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

Abstract

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
1*
, Alain C. Frantz
2,3*
, Ralph Kuehn
4, 5
, Sabine Bertouille
6
, Marc Colyn
7
,
Magdalena Niedzialkowska
8
, Javier Pérez-González
9
, Anna Skog
10, 11
, Nikica Sprĕm
12
, Marie-
Christine Flamand
13
* These authors contributed equally.
1: Natural History Museum Vienna, Burgring 7, 1010 Vienna, Austria, frank.zachos@nhm-wien.ac.at
2: Musée National d’Histoire Naturelle, 25, Rue Munster, L-2160 Luxembourg, alain.frantz@mnhn.lu,
alainfrantz@yahoo.co.uk
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: Département 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, frank.zachos@nhm-wien.ac.at
Journal of Heredity Advance Access published February 24, 2016
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Abstract
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
ST
= 0.166,
Jost’s D
est
= 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
e
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.
Introduction
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
e
) 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)
subspecies.
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, Pérez-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
profile.
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
u
) 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=1–10 were carried out with 10
6
Markov chain
Monte Carlo (MCMC) iterations after a burn-in period of 10
5
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
ST
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
est
)
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
e
) using a bias-corrected version of the linkage
disequilibrium (LD) method by Waples and Do (2008) as implemented in the N
E
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
e
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
e
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
Carpathians).
Data availability
We have deposited the primary data underlying these analyses (ie the microsatellite
genotpyes) in Dryad (doi XXX).
Results
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.
S2).
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
ST
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
est
was 0.385 (significant at p = 0.001).
Effective population sizes of European red deer
N
e
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.
Discussion
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
phylogeography.
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
6
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
ST
and Jost’s D
est
showed
significant differentiation at a rather high level. Our F
ST
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
ST
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
ST
was much higher for
mtDNA control region sequences (because within-population diversity is lower; F
ST
= 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
e
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
e
= 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.
Conclusion
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
desirable, because human impacts are one of the most important factors in shaping genetic
structure in red deer and as such are also a relevant aspect of their present-day biology. Our
analysis has presented evidence that not only with respect to mtDNA but also for
microsatellite DNA presumably natural patterns are still visible in red deer across Europe,
and our data set, being the most comprehensive of its kind so far, may serve as a continent-
wide comparison for geographically more restricted studies in the future.
References
Apollonio M, Scandura M, Sprem N. 2014. Reintroductions as a Management Tool for
European Ungulates. In: Putman R, Apollonio M, editors. Behaviour and Management of
European Ungulates. Dunbeath: Whittles Publishing; p. 46-77.
Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F. 2004. Genetix 4.05.2, logiciel sous
Windows™ pour la genetique des populations. Laboratoire Génome, Populations,
Interactions, CNRS UMR 5000, Université de Montpellier II.
Carden RF, McDevitt AD, Zachos FE, Woodman PC, O’Toole P, Rose H, Monaghan NT,
Campana MG, Bradley DG, Edwards CJ (2012) Phylogeographic, ancient DNA, fossil and
morphometric analyses reveal ancient and modern introductions of a large mammal: the
complex case of red deer (Cervus elaphus) in Ireland. Quaternary Sci Rev 42 : 74-84
Corander J, Waldmann P, Marttinen P, Sillanpää MJ. 2004. BAPS 2: enhanced possibilities for
the analysis of genetic population structure. Bioinformatics 20: 2363-2369.
Dellicour S, Frantz AC, Colyn M, Bertouille S, Chaumont F, Flamand MC. 2011. Population
structure and genetic diversity of red deer (Cervus elaphus) in forest fragments in north-
western France. Conserv Genet 12: 1287-1297.
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden R. 2014. N
E
Estimator v2: re-
implementation of software for the estimation of contemporary effective population size
(N
e
) from genetic data. Mol Ecol Resour 14: 209-214.
by guest on February 29, 2016http://jhered.oxfordjournals.org/Downloaded from

Accepted Manuscript
11
Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12: 13–15.
Excoffier L, Lischer HEL. 2010. Arlequin suite version 3.5: a new series of programs to
perform population genetics analyses under Linux and Windows. Mol Ecol Res 10: 564-567.
Fernández-García JL, Carranza J, Martínez JG, Randi E. 2014. Mitochondrial D-loop phylogeny
signals two native Iberian red deer (Cervus elaphus) lineages genetically different to Western
and Eastern European red deer and infers human-mediated translocations. Biodivers
Conserv 23: 537-554.
Feulner PGD, Bielfeldt W, Zachos FE, Bradvarovic J, Eckert I, Hartl GB. 2004. Mitochondrial
DNA and microsatellite analyses of the genetic status of the presumed subspecies Cervus
elaphus montanus (Carpathian red deer). Heredity 93: 299-306.
Fickel J, Bubliy OA, Stache A, Noventa T, Jirsa A, Heurich M. 2012. Crossing the border?
Structure of the red deer (Cervus elaphus) population from the Bavarian-Bohemian forest
ecosystem. Mamm biol 77: 211-220.
Frantz AC, Hamman J-L, Klein F. 2008. Fine-scale genetic structure of red deer (Cervus
elaphus) in a French temperate forest. Eur J Wildl Res 54: 44-52.
Frantz AC, McDevitt AD, Pope LC, Kochan J, Davison J, Clements CF, Elmeros M, Molina-
Vacas G, Ruiz-Gonzalez A, Balestrieri A, Van Den Berge K, Breyne P, Do Linh San E, Ågren EO,
Suchentrunk F, Schley L, Kowalczyk R, Kostka BI, Ćirović D, Šprem N, Colyn M, Ghirardi M,
Racheva V, Braun C, Oliveira R, Lanszki J, Stubbe A, Stubbe M, Stier N, Burke T (2014)
Revisiting the phylogeography and demography of European badgers (Meles meles) based
on broad sampling, multiple markers and simulations. Heredity 113: 443-453
Frantz AC, Tigel Pourtois J, Heuertz M, Schley L, Flamand MC, Krier A, Bertouille S, Chaumont
F, Burke T. 2006. Genetic structure and assignment tests demonstrate illegal translocation of
red deer (Cervus elaphus) into a continuous population. Mol Ecol 15: 3191-3203.
Gelman A, Rubin DB. 1992. Inference from iterative simulation using multiple sequences.
Stat Sci 7: 457–511.
Goudet J. 1995. FSTAT: a computer program to calculate F-statistics. J Hered 86: 485-486.
Haanes H, Røed KH, Flagstad Ø, Rosef O. 2010a. Genetic structure in an expanding cervid
population after population reduction. Conserv Genet 11: 11-20.
Haanes H, Røed KH, Mysterud A, Langvatn R, Rosef O. 2010b. Consequences for genetic
diversity and population performance of introducing continental red deer into the northern
distribution range. Conserv Genet 11: 1653-1665.
Haanes H, Røed KH, Pérez-Espona S, Rosef O. 2011. Low genetic variation support
bottlenecks in Scandinavian red deer. Eur J Wildl Res 57: 1137-1150.
Hajji GM, Charfi-Cheikrouha F, Lorenzini R, Vigne J-D, Hartl GB, Zachos FE. 2008.
Phylogeography and founder effect of the endangered Corsican red deer (Cervus elaphus
corsicanus). Biodivers Conserv 17: 659-673.
Hartl GB, Zachos F, Nadlinger K. 2003. Genetic diversity in European red deer (Cervus elaphus
L.): anthropogenic influences on natural populations. CR Biologies 326: S37-S42.
Hewitt G (2000). The genetic legacy of the Quaternary ice ages. Nature 405: 907-913.
by guest on February 29, 2016http://jhered.oxfordjournals.org/Downloaded from

Accepted Manuscript
12
Hmwe SS, Zachos FE, Eckert I, Lorenzini R, Fico R, Hartl GB (2006a). Conservation genetics of
the endangered red deer from Sardinia and Mesola with further remarks on the
phylogeography of Cervus elaphus corsicanus. Biol J Linn Soc 88:691-701.
Hmwe SS, Zachos FE, Sale JB, Rose HR, Hartl GB (2006b). Genetic variability and
differentiation in red deer (Cervus elaphus) from Scotland and England. J Zool 270: 479-487.
Höglund J, Cortazar-Chinarro, Jarnemo A, Thulin C-G. 2013. Genetic variation and structure
in Scandinavian red deer (Cervus elaphus): influence of ancestry, past hunting, and
restoration management. Biol J Linn Soc 109: 43-53.
Jombart T. 2008. adegenet: a R package for the multivariate analysis of genetic markers.
Bioinformatics 24: 1403–1405.
Jombart T, Devillard S, Balloux F. 2010. Discriminant analysis of principal components: a new
method for the analysis of genetically structured populations. BMC Genetics 11: 94.
Karaiskou N, Tsakogiannis A, Gkagakvouzis K, Operator of Parnitha National Park, Papika S,
Latsoudis P, Kavakiotis I, Pantis J, Abatzopoulos TJ, Triantaphyllidis C, Triantafyllidis A. 2014.
Greece: A Balkan Subrefuge for a Remnant Red Deer (Cervus elaphus) Population. J Hered
105: 334-344.
Krojerová-Prokesová J, Baranceková M, Koubek P. 2015. Admixture of Eastern and Western
European Red Deer Lineages as a Result of Postglacial Recolonization of the Czech Republic
(Central Europe). J Hered 106: 375-385.
Kuehn R, Schroeder W, Pirchner F, Rottmann O. 2003. Genetic diversity, gene flow and drift
in Bavarian red deer populations (Cervus elaphus). Conserv Genet 4: 157-166.
Kuehn R, Haller H, Schroeder W, Rottmann O. 2004. Genetic roots of the red deer (Cervus
elaphus) population in eastern Switzerland. J Hered 95: 136-143.
Linnell JDC, Zachos FE. 2011. Status and distribution patterns of European ungulates:
genetics, population history and conservation. In: Putman R, Apollonio M, Andersen R,
editors. Ungulate Management in Europe: Problems and Practices. Cambridge: Cambridge
University Press; p. 12-53.
Lorenzini R, Garofalo L. 2015. Insights into the evolutionary history of Cervus (Cervidae, tribe
Cervini) based on Bayesian analysis of mitochondrial marker sequences, with first indications
for a new species. J Zool Syst Evol Res 53: 340-349
Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW. 2010. Estimation of census and
effective population sizes: the increasing usefulness of DNA-based approaches. Conserv
Genet 11: 355-373.
Martínez JG, Carranza J, Fernández-García JL, Sánchez-Prieto CB. 2002. Genetic variation of
red deer populations under hunter exploitation in southwestern Spain. J Wildlife Manage 66:
1273-1282.
Mukesh, Kumar VP, Sharma LK, Shukla M, Sathyakumar S. 2015. Pragmatic perspective on
conservation genetics and demographic history of the last surviving population of Kashmir
red deer (Cervus elaphus hanglu) in India. PLoS One 10(2): e0117069.
Nei M. 1978. Estimation of average heterozygosity and genetic distance from a small
number of individuals. Genetics 89: 583-590.
by guest on February 29, 2016http://jhered.oxfordjournals.org/Downloaded from

Accepted Manuscript
13
Niedzialkowska M, Jedrzejewska B, Honnen A-C, Otto T, Sidorovich VE, Perzanowski K, Skog
A, Hartl GB, Borowik T, Bunevich AN, Lang J, Zachos FE. 2011. Molecular biogeography of red
deer Cervus elaphus from eastern Europe: insights from mitochondrial DNA sequences. Acta
Theriol. 56: 1-12.
Niedzialkowska M, Jedrzejewska B, Wojcik JM, Goodman SJ. 2012. Genetic structure of red
deer population in Northeastern Poland in relation to the history of human interventions. J
Wildlife Manage 76: 1264-1276.
Nielsen EK, Olesen CR, Pertoldi C, Gravlund P, Barker JSF, Mucci N, Randi E, Loeschcke V.
2008. Genetic structure of the Danish red deer (Cervus elaphus). Biol J Linn Soc 95: 688-701.
Niethammer G. 1963. Die Einbürgerung von Säugetieren und Vögeln in Europa. Hamburg,
Berlin: Paul Parey.
Nussey DH, Pemberton J, Donald A, Kruuk LEB. 2006. Genetic consequences of human
management in an introduced island population of red deer (Cervus elaphus). Heredity 97:
56-65.
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.
Pérez-Espona S, Pérez-Barbería FJ, Goodall-Copestake WP, Jiggins CD, Gordon IJ, Pemberton
JM. 2009. Genetic diversity and population structure of Scottish Highland red deer (Cervus
elaphus) populations: a mitochondrial survey. Heredity 102: 199-210.
Pérez-González J, Frantz AC, Torres-Porras J, Castillo L, Carranza J (2012) Population
structure, habitat features and genetic structure of managed red deer populations. Eur J
Wildl Res 58: 933-943.
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using
multilocus genotype data. Genetics 155: 945-959
Randi E, Alves PC, Carranza J, Milosevic-Zlatanovic S, Sfougaris A, Mucci N. 2004.
Phylogeography of roe deer (Capreolus capreolus) populations: the effects of historical
genetic subdivisions and recent nonequilibrium dynamics. Mol Ecol 13: 3071-3083.
Raymond M, Rousset F. 1995. GENEPOP (version 1.2): population genetics software for exact
tests and ecumenicism. J Hered 86: 248-249.
Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V, Davoli R,
Apollonio M, Bertorelle G. 2008. Ancient vs. recent processes as factors shaping the genetic
variation of the European wild boar: are the effects of the last glaciation still detectable?
Mol. Ecol. 17: 1745-1762.
Skog A, Zachos FE, Rueness EK, Feulner PGD, Mysterud A, Langvatn R, Lorenzini R, Hmwe SS,
Lehoczky I, Hartl GB, Stenseth NC and Jakobsen KS. 2009. Phylogeography of red deer
(Cervus elaphus) in Europe. J Biogeogr 36: 66-77.
Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary
distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe.
Quaternary Sci Rev 27: 714-733
Verhoeven KJF, Simonsen KL, McIntyre LM (2005) Implementing false discovery rate control:
increasing your power. Oikos 108: 643-647.
by guest on February 29, 2016http://jhered.oxfordjournals.org/Downloaded from

Accepted Manuscript
14
Waples RS, Do C. 2008. LDNE: a program for estimating effective population size from data
on linkage disequilibrium. Mol Ecol Resour 8: 753-756.
Zachos FE, Althoff C, v Steynitz Y, Eckert I, Hartl GB 2007. Genetic analysis of an isolated red
deer (Cervus elaphus) population showing signs of inbreeding depression. Eur J Wildl Res 53:
61-67.
Zachos FE, Hartl GB. 2011. Phylogeography, population genetics and conservation of the
European red deer Cervus elaphus. Mammal Rev. 41: 138-150.
Zachos FE, Mattioli S, Ferretti F, Lorenzini R. 2014. The unique Mesola red deer of Italy:
taxonomic recognition (Cervus elaphus italicus nova ssp., Cervidae) would endorse
conservation. Ital J Zool 81: 136-143.
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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
R
: allelic richness; Ho: observed heterozygosity; He
u
: unbiased expected
heterozygosity.
Country
Region within country
n
Microsatellite diversity
A
R
Ho
He
u
Belgium
Wallonia NE
20
5.31
0.68
0.69
Belgium
Wallonia central
20
5.08
0.67
0.67
Belgium
Wallonia West
20
5.39
0.69
0.69
Croatia
E Croatia
53
4.82
0.63
0.68
Croatia/Slovenia
NW Croatia / S Slovenia
49
5.36
0.70
0.68
France
Central France (Châteauroux)
23
4.51
0.60
0.58
France
E France (Meurthe)
27
4.70
0.56
0.67
France
NW France (Hardouinais, Brittany)
22
4.20
0.61
0.65
Germany
E Germany (Saxony)
15
6.09
0.60
0.69
Germany
N Germany (Schleswig-Holstein)
19
5.53
0.53
0.60
Germany
NE Bavaria (Fichtelberg/Goldkronach)
31
5.27
0.64
0.68
Germany
NE Germany (Mecklenburg)
10
5.15
0.56
0.61
Germany
SE Germany (Berchtesgaden)
29
5.65
0.70
0.73
Italy
N Italy (Southern Tyrol/Vinschgau)
26
5.71
0.61
0.71
Italy
NE Italy (Mesola)
22
2.76
0.43
0.45
Italy
Sardinia
16
2.69
0.39
0.49
Liechtenstein
29
5.85
0.68
0.72
Norway
W Norway (Sogn og Fjordane)
31
2.85
0.65
0.65
Poland
E Poland (Białowieża)
21
6.32
0.66
0.66
Poland
NE Poland (Warmia-Masuria Province)
23
5.84
0.69
0.70
Poland
SE Poland (N Carpathians)
25
6.10
0.29
0.33
Romania
SE Romania (Carpathians)
17
5.36
0.42
0.46
Scotland
25
5.71
0.67
0.69
Serbia
NE Serbia (Bachka)
19
4.55
0.66
0.69
Spain
SE Spain (Andalucía)
15
4.50
0.63
0.62
Spain
W Spain (Extremadura)
15
5.19
0.63
0.68
Sweden
S Sweden (Skåne)
16
2.52
0.64
0.67
Deer Farm
France, Brittany (Boisgervilly)
30
5.59
0.63
0.69
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Table 2. Effective population sizes (N
e
) as calculated with NeEstimator based on the linkage disequilibrium approach. For each population, N
e
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
e
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
e
)
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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