Open Journal of Genetics, 2013, 3, 174-182 OJGen
http://dx.doi.org/10.4236/ojgen.2013.33020 Published Online September 2013 (http://www.scirp.org/journal/ojgen/)
Genetic analysis for geographic isolation comparison of
brown bears living in the periphery of the Western
Carpathians Mountains with bears living in other areas
Ján Graban1*, Jana Kisková1, Pavol Pepich2, Robin Rigg3
1Institute of the High Mountain Biology, University of Žilina Tatranská Javorina, Tatranská Javorina, Slovak Republic
2Institute of Foreign Languages, University of Žilina, Žilina, Slovak Republic
3Slovak Wildlife Society, Liptovský Hrádok, Slovak Republic
Received 24 April 2013; revised 26 May 2013; accepted 10 June 2013
Copyright © 2013 Ján Graban et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Populations of the European brown bear (Ursus arc-
tos L.) differ substantially in size, degree of geo-
graphic isolation and level of genetic diversity. Pre-
sent patterns result from phylogeographic processes
and profound human intervention. We assessed the
genetic variability of a subpopulation of brown bears
near the periphery of their range in the Western
Carpathian Mountains and compared their genetic
properties with those of bears in the core of the same
population and elsewhere. Samples were collected
non-invasively in 2007-2008 and 2010 in Strážovské
Vrchy Protected Landscape Area (PLA) in Slovakia
(included in the NATURA 2000 networking pro-
gramme). Seven polymorphic microsatellite loci
(UaMU26, UaMU64, G10B, G1D, G10L, UaMU50
and UaMU51) were amplified using a nested PCR in
order to assess the following parameters: variability,
allelic combinations, heterozygosity, number of alleles
and inbreeding coefficient. Sufficient brown bear
DNA for analysis was obtained from 57 out of 140
samples (41%), among which 45 different genotypes
were identified. Loci had a mean of 2.71 ± 0.76 alleles.
Average observed heterozygosity was 0.59. The in-
breeding coefficient was negative for all but one o f th e
analysed loci (2007-2008). In the year 2010 was nega-
tive three of seven loci. These results imply that gene
flow with other parts of the population has been
maintained in the reduced level and the isolation level
of bears in the study area was not so low. Neverthe-
less, the genetic variability of bears in Strážovské
Vrchy PLA was lower than that reported from other
localities in the Carpathian Mountains. The results
are discussed in the context of behavioural ecology
and conservation genetics.
Keywords: Carpathian Mountains; European Brown
Bear; Ursu s arcto s L.; Genetic Diversity; Microsatellite
Markers; Non-Invasive Sampling
1. INTRODUCTION
The brown bear (Ursus arctos) re-colonised the entire
European continent after the Last Glacial Maximum
[1,2], and yet its current distribution shows a discon-
tinuous pattern as a result of various human activities
[3,4]. In contrast to the relatively large and contiguous
populations with higher expected heterozygosity and
allelic diversity in Eastern Europe, the Balkans and
Scandinavia [5] population fragments in the western part
of the continent are extremely small and isolated, with
low levels of genetic variability and vulnerable to genetic
drift and inbreeding [6-8].
An additional level of complexity is added by the ex-
istence of three mitochondrial subclades [6,9,10]. They
may be the result of different founder populations having
passed through bottlenecks prior to rapid recolonisation
during the Holocene [11] or recent human-induced popu-
lation fragmentation due to habitat loss and killing [12,13].
It has been suggested that one of the glacial refugia
from which brown bears of the eastern lineage (subclade
3a) re-colonised most of continental Eurasia was in the
Western Carpathian Mountains of present day Slovakia
[2,11,14]. Alternatively, bears may have survived the
glacial period in the cold tundra-steppe of central Europe
[12]. Regardless of when and how they arrived, brown
bears persisted in the Western Carpathians in a large,
continuous population until the Middle Ages [15] and in
most forested upland areas of present-day Slovakia
*Corresponding author.

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 175
through to the 19th century [16]. Deforestation, over-
hunting and eradication programmes then precipitated a
catastrophic decline in range and numbers culminating in
the 1930s, when it was estimated that only c.20 - 60 indi-
viduals remained in an isolated, relict population [16].
A 30-year moratorium on hunting facilitated popula-
tion recovery [17]. The bears continued to increase in
number and reoccupy parts of their former range despite
the resumption of limited hunting from the 1960s [18].
The Western Carpathian bear population now numbers
several hundred individuals and extends across all moun-
tain ranges of central and northern Slovakia [19]. How-
ever, although it was thought that connection with the
much larger Eastern Carpathian population was re-es-
tablished in the 1980s [20], the two populations show a
high degree of differentiation most likely resulting from
genetic divergence during the c.100 years of their divi-
sion and evidence of renewed gene flow is sparse [21].
Populations that have passed through a recent demo-
graphic bottleneck would generally be expected to have
lost genetic diversity through stochastic drift [22]. How-
ever, medium-high levels of allelic variation have been
found in the nuclear DNA of Western Carpathian bears
and their level of genetic diversity seems to be within the
range commonly observed in different populations of
brown bears and other mammals [15,21,23], which sug-
gests the population bottleneck may not have been quite
so severe as feared by contemporary observers. The main
aim of our study was to examine the degree of genetic
diversity and geographic isolation of bears in a subpopu-
lation near the periphery of their current distribution in
the Western Carpathians and compare it with that of
bears in core areas of the population and in select Euro-
pean subpopulations.
Fieldwork was conducted in the Strážovské vrchy
mountain range (49˚23'702"N, 18˚73'46"E), which ex-
tends over an area of c. 900 km2 in northwest Slovakia
(Figure 1) and forms part of the Inner Western Carpa-
thian Mountains. Brown bears were apparently absent
until the mid 1960s [17,19] but re-colonised during the
period 1967-1984, when the recovering Western Carpa-
thian bear population expanded its range 40 km north-
westwards [24].
Intense development in molecular technology, have
moved bear ecology into a significant development in
which genetic analyses can be performed with ease and
with great informative value. Environmentalists can now
routinely utilize genetic information obtained from DNA
to formulate questions about the behavioural ecology and
conservation genetics of bear populations. The highly
variable microsatellites markers that have been analysed
in this study offer an effective tool for individual identi-
fication. These DNA fingerprints can be used in an eco-
logical context for the dendrogram construction based
on the degree of microsatellite profiles similarity to link-
ing individuals. Then we can get a view on the degree of
genetic relatedness of individuals grouped into clusters.
Implementation of population genetic features of the
PLA Strážovské vrchy provides an overview of the mi-
gration rate between neighboring populations of the
brown bears.
2. MATERIAL AND METHODS
2.1. Sample Collection and DNA Isolation
A total of samples 57 (41.8%) out 140 from different
sites of Strazovske vrchy Mts were collected. A 37 sam-
ples collected during the year 2010 (faeces and hair sam-
ples) and 20 samples from Strazovske vrchy territory
(during the period 2007-2008) were examined. Suffi-
cient brown bear DNA for analysis was obtained from 20
(2007-2008) out of 46 samples and 37 (2010) out of 94
samples collected in the field. A total of 45 different
genotypes were identified among 28 samples from faeces
and 29 from hair.
DNA extraction from hairs was performed using 10%
Chelex according to Kruckenhauser et al. [25], Depend-
ing on availability and quality hairs with visible roots
were used, DNA extractions from non-invasive samples
were performed with the QIAamp DNA Stool Kit
(QIAGEN) with a final elution volume of 100 µl.
Samples were collected in the Strážovské Vrchy Pro-
tected Landscape Area (Figure 1). The PLA covers c.300
km2 of which 78% is forest and 19% agricultural land.
Altitude ranges from 315 to 1213 m a.s.l. On the basis of
annual track surveys, opportunistic direct observations
and camera trapping we estimated there to be approxi-
mately 20 different bears using the area, although some
of them may have had home ranges extending beyond
the study area.
2.2. Microsatellites Analysis and Gender
Identification
Seven microsatellite loci Mu26, Mu64, G10B, G1D,
G10L, Mu50 and Mu51 were amplified using poly-
Figure 1. Location of the study area in relation to the dis-
tribution of the Western Carpathian population of brown bears.

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 176
merase chain reaction [7] and fragment length (allele)
analyses were carried out on eight-capillary sequencer
(Genome Lab GeXP, BeckmanCoulter). Analyses were
repeated in order to verify the reliability of individual
allele length determination.
Molecular sexing of the bears identified by DNA fin-
gerprinting was assessed by amplification of the Sry
fragment on theY chromosome [26]. In each cell, the
autosomal microsatellite locus is twice as concentrated as
the Sry gene. PCR products were checked on 2.0% aga-
rose gels. Males show two bands, the microsatellite and
Sry fragment, while females show only the ZFY/ZFX
band. The primer pair used for gender determination is
not bear-specific, but amplifies the Sry fragment in a
wide variety of mammals, including humans [26]. To
avoid contamination PCR reactions were set up by a fe-
male investigator.
DNA was extracted from hair using 10% Chelex solu-
tion [25] and from faeces with the Qiamp DNA Stool®
kit (Qiagen). To test for individuals, seven microsatellite
loci (UaMU26, UaMU64, G10B, G1D, G10L, UaMU50
and UaMU51) were amplified in a nested polymerase
chain reaction (PCR) [7]: a longer fragment of each locus
was amplified prior to amplifying a more specific area.
Two-step PCR procedures improve genotyping success
rate and limit genotyping errors [27].
Observed (HO) and expected (HE) heterozygosity
were calculated using Cervus 3.0 software (Field Genet-
ics). Results were compared with genetic data from
brown bears in core ranges of the Carpathian Mountains
in Slovakia [21,28] and Romania [21] as well as in cen-
tral Austria [25].
2.3. Statistical Methods
Observed (HO) and expected (HE) heterozygosity were
calculated with CERVUS software. Descriptive statistics
for each locus (mean number of alleles per locus, het-
erozygosities and polymorphic information content (PIC)
were computed from allele frequencies. The Fisher’s
exact test was used to check for genotypic linkage dis-
equilibrium for all pairs of loci by employing the Markov
chain method, as implemented in GENEPOP [29]. De-
viations from Hardy-Weinberg (HW) proportions were
evaluated through the Weir and Cockerham’s [30] and
Robertson and Hill’s [31] estimates of FIS to test for
heterozygote deficit with Levene’s correction for small
sample size, using the method described by Guo and
Thompson [32].
The difference between Hardy-Weinberg heterozygo-
sity (HE) and that expected from the observed number of
alleles (HEQ) was tested under the assumption of muta-
tion-drift equilibrium, given the sample size. Evidence
for a recent reduction of population size is assumed when
HE is significantly higher than HEQ. The patterns of
microsatellite mutations appear to be extremely complex
[33] and the evolution mode of bear microsatellite loci
was not known. Therefore, calculations were made ac-
cording to three models: the Infinite Alleles Mode [34],
the strict Stepwise Mutation Model [34] and the Two
Phase Model (TPM) with a 5% of multi-step changes:
[35] Valdes, Slatkin and Freimer [36], an offshoot of the
SMM, which accounts for addition or deletion of more
than one repeat unit.
The loci screened in our study were evaluated for their
reliability and resolving power when performing parent-
age tests. A simulation of parentage analysis was con-
ducted using CERVUS version 2.0 [37]. That programme
uses allele frequencies from the study population to run
simulations of paternity inference when multiple males
are non-excluded, allowing for user-defined inputs, such
as the number of males that are candidates for paternity,
the proportion of candidate males that are sampled and
errors in genotyping. Success rates of parentage tests
were derived assuming HW equilibrium in cases where
one true parent was known and in cases where neither
parent is known a priori, with 80 and 95% confidence
levels. The simulations (10 000 repetitions) were con-
ducted by changing parameters, e.g. the proportion of
loci typed, the number of candidate parents, the fraction
of candidates sampled and the level of potential labora-
tory mistyping.
2.4. Division of Animals into Clusters
Processing of acquired data and in particular in finding
answers to the question of individuals distribution on the
site led them to propose procedures for evaluation of
microsatellite data was processed using our original
software (Java script) (will be published in Oecologia
Montana). Two methods were used Neighbor-joining and
UPGMA (Unweighted Pair Group Method with Arith-
metic Mean) for construction of clusters graphic presen-
tation based on microsatellites data processing (Figure
2).
Neighbor-joining method provides not only the topo-
logy but also the branch lengths of the final tree. A pair
of “neighbors” is a pair of animals connected through a
single interior node in an unrooted, bifurcating tree [38].
UPGMA is a simple agglomerative or hierarchical
clustering method often used for the creation of phenetic
trees (phenograms). UPGMA assumes a constant rate of
similarity between animals. UPGMA was initially de-
signed for use in protein electrophoresis studies, but is
currently most often used to produce guide trees for more
sophisticated phylogenetic reconstruction algorithms.
The mean observed heterozygosity (HO) among the
seven loci examined in the year 2007-2008 was 0.70 and
0.53 in the year 2010, the mean expected heterozygosity
(HE) 0.54 in the year 2007-2008 and 0.57 in the year

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 177
Figure 2. Schematic presentation of animals distribution into “family” clusters (C1 - C4) based on degree of relatedness between
individual microsatellite profiles. (a) present Neighbor-joining method and (b) the UPGMA method (hr—hair samples, sc—scat
samples).

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 178
2010. Only one locus (UaMU26) had a HO lower than HE
in the year 2007-2008. In the year 2010 it was locus
UaMU26, UaMU64, G10B and Ua MU50 (Table 1).
Locus G1D had the most alleles (four), while UaMU26,
G10B and G10L showed low allelic variability (two al-
leles per locus). With the exception of G10L in the year
2007-2008 and UaMU64 in the year 2010, the observed
number of alleles at each locus (na) was greater than the
effective number of alleles (ne). The inbreeding coeffi-
cient was negative for six out of the seven loci (mean
value FIS = −0.27), the exception being UaMU26 (FIS =
+0.33) (2007-2008). In the year 2010 the inbreeding co-
efficient was negative only for three out seven loci (mean
value FIS = 0.08).
Table 1. Genetic variability of brown bears in the Strážovské Vrchy Mountains, Slovakia. na—observed number of alleles, ne—effec-
tive number of alleles, PIC—Polymorphic information content, HO—observed heterozygosity, HE—expected heterozygosity, FIS—
inbreeding coefficient.
(a)
2007-2008
Locus na n
e PIC H O H E F IS
UaMU26 2 1.89 0.35 0.30 0.47 0.36
UaMU64 3 2.38 0.47 0.85 0.58 −0.47
G10B 2 1.49 0.27 0.40 0.33 −0.21
G1D 4 3.03 0.59 0.85 0.67 −0.27
G10L 2 2.04 0.38 0.80 0.51 −0.57
UaMU50 3 2.56 0.51 0.80 0.61 −0.31
UaMU51 3 2.63 0.53 0.90 0.62 −0.45
Mean 2.71 2.29 0.44 0.70 0.54 −0.27
St. Dev 0.76 0.52 0.10 0.24 0.12 0.31
(b)
2010
Locus na n
e PIC H O H E F IS
UaMU26 2 1.89 0.35 0.07 0.47 0.85
UaMU64 3 3.13 0.59 0.54 0.68 0.21
G10B 2 1.96 0.37 0.32 0.49 0.35
G1D 4 3.33 0.64 0.79 0.70 −0.13
G10L 2 1.92 0.36 0.75 0.48 −0.56
UaMU50 3 2.50 0.51 0.50 0.60 0.17
UaMU51 3 2.27 0.48 0.75 0.56 −0.34
Mean 2.71 2.43 0.47 0.53 0.57 0.08
St. Dev 0.76 0.59 0.12 0.27 0.10 0.47
(c)
2007-2010
Locus na n
e PIC H O H E F IS
UaMU26 2 2 0.38 0.14 0.50 0.72
UaMU64 3 2.94 0.58 0.67 0.66 −0.02
G10B 2 1.96 0.37 0.35 0.49 0.29
G1D 4 3.33 0.64 0.81 0.70 −0.16
G10L 2 2 0.37 0.81 0.50 −0.62
UaMU50 3 2.44 0.50 0.61 0.59 −0.04
UaMU51 3 2.27 0.48 0.77 0.56 −0.38
Mean 2.71 2,42 0.47 0.59 0.57 −0.03
St. Dev 0.76 0.53 0.11 0.26 0.08 0.44

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 179
Locus UaMU26, UaMU64, G1D, G10L, UaMU50
andUaMU51 had the equal number of different allelic
combinations (three) (Table 2). The least variability was
found at loci G10B (2 alleles per locus in 2 different
combinations) (2007-2008).
Locus UaMU64 and UaMU50 had the equal number
of different allelic combinations (four), followed by
UaMU26, G1D, G10B and UaMU51 with three each.
The least variability was found at loci G10L (2 alleles
per locus in 2 different combinations) (2010).
3. DISCUSSION AND CONCLUSIONS
The brown bear is a wide-ranging species exhibiting
male-biased dispersal [39]. Adult males commonly use
hundreds of square kilometres in their search for food
and mating opportunities and dispersing subadult male
brown bears may roam over areas up to 12,000 km2 [1].
Such movements facilitate gene flow and, in the case of
dispersing young males, there is evidence that it operates
as a mechanism to avoid inbreeding [39]. The relatively
high level of heterozygosity and low degree of inbreed-
ing we found in bears in Strážovské vrchy (this study)
suggests that the subpopulation is not geographically
isolated and gene exchange with other segments of the
population has been maintained.
The most obvious potential source of migration into
the study area is the Malá Fatra mountain range, which
lies immediately to the east (Figure 1) and has a high
density of bears [19,28]. The eastern edge of the Strá-
žovské vrchy seems to present the least obstruction to
wildlife movement in and out of the study area, as the
unfenced primary road I/64 passes through a heavily
forested landscape for the 20-km section between Ra-
jecká Lesná and Kľačno, forest availability being the
most important habitat constraint on bear distribution in
the Western Carpathians [40]. There are, however, sev-
eral other nearby ranges from which bears could rea-
sonably be expected to reach Strážovské vrchy, including
Vtáčnik, Kremnické vrchy and Veľká Fatra.
To elucidate their movements without the need to
live-trap, immobilise and fit animals with telemetry
equipment, DNA profiling or ‘genetic fingerprinting’ in
combination with GPS localisation of sampling sites en-
ables individuals to be identified and tracked non-inva-
sively [7,28]. This may help to identify important biocor-
ridors in need of protection or improvement [41,42]. Ef-
fective immigration can also be estimated from changes
in observed and expected heterozygosity and heterozy-
gote excess [13,43,44].
Although the results of the present study showed rela-
tively high variability (increasing in the year 2010), mi-
crosatellite analysis of brown bears in Malá Fatra Na-
tional Park [28] found higher numbers of alleles per lo-
cus (Table 3). Moreover, the difference between ob-
served and expected heterozygosity was greater in Malá
Fatra than in Strážovské vrchy and low values of FIS for
each locus demonstrated a higher occurrence of het-
erozygotes. Higher variability has also been found in
central Slovakia, northern Slovakia and Romania [21].
The leading edge of an expanding population might be
expected to become less diverse as a result of a series of
genetic bottlenecks [22]. However, two loci analysed in
the small central Austrian bear population [25] showed a
similar level of variability to that found in Malá Fatra
and central Slovakia, even though all genotyped indi-
viduals were descended from just four founders. These
four individuals arrived in the area 20 - 40 years ago,
which is more recently than bears re-colonised the
Strážovské vrchy, and there is no evidence of there hav-
ing been any subsequent immigration.
The founders of the central Austrian bear population
Table 2. Allelic combinations found at seven microsatellite loci in genomic DNA from brown bears in the Strážovské Vrchy Moun-
tains. Slovakia. The most frequently occurring combination for each locus is shown in bold. AC—allelic combinations detected.
(a)
2007-2008
AC UaMU26 UaMU64 G10B G1D G10L UaMU50 UaMU51
1 182/182 177/194 114/114 171/221 143/143 118/121 110/110
2 182/198 184/194 114/126 179/179 143/171 121/125 110/136
3 198/198 184/184 179/208 171/171 125/125 110/116
(b)
2010
AC UaMU26 UaMU64 G10B G1D G10L UaMU50 UaMU51
1 182/182 177/177 114/114 171/221 143/143 118/121 110/110
2 182/198 177/194 114/126 179/179 143/171 121/121 110/136
3 198/198 184/194 126/126 179/208 121/125 110/116
4 184/184 125/125

J. Graban et al. / Open Journal of Genetics 3 (2013) 174-182 180
Table 3. The selected Carpatian Mts and central Austria Brown
bear subpopulations genetic variability comparison. na—ob-
served number of alleles, HO—observed heterozygosity, HE—
expected heterozygosity, FIS—inbreeding coefficient of Small
Fatra [28]—SF, Northern Slovakia [21]—NS, Central Slovakia
[21]—CS, Central Austria [25]—CA and Romania [21]—R.
Locality and locus na HO H
E F IS
SF-UaMU26 4 0.65 0.44 −0.48
CA-UaMU26 4 0.65 0.57 −0.13
SF-UaMU64 9 0.74 0.46 −0.61
NS-G10B 5 0.59 0.63 0.06
CS-G10B 4 0.60 0.63 0.05
CA-G10B 4 1 0.69 −0.45
R-G10B 8 0.76 0.75 −0.01
SF-G1D 4 0.63 0.43 −0.47
NS-G1D 6 0.68 0.79 0.14
CS-G1D 6 0.76 0.76 0.00
R-G1D 7 0.71 0.73 0.03
NS-G10L 6 0.41 0.48 0.16
CS-G10L 6 0.60 0.61 0.02
R-G10L 8 0.79 0.84 0.06
NS-UaMU50 6 0.76 0.75 −0.01
CS-UaMU50 6 0.72 0.65 −0.11
R-UaMU50 8 0.80 0.82 0.02
NS-UaMU51 6 0.52 0.74 0.30
CS-UaMU51 7 0.73 0.82 0.11
R-UaMU51 7 0.72 0.78 0.08
originated in Slovenia and are therefore part of the west-
ern lineage (subclade 1b), whereas Slovakia’s bears be-
long to the eastern lineage known as subclade 3a [45].
There are bears of both mtDNA lineages in Romania [5]
but the differentiation is not reflected in nuclear loci,
perhaps due to male-mediated gene flow and female
philopatry [21]. The genetic diversity of brown bears is
highest in Romania [46], where the population has never
fallen below 800 individuals, and is also high in Slovenia
[46] as well as neighbouring Croatia [47].
Our results represent a comparative study of a sub-
population which has hitherto received little attention
from researchers. However, the brown bear is one of the
best-studied mammalian species [13]. In the last two
decades there has been a proliferation of genetics studies
mapping populations at different geographical scales
from regional [7,8,48] to continental [49,50]. Standard-
ised procedures have been developed for sampling and
analysis in order to facilitate comparisons between stud-
ies [46,51,52]. Non-invasive genetic methods, especially
appropriate for use with elusive species in small, endan-
gered populations or over large areas, are now available
to allow identification of individual animals, census
populations and monitor migration and gene flow [13].
The potential for further work building on our study is
therefore substantial. Knowledge of population size, dis-
tribution, social and sexual structure, home range and
population trend on the local level as well as migration is
crucial for the proper conservation and management of
species within and between protected areas.
4. ACKNOWLEDGEMENTS
This study was supported by Structural Funds of EU Project of the
Agency of the Ministry of Education, Science, Research and Sport of
the Slovak Republic, Bratislava, ITMS No. 26110230078.
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