Tree Genetics & Genomes (2014) 10:1419–1433
DOI 10.1007/s11295-014-0770-9
ORIGINAL PAPER
Genetic structure in Pinus cembra from the Carpathian
Mountains inferred from nuclear and chloroplast microsatellites
confirms post-glacial range contraction and identifies introduced
individuals
Bertalan Lendvay & Mária Höhn & Sabine Brodbeck &
Marcel Mîndrescu & Felix Gugerli
Received: 23 September 2013 / Revised: 4 June 2014 / Accepted: 26 June 2014 / Published online: 10 July 2014
# Springer-Verlag Berlin Heidelberg 2014
Abstract Genetic differentiation of scattered populations at
neutral loci is characterized by genetic drift counteracted by
the remaining gene flow. Populations of Pinus cembra in the
Carpathian Mountains are isolated and restricted to island-like
stands at high-elevation mountain ranges. In contrast, paleobotanical data suggest an extended early Holocene distribution of P. cembra in the Carpathians and its surrounding areas,
which has contracted to the currently disjunct occurrences. We
analyzed the genetic variation of 11 Carpathian populations of
P. cembra at chloroplast and, in part newly developed, nuclear
microsatellites. Both marker types revealed low levels of
genetic differentiation and a lack of isolation by distance,
reflecting the post-glacial retraction of the species to its current
distribution. Stronger effects of genetic drift were implied by
the higher genetic differentiation found for haploid chloroplast
than for diploid nuclear markers. Moreover, we found no
association between the values of population genetic differentiation for the two marker types. Several populations indicated
recent genetic bottlenecks and inbreeding as a consequence of
decline in population sizes. Moreover, we found individuals in
Communicated by G. G. Vendramin
Electronic supplementary material The online version of this article
(doi:10.1007/s11295-014-0770-9) contains supplementary material,
which is available to authorized users.
B. Lendvay : S. Brodbeck : F. Gugerli (*)
WSL Swiss Federal Research Institute, Birmensdorf, Switzerland
e-mail: gugerli@wsl.ch
B. Lendvay : M. Höhn
Faculty of Horticultural Science, Corvinus University of Budapest,
Budapest, Hungary
M. Mîndrescu
Department of Geography, University of Suceava, Suceava,
Romania
two populations from the Rodnei Mountains that strikingly
differed in assignment probabilities from the remaining specimens, suggesting that they had been introduced from a provenance outside the studied populations. Comparison with
Eastern Alpine P. cembra and individuals of the closely related Pinus sibirica suggests that these individuals presumably
are P. sibirica. Our study highlights the importance of the
maintenance of sufficiently large local population sizes for
conservation due to low connectivity between local
occurrences.
Keywords Disjunct distribution . Gene flow . Genetic drift .
Molecular markers . Population bottleneck
Introduction
Historically disjunct distribution areas and isolated populations are exceptionally interesting from the biogeographic and
population genetic perspective, and they also provide insights
into processes that are relevant in conservation genetics.
Scattered, reproductively isolated populations may display
particular genetic characteristics compared to those in a continuous area due to reduced gene flow, genetic drift, founder
events, or population bottlenecks (Ellstrand and Elam 1993),
and the reduced migration load may enable the development
of locally adapted genotypes (Lenormand 2002). The extent
of population genetic differentiation of isolated populations at
neutral loci will depend on the existing level of gene flow,
genetic drift, and mutations (Latta and Mitton 1997; Allendorf
and Luikart 2007). However, on the short evolutionary scale,
mutation rate may have a negligible effect compared to the
rates of inter-population gene flow at these loci (Petit et al.
1993; Ennos 1994).
1420
Gene flow among plant populations takes place through
dispersal of pollen and seed. In coniferous trees, biparentally
inherited nuclear genes are transmitted both through longdistance wind pollination and shorter-distance seed transport,
whereas chloroplastic genes are paternally inherited in most
species and, hence, are dispersed first by pollen and subsequently by seed. Owing to the capacity of long-distance pollen
dispersal, coniferous trees generally exhibit high levels of
intra-population genetic diversity, but low levels of genetic
differentiation between populations (Provan et al. 2008) as
long as populations remain in contact by at least occasional
gene exchange.
Unlike the nuclear genome, the chloroplast genome behaves as a single haploid locus that does not undergo recombination. The haploid, uniparentally inherited chloroplast
genes have lower effective population size compared to diploid, biparentally inherited nuclear genes. Thus, chloroplastic
loci are prone to higher stochasticity and have higher susceptibility to genetic drift than nuclear loci (Birky et al. 1989;
Petit et al. 1993; Ennos 1994; Hu and Ennos 1997, 1999;
Ribeiro et al. 2002). Consequently, population genetic differentiation is expected to be higher for chloroplast loci than for
nuclear genes, and their spatial autocorrelation will be a better
indicator for genetic drift (Myers et al. 2007).
Populations of Pinus cembra L. in the Carpathian Mountains are an example of a naturally disjunct distribution of a
coniferous tree species. P. cembra is considered a glacial relict
species of high-altitude mountains in Central Europe, with a
disjunct native range in the Alps and the Carpathian Mountains. While P. cembra is widely distributed in the Alps, all of
its scattered occurrences in the Carpathian Mountains are
restricted to summit areas of the highest mountain ranges
(Fig. 1). Despite this currently disjunct range, P. cembra
may have been much wider distributed in earlier times, as
pollen and macrofossil evidence indicates its presence during
the full-glacial and late-glacial periods in areas around the
Carpathian Mountains (Willis and Van Andel 2004;
Jankovská and Pokorný 2008). Moreover, it may have been
widely distributed in lowlands, but also present within the
mountain range even at higher elevations (e.g. Jankovská
1984; Feurdean et al. 2011; Magyari et al. 2012).
Forest management has become extensive even on remote
sites of the Carpathian Mountains in the second half of the
nineteenth century, and despite its rarity in the Carpathian
Mountains, P. cembra was well known for its versatile wood
for making furniture. Therefore, intensive logging raised concerns about the total disappearance of P. cembra from the
Carpathian Mountains already at that time (Rowland 1882;
Fekete 1887). However, the use of P. cembra in forestry plantations in the Carpathian Mountains has only been documented
in the Tatra Mountains. (Western Carpathians) where Alpine
P. cembra and even Siberian Pinus sibirica were planted with
the purpose of assisting the recruitment of heavily exploited
Tree Genetics & Genomes (2014) 10:1419–1433
populations at the end of the nineteenth century (Paryski 1971
cit. in Zwijacz-Kozica and Żywiec 2007).
Only a limited number of studies have focused on genetic
patterns of disjunctly distributed plant species in the
Carpathian Mountains (Ronikier 2011), but the remarkably
small and isolated Carpathian P. cembra populations have
repeatedly attracted the attention of geneticists. Szmidt
(1982) reported surprisingly high variation within a population from the Retezat Mountains (Southern Carpathians) and
substantial differentiation between the southernmost Retezat
Mountains and northernmost Tatra Mountains by analyzing
allozyme variation. This genetic differentiation was not confirmed later, neither by allozymes (Belokon et al. 2005) nor by
chloroplast microsatellites (Höhn et al. 2005, 2010). In fact,
chloroplast microsatellites found low population differentiation and highest similarity among populations from the
Retezat and the Tatra Mountains (Höhn et al. 2010). All these
surveys as well as Goncharenko et al. (1992) and Krutovsky
et al. (1992) reported high intra-population genetic variability
in Carpathian populations of P. cembra, which was recently
found to surpass genetic variability of populations from the
contiguous part of the range in the Alps (Höhn et al. 2009).
Despite the focus of these studies on genetic patterns of
Carpathian P. cembra populations, the relationship of populations from different Carpathian mountain ranges could yet not
be fully clarified (Höhn et al. 2010). A more detailed perception of the genetic characteristics of the scattered Carpathian
P. cembra populations may be a valuable element for the
general understanding of plant species’ phylogeographic patterns in the Carpathian Mountains, and also to better evaluate
the needs and possible actions for conservation purposes.
This study explores genetic patterns within the Carpathian
range of P. cembra at both biparentally inherited nuclear
microsatellites (nSSRs) and paternally inherited chloroplast
microsatellites (cpSSRs). With the two complementary marker
sets and expanded sample size, we aim to more precisely
describe the range-wide population history that has previously
been inferred by cpSSRs only (Höhn et al. 2009, 2010), considering both natural and human-mediated processes. Besides
giving a higher resolution, the additional biparentally inherited
nSSRs also allowed us to test if recent population bottlenecks
occurred and whether there is inbreeding in the populations. We
expect to find low genetic differentiation among populations
due to post-glacial fragmentation of a formerly wide distribution
and wide-ranging gene flow predominantly through pollen.
Alternatively, populations may show high genetic differentiation because they originated from multiple glacial refugia. With
a comparison between genetic differentiation and diversity at
cpSSRs and nSSRs, we examine effects of gene flow and drift
on the population structure. Higher genetic differentiation for
cpSSRs may mean limited gene transfer between populations
and between regions, while higher genetic variability could be
explained by gene flow among regions. We expect to find an
Tree Genetics & Genomes (2014) 10:1419–1433
1421
Fig. 1 Location of the Pinus cembra populations from the Carpathian
Mountains involved in this study with population abbreviations (see
Table 1) and Carpathian mountain ranges. The inset map shows the total
distribution area of P. cembra in the Alps and the Carpathian Mountains
(source: European Forest Genetic Resources Programme, euforgen.org)
association between population-wise genetic differentiation and
diversity of the two marker types. Furthermore, we introduce
and apply a set of nuclear microsatellite markers newly developed for P. cembra.
P. cembra is closely related to P. sibirica Du Tour, a
widespread species of the Siberian taiga region. The two
species are sometimes even considered as subspecies due to
their nearly identical morphology (Meusel et al. 1965;
Goncharenko et al.1992; Belokon et al. 2005 and references
therein). The high genetic similarity between P. cembra and
P. sibirica has been supported by the high similarity in
allozymes (Goncharenko et al. 1992; Krutovsky et al. 1992;
Belokon et al. 2005), shared chloroplast haplotypes (Gugerli
et al. 2001), and a high proportion of common nSSR alleles
(S. Brodbeck, unpublished).
In the Western Carpathians, P. cembra exists exclusively in
the Tatra Mountains. The closest occurrences are in the Ukrainian Eastern Carpathians at nearly 300-km distance. Other
stands in the Eastern Carpathians are the Rodnei (also referred
to as Rodna) and Călimani Mountains in Romania. In the
Southern Carpathians, small populations exist in the Bucegi,
Făgăras, Cindrel, and Lotru Mountains, where stands are
found on a few slopes and are often not larger than a few
dozen trees (Blada 2008). More extended populations are
found only in the westernmost Retezat and Parang Mountains.
Fossil remains of P. cembra were recovered from the last
full-glacial, late-glacial, or early Holocene periods from various
sites neighboring the Carpathian Mountains: the Moravian
Materials and methods
Study species
P. cembra is a species reaching highest elevations among trees
of Central Europe: stands occur at the timberline or often
beyond as singular trees among dwarf pine. Well adapted to
the harsh conditions, individuals can reach ages of several
hundred years. Reproduction takes place through wind pollination and distribution of the nutritious seeds by the spotted
nutcracker (Nucifraga caryocatactes). In a particular interaction with P. cembra, this bird collects, carries, and hides seeds
in caches mostly within its territory of several hectares, but
seed depositions up to 20 km away from the collection sites
have been observed (Mattes 1982; H. Mattes, University of
Münster; personal communication). Seeds that survive in the
caches can create clumped growth of different individuals
(Tomback et al. 1993).
1422
basin and Austrian Danube valley in the west, the transition to
the Polish plain in the north, and the bank of the Dniester river
in Moldavia and the side of the Prut river in Romania in the
east, as well as several locations on the Hungarian plain in the
Carpathian basin (for references, see Willis et al. 2000; Willis
and Van Andel 2004; Jankovská and Pokorný 2008). Besides
its frequent persistence at lower altitudes, there are also few
examples of P. cembra found within mountain ranges in lateglacial and early Holocene sediments. Seeds and pollen were
found in the full-glacial period in inter-mountain lowlands and
on the southern piedmonts of the Tatra Mountains, and also in
late-glacial sediments of a glacial lake close to the site where
P. cembra presently occurs in the Tatra Mountains, Western
Carpathians (Jankovská 1984; Rybníčková and Rybníček
2006; Kuneš et al. 2008). In the Eastern and Southern
Carpathians, late-glacial pollen and macro-remains of P. cembra
were reported from glacial lakes in the vicinity of present-day
populations in the Rodnei Mountains, the Călimani Mountains,
and the Retezat Mountains, and also in the Gutâiului Mountains, where it subsequently became extinct (Wohlfarth et al.
2001; Feurdean et al. 2011; Magyari et al. 2012). The fossil
remains of P. cembra suggest that the species may have had a
much broader distribution than today, existing both in lowlands
around and elevated locations within the range of the
Carpathian Mountains during the late-glacial and early Holocene periods. It cannot, however, be ascertained based on the
fossils whether these distant extant occurrences previously
formed part of a single contiguous area and whether they
originated form a common glacial refugial area.
Population sampling and genotyping
Samples were collected from 11 sites covering the major
massifs within the range of P. cembra in the Carpathian
Mountains (Table 1 and Fig. 1). This included subsamples
from populations previously studied (Höhn et al. 2009) and
two newly collected sites (LAL, MUN). Needles were sampled from mature trees at least 30 m apart from each other,
although the scattered occurrence of trees often resulted in
much larger inter-individual distances. Sample sizes ranged
from 7 to 26. Some of the sample sizes are relatively small,
which in part reflects low census sizes of some of the populations. However, our results on the population genetic structure were consistent, whereas we acknowledge that the accuracy of parameter estimates might be critical at least in those
cases in which sample size is not integral part of parameter
estimation. Therefore, we omitted population BOR from part
of the analyses after removing individuals considered nonnative (see below).
The needles were dried on silica gel and genomic DNA was
extracted with DNeasy Plant Mini Kit or DNeasy 96 Plant Kit
(Qiagen, Hilden, Germany) following the manufacturer’s protocol. Six nuclear microsatellite primer pairs (Pc1b, Pc7,
Tree Genetics & Genomes (2014) 10:1419–1433
Pc18, Pc22, Pc23, Pc35) were amplified from the P. cembra
microsatellite primer set designed by Salzer et al. (2009).
Multiplex PCRs were carried out in 10-μl volume containing
4.5 μl 2× Multiplex PCR master mix (Qiagen), ~20 ng
genomic DNA, and fluorescently labeled primers (Table S2).
PCRs started with 95 °C for 15 min followed by 35 cycles of
95 °C for 30 s, 54 °C for 30 s, and 72 °C for 50 s and a final
extension at 72 °C for 30 min.
These nuclear microsatellite loci were complemented with
six newly designed markers (Supplementary Table S1). Primer development followed the procedure described in Schoebel
et al. (2013), and Mendelian inheritance was confirmed using
open-pollinated progenies (S. Brodbeck and F. Gugerli,
unpubl. data). Single-tube multiplex PCR reactions were performed in 10-μl volume containing ~20 ng genomic DNA,
4.5 μl 2× Type-it Multiplex PCR master mix (Qiagen), and
different amounts of fluorescently labeled primers (Supplementary Table S2). PCRs started with 95 °C for 5 min,
followed by 35 cycles of 95 °C for 30 s, 58 °C for 90 s,
72 °C for 30 s and a final extension step of 72 °C for 30 min.
Four chloroplast microsatellite loci (Pt630718, Pt15169,
Pt36480, and Pt26081) were amplified from the primer set
of Vendramin et al. (1996). Compared with the data in Höhn
et al. (2009), locus Pt15169 was additionally considered in
this study, and we re-genotyped the previously collected samples. PCR amplification was carried out in single-tube multiplex reactions with fluorescently labeled primers (Supplementary Table S2). PCRs were performed in 10-μl reaction mixtures containing ~10 ng genomic DNA, 4.5 μl 2× Multiplex
PCR master mix (Qiagen), additional 10 nmol MgCl2, and
optimized amounts of primers (Supplementary Table S2).
PCR reactions started with 95 °C for 15 min followed by
25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 60 s
and a final extension at 72 °C for 20 min.
PCR reactions were performed on a Veriti Thermal Cycler
(Applied Biosystems, Foster City, CA, USA), and fragments
were separated on an ABI 3130 capillary sequencer (Applied
Biosystems) with GeneScan ROX–400 (for cpSSRs and newly designed nSSRs) or LIZ–500 (for nSSRs of Salzer et al.
(2009)) as internal size standards. Alleles were scored with
GeneMapper v3.7 (Applied Biosystems).
Data analysis
Nuclear microsatellites
Other than the remaining loci, one locus, SWK, showed a
significant deviation from Hardy-Weinberg equilibrium
(HWE) in several Carpathian populations, suggesting the
presence of null alleles or non-neutral behavior; thus, this
marker was omitted from all analyses. All other loci largely
conformed to the expectations of HWE within single populations (data not shown).
Tree Genetics & Genomes (2014) 10:1419–1433
1423
Table 1 Location and number of genotyped samples of Pinus cembra populations from the Carpathian Mountains
Population
Abbreviation
Mountain range
Part of Carpathian Mountains
Coordinates
Na
Morskie Oko
Velka Studena Dolina
Kedryn Forest Reserve
Borșa
Valea Lala
Neagra Şaralui
Negoiu
Cibin
Muntinul Mare
Gentiana
Gemenele
MOR
VEL
KED
BOR
LAL
NEA
NEG
CIN
MUN
GEN
GEM
Tatra Mts.
Tatra Mts.
Ukrainian Carpathians
Rodnei Mts.
Rodnei Mts.
Călimani Mts.
Călimani Mts.
Cindrel Mts.
Parâng Mts.
Retezat Mts.
Retezat Mts.
Western Carp., Poland
Western Carp., Slovakia
Eastern Carp., Ukraine
Eastern Carp., Romania
Eastern Carp., Romania
Eastern Carp., Romania
Eastern Carp., Romania
Southern Carp., Romania
Southern Carp., Romania
Southern Carp., Romania
Southern Carp., Romania
49.20° N, 20.08° E
49.17° N, 20.20° E
48.42° N, 24.00° E
47.58° N, 24.63° E
47.53° N, 24.92° E
47.17° N, 25.28° E
47.10° N, 25.20° E
45.58° N, 23.80° E
45.36° N, 23.68° E
45.38° N, 22.87° E
45.37° N, 22.83° E
11
17
17
13
26
19
17
7
25
7
16
Populations LAL and MUN were newly sampled in addition to the sampling reported in Höhn et al. (2009)
Mts. mountains, Carp. Carpathians
a
N=sample size; for chloroplast microsatellites, the number of genotyped specimens was 10 in population MOR and 16 in population KED
A Bayesian non-hierarchical assignment test was performed with STRUCTURE 2.3.4 with admixture model and
correlated allele frequencies (Pritchard et al. 2000). Tested
numbers of K were 1–15 with 106 Markov chain Monte Carlo
repetitions with 5×105 burn-in period, using a computer cluster. Runs were repeated 20 times for each K, and an LnP(D)
plot was constructed with STRUCTURE HARVESTER (Earl
and vonHoldt 2012). The number of K best fitting the data was
inferred by evaluation of the LnP(D) plot according to the
guidelines given in the STRUCTURE manual. Label
switching and multimodality among different runs with the
selected K were adjusted with CLUMPP using the Greedy
option (Jakobsson and Rosenberg 2007).
STRUCTURE results identified individuals that remarkably differed from the rest of the samples in both populations
from the Rodnei Mountains (Eastern Carpathians). Eight
specimens from BOR and seven from LAL were grouped into
a unique genetic cluster, leading to the assumption that they
were non-native individuals, originating from outside the
analyzed Carpathian populations. We therefore tested two
hypotheses regarding the possible origin of these individuals
by re-running STRUCTURE with an extended dataset. The
first hypothesis assumed that these individuals were of Eastern
Alpine origin; therefore, genotype data of 436 P. cembra
specimens from 19 Austrian and 1 Swiss sites were added to
our dataset (F. Gugerli et al., unpublished data). The alternative hypothesis assumed that these individuals were planted
P. sibirica; thus, data of 23 P. sibirica individuals from a
natural stand in eastern Siberia (Mujakan basin, Severomuisk)
and 24 planted individuals surrounding Popradske pleso, Tatra
Mountains, were included in STRUCTURE analyses.
STRUCTURE was first run with the settings detailed
above, and an additional analysis was performed with the
applied model switched to USEPOPINFO, with samples of
known origin pooled into four groups: (1) P. sibirica from
eastern Siberia, (2) P. sibirica from the Tatra Mountains, (3)
Eastern Alpine P. cembra, and (4) presumably native
Carpathian P. cembra. Individuals of these groups were used
as “learning samples,” where their population of origin was
pre-specified according to their group at K=4, while the
program was implemented to assign the peculiar individuals
of the Rodnei Mountains to the pre-defined groups. This test
was repeated 20 times and results were adjusted with
CLUMPP.
On the basis of the above analyses, the peculiar individuals
from the Rodnei Mountains were omitted from all further
analyses. STRUCTURE analysis was performed on the remaining, presumably native Carpathian specimens with the
initially described settings. By removing the peculiar specimens, the sample size of BOR was reduced to five. For tests
based on population-wise allele frequency differences, such a
small sample size may lead to imprecise results due to sampling bias (Leberg 2002); hence, the entire BOR population
was excluded from population-wise analyses.
Effective number of alleles, number of unique alleles, and
unbiased genetic diversity (uHe) were calculated for each
population in GenAlEx 6.5 (Peakall and Smouse 2012). Inbreeding (FIS) was calculated for each population, and its
significance was tested in Fstat 2.9.3.2 assuming no HardyWeinberg equilibrium within the samples and using 1,000
permutations (Goudet 1995, 2002). BOTTLENECK 1.2.02
was used to test if recent bottlenecks affected the genetic
structure of the populations (Piry et al. 1999). In this analysis,
the infinite allele model (IAM) was implemented and significance of heterozygosity excess or deficiency was tested with
Wilcoxon two-tailed tests included in the software.
Analysis of molecular variance (AMOVA) without grouping was performed with 9,999 permutations, and values of
1424
pairwise population differentiation (FST) were calculated with
their significance through 9,999 randomization steps.
Isolation by distance was tested with Mantel tests (Mantel
1967) using 9,999 permutations with two different assumptions. First, we assumed long-distance connectivity between
populations through wind pollination; thus, we measured
population distances as Euclidean distances throughout the
range. The distances were logarithmically transformed as
suggested by Rousset (1997). Secondly, we assumed only
shorter gene flow between populations with connectivity approximating a one-dimensional stepping-stone model
(Kimura and Weiss 1964). Accordingly, between-population
geographic distances were measured along a line connecting
the sites through the arch of the Carpathian Mountains. For
this method of distance measurement, pairwise population
FST values were linearized with the FST/(1−FST) transformation following Rousset (1997). These calculations were performed in GenAlEx 6.5.
Spatial variation in genetic differentiation of the populations was analyzed with spatial analysis of genetic variation
(SAMOVA), a method based on Voronoï polygons created
from the geographic location of the populations (Voronoï
1908). The software uses a simulated annealing approach to
establish maximally differentiated (FCT) groups of populations with regard to their geographic position for each number
of groups set by the user (Dupanloup et al. 2002). Runs were
conducted with the number of groups set from two to five.
Chloroplast microsatellites
The four chloroplast microsatellite loci were concatenated into
chloroplast haplotypes. Effective number of haplotypes, number
of unique haplotypes, and unbiased haploid genetic diversity (uh)
were estimated for each population. Pairwise population differentiation values were calculated as ΦPT. Analyses of AMOVA,
Mantel test, and SAMOVA were carried out by using the ΦPT
values with the same settings as described for the nSSRs.
To further investigate the cpSSR population structure of the
presumably native 158 individuals, we ran the Bayesian assignment algorithm implemented in BAPS version 6.0
(Corander et al. 2003; Corander and Tang 2007). We applied
mixture analysis with linked loci for individuals grouped
according to populations. To identify the most probable number of K genetic groups based on the maximum log(marginal
likelihood) values, the number of K was set from 2 to 10, and
the analysis was run ten times for each K. Additional individual runs were performed for K values from 2 to the most
probable cluster number, with 100 repetitions in each run.
Comparison between nuclear and chloroplast microsatellites
Mantel tests were performed to assess the correlation of estimates of pairwise population differentiation (FST for nSSRs
Tree Genetics & Genomes (2014) 10:1419–1433
and ΦPT for cpSSRs) with 9,999 permutations. Furthermore,
mean values of the genetic diversities and pairwise population
genetic differentiation estimates were compared between the
two marker types with a t test for paired samples.
Intra-population variability was estimated through Shannon diversity index (H), a measure that is easy to calculate and
allows comparing among different types of genotypic data
(Sherwin et al. 2006). H was calculated as implemented in
GenAlEx 6.5 with the formula ∑pilnpi, where pi is the proportion of the ith allele in the case of nSSRs or haplotype in
the case of cpSSRs. Correlation was tested between Hs calculated for the nSSR and cpSSR data sets. The Mantel test was
performed in GenAlEx 6.5; Pearson correlation and paired t
tests were carried out with Statistica 11.0 (Statsoft Inc., Tulsa,
OK, USA) with pre-checking the assumption of normal
distribution.
Results
Nuclear microsatellites
Among the 175 Carpathian individuals, eight specimens from
BOR and seven from LAL were assigned to a cluster virtually
absent in other samples of these populations in the results of
STRUCTURE when K>2 (Supplementary Fig. S1). These
samples were again assigned to a unique cluster when samples
from 20 Eastern Alpine P. cembra populations, 1 native, and 1
planted P. sibirica populations were included in the analysis
(Supplementary Fig. S2). With the applied model in STRUCTURE switched to USEPOPINFO, the peculiar samples
showed high assignment rates to the introduced P. sibirica
population from the Tatra Mountains (Fig. 2).
STRUCTURE results indicate that the most likely number
of populations is K=3 when the dataset of the Carpathian
populations were re-analyzed excluding the 15 peculiar individuals (Fig. 3a). Geographic structuring of the clusters was
weak, and only individuals from MUN formed a unique
cluster. The remaining ten populations contained two genetic
groups that were highly admixed; only individuals from NEA
and NEG were largely homogeneous (Fig. 3b).
The mean number of effective alleles per locus ranged
between 2.22 (GEN) and 3.37 (LAL; Table 2). Unique alleles
were present in all but the NEA, CIN, and GEN populations,
with the highest number of six found in LAL. Unbiased
heterozygosity ranged between 0.48 (GEN) and 0.61
(GEM). Five of the populations had FIS values significantly
higher than 0, suggesting inbreeding in half of the populations
(Table 2). The Wilcoxon test implied heterozygote excess due
to recent bottlenecks in populations LAL, CIN, MUN,
and GEM.
Tree Genetics & Genomes (2014) 10:1419–1433
1425
Fig. 2 Assignment of the non-native individuals from BOR and LAL
populations using the USEPOPINFO model in STRUCTURE (Pritchard
et al. 2000). Population of origin is predefined for the following groups:
(1) P. sibirica from eastern Siberia, (2) P. sibirica from the Tatra
Mountains, (3) P. cembra from the Eastern Alps, and (4) P. cembra from
the Carpathian Mountains. The non-native individuals (yellow) were
assigned to the cluster of P. sibirica from the Tatra Mountains (Color
figure online)
Pairwise population FST values were significantly higher
than 0 in 39 of 45 cases (Supplementary Table S4). The
AMOVA revealed an overall FST =0.055, i.e., almost 6 % of
the total genetic variance resided among populations, while
nearly 94 % of the variance was attributed within populations: 17 % among individuals within populations and 77 %
within individuals.
No isolation by distance was discovered among the populations by Mantel tests neither performed using Euclidean
distances (rxy =0.049, p=0.345) nor assuming restricted gene
flow (rxy =0.128, p=0.234).
SAMOVA first separated MUN from the rest of the populations. In the consecutive steps, GEM, GEN-CIN, and NEANEG created individual groups, leaving the populations from
the Western Carpathians and northern part of the Eastern
Carpathians together as a group (Table 3).
number of groups, populations CIN and MUN became separated, and then populations NEA and NEG created a separate
group.
The Bayesian assignment test with BAPS identified K=4
as the most probable number of genetic clusters (98.4 %
probability). Population MUN was separated from all other
populations at K=2 and remained distinct at higher K values.
The remaining populations did not form geographically distinct groups at any value of K tested (Fig. 4).
Chloroplast microsatellites
The four cpSSRs resulted in 33 chloroplast haplotypes in the
153 Carpathian samples after excluding the genetically peculiar individuals based on the results of STRUCTURE and the
remaining five individuals from BOR. The number of haplotypes present in each population varied between 4 (CIN and
GEN) and 11 (VEL); the effective number of haplotypes
ranged from 3.27 (CIN and GEN) to 9.32 (VEL; Table 2).
Unique haplotypes were found in 7 out of 11 populations; the
highest number of four was found in populations VEL and
GEM. Unbiased haploid genetic diversity varied between 0.73
(MUN) and 0.95 (VEL).
The AMOVA resulted in ΦPT =0.095, i.e., 10 % of the total
genetic variance residing among, and 90 % within populations.
The Mantel tests revealed no isolation by distance among the
populations, neither with Euclidean distances (rxy =0.147, p=
0.144) nor assuming the stepping-stone model (rxy =0.049, p=
0.390). ΦPT values were found significantly higher than 0 for
29 out of 45 population pairs (Supplementary Table S4).
No clear geographic pattern of population grouping resulted from SAMOVA at K=2 (Table 3). By increasing the
Comparison between nuclear and chloroplast microsatellites
Pairwise population genetic differentiation was generally
higher for the cpSSRs than for the nSSRs (mean values
0.097 and 0.057, respectively; t=0.013, p<0.001). The Mantel test revealed no significant association between pairwise
population genetic differentiation measured by cpSSRs and
nSSRs (r=0.134, p=0.275, Fig. 5a).
Shannon diversity indices per population were found to be
higher for the cpSSRs (mean value 1.71) than nSSRs (1.01; t=
7.606, p<0.001). Correlation was strong between the diversities measured by the different markers (r=0.738, p=0.015,
Fig. 5b).
Discussion
The aim of the present study was to examine genetic
structure of scattered P. cembra populations from the
Carpathian Mountains on the basis of both biparentally
inherited nSSRs and paternally inherited cpSSRs. While
we detected certain coincidence, and also discrepancies,
between the outcomes of various analyses using the two
marker types, our findings suggest that the effects of
genetic drift dominate over those of historical gene flow
in these populations. The low level of population genetic
differentiation suggests post-glacial contraction of the
species’ range to its currently restricted, disjunct
1426
Fig. 3 STRUCTURE (Pritchard
et al. 2000) results of 160
Carpathian Pinus cembra
individuals from 11 populations:
a Ln of the probability of data
plotted against number of K
clusters and b bar plots showing
individual assignment for K=2–5
Tree Genetics & Genomes (2014) 10:1419–1433
a
b
distribution, which is in line with paleobotanical data.
Recent genetic bottlenecks and respective inbreeding were
observed in several populations, likely as a consequence
of decline in population sizes. Moreover, our moleculargenetic markers allowed us to identify putatively non-
native individuals in two populations from the Rodnei
Mountains, which were likely associated to P. sibirica.
Offspring in these populations will possibly experience
introgression between the two species, which should be
taken into account in conservation strategies.
Tree Genetics & Genomes (2014) 10:1419–1433
1427
Table 2 Population genetic parameters, calculated for eleven nuclear microsatellite (nSSR) and four chloroplast microsatellite (cpSSR) loci in
populations of Pinus cembra in the Carpathian Mountains
Population
nSSRs
cpSSRs
Ne
Nu
uHe
FIS
MOR
2.90±0.42
2
0.60±0.07
0.05
0.139
4.17
2
0.84
VEL
KED
LAL
NEA
NEG
CIN
MUN
GEN
GEM
2.65±0.32
2.56±0.34
3.37±0.77
3.28±0.67
2.60±0.35
2.61±0.44
2.70±0.40
2.22±0.36
2.94±0.39
4
4
6
0
3
0
1
0
2
0.57±0.07
0.56±0.06
0.58±0.08
0.58±0.08
0.55±0.07
0.55±0.09
0.56±0.07
0.48±0.09
0.61±0.07
0.16*
0.04
0.09*
0.04
0.12*
−0.05
0.29*
0.03
0.16*
0.161
0.207
0.007*
0.065
0.188
0.002*
0.027*
0.116
0.002*
9.32
3.77
5.23
6.12
4.31
3.27
3.31
3.27
7.11
4
0
3
2
1
0
2
0
4
0.95
0.78
0.85
0.88
0.82
0.81
0.73
0.81
0.92
Bottleneck
Ne
Nu
Uh
Sample sizes are given in Table 1
Ne average number of effective alleles, Nu cumulative number of unique alleles, uHe unbiased heterozygosity, FIS inbreeding coefficient, Bottleneck: p
values of significance for heterozygosity excess calculated by Bottleneck software (Piry et al. 1999)
*Significant p values (p<0.05) for FIS and Bottleneck
Common origin of distant populations
We detected shallow geographic structuring of genetic groups
in our sample of P. cembra from the Carpathians. Bayesian
assignment of multilocus nSSR genotypes with STRUCTURE discovered highly mixed genetic clusters shared
among eight populations from five mountain ranges, including those of the Western, Eastern, and Southern Carpathians.
The exceptional population was MUN from the Parâng Mountains, and populations NEA and NEG from the Călimani
Mountains. NEA and NEG were genetically more homogeneous and were mainly assigned to a genetic cluster present,
but not dominant, in other populations. In turn, MUN was
characterized by a totally distinct genetic cluster, which was
absent in other regions. The Bayesian assignment of the
populations based on linked cpSSR loci with BAPS found
no strong geographic structuring throughout the range of the
Carpathians but corroborated the peculiarity of MUN (Fig. 4).
For the nSSRs, also SAMOVA first separated MUN and,
with increasing numbers of groups, populations from the
Southern Carpathians. In turn, no distinct geographic structuring was found for the cpSSRs, with Southern Carpathian
populations becoming separated when increasing the number
of groups. Populations MOR-KED-LAL and NEA-NEG assembled into the same groups for both markers at all group
numbers. The higher effect of genetic drift for uniparentally
inherited cpSSRs compared to biparentally inherited nSSRs
may cause higher stochasticity, resulting in shallower geographic structure and common grouping of distant populations, e.g., the northern VEL together with southern GEN and
Table 3 Population groups identified by spatial analysis of molecular variance (SAMOVA; Dupanloup et al. 2002) for 11 Carpathian populations of
Pinus cembra with both nuclear and chloroplast microsatellite markers
Population grouping
Marker type
Number of groups
F CT=ΦPT
nSSRs
2
0.058
{MUN} {GEM/GEN/CIN/NEA/NEG/VEL/MOR/KED/LAL}
cpSSRs
3
4
5
6
2
3
4
5
6
0.053
0.054
0.057
0.061
0.071
0.085
0.085
0.091
0.095
{MUN} {GEM} {GEN/CIN/NEA/NEG/VEL/MOR/KED/LAL}
{MUN} {GEM} {GEN/CIN} {NEA/NEG/VEL/MOR/KED/LAL}
{MUN} {GEM} {GEN/CIN} {NEA/NEG} {VEL/MOR/KED/LAL}
{MUN} {GEM} {GEN/CIN} {NEA/NEG} {VEL} {MOR/KED/LAL}
{CIN/MUN/GEN/GEM/VEL/NEA} {NEG/MOR/KED/LAL}
{CIN} {MUN/GEN/GEM/VEL/NEA} {NEG/MOR/KED/LAL}
{CIN} {MUN} {GEN/GEM/VEL} {NEA/NEG/MOR/KED/LAL}
{CIN} {MUN} {GEN/GEM/VEL} {NEA/NEG} {MOR/KED/LAL}
{CIN} {MUN} {GEN/GEM} {VEL} {NEA/NEG} {MOR/KED/LAL}
Predefined number of groups range from two to six
1428
Tree Genetics & Genomes (2014) 10:1419–1433
Fig. 4 Bayesian assignment of
the 11 Carpathian Pinus cembra
populations based on chloroplast
microsatellites using BAPS
(Corander and Tang 2007) at
group numbers K=2–4
GEM—as also found in the BAPS results of cpSSRs—or the
immediate separation of CIN with an extremely small census
size (Höhn et al. 2009). The overall low geographic congruity
reflects the preponderance of drift over gene flow and a lack of
isolation by distance (Provan et al. 2008). The clustering of
MUN, NEA-NEG, and the remaining populations into three
groups of populations, as found in the STRUCTURE results,
might also indicate three evolutionary lineages that have recolonized the Carpathians from separate refugia during the
Last Glacial Maximum. However, this explanation seems less
likely given the inconsistent grouping found among different
analytical approaches and marker types, and also the genetic
admixture of the majority of populations. Hence, we presume
that common ancestry combined with genetic drift better
explains the patterns observed.
The genetically distinct population MUN occurs on a remote, rocky north facing slope in the Parang Mountains,
Southern Carpathians. Our results indicate a long-term reproductive isolation of MUN from the rest of the sampled populations, which is corroborated by a significantly positive FIS,
indicative of inbreeding, and a significant test statistic for a
historical bottleneck (Table 2). Therefore, this population
might have descended from a distinct refugial population,
with restricted connectivity to surrounding populations
through pollen and seed dispersal.
The high similarity found between the P. cembra populations from the Tatra Mountains and the Retezat Mountains is
in agreement with results presented by Höhn et al. (2010).
However, the genetic structure observed does not follow the
trend of deep phylogeographic separation found in many other
high-mountain plant species from the Carpathians, where
long-term isolation and restricted gene flow is typical between
different mountain ranges (Ronikier 2011). Instead, patterns in
P. cembra are mostly analogous to two high-elevation woody
species with scattered populations in the Carpathian Mountains. In Pinus mugo occurring in ecologically similar habitats
as P. cembra, Wachowiak et al. (2013) found low genetic
variation among Carpathian populations. Their study revealed
a common origin of P. mugo in Central Europe, with a gradual
retreat to higher elevations from post-glacial lowland distribution. Similarly, the wind-dispersed dwarf shrub Salix
herbacea showed low genetic differentiation in the Carpathian
Mountains, suggesting extensive gene flow even after the last
full glaciation (Alsos et al. 2009). In fact, even higher genetic
similarity was found between populations from the Western
and Southern Carpathians (Tatra and Bucegi Mountains, respectively) than between these regions and the Eastern
Carpathians (Rodnei Mountains), which coincides with our
findings. The modeled distribution of S. herbacea showed that
it could have persisted in a large area around and within the
Carpathian Mountains during the Last Glacial Maximum. Similar patterns and inferred underlying processes were described
in the herbaceous alpine plants Carex curvula (Puşcaş et al.
2008) as well as Geum montanum and Geum reptans (ThielEgenter et al. 2009). Hence, these comparisons underline our
assumption that extant occurrences of P. cembra represent
remnants of a common, previously more widespread gene pool
from the surrounding lowlands, which has shifted to higher
elevations and has lead to a concurrent increase in fragmentation in response to climate warming during the Holocene.
Late-glacial and early Holocene fossils of P. cembra were
found in glacial lakes of the Tatra, Rodnei, Călimani, and
Tree Genetics & Genomes (2014) 10:1419–1433
1429
different from the others in their nSSR patterns, as they
comprised individuals that were mainly assigned to a single
genetic cluster (Fig. 3b). This may mean that P. cembra had a
permanent common gene pool until its recent post-glacial
retraction, and insufficient numbers of generations have
passed to allow for genetic drift to induce strong genetic
differentiation. Alternatively, local genotypes of independent
glacial refugia were swamped during a possible post-glacial
range expansion, except for MUN. However, both scenarios
remain hypothetical.
Population genetic comparison of nSSR and cpSSR markers
a
b
Fig. 5 Comparison between the biparentally inherited nuclear and parentally inherited chloroplast microsatellites in ten Pinus cembra populations from the Carpathian Mountains. a Pairwise population genetic
differentiation measured by FST and ΦPT for the nuclear and chloroplast
data, respectively; b genetic diversity measured by Shannon diversity
index (H) for both marker types. The dashed line indicates a 1:1 ratio, the
linear trendline is fit on the data points, and the results of correlation
statistics (see text) are displayed
Retezat Mountains, suggesting that the species may have been
present in the vicinity of these mountains during the last
glaciation. From these regions, only the two populations from
the Călimani Mountains (NEG, NEA) appeared somewhat
For both marker types, rates of genetic variation among populations were slightly lower than the average found in conifers
(Petit et al. 2005). For cpSSRs, 9.5 % inter-population genetic
variation was found, while the average in paternally inherited
markers for conifers is 16.5 %. For nSSRs, the 5.5 % interpopulation genetic variation is about half of the 11.6 % average for biparentally inherited markers reported in conifers.
However, such comparisons should not be taken too strictly
because values depend on the sampling scheme. The lack of
isolation by distance throughout the range, independent of
marker type and method of gene flow estimation, also implies
that even distant populations may either be within gene flow
distance and/or have not substantially diverged from their
common ancestry. Alternatively, populations originating from
distinct lineages may be arranged so that genetic differentiation does not gradually change over distance.
Genetic differentiation was significantly different from zero in the majority of population pairs for both marker types
(Supplementary Table S3). Except for MUN, the high number
of significantly positive FST and ΦPT values can to some extent
be attributed to the low sample sizes, as there is a trend of
larger average genetic differentiation of populations with
smaller sample sizes (data not shown). Between populations
from the same mountain ranges, population pairs NEA–NEG
(Călimani Mountains) did not significantly differ from each
other with both marker types, while MOR–VEL (Tatra Mountains) and GEN–GEM (Retezat Mountains) population pairs
were not significantly different for the nSSRs and cpSSRs,
respectively. This means that moderate rates of gene flow may
still exist between adjacent populations, or genetic drift has
not yet been sufficiently strong and long-lasting to invoke
significant genetic differentiation. At the same time, low genetic differentiation between these neighboring population
pairs substantiates that the genetic estimates are fairly representative despite low sample sizes.
According to theoretical expectations, population genetic
differentiation should be higher for chloroplast than for nuclear genes because of uniparental inheritance and lower effective number of chloroplast genes when gene exchange is
limited (Birky et al. 1989; Petit et al. 1993; Ennos 1994; Hu
1430
and Ennos 1997, 1999). Similar levels of genetic differentiation of nuclear and chloroplast markers in conifers were found
only in cases of extensive pollen dispersal (Latta and Mitton
1997; Ribeiro et al. 2002). In the present study, population
genetic differentiation values were considerably higher for
cpSSRs than for nSSRs. This finding suggests restricted gene
flow, which may be due to the fact that extant populations of
P. cembra in the Carpathians are often separated by dozens or
hundreds of kilometers. Accordingly, we presume that the
levels of genetic differentiation mirror levels of connectivity,
either because of current gene flow or as a result of the
formerly contiguous occurrence of P. cembra in and around
this mountain range.
Values of pairwise population genetic differentiation are
expected to be proportional when measured with different
marker types under high levels of gene flow (Ribeiro et al.
2002). However, we found only weak correlation between
pairwise population genetic differentiation for the cpSSRs
and nSSRs (Fig. 5a). A possible explanation for this phenomenon is that the cpDNA genome behaves as a single locus and
the spatial autocorrelation of chloroplast haplotypes is subject
to higher stochasticity than the multi-locus nSSRs. Thus, drift
may have a stronger effect on the pattern of genetic differentiation of the chloroplast haplotypes than on that measured by
nuclear markers (Myers et al. 2007). Nevertheless, we cannot
completely rule out that partly low sample sizes contributed to
the overall variance in values of genetic differentiation.
Unlike in the case of genetic differentiation, diversity
values will depend not only on the extent of gene flow but
also on the ploidy level and mutation rate of the molecular
markers (Ribeiro et al. 2002). Theoretically, diversity is expected to be similar or higher for biparentally inherited diploid
than for paternally inherited haploid markers (Birky et al.
1989), and generally nSSRs have higher mutation rates than
cpSSRs (Provan et al. 1999). However, Shannon diversity of
non-recombining chloroplast haplotypes depends on the number of loci genotyped, and a higher number of markers will
inevitably result in a higher number of haplotypes. In the
present case, although the values of population genetic diversity have high correlation between cpSSRs and nSSRs, mean
values are markedly higher for the cpDNA haplotypes. Further studies comparing diversity at paternally and biparentally
inherited markers may elucidate if this unexpected result is
extraordinary or a common trend for species with long life
span, wide-ranging gene flow, and disjunct occurrence.
When a population experiences a bottleneck after its size
sharply declines, mating between related individuals will occur even under random mating. This may result in substantial
inbreeding promoted by the bottleneck event, which may lead
to a reduction in genetic diversity if measured in terms of
heterozygosity. From the ten P. cembra populations studied,
four and five were identified as showing a bottleneck effect
and inbreeding, respectively. Three populations LAL, MUN,
Tree Genetics & Genomes (2014) 10:1419–1433
and GEM indicated both bottleneck and inbreeding. This may
be an effect of the decline of P. cembra stands due to logging
and deforestation for creating pastures in past centuries
(Feurdean et al. 2011). One population (CIN) showed a significant test for bottleneck effects, but no inbreeding, which
may be the case when the reduction in population size was too
recent for a detectable increase in inbreeding. In turn, populations VEL and NEG revealed significantly positive inbreeding
parameters without apparent bottleneck effects. Such a situation may arise in expanding populations. A similar situation
has been documented for the disjunct remnant populations of
Pinus radiata, which show inbreeding despite signatures of
recent population decline (Karhu et al. 2006). Even though we
acknowledge that our sample sizes might be at the lower end
of the sensitivity range of the bottleneck test, this test statistic
takes the sample size into consideration. However, the ultimate reason for these results requires further analyses.
Identification of non-native P. sibirica in the Rodnei
Mountains
Population BOR from the Rodnei Mountains (Eastern
Carpathians) has previously been identified as the most distinct from all other populations based on cpSSRs (Höhn et al.
2009). Our analyses confirmed the genetic distinctiveness of
BOR as well as a newly sampled population nearby (LAL).
The idiosyncrasy of these populations was unraveled by the
Bayesian assignment test using nSSRs that discovered individuals forming a genetic cluster otherwise missing in these
populations and only scarcely present elsewhere (Supplementary Fig. S1). Moreover, all but one individual shared an
identical chloroplast haplotype. The genetically peculiar trees
originated from easily accessible valley sides, unlike the rest
of the specimens in both BOR and LAL (M. Höhn, personal
observation). These results suggested that a fraction of BOR
and LAL individuals had been introduced. We tested two
alternatives as likely origins of these presumably non-native
individuals. Our first hypothesis was that P. cembra may have
been introduced to the Rodnei Mountains from Eastern Alpine
locations. The alternative hypothesis assumed that individuals
of the closely related P. sibirica might have been planted in or
close to natural P. cembra stands, like in some places in the
Tatra Mountains (Paryski 1971 cit. in Zwijacz-Kozica and
Żywiec 2007). The STRUCTURE analysis including Eastern
Alpine P. cembra, native P. sibirica, and planted P. sibirica
from the Tatra Mountains could not elucidate the origin of the
non-native specimens at basic parameter settings, as they still
formed their unique genetic cluster not present elsewhere
(Supplementary Fig. S2b). As the comprehensive sample
from the Eastern Alps covered a wide range (from the easternmost Alpine populations to the central Alps at longitude
7.83° E), we rejected the hypothesis of an Eastern Alpine
provenance. In the same line, samples from the native eastern
Tree Genetics & Genomes (2014) 10:1419–1433
Siberian population were assigned to a cluster not present in
any Carpathian samples, which is not surprising given the
distant location of this population and strong genetic differentiation across the range of P. sibirica (Krutovsky et al. 1992).
Individuals of P. sibirica introduced to the Tatra Mountains
showed mixed assignment to the genetic groups of the native
Carpathian P. cembra samples. Thus, the non-native individuals of the Rodnei Mountains seemed to be genetically differentiated from either of the P. sibirica populations studied.
However, by applying the USEPOPINFO model, all nonnative specimens grouped to the cluster of P. sibirica from
the Tatra Mountains. We hence conclude that the non-native
individuals in the two populations of the Rodnei Mountains
are P. sibirica trees from a different gene pool than the ones
planted in the Tatra Mountains or the eastern Siberian provenance included here, e.g., from a western Siberian provenance. The strong differentiation of the population BOR found
in cpSSRs by Höhn et al. (2009, 2010) may thus be attributed
to the prevalence of an otherwise rare chloroplast haplotype in
the P. sibirica individuals.
Critchfield (1986) listed P. sibirica and P. cembra among
the inter-fertile species within Pinus section Strobus; accordingly, crossing of the native and introduced individuals may
be forecasted for future generations. Such “genetic pollution”
as mixture between the locally adapted and foreign individuals
may have a negative effect on the population’s fitness through
outbreeding depression (McKay et al. 2005) and should be
considered in conservation efforts. Genotyping juvenile cohorts could elucidate to what degree introgression has already
occurred locally.
Conclusions
Populations of P. cembra are considered to need active conservation management for their survival due to the upslope
shift of its climate envelope and a respective loss of suitable
habitats during the ongoing climate change (Casalegno et al.
2010). Höhn et al. (2009) found higher genetic variability
within P. cembra populations from the Carpathians compared
to those in the central Alps, emphasizing the importance of
these populations for species conservation. The present study
shows that these isolated populations, in general, have not yet
evolved to become genetically unique. However, the effect of
current gene flow is weak, and genetic drift predominates in
characterizing the genetic variation. This means that population persistence will depend on single populations, suggesting
that sustaining sufficiently high population sizes is recommended. Also, quantitative genetic differentiation, as found
in provenance tests (e.g., Blada 1997), substantiates the relevance of local populations in conserving genetic diversity in
Carpathian P. cembra as a whole. The genetic differentiation
1431
of population MUN suggests that this population should receive special attention because of its peculiar gene pool
among Carpathian populations of P. cembra. In turn, the
genetic composition of the populations in the Rodnei Mountains, which is influenced by presumably non-native
P. sibirica, should be observed for possible introgression. This
has to be kept in mind in forest management and conservation
actions in the region, and further genetic studies may help to
elucidate the exact origin and amount of P. sibirica individuals
introduced in the Rodnei Mountains.
Acknowledgments We acknowledge the CRUS-Sciex grant (12.071)
that allowed the stay of BL at WSL.
Data archiving statement Sequence data of nuclear microsatellite loci
are retrievable from NCBI GenBank; cp/nSSR data are uploaded on the
TreeGenes database.
References
Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell, Oxford
Alsos IG, Alm T, Normand S, Brochmann C (2009) Past and future range
shifts and loss of diversity in dwarf willow (Salix herbacea L.)
inferred from genetics, fossils and modeling. Glob Ecol Biogeogr
18:223–239
Belokon MM, Belokon YS, Politov DV, Altukhov YP (2005) Allozyme
polymorphism of Swiss stone pine Pinus cembra L. in mountain
populations of the Alps and the Eastern Carpathians. Russ J Genet
41:1268–1280
Birky CW Jr, Fuerst P, Maruyama T (1989) Organelle gene diversity
under migration, mutation, and drift: equilibrium expectations, approach to equilibrium, effects of heteroplasmic cells, and comparison to nuclear genes. Genetics 121:613–627
Blada I (1997) Stone pine (Pinus cembra L.) provenance experiment in
Romania. Silvae Genet 46:197–200
Blada I (2008) Pinus cembra distribution in the Romanian Carpathians.
Ann For Res 51:115–132
Casalegno S, Amatulli G, Camia A, Nelson A, Pekkarinen A (2010)
Vulnerability of Pinus cembra L. in the Alps and the Carpathian
mountains under present and future climates. For Ecol Manag 259:
750–761
Corander J, Tang J (2007) Bayesian analysis of population structure based
on linked molecular information. Math Biosci 205:19–31
Corander J, Waldmann P, Sillanpää MJ (2003) Bayesian analysis of
genetic differentiation between populations. Genetics 163:367–374
Critchfield WB (1986) Hybridization and classification of the white pines
(Pinus section Strobus). Taxon 35:647–656
Dupanloup I, Schneider S, Excoffier L (2002) A simulated annealing
approach to define the genetic structure of populations. Mol Ecol 11:
2571–2581
Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website
and program for visualizing STRUCTURE output and implementing
the Evanno method. Conserv Genet Resour 4:359–361
Ellstrand NC, Elam DR (1993) Population genetic consequences of small
population size: implications for plant conservation. Annu Rev Ecol
Syst 24:217–242
Ennos RA (1994) Estimating the relative rates of pollen and seed migration among plant populations. Heredity 72:250–259
1432
Fekete L (1887) Abauj-Torna-, Szepes- és Gömör vármegyék erdőtenyésztési viszonyai. Erdészeti lapok 26:525–549 (Silvicultural
characteristics of Abauj-Torna, Szepes and Gömör counties)
Feurdean A, Tantau I, Farcas S (2011) Holocene variability in the range
distribution and abundance of Pinus, Picea abies, and Quercus in
Romania; implications for their current status. Quat Sci Rev 30:
3060–3075
Goncharenko GG, Padutov VE, Silin AE (1992) Population structure,
gene diversity, and differentiation in natural populations of Cedar
pines (Pinus subsect. Cembrae, Pinaceae) in the USSR. Plant Syst
Evol 182:121–134
Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate Fstatistics. J Hered 86:485–486
Goudet J (2002) Fstat 2.9.3.2. URL: http://www2.unil.ch/popgen/
softwares/fstat.htm
Gugerli F, Senn J, Anzidei M, Madaghiele A, Büchler U, Sperisen C,
Vendramin GG (2001) Chloroplast microsatellites and mitochondrial nad1 intron 2 sequences indicate congruent phylogenetic relationships among Swiss stone pine (Pinus cembra), Siberian stone pine
(Pinus sibirica), and Siberian dwarf pine (Pinus pumila). Mol Ecol
10:1489–1497
Höhn M, Ábrán P, Vendramin GG (2005) Genetic analysis of Swiss stone
pine populations (Pinus cembra L. subsp. cembra) from the
Carpathians using chloroplast microsatellites. Acta Silv Lign Hung
1:39–47
Höhn M, Gugerli F, Ábrán P, Bisztray G, Buonamici A, Cseke K,
Hufnagel L, Quintela-Sabarís C, Sebastiani F, Vendramin GG
(2009) Variation in the chloroplast DNA of Swiss stone pine (Pinus
cembra L.) reflects contrasting post-glacial history of populations
from the Carpathians and the Alps. J Biogeogr 36:1798–1806
Höhn M, Hufnagel L, Cseke K, Vendramin GG (2010) Current range
characteristics of Swiss stone pine (Pinus cembra L.) along the
Carpathians revealed by chloroplast SSR markers. Acta Biol Hung
61(Suppl):61–67
Hu X-S, Ennos RA (1997) On estimation of the ratio of pollen to seed
flow among plant populations. Heredity 79:541–552
Hu X-S, Ennos RA (1999) Impacts of seed and pollen flow on population
genetic structure for plant genomes with three contrasting modes of
inheritance. Genetics 152:441–450
Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and
permutation program for dealing with label switching and
multimodality in analysis of population structure. Bioinformatics
23:1801–1806
Jankovská V (1984) Late glacial finds of Pinus cembra L. in the
Lubovnianská kotlina Basin. Folia Geobot Phytotaxon 19:323–325
Jankovská V, Pokorný P (2008) Forest vegetation of the last full-glacial
period in the Western Carpathians (Slovakia and Czech Republic).
Preslia 80:307–324
Karhu A, Vogl C, Moran GF, Bell JC, Savolainen O (2006) Analysis of
microsatellite variation in Pinus radiata reveals effects of genetic
drift but no recent bottlenecks. J Evol Biol 19:167–175
Kimura M, Weiss GH (1964) The stepping stone model of population
structure and the decrease of the genetic correlation with distance.
Genetics 49:561–576
Krutovsky KV, Politov DV, Altukhov YP (1992) Genetic differentiation
and phylogeny of stone pine species based on isozyme loci.
International Workshop on Subalpine Stone Pines and Their
Environment: The Status of Our Knowledge, St. Moritz,
Switzerland, September 5-11, 1992, Proceedings 19–30
Kuneš P, Pelánková B, Chytrý M, Jankovská V, Pokorný P, Petr L (2008)
Interpretation of the last-glacial vegetation of eastern-central Europe
using modern analogues from southern Siberia. J Biogeogr 35:
2223–2236
Latta RG, Mitton JB (1997) A comparison of population differentiation
across four classes of gene marker in limber pine (Pinus flexilis
James). Genetics 146:1153–1163
Tree Genetics & Genomes (2014) 10:1419–1433
Leberg PL (2002) Estimating allelic richness: effects of sample size and
bottlenecks. Mol Ecol 11:2445–2449
Lenormand T (2002) Gene flow and the limits to natural selection. Trends
Ecol Evol 17:183–189
Magyari E, Jakab G, Bálint M, Kern Z, Buczkó K, Braun M (2012) Rapid
vegetation response to Lateglacial and early Holocene climatic
fluctuation in the South Carpathian Mountains (Romania). Quat
Sci Rev 35:116–130
Mantel N (1967) The detection of disease clustering and a generalized
regression approach. Cancer Res 27:209–220
Mattes H (1982) Die Lebensgemeinschaft von Tannenhäher und Arve,
2nd edn. Eidgenössische Forschungsanstalt für das forstliche
Versuchswesen, Birmensdorf
McKay JK, Christian CE, Harrison S, Rice KJ (2005) “How local is
local?”—A review of practical and conceptual issues in the genetics
of restoration. Restor Ecol 13:432–440
Meusel H, Jäger E, Weinert E (1965) Vergleichende Chorologie der
Zentraleuropäischen Flora. Gustav Fischer, Jena
Myers ER, Chung MY, Chung MG (2007) Genetic diversity and spatial
genetic structure of Pinus strobus (Pinaceae) across an island landscape inferred from allozyme and cpDNA markers. Plant Syst Evol
264:15–30
Paryski W (1971) Sadzenie i przesadzanie limby (The planting and
transplanting of Pinus cembra). In: Białobok S (ed) Limba Pinus
cembra L. (The stone pine Pinus cembra L.) Nasze drzewa leśne 2:
50–56 (in Polish with an English summary) cit. in: Zwijacz-Kozica
T, Żywiec M (2007) Fifty-year changes in a strictly protected stone
pine population in the Tatra National Park. Nat Conserv 64:73–82
Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel.
Population genetic software for teaching and research—an update.
Bioinformatics 28:2537–2539
Petit RJ, Kremer A, Wagner DB (1993) Finite island model for organelle
and nuclear genes in plants. Heredity 71:630–641
Petit RJ, Duminil J, Fineschi S, Hampe A, Salvini D, Vendramin GG
(2005) Comparative organization of chloroplast, mitochondrial and
nuclear diversity in plant populations. Mol Ecol 14:689–701
Piry S, Luikart G, Cornuet J-M (1999) BOTTLENECK: a computer
program for detecting recent reductions in the effective population
size using allele frequency data. J Hered 90:502–503
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:
945–959
Provan J, Soranzo N, Wilson NJ, Goldstein DB, Powell W (1999)
A low mutation rate for chloroplast microsatellites. Genetics
153:943–947
Provan J, Beatty GE, Hunter AM, McDonald RA, McLaughlin E,
Preston SJ, Wilson S (2008) Restricted gene flow in fragmented
populations of a wind-pollinated tree. Conserv Genet 9:1521–
1532
Puşcaş M, Choler P, Tribsch A, Gielly L, Rioux D, Gaudeul M, Taberlet P
(2008) Post-glacial history of the dominant alpine sedge Carex
curvula in the European Alpine System inferred from nuclear and
chloroplast markers. Mol Ecol 17:2417–2429
Ribeiro MM, Mariette S, Vendramin GG, Szmidt AE, Plomion C, Kremer
A (2002) Comparison of genetic diversity estimates within and
among populations of maritime pine using chloroplast simplesequence repeat and amplified fragment length polymorphism data.
Mol Ecol 11:869–877
Ronikier M (2011) Biogeography of high-mountain plants in the
Carpathians: an emerging phylogeographical perspective. Taxon
60:373–389
Rousset F (1997) Genetic differentiation and estimation of gene flow
from F-statistics under isolation by distance. Genetics 145:1219–
1228
Rowland V (1882) A czirbolya-fenyő (Pinus cembra) elöjövétele- és
tenyésztéséről a központi Kárpátokban. Erdészeti lapok 21:422–
Tree Genetics & Genomes (2014) 10:1419–1433
427 (On the discovery of Swiss stone pine (Pinus cembra) and its
propagation in the Central Carpathians)
Rybníčková E, Rybníček K (2006) Pollen and macroscopic analyses of
sediments from two lakes in the High Tatra mountains, Slovakia.
Veget Hist Archeobot 15:345–356
Salzer K, Sebastiani F, Gugerli F, Bounamici A, Vendramin GG
(2009) Isolation and characterization of polymorphic nuclear
microsatellite loci in Pinus cembra L. Mol Ecol Resour 9:858–
861
Schoebel CN, Brodbeck S, Buehler D, Cornejo C, Gajurel J, Hartikainen
H, Keller D, Leys M, Říčanová Š, Segelbacher G, Werth S,
Csencsics D (2013) Lessons learned from microsatellite development for nonmodel organisms using 454 pyrosequencing. J Evol
Biol 26:600–611
Sherwin WB, Jabot F, Rush R, Rosetto M (2006) Measurement of
biological information with applications from genes to landscapes.
Mol Ecol 15:2857–2869
Szmidt AE (1982) Genetic variation in isolated populations of stone pine
(Pinus cembra). Silva Fenn 16:196–200
Thiel-Egenter C, Holderegger R, Brodbeck S, Intrabiodiv Consortium,
Gugerli F (2009) Concordant genetic breaks, identified by combining clustering and tessellation methods, in two co-distributed alpine
plant species. Mol Ecol 18:4495–4507
1433
Tomback DF, Holtmeier F-K, Mattes H, Carsey KS, Powell ML (1993)
Tree clusters and growth form distribution in Pinus cembra, a birddispersed pine. Arct Alp Res 25:374–381
Vendramin GG, Lelli L, Rossi P, Morgante M (1996) A set of primers for
the amplification of 20 chloroplast microsatellites in Pinaceae. Mol
Ecol 5:595–598
Voronoï MG (1908) Nouvelles application des paramètres continus à
la théorie des formes quadratiques. Deuxième mémoire
Recherche sur le paralléloedres primitifs. J Reine Angew Math
134:198–207
Wachowiak W, Boratyńska K, Cavers S (2013) Geographical patterns of
nucleotide diversity and population differentiation in three closely
related European pine species in the Pinus mugo complex. Bot J
Linn Soc 172:225–238
Willis KJ, Van Andel TH (2004) Trees or no trees? The environments of
central and eastern Europe during the Last Glaciation. Quat Sci Rev
23:2369–2387
Willis KJ, Rudner E, Sümegi P (2000) The full-glacial forests of central
and southeastern Europe. Quat Res 53:203–213
Wohlfarth B, Hannon G, Feurdean A, Ghergari L, Onac BP, Possnert G
(2001) Reconstruction of climatic and environmental changes in
NW Romania during the early part of the last deglaciation (~15,
000–13,600 cal yr BP). Quat Sci Rev 20:1897–1914