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Conservation Genetics ISSN 1566-0621 Conserv GenetDOI 10.1007/s10592-013-0537-0

Estimating the genetic diversity and spatialstructure of Bulgarian Castanea sativapopulations by SSRs: implications forconservation

Ilaria Lusini, I. Velichkov, P. Pollegioni,F. Chiocchini, G. Hinkov, T. Zlatanov,M. Cherubini & C. Mattioni

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RESEARCH ARTICLE

Estimating the genetic diversity and spatial structure of BulgarianCastanea sativa populations by SSRs: implicationsfor conservation

Ilaria Lusini • I. Velichkov • P. Pollegioni •

F. Chiocchini • G. Hinkov • T. Zlatanov •

M. Cherubini • C. Mattioni

Received: 8 February 2013 / Accepted: 21 September 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Sweet chestnut (Castanea sativa Mill.) is a

multipurpose species of great ecological and economic

importance in southwest Bulgaria. Bulgarian chestnut for-

ests are severely degraded, however, due to the intensive

exploitation and bad management that have occurred over

the last 2000 years. Given the urgent need to define con-

servation strategies to preserve the biodiversity of Bul-

garian chestnut, we estimated its genetic variability. A set

of eight microsatellite primers were used to analyze the

genetic diversity and structure of six C. sativa populations

distributed throughout the range of species in Bulgaria.

Results showed a generally high level of genetic diversity

but little divergence among populations. A significant,

positive, within-population inbreeding coefficient (Fis) was

observed in four populations. A STRUCTURE analysis

revealed three genetic clusters. Using a landscape

approach, significant genetic barriers among populations

were found by integrating genetics with geographical dis-

tance. We hypothesize that one population is a relict from a

glacial refugium; the structure of the remaining populations

is probably the result of a combination of natural events

and human impacts. For the purposes of conservation

planning, we have identified populations that are particu-

larly rich in diversity and private alleles that are good

candidates for preservation.

Keywords Castanea sativa � Genetic diversity �Microsatellite � Landscape genetics � Conservation

Introduction

Castanea sativa Mill. (European chestnut) is widely cul-

tivated and exploited as a multi-purpose tree species of

great ecological and economic importance. C. sativa grows

in natural forests and in several domesticated forms as

managed coppiced and grafted fruit orchards across

southern Europe from the Caucasus region through the

Balkans to the Italian and Iberian peninsula (http://www.

euforgen.org/distribution_maps.html). In Bulgaria, sweet

chestnut has a limited distribution in the forests of the south

west, which are considered the most biologically important

since they are in a transition zone between continental and

Mediterranean climates, and they are rich in biodiversity.

Sweet chestnut is spread over about 3,315 ha of this area,

corresponding to 0.1 % of the total forest, where it coexists

with other deciduous species such as Fagus sylvatica L.,

Quercus sp., Tilia sp., C. betulus L., and Fraxinus ornus L.

(Mihaylov et al. 2007). The largest chestnut forest in

Bulgaria is situated on the northern slopes of Belasitsa

mountain (Lubenova et al. 2004), and a few forests extend

further north. The sweet chestnuts on Belasitsa mountain,

were probably introduced from southern localities, (Dobrev

1914; Stoyanov 1921), or their presence could be related to

their relict distribution (Bratanova-Doncheva et al. 2005).

I. Lusini (&) � P. Pollegioni � F. Chiocchini � M. Cherubini �C. Mattioni

Istituto di Biologia Agroambientale e Forestale (IBAF),

Consiglio Nazionale delle Ricerche (CNR), Viale Marconi,

2, 05010 Porano, Terni, Italy

e-mail: ilaria.lusini@ibaf.cnr.it

I. Lusini � G. Hinkov � T. Zlatanov

Department for Innovation in Biological, Agro-food and Forest

Systems, University of Tuscia, Via San Camillo de Lellis s.n.c.,

01100 Viterbo, Italy

I. Velichkov

Forest Research Institute, 132 St. Kl. Ohridski blvd., 1756 Sofia,

Bulgaria

123

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DOI 10.1007/s10592-013-0537-0

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Timber macroremains and fossil pollen analysis have

demonstrated that the chestnut forests in Bulgaria are of an

ancient origin: quaternary refugia, dated 7.5 Kyr B.P., have

been located in south-west Bulgaria and along the west

coast of the Marmara Sea (Krebs et al. 2004).

Since ancient times, sweet chestnut forests have been

intensively exploited by the local population. Villagers,

particularly those from the foothills of Belasitsa, exploited

the forests for wood, seed production, and pasture (Pano-

vska et al. 1990). After the Second World War forests in

Bulgaria were nationalized, and the sweet chestnut stands

were abruptly abandoned.

The natural replacement of managed pure chestnut for-

ests by other native species such as beech (F. sylvatica),

hornbean (Carpinus betulus) and silver lime (Tilia tomen-

tosa) formed mixed communities. More recently a fungal

pathogen, Cryphonectria parasitica, has been introduced,

becoming one of the major causes of sweet chestnut deg-

radation in Bulgaria (Petkov and Rossnev 2000). There is

thus an urgent need for long-term forest management

measures and an evaluation of the genetic resources of the

Bulgarian chestnut forest, whose biodiversity is under

threat. Studies of the genetic diversity and structure of

current chestnut populations may provide valuable infor-

mation for management planning and conservation strate-

gies. Tools such as molecular markers are widely used to

characterize the DNA variation of long-lived species, since

they can greatly facilitate the prioritization of conservation

decisions (Allendorf et al. 2010).

The genetic diversity and population structure of Euro-

pean chestnut has been widely investigated using bio-

chemical and molecular markers. Villani et al. (1999),

using isozymes, observed two different gene pools in the

eastern and western Turkish population and an introgres-

sion zone between them. Studies conducted with cpDNA

(Fineschi et al. 2000) indicated a low genetic structure

throughout southern Europe while investigations using

ISSR and SSR revealed a genetic divergence between

eastern (Greek and Turkish) and western (Italian and

Spanish) populations as well as a high level of genetic

diversity in Spanish chestnut populations (Mattioni et al.

2008; 2013, Martın et al. 2012). However, the genetic

structure of C. sativa in Bulgaria is still unknown. The

principal aims of our research were to use microsatellite

markers to evaluate the genetic structure and diversity of

natural populations in Bulgaria, and to identify valuable

sweet chestnut reservoirs/areas of genetic resources and

possible means for their conservation. Our study contrib-

utes to the preservation of Bulgarian forest biodiversity by

monitoring the state of genetic diversity of populations and

by giving valuable information for the sustainable man-

agement and optimal maintenance of chestnut forests.

Methods

Plant material

A total of 336 wild chestnut trees were sampled in six

different forests which cover the current distribution of C.

sativa in Bulgaria, about 3,000 ha in the southwest of the

country (Lubenova and Bratanova 2011). The number of

samples collected from each site was proportional to the

population size.

Nearly 2/3 of the samples were obtained from the

chestnut forest on the northern slopes of Belasitsa mountain.

This forest is the biggest and the most well-preserved in

Bulgaria. Considering the high natural value of this area, we

collected samples at different altitudes (400–1,100 m) from

stands in the eastern, central and western part of the

northern slope. The other populations, counted few hun-

dreds of trees, were collected from five sites: the Slavyanka

Mts., the southern slopes of Ograzhden Mts., the north-

western slopes of Pirin Mts. near the village of Brezhani, the

southwestern Pirin Mts near the village of Zlatolist, and the

northern slopes of the West Balkan Range near the town of

Berkovitsa. The distance between each sampled tree was

around 50 m, only individuals estimated to be older than

100–120 years (based on DBH—diameter at breast height)

were sampled to avoid sampling from plantations that were

established during the 1950s. It was assumed that trees with

a DBH higher than 100 cm would be about 100 years old

(Maroschek et al. 2011). The detailed location of the pop-

ulations and the number of trees analyzed are shown in

Table 1, and the sampling localities are shown in Fig. 1.

DNA extraction

Total genomic DNA was isolated by grinding 20 mg of

dehydrated leaf tissue. The tissue was cooled with liquid

nitrogen and then homogenized in a Mixer Mill 300

(Qiagen, Valencia, CA, USA). Genomic DNA was

extracted and purified using the DNeasy96 Plant Kit

(Qiagen) according to the manufacturer’s instructions.

Table 1 Number of samples (N), geographical coordinates (Lat.,

Long.), ID and elevation above sea level (Elev.) for six natural

populations of chestnut in Bulgaria

Populations Lat. Long. ID Elev. (m) N

Belasitsa 41�2105700N 23�1200000E BL 757 211

Slavyanka 41�2405300N 23�3103300E SL 828 21

Brezhani 41�5004800N 23�1105900E BR 803 20

Zlatolist 41�3004100N 23�2403800E ZL 435 21

Ograzhden 41�2704000N 23�0001500E OG 737 42

Berkovitsa 43�1204800N 23�0603400E BK 766 21

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Microsatellite analysis

A set of eight microsatellites primers (CsCAT1, CsCAT2,

CsCAT3, CsCAT6, CsCAT14, CsCAT16, EMCs25 and

EMCs38) developed in C. sativa (Buck et al. 2003; Mari-

noni et al. 2003) were selected and used for the multiplex

PCR analysis. The forward primer of each pair was labeled

with one fluorescent dye (FAM, NED or VIC) (Table 2).

Polymerase chain reactions were carried out on a Gene-

Amp 2700 Thermal Cycler (Applied Biosystems, CA,

USA). The reaction was performed using the Type-it

Microsatellite PCR Kit (QIAGEN, Hilden, Germany) in

20 lL total volume containing 20 ng of genomic DNA and

primer mix with a concentration of 0.2 lM. Following the

Qiagen type-it kit protocol (Qiagen) the cycling parameters

were as follows: 5 min at 95 �C, 28 cycles for 30 s at

95 �C, 90 s at 57 �C and 30 s at 72 �C, and a final step of

30 min at 60 �C. Amplification products (0.1 lL) were

added to 9.8 lL formamide and 0.2 lL Genescan-500

ROX and denatured at 95 �C for 5 min. The samples were

run on an ABI PRISM 3100 DNA sequencer. The raw data

were collected using GeneScan 3.7, and alleles were scored

using Genotyper 3.7 (Applied Biosystems).

Data analysis

Genetic diversity indices

A set of intra and inter population genetic diversity

parameters were calculated. The observed (Na) and

Fig. 1 Map illustrating the

distribution of Castanea sativa

Mill. in Bulgaria (red circle)

and the location of the six

populations examined in the

study (black stars). The

distribution map is taken from

the Red Data Book of the

Republic of Bulgaria, digital

edition. (Color figure online)

Table 2 Number of alleles (Na), range of alleles of seven microsat-

ellite loci for six chestnut populations, number of effective alleles

(Ne), expected heterozygosity (He), observed heterozygosity (Ho),

unbiased estimate of Wright’s fixation indices within-population

inbreeding coefficients f(Fis), total-population inbreeding coefficients

F(Fit), among-population genetic differentiation coefficients h(Fst)

and estimator of actual differentiation (Dest)

Locus Dye Range (pb) Na Ne He Ho f(Fis)a F(Fit)a h(Fst)a Dest

CsCAT1 NED 194–223 12 3.76 0.73 0.65 0.08* 0.16* 0.08* 0.34

CsCAT2 6-FAM 207–231 11 3.62 0.71 0.64 0.17* 0.27* 0.10* 0.46

CsCAT3 6-FAM 190–269 18 3.74 0.70 0.70 0.01 0.06 0.05* 0.20

CsCAT6 VIC 161–197 19 4.74 0.78 0.80 -0.01 0.02 0.04* 0.23

CsCAT14 6-FAM 130–164 8 3.49 0.64 0.69 -0.02 0.04 0.07* 0.23

CsCAT16 6-FAM 127–157 12 4.29 0.76 0.78 0.02 0.08* 0.05* 0.39

EMCs38 NED 228–270 19 3.85 0.72 0.49 0.17* 0.25 0.10* 0.46

a Level of significance of unbiased estimate of Wright’s fixation indices were tested using a non-parametric approach described in Excoffier

et al. (1992) with 1,000 permutations: * p \ 0.05

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effective (Ne) number of alleles, the observed (Ho) and

expected (He) heterozygosity, the expected heterozygosity,

weighted on the number of samples (UHE) were calculated

at each locus, over all loci and for each population using

GeneAlEx 6 (Peakall and Smouse 2005). The unbiased

estimators of Wright’s F-statistics (Weir and Cockerham

1984), within-population inbreeding coefficient f (Fis),

total-population inbreeding coefficient F (Fit) and among-

population genetic differentiation coefficient h (Fst) were

computed for each locus across all populations, over all

loci and for each population over all loci, using hierarchical

locus-by-locus AMOVA as implemented in Arlequin ver-

sion 3.11 (Excoffier et al. 2005). Because the dependence

of Fst values on within population heterozygosity can lead

to an underestimation of the true level of genetic differ-

entiation, we calculated the unbiased estimator of Jost’s

(Dest) (Jost 2008) as an alternative measure of differenti-

ation for each locus across all populations and over all loci

using SMOGD 1.2.5 software (Crawford 2010). The sta-

tistical significance of Fis, Fit and Fst were tested using a

non-parametric approach described in Excoffier et al.

(1992) with 1,000 permutations. Because the presence of

null alleles can affect the estimation of population differ-

entiation for instance, by reducing the genetic diversity

within populations and overestimating the population dif-

ferentiation, null allele frequencies were estimated for each

locus and population following the Expectation Maximi-

zation (EM) algorithm of Dempster, Laird and Rubin

(1977), implemented in FreeNA software (Chapuis and

Estoup 2007). The loci affected by the presence of null

alleles were excluded from the analysis.

The estimation of mean number of alleles per locus as a

measure of allelic richness (Rs) can be affected by differ-

ences in sample size. For this reason, allelic richness and

the number of private alleles (pRs),were computed by the

statistical technique of rarefaction method implemented in

HP-Rare 1.1 (Kalinowski 2005). This approach use the

frequencies of alleles at locus to estimate the expected

number of alleles and/or private alleles in a subsample of

N individuals selected at random from a sample of N indi-

viduals in each population.

Population structure analysis

The population structure and proportion of membership (Q

value) for each predefined population and each individual

sample in each of the predicted clusters were inferred using

the Markov Chain Monte Carlo (MCMC) and Bayesian

clustering algorithms implemented in STRUCTURE 2.3.3

(Pritchard et al. 2000). This method attempts to assign

individuals to several genetic groups in order to minimize

within-group linkage disequilibrium and deviation from the

Hardy–Weinberg equilibrium. The analysis was performed

using the admixture model on the whole dataset (with no

previous population information) and the correlated allele

frequencies between population options (Falush et al.

2007). The range of possible clusters (K) tested was from 1

to 7 (the putative number of provenances plus one). A

series of six independent runs were performed for K

between 1 and 6 with a burn in period of 10,000 steps

followed by 105 MCMC replicates. The ad-hoc statistic DK

defined by Evanno et al. (2005) was used to detect the most

likely number of populations. The six runs from the most

probable number of clusters were averaged by applying a

FullSearch algorithm provided by CLUMPP 1.1.2 (Ja-

kobsson and Rosenberg 2007). The corresponding

Q-matrices were graphically displayed using DISTRUCT

(Rosenberg 2004). A spatial interpolation of population

membership values (Qi), in the inferred K = 3 clusters

estimated by STRUCTURE and CLUMP, was calculated

using the Inverse Distance Weighting (IDW) interpolator

implemented in ArcGIS 9.3, and the clustering surface

maps were produced to better understand the various

‘‘spatial patterns’’ of the genetic clusters. The IDW inter-

polation is a spatial prediction technique that determines

the values of target variables at a new location, using a

linearly weighted combination of a set of sample points.

IDW is based on the assumption that things that are close to

each other are more related than things that are a long way

from each other. It then assumes that these measured points

that are closer to the prediction location have more influ-

ence on the predicted value than those farther away. A

synthetic map, representing the genetic structure, was

generated by overlaying the three clustering surface maps.

Genetic barriers, corresponding to abrupt change in the

patterns of genetic variation among populations were

identified using Barrier 2.2 (Manni et al. 2004). The geo-

graphic coordinates of each population were connected by

Delaunay triangulation and the corresponding Voronoi

tessellation was derived. The genetic barriers were identi-

fied using Monmonier’s (1973) maximum difference

algorithm. This method traces the barriers along the

Voronoi tessellation, starting from the perpendicular

boundary to the edge of the network, for which the distance

value is a maximum, and proceeding across adjacent edges

until the forming boundary has reached either the limits of

the triangulation (map) or closes on itself by forming a loop

around a population. The barriers were constructed

hierarchically.

Based on the number of sampled populations, we tested

from 1 to 6 genetic barriers, and their significance was

tested using 100 resampled bootstrap matrices of Nei’s

(1972) genetic distances calculated using SEQBOOT and

GENDIST in the PHYLIP software package (Felsenstein

2005). A score is associated with the three different barriers

and indicates how many times each edge that constitutes

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the barrier is included in one of the three boundaries

computed from the 100 matrices. Sampling sites and

genetic barriers were overlaid on Digital Elevation Model

(DEM) maps using ArcGis 9.3 to search for the spatial

coincidence of these genetic structures with landscape

elements.

Results

Genetic diversity

In general, measures of genetic diversity were high for all

populations (Table 2, 3). Results from FreeNa showed the

EMCs25 locus was affected by the presence of null alleles,

so this locus was excluded from the analysis. All assayed

SSR loci were polymorphic, and the number of alleles

detected for each locus varied between 8 (locus CsCAT1)

and 19 (locus CsCAT16) (Table 2).

The CsCAT14 and CsCAT6 loci showed the lowest

(0.64) and the highest (0.78) expected heterozygosity (He),

respectively.

The observed heterozygosity ranged from 0.49 in EMCs

38 to 0.80 in CsCAT6. The Fixation index (Fis) was posi-

tive and significant (p \ 0.05) only at EMCs38, CsCAT1

and CsCAT2. Population genetic differentiation (Fst) val-

ues were low but significant (p \ 0.05) for each SSR locus.

The high variability of microsatellite markers and conse-

quently the high He values mean that differentiation may

be underestimated. Consequently, we also calculated the

actual differentiation (Dest), which is an alternative mea-

sure of genetic differentiation. As shown in Table 2, Dest

values were higher than Fst values.

The average value of the effective number of alleles

(Ne) that minimizes differences in sample size, was 3.93,

ranging from 3.28 in the Berkovitsa population to 5.06 in

the Belasitsa population. The lowest observed heterozy-

gosity (Ho) was found in the Slavyanka population (0.62),

and the highest in the Belasitsa population (0.76) with a

mean value of 0.68. Similarly, the expected heterozygosity

(He) ranged from 0.67 in the Slavyanka population to 0.80

in the Belasitsa population. These values may have been

influenced by the sampling effect due to the imbalance on

the sampling size. Nevertheless they are in line with

unbiased expected heterozygosity (UHe) values, weighted

on the number of individuals in a population, which min-

imize the difference in unequal sample sizes. Fis values

were positive in all populations but only four showed a

significant deviation from zero (Belasitsa, Slavyanka,

Zlatolist and Ograzhden) (Table 3). Private allelic richness

values, calculated with the rarefaction method, ranged from

0.17 (Zlatolist) to 1.08 (Slavyanka), while allelic richness

(Rs) values ranged from 7.73 (Belasitsa) to 5.14 (Brezhani)

(Table 3).

AMOVA showed the largest proportion of molecular

variance was within individuals (86.97 %); the variation

among populations was 7.27 %. The Fst values, which are

a measure of population differentiation, were weak but

significant (Fst = 0.0727 p \ 0.001) (Table 4). Pairwise

Fst values were all significant and ranged from 0.042

between Berkovitsa and Belasitsa to 0.180 between Bre-

zhani and Slavyanka (Table 5).

Population genetic structure

The genetic structure of the six chestnut populations was

inferred using STRUCTURE 2.3.3. The results of the Ev-

anno test indicated that K = 3 was the most probable

cluster number (Fig. 2). The estimated population structure

inferred for K = 3 is shown in Fig. 3. The percentage

membership of each individual in each cluster was deter-

mined by the values of Q, and each individual was assigned

to a specific cluster by using an arbitrary threshold of

Table 3 Genetic diversity parameters for the six chestnut popula-

tions analyzed through seven microsatellite loci: mean number of

different alleles (Na), mean number of effective alleles (Ne), observed

heterozygosity (Ho), expected heterozygosity (He), unbiased expec-

ted heterozygosity (UHe), inbreeding coefficient (Fis), allelic richness

(Rs) and private allelic richness (pRs), standardized to twenty indi-

viduals from the original number of trees per population

Populations Na Ne Ho He UHe Fis Rs pRs

Belasitsa 12.86 5.06 0.76 0.80 0.80 0.05* 7.73 0.81

Slavyanka 5.71 3.78 0.62 0.67 0.69 0.10* 5.68 1.08

Brezhani 5.14 3.39 0.67 0.69 0.71 0.06 5.14 0.37

Zlatolist 6.43 4.15 0.66 0.75 0.77 0.15* 6.39 0.17

Ograzhden 6.57 3.91 0.66 0.73 0.74 0.10* 6.04 0.23

Berkovitsa 5.71 3.28 0.69 0.68 0.70 0.01 5.69 0.36

* Significance of inbreeding coefficient Fis was tested using a non-

parametric approach described in Excoffier et al., (1992) with 1,000

permutations: * p \ 0.05

Table 4 The hierarchical AMOVA (Excoffier et al. 2005) and F-

Statistics analysis calculated for six Bulgarian chestnut populations

Source of variation df Variance

components

%

variation

F statistic

Among populations 5 0.21205 Va 7.27 Fst 0.07273*

Among individuals

within populations

330 0.16773 Vb 5.75 Fis 0.06204*

Within individuals 336 2.53571 Vc 86.97 Fit 0.13026*

Total 671 2.91550

Significance of F Statistic values was tested using a non-parametric

approach described in Excoffier et al. (1992) with 1,000 permutations:

* p \ 0.001

df degrees of freedom

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Q C 0.75. The Slavyanka population had the highest Q

membership in the first cluster (Q1 = 0.859, cluster red),

while samples from Brezhani clearly belonged to a second

gene pool (Q2 = 0.782, cluster blue). The individuals from

the Belasitsa, Zlatolist, Ograzhden and Berkovitsa popu-

lations showed an admixture of genotypes, with Q values

indicating some level of membership in each of the three

clusters (cluster 1, cluster 2 and cluster 3—green) but no

single cluster accounting for [0.75. The majority of indi-

viduals from Ograzhden belonged to the first cluster,

whereas the majority of individuals from Zlatolist were

part of the second cluster.

Principal coordinates analysis (PCA) based on Nei’s

unbiased genetic distance (1978) partially confirmed the

repartition of chestnut populations as inferred by the

Bayesan approach (Fig. 4). In line with the STRUCTURE

analysis, the first axis, which accounted for 41.04 % of the

molecular variance, clearly separated Slavyanka from the

other populations. The second axis, which accounted for

22.55 % of variance, separated the Zlatolist population.

Pairwise Fst estimates among the six chestnut popula-

tions reinforced the results of the STRUCTURE analysis

(Fig. 3), revealing that Slavyanka (cluster 1) and Brezhani

(cluster 2) were the most genetically differentiated popu-

lations (Fst = 0.180; Table 5). Figure 5 reports three

clustering surface maps, a result of the spatial interpolation

of population membership values (Qi) in the inferred

clusters for K = 3 (Fig. 5a–c). Each figure shows a dif-

ferent cluster of populations based on an IDW interpolation

of the Q values. In all these figures, the more intensely

colored area indicates the strongest genetic similarity

between populations belonging to the same cluster, and the

gradual change to white indicates a gradual decrease in

genetic similarity. Figure 5a shows the first cluster,

including Slavyanka and Ograzhden; Fig. 5b the second

cluster including Brezhani and part of Zlatolist, and Fig. 5c

the third cluster. Finally, Fig. 5d is a map of the synthesis

of these three interpolations.

The locations of genetic barriers were overlaid on a

Digital Elevation Model in order to reveal overlaps

between geographical and genetic discontinuities (Fig. 6).

Using Monmonier’s maximum difference algorithm, two

main significant genetic boundaries were identified with

bootstrap support [80 % (Fig. 6). The main genetic dis-

continuity separated Slavyanka from all the other popula-

tions. A second boundary (b in Fig. 6) indicated a

significant genetic separation between the Brezhani and

Zlatolist populations. All the others were weak and indi-

cated non-significant separations among the other popula-

tions with bootstrap support \49 %.

Discussion

We believe that this study is the first report on the genetic

diversity and structure of C. sativa populations in Bulgaria.

Table 5 Pairwise Fst estimates among six Bulgarian chestnut populations

Belasitsa Slavyanka Brezhani Zlatolist Ograzheden Berkovitsa

Belasitsa 0.000

Slavyanka 0.112* 0.00000

Brezhani 0.066* 0.180* 0.000

Zlatolist 0.064* 0.157* 0.109* 0.000

Ograzheden 0.043* 0.132* 0.102* 0.092* 0.000

Berkovitsa 0.042* 0.164* 0.110* 0.108* 0.089* 0.000

* Significance of pairwise Fst: * p \ 0.05

Fig. 2 Detection of the most probable number of clusters (K) using

STRUCTURE, based on the analysis of 336 chestnut samples in

Bulgaria through 7 SSR loci. a Log of the most probable number of

clusters L (K) as a function of a value of K calculated average for 7

replicates. b Variation rate of the second order of L (K) between two

successive values of K for 7 replicates (DK)

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Our results revealed a relatively high level of genetic

diversity in all the chestnut populations analyzed. The

values of expected and observed heterozygosity and the

mean number of alleles per locus of Bulgarian populations

were comparable with those obtained from other European

chestnut populations (Marinova et al. 2012; Mattioni et al.

2013). Although sample sizes differed among populations,

using statistical techniques that minimized the effect of

sample size discrepancy (Pruett and Winker 2008), we

found that Belasitsa was the population with the highest

level of diversity. The high genetic diversity observed

could be the consequence of either an 8,000-year-long

spontaneous natural presence of these species in Belasitsa

mountain or of intensive cultivation from the first Neolithic

settlements until the Second War World that may have led

to the introduction of foreign germplasm. Such events tend

to increase genetic diversity which is then conserved for a

long time (Alcala et al. 2013).

The STRUCTURE analysis grouped the populations

into three clusters (K = 3). Slavyanka and Brezhani were

the most genetically divergent populations. In both popu-

lations a homogenous gene pool was observed. The

Slavyanka population showed positive and significant

inbreeding (Fis), along with high values of private allelic

richness. This population was separated from Zlatolist and

Belasitsa by a significant genetic barrier, despite their

physical proximity. These data suggest that an ancient

recolonization of this area took place from a distinct

refugium, followed by a subsequent genetic isolation. In

fact an important natural driving force shaping plant

diversity patterns in Europe was the distribution of glacial

refugia around the Mediterranean basin. Species survived

in these refugia during the Last Glacial Maximum (LGM)

thanks to favorable environmental conditions (Medail and

Diadema 2009). Our hypothesis is supported by the pre-

sumed presence of chestnut glacial refugia in southwestern

and southeastern Bulgaria (Krebs et al. 2004). Nevertheless

present-day topographical features do not explain the pat-

tern of genetic isolation that we observed for the Slavyanka

population. The valley where this population was found is

open to the north and west and is closed to the south and

east by the main ridge of Slavyanka mountain and by the

Pirin mountains. The genetic barrier that we observed may

be due to factors such as the presence of human settlements

and cultivated fields that could have led to the fragmen-

tation and isolation of this population. This hypothesis of

the continuous presence of human activity in southwestern

Bulgaria during the last eight millennia (with a maximum

peak during the Roman period) can be supported by the

fact that Cerealia-type fossil pollen, which is a primary

anthropogenic indicator species, has been found in this area

(Marinova et al. 2012).

The genetic structure of the Brezhani population was

probably also affected by the anthropogenic impact over

the centuries. The samples from Brezhani showed homo-

geneous gene pools, as in the case of Slavyanka, but the

low values of private allelic richness and the non-signifi-

cant Fis suggest that germplasm may have been imported

into the area by humans. However Brezhani’s chestnuts

may simply be derived from a recent natural colonization

from southern tree populations. Both these hypotheses are

supported by Comps et al. (2001), who suggest that pop-

ulations derived from recent colonization show relatively

low heterozygosity deficits.

The levels of genetic admixture in the other populations

could be explained by considering that chestnuts have been

widely cultivated since ancient times using a variety of

silvicultural systems. Until recently it was thought that the

Roman had introduced C. sativa to Belasitsa (Zlatanov

et al. 2013). However Tonkov et al. (2012) proved that

Fig. 3 Population structure of 336 samples of Bulgarian chestnut

calculated using STRUCTURE (K = 3). Each individual is repre-

sented by a vertical line and the populations are separated by a vertical

black line. Different colors in the same row indicate for each individual

the percentage of estimated membership of three different clusters: red

cluster 1, blue cluster 2, green cluster 3. (Color figure online)

Fig. 4 Principal Coordinates Analysis (PCA) of six Bulgarian sweet

chestnut populations based on Nei’s unbiased genetic distance (1978)

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Fig. 5 Geographical display of the membership values (Q) of the

different three clusters (K = 3). A strong color indicates high genetic

similarity, a gradual decrease to white indicates a gradual decrease in

genetic similarity. a Geographical display of the first cluster (red),

b Geographical display of the second cluster (blue), c geographical

display of the third cluster (green), d: synthetic map of the three

clusters. (Color figure online)

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chestnut has been a native tree species in Belasitsa for the

last 8000 years, and that the inhabitants of the surrounding

villages managed the chestnut forests. Only in the last

50–60 years, after the nationalization of the forests in

1948, have the human impacts on chestnut stands dimin-

ished. Although the genetic patterns we observed among

chestnuts of Ograzhden, Berkovitsa, and Belasitsa, could

be a result of intensive human manipulation, they may

instead have been established by recolonization during the

postglacial period. In fact, the admixed zones that showed a

high level of genetic diversity were also found in Fagus

sylvatica, Fraxinus excelsior, Pinus resinosa, and Kalo-

panax septemlobus (Comps et al. 2001; Walter and Ep-

person 2001; Petit et al. 2003; Heuertz et al. 2004;

Sakaguchi et al. 2011), which expanded their ranges after

the LGM .

Zlatolist, which is located between Slavyanka and Bre-

zhani, also showed an admixture of these two gene pools, a

high value of allelic richness and high unbiased expected

heterozygosity. The intermediate genetic make-up of this

population was probably due to human-mediated transfers

of genetic material, although it may have been a result of

natural colonization.

Implications for conservation

As reported by Zlatanov et al. (2013), the forests in Bul-

garia, particularly on Belasitsa mountain, began to be

abandoned in the first half of the 20th century. The sub-

sequent lack of management led to the degradation of

chestnut forests, the invasion of parasites, and poor

regeneration caused by strong competition from other tree

species which are replacing chestnut. Zlatanov et al. (2013)

propose that regenerating chestnut using seeds could be the

best management techniques for recovering this species.

The results of the molecular analysis carried out in our

study provide additional information about the diversity, at

the genetic level, of chestnut forests and suggest some

conservation measures regarding genetic resources and

populations.

According to Petit et al. (1998), allelic richness is the

most useful measure of genetic variation for identifying

populations for conservation since high levels of genetic

variation are expected to increase the potential of popula-

tions to respond to the selection and health maintenance of

individuals (Kalinowski 2004). Based on our results, the

most important sweet chestnut genetic material to be pre-

served in Bulgaria are the populations of Belasitsa and

Zlatolist, which, despite probably being highly manipu-

lated by humans, have the highest values of allelic richness.

Slavyanka may also be an important genetic resource in

terms of its high value of private allelic richness. These

populations could be a reservoir of germplasm for refor-

estation practices.

In conclusion, this study revealed, for what we believed

is the first time, the genetic structure of Bulgarian chestnut

populations. We have underlined the probable influence of

human activities on chestnut genetic diversity, especially

regarding the Belasitsa population. We have also high-

lighted the importance of glacial events on the distribution

of different genetic pools. Our results show that Belasitsa,

Zlatolist and Slavyaka populations are the most important

Fig. 6 Genetic barriers among

Bulgarian chestnut populations

identified using Barrier

software. The barriers were

identified using Monmonier’s

(1973) maximum difference

algorithm, and their significance

was tested with bootstrap

support [80 %. The significant

barriers are represented by a

solid red line. (Color figure

online)

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genetic resource for preserving the biodiversity of sweet

chestnut in Bulgaria.

We believe that our work will provide useful informa-

tion for improved forest management practices.

Acknowledgments This paper is part of the Master’s thesis of the

first author who is grateful to her supervisor Prof. Roberto Bargagli of

the University of Siena, Italy. The authors thank Dr. Keith Woeste for

his critical review of the manuscript.

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