FEMS Microbiology Ecology 49 (2004) 281–293

www.fems-microbiology.org

Diversity of planktonic photoautotrophic microorganisms alonga salinity gradient as depicted by microscopy, flow

cytometry, pigment analysis and DNA-based methods

Marta Estrada a,*, Peter Henriksen b, Josep M. Gasol a, Emilio O. Casamayor c,Carlos Pedr�os-Ali�o a

a Institut de Ci�encies del Mar, CMIMA (CSIC), Pg. Mar�ıtim de la Barceloneta, 37-49, 08003 Barcelona, Spainb Department of Marine Ecology, National Environmental Research Institute, DK-4000, Roskilde, Denmark

c Unitat de Limnologia, Centre d’Estudis Avanc�ats de Blanes (CSIC), Acc�es a la Cala St. Francesc, 14, 17300 Blanes, Spain

Received 12 December 2003; received in revised form 24 March 2004; accepted 1 April 2004

First published online 20 April 2004

Abstract

The diversity of prokaryotic and eukaryotic phytoplankton was studied along a gradient of salinity in the solar salterns of Bras

del Port in Santa Pola (Alacant, Spain) using different community descriptors. Chlorophyll a, HPLC pigment composition, flow

cytometrically-determined picoplankton concentration, taxonomic composition of phytoplankton (based on optical microscopy)

and genetic fingerprint patterns of 16S (cyanobacteria- and chloroplast-specific primers) and 18S rRNA genes were determined for

samples from ponds with salinities ranging from 4% to 37%. Both morphological and genetical descriptors of taxonomic compo-

sition showed a good agreement and indicated a major discontinuity at salinities between 15% and 22%. The number of classes and

the Shannon diversity index corresponding to the different descriptors showed a consistent decreasing trend with increasing salinity.

The results indicate a selective effect of extremely high salinities on phytoplanktonic assemblages.

� 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

Keywords: Diversity; Solar salterns; Plankton; Pigments; Genetic fingerprinting

1. Introduction

Diversity, which can be defined as the richness of

biological elements – such as genes, species or genera –

in a community, has been linked to a number of eco-

system processes. However, in spite of much theoretical

and empirical work, the relationships between different

ecosystem properties such as diversity or productivitycontinue to be open to debate. For example, Lehman

and Tilman [1] and Hector et al. [2] have suggested that

increased diversity leads to increased productivity, while

others [3,4] have challenged this conclusion. Another

important open question is to what extent biotic factors,

* Corresponding author. Tel.: +34-93-2309500; fax: +34-93-2309555.

E-mail address: marta@icm.csic.es (M. Estrada).

0168-6496/$22.00 � 2004 Federation of European Microbiological Societies

doi:10.1016/j.femsec.2004.04.002

such as predation or competition, or abiotic factors such

as habitat harshness, heterogeneity or size, control di-

versity in each particular system. Some theoretical work

[5,6] has suggested that interactions within a commu-

nity may lead to fluctuations in species abundances.

King et al. [7] have shown that differences in species

composition in vernal pools in California resulted from

physico-chemical differences in the habitat. Moreover,Therriault and Kolasa [8] studied 49 coastal pools in

Jamaica and concluded that much of the species richness

was determined by the abiotic pool conditions either

directly or indirectly (after modulation by biotic inter-

actions), while biotic factors appeared to be more im-

portant in controlling species population densities.

Based on general ecological theory, authors like Fron-

tier [9] have suggested that an extreme environmentcould be expected to be less diverse.

. Published by Elsevier B.V. All rights reserved.

282 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

As pointed out by Margalef [10], any attempt to

quantify diversity needs to take into account both the

spatial structure and temporal dynamics of ecosystems.

For example, species richness in a sample of a particular

ecosystem depends, among other factors, on the methodused to examine the organisms and on sample size. In

fact, the curvature of the relationship between species

richness and sample size conveys important information

on the ecosystem structure. This dependence of mea-

surements on the chosen spatial and temporal scales

hinders testing of hypotheses on the factors controlling

biodiversity and its relationships with other ecosystem

properties. In this context, the microbial communitiesinhabiting solar salterns offer attractive possibilities.

Solar salterns comprise typically a gradient of environ-

ments with salinities ranging from that of seawater to

sodium chloride saturation or even beyond. Thus, in a

limited space, salterns may present conditions [11,12]

ranging from those of one of the most common habitats

in the planet (seawater) to one of the most extreme en-

vironments on earth (brines). The relatively small ex-tension of salterns limits the number and scale of sources

of variability to be considered. In addition, temperature

and salinity conditions of each location are kept rela-

tively constant at time scales of weeks, due to the mode

of functioning of these systems, in which water evapo-

ration in the high salinity ponds is compensated with

less saline water from the sea or neighbouring ponds, fed

by pumping or gravity.The different ponds in the salterns provide habitats

for many planktonic prokaryotic and eukaryotic pro-

tists. The autotrophic microbes (including prokaryotes

such as cyanobacteria and eukaryotes like diatoms and

other algae) found in hypersaline environments, have

often been the subject of taxonomic and physiological

research [11–16]. However, although the existence of a

decreasing trend in the number of microbial species assalinity increases has been reported [11,17,18], there is a

lack of quantitative attempts to test this relationship.

One of the underlying problems is the need for robust

measures of microbial diversity. Quantification of this

parameter requires the grouping of individual elements

into non-overlapping classes, according to a consistent

classification criterion [19,20]. The use of species as the

basic unit of grouping is in principle desirable, but inpractice it is difficult to apply to microorganisms be-

cause special preparations or even culture techniques are

required. As a consequence, studies of the diversity of

microbial autotrophs have been generally based on

morphological analyses leading to a combination of

taxonomic identification and morphotypic categories. A

new approach was introduced by Li [21], who applied

diversity concepts to phytoplankton categorized by flowcytometry measurements related to size and pigment

content. HPLC pigment analyses and more recently,

molecular genetics techniques, have provided additional

cultivation-independent methods for determining di-

versity, but there are very few examples of their parallel

use in the same community [22].

The aims of this paper are: (1) to quantify the rela-

tionships between the salinity gradient and the diversityof phototrophic microorganisms, using an array of

methods including morphology by microscopic obser-

vations, pigment composition by HPLC, flow cytome-

try-identified populations and DNA-based approaches

(genetic fingerprinting based on the prokaryote and

eukaryote small subunit (SSU) rDNA gene sequences),

(2) to test whether measures of diversity based on dif-

ferent properties of the same group (like pigments ormorphology for autotrophic microbes) offer consistent

results, and (3) to determine whether the components of

the microbial community described by the different di-

versity estimates (e.g. microalgal morphotypes, flow

cytometric populations, prokaryotic and eukaryotic

microorganisms represented by DGGE bands) show

similar or different patterns of diversity variation. The

work was part of a series of joint experiments, carriedout between 17 and 28 May 1999, in the ‘‘Bras del Port’’

salterns in Santa Pola, Alacant, Spain (38�120N,

0�360W).

2. Materials and methods

2.1. Study site

The Bras del Port salterns, devoted to year-round

commercial salt extraction, consist of over 100 shallow

ponds (depth <1 m). Research dealing with different

aspects of the microbial food web and the composition

of the bacterial and archaeal populations in the salterns

has been published in [11,23–26]. The work described in

this paper is based on two ‘‘salinity gradient’’ surveyscarried out, respectively, on 18 and 26 May, 1999. Water

samples for the surveys were taken with a plastic bucket,

from eight to nine ponds with salinities ranging from 4%

to 37% (the last one is called crystallizer). Salinities were

determined with a hand-held refractometer [11]. Infor-

mation dealing with the molecular biodiversity of mi-

crobes and trophic relationships in the salterns, during

the study period, can be found in [27–32].

2.2. Pigment determinations

Pigment analyses were carried out by HPLC, ac-

cording to the method of Wright et al. [33]. Water

samples were kept in the dark, filtered through glass fi-

bre filters (Advantec GF 75, Toyo Roshi Kaisha, Japan)

and stored frozen in liquid nitrogen generally withinthree hours of sampling. Filters were subsequently

transferred to 3 ml acetone, sonicated on ice for 15 min,

and left to extract for 24 h at 4� C prior to filtering (0.2

Table 1

Phytoplankton taxa and morphotypes identified by optical microscopy

1 Amphidinium sp.

2 Gymnodinium sanguineum(¼Akashiwo sanguinea)

3 Oxyrrhis marina

4 Pentapharsodinium tyrrhenicum

5 Prorocentrum lima

6 Prorocentrum scutellum

7 Prorocentrum triestinum

8 Scrippsiella ‘‘trochoidea-like’’, small

10 Scrippsiella sp.

11 Unidentified dinoflagellates, small

12 Unidentified dinoflagellates, large

13 Cysts? A

14 Cysts? B

15 Amphora sp.

16 Amphora coffaeformis

17 Nitzschia cf. closterium

18 Gyrosigma sp., large

19 Gyrosigma sp., small

20 Nitzschia ‘‘sigma-like’’, large

21 Unidentified pennate diatoms A, small

22 Unidentified pennate diatoms B, small

23 Unidentified pennate diatoms, large

24 Dictyocha fibula

25 Aphanothece spp.

26 Spirulina spp.

27 Cyanobacteria, unicells <2 lm diam.

28 Cyanobacteria, unicells >3 lm diam.

29 Cyanobacteria, unicells >6 lm diam.

30 Cyanobacteria, rectangular unicells

31 Cyanobacteria, filaments >20 lm diam. in 10 lm cell eq.

32 Cyanobacteria, filaments <15 lm diam. in 10 lm cell eq.

33 Cyanobacteria, filaments, rectang. cells

34 Cyanobacteria, filaments, orange color

35 Chroococcales, rounded

36 Chroccocales, elongated

37 Unidentified flagellates, small

38 Green flagellates

39 Unidentified flagellates, large

40 Cryptophyceae

41 Leucocryptos?

42 Haptophyceae

43 Dunaliella cf. salina

44 Dunaliella ‘‘viridis’’, small

45 Mesodinium sp.

M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293 283

lm) 1 ml extract into HPLC-vials and mixing with 300

ll water. HPLC analyses were performed on a Shima-

dzu LC 10A system with a Supelcosil C18 column

(250� 4.6 mm, 5 lm). Pigments were identified by re-

tention times and absorption spectra identical to thoseof authentic standards, and quantified against standards

purchased from DHI Water & Environment, Hørsholm,

Denmark.

The contribution of different algal groups was esti-

mated using the CHEMTAX program [34]. The pigment

composition of Dunaliella cf. salina was calculated using

pigment data from samples with different concentrations

of Dunaliella. The resulting ratios were introduced intothe initial pigment ratio matrix of the program, which

was taken from Henriksen et al. [35]. Given that these

initial ratios reflect mainly major trends in pigment

composition, the results of CHEMTAX should be

considered with caution when dealing with minor con-

tributions. The CHEMTAX calculations were carried

out to allow comparison with inverted microscopy

counts. The number of algal classes present, as derivedfrom CHEMTAX, was used in the diversity estimates.

Other diversity indices (see below) were calculated di-

rectly from the different pigment concentrations.

2.3. Phytoplankton composition

The abundance of nano and microplankton was de-

termined by the inverted microscope technique [36].Samples of 100 ml of water were placed in Pyrex bottles

and fixed immediately with 0.4% final concentration of

formaldehyde neutralized with hexamethylenetetramine

[37]. For microscopic observation, volumes of 10 ml

were introduced in sedimentation chambers and allowed

to settle for at least 24 h. The whole bottom of the

chamber was examined to count the larger and less

abundant organisms. Cells were assigned to the lowestpossible taxonomic category. However, in many cases it

was only feasible to classify the organisms into mor-

photypes. This happened, in particular, for flagellates

and cyanobacteria (Table 1). All dinoflagellates were

included, although some of them are heterotrophic or

mixotrophic. Dunaliella spp. includes at least a large

(Dunaliella cf. salina, approximately 16 lm� 12 lm)

and a small form (9 lm� 5 lm). In the case of fila-mentous cyanobacteria, the total length of filaments of

each morphotype was transformed into cell number by

assuming an arbitrary cell length of 10 lm. It must be

noted that the inverted microscope method is not ade-

quate for picoplankton-sized organisms and that many

forms degrade rapidly in fixed samples. For consistency,

the term ‘‘phytoplankton’’ will be used to designate the

set of organisms included in the inverted microscopecounts, although some of them, like heterotrophic fla-

gellates, may not be considered as ‘‘phytoplankton’’

sensu stricto.

2.4. Flow cytometry

Samples for flow cytometry were fixed with parafor-

maldehyde and glutaraldehyde (1%+0.05% final con-

centrations), deep frozen in liquid nitrogen and later

stored at )80� C. In the laboratory, 1 lm yellow-green

latex Polysciences beads were added to 0.4 ml subsam-

ples as an internal standard and run in a Becton &Dickinson flow cytometer FACScalibur bench machine

with a laser emitting at 488 nm. The subsamples were

diluted 2� to 4� to reduce effective salinity and prevent

optical problems related to the difference of salinities

between the sheath fluid (MilliQ water) and the sample,

although we did not use the values of forward scatter

284 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

which are those most affected by density differences.

Samples were run at the highest possible speed (around

60 ll min�1) and 15,000 events were acquired in log

mode. Abundances were calculated by the ratiometric

method from the known amount of added beads, cali-brated daily against TrueCount (Becton & Dickinson)

beads. We differentiated several algal populations by

their FL2 (orange fluorescence) vs. FL3 (red fluores-

cence) and by their SSC (side scatter) vs. FL3 signatures.

For example, cells with a SSC similar to that of the

beads, and with similar FL2 and FL3 signatures were

considered to be Synechococcus following standard

procedures [38,39].

2.5. Genetic fingerprinting

Genetic diversity determinations were carried out for

the samples obtained from the different ponds on the

survey of 18 May. The molecular methodology used in

this study was based on denaturing gradient gel elec-

trophoresis (DGGE) separation of 16S and 18S rRNAgene segments amplified by PCR. A detailed account of

the PCR-fingerprinting procedures used can be found

elsewhere [22,28,40]. Briefly, microbial biomass was

collected from different water volumes (see Table 1 in

[28]) using a peristaltic pump in 0.2 lm Sterivex filters

(Millipore Corp., Bedford, MA) and DNA was further

extracted with proteinase K, SDS and phenol–chloro-

form–isoamylalcohol, according to the ICM protocoldetailed in [28]. Purified DNA was amplified by PCR

using two primer combinations and protocols. Thus,

fragments of the 16S rRNA gene suitable for subsequent

DGGE-analysis were obtained with one primer combi-

nation specific for oxygenic phototrophs (CYA359 for-

ward-GC clamp: 50-CGC CCG CCG CGC CCC GCG

CCC GTC CCG CCG CCC CCG CCC G- GGG GAA

TYT TCC GCA ATG GG-30, and CYA781 reverse: 50-GAC TAC T/A GG GGT ATC TAA TCC C A/T T T-

30) targeting the 16S rRNA genes of cyanobacteria and

algal chloroplasts [22]. In addition, some 16S rRNA

gene bands were excised from the gel and sequenced as

reported [40]. Sequences were submitted to BLAST

(www.ncbi.nlm.nih.gov) for a first phylogenetic affilia-

tion. Nucleotide sequence accession numbers at EMBL

are AJ580966 to AJ580973.The second primer combination targeted 18S rRNA

eukaryotic genes and the genetic fingerprints were ob-

tained from a former work (Fig. 3 in [28]). DGGE gels

were stained with a solution of GelStar (1:5000 dilution;

FMC BioProducts) and the band patterns were visual-

ized under UV radiation with the Fluor-S MultiImager

(Bio-Rad) and the Multi-Analyst software (Bio-Rad).

High resolution digitized images were processed with theDiversity Database (Bio-Rad) software. The program

carried out a density profile through each lane, detected

the bands, and calculated the relative contribution of

each band to the total band signal in the lane. A band

was defined as a stain signal whose intensity was more

than 0.2% of the total intensity for each lane.

2.6. Estimates of diversity

The number of classes (Sx) and their relative abun-

dance was determined for each of the community de-

scriptors (x) considered (x¼M, light microscopy

phytoplankton; x¼F, flow cytometry picoplankton;

x¼P, HPLC pigments; x¼ 16S or 18S, DGGE bands

for oxygenic phototrophs and eukaryotes, respectively).

In the case of light microscopy and flow cytometriccounts, the abundance of each taxon or population was

given as cell numbers per unit volume. For the HPLC

analyses, the descriptors were the different pigments

detected and diversity was calculated from their con-

centrations (lg l�1). Numbers and proportional abun-

dances of rRNA genes were estimated from the

denaturing electrophoresis gels using the Multi-Analyst

image analysis software facilities. We were aware ofbiases of PCR-based methods and we were cautious in

the number of PCR cycles carried out and in using the

same amount of template in each reaction. The samples

that we compared were all run in the same PCR and

analyzed in the same DGGE gel. Therefore, any biases

should have been the same for all samples and the

comparison would still be valid. The 16S rRNA finger-

prints were taken as representative of the genetic di-versity of cyanobacteria and plastids from algae. The

18S rRNA fingerprints are characteristic of eukaryotes,

both autotrophic and heterotrophic.

The Shannon diversity index (Dx) was calculated for

all descriptors [41] as:

Dx ¼ �XS

i¼1

pi log2 pi;

where S is the number of classes, pi is the relative

abundance of class i (P

pi ¼ 1) and x stands for M, F, P

16S or 18S, depending on the descriptor considered.

In addition, the index KM ¼ log SM= logN [42], where

SM is the number of taxa and N the total number of

individuals, was also calculated for the inverted mi-croscopy phytoplankton counts. This index is less in-

fluenced by the distribution of abundances of the

different taxa (or evenness).

3. Results

3.1. Pigment analyses and CHEMTAX results

Chlorophyll a (Chl a) concentration (Fig. 1) pre-

sented maxima in the 8% pond and in the 37% crystal-

lizers. Between the first and second surveys, Chl a

0

5

10

15

20

0 5 10 15 20 25 30 35 40

18 May 199926 May 1999

Chl

a (

µg l-1

)

Salinity (%)

Fig. 1. Chlorophyll a concentration determined by HPLC analysis in

the ponds sampled on 18 and 26 May 1999.

Table

2

Pigmentcomposition,in

lgl�

1,alongthesalinitygradient,asdetermined

from

HPLC

analyses

Survey

date

Salinity(%

)Chlc

Per

Fuc

Neo

Pra

Vio

Diadino

All

Lut

Zea

Chlb

Chla

a-C

arotene

b-Carotene

18/05/03

40.763

0.121

0.224

0.110

0.202

0.128

0.065

0.880

0.048

00.855

4.064

0.137

0.289

5.4

2.052

0.980

0.234

00

0.228

0.630

1.214

0.042

00.046

5.498

0.064

0.181

81.155

0.412

0.852

0.194

01

1.102

0.237

0.646

0.382

1.180

8.411

00.588

11

0.058

00.283

0.037

00

0.156

00.140

0.076

0.176

1.714

00.117

15

0.200

00.515

00

00.422

00

00

2.008

00.102

22.4

00

00

00

00

00

00.531

0.206

0.877

31.6

00

00

00

00

0.219

0.561

02.374

1.193

17.38

37

00

00

00

00

1.230

6.657

1.805

18.80

15.68

508.5

26/05/03

40.755

0.087

0.294

0.036

00.018

0.078

0.650

0.025

00.176

3.220

0.118

0.152

5.4

2.719

1.198

0.788

00

0.088

1.044

0.865

0.060

00.063

7.644

0.119

0.316

81.330

0.432

0.183

0.202

01.258

0.652

1.332

0.664

0.303

1.468

8.595

0.168

0.464

11

0.909

0.191

0.190

0.128

00.982

0.561

0.241

0.886

0.139

1.366

7.982

0.022

0.381

15

0.139

00.377

00

00.144

00

0.043

01.766

00.362

22.4

00

0.181

00

00

00

00

3.492

0.150

0.870

25

00

00

00

00

00

05.350

0.388

2.319

31.6

00

00

00

00

0.540

2.847

09.410

4.684

182.0

35.6

00

00

00

00

0.909

3.909

1.626

13.57

21.38

363.4

Chlc,

chlorophyllc;

Per,peridinin;Fuc,

fucoxanthin;Neo,neoxanthin;Pra,prasinoxanthin;Vio,violaxanthin;Dia,diadinoxanthin;All,alloxanthin;Lut,lutein;Zea,zeaxanthin;Chlb,

chlorophyllb;

Chla,

chlorophyll.Diadinoxanthin

anda-carotenewerenotincluded

intheCHEMTAX

calculations.

M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293 285

concentrations changed significantly in some of theponds, but the overall pattern of variation of chloro-

phyll with salinity remained similar.

The HPLC analyses detected up to 13 different pig-

ments, including chlorophylls a, b and c. b-Carotene wasa major component of the highest salinity ponds, in

which Dunaliella cf. salina was dominant (Table 2). The

contribution of major phytoplankton groups to total

Chl a, as derived from CHEMTAX, is shown in Fig. 2.There were marked differences between surveys in the

contribution of some groups, but the general patterns

were similar. Dunaliella spp. was dominant at the

highest salinities, including the crystallizers, during both

surveys, and also at the 31.6% pond on 26 May. Other

chlorophytes were found at all salinities below 37% (first

survey) or 31.6% (second survey). Dinoflagellates and

cryptophytes were present between 4% and 8–11% sa-linity and diatoms were not present at salinities above

22.4% (disregarding the minor contribution at 31.6%,

during the second survey). Cyanobacteria were only

important in the 31.6% ponds and to a much lesser ex-

tent in the 8% pond.

3.2. Morphotypic composition of autotrophs

The distribution of total phytoplankton numbers

(Fig. 3), derived from microscopic counts, was compa-

rable to that of Chl a, with maxima around 8% salinity

and in the crystallizers. A list of the identified taxa and

morphotypes is given in Table 1. Dunaliella cf. salina

was found at salinities of 25% and higher; on 18 May, its

population reached 24,000 cellsml�1 in one of the crys-

tallizers, but decreased to 5300 cells ml�1 in the secondsurvey. Other important contributors to the phyto-

plankton community were filamentous and chroococcal

cyanobacteria across all the salinity range, diatoms and

dinoflagellates up to 15% and 11% salinities, respec-

tively, and cryptophytes in the 5% pond on 18 May and

from the 5% to the 11% ponds on 26 May. Mesodinium

sp., an autotrophic ciliate with endosymbiotic crypto-

Fig. 2. Contribution of different algal classes to total chlorophyll aalong the salinity gradient, on 18 May 1999 (a) and 26 May 1999 (b),

as determined by application of the CHEMTAX programme to the

HPLC analyses.

Fig. 3. Composition of the phytoplankton assemblage as determined

by inverted microscopy, along the salinity gradient on 18 May 1999 (a)

and 26 May 1999 (b).

286 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

phyte chloroplasts, was present with 67 cells ml�1 in the

4% pond, on 18 May, although most cells appeared to

be in bad shape. The dinoflagellate assemblage of the 4,5 and 8% ponds included Pentapharsodinium tyrrheni-

cum, Prorocentrum scutellum, Prorocentrum lima, and

concentrations up to 88 cells ml�1 of Gymnodinium

sanguineum (¼Akashiwo sanguinea, according to [43]), a

typical red tide species. The most abundant diatoms

were small pennates, Amphora coffaeformis, and several

species of Nitzschia, including a large form (N. cf. sigma)

which reached concentrations of 100 cells ml�1 at 8%salinity on 18 May. The cyanobacteria included Apha-

nothece, Spirulina, and non-identified morphotypes of

Chrooccocales (unicellular) and Oscillatoriales (fila-

mentous).

3.3. Flow cytometry

The flow cytometric analyses of the survey samplesallowed the detection of 16 distinct populations, char-

acterized by their pigment and size signatures. Three of

them contained phycobilins (orange fluorescence) and

we considered that consisted of two cyanobacterial

(example, population #A in Fig. 4) and one cryptomo-

nad-like population (population #B in Fig. 4). One of

the presumed cyanobacteria (#A) resembled Synecho-

coccus and the other was composed of larger units,

presumably corresponding to filamentous colonies. The

other 13 populations were considered to be different

picoeukaryotes although the possibility that some of

them could be chlorophyll-positive (red fluorescent) andphycobilin-negative bacteria cannot be discarded. Fig. 4

shows three examples of different ponds, with the de-

tected populations and the codes we assigned to them.

The total concentration of picoplankton presented a

distribution pattern similar to that of all phytoplankton,

with maxima at salinities of 8% and 32–37% (Fig. 5).

The number of populations (Table 3) ranged from 6 for

the 4% pond to 2–3 in the crystallizers. The mostabundant population (population #F in Fig. 4), which

peaked at 8% in both surveys (Fig. 5), reached concen-

trations of around 300,000 cells ml�1.

3.4. Genetic fingerprinting

Genetic fingerprinting of oxygenic phototrophs tar-

geted cyanobacteria and algal chloroplasts. Heterotro-phic bacteria were, therefore, mostly excluded from this

analysis. The number of bands decreased from 12 to 2

along the gradient (Table 3). Sequences from excised

Fig. 4. Red (chlorophyll) fluorescence vs. side scatter (left) and vs. orange (phycobilin) fluorescence (right) of three saltern samples selected as ex-

amples of the flow cytometric detection of different photosynthetic microbes. Saltern of 4% salinity (upper panel), 8% salinity (central) and 32%

salinity (lower panels). The different populations are marked with letters. Population F corresponded to the most abundant picoalgae detected by

flow cytometry in the samples (see also Fig. 5).

M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293 287

bands indicated a shift from marine Cryptomonadaceae

in the first two ponds to halophilic Cyanobacteria (99%similarity in 16S rRNA sequence to Euhalothece sp.) in

the last two ponds, with a predominance of chloro-

phytes in the intermediate salinity ponds (Fig. 6). Given

the dearth of sequences in the data base and the rela-

tively low similarities of these bands to Chlorella, they

could very well represent Dunaliella.

The 18S rRNA fingerprints, which targeted eukary-

otic microorganisms (both auto and heterotrophs) yiel-ded between 10 and 32 DGGE bands and each pond

presented a particular fingerprint, indicating a quite

different community composition for each salinity level

(see [28] for details). The number of bands decreasedfrom the 4% to 15% ponds and remained between 10

and 12 in the other ponds (Table 3). As discussed later,

this high number could be influenced by the presence of

heterotrophic microorganisms and by methodological

biases.

3.5. Diversity indices

For all the descriptors ðxÞ considered, the number

of classes, Sx, presented a clear decreasing trend with

102

103

104

105

106

0 5 10 15 20 25 30 35 40

Total picoplankton - May 18, 1999Total picoplankton - May 26, 1999Population #F - May 18Population #F - May 26Population #C - May 18Population #C - May 26

Salinity (%)

Pho

tosy

nthe

tic p

icop

lank

ton

(cel

ls-1

)

Fig. 5. Total picoplankton abundance as detected by flow cytometry in

the experimental ponds sampled on 18 and 26 May 1999, as well as the

contribution of some examples of the different picoplankton popula-

tions identified by flow cytometry on 18 May 1999 and 26 May 1999.

Population #F was the most abundant picoplankton population ap-

pearing in salinities <25% and contributing most of the picoplankton

numbers in salterns from 5% to 15%. Population #C, a phycobilin-

containing organism of a rather large size, in contrast, appeared in

salterns of salinity >31% and contributed less than 10% to total

picoplankton abundance.

Fig. 6. DGGE gel after PCRperformedwith 16S rRNA specific primers

for oxygenic phototrophicmicroorganisms, in planktonic samples taken

along the salinity gradient on 18May 1999. Someminor bands, counted

in Table 3, are not visible in the figure. Numbered bands (1–8) were

excised from the gel and sequenced. Closest relative and percentage of

similarity (in parenthesis) are: 1, Marine clone OM283* (97%); 2, Py-

renomonas salina* (94%); 3, marine clone OM283* (99%); 4,Cyanothece

sp.** (92%); 5,marine cloneOCS20* (98%); 6,Chlorarachnion sp. (98%);

7, Chlorella sp. (93%); 8, Euhalothece sp.** (99%). *, Cryptomonada-

ceae; **, Cyanobacteria. Nucleotide sequence accession numbers at

EMBL are AJ580966 to AJ580973.

288 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

salinity (Tables 3 and 4). The minimal number of classes

identified ranged from 2 for phytoplankton, flow cy-

tometry and 16S rRNA bands, to 10 for 18S rRNA; the

maximal number ranged from 6 for flow cytometry to 32

for 18S rRNA. In spite of the differences in range of

variation, all the Sx corresponding to the different de-scriptors were well correlated (Table 4).

Table 3

Number of classes, detected for different descriptors (Sx, see Section 2) and CHEMTAX-derived taxa, along the salinity gradient

Survey Salinity (%) SM Phyto-plankton

taxa

SF Pico-plankton

populations

SP Pigments

detected by HPLC

CHEMTAX-

derived taxa

S16S 16S rRNA

bands

S18S 18S rRNA

bands

18/5/99 4 13 6 13 5 12 32

5.4 17 5 11 4 9 28

8 17 4 12 6 7 31

11 9 4 9 2 4 21

15 15 3 5 2 2 12

22.4 6 3 3 1 2 10

31.6 4 4 5 3 2 12

37 2 2 6 1 11

26/5/99 4.0 16 6 12 4

5.4 13 5 11 4

8.0 17 4 13 6

11 15 4 13 6

15.0 7 2 6 2

22.4 5 3 4 2

25.0 9 2 3 1

31.6 4 2 5 2

36.0 7 3 6 1

37.0 6 3

In the case of CHEMTAX-derived taxa, the potential maximum is 9. Only estimated contributions to total Chl a exceeding 0.2 lg l�1 have been

considered.

Table 4

Linear (Pearson) correlation coefficients among the salinity and the number of classes for the different descriptors

Salinity SM SF SP S16S

SM )0.85SF )0.66 0.58

SP )0.77 0.78 0.74

S16S )0.81 0.59 0.83 0.91

S18S )0.83 0.73 0.76 0.98 0.92

The number of observations was 16–18 for pairs involving salinity, SM, SF and SP and 7–8 for those with S16S and S18S. All values are significant

(p < 0:05).

0

1

2

3

4

5

0 5 10 15 20 25 30 35 40

DGGE bands, 18 May 1999

Sha

nnon

div

ersi

ty

Salinity (%)

DGGE16SDGGE18S

Fig. 8. Distribution of the Shannon diversity index based on the

number and intensity of bands in DGGE gels from samples along the

salinity gradient on 18 May, after a PCR with primers for 16S rRNA

(D16S, filled symbols) or for 18S rRNA (D18S, empty symbols).

0.5

1

1.5

2

2.5

3

0 5 10 15 20 25 30 35 40

HPLC pigments

18 May 199926 May 1999

Sha

nnon

div

ersi

ty (

Dp)

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30 35 40

Phytoplankton

Km

in

dex

Salinity (%)

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20 25 30 35 40

Sha

nnon

div

ersi

ty (

Dm

)

Phytoplankton

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35 40

Picoplankton

Sha

nnon

div

ersi

ty (

Df)

Salinity (%)

(a)

(c)

(b)

(d)

Fig. 7. Distribution of the Shannon diversity indices on 18 (filled symbols) and 26 May 1999 (empty symbols) for (a) pigment concentrations de-

termined by HPLC (DP), (b) phytoplankton counts by the inverted microscope technique (DM) and (d) fluorescent picoplankton counts (DF). (c)

Distribution of the KM diversity index for phytoplankton (see text).

M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293 289

The KM index for phytoplankton and the Shannon

diversity indices for phytoplankton (DM), pigments (DP)

and DGGE (D16S and D18S) presented in general the

highest values at salinities below 10–15% (Figs. 7 and 8).

Except for DF (picoplankton) and for the correlation

between the DGGE indices (D16S and D18S) and the

phytoplankton ones (DM and KM), all these indices were

negatively correlated with salinity and positively corre-lated among themselves (Table 5). In the case of DGGE

bands, HPLC pigments and KM index for phytoplank-

ton, there was a clear trend of lower index values at

higher salinities. The pattern was more complex for the

picoplankton DF index, with minima at 8–15% and 37%

and for the phytoplankton DM index, which is more

sensitive than KM to changes in relative abundance

among groups and showed minima at salinities of 5%,22% and 37% (on 26 May).

Table 5

Linear (Pearson) correlation coefficients among salinity and several diversity indices

Diversity indices

Salinity Phytoplankton Picoplankton Pigments DGGE

DM KM DF DP D16S

DM )0.51KM )0.89 0.74

DF n.s. n.s. n.s.

DP )0.85 0.61 0.73 n.s.

D16S )0.89 n.s. 0.69 n.s. 0.96

D18S )0.79 n.s. 0.64 n.s. 0.89 0.95

The number of observations was 16–18 for pairs involving salinity, DM, KM and DP, and 7–8 for those with D16S and D18S. All values, except those

marked ‘‘n.s.’’ are significant (p < 0:05).

290 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

4. Discussion

4.1. Autotrophic biomass along the salinity gradient

All indicators of autotrophic biomass, including Chla concentration, picoplankton and phytoplankton cell

numbers (Figs. 1–3 and 5), presented a bimodal distri-

bution, with maxima at salinities 5–11% and 37%. This

distribution of Chl a concentration and phytoplankton

abundance agrees with the findings of Pedr�os-Ali�o et al.

[11] in the same study area, although there were differ-

ences in the particular salinity at which the chlorophyll

peaks were found and in the dominant groups along thesalinity gradient. Given that the survey of [11] took

place in July, 1993, part of these differences may be due

to seasonal successional changes. Unfortunately, studies

following the microbial community through a seasonal

cycle have not been done in these salterns and there is no

available background information to extract further

conclusions. The Chl a peak at the lower salinities co-

incided with maxima of carbon fixation and Chl a-spe-cific carbon fixation rates, as determined using the 14C

method [29]. However, carbon fixation rates were very

low in the crystallizers, in spite of their high Chl aconcentration.

The comparison between the 18 and 26 May surveys

showed significant variability, in spite of the relative sta-

bility of the temperature and salinity conditions. The

changes affected both biomass and taxonomic composi-tion, especially in the lower salinity ponds, andmay reflect

both spatial heterogeneity [29] and temporal variability.

An important factor that must be considered in these

shallow ponds is the resuspension of benthic organisms,

due to wind events or to the activity of water birds. Such

mechanisms could explain the high concentration of large

Nitzschia cells found in the 8% pond on 18 May.

4.2. Comparison between optical microscopy observations

and CHEMTAX results

Results of the CHEMTAX program for the con-

tribution of dinoflagellates and diatoms were in gen-

eral agreement with those of microscopy (Figs. 2 and

3). The correspondence was also good for Dunaliella

spp., as could be expected because the corresponding

initial pigment ratio had been adjusted with data

from the present study. However, the relationshipbetween cell numbers and pigment-derived contribu-

tion was not significant for cyanobacteria. The pig-

ment ratios for cyanobacteria used as input to the

CHEMTAX calculations were derived from a labo-

ratory culture of the unicellular Synechococcus. This

species was characterized by the presence of the ca-

rotenoid zeaxanthin and changes in irradiance were

found to have a pronounced effect on the cellularratio of zeaxanthin to chlorophyll a [35]. In the

shallow salterns, irradiance by far exceed the experi-

mental conditions and thus the zeaxanthin to chlo-

rophyll a ratio of cyanobacteria used in the

CHEMTAX calculations may not have been repre-

sentative for the cyanobacterial community in the

salterns. Furthermore, several filamentous cyanobac-

teria have different zeaxanthin to chlorophyll a ratiosthan the unicellular species [44] and therefore this

group of organisms may have been consistently un-

derestimated. Unfortunately no pigment ratios from

species of Oscillatoriales were available for inclusion

in the calculations. In the case of cryptomonads, the

agreement was good for part of the samples but not

for others, in which these organisms could not be

detected by microscopy. In one of the samples (8% of18 May) at least, this lack of correspondence could be

due to the presence of the ciliate Mesodinium, which

contains cryptophyte pigments. According to the

CHEMTAX analyses, prasinophytes and chlorophytes

were relatively important in some samples; organisms

of these groups were also detected in the microscopic

observations, but most of them could not be posi-

tively identified as such and were included in cate-gories such as ‘‘others’’ (Fig. 3). As discussed in

Section 2.2, the minor CHEMTAX-derived contribu-

tion of cryptophytes and diatoms in the 32% pond

during the second survey should be considered as

doubtful.

M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293 291

4.3. Phytoplankton composition along the salinity gradi-

ent

The results reported here confirm the dramatic

ecological variability originating from the salinitygradient in the salterns. Basically, the phytoplankton

communities in the studied salterns could be divided

into a marine assemblage, occurring at salinities up to

15%, and a halophilic assemblage, found at higher

salinities. There seemed to be a gap at intermediate

salinities (22.4%), with minima of phytoplankton and

picoplankton abundance and relative minima of phy-

toplankton diversity. Dinoflagellates were present upto salinities around 11% and diatoms practically dis-

appeared at salinities >22%, in accordance with pub-

lished data [16]. These observations were supported by

the results of a perturbation experiment (data not

shown) in which water from selected ponds (11%,

22.4% and 37%), either untreated or diluted to 85%,

75% and 60% of the original salinity with distilled

water, was placed in 30 l tanks [30]. Due to evapo-ration, salinity in the tanks increased significantly

between the beginning (20 May) and the end (27 May)

of the experiments. Diatoms and dinoflagellates,

originally present in the 11% community, disappeared

in the untreated containers, which reached a salinity

of 13.5%. Dinoflagellates disappeared also in the 85%

dilution tank, which ended up with a salinity of 13%.

The taxonomic changes around 22% salinity were alsopresent in the picoplankton (e.g. population F in

Fig. 5) and in the genetic composition of prokaryotes,

which showed a consistent discontinuity between 10%

and 22% salinity [28]. It is interesting to note that

zooplankton presented a marked biomass maximum

(due to Artemia salina) at salinities between 15% and

31.6% [30]. Probably, this Artemia peak contributed to

low phytoplankton biomass at the 15%–22% salinityinterval. However, Artemia abundance varied rela-

tively little between 15% and 31.6% salinity, so that it

seems unlikely that the qualitative changes in micro-

bial composition around 22% could be attributed to

grazing effects. Microzooplankton grazing was signifi-

cant in the 4% and 8% salinity ponds while no sig-

nificant microzooplankton grazing on the total

phytoplankton community was found in the 11%pond and the crystallizer [29]. In addition to the po-

tential effect on the total phytoplankton biomass, this

grazing may have influenced the composition and di-

versity of the plankton community through selective

grazing on some groups of phytoplankton. Thus,

grazing in the 4% pond was primarily on prasino-

phytes and diatoms while in the 8% pond mainly

cryptophytes, chlorophytes and possibly cyanobacteriawere grazed by microzooplankton [29]. Unfortunately

no data are available to quantify the abundance of

microzooplankton along the salinity gradient.

4.4. Diversity patterns

As can be seen in Table 3, the number of classes of the

different variables considered (Sx) tended to decrease

with salinity and reached the minimal values in thecrystallizers. All Sx indices were significantly correlated

(Table 4). Due to the variety of methods used, different

numbers of classes must be expected, even when dealing

with the same organisms. For example, morphological

differences among filamentous cyanobacteria, will not

have been reflected by the HPLC analyses. Similarly,

Mesodinium would not be separated from cryptophytes

using information derived from only pigment analysis.The results of the molecular analyses deserve, however,

some comments. In a parallel study carried out in the

same salterns we detected that the number of groups of

Bacteria and Archaea decreased as salinity increased,

until only one group became dominant, but with a high

degree of microdiversity [27]. Such microdiversity cor-

responds to clusters of closely related 16S rRNA se-

quences below the ‘‘species-level’’ (98–99.9% similarityin the sequence) and may represent the coexistence of

several closely related clones of microorganisms that

form ecologically distinct populations ([45,46] and ref-

erences therein). On the other hand, it has been de-

scribed that some DGGE bands (considered here as

operational taxonomic units, OTUs) could correspond

to artifacts, because simultaneous presence of several

closely related 16S rRNA fragments may easily result inheteroduplex formation [47]. Therefore, in extremely

low diversity assemblages, such as crystallizers, DGGE

fingerprints require careful interpretation because the

number of OTUs detected can overestimate the actual

microbial richness [28]. In the case of eukaryotes, the

relatively high number of 18S rRNA DGGE bands at

the high salinity end of the gradient could be, in part, an

artifact due to the formation of heteroduplexes derivedfrom the presence of several closely related 18S rRNA

sequences [28]. We cannot discard either the presence of

eukaryal heterotrophs, such as yeasts, which have been

recently reported in hypersaline waters [48]. Such or-

ganisms could have been overlooked in the microscopic

counts or included in the ‘‘others’’ category as non-

identified cells.

The values of the Shannon (Dx) and KM diversityindices (Figs. 7 and 8), are affected both by the number

of classes and by their individual abundances and,

therefore, their distribution patterns should be more

influenced than class numbers by the short-term eco-

logical dynamics within the communities. In general,

these indices showed decreasing values with increasing

salinity, but DM and DF presented additional minima at

intermediate salinities (Figs. 7 and 8). The phytoplank-ton DM minimum at 22% can be related to the changes

of community dominance and the minimum in Chl aconcentration discussed above. The high diversity values

292 M. Estrada et al. / FEMS Microbiology Ecology 49 (2004) 281–293

of this index at 8% coincided with Chl a (Fig. 1) and

primary production maxima as measured on 26 May

[29] but the correspondence did not hold at the high

salinity extreme, which presented very high Chl a con-

centration, very low primary production values and in-termediate or low DM diversity. The picoplankton DF

minimum at 8–15%, which is not reflected in the number

of populations, can be related to the strong dominance

of population F (Fig. 5).

Potential drawbacks of different diversity estimates

have been considered by Margalef [42] and N€ubel et al.[22] among others. As recognized by N€ubel et al. [22],who attempted a quantification of microbial diversitybased on morphotypes, 16S rRNA genes and carotenoid

analyses, none of these approaches allows an exact de-

termination of the number of existing classes and their

abundance in the community. The problem does not lie

only in the classification of elements, but starts with the

selective effect of the sampling method and strategy

adopted. In this context, it may be useful to adopt the

proposal of Margalef [42], of distinguishing betweendiversity and biodiversity. Diversity is a measure of the

richness of components of the biosphere which are ac-

tive or abundant at a particular time and location, while

biodiversity refers to set of non-redundant genetic in-

formation contained in this location. At any point in

time, the differences between diversity and biodiversity

are likely to be higher in a strongly dynamic environ-

ment. In the case of the salterns, the agreement of theresults obtained with different approaches strongly

suggests that there is a consistent trend of decreasing

biodiversity with increasing salinity. The observation

that this trend affects both prokaryotic and eukaryotic

microautotrophs suggests that, as discussed by Brock

[17] and Pedr�os-Ali�o et al. [11], the underlying cause is

likely the selective effect of extremely high salinities.

5. Conclusions

A varied set of diversity estimates based on micros-

copy, pigment analysis, flow cytometry, and DNA-based

approaches confirmed a decrease in diversity with in-

creasing salinity, indicating the selective effect of extreme

environmental conditions on autotrophic microorgan-isms, both prokaryotic and eukaryotic. The numbers of

elements of the different descriptors used (number of

microalgal taxa counted by optical microscopy, flow cy-

tometry-determined populations, pigment types, DGGE

bands) were significantly correlated among themselves

and negatively correlated with salinity. The Shannon di-

versity indices, which are influenced by the relative

abundances of the different elements, also showed anoverall decreasewith salinity, but in the case ofmicroalgal

taxa and flow cytometric picoplankton presented marked

minima at intermediate salinities. The phytoplankton

diversity minimum around 22% salinity appeared to be

related to a marked change in community composition,

from an assemblage with mixed participation of dinofla-

gellates and diatoms, to another dominated byDunaliella

and cyanobacteria. In the case of picoplankton, low di-versities (as measured by the Shannon index) at salinities

10–15% were due to the presence of a strongly dominant

population that disappeared at salinities above 22%. A

qualitative change around 22% was also clearly apparent

from the fingerprinting analyses of 16S rRNA and 18S

rRNA, indicating a similar response of the prokaryotic

and eukaryotic communities. Our results indicate that

general patterns of diversity variation along the salinitygradient in the salterns were comparable for different de-

scriptors of themicroautotrophic planktonic community.

Acknowledgements

This study was supported by the CSIC, by the

Spanish project REN2001-2120/MAR (MicroDIFF)and by EU contract MAS3-CT97-0154 (MIDAS pro-

ject), in the framework of MAST 3 Programme. We

thank Mr. Miguel Cuervo-Arango for permission to

work in the Santa Pola salterns. E.O.C. benefits from

the Programa Ram�on y Cajal of the Spanish Ministerio

de Ciencia y Tecnolog�ıa.

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