SHALLOW LAKES

Bacterioplankton in the littoral and pelagic zonesof subtropical shallow lakes

Haig-They Ng • David da Motta Marques •

Erik Jeppesen • Martin Søndergaard

Published online: 10 March 2010

� Springer Science+Business Media B.V. 2010

Abstract We measured bacterioplankton (phylo-

types detected by fluorescent in situ hybridisation,

morphometric forms, abundance and production) in

samples collected in summer in the littoral and pelagic

zones of 10 subtropical shallow lakes of contrasting

area (from 13 to 80,800 ha). Compared to the pelagic

zones, the littoral zones were overall characterised by

higher macrophyte dominance and lower concentra-

tions of total phosphorus and alkalinity and higher

concentrations of dissolved organic carbon (DOC) and

humic substances. Similarities of bacterial production

and biomass turnover and density of active phylotypes

and morphotype proportions were related to similar-

ities in a set of environmental variables (including

nutrients, humic substances content, predator density

and phytoplankton biomass), and some additionally to

lake area. Horizontal heterogeneity in bacterioplank-

ton variables (littoral versus pelagic) increased with

lake area. Bacterioplankton biomass and production

tended to be lower in the littoral zone than in the

pelagic zone despite higher concentrations of DOC

and humic substances. A likely explanation is higher

predation on bacterioplankton in the littoral zone,

although allelophatic effects exerted by macrophytes

cannot be excluded. Our results indicate that organic

cycling via bacterioplankton may be less efficient in the

littoral zone than in the pelagic zone of shallow lakes.

Keywords Horizontal zonation �Physiological status � Bacterial morphotypes �Bacterial predation � Macrophytes � Phytoplankton

Introduction

Searching for patterns in bacterial distribution has

emerged as a challenging frontier within the science

of microbial ecology (Van der Gucht et al., 2007).

Current knowledge indicates that evolutionary back-

ground and ecological processes may play an impor-

tant role in determining the patterns of bacterial

distribution (Pommier et al., 2007; Van der Gucht

et al., 2007, Pinel-Alloul & Ghadouani, 2007; Shade

Guest editors: M. Meerhoff, M. Beklioglu, R. Burks, F. Garcıa-

Rodrıguez, N. Mazzeo & B. Moss / Structure and Function of

World Shallow Lakes: Proceedings from the 6th Shallow Lakes

Congress, held in Punta del Este, Uruguay, 23–28 November,

2008

H.-T. Ng (&)

Programa de Pos-Graduacao em Ecologia, Universidade

Federal do Rio Grande do Sul, Porto Alegre,

RS 91501-970, Brazil

e-mail: haig.they@gmail.com

D. da Motta Marques

Instituto de Pesquisas Hidraulicas, IPH-Universidade

Federal do Rio Grande do Sul, PO Box 15029,

Porto Alegre, RS 91501-970, Brazil

E. Jeppesen � M. Søndergaard

Department of Freshwater Ecology, National

Environmental Research Institute, Aarhus University,

Vejlsøvej 25, PO Box 314, 8600 Silkeborg, Denmark

123

Hydrobiologia (2010) 646:311–326

DOI 10.1007/s10750-010-0177-z

et al., 2008). In lakes, bacterial diversity has been

related to habitat size (Horner-Devine et al., 2004;

Reche et al., 2005; Bell et al., 2005) and spatial

factors, such as the arrangement of lakes in the

landscape and scaling (Lindstrom & Leskinen, 2002;

Yannarell & Triplett, 2004; Green & Bohannan,

2006). So far, most bacteria studies have been

conducted in temperate lakes, whereas subtropical

shallow lakes have only received negligible attention;

in particular, drivers of among-lake differences in

bacterial composition and diversity are poorly eluci-

dated. A recent study suggests that subtropical

shallow lakes as temperate lakes show horizontal

heterogeneity in bacterial taxonomic composition

associated with the presence of submerged aquatic

macrophytes (Wu et al., 2007).

In shallow temperate lakes aquatic, particularly

submerged, macrophytes can have major effects on

trophic structure and thereby influence the existing

communities of invertebrates and fish (Schriver et al.,

1995; Scheffer & Jeppesen, 1997; Burks et al., 2001).

Macrophytes influence phytoplankton and bacterio-

plankton biomass and production (Søndergaard &

Moss, 1997; Søndergaard et al., 1997; Jeppesen et al.,

2002) and, consequently, nutrient concentrations and

cycling. How bacterioplankton respond to heteroge-

neity in littoral community structure is less well

studied in subtropical lakes. Climate may also play an

important role in regulating trophic structure, as the

cascading effect of grazers may differ among climate

zones due to an increase in fish predation as lakes

become warmer (Gyllstrom et al., 2005; Jeppesen

et al., 2007). Under high fish predation, zooplankton

communities are dominated by small-bodied forms

(Meerhoff et al., 2007a, b; Havens et al., 2009) with

higher abundances of rotifers, ciliates (Crisman &

Beaver, 1990) and nanoflagellates. Potentially, this

could result in a higher grazing pressure on bacte-

rioplankton. Accordingly, Lobao et al. (unpublished

data) found twofold lower bacterioplankton abun-

dances per unit of chlorophyll a in tropical Brazilian

lakes than in comparable temperate Danish lakes,

corroborating the hypothesis that lakes under differ-

ent climate regimes and with different trophic

structures exhibit different cascading effects on

bacterioplankton. In addition, the structuring role

of macrophytes may vary among climate regions

(Meerhoff et al., 2007a, b) as the refuge effect for

medium-large bodied zooplankton is much lower in

warm lakes due to higher aggregation of fish, and

thus a higher predation pressure inside the plant beds

(Meerhoff et al., 2007a, b).

Submerged macrophytes may also exert direct

influences on the abundance and composition of

bacterioplankton communities via exudation of dis-

solved organic carbon (DOC) (Wetzel, 2000; Wu

et al., 2007). Whether this DOC is used by bacterio-

plankton depends on the nutrient state and grazers that

both are modulated directly and indirectly by the

macrophytes or epiphytes on the plants (e.g. Søndergaard

& Wetzel, 1997; Jeppesen et al., 2002; Huss & Wehr,

2004). Some macrophytes may also affect phyto-

plankton, trophic dynamics and likely then also

bacterioplankton through allelopathy (Korner &

Nicklisch, 2002; Hilt et al., 2006; Cerbin et al.,

2007; Gross et al., 2007; Hilt & Gross, 2008), although

little is known about the allelopathic effect on

bacterioplankton.

We studied bacterioplankton in the littoral and the

pelagic zones of 10 subtropical shallow lakes of

contrasting area. We used three independent meth-

ods: (i) fluorescent in situ hybridisation (FISH),

which is based on hybridisation of a fluorescent

labelled DNA probe complementary to a bacterial

rRNA (ribosomic RNA) sequence. Since the cell

detection signal depends on the rRNA pool in the

cell, it may yield useful information on bacterial

assembly physiology (Bouvier & del Giorgio, 2003);

(ii) bacterial morphotypic distribution. The propor-

tion of bacterial morphotypes in the assembly is

seldom measured, but the few studies available show

good agreement between molecular and morpho-

physiological approaches under different conditions,

ranging from guano decomposition experiments

(Zdanowski et al., 2005) to studies in bioreactors

(Fernandez et al., 2000). A study in Brazilian tropical

reservoirs reported an increase in coccus-like forms

with increasing eutrophication, a finding associated

with an increase in ‘r strategy’ (Racy, 2004) and

(iii) the bacterial production rate as a direct measure

of bacterial activity. We tested for similarities among

these measures and their patterns relative to horizon-

tal heterogeneity in the lake (littoral versus pelagic)

and lake area. We hypothesise that a higher propor-

tion of macrophyte coverage in littoral zones

promotes in-lake heterogeneity for bacterioplankton

that increases with lake area as the difference in

environmental conditions between the littoral and

312 Hydrobiologia (2010) 646:311–326

123

pelagic zone expectedly becomes larger when we

move from a mixed littoral–pelagic zone in small

lakes to more distinct littoral and pelagic zones in

large, wind-exposed lakes (Søndergaard et al., 2005).

Materials and methods

Sampling

The study area is the homogeneous 630-km long

coastal plain of the Rio Grande do Sul state (southern

Brazil), where several lakes formed during the

Quaternary period as a result of a marine regression

(Holz, 1999) (Fig. 1, Tables 1, 2). The coastal plain is

covered by grass fields and wetlands and on the ocean

side by fixed dunes. Human activities focus on rice

production on old wetlands, and there are also small

urban areas draining directly to the Atlantic Ocean.

We sampled 10 lakes (Fig. 1) ranging in area from 13

to 80,800 ha, with the distance between lakes varying

from *1 to *490 km. All lakes are shallow, the

deepest reaching a maximum depth of 11 m

(Schwarzbold & Schafer, 1984; Schafer, 1988). Each

lake was sampled in an identical fashion for all

variables at two sampling points: at the lake centre

(pelagic zone) and in the lake margin (littoral zone). Of

the 10 lakes, surface water samples in Lake Mangueira

were taken in late October 2007 (with HCl 10% and

distilled water rinsed polyethylene bottles). The other

nine lakes were sampled once between January and

May 2007 (with a PVC pipe used to integrate the water

column until *10 cm above the sediment).

Physical, chemical and biological variables

including chlorophyll a, phytoplankton,

macrophytes and ciliates

Nutrients were determined using colorimetric methods:

total nitrogen (TN), nitrate (NO3-), total phosphorus

(TP) and soluble reactive phosphorus (PO43-) following

Mackereth et al. (1989). Chlorophyll a (Chl a) was

quantified after cold ethanol extraction (Jespersen &

Christoffersen, 1987) and the carbon series was deter-

mined via a carbon analyzer (Shimadzu Vcph 5000).

DOC was determined as the fraction passing through a

450�C pre-combusted glass fiber filter (Macherey–

Nagel GF6—0.6 lm average mesh size), while total

organic carbon (TOC) was the unfiltered sample. Total

suspended solids (TSS) were determined gravimetri-

cally following APHA) (1999). Water colour (Col),

turbidity (Turb) and the humic substances (HS) ratio

were determined as absorbance at 430, 750 and the ratio

between 250 and 365 nm, respectively, in a 1 cm quartz

cuvette and spectrophotometer (Varian Cary 1-E).

Alkalinity (Alk) was measured by gran titration (Wetzel

& Likens, 2000) and silicate (Sil) by a photometric

method (Si Merck Spectroquant� kit for silicates—

silicilic acid).

For phytoplankton biomass determination, 20 l

were concentrated in a 20 lm mesh sieve and fixed in

formaldehyde solution (4% vol:vol). Samples were

stored at room temperature in the dark until analysis.

At least 20 individuals (when possible) from each

species were measured in Sedwick-Rafter chambers

(APHA, 1999) and their biovolume was subsequently

determined (Hillebrand et al., 1999; Sun & Liu,

2003). Fresh biomass (mm3 l-1 = mg l-1) was esti-

mated by multiplying biovolume with the density of

each taxon. Fresh biomass was then converted into

carbon biomass by empirical conversion factors

(Wetzel & Likens, 2000).

Aquatic macrophyte coverage (%) was estimated

using the technique of points interception (Madsen,

1999). This method consists of mapping regularly

spaced sampling points on an ordinated grid com-

posed of orthogonal transects placed equidistantly to

the main axis of the lake. The larger the lake, the

higher the number of transects and the wider the

space between points and transects. For this lake data

set, on average 30 points were sampled. Along each

transect, the macrophyte coverage type was estimated

visually in plots of 10 9 10 m or 5 9 5 m, depend-

ing on the degree of heterogeneity of the macrophyte

coverage. Through this method, littoral and pelagic

zones were sampled uniformly, and the data were

used to generate macrophyte coverage type interpo-

lation and estimates of total coverage and distribution

by zones.

Total ciliate density was determined through

whole sample counting in sedimentation chambers

using an inverted microscope (Olympus IX70) at

4009 magnification. For Lake Mangueira, 2 l of lake

water were concentrated in a 20 lm mesh size sieve

in 50 ml samples for carrying and storage. For the

other nine lakes, unfiltered 50 ml were collected. All

samples were fixed with lugol (9:1 vol:vol) and stored

in the dark under refrigeration until analysis.

Hydrobiologia (2010) 646:311–326 313

123

Fluorescent in situ hybridisation

FISH was employed using six oligonucleotide DNA

probes marked with Cy3 (UNISCIENCE) comple-

mentary to a region of the bacterial 16S rRNA:

Eub338R—is a general probe for domain bacteria,

complemented by four sub-groups: Alf968—

a-proteobacteria; Bet42a—b-proteobacteria; Gam42a—

c-proteobacteria; CF319a—Cytophaga Flavobacter

and also a negative control ([Cy3]50-CCTAGTGAC

GCCGTCGAC-30) which was non-complementary to

any bacterial group (Glockner et al., 1999). The

samples were pre-filtered on MN 640d Macherey–

Nagel filters (average retention size of 2.0–4.0 lm) in

order to exclude larger organisms other than bacteria.

Of the pre-filtered samples, 2 ml were filtered onto

0.2 lm polycarbonate membranes (GE) under low

pressure (\5.0 kPa). The filters were fixed under

immersion in a 4% paraformaldehyde phosphate buffer

pH 7.2 solution (max. 24 h) and kept frozen at -15�C

until hybridisation. For the hybridisation the filters

were cut into two halves and one of the halves was then

divided in six parts, each being hybridised (for 24 h at

46�C) with 40 ll of one of each probe hybridisation

solution [NaCl 0.9 M; Tris–HCl 20 mM pH 7.2;

formamide 35% (CF319a) and 30% (other probes);

EDTA 5 mM; sodium dodecyl sulphate 0.01% and

100 ng of oligonucleotide]. For total cell counts DAPI

stain was used (20 lg ml-1). Once the slides had been

prepared, they were processed within a week (Pernt-

haler et al., 2001). For cell counting we used an image

grab system: a refrigerated camera (MOTIC 5000)

coupled to an inverted epifluorescence microscope

(Olympus IX70). Microscope filter set used were

U-MWU2 330–385 nm for DAPI and U-MWG2

520–550 nm for Cy3. Image capture (MOTIC

Image 3.2) and processing followed Massana et al.

(1997) with the help of the Freeware Image Tool

(v.1.27). A total of 10 fields per probe were processed

in each filter, resulting in a total of 120 images (60 total

DAPI counts and 60 labelled cells counts) per filter.

Number of cells counted was always [500. Auto-

trophic cells, although practically never present, were

discounted whenever detected. Group targeted density

was calculated as the average of the mean countings of

each probe labelled cells per field subtracted from the

negative control and according to the Kepner & Pratt

(1994) cell density equation. For data presentation in

figures and computations, phylotypes densities were

calculated for ‘a-proteobacteria’, ‘b-proteobacteria’,

‘c-proteobacteria’ and ‘Cytophaga Flavobacter’ by

specific probes. ‘Other Bacteria’, not targeted by these

specific probes, were estimated by subtracting

EUB338 probe labelled cells (total ‘Bacteria’ density)

from the sum (a ? b ? c-proteobacteria ? Cytoph-

aga Flavobacter). When this sum was higher than the

Fig. 1 Lakes sampled in

the coastal plain of Rio

Grande do Sul state

(Brazil): Horacio (58 ha),

Inacio (30 ha), Rincao

(60 ha), Ipe (13 ha), Passos

(69 ha), Veados (222 ha),

Suzana (97 ha), Lake 10

(192 ha), Lake 02 (36 ha)

and Lake Mangueira

(80,800 ha). Sources:

modified from IBGE (2005)

and Google Earth (2008)

314 Hydrobiologia (2010) 646:311–326

123

total ‘Bacteria’ estimated by EUB338, ‘other Bacteria’

were considered equal to zero. Undetermined groups

were computed as total DAPI counts (unspecific stain,

total density) minus EUB338 counts or the sum

(a ? b ? c-Proteobacteria ? Cytophaga Flavobact-

er), whichever the highest. All probe counts were

discounted from negative control (incompatible to any

bacterial group).

Bacterial biomass and proportion of bacterial

morphotypes

Samples were collected (as described in the Sampling

section above) and fixed in 4% formaldehyde in

polyethylene bottles in the field and stored in the dark

and refrigerated until analysis. In the laboratory,

samples were pre-filtered as described for the FISH

protocol (above), and 2 ml of each sample were

filtered (\5.0 kPa) in a Vacuum Manifold Filtration

Tower (Millipore) with 1 ml of pre-filtered (0.2 lm)

Mili-Q water to improve cell dispersion. Cells were

concentrated onto 0.2 lm black polycarbonate

membranes (GE). Approximately 1 ml of a 10%

(weight:vol) acridine orange stain was added to the

filters for 5 min. They were then washed with Mili-Q

water and air dried. Filters were mounted in slides

with mineral oil (Nujol) and were processed within

3 days because of quick faint of acridine. A total of

10 images were captured per filter and image

processing was carried out on six of them. Image

processing and capture were the same used for FISH,

as described above, except for the microscope filter

set, which was U-MWB2 460–490 nm for acridine.

Once the images had been processed and binarised,

they were submitted to CMEIAS software (Liu et al.,

2001) for morphotype classification. This program

consists of a series of plug-ins for Image Tool (1.27)

and utilises multiple measures of each object, work-

ing hierarchically through statistical classification of

11 possible morphotypes (Coccus, Spiral, Curved Rod,

U-Shaped Rod, Regular Rod, Unbranched Rod, Ellip-

soid, Club, Prosthecate, Rudimentary Branched Rod

and Branched Filament). Biovolume was calculated

using specific equations for the different morphotypes

by Massana et al. (1997) and converted to carbon

(C = 0.09 9 biovolume0.9 in pgC; Norland, 1993).

Bacterial production rate and duplication time

of biomass

Bacterial production was estimated using L[4.5-3H]

radio-labelled leucine and microcentrifugation (Simon

& Azam, 1992; Kirchman, 2001). We added 11 ll of

leucine solution (approximately 20 nmoles l-1, spe-

cific activity of 65 Ci mmol-1) to 1,700 ll of sample

in Eppendorf vials (2 ml). An initial vial was fixed at

the beginning and 2–3 replicates for each sample were

incubated for 30 min with lake water at room temper-

ature (15–16�C). Interruption of bacterial activity was

achieved with the addition of 89 ll of concentrated

trichloroacetic acid (TCA). The vials were then stored

in the dark and cool until centrifugation. Samples were

centrifuged three times (cell concentrate, ?TCA 5%

and ?ethanol 80%) and left to dry for approximately

1 week at 4�C. For beta-particles counting, 1 ml of

scintillation liquid (Optiphase HiSafe III Wallac) was

Table 1 Location of sampling points and some limnological

variables for the 10 lakes data set sampled from January to

October 2007

Lake SP Location A Z pH Col HS ratio

Ip L 29�550170 0S 13 0.8 7.5 0.034 4.58

P 50�110550 0W 7.5 0.035 4.60

In L 29�540330 0S 30 1.37 7.3 0.024 4.74

P 50�130210 0W 6.9 0.025 4.64

L2 L 30�330080 0S 36 1.81 6.5 0.033 4.14

P 50�250600 0W 7.4 0.034 4.19

Ho L 29�540470 0S 58 1.09 7.0 0.019 4.83

P 50�130590 0W 7.0 0.020 4.69

Ri L 29�540370 0S 60 0.76 7.4 0.032 4.68

P 50�110370 0W 7.4 0.029 4.82

Pa L 29�540290 0S 69 1.2 6.7 0.038 4.46

P 50�120370 0W 7.3 0.035 4.52

Sz L 30�090570 0S 97 1.02 7.3 0.039 4.61

P 50�160530 0W 7.4 0.039 4.59

L10 L 30�240440 0S 192 1.1 7.6 0.035 4.23

P 50�210430 0W 7.5 0.033 4.27

Ve L 29�540210 0S 222 1.22 6.8 0.015 5.42

P 50�100300 0W 6.7 0.018 5.18

Ma L 33�310220 0S 80,800 2 7.6 0.035 0.80

P 53�070480 0W 8.1 0.019 1.02

SP sampling point, L littoral, P pelagic, A area (ha), Z mean

depth (m), Col water colour (absorbance at 430 nm) and HSRatio humic substances ratio (ratio between the absorbances at

250/365 nm; Lakes: Ip Ipe, In Inacio, L2 Lake 02, Ho Horacio,

Ri Rincao, Pa Passos, Sz Suzana, L10 Lake 10, Ve Veados, MaMangueira)

Hydrobiologia (2010) 646:311–326 315

123

added to each vial. Reading was done twice (LKB

Wallac 1209 Rack beta Liquid Scintillation Counter)

during 180 s for each reading. For calculations, we

assumed an intracellular isotopic dilution of 2, a molar

percentage of leucine in the protein pool of 0.073 and a

carbon:protein ratio of 0.86. Results were expressed in

lg C l-1 h-1 (Simon & Azam, 1992; Kirchman,

2001). Duplication time of bacteria (DT) was calcu-

lated as tdup ¼ lnð2Þ=r; where r is the intrinsic growth

rate [r = biomass (lg C l-1)/production

(lg C l-1 h-1), Gotelli, 2007].

Analytical approaches

A set of bacterial descriptor and explanatory envi-

ronmental variables were generated for the littoral

and the pelagic zone of each lake. Bacterial variables

included the phylotype density distribution (bacterial

assembly composition—FISH), morphotype propor-

tion distribution (these two multivariate), bacterial

biovolume, biomass, secondary production rate, bio-

mass duplication time (the last ones univariate);

environmental variables included macrophyte type

(emergent, floating, submerged, in %), an extensive

environmental data set (18 variables) and lake area.

Each variable generated a similarity matrix by

pairwise comparisons between the zones of all lakes.

In a second step, we generated similarity matrices for

each of the two zones separately. We tested whether

the similarity of a given environmental variable could

be associated with the similarity of a bacterial

variable. The bacterial similarity matrices were:

(i) bacterial assemblies similarity (BAS) using the

Morisita–Horn index on group abundance (phylo-

types, assessed by FISH) data log (x ? 1) trans-

formed; (ii) bacterial morphotype similarity (BMS),

computed also as the Morisita–Horn index on

percentage data and (iii) bacterial production simi-

larity (BPS), calculated as {1 - (standardised

Euclidean distance)}. The environmental similarity

matrices were: (i) TSS, silicon, TN, TP, alkalinity,

chlorophyll a, DOC, TOC, water colour, water

turbidity, HS ratio, ciliate density and the mmol:

mmol ratios between DOC:PO43-, DOC:NO3

-,

DOC:TOC, TN:TP and TOC:TN, and bacterial

biomass relative to phytoplankton (BBiom:Pbiom

Table 2 Catchment information available for the 10 lakes data set sampled from January to October 2007

Lakes Municipality

where the lakes

are founda

Municipality

Area

(ha)aTotal

population

(inhabitants)a

(inhab/ha) Perennial ? annual

agriculture area

(ha)b

Perennial ? annual

agriculture area

(% of municipality)b

Rice

farm

areac (ha)

Ipe

Horacio

Rincao Osorio 666,612 36,131 0.05 8,399 1.26 5,015d

Passos

Veados

Suzana Cidreira 23,187 8,882 0.38 1,966 8.48 939d

Lake 02 Palmares do Sul 109,143 10,854 0.10 16,639 15.24 33,300

Lake 10 Mostardas 190,520 11,658 0.06 46,851 24.59 41,140

Mangueira Santa Vitoria do Palmar 527,854 33,304 0.06 73,688 13.96 68,000

a FEPAM—Fundacao Estadual de Protecao Ambiental Luis Roessler (State Environmental Protection Agency). Assessed 20/07/

2009. Available at http://www.fepam.gov.rs.brb IBGE—Instituto Brasileiro de Geografia e Estatıstica (Brazilian Institute for Geography and Statistics). Agriculture and Cattle

Raising Census 2006. Assessed 21/07/2009. Available at: http://www.ibge.gov.brc IRGA. Instituto Rio Grandense do Arroz. Acompanhamento da semeadura de arroz irrigado no Rio Grande do Sul (Rio Grande do

Sul Rice Institute—following of the irrigated rice seeding in Rio Grande do Sul). Data from 2009 harvest. Assessed 20/07/2009.

Available at: http://www.irga.rs.gov.brd Rice cultivation area in the IBGE database census 2007. IBGE—Instituto Brasileiro de Geografia e Estatıstica (Brazilian Institute

for Geography and Statistics). Agricultural census 2006. Assessed 21/07/2009. Available at: http://www.ibge.gov.br

316 Hydrobiologia (2010) 646:311–326

123

ratio), computed in the same way as for BPS; (ii)

aquatic macrophyte coverage similarity all computed

from each zone separately and (iii) area similarity

between lakes, measured as {1 - (standardised Man-

hattan distance)} assumed to be identical for each zone.

Standardisation for Euclidean distance was based on the

total and for Manhattan distance by range in order to

obtain similarity indexes between 0 and 1.

For environmental variables, DOC:PO43- and

DOC:NO3- are considered as measures of the propor-

tion of organic to inorganic nutrients in the system,

which is assumed to decrease along trophic gradients

(Cotner & Biddanda, 2002). We also included

TOC:TN; TN:TP, referring to the classical Redfield

ratio; DOC:TOC, the ratio of dissolved to total carbon,

which is assumed to positively favour bacterioplankton

(Cotner & Biddanda, 2002), and the bacterial to

phytoplankton carbon biomass ratio, which is often

higher in oligotrophic systems (del Giorgio et al., 1997;

Cotner & Biddanda, 2002).

Statistical tests

All bacterioplankton variables (BBiov, BBiom, BP

and DT) were tested (after log (x ? 1) transforma-

tion) for association with lake area (log transformed)

for both the littoral and the pelagic zone in order to

avoid pseudoreplication (n = 10). In order to assess

whether the difference between lake zones could be

associated with lake area, littoral minus pelagic

differences (also after log (x ? 1) transformation)

of all limnological and bacterioplankton variables

were tested against lake area (log transformed). These

associations were tested through linear regression in

PAST v.1.80 (Hammer et al., 2001).

Differences between littoral and pelagic zones of

all lakes and also groups of lakes arranged according

to area were tested using ANOSIM. For this analysis

we included, besides the variables listed above,

bacterial phylotypes and morphotypes. For these we

used the Morisita–Horn similarity index ðCk ¼2P

n1in2i= k1 þ k2ð ÞN1N2 with kj ¼P

n2ji=N2

j ; nji is

the number of individuals of species i in the sample j

and Nj is the number of individuals in sample j), the

same used for the construction of the similarity

matrices, as it is very robust to sample size and gives

more weight to abundance than presence/absence

(Wolda, 1981). For others, we employed the Euclid-

ean distance (often used for environmental variables).

Computations were performed using PAST v.1.80

(Hammer et al., 2001). For the similarity matrix

approach we first calculated the Pearson’s correlation

to test for relationships between the similarity

matrices of the bacterial variables.

We elucidated the effects of environmental vari-

ables on the four bacterial variables testing the

explanatory power of environmental similarity, lake

area similarity and the three types of macrophyte

coverage similarity on bacterial production, biomass

duplication time, bacterial assembly composition and

bacterial morphotype proportion. We first employed

all possible pairwise comparisons and then separated

the similarity between only littoral and between only

pelagic zones. For this similarity matrices associa-

tion we used linear regression between matrices. We

aligned the matrices in order to match each similar-

ity index between two sampling points to another

index between the same points and calculated the

linear regression parameters. Computations were

performed in FitoPacShell (v. 1.6.4.25)—Morisita–

Horn similarity matrices, Multiv (v.2.3.10)—Euclid-

ean distance matrices, R (v. 2.6.0) and Origin

(v. 6.0) Linear Regressions and graphic presentation,

respectively.

Results

Overall, in spite of differences in morphometric

variables and human activities intensity in the catch-

ment where the lakes are located, the lakes are similar

in nutrients concentration and other physical and

chemical variables (Tables 1, 2 and Figs. 1, 2, 3).

ANOSIM revealed that the littoral and pelagic zones of

the lakes differ only for the three types of macrophyte

coverage (higher in littoral zones); the smallest lakes

differed from the other lakes for TN, Chl a and the

Bbiom:Pbiom ratio (higher in the smallest lakes) while

the largest lakes differed from the others for Alk, Col,

Turb, HS, DOC:TOC, DT and emergent, floating and

submerged macrophyte coverage (except for Col, all

the other variables were higher in the largest lakes)

(Table 3). The regression of all bacterioplankton

variables against lake area revealed that only littoral

bacterioplankton production was related to lake area,

decreasing along a lake area gradient (log(BP ? 1) =

-0.04(log area) ? 0.03, R2 = 0.77).

Hydrobiologia (2010) 646:311–326 317

123

When subtracting the pelagic zone data from the

littoral zone data, only Chl a was associated nega-

tively and emergent macrophyte coverage positively

with lake area (log (diff(L - P)Chl a ? 1) =

-0.138(log area) ? 0.32, r2 = 0.90, P = 0.0003

and log(diff(L - P)Emerg ? 1) = 0.691(log area)

- 0.92, r2 = 0.70, P = 0.0237). For the bacterial

variables, the differences in BP and TP between

zones correlated positively (R2 = 0.50, P = 0.019)

and the same was true for differences in BP and in

DOC (R2 = 0.51, P = 0.015).

Similarity matrices

FISH and morphotype data are shown in Table 4. The

lakes were overall dominated by cells belonging to

the undetermined group (not Domain Bacteria) and

the morphotypes by cocci and regular rods. Among

the matrices for BPS, biomass duplication time

similarity (DTS), BAS and BMS, BPS correlated

significantly with DTS and BMS, while DTS addi-

tionally correlated with BAS and BMS (Table 5).

Similarities of BPS, DTS, BAS and BMS were

related to similarities in environmental variables (ES)

and lake area (LAS) for all comparisons possible

(those where we included similarities between littoral

zones, among pelagic zones and between littoral and

pelagic zones of all lakes in the same matrix). Most

Ip In L2 Ho Ri Pa Sz L10 Ve Ma

0

20

40

60

80

100

Subm

(%

)

0

10

20

30

40

50

60

Flo

at (

%)

0

20

40

60

80

100 Littoral zone

Pelagic zone

Em

erg

(%)

Fig. 2 Percentages of aquatic macrophyte coverage type in

littoral and pelagic zones along a lake area gradient. Emergemergent, Float floating, Subm submersed. Lakes: Ip Ipe, InInacio, L2 Lake 02, Ho Horacio, Ri Rincao, Pa Passos, SzSuzana, L10 Lake 10, Ve Veados, Ma Mangueira

Ip In L2 Ho Ri Pa Sz L10 Ve Ma

0750

1500

Littoral zone

Pelagic zone

Alk

0,00,5

TN

0,000,050,10

TP

01020

Ch

la

036

Cil

0,07,5

15,0

DO

C

0,07,5

15,00.227 0.2050.367* 0.6640.631 0.6070.335

DT

0,00,10,2

BP

01020

BB

iom

0,000,05

BB

iov

Fig. 3 Bacterial biovolume (BBiov, lm-3), biomass (BBiom,

ng C ml-1), production (BP, lg C l-1 h-1), biomass duplica-

tion time (DT, 910-3 h-1), dissolved organic carbon (DOC,

mg l-1), ciliate density (Cil, 9105 l-1), chlorophyll a (Chl a,

lg l-1), total phosphorus (TP, mg l-1), total nitrogen (TN,

mg l-1) and alkalinity (Alk, lEq l-1) in littoral and pelagic

zones along the lake area gradient sampled. Arrows indicate

where pelagic values exceed littoral values. Lakes: Ip Ipe, InInacio, L2 Lake 02, Ho Horacio, Ri Rincao, Pa Passos, SzSuzana, L10 Lake 10, Ve Veados, Ma Mangueira [DT: figures

shown on the graph where they are too small for the scale;

(*) data not calculated for the littoral zone of Lake Ho, littoral

zone, where BP was negative]

318 Hydrobiologia (2010) 646:311–326

123

distinctly, BAS showed the same dependence on ES,

but not on LAS (Table 6). In comparisons between

littoral zones only, the same pattern emerged,

suggesting that the similarity between littoral zones

may be responsible for the overall pattern (Table 6).

Thus, when comparing pelagic zones, only BPS and

DTS continued to show strong dependency on ES and

LAS, while BMS was more weakly related to these

factors. BAS showed no association with ES and LAS

(Table 6). For BMS and BAS the environmental

Table 3 Comparisons between littoral (L) and pelagic (P) zones and groups of lakes arranged according to area using ANOSIM for

limnological and bacterioplankton variables and macrophyte coverage type

Variable Comparison R P

TN Ip 9 others 0.4174 0.0271*

0.57 (0.37) 9 0.31 (0.20)

Chl a Ip 9 others 1 0.0051*

21.33 (2.62) 9 2.91 (1.72)

Ip ? In 9 others 0.5006 0.0113*

12.73 (10.05) 9 2.75 (1.77)

Alk Ma 9 others 0.7157 0.0173*

1,157 (509.12) 9 317.39 (190.44)

Col Ma 9 others 0.4159 0.0339*

0.027 (0.011) 9 0.030 (0.008)

Turb Ma ? Ve 9 others 0.3214 0.0419*

0.004 (0.004) 9 0.002 (0.002)

HS Ma 9 others 0.5988 0.0334*

0.91 (0.16) 9 4.62 (0.32)

DOC:TOC Ma 9 others 0.5321 0.0433*

1.6 (0.14) 9 1.06 (0.47)

B:P biomass ratio Ip ? In 9 others 0.2919 0.049*

0.026 (0.033) 9 0.009 (0.007)

BP Ma 9 others 0.928 0.006*

0.241 (0.008) 9 0.045 (0.050)

DT Ma ? Ve 9 others 0.3439 0.027*

0.807 (0.509) 9 0.511 (0.336)

Ma 9 others 0.787 0.007*

1.142 (0.151) 9 0.507 (0.344)

Morphotypes Ma 9 others (not applicable) 0.363 0.042*

Emerg L 9 P 0.349 0.002*

42.19 (22.16) 9 16.89 (11.26)

Ma 9 others 0.753 0.008*

50 (70.71) 9 27.27 (12.91)

Float L 9 P 0.193 0.036*

34.22 (19.21) 9 13.26 (15.80)

Subm L 9 P 0.253 0.007*

30.51 (30.11) 9 3.43 (7.7.39)

(Lakes: Ip Ipe, In Inacio, L2 Lake 2, Ho Horacio, Ve Veados and Ma Mangueira. Variables: TN total nitrogen, Chl a chlorophyll a,

Alk alkalinity, Col water colour, Turb turbidity, HS humic substances ratio, DOC:TOC dissolved:total organic carbon ratio, B:Pbiomass ratio bacterial:phytoplankton biomass ratio, BP bacterial secondary production, DT duplication time of bacterial biomass,

Morphotypes bacterial morphotypes proportion, Emerg, Float and Subm emergent, floating and submersed macrophytes coverage,

respectively); numbers below type of comparisons indicate the mean (±standard deviations) of each group; (*) significant

relationships

Hydrobiologia (2010) 646:311–326 319

123

Ta

ble

4B

acte

rial

asse

mb

lyan

dm

orp

ho

typ

esfo

rth

e1

0la

kes

dat

ase

tin

litt

ora

lan

dp

elag

iczo

nes

Ipe

(13

ha)

Inac

io(3

0h

a)L

ake

02

(36

ha)

Ho

raci

o

(58

ha)

Rin

cao

(60

ha)

Pas

sos

(69

ha)

Su

zan

a

(97

ha)

Lak

e1

0

(19

2h

a)

Vea

do

s

(22

2h

a)

Man

gu

eira

(80

,80

0h

a)

LP

LP

LP

LP

LP

LP

LP

LP

LP

LP

FIS

H

a-P

rote

ob

acte

ria

0.1

8

(4)

0.2

6

(30

)

0.0

6

(2)

1.4

4

(48

)

0.5

4

(28

)

0.3

0

(16

)

1.4

2

(26

)

1.1

2

(41

)

0.0

5

(8)

0.0

4

(4)

1.0

6

(40

)

0.8

1

(24

)

0.4

8

(21

)

0.1

9

(10

)

0.0

9

(4)

0.0

5

(1)

0.0

7

(9)

0.0

5

(19

)

0.0

4

(2)

0.1

9

(4)

b-P

rote

ob

acte

ria

1.9

6

(40

)

0.0

4

(5)

0.2

2

(7)

0.6

0

(20

)

0.3

9

(20

)

0.3

3

(17

)

0.6

8

(12

)

0.0

0

(0)

0.2

3

(37

)

0.0

0

(0)

0.2

6

(10

)

0.4

7

(14

)

0.3

1

(14

)

0.1

4

(7)

0.0

7

(3)

0.0

2

(0)

0.0

3

(4)

0.0

3

(11

)

0(0

)0

.08

(2)

c-P

rote

ob

acte

ria

0.0

3

(9)

0.0

7

(8)

0.1

7

(6)

0.6

0

(20

)

0.2

8

(15

)

0.4

3

(23

)

1.3

1

(24

)

0.5

2

(19

)

0.0

5

(9)

0.7

4

(79

)

0.1

7

(6)

0.3

0

(9)

0.4

4

(20

)

0.1

3

(7)

0.0

2

(1)

0.0

3

(1)

0.0

5

(7)

0.0

5

(19

)

0.0

0

(0)

0.1

9

(4)

Cyt

op

ha

ga

Fla

vob

act

er0

.03

(1)

0.1 (1

1)

0(0

)0

.32

(10

)

0.0

4

(2)

0.0

1

(0)

0.1

1

(2)

0(0

)0

(0)

0.0

0

(0)

0.0

4

(1)

0.0

5

(1)

0.0

1

(0)

0.0

2

(1)

0.0

2

(1)

0.0

3

(1)

0.0

1

(1)

0.0

1

(4)

0.0

0

(0)

0.0

8

(2)

Oth

erE

ub

acte

ria

0(0

)0

(0)

0.9

3

(31

)

0(0

)0

.22

(11

)

0(0

)0

.74

(14

)

0(0

)0

(0)

0(0

)0

(0)

0(0

)0

(0)

0.2

4

(12

)

0(0

)0

.11

(2)

0.0

3

(4)

0.0

5

(19

)

0.3

1

(15

)

0(0

)

Un

det

erm

ined

gro

up

s

2.3

1

(47

)

0.4

0

(47

)

1.6

4

(54

)

0.0

5

(2)

0.4

8

(24

)

0.8

3

(44

)

1.2

1

(22

)

1.1

0

(40

)

0.2

9

(47

)

0.1

5

(16

)

1.1

0

(42

)

1.8

0

(52

)

1.0

0

(45

)

1.2

5

(63

)

2.3

4

(92

)

5.0

0

(95

)

0.5

8

(74

)

0.0

7

(27

)

1.7

3

(83

)

3.8

6

(88

)

Mo

rph

oty

pes

Co

ccu

s6

57

37

67

86

36

47

47

47

88

07

47

56

98

16

76

88

08

06

58

4

Reg

ula

rro

d2

11

91

71

62

72

72

02

11

61

62

11

82

21

12

42

41

41

42

11

2

Cu

rved

rod

65

65

87

54

54

56

88

86

55

12

3

U-s

hap

edro

d1

00

01

11

01

00

00

01

10

11

0

Ell

ipso

id0

00

00

00

00

00

00

00

00

00

0

Pro

sth

ecat

e0

10

01

00

00

00

01

01

10

11

0

Ru

dim

enta

ry

bra

nch

edro

d

00

00

00

00

00

00

00

00

00

00

Clu

b0

00

00

00

00

00

00

00

00

00

0

FIS

Hfl

uo

resc

ent

insi

tuh

yb

rid

izat

ion

(up

per

fig

ure

sar

ep

hy

loty

pe

den

sity

cell

s9

10

6m

l-1

and

low

erfi

gu

res

inp

aren

thes

esar

ep

erce

nta

ge

of

the

ph

ylo

typ

esin

the

sam

ple

);fo

r

mo

rph

oty

pes

the

pro

per

cen

tag

eo

fea

chm

orp

ho

typ

ein

the

sam

ple

isg

iven

320 Hydrobiologia (2010) 646:311–326

123

similarity, and for BMS also the area similarity, was

more important in littoral zones.

Several of the bacterial variable similarities were

related to macrophyte coverage similarities, separated

into emergent, floating and submerged macrophyte

coverage similarity (EMCS, FMCS and SMCS,

respectively) (Table 7). In all comparisons, EMCS

was highly significantly related to BPS, DTS and

BMS, while FMCS was highly significant for only

DTS and BAS. SMCS was significantly related to

particularly BPS and DTS and more weakly to BAS

and BMS (Table 7). When only similarities between

littoral zones were considered, EMCS explained

significantly only the variation in BPS and DTS,

FMCS in DTS and BAS, and SMCS in BPS, DTS and

BMS (Table 7). In comparisons between pelagic

zones, only EMCS was significantly associated to

BPS, DTS and BMS, while FMCS was also associ-

ated to DTS (Table 7).

Discussion

We found that the similarities of BPS, DTS and BMS

were highly correlated and largely showed the same

patterns of dependence on environmental and macro-

phyte variables. Bacterial morphotype proportions

may therefore be a useful, simple tool to be applied in

further comparative approaches. BAS, assessed by

FISH, was least correlated to BPS and BMS, which

may be attributed to methodological constraints (e.g.

amount of ribosomes) of the FISH technique (Bouvier

Table 6 Environmental

similarity (ES) and lake

area similarity (LAS) as

explanatory variables for

bacterial production

similarity (BPS),

duplication time of biomass

similarity (DTS), bacterial

assembly similarity (BAS)

and bacterial morphotype

similarity (BMS) for all site

by site comparisons (all

comparisons possible) and

between littoral zones only

and pelagic zones only

(*) Significant relationships

at 0.05a probability;

(–) non-applicable

equations because the

relationship is assumed to

be non-existent given the

associated P value. DTS

exhibits a lower n because

BP was negative for the

littoral of Lake Ho,

preventing calculation of

DT

Explanatory Dependent Equation R2 P n

All comparisons possible

ES BPS y = 0.72 ? 0.27x 0.55 \0.0001* 210

LAS y = 0.85 ? 0.11x 0.56 \0.0001* 210

ES DTS y = 0.79 ? 0.20x 0.46 \0.0001* 190

LAS y = 0.88 ? 0.09x 0.66 \0.0001* 190

ES BAS y = 0.81 ? 0.03x 0.27 \0.0001* 210

ES BMS y = 0.96 ? 0.01x 0.35 \0.0001* 210

LAS y = 0.98 ? 0.001x 0.32 \0.0001* 210

Comparisons only between littoral zones

ES BPS y = 0.76 ? 0.23x 0.59 \0.0001* 55

LAS y = 0.85 ? 0.12x 0.79 \0.0001* 55

ES DTS y = 0.77 ? 0.21x 0.45 \0.001* 45

LAS y = 0.83 ? 0.14x 0.81 \0.0001* 45

ES BAS y = 0.77 ? 0.20x 0.45 \0.001* 55

ES BMS y = 0.97 ? 0.03x 0.47 \0.001* 55

LAS y = 0.95 ? 0.008x 0.32 0.015* 55

Comparisons only between pelagic zones

ES BPS y = 0.53 ? 0.49x 0.78 \0.0001* 55

LAS y = 0.86 ? 0.11x 0.71 \0.0001* 55

ES DTS y = 0.76 ? 0.23x 0.65 \0.0001* 55

LAS y = 0.92 ? 0.05x 0.53 \0.0001* 55

ES BMS y = 0.96 ? 0.04x 0.28 0.035* 55

LAS y = 0.98 ? 0.009x 0.26 0.049* 55

Table 5 Pearson’s pair-wise correlation coefficients (only

significant relationship at 0.05 a probability shown) between

bacterial production similarity (BPS), bacterial biomass

duplication time similarity (DTS), bacterial assembly similarity

(BAS) and bacterial morphotype similarity (BMS) for the 10

lakes data set

BPS DTS

DTS 0.786

BAS 0.156

BMS 0.263 0.260

Hydrobiologia (2010) 646:311–326 321

123

& del Giorgio, 2003), explaining why this technique

has only seldom been employed to assess the physi-

ological status of the bacterial assembly (Bouvier &

del Giorgio, 2003). Moreover, the large number of

bacteria associated with the ‘undetermined group’

class may likely reflect low metabolic activity (few

ribosomes copies) rather than provide a snapshot of

major bacterial groups diversity; thus, we decided to

use FISH data for comparisons with caution when

discerning diversity patterns. To improve assessment

of diversity patterns future works ought to include

the universal bacterial domain cocktail of probes

EUBMIX, which targets more than 90% of the

bacteria, and/or use CARD-FISH, which relies less

on the ribosomic pool inside the cells.

We found that similarities in the bacterioplankton

phylotypes detection, production, duplication time and

morphotypes were related to similarities in environ-

mental physico-chemical variables and, for some

variables, also to lake area. The importance of

similarities in environmental variables (ES) and

similarities in lake area (LAS) in littoral zones was

higher, suggesting that the littoral data are mainly

responsible for the overall dependence of bacterial

variables on ES and LAS. We found increasing HS,

TN, DOC:PO43-, DOC:NO3

-, TN:TP, BP, and DT

values and decreasing TSS and floating emergent

macrophyte coverage along the lake area gradient. In

order to test the effect of increasing lake area on

increasing differences between littoral and pelagic

zones of the lakes, we subtracted, for all variables

sampled, the values of littoral minus pelagic zone of

each lake and regressed the differences with lake area.

This approach revealed that these differences

decreased for chlorophyll a and increased for emergent

macrophyte coverage with increasing lake area. How-

ever, due to lack of catchment information for some of

the lakes (Table 2), it is not possible to test whether the

difference is actually a result of change in lake area or

of differences in the loading of nutrients to the lakes.

In addition, BPS, DTS, BAS and BMS correlated

equally well with similarities of macrophyte coverage

Table 7 Macrophyte

coverage type similarity as

explanatory variables for

Bacterial production

similarity (BPS),

Duplication time of biomass

similarity (DTS), Bacterial

assembly similarity (BAS)

and Bacterial morphotype

similarity (BMS) for all site

by site comparisons (all

comparisons possible) and

between littoral zones only

and pelagic zones only

(*) Significant relationships

at 0.05 a probability;

(–) non-applicable

equations because the

relationship is assumed to

be non-existent given the

associated P value. EMCS,

FMCS and SMCS are

Emergent, Floating and

Submersed macrophyte

coverage similarity,

respectively

Explanatory Dependent Equation R2 P n

All site by site comparisons

EMCS BPS y = 0.13 ? 0.84x 0.52 \0.0001* 210

DTS y = 0.04 ? 0.94x 0.68 \0.0001* 190

BMS y = 0.88 ? 0.12x 0.34 \0.0001* 210

FMCS DTS y = 0.69 ? 0.27x 0.19 0.007* 190

BAS y = 0.42 ? 0.53x 0.30 \0.0001* 210

SMCS BPS y = 0.70 ? 0.26x 0.36 \0.0001* 210

DTS y = 0.56 ? 0.41x 0.63 \0.0001* 190

BAS y = 0.79 ? 0.15x 0.19 0.006* 210

BMS y = 0.98 ? 0.02x 0.15 0.028* 210

Only littoral zones site by site comparisons

EMCS BPS y = -0.19 ? 1.17x 0.79 \0.0001* 55

DTS y = -0.31 ? 1.29x 0.80 \0.0001* 45

FMCS DTS y = 0.43 ? 0.54x 0.30 0.048* 45

BAS y = -0.05 ? 1.02x 0.53 \0.0001* 55

SMCS BPS y = 0.51 ? 0.48x 0.69 \0.0001* 55

DTS y = 0.43 ? 0.55x 0.75 \0.0001* 45

BMS y = 0.96 ? 0.03x 0.32 0.018* 55

Only pelagic zones site by site comparisons

EMCS BPS y = -0.85 ? 1.83x 0.51 \0.0001* 55

DTS y = -0.12 ? 1.09x 0.52 \0.0001* 55

BMS y = 0.75 ? 0.24x 0.44 \0.001* 55

FMCS DTS y = 0.61 ? 0.37x 0.32 0.0167* 55

322 Hydrobiologia (2010) 646:311–326

123

for the different plant categories, which indicates that

the plants impact heterogeneity among the lakes, the

strongest relationships occurring in the littoral zone.

The higher macrophyte coverage in the littoral zone

may affect the quantity of nutrients and carbon in the

adjacent water and, thereby, bacterial metabolism

(Rooney & Kalff, 2003; Stanley et al., 2003; Huss &

Wehr, 2004). A dependence of bacterial metabolism

on nutrients and DOC may explain the significant

correlation between the littoral–pelagic difference for

BP and TP and also for BP and DOC. We found

higher concentrations of DOC and higher levels of

humic substances (lower HS ratio) in the littoral zone

than in the pelagic, which likely reflects release of

organic substances from macrophytes. This should

potentially stimulate bacterioplankton metabolism,

since higher concentrations of substrates typically

result in higher bacterioplankton production (Cotner

& Biddanda, 2002). However, bacterioplankton bio-

mass and production tended to be higher in the

pelagic zone in most lakes. Several factors may be

involved, including (i) reduced availability of nutri-

ents, as we found reduced concentrations of phos-

phorus in the littoral zones associated with

macrophyte presence; (ii) allelopathic effects of

macrophytes on phytoplankton, leading indirectly to

a reduction in bacterioplankton production as less

algal derived extracellular labile carbon is released

(Korner & Nicklisch, 2002; Van Donk & Van de

Bund, 2002; Leu et al., 2002). However, we found a

tendency to higher chlorophyll a concentrations

(Table 1, Fig 3) in the littoral zone (except for the

wind-exposed largest lake). Besides, direct allelo-

pathic effects on bacteria may occur, as previously

suggested by Wu et al. (2007), and seemingly

suggested in our study by dominance of the coccus

form in all samples (the morphotype with the highest

surface:volume ratio) and low detection of bacteria

by FISH probes, factors that may both indicate

metabolic deprivation (Cotner & Biddanda, 2002;

Bouvier & del Giorgio, 2003); and (iii) grazing may

potentially be an important factor controlling bacte-

rial biomass and morphotypes (by selecting higher,

fast growing cells), and it may differ among the

pelagic and littoral zone due to differences in

macrophyte coverage. Studies in temperate lakes

have shown substantially lower concentrations of

ciliates and flagellates and lower bacterioplankton

biomass in dense macrophyte beds, which could be

attributed to much higher grazing by macrozooplank-

ton hiding in the vegetation to avoid fish predation

(Jurgens & Jeppesen, 1997; Jeppesen et al., 2002).

Unfortunately, we have only information about the

density of ciliates and we found no direct, univariate

relationship (only multivariate and when embedded

in the environmental similarity matrix), between

ciliate density and any of the bacterioplankton

variables. However, a recent study from tropical

lakes revealed a lower bacterioplankton biomass in

tropical freshwaters than in temperate lakes with a

similar trophic state, which likely indicates higher

grazing on bacterioplankton in the tropics (Lobao

et al., unpublished data), where bacterioplankton

grazers are dominated by small-sized forms of

crustaceans, rotifers, ciliates and heterotrophic fla-

gellates due to high fish predation on macrozoo-

plankton (Jeppesen et al., 2007; Meerhoff et al.,

2007a, b; Havens et al., 2009). Nevertheless, little is

known about differences in grazing inside and outside

the vegetation in (sub)tropical lakes, where plants

constitute a poor refuge for zooplankton due to high

densities of fish inside the plant beds (Meerhoff et al.,

2007a, b).

The bacterioplankton assembly may composition-

ally and functionally reflect the whole lake condi-

tions; however, it is of major importance to stress that

we only considered the water phase in our study.

There is growing evidence that shallow aquatic

environments may present higher rates of bacterial

production in sediment and comparable rates in the

epiphytic environment when compared to the pelagic

environment (Fischer & Pusch, 2001; Vadeboncoeur

et al., 2002). Do bacteria in the two habitats, benthic

(sediment ? epiphytes) and plankton, rely on the

same pools of DOC and nutrients? This question

raised by Vadeboncouer et al. (2002) still remains

unanswered; is it likely that the benthic bacteria are

taxonomically different from the adjacent bacterio-

plankton and therefore adapted to thrive under high

loads of humic acids, allelopathic substances and

lower concentrations of nutrients, conditions less

suitable for their planktonic counterpart?

Regardless of the cause for the apparently lower

bacterioplankton biomass and production in littoral

zones, the implication is lower turnover of carbon in

the water, implying that less dissolved carbon,

proportionally to the pelagic zone, is reintroduced

into the food web through bacterivory (as

Hydrobiologia (2010) 646:311–326 323

123

demonstrated elsewhere, Azam et al., 1983). Never-

theless, it is important to include in future studies

measures of epiphytic bacterial metabolism, as these

may counterbalance this deficit. Understanding the

underlying mechanisms involved in these patterns is

essential for accurately assessing whole lake metab-

olism in (sub)tropical lakes, and controlled follow-up

experiments are needed to elucidate this topic.

Acknowledgements This work was supported by the Brazilian

agency CNPq/MCT Taim Hydrological System, Site 7, part of

the Brazilian network of Long Term Ecological Research-PELD

(Grant 520017-98-1) and undertaken via the Programa de

Pos-Graduacao em Ecologia, UFRGS and Instituto de Pesquisas

Hidraulicas, UFRGS. Special thanks go to Dr. Luciane Crossetti for

phytoplankton analyses, Dr. Laura Utz for helping with ciliates

handling, Dr. Luiz Kucharski for lending us the scintillator counter;

Dr. Tiago Finkler Ferreira for macrophyte coverage data, valuable

suggestions and comments and Anne Mette Poulsen for editorial

assistance. EJ and MS were supported by the Villum Kann

Rasmussen Foundation (CLEAR project), The Research Council

for Nature and Universe (272-08-0406) and the EU projects

EUROLIMPACS and WISER.

References

American Public Health Association (APHA), 1999. Standard

Methods for the Examination of Water and Wastewater,

20th ed. American Public Health Association Inc.,

Washington DC.

Azam, F., J. G. Fenchel, J. G. Field, J. S. Gray, L. A. Meyer-

Reyl & F. Thingstad, 1983. The ecological role of water-

column microbes in the sea. Marine Ecology Progress

Series 10: 257–263.

Bell, T., D. Ager, J.-I. Song, J. A. Newmann, I. P. Thompson,

A. K. Lilley & C. J. van der Gast, 2005. Larger islands

house more bacterial taxa. Science 308: 884.

Bouvier, T. & P. A. del Giorgio, 2003. Factors influencing the

detection of bacterial cells using fluorescence in situ

hybridization (FISH): a quantitative review of published

reports. FEMS Microbiology Ecology 44: 3–15.

Burks, R. L., E. Jeppesen & D. M. Lodge, 2001. Littoral zone

structures as Daphnia refugia against fish predators.

Limnology and Oceanography 46: 230–237.

Cerbin, S., E. Van Donk & R. D. Gulati, 2007. The influence of

Myriophyllum verticillatum and artificial plants on some

life history parameters of Daphnia magna. Aquatic

Ecology 41: 263–271.

Cotner, J. B. & B. A. Biddanda, 2002. Small players, large

role: microbial influence on biogeochemical processes in

pelagic aquatic ecosystems. Ecosystems 5: 105–121.

Crisman, T. L. & J. R. Beaver, 1990. Applicability of plank-

tonic biomanipulation for 6 managing eutrophication in

the subtropics. Hydrobiologia 200: 177–185.

del Giorgio, P. A., J. J. Cole & A. Cimbleris, 1997. Respiration

rates in bacteria exceed phytoplankton production in

unproductive aquatic systems. Nature 385: 148–151.

Fernandez, A. S., S. A. Hashsham, S. L. Dollhopf, L. Raskin,

O. Glagoleva, F. B. Dazzo, F. Hickey, C. A. Criddle &

J. M. Tiedje, 2000. Flexible community structure corre-

lates with stable community function in methanogenic

bioreactor communities perturbed by glucose. Applied

and Environmental Microbiology 66: 4058–4067.

Fischer, H. & M. Pusch, 2001. Comparison of bacterial pro-

duction in sediments, epiphyton and the pelagic zone of a

lowland river. Freshwater Biology 46: 1335–1348.

Glockner, F. O., B. M. Fuchs & R. Amann, 1999. Bacterio-

plankton compositions of lakes and oceans: a first com-

parison based on fluorescence in situ hybridization. Applied

and Environmental Microbiology 65: 3721–3726.

Google Earth, 2008. Accessed on: 06/2008.

Gotelli, N. J., 2007. Ecologia, 3rd edn. Planta, Brazil: 8.

Translation: A Primer of Ecology.

Green, J. & B. J. M. Bohannan, 2006. Spatial scaling of

microbial diversity. Trends in Ecology and Evolution 21:

501–507.

Gross, E., S. Hilt, P. Lombardo & G. Mulderij, 2007. Searching

for allelopathic effects of submerged macrophytes on

phytoplankton – state of the art and open questions.

Hydrobiologia 584: 77–88.

Gyllstrom, M., L.-A. Hansson, E. Jeppesen, F. Garcıa-Criado,

E. Gross, K. Irvine & T. Kairesalo, 2005. The role of

climate in shaping zooplankton communities of shallow

lakes. Limnology and Oceanography 50(6): 2008–2021.

Hammer, Ø., D. A. T. Harper & P. D. Ryan, 2001. PAST:

palaentological statistics software package for education

and data analysis. Palaentologia Electronica 4: 1–9.

Havens, K. E., A. C. Elia, M. I. Taticchi & R. S. Fulton, 2009.

Zooplankton–phytoplankon relationships in shallow sub-

tropical vs. temperate lakes Apopka (Florida, USA) and

Trasimeno (Umbria, Italy). Hydrobiologia 628: 165–175.

Hillebrand, H., D. Durseken, D. Kirschiel, U. Pollingher &

T. Zohary, 1999. Biovolume calculation for pelagic and

benthic microalgae. Journal of Phycology 35: 403–424.

Hilt, S. & E. Gross, 2008. Can allelopathically active submerged

macrophytes stabilise clear-water states in shallow lakes?

Basic and Applied Ecology 9: 422–432.

Hilt, S., M. G. N. Ghobrial & E. M. Gross, 2006. In situ allelo-

pathic potential of Myriophyllum verticillatum (Haloraga-

ceae) against selected phytoplankton species. Journal of

Phycology 42: 1189–1198.

Holz, M., 1999. Do mar ao deserto. A Evolucao do Rio Grande

do Sul no Tempo Geologico, 2nd edn. UFRGS, Porto

Alegre: 131–144, 144 pp (Portuguese).

Horner-Devine, M. C., M. Lage, J. B. Hughes & B. J. M.

Bohannan, 2004. A taxa–area relationship for bacteria.

Nature 432: 750–753.

Huss, A. A. & J. D. Wehr, 2004. Strong indirect effects of a

submersed aquatic macrophyte, Vallisneria americana, on

bacterioplankton densities in a mesotrophic lake. Micro-

bial Ecology 45: 305–315.

IBGE, 2005. Brazilian Institute of Geography and Statistics

(Fundacao Instituto Brasileiro de Geografia e Estatıstica,

2008). Brazilian cartographic digital base (1:1000000)

version 2003 (Portuguese): 26, Fig. 1: IBGE.

Jeppesen, E., M. Søndergaard, M. Søndergaard, K. Christof-

fersen, K. Jurgens, J. Theil-Nielsen & L. Schluter, 2002.

Cascading trophic interactions in the littoral zone: an

324 Hydrobiologia (2010) 646:311–326

123

enclosure experiment in shallow Lake Stigsholm, Den-

mark. Archiv fur Hydrobiologie 153: 533–555.

Jeppesen, E., M. Meerhoff, B. A. Jakobsen, R. S. Hansen, M.

Søndergaard, J. P. Jensen, T. L. Lauridsen, N. Mazzeo &

C. Branco, 2007. Restoration of shallow lakes by nutrient

control and biomanipulation – the successful strategy

depends on lake size and climate. Hydrobiologia 581:

269–288.

Jespersen, A.-M. & K. Christoffersen, 1987. Measurements of

chlorophyll-a from phytoplankton using ethanol as

extraction solvent. Archiv fur Hydrobiologie 109: 445–

454.

Jurgens, K. & E. Jeppesen, 1997. Cascading effects on

microbial food web structure in a dense macrophyte

canopy. In Jeppesen, E., M. Søndergaard, M. Søndergaard

& K. Christoffersen (eds), The Structuring Role of Sub-

merged Macrophytes in Lakes. Ecological Studies, Vol.

131. Springer Verlag, New York: 262–274.

Kepner, R. L. J. R. & J. R. Pratt, 1994. Use of fluorochromes

for direct enumeration of total bacteria in environmental

samples: past and present. Microbiological Reviews 58:

603–615.

Kirchman, D., 2001. Measuring bacterial biomass production

and growth rates from leucine incorporation in natural

aquatic environments. In Paul, J. H. (ed.), Methods in

Microbiology, Vol. 30. Academic Press, USA: 227–237.

Korner, S. & A. Nicklisch, 2002. Allelopathic growth inhibi-

tion of selected phytoplankton species by submerged

macrophytes. Journal of Phycology 38: 862–871.

Leu, A., A. Krieger-Liszkay, C. Goussias & E. M. Gross, 2002.

Polyphenolic allelochemicals from the aquatic angio-

sperm Myriophyllum spicatum inhibit photosystem II.

Plant Physiology 130: 2011–2018.

Lindstrom, E. S. & E. Leskinen, 2002. Do neighboring lakes

share common taxa of bacterioplankton? Comparison of

the 16S rDNA fingerprints and sequences from three

geographic regions. Microbial Ecology 44: 1–9.

Liu, J., F. B. Dazzo, O. Glagoleva, B. Yu & A. K. Jain, 2001.

CMEIAS: a computer-aided system for the image analysis

of bacterial morphotypes in microbial communities.

Microbial Ecology 41: 173–194.

Mackereth, F. J. H., J. Heron & J. F. Talling, 1989. Water

Analysis: Some Revised Methods for Limnologists, 2nd

ed. Freshwater Biological Association, USA.

Madsen, J., 1999. Point Intercept and Line Intercept Methods

for Aquatic Plant Management. Aquatic Plant Control

Technical Note. APCRP: 11 pp.

Massana, R., J. M. Gasol, P. K. Bjørnsen, N. Black-Burn, A.

Hagstrom, S. Hietanen, B. H. Hygum, J. Kuparinen &

C. Pedros-Alio, 1997. Measurement of bacterial size via

image analysis of epifluorescence preparations: descrip-

tion of an inexpensive system and solutions to some of the

most common problems. Scientia Marina 61: 397–407.

Meerhoff, M., F. T. M. Clemente, C. Iglesias, A. R. Pedersen &

E. Jeppesen, 2007a. Can warm climate-related structure of

littoral predator assemblages weaken the clear water state

in shallow lakes? Global Change Biology 13: 189–1888.

Meerhoff, M., C. Iglesias, F. T. de Mello, J. M. Clemente, E.

Jensen, T. L. Lauridsen, E. Jeppesen, et al., 2007b. Effects

of habitat complexity on community structure and pred-

ator avoidance behaviour of littoral zooplankton in

temperate versus subtropical lakes. Freshwater Biology

52(6): 1009–1021.

Norland, S., 1993. The relationship between biomass and volume

of bacteria. In Kemp, P. F., B. F. Sherr, E. B. Sherr & J. J.

Cole (eds), Handbook of Methods in Aquatic Microbial

Ecology. Lewis Publishers, USA: 339–345, 777 pp.

Pernthaler, J., G. Frank-Oliver, W. Schonhuber & R. Amann,

2001. Fluorescence in situ hybridization (FISH) with

rRNA-targeted oligonucleotide probes. In Paul, J. H. (ed.),

Methods in Microbiology, Vol. 30. Academic Press, USA:

207–226.

Pinel-Alloul, B. & A. Ghadouani, 2007. Spatial heterogeneity

of planktonic microorganisms in aquatic systems. In

Franklin, R. B. & A. L. Mills (eds), The Spatial Distri-

bution of Microbes in the Environment. Springer, New

York: 203–310.

Pommier, T., B. Canback, L. Riemann, K. H. Bostrom, K. Simu,

P. Lundberg, A. Tunlid & A. Hagstrom, 2007. Global pat-

terns of diversity and community structure in marine bac-

terioplankton. Molecular Ecology 16: 867–880.

Racy, F. P. P., 2004. Aspectos numericos, morfologicos de

morfometricos da comunidade bacteriana em diferentes

escalas trofica e temporal, em reservatorios. Master Thesis

(Universidade Federal de Sao Carlos): 65 pp (Portuguese).

Reche, I., E. Pulido-Villena, R. Morales-Baquero & E. O.

Casamayor, 2005. Does ecosystem size determine aquatic

bacterial richness? Ecology 86: 1715–1722.

Rooney, N. & J. Kalff, 2003. Submerged macrophyte-bed

effects on water-column phosphorus, chlorophyll a, and

bacterial production. Ecosystems 6: 797–807.

Schafer, A., 1988. Tipificacao ecologica das lagoas costeiras do

Rio Grande do Sul, Brasil. Acta Limnolica Brasiliensia

11: 29–55. (Portuguese).

Scheffer, M. & E. Jeppesen, 1997. Alternative stable states.

In Jeppesen, E., M. Søndergaard, M. Søndergaard &

K. Christoffersen (eds), The Structuring Role of Sub-

merged Macrophytes in Lakes. Ecological Studies, Vol.

131. Springer Verlag, New York: 397–406.

Schriver, P., J. Bøgestrand, E. Jeppesen & M. Søndergaard,

1995. Impact of submerged macrophytes on fish-

zooplankton-phytoplankton interactions: large-scale enclo-

sure experiments in a shallow eutrophic lake. Freshwater

Biology 33: 255–270.

Schwarzbold, A. & A. Schafer, 1984. Genese e morfologia das

lagoas costeiras do Rio Grande do Sul – Brasil. Amaz-

oniana 9: 87–104. (Portuguese).

Shade, A., A. E. Jones & K. D. McMahon, 2008. The influence

of habitat heterogeneity on freshwater bacterial commu-

nity composition and dynamics. Environmental Microbi-

ology 10: 1057–1067.

Simon, D. C. & F. Azam, 1992. A simple, economical method for

measuring bacterial protein synthesis rates in sea water using

3H-leucine. Marine Microbial Food Webs 6: 107–109.

Søndergaard, M. & B. Moss, 1997. Impact of submerged

macrophytes on phytoplankton in shallow freshwater

lakes. In Jeppesen, E., M. Søndergaard, M. Søndergaard &

K. Christoffersen (eds), The Structuring Role of Sub-

merged Macrophytes in Lakes. Ecological Studies, Vol.

131. Springer Verlag, New York: 115–132.

Søndergaard, M. & R. G. Wetzel, 1997. The role of submersed

macrophytes for the microbial community and dynamics

Hydrobiologia (2010) 646:311–326 325

123

of dissolved organic carbon in aquatic ecosystems. In

Jeppesen, E., M. Søndergaard, M. Søndergaard & K.

Christoffersen (eds), The Structuring Role of Submerged

Macrophytes in Lakes. Ecological Studies, Vol. 131.

Springer Verlag, New York: 133–148.

Søndergaard, M., J. Theil-Nielsen, K. Christoffersen, L.

Schluter, E. Jeppesen & M. Søndergaard, 1997. Bacte-

rioplankton and carbon turnover in a dense macrophyte

canopy. In Jeppesen, E., M. Søndergaard, M. Søndergaard

& K. Christoffersen (eds), The Structuring Role of Sub-

merged Macrophytes in Lakes. Ecological Studies, Vol.

131. Springer Verlag, New York: 250–262.

Søndergaard, M., E. Jeppesen & J. P. Jensen, 2005. Pond or

lake: does it make any difference? Archiv fur Hydrobi-

ologie 162: 143–165.

Stanley, E. H., M. D. Johnson & A. K. Ward, 2003. Evaluating

the influence of macrophytes on algal and bacterial pro-

duction in multiple habitats of a freshwater wetland.

Limnology and Oceanography 48: 1101–1111.

Sun, J. & D. Liu, 2003. Geometric models for calculating cell

biovolume and surface area for phytoplankton. Journal of

Plankton Research 25: 1331–1346.

Vadeboncoeur, Y., M. J. V. Zanden & D. M. Lodge, 2002.

Putting the lake back together: reintegrating benthic path-

ways into lake food web models. Bioscience 52: 44–54.

van der Gucht, K., K. Cottenie, K. Muylaert, N. Vloemans, S.

Cousin, S. Declerck, E. Jeppesen, J.-M. Conde-Porcuna,

K. Schwenk, G. Zwart, H. Degans, W. Vyverman & L. D.

Meester, 2007. The power of species sorting: local factors

drive bacterial community composition over a wide range

of spatial scales. Proceedings of the National Academy of

Sciences 104: 20404–20409.

Van Donk, E. & W. J. Van de Bund, 2002. Impact of sub-

merged macrophytes including charophytes on phyto- and

zooplankton communities: allelopathy versus other

mechanisms. Aquatic Botany 72: 261–274.

Wetzel, R. G. & G. E. Likens, 2000. Limnological Analyses,

3rd ed. Springer-Verlag, New York: 429 pp.

Wolda, H., 1981. Similarity indices, sample size and diversity.

Oecologia 50: 296–302.

Wu, Q. L., G. Zwart, J. Wu, P. Kamst-van Agterveld, S. Liu &

M. W. Hahn, 2007. Submersed macrophytes play a key

role in structuring bacterioplankton community composi-

tion in the large, shallow, subtropical Taihu Lake, China.

Environmental Microbiology 9: 2765–2774.

Yannarell, A. C. & E. W. Triplett, 2004. Within- and between-

lake variability in the composition of bacterioplankton

communities: investigations using multiple spatial scales.

Applied and Environmental Microbiology 70: 214–223.

Zdanowski, M. K., M. J. Zmuda & I. Zwolska, 2005. Bacterial

role in the decomposition of marine-derived material

(penguim guano) in the terrestrial maritime Antarctic. Soil

Biology & Biochemistry 37: 581–595.

326 Hydrobiologia (2010) 646:311–326

123