ZOOLOGICAL SCIENCE 26: 48–53 (2009)¤ 2009 Zoological Society of Japan

Evidence for Cryptic Genetic Discontinuity in a Recently
Expanded Sika Deer Population on the Boso Peninsula,
Central Japan
Masanobu Yoshio1, Masahiko Asada2, Keiji Ochiai2, Koichi Goka3,
Tadashi Miyashita1 and Haruki Tatsuta3*†
1

Laboratory of Biodiversity Science, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo 113-8657, Japan
2
Natural History Museum and Institute, Chiba 260-8682, Japan
3
Research Center for Environmental Risk, National Institutefor Environmental Studies, Tsukuba 305-8506, JapanThe genetic structure of populations is not necessarily reflected in the geographical proximity of
individuals, because environmental gradients such as those of vegetation or climate can function
as cryptic barriers to gene flow. We examined polymorphisms at nine microsatellite loci todetermine and discuss whether a distinctive genetic structure was detectable in a spatiallycontinuous population of the sika deer (Cervus nippon) on the Boso Peninsula of central Japan.
Spatially explicit Bayesian analysis revealed that two genetically distinctive clusters exist in the
Boso population. The spatial boundary of the two clusters approximately conformed to the borderdefined previously from a mitochondrial DNA dataset. By combining information on thegeomorphic features surrounding the boundary and that on the lineage of 1970s population, we
propose a schematic scenario for characterizing the population genetic structure to the present.
The current population consists of genetically different lineages, and spatially discontinuous
clusters have come into contact in the vicinity of a local road running along a steep-walled ravinethat could act as principal barrier to gene flow. Biological factors such as distribution of vegetationand philopatric behavior might also have helped strengthen the cryptic genetic structure of the
Boso population.
Key words: Cervus nippon, population structure, gene flow, microsatellite DNA, lineage, secondary contact
INTRODUCTION
The use of molecular genetics to detect population
structure is a well-established, practical alternative to direct
methods such as mark-and-recapture or biotelemetry, and it
has the added advantage of giving a temporal perspective
to gene flow (Slatkin, 1985; Neigel, 1997). Wright’s F statistics
are used extensively for estimating the levels of gene flow
and genetic dissimilarity among predefined patches or populations. However, there is not always much point in using
such predetermined definitions of groups, because thegenetic structure of a population is not necessarily reflectedin the geographical proximity of individuals. Populations thatare distributed in a spatially continuous way can be genetically structured along environmental gradients, such as
climate or vegetation gradients, that function as cryptic
barriers to gene flow (e.g., Zannèse et al., 2006).
* Corresponding author. Phone: +81-98-895-8799;
Fax : +81-98-895-8734;
E-mail : htatsuta@agr.u-ryukyu.ac.jp†
Present address: Laboratory of Subtropical Zoology, Graduate
School of Agriculture, University of the Ryukyus, Okinawa 9030213, Japan
doi:10.2108/zsj.26.48

Recent advances in the development of landscape
genetic tools have enabled us to detect dissimilar individuals
within spatially continuous populations. These tools weredeveloped under the framework of Bayesian statistics, especially for approximations of the posterior probability ofassigning individuals to their groups of origin; the updatingof plural parameters is estimated in a deterministic order(Pritchard et al., 2000; Falush et al., 2003; Guillot et al.,2005). In addition to being used to find the number of genetically distinctive clusters, these approaches are particularly
useful for mapping the borders of clusters and detecting
migrants; they could therefore be powerful tools for monitoring the illegal translocation of nonnative individuals (Frantz
et al., 2006) and for inferring the level of admixture in agenome (Corander and Marttinen, 2006).
Studies of genetic structure based on mitochondrial Dloop variation have demonstrated that the Boso populationof the sika deer (Cervus nippon) consists of two major haplotypes (Nagata et al., 1999; Yoshio et al., 2008). Marked
heterogeneity has been detected in the distribution of these
haplotypes, and spatial discontinuity in the constitution of
the haplotypes has been detected at the edge of a local
motorway, Route 81; for details see Yoshio et al. (2008).
Because Route 81 on the Boso peninsula passes along a

Genetic Structure in Sika Deer

49

PCR was carried out in reaction volumes of 25 μl containing 2.5 μlof 10×NH4 buffer (Bioline, London, UK), 2.5 mM MgCl2, 0.2 mM
each dNTP, 0.4 μM fluorescently labeled forward primer, 0.4 μM
reverse primer (see Table 1), and 1.25 units of BIOTAQ DNA polymerase (Bioline). PCR cycling conditions consisted of an initialdenaturation at 94°C for 1 min, followed by 30 cycles of denaturation at 94°C for 30 s, annealing at an appropriate temperature (seeTable 1) for 30 s, and extension at 72°C for 30 s. After PCR, 0.1 μlof the product for each locus was mixed with 10 μl of deionized
formamide and run on an ABI 3730 capillary sequencer (Applied
Biosystems, Foster, CA, USA) with 0.2 μl of GeneScan-500 LIZ size
standard (Applied Biosystems). Genotyping was performed with
GeneMapper software, version 3.5 (Applied Biosystems).

steep-walled ravine, the natural landscape here may havefunctioned as a cryptic barrier to gene flow (Yoshio et al.,2008). Because the resolution of mtDNA polymorphism
analysis is generally lower than that of other polymorphic
marker analyses, such as AFLP or microsatellite analyses,
more details of the genetic structure can be detected byusing such other markers.
Our primary purpose was to dissect the genetic discontinuity by using microsatellites, which are codominant nuclear
markers with higher resolution than mtDNA. If potential
configurations or other environmental factors suggested by the
analysis of mitochondrial D-loop variation were to impede
migration and promote the genetic differentiation of underlying
groups, then we expected that a similar pattern of genetic
discontinuity would be detected by using other molecularmarkers. We conducted a spatially explicit Bayesian analysis
using nine microsatellite markers to delineate the heterogeneityof the genetic structure of the Boso population.

Genetic diversity and detection of genetic discontinuity in the
Boso population
To document the extent of genetic diversity, the numbers ofalleles and observed and expected heterozygosities (Ho and He)
were calculated for each locus. Significant deviations from linkage
equilibrium between pairs of loci and from Hardy-Weinberg equilibrium at each locus were tested by the method of Raymond andRousset (1995). The significance of P values was adjusted by aBonferroni method (Rice, 1989). These calculations were carriedout in GENEPOP, version 4.0 (Raymond and Rousset, 1995).
Before the spatial analysis of genetic structure, the number ofgenetically identified clusters (K) within the population was determined with a Bayesian approach implemented in the software
STRUCTURE, version 2.2 (Pritchard et al., 2000). We chose theadmixture model and the option of correlated allele frequencies, as
this configuration is considered best in cases of subtle population
structure (Falush et al., 2003). Similarly, we let the degree of admixture alpha be inferred from the genotyping data. In a pilot study, we
found that a burn-in and Markov chain Monte Carlo (MCMC) lengthof 104 was sufficient. Longer burn-in or MCMC did not change theresults significantly. As different runs could produce different likelihood values, even with much longer chains (e.g., 106; Evanno et al.,2005), five runs were performed to quantify the degree of variationof the likelihood for each K. From the results of the preliminary
study, the range of possible K values tested was set at 1 to 4. We
fitted a linear regression and second-order polynomial to the likelihood scores and compared Akaike’s information criterion (AIC)(Akaike, 1987) scores to choose the best K.
After determination of the best K, the two-dimensional population genetic structure was investigated by using GENELAND software (Guillot et al., 2005); this method uses Bayesian statistics topartition individuals into groups that are as close as possible to
Hardy-Weinberg and linkage equilibria (but see Frantz et al., 2006),
in consideration of the spatial information explicitly associated with

MATERIALS AND METHODSSpecimens and DNA extraction
Whole blood, tail or ear tissue, or hair was obtained from 456
individual sika deer (260 females and 196 males) culled by hunters
at Kimitsu, Otaki, Ichihara, Kamogawa, and Katsu-ura in Chiba
Prefecture, central Japan, between January 2005 and September
2006. Details, including geographic information on capture points(latitude and longitude), date of collection, and sex of the deer, wereindividually recorded. Whole-blood samples were stored on FTA
Mini Cards (Whatman, Middlesex, UK), and other tissues werestored in a freezer until the extraction of total DNA. DNA wasextracted from the FTA Mini Cards in accordance with the manufacturer’s specifications and from the tissues by using a DNeasy
Tissue Kit (Qiagen, Hilden, Germany) in accordance with the
manufacturer’s protocol.
Amplification and genotyping of microsatellite DNA
Nine microsatellite loci (BOVIRBP, OarFCB193, BL42, BM203,
BM888, BMC1009, Cervid14, ETH225, and BM4107) were used forgenotyping (Table 1). These loci, apart from Cervid14, which isderived from the white-tailed deer (Odocoileus virginianus;
DeWoody et al., 1995), are derived from cattle or sheep (Bishop etal., 1994; Crawford et al., 1995) and are mutually unlinked on a
genetic map of red deer (Cervus elaphus), which is closely related
to the sika deer (Slate et al., 2002). All of these loci have been
revealed as polymorphic markers in the Kinkazan Island populationof the sika deer (Okada and Tamate, 2000; Tamate et al., 2000).

Table 1. The nine microsatellite loci used; annealing temperatures for amplification; allele sizes(in base pairs); numbers of alleles observed; observed and expected heterozygosities (Ho andHe, respectively); FIS (coefficient of inbreeding) values, and deviation from Hardy-Weinberg
equilibrium (P).
Locus

Annealing
temperature

Allele size(bp)

No. ofalleles

Ho

He

FIS

Pa

BOVIRBPOarFCB193
BL42
Cervid14
BM203
BMC1009
BM888
ETH225
BM4107

57
57
60
64
60
60
60
60
60

138–142
100–129
250–259
220–230
216–230
278–293
198–206
139–195
153–169

2
13
3
5
7
6
5
10
5

0.06
0.71
0.46
0.38
0.54
0.71
0.57
0.66
0.63

0.07
0.67
0.49
0.37
0.57
0.72
0.61
0.68
0.61

0.04
–0.06
0.05
–0.02
0.06
0
0.06
0.02
–0.03

0.395 ns0.004*
0.256 ns0.186 ns
0.000***
0.011 ns0.001**
0.251 ns0.045 nsaSignificance levels for P value after Bonferroni correction: *, α<0.05; **, α<0.01; ***, α<0.001.

50

M. Yoshio et al.

each individual. Partitions of spatially proximal individuals were
given higher priority than partitions of individuals farther apart(Guillot et al., 2005). We ran five independent Monte Carlo chainswith 106 iterations each (parameter values: maximum rate ofPoisson process, 456; no coordinate noise; maximum number ofnuclei in the Poisson-Voronoi tessellation, 1368; thinning, 10)(Guillot et al., 2005) and looked for consistency in the results. We
adopted both the spatial Dirichlet model (D-model) and the Falush
model (F-model) to determine whether the results varied with thepriors for allele frequencies; for details see Guillot et al. (2005).
Finally, to define the spatial boundary between different clusters, wefirst applied local polynomial regression fitting (LOESS) to dummy
scores (e.g., “0” assigned to individuals of one group and “1” tothose of the other when K=2) and then regarded the median of thehighest and lowest predicted values from the fitting as the mostappropriate boundary between two distinctive clusters. To show thelevels of uncertainty of the boundary, we also estimated thestandard errors of the predicted values. Data fitting was performed
with the R statistical package, version 2.6.2 (R Development Core
Team, 2008). The locations of all the samples belonging to eachgroup were plotted, and artificial structures such as golf courses
and major roads that could act as barriers to gene flow (e.g.,
Forman and Alexander, 1998) were overlaid on the samegeographic information system (GIS) map using ArcGIS (version
9.1; ESRI Inc., Redlands, CA, USA).

RESULTS
Genetic variation in nine microsatellite markers
In 36 individual tests for linkage disequilibrium, four
pairs of loci deviated significantly from equilibrium after
Bonferroni adjustment (BOVIRBP and ETH225, P<0.001;
OarFCB193 and BMC1009, P<0.005; BL42 and ETH225,
P<0.005; Cervid14 and BM4107, P<0.005). Five of the six
loci in the first three pairs are unlinked on the genetic mapof red deer, which is closely related to the sika deer (Slateet al., 2002), and Cervid14 is known to be unlinked with anyof the other loci that we examined here (Tamate et al.,2000). Therefore, all these loci were regarded as independent and were used in subsequent analyses.
The deviation from Hardy-Weinberg equilibrium was not
significant at six loci, whereas a significant heterozygote
excess was observed at OarFCB193 and significant
heterozygote deficits at both BM203 and BM888, after
Bonferroni adjustment (Table 1). These three loci werehighly polymorphic (Table 1). The largest number ofobserved alleles was 13, on the OarFCB193 locus, and thesmallest was two, on the BOVIRBP locus. Ho ranged from0.06 at BOVIRBP to 0.71 at OarFCB193 and BMC1009;
average observed and expected heterozygosities in the
Boso population were 0.52±0.21 (mean±SD) and 0.53±0.20,
respectively. In a previous study, Ho and the number of alleles observed were 0.08 and 2, respectively, at the BOVIRBP
locus and 0.39 and 4 at OarFCB193 (Nagata et al., 1998).
The large discrepancy in Ho at the OarFCB193 locus might
have resulted from a difference in sample sizes.
Spatial genetic structure of the Boso population
Five replicated runs using STRUCTURE showed thatthe mean posterior probability [LnP(D)] was highest when
K=2 (Fig. 1). We found that the second-order polynomial
fitted better (AIC=195 for the linear regression and AIC=169
for the second-order polynomial) and that the posterior probability was ~1 when K=2. Along these lines of evidence, weFig. 1. Plot of estimated logarithms of probability of data [LnP(D)]against number of clusters tested (K).

concluded that K=2 was the best K. We then estimated thegenetic population structure by using GENELAND (Fig. 2).
The boundary estimated between identified clusters was
situated at 140.11°E±0.0221° (median±SE) longitude; nodistinctive latitudinal boundary was detected. The position of
the boundary detected between the two clusters was fairly
consistent, irrespective of the different priors used in the Dmodel and the F-model (data not shown). The distribution
boundary of the two clusters is surrounded by Route 81 andtwo dirt forest roads (the Nishimine and Matsumori routes).
Six of the nine loci contained private alleles, and most ofthese were found in individuals in the eastern area (see Fig.
2). Weir and Cockerham’s θ, an estimator of genetic differentiation between the western and eastern individuals thatwas identified from the discontinuity line described above,
was 0.017, deviated significantly from zero (P<0.001, 5×104
permutations), implying that a distinguishing genetic differentiation existed in the population.
DISCUSSION
All nine microsatellite loci tested were polymorphic inthis isolated sika deer population on the Boso Peninsula,central Japan. Significant heterozygote excess and deficit
were found at a single locus (OarFCB193) and at two loci
(BM203 and BM888), respectively (Table 1). The deficits inheterozygosity might have been due to a bottleneck effectand subsequent inbreeding resulting from a severe reduction in population size in the past (Koganezawa et al., 1976;
Chiba Prefecture and Deer Research Group on Boso,
1994).
There is serious concern worldwide about the ecologicalimpact of an overabundance of deer in relation to direct economic losses in agriculture and forestry and the transmission of several animal and human diseases (Côté et al.,2004). From an ecological viewpoint, deer overabundance
has indirect negative effects on the whole community structure and nutrient cycle (Rooney and Waller, 2003). On the
Boso Peninsula, intensive studies of the impacts of overabundance of the sika deer have revealed a marked

Genetic Structure in Sika Deer

51

Fig. 2. GENELAND assignment of individuals. Individuals assigned to the western cluster are represented by orange triangles and those
assigned to the eastern cluster are represented by yellow circles. Golf courses are shown by green round-edged squares, arrows point at
Route 81 and dirt roads (see text), and roads broader than 5 m are shown by red lines. Genetic discontinuity was estimated from local polynomial fitting (LOESS) indicated by the blue dotted line (the thin blue dotted lines indicate±SE). The vertical axis on the graph indicates dummy
variables (“0” was assigned to individuals with yellow circles and “1” was assigned to individuals with orange triangles).

increase in damage to the richness of plant and arthropod
communities with increasing population density of the deer
(Miyashita et al., 2004; Suzuki et al., 2008). In the 19th

century, sika deer were distributed throughout the Boso
Peninsula (Chiba Prefecture and Deer Research Group on
Boso, 2004). A severe population reduction was observed

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M. Yoshio et al.

around the 1940s, probably as a result of human settlementof the foothills and indiscriminate hunting (Chiba Prefecture
and Deer Research Group on Boso, 2004). In 1973–74, thelineage population became very small and was restricted to
one area in the southern part of the peninsula (Koganezawa
et al., 1976). In addition to the relaxation of hunting pressure
when protection of the deer was instigated, extensive deforestation for conifer planting, which provided new feedingsites, was probably the impetus for the population resurgence that began in the late 1980s. This range expansion of
the deer population has continued up to the present. The
estimated number of deer had increased to around 3000 by
2001, and the distribution area changed from an estimated
40 km2 in 1973–74 to about 440 km2 in 2001 (Chiba
Prefecture and Deer Research Group on Boso, 2004). Our
principal purpose in this study was to examine whether agenetic population structure was detectable in this spatiallycontinuous sika deer population on the Boso Peninsula,and, if so, to compare the boundary positions of clusters as
inferred by both mtDNA and microsatellite analyses.
We found two meaningful clusters, in the western andeastern areas flanking Route 81. In a study of mitochondrial
D-loop variation, spatial genetic discontinuity between two
main haplotypes was detected in the area surrounding
Route 81 (Yoshio et al., 2008); the results of this mtDNA
analysis were in agreement with those of our present study,
although the mtDNA analysis also suggested that a networkof artificial features (two golf courses and two dams) might
have contributed to the restriction of deer dispersal.
Although further details of the genetic structure were notrevealed by using the microsatellites, such incongruence inthe results obtained by using the two different types of markers is not extraordinary, because mtDNA is generally inherited from the dam while microsatellite polymorphism arises
from both the sire and the dam (e.g., Elmer et al., 2007;
Ronka et al., 2008). The finding of an approximate concordance in the spatial patterns of genetic discontinuity, despite
the use of disparate genetic markers, is intriguing in termsof dispersal patterns of males and females.
The sika deer might have been fragmentarily distributedin the southern area in the 1970s, because at this time fieldsigns were found only on the margins of this area
(Koganezawa et al., 1976). One conceivable explanation isthat the present sika deer population on the Boso Peninsula
consists of genetically different lineages, and that spatially
discontinuous clusters have come into contact in the vicinityof Route 81, where a steep-walled ravine exists. Not Route81 itself (because of its low volume of traffic), but two rivers
and three ridges encompassing Route 81, are also potential
candidate geographic barriers to gene flow. Recent studies
of red deer have revealed that some landscape features,such as roads and slopes, can impede migration on a local
spatial scale, supporting the above view (Pérez-Espona etal., 2008).
Because the mountains on the Boso Peninsula are relatively low and thus probably do not hamper migration, otherpotential factors, such as the distribution of suitable foods(Takada et al., 2002), are worth considering as facilitators of
population structuring. In terms of plant communities, thelandscape of the Boso Peninsula is a mixture of fine-scaleelements, including broad-leaved forests, Japanese cedar

forests, and agricultural fields (Miyashita et al., 2007). In fact,
the forest edges distributed sporadically in the study area are
generally known to act as large sources of food for the deer(e.g., Sinclair, 1997); furthermore, a wide area of agricultural
fields in the southern part of the distribution, to the west of
Route 81, supplies the deer with high-quality food (Takada etal., 2002; Miyashita et al., 2007). These spatially biased
distributions of food tend to affect migration distance (Kareiva
and Odell, 1987; Dwyer and Morris, 2006); this variation in
migration distance would, in turn, promote the formation of acryptic genetic structure in the population.
It is also worth mentioning the effect of social behavior
in the sika deer. In general, sika deer are potentially capableof dispersing great distances. According to knowledge of thewhite-tailed deer and roe deer, for example, dominantfemales occupy a home range that is at the center of thesocial group, with younger individuals occupying homeranges that overlap and extend the periphery (Porter et al.,1991). Populations expand as an array of family units composed of females and showing strong philopatry (“the petalsof a rose” metaphor; see Porter et al., 1991), while males
tend to disperse further than females (Purdue et al., 2000;
Wang and Schreiber, 2001). It remains an open question
whether such social behavior also facilitates the formation ofa global spatial genetic structure; such a characteristic ofdispersal could be a factor in the acceleration of genetic
structuring on a restricted spatial scale (e.g., Mathews andPorter, 1993; Purdue et al., 2000). In a future study we willneed to examine the extent to which the factors mentioned
above have contributed to the organization of spatial genetic
structure in the Boso population.
ACKNOWLEDGMENTS
We are grateful to Yuji Nagahata (Kamogawa City Hall), Ikuo
Yamanaka (University of Tokyo), Takashi Ishihara and Ken
Kumagawa (Deer Research Group on Boso), Makoto Kurokawa(Katsu-ura City Hall), and Yukihiro Takahashi (Kamogawa City) for
their great help in collecting specimens and to Keiko Ikeda (National
Institute for Environmental Studies) for technical assistance. Thanks
are also due to all members of the Boso Sika Project and to K. Ryo
Takahasi (National Institute of Genetics) and Hirohisa Kishino(University of Tokyo) for discussions. This study was supported by
an Environmental Technology Development Grant (No. 60029) from
the Ministry of the Environment of Japan.

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