Genetic structure of island populations of wild cardoon

[Cynara cardunculus L. var. sylvestris (Lamk) Fiori]

detected by AFLPs and SSRs

Ezio Portis a, Alberto Acquadro a, Cinzia Comino a, Giovanni Mauromicale b,Eva Saba c, Sergio Lanteri a,*

a Di.Va.P.R.A. Plant Genetics and Breeding, University of Turin, via L. da Vinci 44, I-10095 Grugliasco (Turin), Italyb Dipartimento di Scienze Agronomiche, Agrochimiche e delle Produzioni Animali – sez. Scienze Agronomiche,

University of Catania, via Valdisavoia 5, I-95123 Catania, Italyc Dipartimento di Scienze Agronomiche e Genetica Vegetale Agraria, University of Sassari,

via E. De Nicola, I-07100 Sassari, Italy

Received 28 February 2005

Available online 29 March 2005

www.elsevier.com/locate/plantsci

Plant Science 169 (2005) 199–210

Abstract

Wild cardoon [Cynara cardunculus L var. sylvestris (Lamk) Fiori] is a non-domesticated robust perennial plant characterized by a rosette

of large spiny leaves and branched flowering stems. It is native to the Mediterranean basin where it grows in a variety of habitats, from mesic

riparian sites to xeric rocky slopes. Wild cardoon is potentially exploitable for the production of lignocellulosic biomass, oil from seeds, and

biopharmaceuticals, such as caffeoylquinic acids (cynarine) and luteolin from leaves, and inulin from roots. The genetic variation present at

microsatellite (SSR) and amplified fragment length polymorphism (AFLP) loci has been assessed in seven Italian populations of wild

cardoon, collected from Sicily and Sardinia. Thirty individuals, randomly sampled from each population were genotyped at five SSR loci and

fingerprinted using seven AFLP primer combinations. Genetic distance estimates both within and between populations were consistent

between the two marker systems. As a result of the geographical isolation, the Sardinian and Sicilian populations were clearly differentiated,

forming two distinct gene-pools. Most of the genetic variation was partitioned within rather than between populations. The genetic

distribution will allow the design of rational sampling strategies to capture the genotypic range present.

# 2005 Elsevier Ireland Ltd. All rights reserved.

Keywords: AFLP; Genetic variability; Microsatellites; Molecular markers; Natural populations; Cynara cardunculus

1. Introduction

Wild cardoon [Cynara cardunculus L. var. sylvestris

(Lamk) Fiori] is a non-domesticated robust perennial plant,

characterized by its rosette of large spiny leaves, branched

flowering stems and blue-violet flowers. It belongs to the

family of Asteraceae, tribe Cynareae and is native to the

Mediterranean basin, where it colonizes dry and undisturbed

areas. Molecular [1–4], as well as cytogenetic and isozyme

[5] studies indicate that the species is the ancestor of the

* Corresponding author. Tel.: +39 0116708806; fax: +39 0112368806.

E-mail address: sergio.lanteri@unito.it (S. Lanteri).

0168-9452/$ – see front matter # 2005 Elsevier Ireland Ltd. All rights reserved

doi:10.1016/j.plantsci.2005.03.014

cultivated forms, globe artichoke (C. cardunculus var.

scolymus L.) and cardoon [C. cardunculus. var. altilis (DC)],

since they are fully cross-compatible and their F1 hybrids are

fully fertile [6].

Wild cardoon is allogamous and is propagated by seeds

(achenes). The majority of seed is shed close to the parent

plant, and germinates after the first autumn rains, although

germination may occur all year round under favourable

conditions; the growth period spans September (emer-

gence) to July (achene maturity), and flowers are usually

produced by 2-year-old plants. Like globe artichoke, the

fleshy heads (immature flowers) as well as the petioles and

roots, if properly prepared, are edible. Previous studies

.

E. Portis et al. / Plant Science 169 (2005) 199–210200

Fig. 1. Geographic location of the seven wild cardoon populations. Roc-

cella (SIC-ROC), Bronte (SIC-BRO), Piano Tavola (SIC-TAV), Palazzolo

(SIC-PAL), Sassari (SAR-SAS), Nuoro (SAR-NUO), Oristano (SAR-ORI).

have shown that wild cardoon is also a promising source of

seed oil, both with respect to quality and quantity, and the

residue flour after extraction is usable as a component of

animal feed. Seed yield is in the region of 2.0 tonnes/ha

(at 5% (w/v) moisture), of which about 25% is oil [7]. The

high proportion of oleic and linoleic acids, in a balanced

ratio, and the low amount of free acids, peroxides,

saturated and linoleic acids are responsible for its good

alimentary quality. In addition, a favourable a-tocopherol

content provides good stability against oxidation [8]. Wild

cardoon could also be exploited for the production of

lignocellulosic biomass for energy or paper pulp [9], as the

biomass yield is up to 19.0 tonnes/ha dry matter [7,10].

Furthermore, wild and cultivated forms of Cynara

cardunculus are a source of biopharmaceuticals [11–14].

The roots contents include inulin, a known improver of

human intestinal flora, while the leaves are a source of

antioxidant compounds, such as luteolin and di-caffeoyl-

quinic acids, which (i) protect proteins, lipids and DNA

from oxidative damage caused by free radicals [15–17],

(ii) inhibit cholesterol biosynthesis and contribute to the

prevention of arteriosclerosis and other vascular disorders

[16,18–20], (iii) inhibit HIV integrase, a key player in HIV

replication and its insertion into host DNA [21,22], and

(iv) possess antibacterial activity [23]. Little is known

of the extent or pattern of genetic variation in natural

populations of wild cardoon, particularly with respect to

variation in the chemical profiles of leaf extracts.

Furthermore, there has been no attempt as yet to

genetically improve the taxa, with a viewing to increasing

seed oil or biomass yield.

DNA markers well suited to assess genetic diversity.

Their advantages are that most exhibit no plasticity, that they

are unlikely to be similar because of convergent evolution,

and they can generate information at many different loci

[24]. The two most widely applied marker systems are

amplified fragment length polymorphism (AFLP) [25] and

microsatellites (simple sequence repeats, SSR) [26]. Some

SSR assays have been developed recently for Cynara

cardunculus [4,27,28]. Owing to their co-dominant inheri-

tance, SSRs detect multiple alleles at a given locus while

AFLP, being dominant, detect multiple loci distributed

throughout the genome. Hence different data analysis

approaches are needed to obtain comparable statistics

[24,29,30]. However, comparative studies over a wide range

of species have generally revealed a good congruence

between genetic parameters revealed by these two marker

systems [24,31–33].

The objectives of the present study were: (i) to investigate

the level of AFLP and SSR variation in seven populations of

wild cardoon, (ii) to quantify the genetic diversity within and

between wild cardoon populations, (iii) to compare the

informativeness of the AFLP and SSR assays employed at

the level of populations and individuals, and (iv) to identify

approaches towards a rational sampling strategy programme

in wild cardoon.

2. Materials and methods

2.1. Plant material and DNA extraction

Four populations of wild cardoon were identified in Sicily

[Roccella (SIC-ROC), Bronte (SIC-BRO), Piano Tavola

(SIC-TAV), and Palazzolo (SIC-PAL)], and three in Sardinia

[Sassari (SAR-SAS), Nuoro (SAR-NUO) and Oristano

(SAR-ORI)] (Fig. 1). The sites vary in altitude from 50 to

700 m a.s.l., and each covers an area of ca. 1–2 ha. From

each population, leaf material from 30 randomly chosen

individuals was collected and used as a source of genomic

DNA, which was extracted following methods described by

Lanteri et al. [34]. This generated a genotyping set of 210

individuals.

2.2. SSR analysis

We previously generated in globe artichoke the five SSR

primers used in this study (CDAT-01, CLIB-02, CMAL-06,

CMAL-24 and CMAL-108) [4,27]. All produced clear

electrophoretic profiles, uncovering a high level of poly-

morphism in wild cardoon. Amplification and detection

were performed following Acquadro et al. [4]. Amplified

fragments were resolved on 6% denaturing polyacrylamide

gels and silver stained as described by Bassam et al. [35].

Alleles were scored according to molecular weight, and

between-gel differences were controlled by the inclusion of

molecular weight markers on each gel.

E. Portis et al. / Plant Science 169 (2005) 199–210 201

2.3. AFLP analysis

The protocol was adapted from Vos et al. [25], as

described by Lanteri et al. [36]. Initially, 32 primer

combinations (four EcoRI primers � eight MseI primers)

were tested against one individual from three populations.

All primer pairs generated profiles with a high proportion of

polymorphic bands. From this pilot study, the seven primer

combinations E35/M47 (E + ACA/M + CAA), E35/M60

(E + ACA/M + CTC), E37/M47 (E + ACG/M + CAA),

E38/M47 (E + ACT/M + CAA), E32/M17 (E + AAC/

M + CG), E32/M48 (E + AAC/M + CAC), E38/M48

(E + ACT/M + CAC) were selected. A reproducibility test

was performed on four plants with all seven primer

combinations. For each sample, three independent DNA

extractions were carried out and AFLP fingerprints were

compared. Amplified fragments were separated and silver

stained as for SSR, except that the acrylamide content of the

gels was reduced to 5%.

Electrophoretic patterns were captured by the Gel

Documentation System (Quantity One Programme,

BioRad). Each PCR product (between 60 and 650 bp)

was assumed to represent a single locus with two alleles and

data were scored as the presence (1) or absence (0) of each

polymorphic band.

2.4. Microsatellite data analysis

Statistical parameters were derived using GENEPOP

version 3.4 [37]. In order to estimate overall levels of genetic

diversity, the following were calculated for all populations:

average number of microsatellite alleles observed per locus

(no), effective number of alleles per locus (ne), mean

observed (HO) and mean expected (HE) heterozygosity.

Unbiased estimates for the exact P-values for tests of

conformation to Hardy–Weinberg (HW) equilibrium were

calculated using the Markov chain randomization method of

Guo and Thompson [38]. All results were adjusted for

multiple simultaneous comparisons using a sequential

Bonferroni correction. For analyses of HW equilibrium

tests, the Markov chain parameters were as follows:

dememorization number = 1000, number of batches = 100,

and number of iterations per batch = 1000. Each parameter

was increased 10-fold for tests of genotypic linkage

disequilibrium.

The extent and significance of the genetic differentiation

among populations was investigated by estimating the

fixation indices based on two models: (i) the infinite allele

model [39], and (ii) the stepwise mutation model [40]. F-

statistics [41], were calculated under the infinite allele model

[FST: the Wright’s fixation index, which measures the

proportion of variation due to population differentiation;

F IS: the within population inbreeding, which measures the

correlation of allele frequencies among individuals within

populations; F IT: the overall inbreeding that measures the

correlation of allele frequencies within individuals in

different populations] using POPGENE version 1.32 [42].

Genetic differentiation under the stepwise mutation model

was assessed by an estimator of Slatkin’s RST [43] developed

as a counterpart of FST. The RST estimate was obtained by

calculating the within and between population components

of variance of allele sizes for all loci and populations using

the software RSTCALC version 2.2 [44]. We have not used

different notations to distinguish FST and RST from their

respective estimators u and r. Permutation tests were carried

out to determine whether observed values of F- and R-

statistics were significantly different from zero.

The Nei and Li index of similarity [45] was calculated in

all possible pair-wise comparisons to estimate genetic

similarities (GS) between pairs of plants. A principal

coordinate (PCO) analysis was performed based on the

triangular matrix of genetic similarity estimates, and the first

three axes were plotted graphically, according to the

extracted eigen vectors. All these calculations and analyses

were conducted using the appropriate routines of the

software package NTSYS version 1.80 [46]. In addition to

the Nei and Li estimates for genetic similarity, genetic

relatedness based on SSR data was estimated following the

stepwise mutation model of Goldstein et al. [47]. In contrast

to the Nei and Li index, which represents the quotient of

shared alleles and total number of alleles, the dm2 distance

[47] is the squared average allelic difference in terms of

tandem repeats overall SSR loci. dm2 distances between

pair of plants were computed using MICROSAT software

(http://hpgl.stanford.edu/projects/microsat/). Genetic dis-

tances (GD) between populations were also estimated for

SSR markers using Nei’s standard distance D [48], by means

of POPGENE software, and dm2 [47], using the program

SPAGeDI 1.1 [49]. The PHYLIP package [50] was used to

construct two neighbour-joining trees based on 1000

permuted trees bootstrapped across loci, based on both

distance measures used.

2.5. AFLP data analysis

Allelic variation and differentiation statistics were

computed using AFLP-SURV version 1.0 [30]. Allelic

frequencies at polymorphic AFLP loci were calculated from

the observed frequencies of fragments using a Bayesian

approach with non-uniform prior distribution of allele

frequencies, as proposed by Zhivotovsky [51] for diploid

species, assuming an HW equilibrium (as estimated from

SSR data, see results). Under this approach, the frequency of

the null allele (q) at each locus is computed from the sample

size and the number of individuals in the sample that lack the

AFLP fragment, using a Bayesian method which also

estimates the distribution of allele frequencies based on the

variation over loci of the frequencies of AFLP fragments in

the sample.

These allelic frequencies were used as input for the

analysis of genetic diversity within and between popula-

tions, and for the estimate of basic statistics, using Microsoft

E. Portis et al. / Plant Science 169 (2005) 199–210202

Excel. The following measures were calculated for all

populations: average number of alleles observed per locus

(no), effective number of alleles per locus (ne) and genetic

diversity within a population quantified by estimating Nei’s

unbiased expected heterozygosity (H0) [52]. Pearson

correlation coefficients between fragment sizes and frag-

ment frequencies (together with the P-value associated with

the correlation) were calculated for each primer combina-

tion, and on the overall sample.

Nei genetic diversity statistics [53] were computed for

each primer combination and overall data in order to

evaluate the proportion of genetic diversity distributed

among populations (GST). The significance of the genetic

differentiation between groups was tested by comparison of

the observed GST with a distribution of GST under the

hypothesis of no genetic structure, obtained by means of

1000 random permutations of individuals among groups,

using AFLP-SURV.

The Nei and Li index of similarity was calculated in all

possible pair-wise comparisons to estimate genetic simila-

rities (GS) between pairs of plants. A principal coordinate

(PCO) analysis was performed based on this matrix using

NTSYS software and the first three axes were plotted

graphically. Furthermore, the clustering power of the seven

selected AFLP primer combinations was tested to determine

the optimal number of primer pairs needed to resolve the

210 individuals into distinct groups for each population

analysed, using similarity coefficients to construct a

dendrogram by the UPGMA method. Correlations between

the genetic similarity matrices obtained with each individual

primer combination, and the matrix obtained with the whole

AFLP data set were calculated using the Mantel test [54].

The primer combination with the highest correlation was

initially analysed on its own, and then in combination with

the other primer combinations with a progressively lower

clustering ability.

Table 1

Genetic diversity statistics within wild cardoon populations for both SSR and A

Population SSR

no ne Ho

SIC-BRO 7.00 3.31 0.653

SIC-PAL 8.00 4.05 0.740

SIC-ROC 6.80 4.38 0.795

SIC-TAV 4.80 2.57 0.650

SAR-ORI 6.80 4.11 0.769

SAR-SAS 5.00 3.10 0.666

SAR-NUO 4.40 3.04 0.720

Overall populations 13.40 5.63 0.713

S.D. 4.28 2.57 0.058

Over Sicilian populations 12.00 4.77 0.709

S.D. 3.81 2.19 0.071

Over Sardinian populations 8.40 4.11 0.718

S.D. 3.13 1.77 0.051

no: mean number of observed alleles; ne: mean number of effective alleles; HO:

genetic diversity. Sample codes described in the text

Pair-wise Nei’s D genetic distances [48] were calculated

between each population by means of POPGENE software.

A neighbour-joining tree was computed, based on these

distance measurements and 1000 bootstraps were performed

over AFLP loci using AFLP-SURV and PHYLIP.

2.6. Marker system efficiency and correlation between

SSRs and AFLPs

The efficiencies of SSR and AFLP were compared by

calculating the assay efficiency index (Ai) [32], which

combines the effective number of alleles identified per locus

and the number of the polymorphic bands detected in each

assay. In addition, the marker index (MI) (equal to the

product of the expected heterozygosity and the effective

multiplex ratio) was used for evaluating the overall utility of

each marker system [31]. Indices of genetic relatedness were

compared by calculating Spearman’s rank correlation

coefficients. The correlations between the two SSR data

sets and between SSR and AFLP data sets were checked at

two levels: individual genetic distance (based on 210

individuals) and population distance (based on seven

populations).

3. Results

3.1. Microsatellite genetic diversity and HW equilibrium

The statistical parameters relating to the SSR loci for

each population are reported in Table 1. The five SSR loci

detected 67 alleles across the 210 individuals. The number of

alleles per locus ranged from 9 (CMAL-108) to 20 (CMAL-

06) with a mean of 13.4. Within each population, the allele

frequency ranged between 0.017 and 1, with an overall mean

value of 0.170. The average number of observed alleles (no)

FLP markers

AFLP

HE FIS no ne H0

0.616 �0.059 1.72 1.35 0.218

0.710 �0.042 1.74 1.38 0.237

0.735 �0.081 1.64 1.33 0.205

0.601 �0.080 1.69 1.34 0.211

0.723 �0.064 1.60 1.30 0.192

0.650 �0.025 1.57 1.27 0.172

0.657 �0.095 1.53 1.26 0.167

0.670 �0.064 2.00 1.32 0.200

0.053 0.024 0.08 0.04 0.025

0.666 �0.066 1.88 1.35 0.218

0.067 0.019 0.05 0.02 0.014

0.677 �0.061 1.70 1.28 0.177

0.040 0.035 0.04 0.02 0.013

observed heterozygosity; HE: expected heterozygosity; H0: Nei’s unbiased

E. Portis et al. / Plant Science 169 (2005) 199–210 203

Table 2

Descriptive and Nei’s genetic diversity statistics evaluated overall popula-

tions for each AFLP primer combination

NTB NPB % r GST

E35/M47 76 59 77.6 0.141 (P < 0.1) 0.224

E35/M60 77 60 77.9 �0.038 (P < 0.1) 0.162

E37/M47 86 71 82.6 0.183 (P < 0.1) 0.214

E38/M47 96 84 87.5 0.067(P < 0.1) 0.199

E32/M17 87 75 86.2 �0.285 (P < 0.01) 0.201

E32/M48 80 67 83.8 �0.306 (P < 0.01) 0.231

E38/M48 78 60 76.9 �0.414 (P < 0.01) 0.246

Total 580 476

Average 82.9 68 81.8 �0.069 (P < 0.1) 0.211*

S.D. 7.2 9.3 4.3

NTB: number of total bands amplified; NPB: number of polymorphic bands;

%: percentage of polymorphic bands; r: correlation between fragment size

and band frequency; GST: Nei’s fixation index.* P < 0.0001.

was 12.0 and 8.4, respectively, for the Sicilian and Sardinian,

populations; the average number of effective alleles (ne) was

4.77 for the Sicilian populations, 4.11 for the Sardinian

populations and 5.63 overall entries (Table 2). The assay

efficiency index (Ai) was 5.6 and the marker index (MI)

0.67.

Fig. 2. Typical SSR profiles: (A) CDAT-01, (B) CLIB-02, (C) CMAL-06,

(D) CMAL-24, (E) CMAL-108.

The mean observed heterozygosities (HO) of the

Sardinian and Sicilian populations were similar (0.709

versus 0.718); the lowest and highest HO values were

detected in two populations from Sicily: i.e. SIC-BRO

(0.653) and SIC-ROC (0.795). The same populations also

showed the lowest (SIC-BRO = 0.616) and highest (SIC-

ROC = 0.735) mean expected heterozygosity (HE), whose

mean overall value was 0.670. Based on the estimates of

the within population inbreeding coefficient (F IS), no

significant deviation from HW equilibrium was observed

(Table 1, Appendix A). Examples of allelic polymorphism at

microsatellite loci are shown in Fig. 2.

A significant departure from HW equilibrium was

observed at seven of the 35 locus/population combinations.

Significant deficiencies in heterozygotes were detected at

CMAL-06 (SIC-BRO population) and CMAL-24 (SIC-PAL

and SIC-ROC populations), while an excess of hetero-

zygotes was observed at CDAT-01 (SIC-ROC and SAR-

NUO populations), CMAL-06 (SIC-ROC) and CMAL-24

(SIC-TAV). However, overall, the seven populations did not

deviate significantly from HW equilibrium (Appendix A).

All the loci showed independent inheritance, with no

pairs of loci showing any significant linkage disequilibrium

across the populations. The populations from SIC-PAL, SIC-

ROC, SIC-TAV and SAR-ORI showed significant linkage

disequilibrium at three or fewer of the ten possible pair-wise

combinations of the five loci; on the other hand the

Bonferroni correction removed all but one of these cases

(between CDAT-01 and CLIB-02 at SIC-TAV).

3.2. AFLP genetic variability

The statistical parameters relating to the AFLP markers

for each population are reported in Table 1. The seven primer

combinations defined 580 distinct bands, of which 476

(82%) were polymorphic; the highest number of poly-

morphic bands was generated by E38/M47 (84 polymorph-

isms, Table 2). The values for average number of observed

alleles (no), effective alleles (ne) and genetic diversity (Nei’s

unbiased H0) were significantly higher for the Sicilian

populations (1.88, 1.35 and 0.218, respectively) than for the

Sardinian ones (1.70, 1.28 and 0.177, respectively)

(Table 1). The assay efficiency index (Ai) was 89.5 and

the marker index (MI) 10.9. All the genotypes were

characterized by a unique banding pattern: an example of an

AFLP profile is shown in Fig. 3.

The overall reproducibility value was high, as 98.1% of

the markers were scored identically in the three replicates

of each of the four individuals tested. A negative

correlation between fragment size and band frequency

(�0.069, P < 0.1) was computed, with a high range of

variation between primer combinations (Table 2); this

may indicate a degree of homoplasy in the data set

[24,30,55]. On the other hand, the highest values of

population differentiation were detected by E38/M48 and

E32/M48 (Table 2), which also gave the most negative

E. Portis et al. / Plant Science 169 (2005) 199–210204

Fig. 3. Subset of AFLP amplification products generated using primer combination E38/M47 in 24 (out of the 30 plants analysed) in each of the four Sicilian

populations. Sample codes described in the text.

correlation between fragment size and band frequency

(�0.414, P < 0.01 and �0.306, P < 0.01, respectively).

This may suggest that the potential presence of size

homoplasy for AFLP fragments did not cause an

underestimate of genetic divergence between populations.

Data analysis was also performed by ignoring AFLP

fragments smaller than 150 bp (338 markers retained);

this resulted in a less negative correlation between

fragment size and band frequency (�0.055, P < 0.1).

However, the differentiation values among populations

Fig. 4. PCO plot of first three principal coordinates, depicting the genetic relations

five SSR loci. Principal axes explain 34.7, 8.6 and 6.1%, respectively, of the varianc

1), 11.2% (axis 2) and 5.9% (axis 3). Sample codes described in the text.

were similar to those obtained with the complete data set

(results not shown).

3.3. Cluster analysis of individuals

Both principal-coordinate (PCO) plots (based on SSR and

AFLP data, Fig. 4) show congruent groupings: individuals

from both the Sicilian and the Sardinian populations formed

two separate groups. For the SSR loci, the first three axes

explain 49.4% of the variation; within the Sicilian group

hip among the 210 accessions from seven populations. (A) Calculated from

e. (B) Calculated from 416 AFLP markers. Variance explained: 39.8% (axis

E. Portis et al. / Plant Science 169 (2005) 199–210 205

Table 3

Single and overall locus estimates of Wright’s F-statistics and Slatkin’s R-

statistics for seven populations of wild cardoon

Locus FIS FIT FST RST

CDAT-01 �0.119 �0.016 0.093 0.078

CLIB-02 �0.089 0.139 0.201 0.178

CMAL-06 �0.076 0.068 0.133 0.182

CMAL-24 �0.036 0.136 0.166 0.210

CMAL-108 �0.014 0.071 0.083 0.185

Overall loci �0.064 0.086* 0.141* 0.168*

* P < 0.0001.

individuals were mainly distributed according to geographi-

cal origin (showing little overlap), while Sardinian materials

could not be so clearly differentiated, as the three populations

appeared to be overlapping (Fig. 4A). The first two axes of the

PCO analysis for the individual AFLP distance matrix explain

56.9% of the population genetic variation, and different

groups for each population were defined for both Sicilian and

Sardinian provenances (Fig. 4B).

AFLP- and SSR-derived PCO plots were very similar,

although plots based on SSR data showed more hetero-

geneity (mainly with respect to the Sardinian materials),

and the clusters appeared less distinct, reflecting more

variation between the individuals. To achieve separation

into seven distinct groups, the minimum number of AFLP

primer combinations required was four (297 polymorphic

bands). The addition of further primer combinations

resulted in a minor modification of the dendrogram (data

not shown).

3.4. Populations differentiation

The neighbour-joining trees based on SSR and AFLP

data are shown in Fig. 5. As for the PCO plots, consistent

results were obtained from the two marker types. Very

similar trees were obtained from distance measures

calculated from SSR data (i.e. Nei’s D and dm2). The

Sicilian populations always clustered separately from the

Sardinian ones; bootstrap support values were particularly

high in the internal node within the Sardinian populations

(from 90 to 100%), but were lower in the internal nodes of

the Sicilian materials (from 50 to 69% for SSR trees and

from 62 to 96% for the AFLP tree).

F- and R-statistics for each SSR locus are reported in

Table 3. Overall loci and populations, the mean coefficient

of inbreeding within population (F IS) was not significantly

different from zero. The value of F IT was significantly

different from zero (P < 0.0001), providing some evidence

for population structure. The differentiation among all

Fig. 5. Neighbour-joining trees with bootstrap support values (based on 1000 boots

SSR data and dm2 distance; (C) AFLP data and Nei’s genetic distance. Sample c

samples was significant for both marker systems, as the three

overall measures of population differentiation (GST, FST and

RST) were significantly greater than zero: GST = 0.211 for

AFLP data (P < 0.0001, Table 2), FST = 0.141 (P < 0.0001)

and RST = 0.168 (P < 0.0001) for SSR data (Table 3). A

higher range of variation was observed among the SSR loci

as compared to the AFLP loci in relation to two components

of genetic diversity: estimates of RST were numerically

greater than the estimates of FST for three out of the five SSR

loci analysed (Tables 2 and 3).

3.5. Correlation between SSRs and AFLPs

Since distance estimates according to Goldstein’s

stepwise mutation model (dm2) [47] are expressed as the

squared mean of the differences in tandem repeats overall

SSR loci taken into account, absolute dm2 values are not

comparable to Nei and Li [45] and Nei [48] based estimates

of genetic similarity. Spearman’s rank correlation coeffi-

cients were higher at population than at individual level

(Table 4). Furthermore, at the individual level the correlation

between the Nei and Li’s AFLP and SSR data was lower than

that between AFLP and dm2-SSR estimates (0.54 versus

0.63, Table 4).

traps) at forks, calculated from: (A) SSR data and Nei’s genetic distance; (B)

odes described in the text.

E. Portis et al. / Plant Science 169 (2005) 199–210206

Table 4

Spearman’s rank correlation coefficients among diversity indices derived

from SSR and AFLP markers at the population (P < 0.001) and individual

(P < 0.01) level

Population level Individual level

AFLPa SSRa dm2-SSRb AFLPa SSRa dm2-SSRb

AFLPa 1.00 1.00

SSRa 0.91 1.00 0.54 1.00

dm2-SSRb 0.91 0.97 1.00 0.63 0.79 1

a Genetic similarity data estimated according to Nei [48] at population

level and to Nei and Li [45] at individual level.b Genetic similarity data estimated according to Goldstein et al. [47].

4. Discussion

This report represents the first analysis of SSR-based

genetic differentiation between and within populations of

wild cardoon. In a previous work Raccuia et al. [56] by

means of AFLP markers analysed genetic variation in six

Sicilian populations, however only four plants per popula-

tion were sampled and thus their data do not appear

comparable. As the species is outcrossing and seed-

propagated, it is expected to display high levels of

heterozygosity. Wild cardoon grows in dense stands and

can produce viable seed across a variety of habitats, ranging

from mesic riparian sites to xeric rocky slopes, and the seven

populations sampled in this report cover locations, in the two

major Mediterranean islands Sicily and Sardinia, character-

ized by different pedo-climatic conditions. The stands were

selected to be far from each other to ensure their

reproductive isolation. In choosing a number of plants to

sample, we have followed the suggestion of Gillet [57] who

has estimated that from populations in HW equilibrium, a

random sample of 30 individuals ensures the detection of all

alleles present at relative frequencies between 0.05 and 0.09

(P � 0.95). The two marker types applied have different

properties and they revealed different levels of polymorph-

ism. Dominant markers can only identify two alleles per

locus, with a detectable maximum level of heterozygosity of

0.5 [33]. Thus the mean expected heterozygosity level based

on AFLP (0.196) was, as expected, significantly lower than

the equivalent for SSRs (0.670). On the other hand, AFLP

markers had a higher effective multiplex ratio value (55.8)

and the estimated efficiency index (Ai) and marker index

(MI) parameters revealed their much higher relative

information content in comparison to SSR.

Nevertheless, the two marker systems generated compar-

able descriptions of population structure, and significant

correlations were detected from relatedness matrices

between populations and between individuals. As reported

by others [24,33], the concordance between AFLP- and

SSR-based genetic similarities was lower at the individual

than at the population level. At individual level there

was a higher correlation between AFLP and dm2-SSR data

in relation to Nei and Li’s AFLP and SSR similarities

(Spearman rank correlations: 0.632 versus 0.545; see

Table 4), and analogous results have been reported by

Uptmoor et al. [58]. The SSR (CMAL-24, CMAL-108 and

CMAL-06) allele distributions were continuous (with the

exception of two alleles unique to the SIC-BRO and SAR-

SAS populations). The other SSR allele distributions were

also nearly continuous, with only one size class missing for

CLIB-02, and two size classes for CDAT-01. Thus the

stepwise mutation model (SMM) seems more suitable for

the analysis of individual relationships based on SSR

variation. The higher correlation between the fixation index

based on SMM (RST) and the mean GST value based on 476

AFLP markers provides further confirmation. Generally,

GST for AFLP data (0.211) and both RST and FST for SSR

data were significantly greater than zero (i.e. 0.141 and

0.168, respectively) indicating a moderate to large

differentiation [59].

Pejic et al. [32] have reported that 150 polymorphic bands

can provide a reliable estimate of genetic similarities among

genotypes within a species. In wild cardoon, we have shown

that two primer combinations generate about 140 poly-

morphic bands, making it practicable to fingerprint all 210

genotypes included in this study. However, for the maximum

resolution of seven distinct clusters (one per each popula-

tion) four primer combinations, amplifying 297 poly-

morphic bands, were necessary. As we have based our

analysis on 476 polymorphic bands, we are confident of the

accuracy of our estimates of the genetic relationships

between genotypes and of the genetic divergence between

populations.

The results reported in this paper illustrate a number of

significant features. As expected, due to geographical

isolation which prevents gene flow, the Sardinian and

Sicilian populations are clearly differentiated from one

another, and form two separate gene-pools. Both marker

systems show that the Sardinian and Sicilian populations

possess a remarkable number of private alleles (see Table 5)

so that the two gene-pools must have evolved independently.

A higher degree of genetic differentiation was found among

the Sicilian populations, which might be a consequence of a

more ancient colonization of this island. This idea is

supported by the higher number of SSR alleles and AFLP

polymorphisms found in the Sicilian gene-pool (Table 5).

A limited degree of gene flow presumably occurs

between populations within Sicily and Sardinia. Wild

cardoon has a discrete reproductive phase of plant growth,

with flowering occurring from March through July; although

flowers can be produced at different stages of plant maturity

and seed set occurs over several months, only limited gene

flow between populations can occur, primarily via seed

dispersal by birds and wind, the latter aided by the presence

of a well developed pappus. This is presumably also the

route for colonization of new habitats.

In both Sardinia and Sicily a high genetic variation was

present within populations. This is consistent with the

conclusions of Gaudel et al. [60], who compared FST

estimates in natural populations of cross- and self-pollinating

E. Portis et al. / Plant Science 169 (2005) 199–210 207

Table 5

Number of polymorphic bands, common and private alleles within a location, detected with SSR and AFLP markers in seven population of wild cardoon

(sample codes described in the text)

Population Number of polymorphic bands Number of locally common alleles Number of private alleles

SSR AFLP SSR AFLP SSR AFLP

SIC-BRO 35 342 7 22 3 6

SIC-PAL 40 347 10 25 5 5

SIC-ROC 34 302 6 15 3 5

SIC-TAV 24 328 4 13 2 3

SAR-ORI 34 286 6 16 1 5

SAR-SAS 25 269 3 12 1 4

SAR-NUO 22 250 2 13 0 4

Sicilian populations 60 419 26 70 2 22

Sardinian populations 42 333 10 36 3 19

species made by several investigators, which generated an

average FST for out-breeding species of about 15%. Never-

theless, we have detected a remarkable extent of within-

population clustering. We could not identify any overlap

between populations on the basis of 416 AFLP bands

(Fig. 4B), and thus, despite the high ratio of within-to-between

population genetic variance, the AFLP banding pattern of

each genotype is a relatively reliable predictor of its parental

population. In contrast, on the basis of SSR genotyping, the

Sardinian populations appeared to share part of their gene-

pool, and the Sicilian populations partially overlap (Fig. 4A);

this lack of agreement is probably an artefact of the small

number of SSR loci employed. Furthermore, several

characteristics of SSRs, among which are constraints on

allele size, and rapid and complex mutational processes

within the SSR and flanking regions (that result in size

homoplasy), can lead to underestimates of genetic diversity

and divergence between populations [61,62]. These factors

can usually be minimised by increasing the number of SSR

loci which, as in most studies to date, are far smaller than

typical numbers of AFLPs [33].

Because of wild cardoon’s adaptability to a range of

environmental conditions and to habitat fragmentation, the

geographical–genetic distance relationship was weak;

landscape topography therefore may play at least as

important a role as does life history in establishing the

population structure—in the same way as described for a

grassland herb in a recent paper by Bullock et al. [63].

Previous studies [7,10], which have aimed to evaluate the

potential of wild cardoon as a biomass and seed oil extraction

crop, have indicated that some genotypes are more promising

than others. The genetic distribution observed among

populations detected in this study will aid in the identification

of appropriate sampling strategies to capture the maximum

possible genetic variation. Several criteria have been

suggested to identify the most important alleles to target in

a sampling strategy [33,55]: (i) allelic richness, or the number

of alleles per locus; (ii) the evaluation of the ‘locally common

alleles’, i.e. alleles that are common in one to several

populations, but not in the species as a whole; (iii) the

identification of unique alleles. Table 5 reports, for each

population analysed, the numbers of AFLP bands and SSR

alleles detected, the locally common alleles (defined as AFLP

bands or SSR alleles with frequency �0.05) restricted to one

or two populations and the number of unique alleles, defined

as AFLP bands/SSR alleles restricted to a single population.

Based on any one of these criteria, using either the AFLP or

the SSR data sets, the same priority populations are identified:

SIC-PAL and SIC-BRO in Sicily and SAR-ORI in Sardinia.

Representative individuals will clearly need be tested across

different environments, in order to evaluate the effect of soil

and climate variation on the chemical composition of oil and

leaf extracts, seed set and biomass production. Wild cardoon,

with its autumn–summer growth cycle and deep root system,

which is able to extract water and nutrients from very deep soil

zones, shows good adaptation to environments characterized

by high abiotic stress, and there is potential for its cultivation

to be extended to the unfavourable edaphic and climatic

conditions common to many inland hilly areas of Mediterra-

nean basin.

Acknowledgements

We thank Dr. L. Barchi for technical assistance and Dr. A.

Rottenberg for the critical reading of the manuscript.

E. Portis et al. / Plant Science 169 (2005) 199–210208

Appendix A. Summary of allelic data and statistics for the five SSR loci across seven wild cardoon populations

SIC-

BRO

SIC-

PAL

SIC-

ROC

SIC-

TAV

SAR-

ORI

SAR-

SAS

SAR-

NUO

Over Sicilian

populations

Over Sardinian

populations

Overall

populations

CDAT-01

no 3 6 5 6 4 3 4 10 5 11

Ho 0.208 0.625 0.750 0.667 0.583 0.542 0.667 0.563 0.597 0.577

He 0.190 0.580 0.593 0.627 0.556 0.497 0.568 0.497 0.540 0.516

F IS �0.096 �0.078 �0.265* �0.064 �0.048 �0.091 �0.174* �0.131 �0.105 �0.119

Allele size range 178–245

CLIB-02

no 8 6 5 4 5 5 3 11 6 12

Ho 0.762 0.773 0.792 0.609 0.737 0.714 0.708 0.734 0.720 0.728

He 0.692 0.702 0.713 0.556 0.652 0.728 0.635 0.666 0.672 0.668

F IS �0.102 �0.100 �0.111 �0.095 �0.130 0.019 �0.116 �0.102 �0.076 �0.089

Allele size range 180–220

CMAL-06

no 9 13 6 5 8 8 5 18 13 20

Ho 0.773 0.909 0.826 0.765 0.882 0.800 0.857 0.818 0.846 0.830

He 0.804 0.855 0.705 0.697 0.813 0.774 0.754 0.765 0.780 0.772

F IS 0.039* �0.063 �0.172* �0.097 �0.085 �0.034 �0.137 �0.069 �0.085 �0.076

Allele size range 122–188

CMAL-24

no 9 10 11 5 8 4 5 13 9 15

Ho 0.696 0.727 0.783 0.636 0.810 0.650 0.682 0.710 0.714 0.712

He 0.630 0.801 0.869 0.543 0.765 0.584 0.618 0.711 0.656 0.687

F IS �0.103 0.092* 0.099* �0.171* �0.058 �0.113 �0.104 0.000 �0.089 �0.036

Allele size range 180–208

CMAL-108

no 6 5 7 4 9 5 5 8 9 9

Ho 0.826 0.667 0.826 0.571 0.833 0.625 0.684 0.723 0.714 0.719

He 0.767 0.612 0.798 0.584 0.827 0.668 0.711 0.690 0.735 0.709

F IS �0.078 �0.089 �0.036 0.021 �0.007 0.064 0.037 �0.047 0.028 �0.014

Allele size range 80–104

Overall loci

no 35 40 34 24 34 25 22 60 42 67

Ho 0.653 0.740 0.795 0.650 0.769 0.666 0.720 0.709 0.718 0.713

He 0.616 0.710 0.735 0.601 0.723 0.650 0.657 0.666 0.677 0.670

F IS �0.059 �0.042 �0.081 �0.080 �0.064 �0.025 �0.095 �0.066 �0.061 �0.064

no: number of observed alleles; Ho: observed heterozygosity; He: expected heterozygosity; F IS: within population inbreeding coefficient.* Significant (P < 0.05) departure from Hardy–Weinberg genotypic proportions.

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