Genetic and Metabolite Diversity of Sardinian Populations of Helichrysum italicum
et al. (2013) Genetic and Metabolite Diversity of Sardinian Populations of Helichrysum italicum. PLoS
ONE 8(11): e79043. doi:10.1371/journal.pone.0079043
Genetic and Metabolite Diversity of Sardinian Populations of Helichrysum italicum
Sara Melito 0
Angela Sias 0
Giacomo L. Petretto 0
Mario Chessa 0
Giorgio Pintore 0
Andrea Porceddu 0
Keith A. Crandall, George Washington University, United States of America
0 1 Dipartimento di Agraria, Universita` degli Studi di Sassari , Sassari , Italy , 2 Dipartimento di Chimica e Farmacia, Universita` degli Studi di Sassari , Sassari , Italy , 3 Centro Interdipartimentale per la Conservazione e Valorizzazione della Biodiversita` Vegetale , Loc. Surigheddu, Sassari , Italy
Background: Helichrysum italicum (Asteraceae) is a small shrub endemic to the Mediterranean Basin, growing in fragmented and diverse habitats. The species has attracted attention due to its secondary metabolite content, but little effort has as yet been dedicated to assessing the genetic and metabolite diversity present in these populations. Here, we describe the diversity of 50 H. italicum populations collected from a range of habitats in Sardinia. Methods: H. italicum plants were AFLP fingerprinted and the composition of their leaf essential oil characterized by GC-MS. The relationships between the genetic structure of the populations, soil, habitat and climatic variables and the essential oil chemotypes present were evaluated using Bayesian clustering, contingency analyses and AMOVA. Key results: The Sardinian germplasm could be partitioned into two AFLP-based clades. Populations collected from the southwestern region constituted a homogeneous group which remained virtually intact even at high levels of K. The second, much larger clade was more diverse. A positive correlation between genetic diversity and elevation suggested the action of natural purifying selection. Four main classes of compounds were identified among the essential oils, namely monoterpenes, oxygenated monoterpenes, sesquiterpenes and oxygenated sesquiterpenes. Oxygenated monoterpene levels were significantly correlated with the AFLP-based clade structure, suggesting a correspondence between gene pool and chemical diversity. Conclusions: The results suggest an association between chemotype, genetic diversity and collection location which is relevant for the planning of future collections aimed at identifying valuable sources of essential oil.
Funding: This research was supported by the RAS (Autonomous Region of Sardinia) Postdoctoral Fellowship Promotion of the Scientific Research and of the
Technological Innovation in Sardinia (Sardinia POR, FSE 20072013 funds, L.R. 7/2007)(ID:CRP1_385) and Fondazione Banco di Sardegna. The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: This study was partly funded by Fondazione Banco di Sardegna. This does not alter the authors adherence to all the PLOS ONE policies
on sharing data and materials.
The genus Helichrysum (Asteraceae, Gnaphalieae) includes over
500 species, distributed worldwide . The species are highly
diverse with respect to both phenotype and metabolite profile [2
4]. H. italicum (Roth) G. Don is a typical endemic Mediterranean
species, able to colonize environments ranging in altitude from
zero to 2,200 m a.s.l. [2,5]. It has been sub-divided into ssp.
italicum (distributed as isolated populations in Morocco, Cyprus,
Corsica, some Aegean islands and Italy) , ssp. michrophyllum
(Willd.) Nyman (present in the Balearic Islands, Corsica, Crete and
Sardinia)  and ssp. siculum (Jord. & Fourr.) Galbany, L. Saez &
Bened (endemic to Sicily) . Both ssp. italicum and ssp.
michrophyllum are found throughout Sardinia, from sandy beaches
to holm oak forests at an altitude of 1,250 m a.s.l. Although certain
morphological traits have been proposed to discriminate between
the two subspecies, their phenotypic plasticity has caused problems
in their taxonomic assignment. A number of molecular marker
studies have attempted to address this taxonomic problem [5,6],
but little attention has as yet been paid to characterizing the
relationship between intra-specific genetic diversity and either
growing environment or metabolite profile.
The species complex has attracted attention on account of its
secondary metabolite content, specifically flavonoids,
sesquiterpene lactone and essential oils [3,813]. H. italicum extracts have
been shown to exhibit both antioxidant and anti-inflammatory
activity [14,15], and its antimicrobial activity (against both
Staphylococcus aureus and Candida albicans) has been ascribed to the
presence of terpenes and terpenoids .
The composition of essential oils is known to depend both on
the collection site and on the developmental stage of the plant.
The essential oil profiles derived from a set of Corsican and
Tuscan ssp italicum accessions produced two distinct groups, with
the profiles of the Corsican oils being dominated by neryl acetate,
neryl propionate, nerol, acyclic ketones and b-diketones, while that
of the Tuscan ones featured a-pinene and b-caryophillene.
Santa Sofia - Laconi
Meanwhile, ssp. microphyllum accessions collected in Sardinia and
Corsica showed a similar composition, characterized by a high
content of neryl acetate, nerol and neryl proponiate . In spite
of some sustained effort directed towards characterizing the
metabolite profile of H. italicum, little emphasis has as yet been
given to linking genetic with metabolite diversity [20,21]. Such
information would be useful in the context of formulating rational
Based on a combination of molecular marker and
morphological evidence, it has been argued that Corsican and Sardinian
populations of ssp. italicum and ssp. microphyllum share the same
gene pool . The high levels of morphological diversity observed
have been seen as reflecting adaptation to a wide range of
ecological conditions [6,22]. This findings established the rational
for exploring the genetic and chemical diversity of Sardinian
populations of H. italicum without reference to the subspecies to
which they belonged.
We report the application of the AFLP (amplified fragment
length polymorphism) DNA fingerprinting platform in
combination with essential oil analysis obtained by gas chromatography
mass spectrometry (GC-MS), aimed at evaluating simultaneously
the genetic and metabolite diversity of H. italicum populations
sampled from disparate sites in Sardinia. The data should be
informative for collection and conservation activities, as well as for
the wider exploitation of the secondary metabolite content of the
Materials and Methods
H. italicum collection and sampling sites
Young stems of about 10 cm in length were collected from 294
H. italicum plants between March and July 2010 and stored at
220uC at the Centro Interdipartimentale per la Conservazione e
Valorizzazione della Biodiversita` vegetale (University of Sassari,
IT). To avoid the collection of clonal material, only well spaced
plants (at least 10 m apart from one to other) were sampled. When
this was not possible the number of sampled individuals was
reduced from 6 up to a minimum of 4 (Table 1). All plants were
harvested at the full blooming stage.
The location of the 50 collection sites (Figure 1) was determined
by GPS, which along with other features of the sites, is recorded in
Table 1. No endangered or protected species were involved and
no specific permissions were required at any of the sites.
Meteorological data relevant for each site were provided by the
Environmental Protection Agency of Sardinia (ARPAS),
Hydrology, Meteorology and Climatology Department, derived from
facilities located close to each site (Table S1 in File S1). Mean
monthly parameters reflected historical (19972010) data. The
habitat and soil type at each site are given in Table S2 in File S1.
Total genomic DNA was extracted using a DNeasyH Plant kit
(QIAGEN, Hilden, Germany) following the manufacturers
protocol. AFLP analysis, based on the two restriction enzymes
MseI and EcoRI and the three primer combinations
E-AAC/MCAT, E-AGC/M-CTA and E-ACC/M-CAT, was carried out
according to Vos et al.  with minor modifications. Thus, the
genomic DNA (250 ng) was double digested with 5 U of EcoRI
and MseI (New England Biolabs, Ipswich, MA, USA) in 106
Restriction/Ligation buffer (100 mM Tris base, 100 mM MgAc,
500 mM KAc, 50 mM DTT, 100 ng/ml BSA). Ten microliters of
ligation mix (5 mM EcoRI adapters +1A, 50 mM MseI adapters
+1C, 10 mM ATP, 1 U T4 ligase) was added to the restriction mix
and incubated for 3 h at 37uC. A 5 ml aliquot of a 1:9 dilution of
this reaction represented the template for a 20 ml pre-amplification
reaction containing 1.5 mM MgCl2, 2 ml 106 buffer, 10 mM
dNTP, 2.75 mM each of the EcoRI and MseI primers and 1 U
Taq polymerase. The resulting amplicon was diluted 1:4 with
water and a 5 ml aliquot was used as the template for a selective
PCR, primed with one of the three EcoRI/MseI primer
combinations E-AAC/M-CAT, E-AGC/M-CTA,
E-ACC/MCAT. All PCRs were performed using Platinum H Taq DNA
Polymerase High Fidelity. The amplicons were electrophoresed
through 6% denaturing polyacrylamide gels, with the 100 bp
DNA Ladder 100 (Invitrogen Life Technologies) included to allow
for the estimation of fragment sizes. The fragments were visualized
by silver staining, using a protocol adapted from Bassam et al.
. Fragments were scored on a presence (1)/absence (0) basis.
Only strongly amplified fragments in the size range 80 bp-500 bp
The population structure was investigated using the Bayesian
clustering model implemented in STRUCTURE 2.3.3 .
The settings were based on the recessive allele mode with an
admixture model correlated to allele frequencies and no a priori
information regarding population origin. The range in K
considered was 115, and for each value of K, 20 replicate chains
of 200,000 MCMC interactions were run with a burn length of
100,000. The most likely number of genetic clusters (K) was
evaluated as suggested by Evanno et al. . To incorporate
geographical coordinates into the assignment analyses, the
program TESS 2.3  was run, employing an admixture model
with 300,000 sweeps and a burn-in period of 100,000.
Again, the number of clusters studied ranged from 1 to 15, and
20 replicates were considered for each K.
Population divergence was evaluated by analysis of molecular
variance (AMOVA) as implemented in ARLEQUIN v220.127.116.11
. Loci which were putatively either neutral or under selection
loci were identified by BAYESCAN 1.0 software  (99%
confidence interval, pilot run length 5,000). The loci identified
were used for subsequent AMOVA and a Mantel test. A
phylogeny was constructed based on UPGMA clustering ,
using TFPGA v1.3 software .
Extraction, isolation and identification of essential oils
The essential oil samples were isolated from young stems by
hydrodistillation in a Clevenger type apparatus for 1 h with
Loci under selection Among population 49
Among population 49
Within population 244
Within population 244
Among population 49
Within population 244
*d.f. = degree of freedom.
full set includes all loci, loci under selection only loci deemed to be under
putative selection, and neutral loci only loci deemed not to be under
500 ml distilled water, following an established protocol .
Subsequent GC-MS analysis was carried out using a Hewlett
Packard 5890 GC-MS system operating in EI mode at 70 eV and
equipped with either (1) an HP-InnoWax capillary column
(30 m60.25 mm, film thickness 0.17 mm), over a temperature
gradient of 4uC per minute, starting at 60uC for three minutes and
ending at 210uC for 15 min; or (2) a HP-5 capillary column
(30 m60.25 mm, film thickness 0.25 mm) over a temperature
gradient of 4uC per minute, starting at 60uC for three minutes and
ending at 300uC for 15 min. The injection and transfer line
temperatures were 220uC and 280uC, respectively. Helium was
used as the carrier gas at a flow rate of 1 ml per minute and a split
ratio of 1:10. The identification of components was achieved by
comparing the GC retention index (RI) on the apolar and polar
columns with those of authentic samples of various essential oils
and by matching the MS fragmentation patterns and retention
index with stored Wiley 7 mass computer library, NIST (National
Institute of Standards and Technology) or data in the literature
[33,34]. A hydrocarbon mixture of alkanes (C9-C22) was analyzed
separately under the same chromatographic condition to calculate
the RIs using a generalized equation . The following standards
were included: linalool (Purity $95%, Fluka), 1,8-cineole (99%
purity, Aldrich), nerol (Purity $90%, Fluka), geraniol (Purity
$96%, Fluka). C9-C22 alkane standards (purity 9899%) were
purchased from Aldrich.
Correlations between metabolites, site altitude, site climate and
the genetic coefficient of membership (Q) were calculated. A
Pearsons x2 test for 262 contingency tables was performed for the
categorical variables soil type and habitat class  to test
correlations with the clades identified by STRUCTURE .
Principal Component Analysis (PCA) was employed to reduce the
complexity of the meteorological data. All variables were
standardized for PCA analysis (Table S3 in File S1), and the
analyses were carried out using JMP 7 software . Climatic,
geographic and genetic pairwise distance matrices were calculated
for the purpose of the Mantel test. The climatic distance matrix
was calculated by considering the first five principal components,
based on the Euclidean method; geographical distance matrices
between populations were computed from GPS coordinates, while
genetic distance matrices were calculated in the form of Fst .
The Mantel test and partial Mantel tests between geographic,
climatic, and genetic distance matrices were performed using
XLSTAT 2007 software .
Figure 4. Average coefficient of membership (Q) for the
lowland (,300 m a.s.l.), mid altitude (300600 m a.s.l.) and
highland (.600 m a.s.l.) populations. A) At K = 2, Q is significantly
correlated with elevation (Cluster A, r = 20.42, P,0.0001, Cluster B,
r = 0.42, P,0.0001). B) At K = 4, members of Clusters A (identical to
Cluster A at K = 2) and D predominate in the lowland sites, while
members of Clusters B and C are found in the mid altitude and highland
Population genetic structure and divergence
The genotypic data set comprised presence/absence scores for
125 AFLP fragments. STRUCTURE supported the presence of
differentiation among the populations, and the DK method 
identified two main clusters as the most likely structure
(DK = 37.00, Figure S1 in File S1). The incorporation of spatial
information using TESS software  similarly identified the
populations to have a bipartite structure (Figure S1 in File S1).
The clades defined by the two independent clustering methods
were substantially congruent with one another (Figure S1 in File
S1). Cluster A was populated by material originating from the
southwestern region (zone A in Figure 1), while Cluster B was
dominated by collection sites in the interior and northern regions
(zones B and C, respectively). Zones B and C are separated from
each other by the Marghine mountain chain, and zones A and B
are interrupted by a region of intensive cultivation (Campidano).
The coefficients of membership of individuals to the clades were
quite high, except for samples from populations 10 through 16,
which were collected from the northern coast (Q value of 0.88)
(Figure S1 in File S1, Table S4 in File S1). As K was increased, the
new sub-clades which formed all split off from Cluster B, with
Cluster A remaining essentially intact (Figure 2 A, B). The initial
populations to drop out of Cluster B were those collected in the
northern interior and coastal regions, followed by materials
originating from the interior region (Figs. 1, 2B). Inter-population
relationships were graphically illustrated by a UPGMA-based
dendrogram (Figure 3). This analysis showed that the two main
groups, which were only 60% genetically similar to one another,
corresponded fairly well to Clusters A and B (as identified by
Bayesian clustering). Cluster B was divided in eight sub-clusters
(labelled B through I in Figure 3).
The AMOVA showed that about one third of the molecular
variance was attributable to the difference between Clusters A and
B, a proportion which was not much altered when the higher
order partitions (K.2) were tested (data not shown); a further one
third of the molecular variance was explained by differences within
each clade (Figure S2 in File S1). The pairwise distances between
populations assigned to Cluster A members and members of
Cluster B (Figure 2A, B) were greater than those between
populations assigned to other clusters (Figure S2 in File S1).
Several rather high pairwise distances obtained between members
of Cluster B, as for instance between populations 3436 and 3133
(Figure 2A, B), which produced an Fst of 0.63, compared to the Fst
of 0.54 between populations 3133 and 1719 (Figure S2 in File
S1). The BAYESCAN 1.0 tool  identified 43 AFLP loci (34%)
which exceeded the threshold log10 value of 2.0 (posterior odds
probability .0.99) (Figure S3 in File S1). The molecular variance
was then re-calculated considering either only the 43 loci
putatively under selection or those which were neutral (Table 2).
The variance based on the former was partitioned into 75.06%
between populations and 24.94% within populations, while
the apportioning of the variance based on neutral loci produced a
picture similar to that obtaining for the marker set as a whole.
Relationships between the collection site and genotype
The congruence between the AFLP-based clustering and
accession origin prompted a consideration of the relationship
between genetic diversity and aspects of the collection sites
physical environment. The sites were first grouped on the basis of
their elevation above sea level into lowland (,300 m), mid
altitude (300600 m) and highland (.600 m) and the
AMOVA was then repeated. Partitioning of the genetic variance
showed that 4.90% resided between elevation classes, 55.48%
between populations within an elevation class and 39.54% within
populations of same altitude class (Table 3). When the germplasm
was re-organized to fit a K = 4 model (Figure 2), Cluster A4 and D4
members all fell into the lowland sites (Figure 4A, B), while Cluster
B4 and C4 members dominated the mid and higher altitude class
(Figure 4B). The CORINE-based classification of habitat type
defined 14 habitats (Table S2 in File S1) . At first glance, there
was little evidence of any relationship between AFLP-based clade
and habitat (Figure 5A, B), but it was noticeable that Cluster A
members dominated the salt pioneer swards habitat (Figure 5A).
The resulting hierarchical AMOVA showed that 3.26% of the
molecular variance could be explained by habitat type (Table 3). A
x2 test indicated a non-random distribution between AFLP clade
and habitat (P,0.0001), a result which was strengthened when the
rarer habitats were excluded (Table S5 in File S1). The next
physical aspect of the collection sites to be tested was soil type
(Table S2 in File S1) . The AMOVA indicated that 9.38% of
Geographic vs Ecological 0.24
Geographic vs Ecological 0.24
Alleles under selection Geographic vs Ecological 0.24
Genetic vs Geographic
Genetic vs Ecological
Genetic vs Geographic
Genetic vs Ecological
Genetic vs Geographic
Genetic vs Ecological
molecular variance could be explained by this factor, and the
subsequent x2 test again showed a non-random association
between soil type and AFLP clade (Table S5 in File S1). The
distribution of the populations with respect to soil type is shown in
Figure 6. Note that the sole colonizers of gleyic solonchak soils
were Cluster A members, some of which were also sampled from
The same test was repeated on the subsets of marker data referred to in Table 3.
gleyic arenosol and haplyic nitosol soils. Group C4, a population
derived from cluster B (see Figure 2A, B), dominated rocky
outcrops, but was also represented on some other soil types, while
the haplic soils were preferentially populated by individuals
belonging to group D4 (derived from cluster B, K = 2) (Figure 6B).
Geographic zone (A, B, C)
Altitude Classes (AC)
Habitat classes (CB)
Soil Classes (SC)
Source of variation
Among populations within zone
Within populations of the same geographic zone
Among populations within AC
Within populations of the same AC
Among populations within CB
Within populations of the same CB
Among populations within SC
Within populations of the same SC
Correlation between genetic distance and local climate
Relevant meteorological data for the various collection sites are
given in Table S1 in File S1 and the five principal components
(PCs) identified by the PCA in Table S3 in File S1. The first PC
(PC1) explained about half of the overall meteorological variance
and was positively correlated with temperature and negatively with
rainfall, while PC2 (19% of the variance) was dominated by the
mean maximum summer temperature. The remaining three PCs
each explained between 5% and 12% of the variance, and the first
five components together more than 90% of the variance. The
pairwise climatic distances between sites were expressed as a
Euclidean distance based on PC1 through PC5 and are here
referred to as ecological distances. According to a Mantel test, the
pairwise genetic distances were positively correlated with both
climate and geographic separation (Table 4). The latter two
variables were also highly correlated with one another. The
correlation between genetic and climatic distance decreased by
one third when controlling for geography. A similar result
obtained for the correlation between genetic distance and
geographical separation when the effect of climate was factored
out. When the correlations were re-calculated to reflect the set of
AFLP loci which were either neutral or putatively under selection
(Table 4), the one between genetic distance and ecological distance
was reduced by about one third when the neutral markers were
considered, while it rose by a third for the set of loci which were
putatively under selection. The suggestion is that the distribution
of genetic variation has been shaped by natural selection, but in
spite of being correlated with ecology, geographical location on its
own has still been an important factor. The correlation between
The latter were classified into four major chemical groups.
genetic distance and geography was statistically significant even
after controlling for the effect of ecology for both the neutral
markers and those putatively under selection.
Essential oil composition
The GC-MS analysis highlighted 35 distinct compounds,
comprising five monoterpenes, ten oxygenated monoterpenes, 15
sesquiterpenes and five oxygenated sesquiterpenes (Tables S6S9
in File S1). A representative chromatogram is given in Figure S4 in
File S1. The concentration of some of these compounds varied
among the accessions. For instance, nerol and its derivatives were
absent from populations 19, but were present in significant
amounts in population 16. To derive the relationship between
essential oil profile and AFLP clade, the compounds were initially
grouped into the above four terpene classes. A contingency
analysis showed that only the oxygenated monoterpenes were
correlated with the average coefficient of membership at K = 2
(Table 5). When the concentration of each of the 35 compounds
was compared separately, nine proved to be significantly
correlated to AFLP clade at K = 2 (Table 6). The relationship
between the concentration of these nine compounds and the
genetic structure of the material at K = 2 is shown in Figure 7A and
B. Two predominant chemotypes could be recognized, based on
the presence/absence of eudesm-5-en-11-ol and neryl acetate. The
populations collected from zone A (Cluster A members) produced
significant levels of eudesm-5-en-11-ol and almost negligible
quantities of the other compounds (Chemotype 1), while samples
collected in zones B and C (populations 9 through 50, Cluster B
members) contained substantial quantities of neryl acetate, but
relatively little eudesm-5-en-11-ol (Chemotype 2) (Figure 7 B).
Only compounds significantly correlated to the genetic structure are included.
AFLP profiling of the Sardinian populations of H. italicum
strongly supported the existence of two major clades. The
coefficient of membership of individuals to either cluster was, in
general, rather high. For the most part, the AFLP-based clades
were congruent with the collection site of the populations. The
only exception to this scenario was the admixture noticed in the
northern coast populations. The suggestion is therefore that gene
flow between the various populations is rather limited. H. italicum is
an allogamous species which relies on insects for pollination. A
reduced mobility of pollinators may have been responsible for the
limited gene flow observed between the populations. Note
particularly that the membership of Cluster A was hardly affected
by increasing the K value, although this was not the case for
Cluster B. The indication is thus that a bipartite genetic structure
is an intrinsic feature of Sardinian H. italicum populations. This
conclusion needs to be viewed in the light of the proposal that the
two H. italicum ssp. are part of the same gene pool, and that local
populations have become differentiated by natural selection .
The congruence between genetic structure and the physical
environment is suggestive of the diversity being shaped by adaptive
selection. Cluster A was largely restricted to lowland sites, while
Cluster B members were concentrated in the mid altitude and
highland sites. In contrast, Galbany-Casals et al. [22,40] have
proposed that the species genetic structure in the western and
central Mediterranean Basin takes the form of a continuous
gradient in allele frequency rather than exhibiting any sharp
division to form distinct populations; they describe a high
correlation between geographic and genetic inter-population
distances, interpreted as reflecting a typical pattern of isolation
by distance. The apparent discrepancy with the present findings
may, at least in part, be accounted for by the choice of sampling
method. Since the Galbany-Casals et al.  experiments were
designed to address whether ssp. italicum and ssp. microphyllum had
evolved independent gene pools, their focus was on morphological
intermediates. In contrast, the present approach based its sampling
on as wide as possible a range of ecological conditions. A risk of
this strategy is the introduction of an unintended bias against sites
in transitional zones. Working at a single population level led to
the recognition of a positive correlation between genetic distance
and geographical separation, implying the action of dispersive
forces and limited gene flow. Nevertheless, the full analysis
indicated that the genetic distance between populations was better
explained by local ecology rather than by geographical separation.
The sampling of the marker set to include loci presumed to be
under selection indicated that these too tended to be associated
with the climatic variables, underlining the contribution of
The composition of the essential oils was rather uniform within,
but differed markedly between the populations. The profile of
Italian populations of H. italicum is reportedly quite variable ,
but as yet there has been no attempt to correlate it with either
genetic diversity or ecological/environmental variables. Three
major chemotypes have been reported in the literature [4,19], but
much of the variability observed was ascribed to geographical
origin and flowering time. With respect to the Sardinian
populations, only limited information of this type has until now
been documented . The present data show for the first time,
that the presence/absence of specific oxygenated monoterpenes
was consistent with the genetic structure of the populations, as
defined by AFLP fingerprinting. As already described elsewhere
for Corsican and Sardinian H. italicum populations , the
predominant essential oil components were neryl acetate, nerol,
neryl propionate and eudesm-5-en-11-ol. The presence/absence
of neryl acetate and eudesm-5-en-11-ol were well correlated with
the K = 2 genetic structure of the populations, although some of
the other compounds also showed some correlation.
The data strongly support the proposition that the two
AFLPbased clades identified are associated with both ecological factors
(such as altitude, soil type and habitat) and specific chemotypes
(Figure 7). The association between genetic structure and the
physical properties of the sampling site prevented any analysis of
the relative importance of location and genotype on the
determination of the essential oil profile. The association between
chemotype and collection location will need to be considered when
elaborating a strategy for any future collection exercise aimed at
identifying valuable sources of essential oil.
File S1 Supporting figures and tables. Figure S1, Admixture
proportions at K = 2 of 294 H. italicum individuals sampled from 50
populations. A Bayesian clustering analysis was performed using
STRUCTURE and TESS software. The highest likelihood run
was identified based on minimum values for DK  and DIC
, respectively. Figure S2, Pairwise Fst between the AFLP-based
clades identified at A) K = 4 and B) K = 10. At K = 4, clades A and
B were the most divergent, but at K = 10, they were replaced by
clades G and I. Figure S3, The identification of AFLP loci
putatively under selection loci using Bayescan 1.0 software .
Each point corresponds to a single AFLP fragment. Fst is plotted
against the log10 of posterior odds. The vertical dashed line
indicates the chosen threshold value of 2.0. Figure S4,
Representative chromatogram of the Essential oil of H. italicum. The main
peak in the chromatogram represents neryl acetate. Table S1,
Location of the meteorological stations, in terms of their latitude,
longitude, elevation and distance from the sea. Table S2, Soil and
habitat description. For each population the collection site was
described for habitat, CORINE Biotope code (CB) and soil.
Habitats and soils description is shown the corresponding columns.
The habitats as well as the soils were divided in groups with similar
characteristics. Fourteen CB groups (I to XIV) and ten soil types
(110) were identified; missing data are indicated as n.d. Table S3,
The first five PCs explaining .90% of the variation in climate,
reduced from a set of 48 climate parameters. Significant
correlations between each PC and individual parameters are
indicated in bold. Air temperature and rainfall amounts are
annual averages. Table S4, Coefficient of membership (Q) with the
major clades of individual accessions of H. italicum. A) K = 2, B)
K = 4, C) K = 10. Table S5, Contingency analysis for habitat and
soil type with respect to AFLP-based clades at K = 2 and K = 4.
The analysis was repeated with a restricted set of habitat and soil
types. Table S6, The monoterpene fraction of H. italicum essential
oil. Table S7, The oxygenated monoterpene fraction of H. italicum
essential oil. Table S8, The sesquiterpene fraction of H. italicum
essential oil. Table S9, The oxygenated sesquiterpene fraction of
H. italicum essential oil.
We thank the Department of Territory Engineer Prof. S. Madrau of the
University of Sassari for his expert assistance in the pedological data
analysis; Dr. Gianluca Fois and the Environmental Protection Agency of
Sardinia (ARPAS) Hydrology, Meteorology, and Climatology Department
for meteorological data; Dr Dora Ceralli and ISPRA 11 (Istituto
Superiore per la Protezione e Ricerca Ambientale) sistema informatico di
Carta della Natura della Regione Sardegna, for the habitat data; Dr.
Luisa Carta and Prof. Ignazio Camarda for their assistance in collecting
plant material; Dr. Domenico Rau for his guidance with statistical analyses
and population genetics tools; and Dr. Emma Rapposelli for her assistance
in AFLP genotyping.
Conceived and designed the experiments: SM. Performed the experiments:
SM GLP AS MC. Analyzed the data: SM AP GP. Contributed reagents/
materials/analysis tools: SM AP GP MC. Wrote the paper: SM AP.
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