Eichhornia azurea decomposition and the bacterial dynamic: an experimental research

h tt p : / / w w w . b j m i c r o b i o l . c o m . b r /

Environmental

Microbiology

Eichhornia

azurea

decomposition

and

the

bacterial

dynamic:

an

experimental

research

Zaryf

Dahroug

_,

_Natália

_Fernanda

_Santana

_,

_Thomaz

_Aurélio

_Pagioro

a_{ProgramadePós-graduac¸ãoemEcologiadeAmbientesAquáticosContinentais,UniversidadeEstadualdeMaringá(UEM),Mariná,} PR,Brazil

b_{UniversidadeTecnológicaFederaldoParaná(UTFPR),Curitiba,PR,Brazil}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received13December2012 Accepted19August2015 Availableonline2March2016 AssociateEditor:LaraDurãesSette

Keywords: Decompositionrate Dryweight Aquaticmacrophyte COD

a

b

s

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t

Organicdecompositionisacomplexinteractionbetweenchemical,physicalandbiological processes,wherethevarietyofaquaticvascularplantsisessentialforthetrophicdynamics offreshwaterecosystems.Thegoalofthisstudywastodeterminetheaquaticmacrophyte

Eichhornia azurea(Sw.)Kunthdecompositionrate,the timerelationwith the limnologi-calparameters,andwhetherthisrelationshipisaresultofdecompositionprocesses.To thatend,wecollectedwaterandleavesofE.azureainSurfLeopoldo,PR.Theexperiment consistedoftwotreatments:25containerswith450mLofwaterand0.8gofbiomassdry weightwereusedwithorwithouttheadditionofmacrophytes.Sampleswerecollectedin triplicateattimes0,3h,6h,12h,24h,72h,120h,168hand240h.Whenthecontainerwas removed,theplantmaterialwasdriedinanoven.After48h,thematerialwasmeasured toobtainthefinaldryweight.AnalysesofpH,conductivity,dissolvedoxygen,total phos-phorusN-ammonia(NH4),solublereactivephosphorus(PO4)anddissolvedorganiccarbon

wereperformed,andthedecompositionratewascalculated.Theresultsshowedsignificant temporalvariationoflimnologicalparametersinthestudy.Additionally,dissolvedoxygen, conductivity,dissolvedorganiccarbonandtotalphosphoruswerecorrelatedwiththedry weightofthebiomass,suggestingthatE.azureadecompositionsignificantlyinterfereswith thedynamicsofthesevariables.

©2016SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.Thisis anopenaccessarticleundertheCCBY-NC-NDlicense

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Introduction

Aquaticmacrophytes areimportantcomponentsof ecosys-temsthatprovidefloodpulse.Theyhavespatialandtemporal characteristicsthat maketheminterestingforthestudy of decompositioninaquaticplants.1–3_{Macrophytesareoftenthe}

∗ _{Correspondingauthor.}

E-mail:pagioro@utfpr.edu.br(T.A.Pagioro).

primary producers,especiallyin lenticenvironments. They haveamajorroleinnutrientcyclingandindebrisformation, beinganabundantsourceoforganicmatter.4,5_{Additionally,}

theyareamixedstands,whichinfluencesthephysicaland chemical characteristics of water, altering the turbulence, temperature,sunlightpenetration,concentrationand distri-butionofdissolvedoxygenandnutrients.6

http://dx.doi.org/10.1016/j.bjm.2015.08.001

1517-8382/©2016SociedadeBrasileiradeMicrobiologia.PublishedbyElsevierEditoraLtda.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Eichhorniaazurea(Sw.)Kunthisamajormacrophytein con-strained,floodedenvironments.Itisafloatingfixingspecies, perennial and rhizomatous.7,8 _{It is distributed in natural}

and artificial reservoirsfrom south ofthe UnitedStates to ArgentinaandinalloftheregionsandecosystemsofBrazil.9

It serves as food for capybaras, pigs and other herbivores andasahabitatformanyfish,insectlarvae,snailsandtheir eggs,etc.10_{Aquaticmacrophytesreachhighbiomassvalues,}

makingthemanimportantsourceoforganicmaterialtobe decomposed.11 _{Severalstudieshavehypothesizedaboutthe}

relationship of this macrophyte with others species, such asephemeropteras,12_fishes,13–17_insects10,18,19_andthe

endo-phyticfungalcommunity.20

Plant material decomposition releases much dissolved organicmatter intoaquaticenvironments. Thisproducesa quantityofdebriscapableofregulatingthenutrientflowinthe ecosystembothspatiallyandtemporally.21_{Thereisastrong}

linkbetweenprimaryproduction,decompositionandnutrient cycling.22_{Thus,thecompoundsreleasedduringtheaquatic}

macrophytedecompositioncanberesponsibleformostofthe energyflowinaquaticecosystems.23_{Onewaytomonitorthe}

masslossovertimeisbycalculatingthedecompositionrate.It affectsthenutrientrelease,theaccumulationofdecomposing materialinthesedimentandthequalityofthedetritus,22_and

itisusuallyexpressedbytheweightlossinacertainperiodof time.

Themetabolicactivityofheterotrophicbacteriahas impor-tantimplications forthe functionofaquaticecosystems.24

Bacteria and fungi are essential for organic matter decomposition.25 _{They useavarietyoforganiccompounds}

underdifferentenvironmentalconditions,extractingenergy from these compounds by fermentation and aerobic and anaerobic respiration.26 _{They convert large amounts of}

matterinto inorganicnutrients.Thefactors that affectthe compositionofthebacterialcommunityanditsactivityhave beenthebasisformanystudiesinrecentyears.1,2,27–29 _With

no evaluation of the mechanisms that regulate microbial foodwebsandgiventheareacoveredbyaquaticecosystems, thefunctioningofaquaticecosystemshasbeenonlypartly described.30

Thisstudyinvestigatedtheexistenceoftemporal fluctu-ations oflimnological parameters during E. azurea decom-position that simulates the flood pulse because in this period, there is an organic matter input from aquatic macrophytes.

Materials

and

methods

Collectionandassemblyoftheexperiment

SamplesofwaterandE.azurealeavesweregatheredinthe RessacoLeopoldo(22◦4524S,53◦167W),locatedinPuerto Rico,intheFloodPlainoftheAltoParanáRiver.

The species E. azurea was chosen because it is more common in areas subjected to flooding and because it has high biomass levels.11 _{In addition, many}

decomposi-tion studieshave beenperformed withthis macrophyte in floodplains.30,31

Inthelaboratory,thewatercollectedwaskeptunder aera-tionuntiltheexperimentalassembly.Themacrophyteswere driedin anovenfor sevendaysinorder toobtain the dry weight.

Fortheexperimentalassembly,51bottlesofpolyethylene wereused(500mL).Anaerationsystemwasusedineach indi-vidual container.Theexperiment occurred in aninsulated environment inorder tomaintain astabletemperature. In theexperiment,450mLofwaterand0.8gofmacrophytedry weightwereaddedtothebottles.

Themasswasbasedonthevaluesofmacrophytebiomass obtained in the environments of the Upper Paraná River Floodplain30_{andsimulatedthedecompositioneventsduring}

thefloodpulse.

TheincreaseinE.azureabiomasscansimulatethenutrient inputeffectcharacteristicofthefloodpulseprocess.Inthis process,alargebiomassconcentrationdecomposes,leading toanincreaseinnutrientcyclingandaffectingthemicrobial loop.32

Acontrolwasalsoperformed,inwhichthesamevolume ofwaterwasaddedwithouttheadditionofthemacrophyte dryweight.Samplesweretakenat0(experimentinitiation), 3h,6h,12h,24h, 72h,120h,168hand240h.These samp-ling times represent the leachingperiod oforganic matter decomposition.Ateachtimepoint,threecontainersfromeach treatment were randomlyremoved,and theplantmaterial containedwithinwassenttotheovenfordrying.After48h, thematerialwasweighedtoobtainthefinaldryweight. Fur-thermore,5mLofwaterwasseparatedforthebacteriological analysis. The remainingvolume was used inphysical and chemicalanalyses.

Bacterialdensityandbiomass

Thedensityandbacterialbiomasswereestimatedbyfiltering 0.1mL of water from the experiment. Black polycarbo-nate filters (Nucleopore®_{) with pore openings of 0.2␮m,}

stainedwith1mLofthefluorochromeDAPI (4,6-diamidino-2-phenylindole),wereusedfor5mininthedark.Thefilterswere mountedonslidesand storedinthefreezer. Bacteriawere quantifiedusinganepifluorescencemicroscope(1000×).The biovolumewasdeterminedusingtheequationproposedby Fry(1990):v=(p/4)·w2(l×w/3),wherev=cellvolume,l=length,

w=width.Fortheconversionofbiovolumetobiomass,itwas consideredthat1␮m3_=3.5×10−13_gC.13

Abioticanalysis

Thedissolvedoxygen(mgL−1)levelsandthewater tempera-tureweredetermineddirectlyinthebottlesusingaportable digitaloxymeter(YSI-550A).Theelectricalconductivity and pH values were determined using portable digital poten-tiometers.Analiquot of50mLwasusedfortotaldissolved organic carbon(DOC)determination,whichwascarriedout by catalyticoxidation ata high temperature (720◦C) using theShimadzuTOCanalyzerV-CSN.Theremainingwaterwas filteredthroughafiberglassfilter(Whatman®_GF/C)to

deter-minethesolublephosphorus(P-PO43−),ammonia(NH4+),and

0 3 6 12 24 48 72 96 120 144 168 192 216 240 Time (h) 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 Dry weight (g)

Fig.1–DryweightlossofE.azureaduringdecompositionprocess.

Decompositionrate

Formacrophytedecompositionratedetermination,the neg-ativeexponentialmodel“Wt=W0e−kt”wasused,whereWtis

theremnantweightfractionofthevegetationattime“t”,W0

istheinitialweightandkisthedecompositionrate(day−1). Thismodelassumesthataconstantfractionoftheremaining masskdecaysineachtimeunit.37

Statisticalanalysis

TheANCOVAcovariancetestwasconductedtodeterminethe existenceofsignificantdifferencesbetweentreatments.The timeparameterwasusedasacovariate.

ThedatawerecorrelatedusingthePearson’slinear corre-lationcoefficient(r)toidentifywhichlimnologicalvariables wereassociatedwithbacterialdensityandbiomass.

Aregressionanalysiswasperformedtoevaluatethemain variablesofthebacterialcommunityandtheirpowerto pre-dictthe dependentvariable.Theregression wasperformed including all significant factors in the Pearson correlation (independentvariables)andexcludingnon-significantfactors (p>0.05), looking for the simplest model by which to pro-ducethemostrepresentativeparameters(backwardstepwise method).Theassumptionsoftheseanalyseswereverifiedby residuesanalysis.

Allstatisticalanalyseswereperformedusingthestatistical package“Statistica”version5.5.38_{Thesignificancelevelwas}

setatp<0.05.

Results

E.azureadecompositionrate

The reduction inthe dry weight was the mostprominent duringthefirst6h(12.75%).Afterthis time,thedryweight graduallydiminished,withtheexceptionofT5days,whenan increasewasobtainedrelativetotheearlierandlatertimes. AtT8,whichrepresentstendaysofdecomposition,therewas adecreaseof22.38%indryweight(Fig.1).Thedecomposition exponentialrate(k)ofthemacrophyteE.azureawas0.0025d−1. According to the results, it can be presumed that the decomposition rate is inversely proportional to time, thus showingthatthe decompositionisgreatestinthefirst few hoursandslowsinthefollowinghours.

Abioticanalyzes

Theabioticparameters(Table1)differedbetweenthe treat-mentandcontrolgroups.

Theparametersanalyzedwere pH, conductivity(Cond.), dissolvedoxygen(DO),watertemperature(TH2O),ammonia

(NH4),solublephosphorus(P-PO43−),TPandDOC.

Table1–Limnologicaldataforcontrolandtreatmentduringexperimentsamplingtime(T=treatment;C=control;DOin mgL−1;conductivityin␮Scm−1;NH4,P-PO43−,TPandDOCin␮gL−1).

pH Cond. DO TH2O NH4 P-PO4 TP DOC

T C T C T C T C T C T C. T C T C T0–0h 7.2 7.24 72.5 72.5 6.74 6.93 22.4 22.4 48 48 13.5 13.5 24.77 24.7 3.86 3.86 T1–3h 6.88 7.28 214 70.73 4.9 6.69 25.1 25.7 32.9 49.58 351.7 9.64 440.4 33.6 8.76 4 T2–6h 6.9 7.18 216.6 67.83 4.45 6.32 27.1 27.8 53.8 34.29 575.9 10.6 657.7 49.4 11.3 4.33 T3–12h 6.54 7.17 215.6 72.4 2.98 3.86 28.9 29.4 26.9 28.31 471.5 12.5 596.8 36.2 18.7 5.76 T4–24h 6.23 7.28 222.5 69.13 0.59 4.51 28.6 28.8 30.0 16.88 47.34 9.64 710.1 44.7 23.4 5.2 T5–72h 6.57 7.15 233.6 72.8 1.48 4.31 24.93 25.5 78.8 16.36 344.5 4.72 661.4 45.6 14.0 5.46 T6–120h 6.64 7.29 218 74.2 1.71 4.12 27.86 27.6 55.3 19.26 235.6 5.2 623.9 59.6 33.9 5.16 T7–168h 6.76 7.24 249.5 77.96 2.61 4.18 26.6 26.4 42.4 8.97 415.4 3.78 695.2 75.8 25.9 5 T8–240h 7 7.36 308 76.6 2.42 4.25 27.6 27.3 55.3 21.89 453.2 6.4 845. 63.3 36.5 4.76

Table2–ANCOVAresultsfordifferencebetweencontrolandtreatment.

pH Cond. OD NH4 PO4 Ptotal COD

ANCOVA results

F=51.50,p<0.05 F=244.9,p<0.05 F=37.9,p<0.05 F=16.3,p<0.05 F=118.6,p<0.05 F=144.1,p<0.05 F=75.2,p<0.05

Table3–ANCOVAresultsfordifferencebetweentemporaldata.

pH Cond. OD NH4 PO4 Ptotal COD

ANCOVA results

F=0.657,p<0.05 F=62.42,p<0.05 F=52.71,p<0.05 F=3.04,p<0.05 F=3.43,p<0.05 F=7.71,p<0.05 F=24.10,p<0.05

Inalloftheabioticparametersstudied,therewere signif-icantdifferencesbetweenthecontrolandtreatmentgroups

(Table2).All parametersalsosignificantlyvariedwithtime

(Table3).

Effectofdecompositioninthelimnologicalparameters

ThedryweightofE.azureahadasignificantnegative corre-lationwithDOC(r=−0.73,p<0.0001),conductivity(r=−0.73,

p<0.0001) and P-PO43− (r=−0.70, p<0.0001) and a positive

correlation with DO (r=0.80, p<0.0001).This suggests that thedynamicsofthesefactorsarecloselyassociatedwiththe decompositionprocess.

Bacterialdensityandbiomass

The total bacterial density ranged from 4.76×107 _to

9.31×107_cellsmL−1 _{in the controlgroup. In the treatment}

group,thehighestdensityreached2.95×108_cellsmL−1_atthe

endoftheexperiment(Fig.2).

Thedensityvariationinthecontrolgrouphadadynamic verysimilartothesigmoidcurve.Inthetreatmentgroupthere wasacontinuousincreaseindensity,exceptforadecrease thatoccurredwithin72h.Atthissampling time,therewas alsoadecreaseinDOC(14.0␮g/L).

TheANCOVAcovariancetestshowedasignificant differ-ence indensity betweenthe controland treatmentgroups (F=20.33p<0.05). 0 3 6 12 24 48 72 96 120 144 168 192 216 240 Time (h) –1E8 0 1E8 2E8 3E8 4E8 5E8 6E8

Bacterial density (cel/mL)

Control Treatment

Fig.2–Meanvaluesandstandarddeviationofdensityin controlandtreatment.

In the control group (Fig. 3A), the rod density started at 2.95×107_cellsmL−1_{, reaching its highest value in 12h}

(5.16×107_cellsmL−1_{)andthendecreasinguntilreachingits}

lowestdensity at240h(2.4×107_cellsmL−1_).Thecocci

den-sity was the highest at 120h (2.78×107_cellsmL−1_{), and}

then,itdecreasedat168h(2.7×107_and1.61×107_cellsmL−1_,

respectively). Thevibriosdensity increasedwiththe samp-ling times, starting with 6.5×106_cellsmL−1 _{and reaching}

4.03×107_cellsmL−1_{atthefinaltimepoint.Thespirillumwere}

onlyobservedatthelastsamplingtimepoint,withalow den-sityof6×105_cellsmL−1_.

Inthetreatmentgroup(Fig.3B),thebacillusdensity,except fortime T7(8×107_cellsmL−1_{), increasedwithtime,}

reach-ingitshighestvalueatT8(1.26×108_cellsmL−1_).Thehighest

coccidensitywasobtainedat120h(4.66×107_cellsmL−1_),and

itslowestdensitywasat240h(6.83×106_cellsmL−1_).The

vib-rioshaditshighestdensityat240h(4.36×107_cellsmL−1_).The

spirillumwereonlyobservedatthelastfoursamplingtime points, with adensity ranging from 1.66×105_cellsmL−1 _at

120hto2.16×106_cellsmL−1_at240h.

Thebiomassvalueswerehigherinthetreatmentthanin thecontrolgroup(Fig.3A).Inthecontrolgroup,thebiomass attimeT0ofthe experimentwas0.11mgCL−1,reachingits maximumvalueof0.22mgCL−1at24and168h.Inthe treat-ment group, the biomass values were crescent, except at 72 and 168h (0.8 and 1.81mgCL−1, respectively), reaching 2.78mgCL−1bytheendoftheexperiment.

TheANOVAcovariancetestshowedasignificantdifference betweenthebiomassobtainedinthecontrolandtreatment groups(F=59.61,p<0.05).

Abioticandbioticparameters

The parametersDO, NH4+, P-PO43−, PT and DOC were

sig-nificantlyassociatedwiththebacterialdensityandbiomass duringtheexperimentalcontrolgroup.Inthetreatmentgroup, theparametersconductivity,PTandDOCweresignificantly associated with biomass and density, while DO was only associated withbiomass(Table 4). Both inthe control and inthetreatmentgroups,thestrongestassociationsbetween biomassanddensitywerewithDOC.

Multiple linear regression analyses were calculated to developapredictionmodelforbacterialdensityinthe con-trolgroup.TheanalysissuggestedthatDOandDOCcompose themodel(N=27,F(5,21)=152.23,p<0.0001),withanR2=0.97.

0.00E+00 1.00E+07 2.00E+07 3.00E+07 4.00E+07 5.00E+07 6.00E+07 7.00E+07 8.00E+07 9.00E+07 1.00E+08 1.10E+08 1.20E+08 1.30E+08 1.40E+08 T0 T1 T2 T3 T4 T5 T6 T7 T8 Density (cells mL –1 ) Sampling time Control 0.00E+00 1.00E+07 2.00E+07 3.00E+07 4.00E+07 5.00E+07 6.00E+07 7.00E+07 8.00E+07 9.00E+07 1.00E+08 1.10E+08 1.20E+08 1.30E+08 1.40E+08 T0 T1 T2 T3 T4 T5 T6 T7 T8 Density (cells mL –1) Sampling time Treatment

A

B

Bacillus Coccus Vibrio Spirilum

Fig.3–Densityvalueofeachbacterialmorphotypeobservedincontrol(A)andtreatment(B).

The model predictions are represented by the equation below:

Bacterialdensityincontrol=31829+6047DO+195937DOC ThebacterialdensityinthetreatmentgrouponlyhadDOC asasignificantparameterintheregressionanalysis(N=27,

F(1,25)=26.734,p<0.00002),withR2=0.93.Themodel

predic-tionsarerepresentedbytheequationbelow:

Bacterialbiomassincontrol=6545×104+5876433DOC Thebacterialbiomassinthecontrolgrouphad onlyDO andDOCassignificantparametersinthemultipleregression analysis(N=27,F(2,24)=138.94,p<0.000001),withR2=0.9204.

Themodelpredictionsarerepresentedbytheequationbelow:

Bacterialdensityintreatment=0.01348+0.002303DO +0.045362DOC

Thebacterial biomassin the treatmentgroup was only significant with COD in the analysis of multiple linear regression(N=27,F(1,25)=440.56,p<0.000001)withR2=0.9463.

The model predictions are represented by the equation below:

Bacterialbiomassintreatment=0.021822+0.079539DOC

Discussion

E.azureadecomposition

Dissolved and particulate organic detritus decomposition influencestheenergyandmaterialflowsinlakeecosystems.23

Thegradualbiomasslosswasmostintenseinthe early timepointsandslowedovertime.Thebiomasslossdynamics areassociatedwithwater-solublecompoundrelease,which is most intensein the early hours.39 _{The weight loss and}

changesinthechemicalcompositionoftheE.azureadebrisare affectedbythesechemicalcharacteristicsatthebeginningof thedecompositionprocessandbywhere,geographically,the processoccurs.30

The k value for this study was 0.023d−1. The average decomposition coefficient of E. azurea leaves proposed by PetersenandCumminswas0.0033d−1.40_{Thiscoefficientwas}

classifiedas“slow”bythesameauthors.Inthedecomposition studyofE.azureaperformedbyPadialandThomaz,30_thekfor

sevendayswas0.010d−1.Thesevaluesshowthatthereis vari-ationinthedecompositionrate,evenwithinthesamespecies. Thisvariationisinfluencedbythechemicalandphysical con-ditionsoftheenvironments,themicrobialdiversity,andthe chemicalcompositionofthedebris.22,30_{Studiesinareas}

sub-jecttofloodinghaveshownthatthedecompositionrateisalso affectedbythetypeofenvironment(loticorlentic)andwater quality.30

Table4–ResultsofPearson’scorrelationtestbetweenbacterialdensityandbiomasswithlimnologicalparametersin controlandtreatment.

Density Biomass

Control Treatment Control Treatment

pH p=0.77;r=0.586 p=0.9633;r=0.009 p=0.8512;r=0.037 p=0.076;r=−0.1316 Cond. p=0.09;r=0.3294 p=0.0212;r=−0.441 p=0.067;r=−0.356 p=0.048;r=0.3832 DO p<0.0001;r=0.88 p=0.067;r=−0.3565 p<0.0001;r=−0.91 p=0.0019;r=−0.568 NH4+ p=0.0005;r=−0.624 p=0.8214;r=−0.64 p=0.0003;r=−0.64 p=0.3242;r=0.1972 PPO43− p=0.034;r=−0.407 p=0.2706;r=0.216 p=0009;r=−0.492 p=0.5357;r=0.1246 TP p=0.044;r=0.3894 p=0.014;r=0.4654 p=0.014;r=0.463 p=0.0112;r=0.4803 DOC p<0.0001;r=0.957 p<0.0001;r=0.8231 p<0.0001;r=0.9024 p<0.0001;r=0.9728

Thedecomposition process still influences the physical andchemicalcharacteristics ofwater.30_{Thismostlyoccurs}

intropicalregions,wheretheriversystemfloodplainsshow markedtemporal variationinphysical,chemicaland biotic factors. Such variations are, mainly, related to changes in hydrometriclevels,whichhavebeenattributedtothe‘flood pulses’theory.41_{Thedryweightlossinthisstudywas}

corre-latedwithDOCandPT.AccordingtoSridharandBarlocher,42

high nutrient concentrations are correlated with increased submerged biomass decay rates. One of the factors that influences the degradation of this material is its nutrient concentration.43_{Thereisapositiverelationshipbetweenthe}

decompositionrateofplantsandthephosphorus concentra-tionoftheirtissues.Total phosphorusisthebest indicator ofthenutrientcontentinanyecosystem.Thismayexplain thesignificantcorrelationbetweenphosphorusandthedry weightofE.azurea.44

Bacterialdensityandbiomass

Themicroorganisms found in anaquatic environment are determined by the physical and chemical conditions that prevailinthatenvironment.45_{Theyqualitativelyand}

quanti-tativelyvaryforlongperiodsoronashorttimescale.46_Thus,

temporalvariationsaffectthepopulationecologyandmodify thestructureandfunctionofthemicrobialcommunities.29

Generally, the bacteria density ranges between 105 _and

108_cellsmL−1_{andcanincreasewiththeenvironmenttrophic}

status.47 _{Inthis study,the bacterialdensitywas 10}7 _inthe

controlgroup,reaching108 _{inthetreatmentgroup.Teixeira}

et al.,48 _{working in similar environments, found}

bacteri-oplankton densitiesvarying between 1.3±0.7×109_cellsL−1

and22.0±4.7×109cellsL−1.Theincreaseofdensitywiththe

E.azureabiomasscansimulatetheeffectofthetypical nutri-tional contribution of the flood pulse process when large amountsofbiomassdecompose,causinganincreasein nutri-entcycling.Inourexperiment,weobservedanincreaseinthe densityandbacterialbiomassduringthedecomposition pro-cess.StudiesperformedintheParanaRiverdeterminedthe bacterialabundanceandproductionratesinthe lowwater period,butthedifferenceswerenotsignificantinthestudies intheParanaRiver, whilethebacterialabundancewas sig-nificantly higherduringtheflood.32 _{Previousstudies found}

greaterbacterialabundancesandproductionratesinthelow waterperiod,butthedifferenceswerenotsignificant.20,49

Gene expression can provide valuable information regarding both structural and functional bacterial popu-lationsinaquaticecosystems.50,51_{Ingeneral,smallbacteria}

(cocci) inhabit waters with low nutrient concentrations. Larger bacteria (bacillus, vibrios and spirillum) are more commoninenrichedenvironmentswithorganicmatter.52,53

Thus,thebacterialpopulationcompositionmaybemodified accordingtothetrophiclevel.54

Thecoccus density wasincreasedduring thetreatment until T6, decreasing thereafter. Concomitantly, the density ofthevibriosandspirilosincreasedafterT6.Becausethese bacteria have a spherical cell shape, environments with a greateravailabilityofresourcesfacilitatethemetabolic activ-ity ofthese organisms with regard to both the amount of nutrientsavailableintheenvironmentandtheabilitytodrive

nutrientsintothecells.55_{However,factorssuchas}

competi-tionmaylimitresources,thuscontrollingorganismdensity.

Abioticfactorsandbacterialbiomassdensity

Bacterioplankton growth is dependent on the availability of inorganicnutrients. Thechange inthe concentrationof nutrients can have direct and indirect effects on bacterial growth.56,57_{Theelectricalconductivitywassignificantly}

cor-related withthe density and bacterialbiomassonlyin the treatmentgroup.AccordingtoEsteves,44_{conductivityvalues}

arerelatedtothetrophicstateofthewater.Thus,increased nutrientconcentrationsfromthedecompositionand subse-quentreleaseofionsaffectedtheconductivity.

In this experiment, there was a negative correlation betweenbiomassandDOinthecontrolandtreatmentgroups. Thisindicatesthemicroorganisms’actioninorganicmatter decompositionandtheformationofanoxiczones.

TheP-PO43− was positivelycorrelated with thebiomass

andbacterialdensityonlyinthecontrolgroup.Inother stud-ies,this correlationhasalsobeenfound.58 _{Thisresultmay}

representabottom-upcontrolofthebacterialcommunity20,59

because phosphorus is an essential nutrient for bacterial growthandisoftenlimitedintheenvironment.60_Thisconcept

ismostevidentbythefactthatthiscorrelationoccurredonly inthecontrolgroup,wheretheP-PO43−decreasedwithtime,

suggestingitsassimilationbythebacterialcommunity. There-fore,inthetreatmentgroup,wherenutrientsweresupplied fromE.azureadecomposition,theP-PO43−wasnotalimiting

factorofbacterialgrowth.

Usingmultiplelinearregressionanalysis,weverifiedthat the DO and DOC are the major variables that explained bacterial dynamics. The DOC strongly affects the bacterial dynamics,astheheterotrophicplanktonicbacteriaare asso-ciatedwiththecarbonmetabolisminapelagicenvironment.1

Agreatpartofthedissolvedorganicmattercanbeconsumed bytheseorganisms.47,61_{AstheDOCisgenerallyregardedasa}

growthlimitingfactorofbacterioplankton,62,63_astrong

asso-ciationbetweenbacterioplanktonandDOClikelyoccurs. Despiteitsecologicalimportance,knowledgeregardingthe diversityofaquaticenvironmentsandthefactorsthat con-trolthefreshwaterbacterioplanktoncompositionisfarfrom complete.49

Conflicts

of

interest

Theauthorhasnoconflictsofinteresttodeclare.

Acknowledgements

We thank Maria do Carmo Roberto (Maringá State Uni-versity) for field assistance and nutrients analysis, CAPES (Coordenac¸ãodeAperfeic¸oamentodePessoaldeNível Supe-rior)scholarshipsandCNPq(BrazilianCouncilofResearch). This study was alsosupportedby the “Long-Term Ecologi-cal Research”(LTER)programofCNPq(BrazilianCouncilof Research).

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