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Genetic Structure and Effective Population Sizes in European Red Deer (Cervus elaphus) at a Continental Scale: Insights from Microsatellite DNA


Abstract and Figures

We analysed more than 600 red deer (Cervus elaphus) from large parts of its European distribution range at 13 microsatellite loci, presenting the first continent-wide study of this species using nuclear markers. Populations were clearly differentiated (overall FST = 0.166, Jost’s Dest = 0.385), and the BAPS clustering algorithm yielded mainly geographically limited and adjacent genetic units. When forced into only three genetic clusters our data set produced a very similar geographic pattern as previously found in mtDNA phylogeographic studies: a western group from Iberia to central and parts of Eastern Europe, an eastern group from the Balkans to Eastern Europe and a third group including the threatened relict populations from Sardinia and Mesola in Italy. This result was also confirmed by a multivariate approach to analysing our data set, a discriminant analysis of principal components (DAPC). Calculations of genetic diversity and effective population sizes (linkage-disequilibrium approach) yielded the lowest results for Italian (Sardinia, Mesola; Ne between two and eight) and Scandinavian red deer, in line with known bottlenecks in these populations. Our study is the first to present comparative nuclear genetic data in red deer across Europe and may serve as a baseline for future analyses of genetic diversity and structuring in this widespread ungulate.
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Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a
continental scale: insights from microsatellite DNA
Frank E. Zachos
, Alain C. Frantz
, Ralph Kuehn
4, 5
, Sabine Bertouille
, Marc Colyn
Magdalena Niedzialkowska
, Javier Pérez-González
, Anna Skog
10, 11
, Nikica Sprĕm
, Marie-
Christine Flamand
* These authors contributed equally.
1: Natural History Museum Vienna, Burgring 7, 1010 Vienna, Austria,
2: Musée National d’Histoire Naturelle, 25, Rue Munster, L-2160 Luxembourg,,
3: Fondation faune-flore, 25, Rue Munster, L-2160 Luxembourg
4: Unit of Molecular Zoology, Chair of Zoology, Department of Animal Science, Technische Universität
München, Freising, Germany
5: Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces,
NM 88003-8003, U.S.A.
6: partement de l’Etude du Milieu naturel et agricole, Service Public de Wallonie, 23 Avenue
Maréchal Juin, 5030 Gembloux, Belgium
7: CNRS-UMR 6553, Université de Rennes 1, Station Biologique, 35380 Paimpont, France
8: Mammal Research Institute, Polish Academy of Sciences, Białowieza, Poland,
9: Grupo de Biología y Etología, Universidad de Extremadura, 10071 Cáceres, Spain
10: Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of
Oslo, 0316 Oslo, Norway
11: Cancer Registry of Norway, 0304 Oslo, Norway
12: Department of Fisheries, Beekeeping, Game Management and Special Zoology, Faculty of
Agriculture, University of Zagreb, Zagreb, Croatia
13: Institut des Sciences de la Vie, Université catholique de Louvain, Croix du Sud 4-15, 1348 Louvain-
la-Neuve, Belgium
Corresponding author: Frank E. Zachos,
Journal of Heredity Advance Access published February 24, 2016
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We analysed more than 600 red deer (Cervus elaphus) from large parts of its European
distribution range at 13 microsatellite loci, presenting the first continent-wide study of this
species using nuclear markers. Populations were clearly differentiated (overall F
= 0.166,
Jost’s D
= 0.385), and the BAPS clustering algorithm yielded mainly geographically limited
and adjacent genetic units. When forced into only three genetic clusters our data set
produced a very similar geographic pattern as previously found in mtDNA phylogeographic
studies: a western group from Iberia to central and parts of Eastern Europe, an eastern
group from the Balkans to Eastern Europe and a third group including the threatened relict
populations from Sardinia and Mesola in Italy. This result was also confirmed by a
multivariate approach to analysing our data set, a discriminant analysis of principal
components (DAPC). Calculations of genetic diversity and effective population sizes (linkage-
disequilibrium approach) yielded the lowest results for Italian (Sardinia, Mesola; N
two and eight) and Scandinavian red deer, in line with known bottlenecks in these
populations. Our study is the first to present comparative nuclear genetic data in red deer
across Europe and may serve as a baseline for future analyses of genetic diversity and
structuring in this widespread ungulate.
The present genetic structure of large mammals in Europe is mainly due to: (1) signatures of
glacial-interglacial cycles with (in temperate species) southern refugia during glacials and
subsequent recolonization of northern regions, particularly after the Last Glacial Maximum
(LGM) (Hewitt 2000); (2) anthropogenic influences over the past centuries, e. g. selective
hunting, habitat fragmentation and translocations (Hartl et al. 2003, Frantz et al. 2006).
Genetic consequences of human interference are thus grafted onto natural phylogeographic
patterns, often blurring the intraspecific structuring of pre-human eras.
The red deer (Cervus elaphus) is arguably the most important European game species and,
consequently, has been impacted by humans for centuries or even millennia (Hartl et al.
2003). A multitude of studies on its genetic structure in Europe have been carried out, both
on a local, regional and continental scale (Zachos and Hartl 2011, Carden et al. 2012,
Niedzialkowska et al. 2012, Fernández-García et al. 2014, Karaiskou et al. 2014, Krojerová-
Prokesová et al. 2015). Studies on mitochondrial DNA (for a review see Zachos and Hartl
2011) have uncovered three main phylogeographic lineages in Europe: a western
haplogroup (designated A) distributed from Iberia through France and northern Central
Europe to the British Isles, Scandinavia and parts of Eastern Europe; an eastern haplogroup
(designated C) in the Balkans and parts of Eastern and Central Europe; and an isolated
lineage B restricted to the Tyrrhenian red deer (C. e. corsicanus) on Sardinia and Corsica and
the North African Barbary red deer (C. e. barbarus). The suture zone between the lineages A
and C appears to run from Austria through Poland and Belarus to the Baltic States
(Niedzialkowska et al. 2011, Fickel et al. 2012, Krojerová-Prokesová et al. 2015).
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While the overall continental pattern does not seem to have been blurred by human
interference (there are only few geographic outliers with respect to the three lineages,
Nussey et al. 2006), the areas where geographic lineages meet may or may not show the
natural distribution patterns. In red deer, this is a general issue, even more so at regional
and local scales, where it is often not clear if and to what extent populations are “pure”, i. e.
free from artificial introductions. Translocations of farmed red deer have occurred countless
times, and what is known about them is still only the tip of the iceberg (Linnell and Zachos
2011, Apollonio et al. 2014, especially Table 3.1, and references therein). Even if, as the
mtDNA phylogeographic studies suggest, translocations have mainly been carried out within
the main lineages rather than between them, these translocations have often covered large
geographic distances (Linnell and Zachos 2011). Of course, between-lineage translocations of
stags would not leave a signature in mitochondrial patterns; however, there does not seem
to be a male bias in translocations, and available documentation confirms that most of the
times females were translocated as well (e. g. Niethammer 1963). Local or regional red deer
stocks have also been intensively studied from a population genetic point of view, often
taking into account human impacts (Kuehn et al. 2003, 2004, Zachos et al. 2007, Frantz et al.
2008, Haanes et al. 2010a, b, Niedzialkowska et al. 2012, Fernández-García et al. 2014).
In this study, we present the first microsatellite data set covering most of the European
range of red deer to infer its nuclear genetic structuring. Contrary to the roe deer and wild
boar, the other two widespread European ungulate species (Randi et al. 2004, Scandura et
al. 2008), no such analysis exists for red deer to date. The present study aims at closing this
gap, for the first time allowing for nuclear genetic comparisons across the whole continent.
In particular, our study aims are:
(1) to uncover the large-scale nuclear genetic structure of the red deer in Europe and
compare it to the known mtDNA phylogeography that is believed to bear signatures of the
Quaternary climatic cycles, especially those of refugia during the LGM and subsequent
recolonization events;
(2) to use our microsatellite data set to calculate genetic diversity and effective population
sizes (N
) at a continent-wide comparative level to get further insights into the distribution
of genetic variability in this game species and to produce important and directly comparable
diversity parameters for the endangered Tyrrhenian (Corsica, Sardinia) and Mesola (NE Italy)
Material and Methods
Sample collection and laboratory work
The present study was based on 638 red deer tissue samples from 27 locations throughout
the continent (see Table 1 and Fig. 1). We also included 30 samples from a French deer farm
(Boisgervilly) in an attempt to understand the origin of these individuals and to test the
potential to identify farmed individuals in the European data set. The samples included both
new material and individuals already analysed in previous studies (Kuehn et al. 2003, 2004,
Feulner et al. 2004, Hmwe et al. 2006a, b, Zachos et al. 2007, Skog et al. 2009, Dellicour et al.
2011, Niedzialkowska et al. 2011, rez-González et al. 2012). DNA was extracted from new
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samples using a chloroform-based extraction method (Doyle and Doyle 1990. All samples
(old and new) were genotyped at 13 microsatellite loci (BM1818, Cer14, CSPS115, CSSM14,
CSSM16, CSSM19, CSSM22, CSSM66, ETH225, Haut14, ILSTS06, INRA35 and MM12; for
references see Kuehn et al. 2003) in three multiplex polymerase chain reactions (PCR) using
the Qiagen Multiplex kit (Qiagen, Hilden, Germany). Detailed information on the PCR
composition and reaction times can be found in Dellicour et al. (2011). Reactions were
performed using a Verity thermocycler (Applied Biosystems, Warrington, UK). PCR products
were separated using an ABI 3100 automated DNA sequencer (Applied Biosystems), and the
data were analysed using GeneMapper version 3.7 (Applied Biosystems). All individuals were
genotyped at 11 loci or more, with 629 of the 668 sampled having a complete 13-locus
Data analysis
We tested for the significance of heterozygote deficiency or excess (i. e. deviation from
Hardy-Weinberg equilibrium) in the 23 European sampling locations with N≥15 (Table S1,
excluding the deer farm) with the Markov-chain method in GENEPOP 3.4 (Raymond &
Rousset 1995), with 10,000 dememorisation steps, 500 batches and 10,000 subsequent
iterations. The populations were tested for pairwise linkage disequilibrium between loci
using an exact test based on a Markov-chain method as implemented in GENEPOP 3.4. The
false discovery rate technique was used to eliminate false assignment of significance by
chance (Verhoeven et al. 2005). Mean allelic richness per locus for each pre-defined
European population was calculated with FSTAT v. 2.9.3 (Goudet 1995) to standardize
measures for a population size of ten diploid individuals. Observed (Ho) and unbiased
expected (He
) heterozygosities (Nei 1978) for the same populations were estimated using
GENETIX 4.05.2 (Belkhir 2004).
We used STRUCTURE v2.3.1 (Pritchard et al. 2000) to estimate the number of
subpopulations (K). Ten independent runs of K=110 were carried out with 10
Markov chain
Monte Carlo (MCMC) iterations after a burn-in period of 10
iterations, using the model with
correlated allele frequencies and assuming admixture. ALPHA, the Dirichlet parameter for
the degree of admixture, was allowed to vary between populations. After deciding on the
most probable number of sub-populations based on the log-likelihood values (and their
convergence) associated with each K, we calculated each individual’s percentage of
membership (q), averaging q over ten runs. Bar plots of assignments were generated using
DISTRUCT 1.1 (Rosenberg 2004). We also used BAPS v5.4 (Corander et al. 2004) to perform a
population mixture analysis based on clustering individuals. This algorithm partitions the
data into populations with non-identical allele frequencies. The program was run for K = 2 to
30 with ten replications for each K.
A discriminant analysis of principal components (DAPC) was performed using the R-package
adegenet (Jombart 2008, Jombart et al. 2010) for R v. 2.12. (R Development Core Team
2011). This method, which is not based on any assumptions regarding the population genetic
model, first extracts information by applying a principal component analysis (PCA). In a
second calculation step a discriminant analysis (DA) maximizes the between-group
component of the genetic variation. The result of the DAPC can be visualized by using RGB
colour coding; the similarity of the dot colour represents the genetic similarity of the
populations (Jombart 2008, Jombart et al. 2010). In the first step of this procedure 50
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principal components of PCA were retained in order to explain approximately 90% of the
total variation of the data set analysed in this study. We carried out the DAPC at the
population level as we did not have coordinates for a large number of single deer specimens.
To quantify overall genetic differentiation within our data set we calculated the overall F
value (indicating which portion of of the overall variance was due to differentiation among
populations) with Arlequin 3.5 (Excoffier and Lischer 2010) and an estimator of Jost’s D (D
with GenAlEx 6.502 (Peakall and Smouse 2012). These calculations were carried out over all
populations listed in Table 1 (excluding the deer farm). GenAlEx was also used to identify
private alleles and their frequencies.
We estimated effective population sizes (N
) using a bias-corrected version of the linkage
disequilibrium (LD) method by Waples and Do (2008) as implemented in the N
Estimator v2
software (Do et al. 2014). This approach is based on the rationale that in small populations
with few parent individuals genetic drift will create non-random combinations of alleles of
different loci, i.e. LD. In general this approach is reliable if effective population sizes are not
much larger than ca. 200 and the data set is based on 10 or more loci and population sample
sizes of 25 or more. These conditions are not met for all our populations, so results should
be viewed with due caution in these cases. Since rare alleles (which occur frequently in
highly polymorphic markers like microsatellites) may have a disproportionately high impact
on the linkage values, the software offers different critical threshold values (we chose the
default values of 0.05, 0.02 and 0.01) below which alleles are not considered. We were
particularly interested in the values for the endangered subspecies from Sardinia and Mesola
which have undergone serious bottlenecks and for which no values of N
derived from
genetic data have ever been published. The present data set, the largest nuclear genetic on
European red deer so far, offers a good opportunity to estimate effective population sizes of
these deer and put them into a comparative perspective. We calculated N
values for pre-
defined populations, not for the clusters retrieved by BAPS because (i) differences between
the two were often small and (ii) BAPS uses marker independence as one of the clustering
parameters, so LD values might be affected by this clustering approach. Additionally, we only
chose those populations which had a sample size of n 15 and which were not obviously part of
a much larger continuous population (which is why we did not include the red deer from the
Data availability
We have deposited the primary data underlying these analyses (ie the microsatellite
genotpyes) in Dryad (doi XXX).
After correcting for multiple tests, we observed 15 instances of a locus deviating from HWE
in one of the 26 pre-defined populations (Table S1). Four loci (BM1818, CSSM19, CSSM66,
ETH225) significantly deviated from HWE in more than one population, 16 populations
showed no deviations from HWE at all. We concluded that no locus systematically deviated
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from HWE, but that the genetic characteristics of some populations (e.g. Wahlund effect,
immigrants, non-random sampling) led to the majority of the significant deviations from
HWE. All loci were therefore retained in subsequent analyses. No pairs of loci were
characterised by systematic linkage disequilibrium. Diversity values (allelic richness,
observed and expected heterozygosities) are given in Table 1. Across all three diversity
parameters, Sardinia and Mesola showed the lowest genetic diversity (as expected), but
other populations showed similarly low values for allelic richness (Norway, Sweden) or
heterozygosity (Polish and Romanian Carpathians). As expected given our comprehensive
geographical sampling we found private alleles in several populations. Most populations only
had a single private allele, but Berchtesgaden in Germany had five (all at low frequencies of
less than 3.5%). Sardinia, arguably the evolutionarily most divergent population in our data
set, only had a single private allele which, however had a frequency of 40.6%!
Genetic structuring across Europe
The results of the STRUCTURE analysis showed that the independent runs did not converge
on an optimal solution. Log-likelihood values gradually increased, without reaching a higher
value of K where they converge reasonable well, and started to decline again after K=12 (Fig.
S1). The best convergence of log-likelihoods was obtained for K=2, K=4 and K=7. However,
even at these three values of K, assignments of individuals differed fairly widely between
runs of the same K. For example, at K=2, we obtained six different clustering solutions (Fig.
The individual-based modal population mixture analysis in BAPS inferred the presence of 26
genetic populations. The majority of the sampling locations formed distinct sub-populations
(Fig. 1). The samples from eastern Poland formed a genetic cluster with the samples from
northern and eastern Germany. Seven clusters consisted of six individuals or less. Four deer,
which had been sampled in Croatia/Slovenia, Norway, SE Germany and SE Poland,
respectively, formed single-individual partitions (not shown in Fig. 1). The SE Polish and NE
Italian (Mesola) populations, as well as the deer farm, each contained a few individuals that
formed distinct clusters (Fig. 1). The remaining deer farm individuals were grouped with the
Scottish and Eastern European clusters and it was not possible to unequivocally identify
farmed individuals in the rest of the data set.
When forced to assign all European individuals to only three clusters (K = 3), BAPS produced
a geographical pattern very much like that known from the three mtDNA lineages (Fig. S3):
there is a clear separation of western from eastern red deer with an overlap of these two
groups in eastern Central Europe and Poland. Sardinia, together with Mesola (NE Italy) and
Norway, constitutes the third cluster (at K = 4, Norway is separated from Sardinia/Mesola).
We checked for the occurrence of otherwise rare alleles in these three populations as a
possible explanation for their clustering. However, while we did find rare alleles at several
loci shared by Mesola and Sardinia, we did not find any that were shared also by Norway.
The DAPC yielded results in accordance with those from BAPS (Fig. 2). Sardinia, Mesola,
Norway and Sweden were most divergent genetically, masking differentiation among the
remaining populations. But again, when removing these four outliers from the analysis, we
found a west-east dichotomy of red deer populations (Fig. 2, right map). The Belgian red
deer were somewhat genetically differentiated from its surrounding populations. The allelic
richness values characterising the populations from Sardinia, Mesola, Norway and Sweden
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were the lowest in the data set (Fig. 3, Table 1). Furthermore, populations in France, eastern
Croatia and Iberia had low levels of allelic richness, while deer in central Europe and Poland
had the highest. The overall F
value was 0.166 (p < 0.00001), indicating that 16.6% of all
genetic variance was due to differences among populations as opposed to variability within
populations (83.4%). Jost’s D
was 0.385 (significant at p = 0.001).
Effective population sizes of European red deer
values as calculated with the LD method are given in Table 2. Values above 200 and for
samples much smaller than 25 should be viewed with due caution (see above). In line with
the diversity parameters (Table 1) values for Sardinia and Mesola were the lowest (between
2.0 and 8.2), and no other populations show similarly low effective population sizes,
although some do show values that are below 50 (e. g. Sweden, Norway and Schleswig-
Holstein in northern Germany), which is often viewed as a threshold below which inbreeding
depression is likely to occur. The comparison of different threshold values also shows that
rare alleles sometimes have a large impact on the result for a given population but do not
change the overall picture.
Our analyses of more than 600 individual multi-locus genotypes of European red deer have
uncovered substantial structuring across the continent and, as expected given the higher
mutation rates in microsatellites, the overall nuclear genetic structure was more complex
than that found in phylogeographic studies based on mtDNA. Microsatellites are generally
more appropriate for the detection of small-scale and/or more recent structuring but the
European data set once more confirmed the genetic uniqueness of both the Sardinian and
the Mesola red deer and also yielded similar patterns to those uncovered by mtDNA
European genetic structure and phylogeography
Due to convergence problems, the STRUCTURE results were inconclusive. Re-running the
analysis using the substantially longer burn-in of 10
did not solve the issue (results not
shown). To the best of our knowledge, STRUCURE does not allow a formal assessment of the
convergence of the MCMC chains, via the statistic by Gelman and Rubin (1992), for example.
While similar problems have been reported (e.g., Frantz et al. 2014), the issue is particularly
striking here. We therefore limited our inference to the results obtained by the BAPS
algorithm. Contrary to STRUCTURE, the overall picture provided by BAPS was consistent in
that Sardinian, Mesola, Norwegian and, to a lesser extent, Swedish red deer were the most
divergent of the European populations. Also, the BAPS and the DAPC analyses retrieved a
clear signal of genetic divergence between western and eastern Europe.
The BAPS analysis yielded 26 distinct genetic clusters across Europe. Even if this is an
overestimate (BAPS can overestimate the number of genetic clusters because it has a
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tendency to identify populations consisting of only a few individuals, as observed here as
well; Latch et al. 2006), it clearly shows the differentiation at a comparatively small
geographical scale in European red deer. In line with this, both F
and Jost’s D
significant differentiation at a rather high level. Our F
of 0.166 is almost exactly the same as
that found for another cervid species with a similar distribution range in Europe, the roe
deer (Capreolus capreolus; F
across Europe based on 704 specimens and 11 microsatellite
loci was found to be 0.16 by Randi et al. 2004). As expected, overall F
was much higher for
mtDNA control region sequences (because within-population diversity is lower; F
= 0.84,
Skog et al. 2009).
Most BAPS clusters comprise local populations and/or mostly geographically adjacent
sampling sites. Given the mutation rates of microsatellites, this is an expected outcome and
in line with many microsatellite studies on red deer at local or regional scales (e. g. Feulner
et al. 2004, Nielsen et al. 2008, Haanes et al. 2011, Niedzialkowska et al. 2012, Höglund et al.
2013, Karaiskou et al. 2014, Krojerová-Prokesová et al. 2015). However, if low K values of the
BAPS analysis are considered, the geographical distribution of the clusters are very
interesting. Indeed, if all European red deer are clustered into only three groups, the
geographical pattern bears a striking resemblance to the phylogeographic pattern derived
from mtDNA: two main groups (west and east, respectively) that show an overlap in Central
Europe and Poland, and a minor group containing the red deer from Sardinia. These groups
correspond to the mtDNA lineages A (west), C (east) and B (Sardinia and North Africa).
Instead of the North-African Barbary deer (of which unfortunately no samples were available
for the present study), the microsatellite analysis particularly groups the Sardinian red deer
with Mesola whose mtDNA affinities are somewhat intermediate between the western and
eastern clade (Skog et al. 2009, Niedzialkowska et al. 2011), with the most recent study
favouring closer relationships to the eastern group (Lorenzini and Garofalo 2015). Norway
becomes separated from this group at the next higher level of K = 4. The DAPC confirmed
these results in that after the removal of the outlier populations there was a clear
differentiation between Western, Central and Central-Eastern Europe on the one hand and
South-Eastern and southern Central Europe on the other. In red deer, concordance between
mtDNA phylogroup distribution and microsatellite structuring has been found before, albeit
at a smaller geographical scale, in the Czech Republic (Krojerová-Prokesová et al. 2015) and
in Greece (Karaiskou et al. 2014).
The most convincing explanation for the biogeographic pattern observed in the present
study is that the microsatellite structure of red deer across Europe still carries a signature of
the postglacial recolonization process from two main glacial refugia (Iberia/southern France
in the west, the Balkans and possibly the Carpathian region in the east, Sommer et al. 2008).
In line with this, values of allelic richness are highest where the two main BAPS clusters and
DAPC groups (west and east) meet in Central Europe and Poland, which is also where the
western and eastern mtDNA lineages co-occur (Niedzialkowska et al. 2011, Fickel et al. 2012,
Krojerová-Prokesová et al. 2015). To what extent this zone of overlap is natural, however,
cannot be definitively answered due to the high number of translocations that are known to
have occurred. It would be interesting to see whether phylogeographic data from one or
more nuclear markers with lower mutation rates than microsatellites also confirm the large-
scale pattern of three groups found in mtDNA and microsatellites.
Within the West-Palaearctic red deer, the Tyrrhenian red deer from Corsica and Sardinia (C.
e. corsicanus) and the North-African Barbary red deer (C. e. barbarus) comprise a distinct
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mtDNA lineage (B), and their phylogeographic history is not entirely clear, e. g. it is still being
debated whether the Tyrrhenian deer are derived from introduced Barbary deer or vice
versa (see Zachos and Hartl 2011 and references therein). If the Tyrrhenian deer are
descendants of Italian mainland deer, then they should be closely related to the red deer
from Mesola (recently described as C. e. italicus, Zachos et al. 2014) which are the last
surviving native Italian red deer. While this is not supported by mtDNA studies, close
affinities between the two have been found based on microsatellites (Hajji et al. 2008). It is
interesting to note that the present data set of 13 microsatellites none of which is identical
to the eight loci used by Hajji et al. (2008) yielded the same result of a close relationship
between C. e. corsicanus and C. e. italicus. It seems therefore unlikely that these results are
simply an artefact due to drift effects in two recently severely bottlenecked populations for
this to be true the bottlenecks would have to have resulted in similar and unique allele
frequencies in two completely non-overlapping sets of altogether 21 loci. Rather, it seems
more likely that the result is indicative of a true signal of phylogeographic relationships
between mainland Italian and Sardinian/Corsican red deer, a question that ultimately only
ancient DNA studies will be able to answer.
Genetic diversity and effective population sizes
Genetic diversity values were in the range known for microsatellite loci in red deer (see
Table 1 in Zachos and Hartl 2011 for a compilation of values from all over Europe). The
lowest values when considering both allelic diversity/richness and heterozygosities were
expectedly found in the severely bottlenecked populations from Sardinia and Mesola in Italy,
and known bottlenecks can also account for the low diversity (at least in terms of allelic
richness) found in Scandinavian red deer from Sweden and Norway which is in accordance
with findings from other studies (Haanes et al. 2010a, b, 2011).
We present here also the first estimation of effective population sizes in red deer across a
large area of their distribution. Overall, N
values were in the range of, although with lower
maximum values than, those previously calculated for German and Spanish red deer based
on genetic and demographic data (Martinez et al. 2002, Kuehn et al. 2003). The calculations
have also confirmed the genetic depletion of the red deer from Sardinia and Mesola as a
consequence of past bottlenecks and near-extinction. Values between two and eight are the
lowest in Europe and even considerably lower than the N
= 20 calculated with the same
approach (LDNe) for the endangered Kashmir red deer or hangul (C. e. hanglu) whose total
census population size is estimated at just above 200 (Mukesh et al. 2015). While both the
Sardinian and Mesola red deer have increased in numbers recently and are not threatened
with immediate extinction anymore, the long-term consequences of low genetic diversity
and inbreeding remain unclear. While overall the LD approach is viewed as a reliable
method, there are many unknowns in any calculation of effective population size (Luikart et
al. 2010). The values therefore might best be viewed in a comparative context rather than as
absolute values for each of the populations separately.
Although a common and locally abundant game animal today, the red deer faced extirpation
in many parts of its range during past centuries. Documentation on recolonization whether
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natural or human-mediated is usually scarce, and what is known from the literature (e. g.
Niethammer 1963, Apollonio et al. 2014) is almost certainly only the tip of the iceberg. In
fact, it is believed that the present gene pool of many if not most free-living populations of
red deer in Europe contains at least some genetic material that goes back to introductions
(Hartl et al. 2003). Evidence for purely autochthonous populations is rare (and usually not
conclusive), with some possible examples being red deer in Mesola (Zachos et al. 2014),
Skåne in southern Sweden (Höglund et al. 2013) and the Scottish Highlands (Pérez-Espona et
al. 2009). Although genetic analyses are a powerful means to elucidate the status of
populations with respect to their natural or anthropogenic origin (see Kuehn et al. 2004,
Frantz et al. 2006), such analyses have not been carried out for most of the distribution
range. Many of the populations analysed in the present study will therefore not be
completely natural units (it is known, for example, that the Châteauroux red deer have
partly been introduced from the Domaine National de Chambord). However, a “purist”
approach allowing only completely native populations in a species as deeply impacted
anthropogenically as the red deer in Europe is neither feasible nor would it, in our view, be
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Fig. 1. Location of the genetic populations inferred using the individual-based BAPS
algorithm. The size of the pie charts indicates the number of samples collected from a
locality, while the pattern of the pie chart indicates the identity of the genetic clusters. The
four deer that had been sampled in Croatia/Slovenia, Norway, SE Germany and SE Poland
and that formed single-individual partitions were omitted from the plot. For the pattern
based on only three genetic clusters, see Fig. S3 in the Appendix.
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Fig. 2. DAPC of European red deer populations. Similar RGB colour codes signify genetic similarity. Sardinia, Mesola, Norway and Sweden are genetically very
different from the rest of Europe, effectively veiling differentiation among the latter (left). When running the analysis without these four outliers (right), the
European pattern largely shows a dichotomy between a western group (red-purple colours) that ranges from Iberia through western Europe and the British
Isles to eastern central Europe, and an eastern group (green-blue colours) in the Balkans and southern central Europe. In Central and Eastern Europe these
groups admix (brownish colours). This is in accordance with both mtDNA phylogeography and the BAPS results for K = 3. The Belgian red deer are the only
outliers in this pattern (yellow dots).
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Fig. 3. Microsatellite-based allelic richness measures for 28 pre-defined European red deer
populations (incl. the deer farm). The estimate of allelic richness is based on a sample of 10 diploid
individuals and 13 microsatellite markers. Sardinia, Mesola, Norway and Sweden show the lowest
values (little black squares).
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Table 1: Geographic distribution of the European samples analysed in this study and summary of
genetic diversity measures. A
: allelic richness; Ho: observed heterozygosity; He
: unbiased expected
Microsatellite diversity
Wallonia NE
Wallonia central
Wallonia West
E Croatia
NW Croatia / S Slovenia
Central France (Châteauroux)
E France (Meurthe)
NW France (Hardouinais, Brittany)
E Germany (Saxony)
N Germany (Schleswig-Holstein)
NE Bavaria (Fichtelberg/Goldkronach)
NE Germany (Mecklenburg)
SE Germany (Berchtesgaden)
N Italy (Southern Tyrol/Vinschgau)
NE Italy (Mesola)
W Norway (Sogn og Fjordane)
E Poland (Białowieża)
NE Poland (Warmia-Masuria Province)
SE Poland (N Carpathians)
SE Romania (Carpathians)
NE Serbia (Bachka)
SE Spain (Andalucía)
W Spain (Extremadura)
S Sweden (Skåne)
Deer Farm
France, Brittany (Boisgervilly)
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Table 2. Effective population sizes (N
) as calculated with NeEstimator based on the linkage disequilibrium approach. For each population, N
values are
given for three different thresholds for the lowest allele frequency used. The values in parentheses are the 95% confidence intervals based on jackknifing on
loci. n: sample size. “infinite” values of N
refer to cases where there is no evidence of variation of the genetic characteristic due to finite numbers of
parental individuals, i. e. all can be explained by sampling error (Do et al. 2014). Only those populations are included for which evidence was present that
they were not just artificially designated sample sites and for which n was ≥ 15. The two Spanish populations, after mostly yielding infinite values separately,
were pooled.
Population n Effective population size (N
Frequency threshold: 0.05 0.02 0.01
Sardinia 16 4.3 (2.3 10.4) 8.2 (3.3 - 17.1) 8.2 (3.3 17.1)
Mesola 22 2.0 (1.4 3.0) 2.6 (1.8 5.3) 2.6 (1.8 5.3)
Sweden 16 infinite (9.8 infinite) 20.4 (7.8 549.8) 20.4 (7.8 549.8)
Norway 31 40.6 (16.9 532.1) 30.2 (15.5 87.8) 10.3 (3.9 21.6)
Schleswig-Holstein 19 19.2 (13.7 29.0) 26.2 (18.6 40.5) 26.2 (18.6 40.5)
Saxony 15 infinite (122.2 infinite) 283.5 (69.8 infinite) 283.5 (69.8 infinite)
Serbia 19 303.7 (41.1 infinite) 131.3 (37.1 infinite) 131.3 (37.1 infinite)
E Croatia 53 480.6 (128.7 infinite) 1384.1 (206,6 infinite) 1127.7 (217.6 infinite)
NW Croatia/S Slovenia 49 84.5 (52.5 177.2) 155.4 (93.1 394.9) 139.5 (80.6 397.4)
Berchtesgaden 29 46.2 (32.1 75.4) 51.7 (35.2 89.1) 41.4 (28.0 71.2)
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Fichtelgebirge 31 28.8 (20.2 44.7) 38.3 (27.7 57.7) 41.1 (30.4 59.9)
Liechtenstein 29 139.6 (60.3 infinite) 166.0 (75.3 infinite) 156.3 (74.0 infinite)
Vinschgau (Italy) 26 101.0 (45.6 infinite) 119.5 (54.9 infinite) 149.5 (62.3 infinite)
E France (Meurthe) 27 42.0 (22.1 144.8) 57.6 (30.3 229.9) 79.2 (40.8 424.2)
NW France (Hardouinais) 22 77.7 (26.6 infinite) 176.4 (45.1 infinite) 176.4 (45.1 infinite)
C France (Châteauroux) 23 62.1 (27.0 infinite) 85.1 (40.6 1797.9) 85.1 (40.6 1797.9)
Spain (pooled) 30 38.0 (24.9 68.8) 42.1 (28.8 70.4) 68.0 (45.3 124.8)
NE Wallonia 20 infinite (118 infinite) infinite (121.4 infinite) infinite (121.4 infinite)
C Wallonia 20 82.8 (35.9 infinite) 114.3 (45.6 infinite) 114.3 (45.6 infinite)
W Wallonia 20 126.2 (44.2 infinite) 167.5 (57.4 infinite) 167.5 (57.4 infinite)
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... The overall population genetic diversity of roe deer in Europe appeared relatively high compared to other large ungulates inhabiting the temperate climatic zone such as Red Deer (Cervus elaphus; Niedziałkowska et al. 2012;Zachos et al. 2016) and Moose (Alces alces; Niedziałkowska et al. 2016a). Observed heterozygosity of roe deer was higher than in Red Deer and Moose populations, whereas allelic richness was similar to Moose and higher than in Red Deer (Zachos and Hartl 2011;Niedziałkowska et al. 2012;Zachos et al. 2016). ...
... The overall population genetic diversity of roe deer in Europe appeared relatively high compared to other large ungulates inhabiting the temperate climatic zone such as Red Deer (Cervus elaphus; Niedziałkowska et al. 2012;Zachos et al. 2016) and Moose (Alces alces; Niedziałkowska et al. 2016a). Observed heterozygosity of roe deer was higher than in Red Deer and Moose populations, whereas allelic richness was similar to Moose and higher than in Red Deer (Zachos and Hartl 2011;Niedziałkowska et al. 2012;Zachos et al. 2016). We found some excess heterozygosity in all except one studied roe deer site. ...
... Such excess has not been commonly observed in other populations of ungulates in Europe (e.g. Niedziałkowska et al. 2012Niedziałkowska et al. , 2016aVeličković et al. 2016;Zachos et al. 2016). According to Cornuet and Luikart (1997) heterozygosity excess or deficit can occur after a recent change of the effective population size but also in a situation where heterozygotes have a selective advantage or disadvantage. ...
Although the European Roe Deer (Capreolus capreolus) is one of the most common and widespread ungulate species in Europe and inhabiting a variety of habitats, few studies have addressed its population structure at a large spatial scale using nuclear genetic data. The aims of our study were to: (i) investigate genetic diversity, level of admixture, and genetic structure across European Roe Deer populations; (ii) identify barriers to gene flow; and (iii) reveal factors that have impacted the observed pattern of population genetic structure. Using 12 microsatellite loci, we analyzed 920 European Roe Deer samples from 16 study sites from northern, southern, central, and eastern Europe. The highest genetic diversity was found in central and eastern sites, and lowest in the northern and southern sites. There were 2 main groups of genetically related populations in the study area—one inhabiting mainly Fennoscandia, and the second in the continental part of Europe. This second population was further divided into 3 to 5 spatially distributed genetic clusters. European Roe Deer belonging to the Siberian mitochondrial DNA clade, inhabiting large parts of eastern Europe, were not identified as a separate population in the analysis of microsatellite loci. No isolation by distance (IBD) was detected between roe deer from the fennoscandian and the continental study sites, but the Baltic Sea was inferred to be the main barrier to gene flow. Only weak IBD was revealed within the continental population. Three lower-level genetic barriers were detected in the western, southern, and eastern parts of the study area. The main factors inferred as shaping the observed genetic diversity and population structure of European Roe Deer were postglacial recolonization, admixture of different populations of the species originating from several Last Glacial Maximum refugial areas, and isolation of several study sites.
... However, there is also evidence of genetic exchange between animals belonging to these two lines before the last glacial maximum disappeared: in a single mortality event caused by a cave collapse in north-western Spain dated 35,000 years ago, 15 deer died and some belonged to the Balkan line, whereas others belonged to the Iberian line [5]. Although some authors defended the existence of some of the subspecies (specifically, C. e. hispanicus) based on nuclear DNA microsatellites as opposed to mitochondrial DNA [6], other studies confirmed using the microsatellites the lines reported by Skog et al. [4,7]. Thus, during these natural migrations and because red deer populations were spread in different habitats, the frequencies of their genes could diverge due to drift, natural selection, and adaptation to given environmental conditions. ...
... Many of such populations were morphologically distinct and assigned as subspecies previous to the studies in the last 15 years [19]. However, as pointed out above, most authors consider the five European subspecies as unique red deer species without subspecies, although with genetic variability among populations or lines, particularly because it takes 90,000-400,000 years for a subspecies to diverge, whereas European populations sheltered in a reduced area in southern Europe up to 12,000 years ago [2,4,7,20]. As a significant comparison, most archaeologists agree that humans arrived in America only during deglaciation, about 15,000 years ago, and nobody considers their native population a different human subspecies [21]. ...
... Several studies have described three main mitochondrial lineages resulting from the genetic variability of the mtDNA D-loop region: the Western European lineage, the Eastern European lineage spread in the Balkans and the Mediterranean lineage that originated from Africa, Sardinia, and Corsica. Despite attempts to support a different subspecies for C. e. hispanicus based on nuclear microsatellite DNA instead of the most appropriate mitochondrial one [6], the three genetic lineage patterns (although not achieving enough divergence to differ at the subspecies level) were also found in several other studies based on nuclear microsatellites ( [7]; even despite being blurred by recent translocations [75]). ...
Full-text available
In this study, we analysed the effect of human-mediated selection on the gene pool of wild and farmed red deer populations based on genotyping-by-sequencing data. The farmed red deer sample covered populations spread across seven countries and two continents (France, Germany, Hungary, Latvia, New Zealand, Poland, and Slovakia). The Slovak and Spain wild red deer populations (the latter one in a large game estate) were used as control outgroups. The gene flow intensity, relationship and admixture among populations were tested by the Bayesian approach and discriminant analysis of principal components (DAPC). The highest gene diversity (He = 0.19) and the lowest genomic inbreeding (FHOM = 0.04) found in Slovak wild population confirmed our hypothesis that artificial selection accompanied by bottlenecks has led to the increase in overall genomic homozygosity. The Bayesian approach and DAPC consistently identified three separate genetic groups. As expected, the farmed populations were clustered together, while the Slovak and Spanish populations formed two separate clusters. Identified traces of genetic admixture in the gene pool of farmed populations reflected a strong contemporary migration rate between them. This study suggests that even if the history of deer farming has been shorter than traditional livestock species, it may leave significant traces in the genome structure.
... Also, in light of a tradition of translocating red deer across Europe for centuries, the fact that there is a relatively clear-cut geographic structure in their mitochondrial genome is perhaps somewhat surprising. This is particularly true given that the available translocation records do not suggest a strong bias toward stags and that the nuclear genetic analysis based on biparental microsatellites confirms the overall mtDNA structure in European red deer (in particular with respect to haplogroups A, B, and C, Zachos et al. 2016). ...
... Zachos and Hartl (2011) give an overview of studies (including diversity values) until 2010. The only continent-wide population genetic study yielding directly comparable diversity values (including estimates of effective population sizes N e ) based on highly variable microsatellite data is the one by Zachos et al. (2016). Expectedly, they found a wide range of genetic diversity across Europe, but the two heavily bottlenecked subspecies that only narrowly escaped extinctionthe Tyrrhenian C. e. corsicanus and the Mesola red deer C. e. italicus consistently showed the lowest values for heterozygosity and allelic richness (a measure of allelic diversity corrected for differences in sample size), followed by populations in Scandinavia (representing the Norwegian and Swedish subspecies), which are also known to have experienced bottlenecks (e.g., Haanes et al. 2011). ...
... We used the apriori population cluster assignment approach (five locations as used in Structure locprior model) where we selected the number of Principal Components based on optimisation of 'α' score through spline interpolation (De et al. 2021). We calculated genetic differentiation (pairwise F ST ) in ARLEQUIN version 3.1 (Excoffier et al. 2005) between the genetic groups used in Structure analysis (Estes-Zumpf et al. 2010;He et al. 2010;Zachos et al. 2016). Finally, we investigated patterns of isolation by distance (IBD) by performing Mantel test (9,999 permutations) on geographic versus genetic distances (pairwise) in GenAlEx version 6.5 (Smouse and Peakall 2012). ...
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Abstract The Indian subcontinent has witnessed disproportionate declines in large mammalian herbivore communities. The northern swamp deer (Rucervus duvaucelii duvaucelii) exemplifies the conservation challenges of typical non-protected area spe- cies, where apart from distribution status other ecological information is limited for the upper Gangetic basin population. We combined elements of radio-telemetry and conservation genetics to evaluate dispersal patterns, population connectivity and assess genetic variation and inbreeding status of this population living across a highly human-dominated area. We genetically identified 266 unique swamp deer and further analyses revealed presence of two spatially-admixed genetic lineages with moderate heterozygosity (Ho=0.51, SD=0.10) and low inbreeding (FIS=0.128) status. Multidisciplinary evidence suggests that the small, isolated grassland patches between Jhilmil Jheel Conservation Reserve (JJCR) and Hastinapur Wildlife Sanctuary (HWLS) are highly preferred by swamp deer during migrations and are genetically con- nected. The southern part of the area in HWLS showed early signatures of genetic discontinuity that require immediate conservation attention. We hypothesized that the human settlement history of this landscape, river dynamics and species’ ability to negotiate various pressures and disperse has helped to maintain such connectivity. While these signatures are encouraging for this small, isolated cervid population, careful management interventions are required to ensure the integ- rity and functionality of this landscape. We recommend a scientifically robust population estimation approach across this landscape and multi-stakeholder-driven strategies to augment population and habitat recovery, combat poaching, plantation and riverscape management to ensure long-term survival of this species.
... The formation of the suture zone on the northeastward line Alps-Baltics rather than a northward line, might have been caused by an earlier, or faster, expansion by wild boar from southern France, that consequently recolonized most of northern Europe. A similar pattern is suggested for red deer by fossil records and microsatellite variation (Sommer et al. 2008;Zachos et al. 2016). An explanation may be that at the start of the Holocene (in particular, around 9000 years ago) the region south of the Tatras Mountains was still relatively cold and dominated by coniferous trees, while in western Europe temperatures were milder and broadleaved forests dominated the vegetation (Brewer et al. 2002;Cheddadi and Bar-Hen 2009). ...
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European wildlife has been subjected to intensifying levels of anthropogenic impact throughout the Holocene, yet the main genetic partitioning of many species is thought to still reflect the late-Pleistocene glacial refugia. We analyzed 26,342 nuclear SNPs of 464 wild boar (Sus scrofa) across the European continent to infer demographic history and reassess the genetic consequences of natural and anthropogenic forces. We found that population fragmentation, inbreeding and recent hybridization with domestic pigs have caused the spatial genetic structure to be heterogeneous at the local scale. Underlying local anthropogenic signatures, we found a deep genetic structure in the form of an arch-shaped cline extending from the Dinaric Alps, via Southeastern Europe and the Baltic states, to Western Europe and, finally, to the genetically diverged Iberian peninsula. These findings indicate that, despite considerable anthropogenic influence, the deeper, natural continental structure is still intact. Regarding the glacial refugia, our findings show a weaker signal than generally assumed, but are nevertheless suggestive of two main recolonization routes, with important roles for Southern France and the Balkans. Our results highlight the importance of applying genomic resources and framing genetic results within a species’ demographic history and geographic distribution for a better understanding of the complex mixture of underlying processes.
... Microsatellite DNA studies showed that in most study sites, the hetorozygosity (expected heterozygosity He) of European moose was relatively high (range He ¼ 0.57-0.75, Haanes et al. 2011, Kangas et al. 2013, Niedziałkowska et al. 2016a in comparison with other common European ungulate species as red deer Cervus elaphus (e.g., Niedziałkowska et al. 2012;Zachos et al. 2016) or roe deer Capreolus capreolus (Lorenzini and Lovari 2006;Olano-Marin et al. 2014) and slightly higher than in moose populations in North America (He ¼ 0.45-0.64, Hundertmark 2009, Schmidt et al. 2009). ...
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The analysis of population genetic structure and individual multilocus heterozygosity are crucial for wildlife management and conservation. Microsatellite markers have traditionally been used to assess these genetic parameters. However, single-nucleotide polymorphisms (SNPs) are becoming increasingly popular. Our goal here was to determine to what extent SNPs can provide better insights than microsatellites into the overall genetic status and population genetic processes in the species. To this end, we genotyped 210 red deer (Cervus elaphus) in the Spanish wild population with both 11 microsatellites and 31,712 SNPs. We compared parameters related to population genetic structure and individual multilocus heterozygosity obtained with both types of markers. Our results showed correlations between parameters measured using both microsatellites and SNPs, particularly those related to the level of genetic diversity and genetic differentiation. However, we found notably lower precision of microsatellites in measuring the distribution of genetic diversity among individuals. We conclude that microsatellites can be used to monitor the overall genetic status and detect broad patterns in red deer populations. Nevertheless, the greater precision of SNPs in inferring genetic structure and multilocus heterozygosity leads us to encourage scientists and wildlife managers to prioritize their use whenever possible.
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Studies about diversification of Neotropical open-habitat organisms are scarce, especially in frogs, in which almost all studied species from Cerrado, Chaco, and Caatinga are mainly associated with gallery forest. Here we tested hypotheses about diversification in Neotropical open habitats (savannas and grasslands) using as model a small treefrog associated with these environments and widely distributed throughout South America. We sampled 68 individuals from 43 localities throughout the geographic distribution of Scinax fuscomarginatus and used one mitochondrial and three nuclear DNA markers to define genetic clusters. Based on population assignments and previously proposed diversification hypotheses for the Cerrado, we designed different models to be tested by approximate Bayesian computation (ABC). We found four genetic clusters which reflect the current distribution of open habitats of the Central Brazilian plateau and of the Cerrado lowlands. Among the models tested with ABC, the best-supported model was a scenario of multiple refugia in which all populations experienced population size reduction (bottleneck) in a relatively recent time, with the most ancient split event surrounding the Last Interglacial (LIG). During interglacial periods, warmer and wetter climate would have favored the geographic expansion of forests, resulting in the retraction and fragmentation of open habitats like savannas and grasslands. Populations of species associated with open habitats, including S. fuscomarginatus, would have remained isolated in relatively small stable areas with reduced gene flow among them.
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Mitochondrial DNA (mtDNA) lineages are recognized as important components of intra- and interspecific biodiversity, and allow to reveal colonization routes and phylogeographic structure of many taxa. Among these is the genus Cervus that is widely distributed across the Holarctic. We obtained sequences of complete mitochondrial genomes from 13 Cervus taxa and included them in global phylogenetic analyses of 71 Cervinae mitogenomes. The well-resolved phylogenetic trees confirmed Cervus to be monophyletic. Molecular dating based on several fossil calibration points revealed that ca. 2.6 Mya two main mitochondrial lineages of Cervus separated in Central Asia, the Western (including C. hanglu and C. elaphus) and the Eastern (comprising C. albirostris, C. canadensis and C. nippon). We also observed convergent changes in the composition of some mitochondrial genes in C. hanglu of the Western lineage and representatives of the Eastern lineage. Several subspecies of C. nippon and C. hanglu have accumulated a large portion of deleterious substitutions in their mitochondrial protein-coding genes, probably due to drift in the wake of decreasing population size. In contrast to previous studies, we found that the relic haplogroup B of C. elaphus was sister to all other red deer lineages and that the Middle-Eastern haplogroup E shared a common ancestor with the Balkan haplogroup C. Comparison of the mtDNA phylogenetic tree with a published nuclear genome tree may imply ancient introgressions of mtDNA between different Cervus species as well as from the common ancestor of South Asian deer, Rusa timorensis and R. unicolor, to the Cervus clade.
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The red deer (Cervus elaphus) is considered a valuable and important ungulate species with significant ecological role and high importance as a game species in Europe. Its local population in Belarus had undergone extended periods of decline in the past, followed by multiple reintroduction campaigns and management policy adjustments during the Soviet and post-Soviet periods, which eventually led to a recent spike in estimated population numbers. Along with increasing the numbers, those reintroductions have made the understanding of the structure and origins of the populations for the purpose of proper management and sustainable long-term growth much more complicated. Information on the origin of the reintroduction stock has often been lacking, while control of the red deer population dynamics in Belarus is currently limited to indirect survey of putative population numbers, with no utilization of contemporary genetic analysis. Here we report an estimate and interpretation of the red deer population structure in Belarus based on the analysis of microsatellite genotype data from 118 individuals of the red deer from the most well-known groups across Belarus. These specimens were genotyped using a novel multiplex panel of 14 microsatellite loci with various levels of polymorphism. We describe two red deer subpopulations with overlapping ranges that form the Belarussian metapopulation. We also report estimates of their genetic diversity, gained from the analysis of molecular variance, Bayesian analysis of genetic structure, differentiation indices, genetic bottleneck event analysis, and standard genetic diversity metrics. Based on the geographical distribution of subpopulations, their genetic differentiation and known history of red deer reintroductions in Belarus, we consider that both these subpopulations emerged mostly out of the patterns of animal release during two separate periods of reintroduction. We also suggest appropriate population management adjustments arising from the issue of anthropogenic reintroductions that determine the population structure in this managed species.
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Molecular forensic methods are being increasingly used to help enforce wildlife conservation laws. Using multilocus genotyping, illegal translocation of an animal can be demonstrated by excluding all potential source populations as an individual's population of origin. Here, we illustrate how this approach can be applied to a large continuous population by defining the population genetic structure and excluding suspect animals from each identified cluster. We aimed to test the hypothesis that recreational hunters had illegally introduced a group of red deer into a hunting area in Luxembourg. Reference samples were collected over a large area in order to test the possibility that the suspect individuals might be recent immigrants. Due to isolation-by-distance relationships in the data set, inferring the number of genetic clusters using Bayesian methods was not straightforward. Biologically meaningful clusters were only obtained by simultaneously analysing spatial and genetic information using the program baps 4.1. We inferred the presence of three genetic clusters in the study region. Using partial Mantel tests, we detected barriers to gene flow other than distance, probably created by a combination of urban areas, motorways and a river valley used for viticulture. The four focal animals could be excluded with a high certainty from the three genetic subpopulations and it was therefore likely that they had been released illegally.
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Red deer populations in the Iberian glacial refugium were the main source for postglacial recolonization and subspecific radiation in north-western Europe. However, the phylogenetic history of Iberian red deer (Cervus elaphus hispanicus) and its relationships with northern European populations remain uncertain. Here, we study DNA sequences at the mitochondrial control region along with STR markers for over 680 specimens from all the main red deer populations in Spain and other west European areas. Our results from mitochondrial and genomic DNA show contrasting patterns, likely related to the nature of these types of DNA markers and their specific processes of change over time. The results, taken together, bring support to two distinct, cryptic maternal lineages for Iberian red deer that predated the last glacial maximum and that have maintained geographically well differentiated until present. Haplotype relationships show that only one of them contributed to the northern postglacial recolonization. However, allele frequencies of nuclear markers evidenced one main differentiation between Iberian and northern European subspecies although also supported the structure of both matrilines within Iberia. Thus, our findings reveal a paraphyletic nature for Iberian red deer but also its genetic identity and differentiation with respect to northern subspecies. Finally, we suggest that maintaining the singularity of Iberian red deer requires preventing not only restocking practices with red deer specimens belonging to other European populations but also translocations between both Iberian lineages.
This book considers a number of problems posed by ungulates and their management in Europe. Through a synthesis of the underlying biology and a comparison of the management techniques adopted in different countries, the book explores which management approaches seem effective - and in which circumstances. Experts in a number of different areas of applied wildlife biology review various management problems and alternative solutions, including the impact of large ungulates on agriculture, forestry and conservation habitats, the impact of disease and predation on ungulate populations and the involvement of ungulates in road traffic accidents and possible measures for mitigation. This book is directed at practising wildlife managers, those involved in research to improve methods of wildlife management, and policy-makers in local, regional and national administrations.
The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Availability: Stable version is available from CRAN: Development version is available from adegenet website: Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2). Supplementary information:Supplementary data are available at Bioinformatics online.
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from