Impact of water quality on bacterioplankton assemblage along C'ertima River Basin (central western Portugal) assessed by PCR--DGGE and multivariate analysis

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Environmental Monitoring andAssessmentAn International Journal Devoted toProgress in the Use of Monitoring Datain Assessing Environmental Risks toMan and the Environment ISSN 0167-6369Volume 184Number 1 Environ Monit Assess (2012)184:471-485DOI 10.1007/s10661-011-1981-2

Impact of water quality onbacterioplankton assemblage alongCértima River Basin (central westernPortugal) assessed by PCR–DGGE andmultivariate analysisDaniela R. de Figueiredo, RaquelV. Ferreira, Mário Cerqueira, TeresaCondesso de Melo, Mário J. Pereira,Bruno B. Castro & António Correia

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Environ Monit Assess (2012) 184:471–485DOI 10.1007/s10661-011-1981-2

Impact of water quality on bacterioplankton assemblagealong Cértima River Basin (central western Portugal)assessed by PCR–DGGE and multivariate analysis

Daniela R. de Figueiredo · Raquel V. Ferreira · Mário Cerqueira ·Teresa Condesso de Melo · Mário J. Pereira ·Bruno B. Castro · António Correia

Received: 16 April 2010 / Accepted: 24 February 2011 / Published online: 24 March 2011© Springer Science+Business Media B.V. 2011

Abstract The information on bacterial commu-nity composition (BCC) in Portuguese water bod-ies is very scarce. Cértima River (central westernPortugal) is known to have high levels of pol-lution, namely organic. In the present work, theBCC from a set of 16 water samples collected fromCértima River Basin and its main tributaries wascharacterized using 16S rDNA–denaturing gra-dient gel electrophoresis, a culture-independentmolecular approach. Molecular data were related

D. R. de Figueiredo (B) · R. V. Ferreira ·M. Cerqueira · M. J. Pereira · B. B. Castro ·A. CorreiaCESAM (Centre for Marine and EnvironmentalStudies), University of Aveiro, 3810-193Aveiro, Portugale-mail: [email protected]

D. R. de Figueiredo · M. J. Pereira · B. B. Castro ·A. CorreiaDepartment of Biology, University of Aveiro,3810-193 Aveiro, Portugal

R. V. Ferreira · M. CerqueiraDepartment of Environment and Planning,University of Aveiro, 3810-193 Aveiro, Portugal

T. C. de MeloCVRM/Geo-Systems Centre—Instituto SuperiorTécnico, Av. Rovisco Pais, 1049-001Lisbon, Portugal

to environmental parameters through multivari-ate analysis to investigate potential impact of wa-ter pollution along the river. Principal componentanalysis using environmental data showed a waterquality gradient from more pristine waters (at themountain tributaries) to waters with increasinglyeutrophic potential (such as Fermentelos Lake).This gradient was mainly defined by factors suchas organic and inorganic nutrient sources, electri-cal conductivity, hydrogen carbonate concentra-tion, and pH. Molecular results showed variationsin BCC along Cértima River Basin but in the mainriver section, a Bacteroidetes phylotype (Flavobac-terium sp.) proved to be dominant throughoutthe river course. Multivariate analysis suggeststhat spatial variation of BCC along the CértimaRiver Basin depended mainly on parameters suchas Chl a, total suspended solid (TSS), total or-ganic carbon, electrical conductivity, and HCO−

3levels. Bacteroidetes phylotypes were all relatedto higher electrical conductivity and HCO−

3levels although some of these were also corre-lated with high SO2−

4 and others with high solu-ble reactive phosphorus, nitrate, TN, and Kjeld-Nlevels. The Gammaproteobacteria occurrence wascorrelated with high SO2−

4 levels. One of the Be-taproteobacteria phylotypes showed to correlatewith low redox potential (Eh) and high temper-ature, pH, TSS, and Chl a levels while anotherone showed a negative correlation with Chl avalues.

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Keywords Riverine bacterioplankton diversity ·16S rDNA–DGGE · Organic pollution ·Nutrients · CCA analysis

Introduction

The impact of natural (climatic change) and an-thropogenic (industrial and domestic effluents)stressors over freshwaters has led to the increaseof water pollution worldwide and the enhance-ment of the eutrophication process (Ducharneet al. 2007; Tong et al. 2007). This has ma-jor impacts on the aquatic communities such asthe bacterioplankton (Paerl et al. 2003; Hall andCotner 2007; Zeng et al. 2009), which may endan-ger the quality and safety of water used for humanpurposes (de Figueiredo et al. 2004; Zaitlin andWatson 2006).

Cértima River (central western Portugal) is anexcellent case study. In spite of the effort for im-plementation of wastewater management plans,Cértima River is still suffering from consider-able pollution levels due to inputs from domesticwastewater, runoffs from agriculture fertilizers,and effluents from industry and animal farming(Cerqueira et al. 2005; Ferreira 2007). The pres-ence of high levels of contamination, not onlyfrom organic and inorganic sources of nutrientsbut also from pesticides used in agriculture andheavy metals from industrial activity, has beenreported over the last two decades (Rino and Gil1987; Calado 1990; Calado et al. 1991; Pereira1999; Almeida 2001; Teles et al. 2007). Althoughthe Cértima River Basin has been a topic forimportant investigation on phytoplankton occur-rence over the past 20 years (Rino and Gil 1987;Almeida 2001; Calado et al. 2005), studies on itsbacterioplankton diversity are very scarce and fo-cused on Fermentelos Lake (located downstreamCértima River; de Figueiredo et al. 2007, 2010).

The main purpose of the present study wasto assess the bacterioplankton diversity shiftsalong the Cértima River Basin, using the culture-independent molecular methodology 16S rDNAPCR–denaturing gradient gel electrophoresis(DGGE; Muyzer et al. 1993; Lyautey et al. 2005).The impact of the water physical and chemical

parameters on spatial BCC diversity was investi-gated through multivariate analysis.

Materials and methods

Sampling

The Cértima River is a relatively small river—approximately 43 km long—and a tributary ofÁgueda River. The river source is at the Buçaco(or Bussaco) Mountain (central Portugal), and theriver mouth is an enlargement area (FermentelosLake) of about 5 km2. The main tributaries in-clude Serra and Levira Rivers and Ribeira doPano. The Cértima River Basin is markedly im-pacted by agriculture but also industrial activ-ity and domestic discharges (Rino and Gil 1987;Cerqueira et al. 2005). Geomorphologically, thisbasin shows heterogeneity between sandy low-lands with altitudes ranging from 8 to 70 m andhighlands (on the eastern margin of the mainriver course) where altitudes are always above200 m and characterized by marked relief andcliffs (Pinho et al. 1988). The sampling sites weredetermined based on previous published studiesabout the Cértima River Basin (Cerqueira et al.2005). Their codes, location, and description arepresented in Fig. 1 and Table 1. In late May2007, during three consecutive days, the sampleswere taken sub-superficially at about 1 m fromthe shore using sterile bottles and assuring thatsediment was not collected. Samples were placedat 4◦C in the dark until further treatment within12 h after collection. Table 1 shows the resultsfor hydrogeochemical variables (Ferreira 2007)which were determined according to standard pro-cedures (APHA 1995).

DNA extraction and PCR amplification ofbacterial 16S rDNA fragments

Total DNA was extracted from water samplesafter filtering 100 to 200 mL (depending on thewater transparency) through 0.22 μm polycarbon-ate sterile filters; cells and particles retained onthe filter were resuspended in 2 mL of TE buffer(10 mM Tris HCl, 1 mM EDTA, pH 8.0) andthen centrifuged. After resuspension in 200 μL

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Environ Monit Assess (2012) 184:471–485 473

Fig. 1 Location of sampling sites along the Cértima River Basin (see sample codes in Table 1)

of TE, lysozyme was added and incubation wasperformed at 37◦C for 1 h. The subsequent DNAextraction and purification steps were carried outusing the Genomic DNA Purification Kit (MBIFermentas, Vilnius, Lithuania). DNA was finallysuspended in TE buffer and stored at −20◦C.The 16S rRNA gene fragments for DGGE analy-sis were amplified using the universal primersfor bacteria 338F-GC/518R (Muyzer et al. 1993).Primers were synthesized by STABVida (Oeiras,Portugal). PCRs were performed in a Bio-RadiCycler Thermal Cycler (Bio-Rad Laboratories,Hercules, CA, USA) with 50 μL reaction mixtureseach containing 3 mM MgCl2, 200 μM of eachnucleotide, 1× PCR buffer with (NH4)2SO4, 5%dimethylsulfoxide, 15 pmol of each primer, 1 Uof Taq DNA polymerase, and 50–200 ng template

DNA. The PCR program had an initial denatura-tion step at 94◦C for 5 min, followed by 30 cyclesof 30 s at 92◦C, 30 s at 55◦C, and 30 s at 72◦Cand a final extension step at 72◦C for 30 min.Negative control reactions without template DNAwere performed simultaneously. The quality ofthe resulting PCR amplicons was confirmed byelectrophoresis in 1.5% agarose gels using a mole-cular weight marker (GeneRuler™ 1 kb DNAladder), after staining with ethidium bromide andvisualization on a UV transilluminator.

Denaturing gradient gel electrophoresis

PCR products were analyzed through DGGE, us-ing a 35–60% denaturing gradient (100% dena-turing gradient is 7 M urea and 40% deionized

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474 Environ Monit Assess (2012) 184:471–485

Tab

le1

Cha

ract

eriz

atio

nan

den

viro

nmen

tald

ata

reco

rded

for

Cér

tim

aR

iver

Bas

insa

mpl

esin

May

2007

(whe

rem

axim

aan

dm

inim

ava

lues

are

inbo

ld)

Sam

ple

Sam

plin

gsi

teO

rigi

nal

pHW

ater

Con

d.E

hC

hla

O2

Dis

s.T

OC

BO

D5

TSS

SO2− 4

HC

O− 3

NO

− 2N

O− 3

NH

+ 4K

jeld

TN

Org

SRP

code

loca

tion

wat

erbo

dyte

mp

( μS

cm−1

)(μ

gL

−1)

sat.

O2

-N-N

-N-N

-N

(◦C

)(%

)(m

gL

−1)

C2

Póv

oado

Lou

reir

oSt

.Cri

stin

a7.

4518

.817

535

23.

091

8.5

2.3

0.75

1.2

1448

<0.

10.

790.

040.

291.

080.

25<

0.01

(Vac

ariç

a,M

ealh

ada)

stre

amC

3V

iado

res

(Pam

pilh

osa,

Can

edo

7.42

20.0

441

324

5.1

948.

65.

62.

507.

040

111

<0.

12.

371.

091.

674.

040.

58<

0.01

Mea

lhad

a)st

ream

C5

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oaSe

ca(A

ntes

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ma

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er7.

3920

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034

33.

466

5.9

8.1

12.2

014

.840

230

<0.

111

.310

.45

12.3

823

.68

1.93

1.30

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lhad

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uso,

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iver

7.20

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132

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989.

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60.

671.

014

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0.1

0.61

0.07

0.30

0.91

0.23

0.03

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lhad

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8P

onte

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tosa

doP

onte

Riv

er7.

3917

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739

42.

291

8.8

6.7

0.91

4.3

2693

<0.

16.

090.

070.

696.

780.

62<

0.01

Bai

rro,

Mea

lhad

a)C

9C

uria

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engo

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ma

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5019

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836

22.

278

7.2

5.0

5.96

5.9

5518

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4.88

1.50

2.16

7.04

0.66

0.26

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dia)

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611

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120

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0.68

1.30

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0.62

0.07

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dia)

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ores

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ma

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5718

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232

611

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910

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165.

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185

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780.

53A

nadi

a)C

16Sã

oJo

ãoda

Aze

nha

Cér

tim

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iver

7.62

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650

335

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2.73

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116

170

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324.

950.

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0.01

(San

galh

os,A

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430.

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0.01

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rô,

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tim

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iver

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18.9

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eda)

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orei

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ira

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eira

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eda)

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tern

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los

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509

318

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ra,Á

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dle

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tern

lake

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aio,

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men

telo

s8.

1221

.650

330

170

.093

8.2

6.2

4.23

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6495

0.2

1.80

0.03

1.08

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1.05

<0.

01R

eque

ixo,

Ave

iro)

Lak

e

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Environ Monit Assess (2012) 184:471–485 475

formamide) in 1 mm vertical polyacrylamide gels(8% (wt/vol) acrylamide in 0.5× TAE buffer).Electrophoresis was performed in a DCode™ uni-versal mutation detection system (Bio-Rad Lab-oratories, Hercules, CA, USA) using 0.5× TAEbuffer containing 20 mM Tris, 10 mM acetic acid,and 0.5 mM EDTA (pH 8.0) during 16 h at 75 V,with an initial step at 20 V for 15 min. The gelwas then stained for 5 min in an ethidium bromidesolution (5%) and then gently distained with ag-itation in distilled water for 15 min before imagedigitalization in a Molecular Imager FX™ system(Bio-Rad Laboratories, Hercules, CA, USA). Themost intense bands from DGGE profiles wereaseptically excised from the gel into 1.5 mL Ep-pendorf tubes and washed in 10 μL of sterilemilli-Q-purified water, from which 5 μL of theeluted DNA was used for PCR amplification withthe original primer pair. The isolation and iden-tity of each DNA band was confirmed throughDGGE, and if necessary, the extraction proce-dure was repeated until the targeted band wasclearly isolated. Each band was then cloned us-ing the TA cloning kit from Invitrogen. Prior tocloning, an A tailing for PCR products was per-formed according to manufacturers’ instructions.The migration point of each cloned sequence waschecked through DGGE after PCR amplificationwith 338F-GC/518R.

Sequencing, nucleotide sequence accessionnumbers, and phylogenetic analysis

The nucleotide sequence of the cloned DGGEbands was made taking advantage of the vec-tor primers M13R/T7. The sequences determinedwere deposited in the GenBank database underthe accession numbers GU908476 to GU908486.A BLAST search (http://www.ncbi.nlm.nih.gov)was used to explore similarity against sequencesdeposited in the GenBank database. The se-quences’ alignment was carried out using theCLUSTAL X software version 1.8 (Thompsonet al. 1994). A phylogenetic tree of the 16S rDNAgene fragments was built using the neighbor-joining method (Saitou and Nei 1987). Bootstrapanalyses were based on 1,000 replicates. TreeView

version 1.6.6 (Page 1996) was used to display thetrees.

Multivariate analysis

The distribution of samples according to en-vironmental parameters was assessed throughprincipal component analysis (PCA) after stan-dardization of environmental data (by subtractingthe mean from each observation and dividing bythe corresponding standard deviation). A clusteranalysis of samples according to environmentalparameters was executed using the unweightedpair group method with mathematical averages(UPGMA). The dendrogram was created withthe similarities calculated using the Pearson cor-relation coefficient (95% probability) and thePRIMER 6 software (Clarke and Gorley 2006).Pearson’s correlation coefficient was also used toassess relationships between environmental para-meters and phylotype occurrence and band in-tensity. The DGGE profiles were analyzed usingthe Diversity Database™ Fingerprinting software(Bio-Rad Laboratories, Hercules, CA, USA), andbands with a relative intensity of less than 0.5%in each lane were not considered for statisticalanalyses.

For DGGE data, the presence or absence of co-migration points was converted to a binary matrix(0/1), and cluster analysis was performed usingalso UPGMA but based on the Bray–Curtis sim-ilarity coefficient. Co-migration points of DGGEprofiles were also used to build a matrix based onthe relative band intensity in each lane after logtransformation. Canonical correspondence analy-sis (CCA; ter Braak and Verdonschot 1995; terBraak 1995) was performed with CANOCO 4.5software (Scientia Software) to extract relation-ships between the distribution of the dominantphylotypes and environmental variables. We usedan a priori forward selection of significant envi-ronmental parameters (P < 0.05) using a MonteCarlo permutation test (499 unrestricted permuta-tions). Environmental data were standardized (asabove) to reduce the relative influence of scale.The relation between bacterial phylotype data andexplanatory variables (reduced model) was testedwith a Monte Carlo unrestricted permutation test.

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I - Easterntributaries

IIIa - Westerntributaries

IIb - FermentelosLake

IV - Main Riverdownstream

IIa - Main Riverupstream

IIIb - Main Rivermiddlestream

-0.6 1.0

-0.8

1.0

pH

Temp

Conductivity

Eh

dissO2

%O2

TOC

BOD

TSS

Kjeld-N

Org-NNO2--N

HCO3-

SO42-

TN

Chl a

C2

C3

C5

C6C8

C9

C13C14

C16

C18

C19

C22

C23

C25

C26

C28

NO3--NNH4

+-N

SRP

I

IIb

IV

III

a

b

Fig. 2 a Cluster dendrogram and b PCA ordination biplot of Cértima River Basin samples according to environmentalparameters recorded in May 2007 (see sample codes in Table 1)

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Environ Monit Assess (2012) 184:471–485 477

Results

Environmental parameters

The environmental parameters recorded for eachsample are summarized in Table 1. The clusteranalysis of samples according to these parameters(Fig. 2a) resulted in two main clusters (negativelycorrelated) which showed to be related to inor-ganic nutrient sources and organic pollution alongwith electrical conductivity and HCO−

3 levels asshown in the PCA (Fig. 2b). This suggests trophicand mineralization gradients, the latter relatedto the geological features of the sampled loca-tions. Cluster I included samples from the easternmountain tributaries (C2, C6, C13, and C18), withthe lowest HCO−

3 concentration and the highestwater quality (lowest electrical conductivity, to-tal organic carbon (TOC), total suspended solid(TSS), SO2−

4 , and low nitrate, org-N, and Chl a),and clusters II included the upstream sample C3plus Fermentelos Lake samples (C25, C26, andC28), which, in spite of the high pH and Chl a lev-els, have relatively low nitrate and HCO−

3 concen-trations. This cluster had intermediate electricalconductivity levels between the high water qualitysampling sites (cluster I) and clusters III and IV.Clusters III included samples from the Westernsandy lowland tributaries (C8 and C22) and themiddle river section samples C5 and C9, all char-acterized by the highest nitrate concentrationsand high electrical conductivity levels; cluster IVincluded samples from downstream sites beforereaching Fermentelos Lake (C14, C16, C19, andC23), and they were mainly characterized by thehighest electrical conductivity and SO2−

4 levelsalong with high HCO−

3 and nitrate concentrations.The PCA biplot puts in evidence this gradient

(Fig. 2b): Samples C2, C6, and C13 (with thehighest water quality) appear on the negative sideof the first axis, and sample C5 (characterizedby a strong organic charge) appears on the posi-tive side. This axis showed to be mainly definedby organic pollutants, electrical conductivity, andHCO−

3 levels, but also inorganic nitrogen sources(Fig. 2b). The second axis was mostly related topH, Chl a, water temperature, TSS, and oxygenlevels; this led to a clear separation between ex-treme samples C25 (on the positive side) and C5

(on the negative side). The first two axes of thePCA accounted for 67% of the total variance ofsamples distribution.

DGGE band patterns and CCA analyses

A total of 299 bands could be detected in theDGGE profiles obtained for Cértima River Basinsamples (Fig. 3) corresponding to 56 differentband migration points. The number of bands persample showed an average of 19 ± 4 (n = 16).DGGE band patterns showed variability betweenthe bacterial assemblages along the Cértima RiverBasin, although strong common bands could bedetected among samples belonging to the mainriver section (see Fig. 3). Band 28 was ubiquitousin all samples while unique phylotypes were de-tected at C3 (bands 4 and 39), C6 (bands 1 and 2),C8 (band 7), C13 (band 3), C14 (bands 6 and 19),C18 (band 52), C22 (band 40), C25 (band 16), andC28 (band 20). Interestingly, in spite of the spatial

C2

C3

C5

C9

C14

C16

C19

C23

C22

C25

C18

C26

C28

C13

C8

C6

1

234

56711 9

8

1213

141516

1819 2022 24

2526 27

28 29 313233343536

373839 40 4142 434547 46

48495051

52

53 55

5657 5859

60

2321

30

44

****** *

Fig. 3 DGGE 16S rDNA band profiles for samples ob-tained along Cértima River Basin in May 2007. The codeabove each lane refers to each sample (see Table 1), and thebands numbering corresponds to the different migrationpositions considered for the analyses

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478 Environ Monit Assess (2012) 184:471–485

I - Eastern tributaries

IV - FermentelosLake

III - Main Riversection

II – Other tributaries

-0.6 1.0

-1.0

1.0

123

4

5

6

8

9

11

12

13

14

15

16

19

20

2122

23

25

26

28

29

30

3132

33

34

35

3637

38

39

40

41

42

43

44

45

46

47 48

49

5051

52

53

55

56

57

5859

60

C2

C3

C5

C6

C8

C9

C13

C14

C16

C18

C19

C22

C23

C25

C26

C28

HCO3-

Conductivity

TOCTSS

Chl a

III

IV

II

I

a

b

Fig. 4 a Dendrogram and b CCA ordination triplot ofCértima River DGGE band patterns according to the envi-ronmental parameters recorded during May 2007. DGGE

bands numbering and samples coding are described inFig. 2 and Table 1, respectively

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Environ Monit Assess (2012) 184:471–485 479

Tab

le2

Sam

ple,

acce

ssio

nnu

mbe

r,cl

oses

tre

lati

ve(a

fter

aB

LA

STse

arch

),an

dco

rres

pond

ing

perc

enta

gesi

mila

rity

for

the

16S

rDN

Aba

cter

ial

part

ial

sequ

ence

sfr

omex

cise

dba

nds

Sam

ple

Ban

dN

CB

IP

hylo

gene

tic

affi

liati

onC

lose

stre

lati

ves

(acc

essi

onno

.)O

rigi

nP

erce

ntag

eac

cess

ion

no.

(%)

sim

ilari

ty

C23

9_cl

8G

U90

8476

Gam

map

rote

obac

teri

aG

amm

apro

teob

acte

rium

MH

154

(EU

0527

46)

Mic

roal

gae

cult

ure

asso

ciat

ion

96G

amm

apro

teob

acte

rium

IMC

C17

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480 Environ Monit Assess (2012) 184:471–485

0.1

Flavobacterium sp. WB3.4-76 (AM934664)

Flavobacterium sp. BF120 (AM934682)

Flavobacterium sp. MH45 (EU182873)

Flavobacterium sp. Man01 (AY788963)

Flavobacterium sp. SOC A4(12) (DQ628951)

Flavobacterium sp. INBF006 (AB426577)

Flavobacterium sp. GOBB3-209 (AF321038)

Flavobacterium sp. A5 (EU860083)

Flavobacterium sp. AKB-2008-JO104 (AM988920)

Flavobacterium sp. AKB-2008-TE19 (AM988929)

C28 cl38; C28 ex3; C28 ex9

55

Flavobacterium sp. WB 2.1-78 (AM167559)

Flavobacterium sp. PDD-14b-7 (DQ512791)

Flavobacterium sp. Q3 (FJ424497)90

66

Flavobacterium sp. BAC226 (EU180563)

60

Uncultured Bacteroidetes bacterium MA00163H11 (FJ533054)

Flavobacterium saliperosum AS 1.3801 (DQ021903)58

63

Flavobacterium sp. YC6274 (EU423319)

Flavobacterium cucumis R2A45-3 (EF126993)100

C22 cl50

Uncultured bacterium M0111_73 (EU104070)100

69

Uncultured Bacteroidetes bacterium FSW11-14 (DQ501319)

Uncultured Bacteroidetes bacterium FFP48 (AY830003)

Uncultured Flavobacteria bacterium LiUU-23-26 (EF061054)

C18 cl53

Uncultured bacterium BAx5 (AF087086)100

63

Sphingobacterium sp. PF-11 (FJ378900)

Sphingobacterium sp. TM5_7 (DQ279371)100

100

72

Uncultured Burkholderiales bacterium clone LW18m-2-21 (EU642387)

Uncultured Comamonadaceae bacterium LW18m-1-47 (EU642305)

Uncultured Burkholderiales bacterium Gap-1-75 (EU642053)62

Beta proteobacterium MWH-BRAZ-DAM2D (FM178226)

Beta proteobacterium MWH-C5 (AJ938026)63C14 cl51

Uncultured bacterium 3C002569 (EU801309)53

56

Beta proteobacterium II-D5 (FM165535)

79

C15 cl24

98

71

C32 cl56

Uncultured Kaistella sp. CL4.E325 (FM176140)94

Uncultured bacterium clone D5-43 (DQ113698)99

Uncultured Bacteroides sp. KS-459 (EU809713)

99

C19 ex1

Uncultured bacterium clone PL14-3B (EU409545)77

Uncultured candidate division TM7 bacterium Skagenf60 (DQ640706)96

TM7 phylum sp. FR058 (AF432141)100

56

62

C31 cI21

Uncultured beta proteobacterium GC1m-1-96 (EU641268)100

Uncultured beta proteobacterium LW1m-1-86 (EU639878)

Uncultured Methylophilaceae bacterium LW1m-3-84 (EU640146)

100

77

Gamma proteobacterium MH154 (EU052746)

Gamma proteobacterium IMCC1704 (DQ664237)

94

Uncultured gamma proteobacterium GOBB3-CL293 (AF493064)

68

C9 clI8

100

Methanobacterium formicicum sk0808-1 (FJ155844)

Bacteroidetes

Betaproteobacteria

Bacteroidetes

Betaproteobacteria

Gammaproteobacteria

Candidate division TM7

Fig. 5 Evolutionary tree showing the phylogeneticaffiliations of the partial bacterial 16S rRNA genesequences obtained from DNA fragments excised fromthe DGGE gel of Cértima River Basin samples (Fig. 3).The archaeal sequence of Methanobacterium formicicum

strain sk0808-1 (FJ155844) was used as outgroup. Scale barindicates 0.1 substitutions per site. Bootstrap values (1,000replicates) that were >50 are placed at the nodes of thebranches

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Environ Monit Assess (2012) 184:471–485 481

gradient for environmental parameters inside thelake, there were only very slight variations in thebacterial assemblage structure among the sampledspots.

Cluster analysis based on band patterns showeda clear grouping of samples according to waterquality and trophic status (Fig. 4a). Two mainclusters were obtained: one including the loticsamples and the other included the Fermente-los Lake samples (cluster IV). This appears tobe related to the hydrodynamic features of theriver and lake (lotic and lentic, respectively) andassociated trophic differences (related to para-meters such as TSS and Chl a levels), as shownby the CCA triplot graph (Fig. 4b). Within theCértima River main cluster, cluster I included themost unpolluted water samples from the easterntributaries, while cluster II incorporated the re-maining tributaries and cluster III included themain River section samples (where high inorganicnutrient and HCO−

3 levels were recorded—seeFig. 4b). The variance explained by CCA analysiswas 59% (from which 47% could be explained bythe first two axes), and the relationship betweenphylotypes and the environmental data matrix wassignificant (P < 0.05, Monte Carlo permutationtest). The first axis was defined by bands 6, 19,and 51 on the negative area while the positiveside was defined by band 16, with Chl a (0.82)and TSS (0.69) as the main factors behind thisdistribution. The second axis was mainly relatedto electrical conductivity (−0.76) and HCO−

3 con-centration (−0.59), and bands 40 (on the negativeside) and 1 and 2 (on the positive side) were wellsegregated along the axis.

Sequencing and phylogenetic affiliationof dominant phylotypes in DGGE profiles

A total of 25 DGGE bands were excised fromthe gel—but only ten bands gave clear resultsin the sequencing reactions; those are shown inTable 2. The phylogenetic affiliation of the se-quenced bands (see Figs. 3 and 5) correspondedto the Bacteroidetes (bands 18, 22, 28, 32 and28), Betaproteobacteria (bands 14, 15 and 31) andGammaproteobacteria (band 9) groups. The mostdominant phylotype present along the main rivercourse (band 28, as shown by sequencing more

than one band corresponding to this same migra-tion point) showed total match with partial 16SrDNA sequences from Flavobacterium sp. strains(Fig. 5).

Discussion

Trophic status and pollution along the CértimaRiver Basin

In this study, the water quality of the river pre-sented signs of degradation from the upstreamtributaries to the downstream part of the mainriver body. This was related to the increase of bio-chemical oxygen demand (BOD), electrical con-ductivity, nutrient levels, and Chl a (suggesting agradient based on pollutants and trophy). Never-theless, an increase of pH and HCO−

3 (as conse-quence of water–rock interaction from carbonaterock dissolution Appelo and Postma 2005) down-stream was also recorded. According to nutrientsand Chl a levels, the upstream tributaries (C2, C6,and C13) were within the range of the oligotrophicto mesotrophic status, while all other samples ingeneral fell into the descriptions for eutrophicand hypereutrophic state (Nürnberg 1996). Theupstream eastern mountain tributaries are locatedin drainage areas with low population pressureand no relevant sources of pollutants (Cerqueiraet al. 2005), and this was reflected in the observedlow levels of electrical conductivity, TOC, TSS,SO2−

4 , and low nitrate, org-N, and Chl a, as usu-ally observed for pristine waters (Cortecci et al.2002; Saksena et al. 2008). The similarity foundbetween C3 (Canedo stream) and FermentelosLake samples may be related to the water reten-tion (by a small weir) in this river section (creat-ing common hydrological features with the lake)and the organic pollution attributed to sewagedischarge (Rino and Gil 1987; Cerqueira et al.2005). At C5 (Lagoa Seca), TOC and BOD lev-els further increased related to urban untreatedwastewater and animal farming effluents (Rinoand Gil 1987; Calado 1990; Cerqueira et al. 2005);the high ammonium levels also suggest a weakoxidation potential of the water as corroboratedby the low oxygen levels. Electrical conductiv-ity, nitrate, soluble reactive phosphorus (SRP),

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and ammonium levels were much higher thanprevious records (Rino and Gil 1987). However,at C9 (Curia), these values tended to decreaseafter the contribution of Luso and Ponte Rivers;this highlights the importance of the tributarieson the water quality maintenance of CértimaRiver. Interestingly, samples from the westernsandy lowland tributaries (C8 and C22) sharedhigh electrical conductivity levels and the highestnitrate concentrations with samples C5 and C9.Levira River (C22) suffers the pressure from adense population along their margins and receiveseffluents from ceramics industry, distilleries, andanimal farms (Rino and Gil 1987). However, atC8 (Ponte River), no previously pollution sourceshave been reported that justify the high nitrateconcentrations recorded; agriculture runoffs con-stitute a potential candidate. The downstreamCértima River samples were mainly character-ized by the highest values of electrical conduc-tivity and SO2−

4 (indicative of industrial pollutionor, eventually, from agricultural activities as de-scribed by Cortecci et al. 2002) although HCO−

3and nitrate concentrations tended to decrease. AtC14 (Malaposta), the discharge of effluents fromwine industry has been previously related to highBOD levels and oxygen depletion (Rino and Gil1987; Calado 1990). However, at C16 (São Joãoda Azenha), BOD decreased and oxygen levelsincreased, lowering the ammonium and pollutionlevels due to water auto depuration and/or thecontribution of small clean tributary streams thatincrease the river’s width and depth (Rino andGil 1987). Nevertheless, SO2−

4 levels achieved themaximum of 116 mg L−1 as recorded in pollutedwaters (Cortecci et al. 2002). Therefore, tribu-taries may have a dual impact over the mainriver, by simultaneously helping depuration ofsome pollutants and adding new ones. At C19(Repolão), the water becomes shallower and itsflow is reduced by floodgates; this enhances theimpact of domestic and industrial effluents usuallydischarged just upstream this spot (Rino and Gil1987). At C23 (Perrães), the water depth risesagain but nitrate, BOD, and TSS levels increasedafter the affluence of the polluted Levira River.Fermentelos Lake samples (C25, C26, and C28),in spite of the high pH, electrical conductivity, andChl a levels, have relatively low nitrate and HCO−

3

concentrations, when comparing to samples fromthe main river section. Inside the lake, a spatialgradient could be observed for some parameterssuch as oxygen levels, TSS, TOC, Chl a, BOD, andammonium. Fermentelos Lake is known to be eu-trophic since decades ago (Gil 1988; Calado et al.1991) due to its high nutrient levels which have asmain sources the runoffs from surrounding fieldsand Cértima River (Cerqueira et al. 2005). Inretrospective, a general trend for the increase ofpH, electrical conductivity, and nitrate levels hasalso been recently recorded (de Figueiredo et al.2007, 2010).

Bacterial assemblage and trophic gradientof the Basin

The phylogenetic affiliation of the most in-tense bands corresponded to phylogenetic groupscommonly found in freshwater bodies such asBetaproteobacteria, Gammaproteobacteria, andBacteroidetes (Cottrell et al. 2005; Lindström et al.2005; Van Der Gucht et al. 2005; Allgaier andGrossart 2006). The clustering analysis based onthe DGGE band patterns suggest the hydrody-namic features (lotic or lentic) and trophic statusof the sampling sites as the main BCC modulators.Multivariate analysis showed Chl a, TSS, TOC,electrical conductivity, and HCO−

3 levels were themost important parameters to determine the BCCvariation. Temperature, pH, and redox potential(Eh) as well as total phosphorus, nitrogen sources,and organic matter have proven to be importantfactors for BCC variation (Lindström et al. 2005;Rooney-Varga et al. 2005; Haukka et al. 2006;Hall and Cotner 2007; Berggren et al. 2009), alongwith the water flow and retention time (Crumpand Hobbie 2005; Lindström et al. 2005), Chl a(Muylaert et al. 2002; Allgaier and Grossart 2006;Šimek et al. 2008) and sulfate levels (Awadallahet al. 1998; Bacelar-Nicolau et al. 2003). However,the impact of HCO−

3 concentration is not usuallyreported although here it showed to be an impor-tant BCC modulator.

The BCC showed variations along CértimaRiver Basin but among most samples commonbands could be detected such as band 28, affiliatedwith Flavobacterium spp. from freshwater lakes(Berg et al. 2008), whose representation was

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stronger in samples with higher HCO−3 , elec-

trical conductivity, and SO2−4 levels (according

to Pearson correlation). In fact, the Cytophaga–Flavobacterium group is well represented inorganic-rich rivers (Brümmer et al. 2000). Band32, affiliated with Bacteroidetes, was correlatedwith higher electrical conductivity and HCO−

3 lev-els and was similar to bacterial sequences fromanimal gastrointestinal tract suggesting a relationwith untreated effluents from domestic wastewa-ter and/or animal farming, which are known toexist near C5 (Cerqueira et al. 2005). Bands 22and 18 showed the highest similarity with partialsequences from uncultured bacteria isolated fromactivated sludge samples; they showed to be re-lated with high ammonium levels although band22 was also related with high electrical conductiv-ity, HCO−

3 , SRP, nitrate, TN, and Kjeld-N levels.In fact, the Bacteroidetes group, in general, isknown to appear abundantly at mesotrophic andeutrophic water bodies (Riemann and Winding2001; Van Der Gucht et al. 2005; de Figueiredoet al. 2007), and it usually correlates with high nu-trient levels (Brümmer et al. 2000; de Figueiredoet al. 2007, 2010; Xi et al. 2007). At C14, band19 matched partial 16S rDNA sequences fromuncultured bacteria also found in wastewater andactivated sludge (Kong et al. 2007). Actually,at C14, a strong pollution from wine industryeffluents has been reported and related to highorganic charge and oxygen depletion (Rino andGil 1987; Calado 1990). The Gammaproteobacte-ria phylotype (band 9) showed to be correlatedwith higher SO2−

4 levels suggesting a preferencefor polluted waters. The highest sequence similari-ties were found with freshwater bacteria; however,this subdivision is not very abundant in fresh-waters although it has been reported in severallakes and rivers (Zwart et al. 2002; Allgaier andGrossart 2006) as well as in wastewater treatmentplants (Kong et al. 2007). Betaproteobacteria arevery abundant in freshwaters (Zwart et al. 2002;Cottrell et al. 2005; Van Der Gucht et al. 2005;Allgaier and Grossart 2006). Bands 14 and 15were affiliated with the family Comamonadaceae(Burkholderiales; Betaproteobacteria) from lakesand rivers (Crump and Hobbie 2005; Mueller-Spitz et al. 2009). In fact, members of this fam-ily have been recorded at Fermentelos Lake (de

Figueiredo et al. 2010) and are abundant in riverswith organic pollution levels such as CértimaRiver (Brümmer et al. 2003). Band 14 correlatedwith low Eh and high temperature, pH, TSS, andChl a levels. Band 31 was affiliated with membersof the family Methylophilaceae (Methylophilales;Betaproteobacteria) isolated from a freshwaterlake (Mueller-Spitz et al. 2009), and its occurrenceshowed a negative correlation with chlorophyll alevels.

Conclusions

The results obtained in this study showed thatthe water quality of Cértima River Basin suffereddegradation from the upstream tributaries andalong the river main body. Parameters such asBOD, electrical conductivity, pH, Chl a, HCO−

3 ,and nutrient levels were the main modulatorsof this water quality gradient suggesting theinfluence of eutrophication, pollution but alsoof hydrogeological features (mineralization gradi-ent). Samples from Lagoa Seca (Mealhada) andCuria (Anadia) but also from the tributary LeviraRiver showed to have the highest pollution levels;this was associated with discharge of wastewatersand effluents from animal farming and industrialactivity. Nevertheless, in general, Cértima Rivertributaries showed to play an important role forthe river pollution depuration.

The variation of the bacterial assemblage alongthe Cértima River Basin showed to depend mainlyon parameters such as Chl a, TSS, TOC, electricalconductivity, and HCO−

3 levels. Bacteroidetes phy-lotypes were all related to higher electrical con-ductivity and HCO−

3 levels. Some of these werealso correlated with high SO2−

4 and others withhigh SRP, nitrate, TN, and Kjeld-N levels. Theoccurrence of a Gammaproteobacteria phylotypewas correlated with high SO2−

4 levels. One of theBetaproteobacteria phylotypes showed to corre-late with low Eh and high temperature, pH, TSS,and Chl a levels while another showed a negativecorrelation with Chl a values. Overall, the bacteri-oplankton assemblage was a good indicator of wa-ter quality, namely of anthropogenic inputs, andthe dominant phylotypes along the Cértima Basin

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were typically associated with organic-enrichedwaters.

Acknowledgements The authors would like to thank thecontributions made by Raquel Silva and Alexandra Mouraduring the development of the present work. The re-search has been supported by Fundação para a Ciênciae a Tecnologia in the form of a Ph.D. grant to Danielade Figueiredo (SFRH/BD/23864/2005) and the projectEcOwEt (POCI/CTE-GEX/58951/2004).

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