlation network of all correlations of SAR11 OTUs, MED-types from
16S and ITS analyses to T4-like myoviruses (p<0.001, r>0.725). Network characteristics are as
described in Figure 8, except blue nodes are SAR11 ITS OTUs and the strength of the correlation is
indicated by the thickness of the edge, whereby a thicker edge represents a stronger correlation.
Negative correlations have been removed for clarity.
90
Table&1.&Rho&values&of&Mantel&and&Partial&Mantel&tests&for&communities&and&
environmental¶meters.&Where&two&values&are&present,&the&former&represents&
the&Mantel&Rho&value&and&the&latter&the&Partial&Mantel&test&Rho&value&(i.e.,&with&time&
factored&out).&Emboldened&values&in&the&upper&table&from&Chapter&2&are&significant&
(p<0.005).&Where&they&are&given,&values&in&parentheses&are&pG values.&&&
&
Mantel'Test'Results'(Reproduced'
from'Chapter'2)'
Date& Attached,&Large& Small,&FreeG Living& PE&
Attached&or&Large& 0.564& & & &
Small/FreeG Living& 0.407% 0.847,%0.819& & &
Photosynthetic&Eukaryotes&(PE)& 0.418% 0.751,%0.687& 0.684,%0.619& &
Environmental&Parameters& 0.582% 0.637,%0.461& 0.484,%0.333& 0.697,%0.614&
Chlorophyll&a& 0.394% 0.481,%0.341& 0.317,%0.187& 0.526,%0.433&
Seawater&Temperature& 0.771% 0.639,%0.389& 0.512,%0.341& 0.555,%0.401&
Wave&Height& 0.11$ 0.083,$0.025& 0.021,$*0.026& 0.077,$0.034&
&
&
$ $ $ $
Mantel'Test'Results'(New'Chapter'
3)'
G23,&full& G23,&march&(12&
days,&average)&
G23(12&days,&1&day&
shift)&
G23(12,&2&day&shift)&
Attached&or&Large& 0.64,0.44' 0.6193(0.001)' 0.3608&(0.006)& 0.1867(0.068)&
Small/FreeG Living& 0.54,0.49% 0.785(0.001)' 0.388'(0.0115)& 0.245(0.051)&
Attached&or&Large&(MED)& 0.66,0.43% & & &
Small/FreeG Living&(MED)& 0.57,0.49% & & &
Date& 0.80% & & &
Photosynthetic&Eukaryotes&(PE)& 0.54,0.40% & & &
Environmental&Parameters& 0.55,0.18$ & & &
91
Overview,(Synthesis,(Conclusion,(Future(Perspectives(
Summary'
! This!dissertation!explored!the!dynamics!associated!with!marine!microbial!
communities!at!a!daily!scale!that!yielded!insights!into!the!dynamics!of!micro8
organisms!enabling!ecological!interpretation.!Chapter!1!explored!bacterial!and!viral!
dynamics!over!about!38!days,!extended!by!weekly!sampling!for!another!month,!
Chapter!2!explored!photosynthetic!eukaryotic!community,!bacteria!and!archaea!
following!the!demise!of!a!diatom!bloom,!and!chapter!3!explored!the!viruses!
associated!with!the!diatom!bloom,!supplementing!the!analysis!with!highly!resolving!
methods!and!highlighting!the!SAR11!group,!often!the!dominant!organism!in!terms!
of!abundance!in!the!ocean.!!
! Chapter!1!reported!the!dynamics!associated!during!a!summer!period!near!
Catalina!Island,!where!the!bacterial!community!was!determined!to!be,!using!the!
DNA!fingerprinting!ARISA!method,!dominated!by!Synechococcus!SAR11!
Actinobacteria,!among!others.!We!found!that!several!clearly!distinct!SAR11!taxa!
were!highly!correlated!over!time!as!they!displayed,!after!an!initial!decline!over!
about!2!weeks,!relatively!stable!patterned!variation!over!the!remaining!283!weeks,!
where!they!stayed!within!about!a!508100%!range!of!their!respective!abundance.!
During!the!initial!period!where!SAR11!decreased,!Synechococcus!increased!and!
then!apparently!was!relatively!variable!over!the!remaining!3!weeks.!Meanwhile!an!
Actinobacterium!was!more!dynamic!showing,!three!times,!patterned!increases!and!
decreases!where!the!decreases!had!them!decrease!to!around!10%!of!the!average!
abundance!and!they!quickly!rebounded.!T48like8myovirus!OTUs,!assayed!via!T8RFLP!
of!a!capsid!gene,!showed!similar!dynamics!with!many!OTUs!decreasing!over!time!
and!oscillating!slightly,!but!with!several!that!increased!and!others!that!showed!
relatively!dynamic!patterns!with!significant!peaks.!Overall!the!taxa!within!
communities!oscillated!within!an!average!range!of!abundance,!thus!the!community!
was!quite!stable!over!time,!but!with!a!slight!decreasing!trend!in!similarity.!
Therefore,!the!community!was!quite!resilient!to!fundamental!change!over!this!time!
period.!
92
! Chapter!2!described!a!very!different!environmental!regime.!We!began!our!
sampling!during!the!decline!of!a!major!phytoplankton!bloom!at!the!San!Pedro!Ocean!
time8series!where!chlorophyll!concentrations!were!between!10820µg/L!and!just!a!
few!days!prior!domoic!acid!concentrations!were!the!highest!ever!measured!at!SPOT.!
For!this!time8series,!high8throughput!16S!sequencing!was!employed!which!enabled!
retrieval!of!all!of!the!marine!bacterial!and!archaeal!taxa!we!are!aware!of!in!the!ocean!
and!also!chloroplast!of!the!major!phytoplankton!species,!sans!dinoflagellates.!As!
part!of!this!research,!a!mock!community!was!developed!to!determine!the!accuracy!
and!precision!of!the!16S!assay!and!serve!as!a!type!of!positive!control,!which!showed!
biased!positive!amplification!in!some!taxa,!but!overall!showing!that!the!assay!was!a!
reasonably!accurate!assessment!of!the!microbial!community,!and!highly!
reproducible.!A!relatively!novel!aspect!of!this!study!(used!only!a!few!times!before,!to!
our!knowledge)!was!the!use!of!chloroplast!16S!rRNA!gene!sequences!to!characterize!
the!photosynthetic!eukaryotes!(in!both!“bacterial”!and!larger!size!fractions),!which!
are!the!major!primary!producers!in!this!community.!It!might!be!argued!that!such!
genes!may!be!a!good!way!to!follow!something!approximating!the!biomass!of!
different!phytoplankton,!because!cells!have!chloroplast!numbers!roughly!
proportional!their!biomass!and!chloroplasts!have!little!known!variation!in!copy!
number!of!this!gene,!whereas!nuclear!18S!rRNA!sequences!have!copy!numbers!that!
can!range!from!a!few!to!tens!of!thousands.!It!was!also!learned!in!the!course!of!this!
work!that!chloroplast!sequences!also!may!better!distinguish!phytoplankton!variants!
(even!possibly!within!species)!compared!to!18S!rRNA!sequences.!In!any!case,!the!
chloroplast!data!showed!fine8scale!day8to8day!dynamics!during!the!initial!bloom!
decline,!including!10!different!taxa!being!dominant!during!the!18!day!spring!time8
series.!Similarly!dynamic!was!the!bacterial!and!archaeal!response,!which!also!
displayed!marked!day8to8day!changes!with!many!taxa!being!highly!correlated!with!
particular!photosynthetic!eukaryotes;!generally!the!bacterial!and!archaeal!
communities!were!more!correlated!with!the!photosynthetic!community!than!they!
were!the!chemical/physical!conditions.!!
! Chapter!3!described!the!phytoplankton!bloom!succession!in!more!detail,!
including!the!T48like8myovirus!community,!and!included!a!focus!on!the!SAR11!
93
group!for!which!we!designed!and!employed!a!sequencing!assay!for!a!region!of!the!
genome!which!is!more!variable!than!the!16S,!the!ribosomal!intergenic!spacer!(ITS).!
Additionally!we!explored!the!extent!to!which!‘classic’!sequence!clustering!
techniques!obscure!taxa!that!are!discernible!by!single!base!differences!and!that!have!
distinct!ecological!niches.!We!found!that!T48like8myovirus!taxa!were!quite!variable!
following!the!phytoplankton!bloom!with!many!strong!correlations!to!bacterial!taxa.!
The!highly!resolving!analyses!showed!that!over!half!of!99%!sequence!clusters!could!
be!decomposed!into!taxa!with!distinct!ecological!niches!based!on!the!single!base!
resolving!methods.!The!highly!resolving!analysis!techniques,!in!addition!to!the!high!
resolving!sequencing!assay!for!SAR11!ITS,!revealed!stronger!correlations!than!those!
with!of!sequence!clusters!and,!in!particular,!showed!a!strong!correlation!between!a!
putative!SAR11!phage!taxon!and!a!SAR11!ITS8based!taxon.!!
'Synthesis,'Conclusion'
! Taken!together,!we!conclude!that,!most!of!the!time,!bacterial!communities!
are!quite!stable,!with!moderate!amounts!of!variation!that!enable!detection!of!co8
occurrence!patterns!of!taxa,!over!a!period!of!weeks.!However,!the!response!to!
blooms!can!generate!rapidly!changing!communities!that!are!distinct.!While!our!
OTU8based!analysis!revealed!some!general!ecological!patterns!of!taxa:!rapidly!
varying!types!of!Flavobacteria!following!a!bloom,!large!or!particle!attached!
Verrucomicrobia,!small/free8living!SAR92!clade!most!associated!with!
phytoplankton!bloom,!generally!stable!SAR11!ecotypes!(but!largely!independent!
variation!of!the!Surface!4!group),!two!types!of!MGII!Archaea!(one!that!bloomed!to!
high!abundances,!and!another!that!tended!to!be!more!stable),!commonly!high!
abundance!of!organisms!during!summer!conditions!including!Actinobacteria,!
SAR86,!Prochlorococcus,!Synechococcus,!SAR11,!as!well!as!particular!lineages!of!
Flavobacteria.!However,!our!analysis!reveals!that!that!closely!related!taxa!can!be!
quite!different!ecologically.!Further,!taken!together!with!previous!research!(e.g.,!
Cram!et!al.!2015)!we!observe!that!correlations!over!short!time8periods!are!different!
than!those!that!are!evident!over!a!decade!of!monthly!sampling,!suggesting!that!the!
ecological!niche!of!organisms!may!be!dependent!on!a!set!of!variables!that!are!
94
somewhat!constant!(for!example,!the!community!composition)!over!the!short8term!
but!is!more!variable!from!years8to8decades.!!
! Ideally,!assays!to!observe!ecological!dynamics!of!taxa!would!assess!them!at!a!
level!that!is!an!ecological!species,!i.e.,!sequences!would!be!split!into!groups!that!have!
the!same!niche;!however,!this!is!likely!to!remain!a!challenging!problem!both!at!the!
population!and!community!level.!However,!the!observation!that!lineages!can!have!
different!ecology!within!very!close!relatives!and!that!lineages!have!varying!degrees!
of!diversity!associated!with!them!provides!insight!into!their!lifestyle!and!the!
evolutionary!and!ecological!processes!involved!in!shaping!the!niche!of!microbial!
lineages.!!For!example,!we!observed!in!chapter!three!that!taxa!which!are!relatively!
similar!phylogenetically!can!have!different!dynamics,!while!those!less!similar!
phylogenetically!may!have!similar!dynamics.!An!evolutionary!explanation!may!be!
that!the!divergence!that!occurs!gives!a!lineage!access!to!a!new!niche,!and!then!at!
particular!times!in!evolutionary!history,!taxa!may!return!to!occupy!similar!niches.!It!
is!likely!that!the!organisms!exhibiting!convergence!are!still!subtly!different,!
however,!and!that!given!a!different!environmental!background!they!would!not!
appear!to!have!similar!dynamics.!In!this!way,!a!lineage!would!establish!a!wide!
breadth!of!niche!space!(“generalists)!that!may!increase!its!overall!success.!Viruses!
may!play!a!role!in!driving!this!diversification,!as!significant!top8down!controllers!on!
the!microbial!community!with!often!very!fine8grained!infection!tendencies.!
Contrastingly,!lineages!without!much!diversity!may!be!optimized!for!a!particular!
niche!(“specialists”)!that!are!not!able!to!expand!their!niche!very!easily!and!go!
through!rounds!of!local!sweeps!whereby!random!drift!is!decreased.!
Regardless,!our!short8term!time8series!reveal!that!much!can!be!learned!from!
studying!the!dynamics!of!microbes!over!short8periods!in!the!ocean.!Technological!
advances!continue!to!push!the!field!of!marine!microbial!ecology!forward!in!terms!of!
our!capabilities!to!collect!environmental!samples!and!make!measurements!of!the!
taxa!and!their!environmental!context.!Application!and!expansion!of!these!tools!to!
study!short8term!dynamics!of!microbial!communities!is!likely!to!reveal!new!
information!on!the!controls!of!these!communities!and!should!be!continued!to!be!
able!to!understand!the!roles!of!organisms!and!how!they!interact!with!one!another.!
95
Continued!application!of!approaches!described!in!this!dissertation!will!continue!to!
reveal!new!observations!while!confirming!others,!allowing!a!more!comprehensive!
view!on!microbial!ecology.!Determining!what!makes!OTUs!and!strains!different!
(both!closely!a!distantly!related)!in!high!throughput!ways!is!an!obtainable!goal!both!
through!metagenomics!sequencing!and!single8cell!sequencing!and!has!already!
begun!to!be!realized!for!some!taxa,!most!notably,!Prochlorococcus(Kashtan!et!al.!
2014).!Continued!inspection!into!the!dynamics!of!strains!during!different!time8
scales!and!windows!will!further!inform!our!understanding!of!marine!microbial!
ecology.!
!
References'
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.!Single8cell!genomics!reveals!hundreds!of!coexisting!
subpopulations!in!wild!Prochlorococcus.!Science!344:!416–20.!
!
!
96

Abstract (if available)

Abstract This dissertation explored the dynamics associated with marine microbial communities at a daily scale that yielded insights into the dynamics of micro-organisms enabling ecological interpretation. Chapter 1 explored bacterial and viral dynamics over about 38 days, extended by weekly sampling for another month, Chapter 2 explored photosynthetic eukaryotic community, bacteria and archaea following the demise of a diatom bloom, and chapter 3 explored the viruses associated with the diatom bloom, supplementing the analysis with highly resolving methods and highlighting the SAR11 group, often the dominant organism in terms of abundance in the ocean. ❧ Chapter 1 reported the dynamics associated during a summer period near Catalina Island, where the bacterial community was determined to be, using the DNA fingerprinting ARISA method, dominated by Synechococcus SAR11 Actinobacteria, among others. We found that several clearly distinct SAR11 taxa were highly correlated over time as they displayed, after an initial decline over about 2 weeks, relatively stable patterned variation over the remaining 2-3 weeks, where they stayed within about a 50-100% range of their respective abundance. During the initial period where SAR11 decreased, Synechococcus increased and then apparently was relatively variable over the remaining 3 weeks. Meanwhile an Actinobacterium was more dynamic showing, three times, patterned increases and decreases where the decreases had them decrease to around 10% of the average abundance and they quickly rebounded. T4-like-myovirus OTUs, assayed via T-RFLP of a capsid gene, showed similar dynamics with many OTUs decreasing over time and oscillating slightly, but with several that increased and others that showed relatively dynamic patterns with significant peaks. Overall the taxa within communities oscillated within an average range of abundance, thus the community was quite stable over time, but with a slight decreasing trend in similarity. Therefore, the community was quite resilient to fundamental change over this time period. ❧ Chapter 2 described a very different environmental regime. We began our sampling during the decline of a major phytoplankton bloom at the San Pedro Ocean time-series where chlorophyll concentrations were between 10-20µg/L and just a few days prior domoic acid concentrations were the highest ever measured at SPOT. For this time-series, high-throughput 16S sequencing was employed which enabled retrieval of all of the marine bacterial and archaeal taxa we are aware of in the ocean and also chloroplast of the major phytoplankton species, sans dinoflagellates. As part of this research, a mock community was developed to determine the accuracy and precision of the 16S assay and serve as a type of positive control, which showed biased positive amplification in some taxa, but overall showing that the assay was a reasonably accurate assessment of the microbial community, and highly reproducible. A relatively novel aspect of this study (used only a few times before, to our knowledge) was the use of chloroplast 16S rRNA gene sequences to characterize the photosynthetic eukaryotes (in both “bacterial” and larger size fractions), which are the major primary producers in this community. It might be argued that such genes may be a good way to follow something approximating the biomass of different phytoplankton, because cells have chloroplast numbers roughly proportional their biomass and chloroplasts have little known variation in copy number of this gene, whereas nuclear 18S rRNA sequences have copy numbers that can range from a few to tens of thousands. It was also learned in the course of this work that chloroplast sequences also may better distinguish phytoplankton variants (even possibly within species) compared to 18S rRNA sequences. In any case, the chloroplast data showed fine-scale day-to-day dynamics during the initial bloom decline, including 10 different taxa being dominant during the 18 day spring time-series. Similarly dynamic was the bacterial and archaeal response, which also displayed marked day-to-day changes with many taxa being highly correlated with particular photosynthetic eukaryotes

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Needham, David M. (author)

Core Title Ecological implications of daily-to-weekly dynamics of marine bacteria, archaea, viruses, and phytoplankton

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Marine and Environmental Biology

Publication Date 07/31/2015

Defense Date 05/14/2015

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

Tag archaea,bacteria,Ecology,marine,OAI-PMH Harvest,phytoplankton,viruses

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Fuhrman, Jed A. (committee chair), Caron, David A. (committee member), Corsetti, Frank A. (committee member), Heidelberg, John F. (committee member), Webb, Eric A. (committee member)

Creator Email dmneedha@gmail.com,dmneedha@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-622414

Unique identifier UC11303155

Identifier etd-NeedhamDav-3776.pdf (filename),usctheses-c3-622414 (legacy record id)

Legacy Identifier etd-NeedhamDav-3776.pdf

Dmrecord 622414

Document Type Dissertation

Format application/pdf (imt)

Rights Needham, David M.

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