ª 2004 The American Genetic Association
Journal of Heredity 2004:95(2):136–143
DOI: 10.1093/jhered/esh019
Genetic Roots of the Red Deer
(Cervus elaphus) Population
in Eastern Switzerland
R. KUEHN, H. HALLER, W. SCHROEDER,
AND
O. ROTTMANN
From the Wildlife Biology and Wildlife Management Unit, Department for Ecosystem and Landscape, Technical University
Munich-Weihenstephan, D-85354 Freising, Germany (Kuehn and Schroeder), Swiss National Park, CH-7530 Zernez,
Switzerland (Haller), and Department for Animal Science, Technical University Munich-Weihenstephan, D-85354 Freising,
Germany (Rottmann). We thank all wildlife, national park, and forest officers involved for their helpful support and all the
hunters for making the samples available. We would like to give special thanks to Dr. R. Gross for useful discussions in
population genetic computations and Dr. Foerster for laboratory assistance. We also thank U. Puszkarz for criticism and
comments on the manuscript. This work was supported by the Swiss National Park.
Address correspondence to R. Kuehn at the address above, or e-mail: [email protected].
Abstract
Overhunting of red deer (Cervus elaphus) in eastern Switzerland led to its extinction in the second half of the 17th century.
Natural recolonization must have taken place later, because red deer were seen again in the canton of the Grisons (eastern
Switzerland) in the 1870s. According to historical data, three different populations could have served as the source
population. To determine the genetic origin of the eastern Swiss red deer population, we collected samples from five
different subpopulations in the canton of the Grisons as well as from four adjacent populations in Germany, Liechtenstein,
Austria, and Italy. We analyzed the samples by genotyping 18 microsatellite loci. FST values, assignment tests,
correspondence analysis, and fuzzy clustering clearly pointed to Liechtenstein as the most probable source population for
the red deer in eastern Switzerland. In addition, our analyses revealed high gene diversity in all examined populations. Gene
flow and the high genetic admixture are discussed.
Colonization of a new habitat or recolonization after
extinction are fundamental processes in metapopulation
biology (Hanski 1998). The genetic structure of a metapopulation is shaped by the geographical scale and pattern of
dispersal leading to colonization (Harrison and Hastings
1996; Wade and McCauley 1988). The emigration of
colonists from an established population can be induced
by resource competition and niche availability (Baker 1978;
Johnson 1969). Dispersal distance, the number of colonists,
and the frequency of emigration also determine the
frequency of recolonization and therefore the spatial genetic
structure of the metapopulation. F statistics can be used to
identify newly colonized sites and to indicate the mode of
colonization (Slatkin 1977; Wade and McCauley 1988). In
many cases, genetic differentiation is minimal, preventing Fstatistic approaches from identifying the source of colonists
unambiguously (Gaggiotti et al. 2002). In addition, Whitlock
and McCauley (1999) mentioned that estimates of dispersal
from FST should be undertaken with great caution, and only
if the biological question behind the attempt at estimating
136
dispersal depends on knowing migration rates within very
large bounds.
In this study, we want to analyze the mode of colonization of red deer (Cervus elaphus) in eastern Switzerland. In
view of the difficulties of the F-statistic approach, we additionally used assignment tests, correspondence analysis, and
fuzzy clustering to identify the possible source population.
Originally red deer were present in all parts of Switzerland. According to historical documents, which were collated
for a comprehensive study of the history, population
development, and habitats of red deer in and around the
Swiss National Park (Haller 2002), overhunting led to their
virtual extinction in the canton of the Grisons (eastern
Switzerland) by the second half of the 17th century. Only
after an increase in red deer in the neighboring countries of
Liechtenstein, Austria, and Italy (South Tyrol), coupled with
enforcement of hunting regulations, did a red deer
population reestablish itself in the canton of the Grisons in
the second half of the 19th century. Toward the end of the
19th century, red deer were seen in Praettigau, an area in the
Kuehn et al. Red Deer in Switzerland
a particularly distinct spreading dynamism. (2) Individuals
from the population in South Tyrol (Vinschgau) dispersed to
Muestair. This colonization was not very successful because
of poaching along the dispersal route between these two
regions. (3) The red deer population of the Ammergebirge
spread from southern Germany and repopulated adjacent
Austrian valleys (e.g., Paznaun) and also the Engadin Valley
in Switzerland (Unterengadin, Mittelengadin, and Oberengadin).
In this study, genetic methods were used to reveal the
relationships between the populations and to reconstruct the
dispersal routes in order to define the origin of the red deer
population of eastern Switzerland. Historical data were used
to formulate the following question: Did the source of the
eastern Swiss red deer originate from Liechtenstein (i.e., gene
flow from the northwest), from South Tyrol in Italy (i.e.,
gene flow from the southeast), or was the possible source
population located in the Ammergebirge along the GermanAustrian border? Despite the fact that the Ammergebirge
population inhabits an area distant from the Grisons, we
were interested in exploring the possibility that long distance
gene flow via the Alps could have happened.
To determine the genetic roots of eastern Swiss red deer,
we analyzed five populations from eastern Switzerland and
four from adjacent countries (Austria, Germany, Italy, and
Liechtenstein). Eighteen microsatellites were genotyped in
order to investigate which population of the adjacent
countries was the possible source. Furthermore, we examined
the influence of founding on gene diversity.
Material and Methods
Animals
A total of 207 red deer of different sex and age, originating
from eight populations, were shot during the 1997 and 1998
hunting seasons, and heart tissue (20 g) was collected from
each animal and stored at 208C. In addition, one popFigure 1. Locations (circle) of five Swiss [Praetigau (PR),
ulation from a previous investigation (Kuehn et al. 2003) was
Oberengadin (OE), Mittelengadin (ME), Unterengadin (UE),
used in this study. Samples were taken from five different
Muestair (MU)] and four adjacent [Paznaun (PZ, Austria),
Swiss areas within the canton of Grisons (Figure 1):
Ammergebirge (AG, Germany), Vinschgau (VI, Italy),
Mittelengadin (ME), Muestair (MU), Oberengadin (OE),
Liechtenstein (FL)] red deer populations. The arrows indicate
Praettigau (PR), and Unterengadin (UE). Four populations
the possible dispersal routes of red deer after 1860 based on
Haller’s (2002) analysis of historical data. The years stand for the from adjacent areas were also examined: Ammergebirge
(Germany; previous study, AG; Kuehn et al. 2003),
first evidence of red deer in the particular regions.
Liechtenstein (FL), Paznaun (Austria, PZ), and Vinschgau
(Italy, VI). Historical and demographic data used in this
north of the canton bordering Austria. By 1916 animals were study were summarized by Haller (2002) and originated prialso sighted in the Val Muestair, in the southeastern part of marily from the Swiss National Park and the hunting agency
the canton. In the first half of the 20th century, red deer of the Grisons.
could be found throughout the canton, and by the second
half, populations had reached the carrying capacity of their
habitats. At the same time, large areas in the central Alps DNA Isolation and Microsatellite Analysis
were repopulated.
The DNA was isolated according to Hogan et al. (1986).
According to historical data, three different dispersal Eighteen microsatellite loci, mostly derived from cattle or
processes took place toward the end of the 19th century sheep, were used for genotyping all populations: BM1818,
(Figure 1): (1) Red deer from the population in Liechtenstein Cer14, CSPS115, CSRM60, CSSM14, CSSM16, CSSM19,
dispersed to the canton of the Grisons. These exhibited CSSM22, CSSM66, ETH3, ETH225, Haut14, Haut24,
137
Journal of Heredity 2004:95(2)
ILSTS06, INRA35, IOBT918, IOBT965, and MM12 (Kuehn
et al. 2003). Generally the loci are physically mapped on
different chromosomes of Cervus (Slate et al. 2002). To reduce the time and cost of the genotyping analyses, multiplex
polymerase chain reactions (PCRs) and multiplex loading
were used according to Kuehn et al. (2003). The genotypes
were scored on an ABI PRISM 377 DNA Sequencer
(Applied Biosystems/Perkin Elmer). The proportion of
missing genotypes was less than 5% per locus.
Descriptive Statistics and Population Differentiation
We documented the extent of intra- and interpopulation
genetic diversity based on the allelic composition of the nine
sample areas. We used the program GENETIX v. 4.02
(Belkhir et al. 1996–2001) to generate allele frequency tables,
to calculate the average number of alleles per locus, and to
calculate expected and observed heterozygosity (He, Ho).
GENEPOP v. 3.3 (Raymond and Rousset 1995b) was used
to test the genotypic distribution for conformity to HardyWeinberg expectations, to test the loci for genotypic
disequilibrium, and to assess the significance of allelic differentiation between pairs of populations. This program was
also used to calculate the pairwise population differentiation,
FST. All probability tests were performed applying the
Markov chain algorithm (Guo and Thomson 1992; Raymond
and Rousset 1995a). Sequential Bonferroni adjustments (Rice
1989) were used to correct for the effect of multiple tests. In
order to assess the possible impact of demographic changes
on genetic diversity due to colonization, we used the
heterozygosity excess test, estimated and tested by the
Wilcoxon signed rank test in the BOTTLENECK (Cornuet
and Luikart 1996) computer program, assuming one step and
multistep mutation models.
The R-PACKAGE software (Casgrain 2001) was used to
calculate the geographical distances (in kilometers) separating
sampling locations based on the approximate latitudes and
longitudes for each region.
To estimate the likelihood of an individual’s multilocus
genotype occurring in a given population, the exclusionsimulation test accompanied by the Bayesian approach of
assignment test (GENECLASS v. 1.0.02; Cornuet et al.
1999) was used. This method considers each population
separately and does not assume a priori that the ‘‘true’’
population of origin has been analyzed. The mean probability of belonging was calculated based on the probability
of the individual assignment, which makes the percentage of
admixture detectable and visualizes it in circular charts.
The extent of population differentiation, or more
precisely the admixture of the red deer population in eastern
Switzerland, based on microsatellite allelic frequencies, was
further assessed by performing a correspondence analysis
(CA) (Benzérci 1973) using STATISTICA 6.0. The application of this analysis to genetic data was developed by
Greenacre and Degos (1977) and She et al. (1987). The
genetic data were transformed into a contingency table
(samples 3 alleles), in which each sample was described by
138
the allelic frequencies. The Euclidian distance, centered on
the marginal distribution of the contingency table, was used
to measure the pairwise population relatedness in the kdimensional space (k ¼ number of alleles). The factors were
tabulated according to their eigenvalues and scattered in
three dimensions. Population similarity is based on the
lowest variation between these three dimensions.
In order to further investigate whether the revealed levels
of similarity are indeed the result of natural recolonization,
fuzzy grouping into different classes of populations was
calculated with the algorithm based on Ohmayer (1982),
Ohmayer and Seiler (1985), and Medjugorac (1995). Using
the Cavalli-Sforza chord distance, this fuzzy-grouping
algorithm, in conjunction with the great deluge algorithm
(Dueck 1989), estimates the probability of populations
belonging to different classes. Fuzzy cluster analysis methods
partition a dataset into classes in which similar data are
assigned to the same class and dissimilar data to different
classes by minimizing the within-class sum of square errors.
Membership degrees ranging from zero to one are used
instead of assignments. A total of 150,000 iterations were
performed with a fuzzy exponent of two for estimating three
to five classes. The optimum number of classes was
established on the basis of minimizing the fuzziness
performance index (FPI) and the modified partition entropy
(MPE). The FPI estimates the degree of fuzziness and the
MPE estimates the degree of disorganization generated by
a specified number of classes (Roubens 1982). In addition,
we tested for the presence of population structure by assigning individuals to populations using the STRUCTURE
program (Pritchard et al. 2000). This program uses a model
Bayesian approach with a Markov chain Monte Carlo algorithm and provides a probability value that can be interpreted
directly as a probability of origin of each individual in populations or classes. The method is described by Pritchard et al.
(2000). A total of 50,000 iterations with 5000 burn-ins were
performed, under the assumption that the regional provenance of the individuals is not known and that there is gene
flow between populations. The individuals were assigned to
four groups and the probability of belonging was compared
with the fuzzy grouping.
Results
Linkage and Hardy-Weinberg Equilibrium
The test for genotypic disequilibrium for each pair of the 18
microsatellite loci across all populations gave two significant
values (P , .05) for 153 comparisons; seven significant values
are expected by chance at the 5% level. After Bonferroni
correction for multiple tests, one combination was significant
(P , .00033) at the experimental level. When each population
was tested separately, a linkage equilibrium between all pairs
of loci was observed.
The probability test by the Markov chain method for the
Hardy-Weinberg equilibrium (Haldane 1954) showed two to
Kuehn et al. Red Deer in Switzerland
Table 1. Number of sampled animals (n), historical background, and geographic coordinates as demographic parameters, and
mean alleles per locus (na), mean expected heterozygosity (He), and mean observed heterozygosity (Ho) in studied red deer
populations over all loci
Population
n
Historical background
Longitude
Latitude
na
He
Ho
Ammergebirge (AG)
Fuerst. Liechtenstein (FL)
Mittelengadin (ME)
Muestair (MU)
Oberengadin (OE)
Praettigau (PR)
Paznaun (PZ)
Unterengadin (UE)
Vinschgau (VI)
43
28
29
27
26
28
13
29
27
Possible founder
Possible founder
Founded
Founded
Founded
Founded
Founded
Founded
Possible founder
11.07
9.52
10.10
10.42
9.83
9.70
10.23
10.30
10.77
47.60
47.13
46.70
46.60
46.50
46.93
47.00
46.80
46.36
6.39
7.00
6.61
7.28
6.61
6.72
5.78
6.50
6.89
0.638
0.689
0.675
0.678
0.679
0.676
0.682
0.651
0.668
0.563
0.581
0.567
0.551
0.558
0.599
0.589
0.548
0.506
five significant deviations from the Hardy-Weinberg equilibrium in each population after Bonferroni correction. The
populations of FL, ME, and VI showed significant deviations
at five loci.
Allele Frequency Distribution and Heterozygosity
The highest number of alleles (19) was detected at the
CSSM19 locus and the lowest (2) at the ETH3 locus. The
expected heterozygosity (He) per locus over all populations
was between 0.887 (ILSTS06) and 0.321 (CSSM14). On
average 9.8 6 5.2 polymorphic alleles per locus were found.
Intrapopulation indices are given in Table 1. The mean
number of polymorphic alleles per locus and population (na)
ranged from 6.4 (AG) to 7.3 (MU). The He of the 18 microsatellites per population ranged from 0.638 to 0.689 and the
observed heterozygosity (Ho) ranged from 0.506 to 0.599.
No significant differences with respect to the heterozygosity
values between the different populations were detectable
based on t tests.
To assess whether the allelic distributions within
populations had been shifted by changes in population sizes
due to colonization, we tested for heterozygosity excess
using the BOTTLENECK program (Cornuet and Luikart
1996). Based on the infinite allele model (IAM) and the
stepwise mutation model (SMM), no population exhibited
significant heterozygosity excess (Wilcoxon test, P , .05).
Genetic Differentiation and Demography
Based on the 18 examined microsatellite loci, pairwise
estimates of FST values showed little to moderate (Wright
1978) differentiation, with values ranging from 0.0015 to
0.0999. Within the Engadin valley (ME, OE, and UE), the
FST values were very low and no significant P values for
population allelic differentiation were detectable after
Bonferroni correction (P , .0014; Table 2). Between the
populations FL and ME, MU and UE, and OE and PR, the
FST values were also very low and the differentiation was not
significant after Bonferroni correction. The other populations showed moderate and significant pairwise FST values,
indicating that the possible source populations VI and AG
are genetically distinct from the remaining populations.
At the microsatellite level, the assignment of the
individuals’ multilocus genotype based on the exclusionsimulation tests (GENECLASS 1.0.02; Cornuet et al. 1999)
revealed that the possible source populations (FL, VI, and
AG) had high probability values for the correct assignments
of individuals to their origin (53%, 46%, and 71%,
respectively). The individuals of the population PZ were
also assigned correctly at 71% (Figure 2). In the case of the
populations ME, OE, and UE within the Engadin valley, the
individuals’ assignments to their populations of origin were
much lower (26–32%). In these as well as in the populations
MU and PR, a high admixture of the multilocus genotypes,
and therefore a high gene flow between the populations, was
Table 2. Matrix of FST values according to Weir and Cockerham (1984) (below diagonal) and the approximate geographical
distances (in kilometers) (above diagonal) between populations
AG
Ammergebirge (AG)
Fuerst. Liechtenstein (FL)
Mittelengadin (ME)
Muestair (MU)
Oberengadin (OE)
Praettigau (PR)
Paznaun (PZ)
Unterengadin (UE)
Vinschgau (VI)
0.0644***
0.0605***
0.0896***
0.0361***
0.0369***
0.0892***
0.0822***
0.0999***
FL
ME
MU
OE
PR
PZ
UE
VI
128
124
65
122
91
27
154
74
30
46
127
26
40
66
49
92
56
35
47
63
41
106
70
19
24
49
48
23
110
110
51
27
73
88
58
40
0.0091**
0.0140***
0.0197***
0.0269***
0.0221***
0.0127***
0.0416***
0.0097***
0.0035
0.0132***
0.0253***
0.0058**
0.0280***
0.0305***
0.0498***
0.0317***
0.0014*
0.0194***
0.0015**
0.0434***
0.0299***
0.0458***
0.0647***
0.0455***
0.0645***
0.0240***
0.0439***
0.0361***
Significant P value for population allelic differentiation: * P , .05, ** P , .001, *** P , .0001.
139
Journal of Heredity 2004:95(2)
Figure 2. Circular charts of adjusted mean probability of belonging of the assigned individuals per population based on the
exclusion-simulation test (GENECLASS; Cornuet et al. 1999). The segments show the probability of belonging of the
assigned individuals to the population noted below the charts (only percentages greater than 10% are displayed).
detectable. Remarkably, however, only 4% of the ME
population, 5% of the OE population, and 7% of the UE
population exhibited the possible founder genotypes of VI.
One percent of ME, 2% of OE, and 2% of UE had
genotypes of the population AG. Eight percent of the MU
population exhibited genotypes of VI and 0% of the PZ
population had genotypes of AG. However, 13–15% of the
populations in the Engadin Valley (ME, OE, and UE)
exhibited genotypes of the possible source population, FL.
Even 13% of the MU population—the population with the
highest geographical distance to FL (Table 2)—had
genotypes of the FL population. Eleven percent of the VI
population exhibited genotypes of the ME population and
13% of the VI population had genotypes of MU. In contrast,
only a small percentage of the ME and MU populations
exhibited genotypes of VI (Figure 2).
The correspondence analysis (CA) was performed using
186 allelic frequencies to summarize population relationships
(Figure 3). The first three dimensions of the CA account for
a total of 60% of the variance. Consistent with the other
analyses used in this study, four well-differentiated ‘‘groups’’
were observed: AG, PZ, VI, and a fourth one consisting of
the possible source population FL and the populations ME,
MU, OE, PR, and UE (Figure 3). In the third dimension, the
140
MU population does not match the remaining group
members (Figure 3).
The fuzzy grouping based on the Cavalli-Sforza chord
distance between the populations and the population
structuring derived by assigning individuals to groups using
the STRUCTURE program (Pritchard et al. 2000) showed
generally the same results (Table 3). Again, four different
groups were observed. With the fuzzy grouping, a clear and
significant (P , 0.05) clustering was obtained. In the case of
the clustering based on the individual classification, however,
the results were somewhat ambiguous. Yet regarding the
significance level, the same classification is evident.
Discussion
Genetic Variability and Differentiation
By applying 18 microsatellites we were able to detect rather
high heterozygosity values (He) ranging from 0.64 to 0.69, as
well as great variation between individuals within red deer
populations. The amount of heterozygosity examined in this
study is consistent with previous studies on ungulate species
(Barker et al. 1997; Forbes et al. 1995; Kuehn et al. 2003).
However, comparison of the genetic variability of Swiss red
deer with that of several endangered, bottlenecked, founded,
Kuehn et al. Red Deer in Switzerland
with a large amount of immigration from one or more source
populations is very likely to have caused the current genetic
structure of the red deer population in eastern Switzerland.
To further examine the foundation of the red deer
population in eastern Switzerland, we calculated pairwise
population differentiation. This analysis revealed that the
possible founders (AG and VI) have higher FST values
compared to the founded ones than the other possible
source population, FL. Particularly noteworthy is that there
is a lower differentiation between FL and MU than between
VI and MU, although the geographical distance between FL
and MU is about 3.5 times higher than that between VI and
MU. The genetic differentiation between the Engadin valley
populations (ME, OE, and UE) is negligible. The FST values
suggest a high gene flow from FL to the Engadin valley and
to MU. Regarding FST values, a genetic connectivity between
MU and VI is likely to exist. These results are consistent with
theories deduced from historical and demographic data
mentioned before.
Population Assignment and Relationships
Figure 3. Correspondence analysis based on frequencies
of 18 microsatellite loci showing the first three components.
The cube shows the populations with the lowest variation
between populations in all three dimensions.
or isolated populations (Goodman et al. 2001; Houlden et al.
1996; Kuehn et al. 2003; Menotti-Raymond and O’Brien
1995) with respect to the examined levels of He did not allow
any conclusions on founder or bottleneck effects. Similarly
the founded populations showed no differences in variability
in the number of polymorphic alleles when compared to the
possible founders. In addition, no allele shifts by changes in
population size were detectable using the test for heterozygosity excess (Cornuet and Luikart 1996). No founder or
bottleneck effects were detectable, therefore colonization
Further investigations to detect possible source populations
or a genetic admixture of red deer in eastern Switzerland are
based on population assignment tests. The exclusionsimulation tests revealed that 13–15% of the populations
of the Engadin valley (ME, OE, and UE) and the PR and
MU populations exhibit genotypes of the FL population.
The probability values of genotypes for the other
possible founders, VI and AG, are low in all Swiss
populations. Gene flow originating from FL through PR to
the Engadin populations and MU is supported by the
assignment test. An interesting result is that 13% and 11% of
the VI genotype belongs to MU and ME, respectively. This
supports the historical and demographic data (Haller 2002),
according to which an increasing population in MU led to the
dispersal of animals to VI. However, due to the small
geographic distance, a former gene flow originating from VI
to MU is likely to have taken place, but this was not clearly
Table 3. Probability of belonging of analyzed red deer populations to different classes based on (1) the fuzzy grouping algorithm
using the chord distance (Ohmayer 1982, 1985; Medjugorac 1995) and (2) the Markov chain Monte Carlo algorithm using the
individual classification (STRUCTURE; Pritchard et al. 2000)
Fuzzy grouping
Structure
Population
OA 1
Class I
Class II
Class III
Class IV
OA 2
Class I
Class II
Class III
Class IV
Ammergebirge (AG)
Fuerst. Liechtenstein (FL)
Mittelengadin (ME)
Muestair (MU)
Oberengadin (OE)
Praettigau (PR)
Paznaun (PZ)
Unterengadin (UE)
Vinschgau (VI)
2*
1
1
1
1
1
3*
1
4*
0.000
0.389
0.487
0.320
0.396
0.375
0.000
0.407
0.000
1.000
0.200
0.160
0.184
0.208
0.234
0.002
0.173
0.024
0.000
0.204
0.166
0.212
0.191
0.193
0.998
0.201
0.027
0.000
0.207
0.188
0.284
0.205
0.198
0.000
0.220
0.949
2*
1*
1*
1
1*
1*
1
1*
1
0.003
0.954
0.960
0.862
0.989
0.973
0.790
0.991
0.650
0.993
0.003
0.002
0.002
0.003
0.002
0.004
0.002
0.003
0.002
0.007
0.014
0.011
0.003
0.014
0.008
0.002
0.003
0.002
0.036
0.024
0.125
0.005
0.011
0.199
0.005
0.344
Columns OA 1 and OA 2 show the object allocation into the most likely class using the different methods.
* Significant P , 0.05.
141
Journal of Heredity 2004:95(2)
deducible by this analysis. Eight percent of MU exhibits
genotypes of VI. The classification with the fuzzy grouping
as well as the Structure analysis identify the FL population as
the source population of the red deer in PR, MU, and the
Engadin valley. The AG and VI populations group into
significantly different classes and can therefore be excluded
as possible founders of the red deer population in eastern
Switzerland. The CA analysis clearly supports the theories
mentioned earlier. According to all investigations, PZ
emerges as an isolated population with no connectivity to
the Engadin valley and AG, but with minor gene flow from
FL (Figures 2 and 3; third dimension).
Management Implications
Animal populations with a long history of anthropogenically
caused change most often show a complex spatial structure
coupled with a mixed genetic history. Demographic and
short-term changes in population growth give rise to largescale changes in genetic variability. Understanding the
historical sequence of population movement across landscapes—long ago fragmented by human development
activities—is difficult and can be helped by appropriate
studies involving conservation genetics. These studies should
envisage endangered species as well as obvious problems
caused by overhunting and habitat fragmentation to assist
wildlife managers in making their decisions for sustainable
conservation.
In the present study, the genetic roots of eastern Swiss
red deer were clearly demonstrated. The results confirm
historical and demographic data by revealing that the red
deer population in eastern Switzerland was founded by the
population in Liechtenstein (FL). Long distance gene flow
and high genetic admixture, coupled with high genetic
variability, were detectable, showing that red deer are capable
of long-distance recolonization, provided that a migration
corridor and a suitable habitat exist. This insight offers
wildlife managers possible justification for unpopular
measures such as the necessary killing of red deer infected
with chronic wasting disease (see Herring 2002), since
a subsequent natural recolonization of the affected area is
likely to occur.
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Received March 23, 2003
Accepted November 11, 2003
Corresponding Editor: Oliver A. Ryder
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