Annals of Botany 114: 1687–1700, 2014
doi:10.1093/aob/mcu197, available online at www.aob.oxfordjournals.org
Evidence for cryptic northern refugia in the last glacial period
in Cryptomeria japonica
Megumi K. Kimura1,†, Kentaro Uchiyama1, Katsuhiro Nakao2, Yoshinari Moriguchi3, Lerma San Jose-Maldia1
and Yoshihiko Tsumura1,*
1
Department of Forest Genetics, Forestry and Forest Products Research Institute, Tsukuba, Ibaraki, 305-8687, Japan,
Department of Forest Vegetation, Forestry and Forest Products Research Institute, Tsukuba, Ibaraki, 305-8687, Japan and
3
Graduate School of Science and Technology, Niigata University, 8050, Igarashi 2-Nocho, Nishi-ku Niigata 950-2181, Japan
* For correspondence. E-mail [email protected]
†
Present address: Forest Tree Breeding Center, Forestry and Forest Products Research Institute, 3809-1 Ishi, Juo, Hitachi, Ibaraki
319-1301, Japan
2
Received: 23 April 2014 Returned for revision: 3 July 2014 Accepted: 26 August 2014 Published electronically: 29 October 2014
† Background and Aims Distribution shifts and natural selection during past climatic changes are important factors
in determining the genetic structure of forest species. In particular, climatic fluctuations during the Quaternary appear
to have caused changes in the distribution ranges of plants, and thus strongly affected their genetic structure. This
study was undertaken to identify the responses of the conifer Cryptomeria japonica, endemic to the Japanese
Archipelago, to past climatic changes using a combination of phylogeography and species distribution modelling
(SDM) methods. Specifically, this study focused on the locations of refugia during the last glacial maximum (LGM).
† Methods Genetic diversity and structure were examined using 20 microsatellite markers in 37 populations of
C. japonica. The locations of glacial refugia were assessed using STRUCTURE analysis, and potential habitats
under current and past climate conditions were predicted using SDM. The process of genetic divergence was also
examined using the approximate Bayesian computation procedure (ABC) in DIY ABC to test the divergence time
between the gene pools detected by the STRUCTURE analysis.
† Key Results STRUCTURE analysis identified four gene pools: northern Tohoku district; from Chubu to Chugoku
district; from Tohoku to Shikoku district on the Pacific Ocean side of the Archipelago; and Yakushima Island. DIY
ABC analysis indicated that the four gene pools diverged at the same time before the LGM. SDM also indicated potential northern cryptic refugia.
† Conclusions The combined evidence from microsatellites and SDM clearly indicates that climatic changes have
shaped the genetic structure of C. japonica. The gene pool detected in northern Tohoku district is likely to have
been established by cryptic northern refugia on the coast of the Japan Sea to the west of the Archipelago. The
gene pool in Yakushima Island can probably be explained simply by long-term isolation from the other gene
pools since the LGM. These results are supported by those of SDM and the predicted divergence time determined
using ABC analysis.
Key words: Approximate Bayesian computation, cryptic refugia, Cryptomeria japonica, genetic divergence time
estimation, last glacial maximum, microrefugia, species distribution modelling, sugi, Taxodiaceae.
IN T RO DU C T IO N
Species distribution shifts and local adaptation through global
climatic changes are important factors determining the genetic
structure of temperate forest species (Savolainen et al., 2004).
Drastic environmental changes have occurred since the last
glacial maximum (LGM) around 21 000– 18 000 years ago,
and most areas of temperate forest vegetation are believed to
have been established since the onset of favourable climates
through expansion from refugial populations in which they survived the cold and harsh LGM conditions (Tsukada, 1982a;
Petit et al., 2003). The contraction and extension of populations
may have caused reductions in genetic diversity in the colonizing
populations and increases in genetic differentiation among them
(Petit et al., 2003), modulated to varying degrees by inputs from
secondary contacts (Widmer and Lexer, 2001). Therefore, information on locations of refugia is particularly relevant, given the
need to predict the effects of the present period of global warming
on species distributions. In many studies using fossil pollen data,
a few distinct refugia were shown to be located in regions that
escaped the major climatic changes of the glaciations, usually
at lower latitudes (Huntley and Birks, 1983; MacDonald, 1993;
McLachlan and Clark, 2004). However, the information on
fossil pollen is limited to certain areas because fossil pollen is
detected mainly in lake sediment or peat deposits. In addition,
most perennial plants possess some capacity for vegetative
propagation, and clonal reproduction is favoured under certain
ecological conditions (Eriksson, 1989, 1996; Pornon et al.,
1997, 2000; Dorken and Eckert, 2001). Especially under
severe conditions, as in peripheral populations of some plant
species, vegetative propagation would be the only means of survival, with little or no flowering (Moriguchi et al., 2001; Eckert,
2002). In this case, the amount of pollen production is low and
fossil pollen may not be detected.
# The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved.
For Permissions, please email: [email protected]
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Kimura et al. — Cryptic refugia in Cryptomeria japonica
Recently, phylogeographic studies have provided support for
the existence of previously unknown refugia at higher latitudes
(Parducci et al., 2012). The locations of these refugia often conflict with interpretations of pollen evidence and predictions of
biogeographical histories of species based on current species
distribution patterns. Such refugia are sometimes referred to
as ‘cryptic’ (Stewart and Lister, 2001; Provan and Bennett,
2008; Opgenoorth et al., 2010). Species distribution modelling
(SDM; Elith and Leathwick, 2009) is a powerful approach to
predict past distribution ranges of plant species. In SDM with
collated modern distribution data and environmental variables,
historical species distributions can be predicted using simulated
past climate data (Maiorano et al., 2012). Although several
advantages of using SDM have been mentioned, they should
be used with caution as they also introduce uncertainty due to
biotic interactions, evolutionary change and species dispersal
(e.g. Pearson and Dawson, 2003). Our understanding of past processes that have shaped population genetic structures will be
improved by combining SDM and phylogeographic data.
However, few studies have used this combined approach to
predict past (e.g. LGM) distributions of climatically suitable
areas or to reconstruct locations of glacial refugia and the dynamics of post-glacial migrations, especially for East Asian temperate forest trees (Sakaguchi et al., 2010, 2011; Qi et al., 2012;
Worth et al., 2013).
The Japanese Archipelago extends in a narrow arc from northeast to south-west, with various mountain ranges that mostly run
parallel to the arc and probably act as physical barriers to the migration or gene flow of many plant species (Tsukada, 1980).
Temperate plant species have generally migrated along the
Pacific Ocean side, the Japan Sea side or the mountain slopes
of the archipelago with past climate change, expanding from
the coasts or to lower altitude refugia to northward or to higher
altitudes during interglacial periods (e.g. Cryptomeria japonica,
Tsukada, 1980; Fagus, Tsukada, 1982b). Therefore, knowledge
of the population history of temperate plant species, including
shifts in the distribution, fragmentation and isolation, especially
after the LGM, facilitates attempts to interpret their current
genetic diversity and structure.
Cryptomeria japonica is an endemic monoecious conifer of
Japan, with an outcrossing breeding system involving wind pollination and the production of wind-dispersed seeds. This species
is the most economically important forestry tree in Japan. Natural
forests of the species are distributed under variable environmental conditions from Aomori Prefecture (40842′ N) to Yakushima
Island (30815′ N) in the Japanese Archipelago (Tsumura et al.,
2012). Fossil pollen of Cryptomeria was abundant during the
interglacial warm intervals in the late Quaternary, reaching a
maximum approx. 350 000–380 000 years ago (Tsukada, 1982a).
This was one of the most representative species throughout the
Quaternary. Cryptomeria japonica was distributed widely over the
Japanese Archipelago until 100 000 years ago, and its distribution
gradually decreased from 70 000 years ago to 30 000 years ago
(Takahara, 1998). Fossil pollen data of this species have been accumulated from many sites, allowing identification of its potential
refugia during the LGM and the most likely routes of post-glacial recolonization (Tsukada, 1982a; Miyake et al., 2011). These studies
indicated that C. japonica had multiple refugia in Japan during the
last glacial period (approx. 18 000 years ago), including the Izu
Peninsula, the margins of Wakasa Bay, Oki Island, Muroto
Peninsula, Yakushima Island and probably around Kii Peninsula
and Toyama Bay (Fig. 1). No clear evidence of northern refugia
exists on the main island of Honshu. If northern populations were
established by expansion from these refugia, C. japonica would
have had to move approx. 130 m per year (Tsukada, 1980). As
this is too fast for this species due to its long regeneration time,
cryptic refugia may have occurred in the northern part of Honshu
where the boreal conifer forests existed during the LGM (Yoshida
and Takeuti, 2009).
The geographical variation between natural forests of
C. japonica has been investigated, focusing on morphological
traits (e.g. needle length or curvature; Murai, 1947), diterpene
components (Yasue et al., 1987) and clonal reproduction
(Kimura et al., 2013). Previous genetic studies based on single
nucleotide polymorphism (SNP) and cleaved amplified polymorphic sequence (CAPS) markers suggested the existence
of two main lines: the Japan Sea side lineage and Pacific
Ocean side lineage (Tsumura et al., 2007, 2012). Bayesian
clustering analyses also provided clear evidence of genetic
divergence among four gene pools using these markers: the
populations in the north region; the populations on the Japan
Sea side; the populations on the Pacific Ocean side; and the
populations on Yakushima Island. Despite extensive population
genetic studies, the historical process of genetic divergence in
this species is still unclear.
This study was performed to identify the responses of
C. japonica to past climatic changes by a combination of phylogeography and SDM methods. Specifically, this study focused on
the locations and numbers of refugia during the LGM. We also
examined the process of genetic divergence among populations
using nuclear microsatellite loci to determine the divergence
times between genetic clusters. Here, we discuss the historical
process of species range formation that could explain the distribution of C. japonica in the Japanese Archipelago.
M AT E R I A L S A N D M E T H O D S
Investigated populations
We sampled 761 individual trees of Cryptomeria japonica
D.Don from 37 populations located in six districts (Tohoku,
Chubu, Kinki, Chugoku, Shikoku and Kyushu) throughout the
entire geographic range of C. japonica (Fig. 1, Table 1). Most
populations were used in previous studies (Table 1: Takahashi
et al., 2005; Tsumura et al., 2007: Kimura et al., 2013). Total
DNA was extracted from the needle tissue of each ramet using
the modified cetyltrimethylammonium bromide (CTAB)
method (Tsumura et al., 1995).
Microsatellite analysis
The DNA of organelles with maternal inheritance has been
used for phylogeography studies in many plant and animal
species because of the clear genetic divergence between populations (McCauley, 1995). However, in this study, we used nuclear
DNA markers to examine the historical genetic structure because
organelle DNAs such as plastid and mitochondrial DNA show
paternal inheritance in this species (Ohba et al., 1971; Kondo
et al., 1998). We determined the genotypes of all samples
using 20 microsatellite markers, including eight genomic
Kimura et al. — Cryptic refugia in Cryptomeria japonica
1689
Shimokita Pen.
1 2
W
E
3
Oga Pen.
5
40°N
4
7
6
8
9
10
11
12
Japan Sea
Tohoku
13
130°E
Toyama Bay
Chubu
16
14
15
17
Oki Isl.
Kanto
Wakasa Bay
28
20
Chugoku
19
22
30
29
27 26
18
Izu Pen.
21
32 31
33
Kinki
23
24
25
Kii Pen.
Muroto Pen.
140°E
Pacific Ocean
Shikoku
Kyushu
0
Yaku Isl.
100
200 km
30°N
34
37
36
35
Refugia based on fossil pollen records
Probable refugia based on precipitation and temperature
F I G . 1. Locations of the 37 sampled populations of Cryptomeria japonica. The grey area represents the current natural distribution of C. japonica (Hayashi, 1960).
The black and grey lines indicate the refugia and probable refugia areas, respectively, during the LGM (Tsukada, 1982a).
(Moriguchi et al., 2003; Tani et al., 2004) and 12 EST (expressed
sequence tag) microsatellite loci (Ueno et al., 2012). The
genomic microsatellite loci were also used in previous studies
(Takahashi et al., 2005; Kimura et al., 2013) and the EST microsatellite loci have been newly included in this analysis.
Polymerase chain reaction (PCR) amplification was performed
in 6 mL reaction volumes containing about 5 ng of genomic
DNA solution, 3 mL of 2× Qiagen Multiplex Master Mix
(Qiagen, Hilden, Germany) and each primer at a concentration
of 2 mM (the forward primer was labelled with dye in each
primer pair). Amplification was performed using a GeneAmp
PCR System Model 9700 thermal cycler (PE Applied
Biosystems, Warrington, UK). PCR conditions were as
follows: denaturation at 95 8C for 15 min followed by 35
cycles consisting of 94 8C for 30 s, annealing at 57 8C for 90 s
and 72 8C for 60 s, with a final extension step at 60 8C for
30 min. The PCR products and DNA size marker (LIZ600;
Life Technologies, Foster City, CA, USA) were separated by
electrophoresis on an ABI 3100 Avant Genetic Analyser
(Applied Biosystems), and their fragment sizes were determined
1690
Kimura et al. — Cryptic refugia in Cryptomeria japonica
TA B L E 1. Location of the 37 Cryptomeria japonica populations
Population no.
1
2
3
4
5
6
7
8
9
10
11*
12
13
14*
15
16*
17*
18
19
20
21
22
23
24
25
26
27*
28
29
30
31*
32*
33
34
35*
36
37
Population
District
n
Latitude
Longitude
Ajigasawa
Owani
Mizusawa
Todosado
Oga
Nibetsu
Shizukuishi
Furukawa
Yamanouchi
Ishinomaki
Sado
Donden
Honna
Hayatsuki
Tateyama
Bijodaira
Obara
Kawadu
Ashitaka
Fuji
Owase
Ashu
Shingu
Yanase
Aki
Tsuyama
Shinjo
Oki
Sanbe
Wakasugi
Yoshiwa1
Yoshiwa2
Azouji
Shiratani
Yakusugiland
Kuromi
Hanayama
Average
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Chubu
Chubu
Tohoku
Chubu
Chubu
Chubu
Chubu
Chubu
Chubu
Chubu
Kinki
Kinki
Kinki
Shikoku
Shikoku
Chugoku
Chugoku
Chugoku
Chugoku
Chugoku
Chugoku
Chugoku
Chugoku
Kyushu
Kyushu
Kyushu
Kyushu
21
17
17
18
20
23
23
23
22
22
22
23
20
21
21
20
19
15
23
9
19
23
23
23
17
20
21
20
23
23
22
22
22
23
23
16
22
20.6
40.675556
40.460833
40.079444
39.946667
39.916111
39.806111
39.680000
38.868333
38.748333
38.328611
38.239963
38.139722
37.426944
36.643056
36.583333
36.576111
36.519533
34.831389
35.232222
35.485556
34.344722
35.307778
33.890000
33.592778
33.565278
35.293056
35.224300
36.268333
35.130278
34.695979
34.512317
34.505433
34.481963
30.378056
30.303491
30.316944
30.326667
140.205278
140.589167
140.247500
140.628611
139.756944
140.260000
140.955278
140.648333
140.074444
141.491944
138.458551
138.383333
139.508889
137.558889
137.462222
137.458889
137.344900
139.000000
138.836111
138.612222
136.222222
135.773889
135.710000
134.095833
133.954444
133.967778
133.518950
133.329167
132.608889
132.080812
132.068183
132.051633
131.963357
130.573333
130.573060
130.510278
130.460000
Elevation (m)
297
218
176
877
564
366
198
779
140
159
585
790
415
923
1007
700
917
634
805
903
918
886
583
762
299
691
805
397
567
468
922
979
1060
795
1047
1767
1247
n, sample size.
*The population was added to this study from previous studies (Takahashi et al., 2005; Tsumura et al., 2007).
using GENEMAPPER version 3.7 analysis software (Applied
Biosystems).
Genetic diversity and characteristics
To assess genetic diversity across all populations, the total number of alleles detected (TA), gene diversity in the total population
(HT), average gene diversity within populations (HS; Nei, 1987)
and observed heterozygosity (HO) were calculated across all populations at each locus and over all loci using FSTAT v. 2.9.3.2
(Goudet, 2002). The fixation index (FIS; Weir and Cockerham,
1984) was also calculated using FSTAT v. 2.9.3.2 (Goudet,
2002) across all populations at each locus and across all loci to
measure departure from Hardy–Weinberg equilibrium (HWE).
Similarly, to assess genetic diversity within each population,
the number of alleles (A), allelic richness (Ar; El Mousadik
and Petit, 1996), HO and unbiased expected heterozygosity
(Nei, 1987) were calculated at each locus and across all loci in
each population using FSTAT v. 2.9.3.2 (Goudet, 2002). Ar
was calculated for a standard sample size of nine (18 gene
copies), which was the smallest sample size, with complete genotypes at all 20 loci, among populations. We also calculated the
number of rare alleles (RA; defined as alleles with a frequency
,1 % in the total population) and private alleles (PA; unique
to one population) per individual in each population. The fixation
index (FIS; 1 – HO/HE) was calculated at each locus and over all
loci in each population to measure departure from HWE. The
deviations of FIS from zero were tested in each population by
false discovery rate test using the ‘stats’ package in R 2.15.0 (R
Development Core Team, 2012).
We also evaluated whether the populations studied had experienced a reduction in effective population size through founding
events or population bottlenecks using Wilcoxon’s signed-rank
test (one-tailed) with the null hypothesis, HE , HEQ, and the alternative hypothesis, HE . HEQ, under the assumption of mutation –drift equilibrium in the infinite allele model (IAM) and the
stepwise mutation model (SMM) with 1000 simulation iterations
using BOTTLENECK v. 1.2.02 (Piry et al., 1999).
Kimura et al. — Cryptic refugia in Cryptomeria japonica
Bayesian genetic clustering
We performed Bayesian cluster analysis using STRUCTURE
2.3.1 (Pritchard et al., 2000; Falush et al., 2003a) to assign individuals to clusters based on their multilocus genotypes. We first
estimated the optimal number of genetic clusters (K ) using 761
individuals from 37 natural populations of C. japonica. Using
an admixture model with correlated allele frequencies and location information (LOCPRIOR model), 20 independent runs were
conducted for each K ( putative cluster number, from 1 to 7) with
100 000 iterations after a 10 000-step burn-in. All other parameters were set at their default values. The optimal number of
clusters, K, was identified using lnPr (X|K) (Pritchard et al.,
2000) or delta K; the rate of change of lnPr (X|K) between successive K values (Evanno et al., 2005) was maximal. The genetic
relationships among the genetic clusters were evaluated based
on net nucleotide distance calculated in STRUCTURE, and
a Neighbor – Joining (NJ) tree based on the net nucleotide
distances was generated using POPULATIONS v. 1.2.30
(Langella, 1999). The net nucleotide distance is the average
probability that the two alleles in a pair from two different
genetic clusters are different (Falush et al., 2003b).
CLUMPP1.1.1 (Jakobsson and Rosenberg, 2007) was used to
calculate the average membership coefficient (Q value) for each
individual by aligning and converging the results of the 20 runs
described above. We also calculated the average membership coefficient (Q value) for each individual excluding the Yakushima
Island population (K ¼ 3) for ABC analyses (see below) because
the genetic cluster of Yakushima Island was particularly different from the other gene pools. In addition, principal co-ordinate
analysis (PCoA) we conducted based on genetic distance in
GeneAlEx 6.41 (Peakall and Smouse, 2006) to visualize the relationships among populations.
Approximate Bayesian computation (DIY ABC)
In the absence of a fossil record, the time scales which estimated solely on genetic data may not be as precise as geological
time scales, but are good enough to make it possible to generate
the temporal hierarchies required for understanding interspecific
gene flow and demographic dynamics cause by distributional
changes (Wakeley, 2008). For probabilistic analysis among alternative hypotheses for the history of gene pool divergence,
we used the approximate Bayesian computation procedure
(Beaumont et al., 2002) in DIY ABC v. 1.0.4.39 (Cornuet
et al., 2008, 2010). We tested the following six competing scenarios (for details, see Fig. 2 and Supplementary Data Fig. S1).
Scenario 1 (three types of refugia). Gene pools B2, B3 and B4
diverged at the same time, and then gene pool B1 arose recently
via divergence from gene pool B2. This scenario suggests that
three types of refugia (e.g. the margins of Wakasa Bay, the Izu
Peninsula and Yakushima Island as sampled sites of fossil
pollen) existed at the LGM and that the population in northern
Tohoku (B1) diverged when this species expanded its distribution to the north.
Scenario 2 (four types of refugia). Four gene pools diverged at the
same time. This scenario suggests that several refugia existed, including cryptic refugia, in the northern Tohoku region at the
LGM.
1691
Scenario 3 (two lineages diverged before the LGM). First, the populations on the Japan Sea side (B1 and B2) and Pacific Ocean side
(B3 and B4) diverged before the LGM, and each gene pool then
separated.
Scenario 4 (divergence and admixture). Scenario 4 includes one
admixture event because most populations showed admixed
gene pools on the Pacific Ocean side, except the Yakushima
populations, in clustering K ¼ 2 (see the Results and
Supplementary Data Fig. S2). First, the populations on the
Japan Sea side (B1 and B2) and Pacific Ocean side (B4) diverged,
and gene pool B3 arose via admixture between Japan Sea side
and Pacific Ocean side populations. Finally, gene pool B1
arose recently via divergence from gene pool B2.
Scenario 5 (divergence three times). First, gene pool B4 diverged
and then gene pool B3 separated. Finally, gene pool B1 arose recently via divergence from gene pool B2. Scenario 5 reflects the
net nucleotide distance among the four gene pools.
Scenario 6. First, we incorporated a bottleneck into the history of
gene pool B1 because northern populations may have suffered
from small population size in the glacial period. However, the
time of the bottleneck in gene pool B1 could not be estimated
well in the best model (Scenario 2), and we also tested the best
model without the bottleneck as Scenario 6.
In this study, we conducted a modified version of FST outlier
loci analysis (Beaumont and Nichols, 1996) under the assumption of the IAM using LOSITAN (Antao et al., 2008).
Simulations were run 10 000 times to generate 95 % confidence
intervals (CIs). The default value for the false discovery ratio
(¼ 0.1) was used in this analysis. ABC analyses were applied
to data with 14 loci, excluding six outlier loci (Supplementary
Data Table S1). Because it is difficult to decide the gene pool
in high admixed genets, 233 individuals with Q values .0.8
were used as samples for ABC analyses. From Yakushima
Island in Kyushu district, 34 individuals were extracted as samples of gene pool B4 in clustering K ¼ 2. In total, 199 individuals
with Q values .0.8 from multiple populations were used as
samples for the other three gene pools (B1, B2 and B3) in the
clustering K ¼ 3, excluding the Yakushima Island population.
In DIY ABC, we employed the higher mutation rate from the
generalized stepwise mutation model (GSM; Estoup et al., 2002)
and the lower rate for single nucleotide indels (SNIs) as mutation
models of simple sequence repeats (SSRs). The default prior
values were used for all of these parameters (Supplementary
Data Table S3). Summary statistics were calculated in each scenario for selecting scenario estimating posteriors of demographic
parameters (Table S4). In total, 4 million data sets were simulated, of which the 1 % closest to the observed data were used
to estimate the relative posterior probabilities of each scenario
via a logistic regression approach. Equal prior probability was
set for each scenario. The relative likelihoods of the four scenarios were compared by logistic regression on 1 % of the closet
simulated data sets.
Current and past distribution modelling
To assess the effects of the last glacial distribution shifts on the
population demography of C. japonica, the potential habitats
present at the LGM (21 000 years BP) and under current climate
1692
Kimura et al. — Cryptic refugia in Cryptomeria japonica
18
1
19
2
20
21
3
4
22
5
23
6
24
7
25
1
2
4
8
3
26
9
27
W
E
7
5
10
28
6
11
29
8
9
12
30
11
13
13
31
14
32
15
15
33
14
16
16
34
10
12
17
35
28
17
Japan Sea
36
27
37
22
26
29
30
31
18
32
19
33
20
21
Pacific Ocean
24
25
23
0
200 km
B2; F = 0·04393
B1; F = 0·05937
34
B3; F = 0·02779
37
35
0·01
B4; F = 0·05026
36
F I G . 2. Distribution of cluster memberships at individual and population levels in the 37 Cryptomeria japonica populations obtained by STRUCTURE analysis
(Pritchard et al., 2000). Number of cluster (K) is 4. Each horizontal bar in the histograms represents the proportion of cluster memberships for each individual.
The pie charts superimposed on the maps show the average proportions of cluster memberships among the individuals sampled in each population.
Kimura et al. — Cryptic refugia in Cryptomeria japonica
conditions were derived by SDM. The generalized additive
model (GAM) (Elith et al., 2006) was allocated based on the
presence and absence data using the ‘mgcv’ package in R
2.15.0 (R Development Core Team, 2012). The distribution
map of C. japonica natural forests by Hayashi (1960) was used
to obtain data for the presence of this species. The area without
natural forests of C. japonica was used as data for absence of
this species. Four climatic variables (Bio6, mean daily
minimum temperature of winter; Bio10, mean temperature of
summer; Bio18, summer precipitation; and Bio19, winter precipitation) at a spatial resolution of 2.5 min were used as explanatory variables when extracting data from the WorldClim database
(http://www.worldclim.org). The four climatic variables that are
generally thought to be important for plant growth and survival
were used as explanatory variables in the SDM. These variables
have been used to obtain highly accurate SDMs in East Asia (e.g.
Tanaka et al., 2012; Tsuyama et al., 2014; Yun et al., 2014). The
dataset is based on meteorological observations collected for
the period 1950– 2000 (Hijmans et al., 2005). The area under
the curve (AUC) value, which was derived from receiver operating characteristic (ROC) analysis (Metz, 1978; Thuiller et al.,
2003), was used to assess the prediction accuracy of the GAM.
The AUC value was calculated by comparing the predicted
presence/absence data and the validation data bootstrapped
from all of the training data with 100 repetitions.
The relative importance of each variable was used to measure
the magnitude of the contribution of each explanatory variable in
the model, which was defined as the sum of the deviance of variables across all models in which the variables occurred
(Burnham and Anderson, 2002). The coefficient values were
also calculated for four current climatic variables at each mesh.
We projected the potential habitat for C. japonica using GAM
on the climate of the LGM as a representative of glacial periods
during the Pleistocene. To project the LGM distribution of
C. japonica, we applied two GCMs for the LGM (approx.
21 000 years ago): the Community Climate System Model
version 3 (hereafter CCSM) (Collins et al., 2006) and the
Model for Interdisciplinary Research on Climate 3.2_hires
(hereafter MIROC) (K-1 Model Developers, 2004). We downloaded the data from the WorldClim dataset (http://www.
worldclim.org/), which were spatially interpolated to 2.5 min.
To validate the relevance of projected LGM occurrences for
C. japonica, we calculated minimum distances between fossil
occurrence and projected potential habitat for the species, and
tested the difference between them by sensitivity analysis.
Calculating the distances between C. japonica pollen data and
the projected potential habitats for the LGM, the distances
between the pollen presence and the predicted presence under
the MIROC simulation were 14.8 + 23.87 km, and the sensitivity was 72.7 % (Supplementary Data Fig. S3A). In the case of
CCSM simulation, the minimum distance and sensitivity were
185.9 + 179.93 km and 0.9 %, respectively (Fig. S3B). From
this result, the MIROC simulation was used in this study.
R E S U LT S
Genetic diversity across all populations and within populations
The 20 microsatellite loci used were highly polymorphic. The
total number of alleles detected over all populations at each
1693
locus ranged from two to 46, with an average value of 15.6
(Supplementary Data Table S2). On average, across all loci,
the gene diversity in the total population (HT) and the average
gene diversity within populations (HS) were 0.651 and 0.628, respectively. Genomic and EST SSR markers that showed high
degrees of polymorphism were used to examine the diversity
and structure in this species, in which the ranges of gene diversity
in the total population (HT) were 0.661– 0.946 and 0.157– 0.884,
respectively (Table S2).
High levels of genetic diversity within populations were also
observed in each population (on average, A ¼ 6.6, Ar ¼ 5.4,
RA ¼ 0.74, HO ¼ 0.592, HE ¼ 0.611; Supplementary Data
Table S2). Rare alleles (RA) were found in all populations.
Private alleles (PA) were found in 21 of 37 populations and heterozygosity excess was detected in four populations (one Tohoku
population, two Chugoku populations and one Kyushu population) after Bonferroni correction under the IAM (Table S2).
The FIS values over all loci significantly positively deviated
from zero in nine populations (four Tohoku populations, one
Kinki population, one Shikoku population and three Chugoku
populations; Table S2).
Genetic structure
The population genetic structure was explored by the
STRUCTURE analysis. The values of log-likelihood of the
data, ln P(X|K), reached a plateau when the numbers of clusters
were K . 4 (Supplementary Data Fig. S4). Furthermore, the
values of delta K, based on the rate of change of ln P(X|K )
between successive K values, were highest when K was 2.
Therefore, we examined the proportional membership of each
cluster of each individual and population at K ¼ 4 and K ¼ 2.
When we set K to 4, the distribution of cluster membership exhibited not only the pattern of population divergence between the
Japan Sea and Pacific Ocean sides, but also genetic substructures among populations along each side (Fig. 2). On the
Japan Sea side, the membership proportion of gene pool B1
was higher in the populations from northern Tohoku district
( population nos 1 – 7) to Sado Island in Niigata Prefecture (nos
11, 12), whereas that of gene pool B2 was higher in the populations (nos 17, 26– 33) from the Chubu to Chugoku districts
(Fig. 2). Similarly, among the populations along the Pacific
Ocean side, the populations (nos 10, 18– 25) from Tohoku to
Shikoku district had higher proportions of gene pool B3,
whereas the Yakushima Island populations (nos 34– 37) in
Kyushu district had higher proportions of gene pool B4. High admixture of several gene pools was found in Kyoto, Toyama
Prefecture and southern Tohoku district (nos 8, 9, 14 – 16, 22).
The F-values of gene pools B1, B2, B3 and B4 were 0.05937,
0.04393, 0.02779 and 0.05026, respectively (Fig. 2). The NJ
tree using net nucleotide distance showed that gene pool B4
was markedly divergent from the other three gene pools
(Fig. 2). The distribution of membership of the two clusters
(A1 and A2) was found to be geographically structured
between the populations along the Japan Sea and Pacific Ocean
sides (Supplementary Data Fig. S2). The proportion of gene
pool A1 (blue) was higher in the populations of northern
Tohoku district, whereas that of gene pool A2 (red) was higher
in populations of Yakushima Island in the Kyushu district. The
1694
Kimura et al. — Cryptic refugia in Cryptomeria japonica
F-values of gene pools A1 and A2 were 0.02810 and 0.03779, respectively (Fig. S2).
This result of population genetic structure by STRUCTURE
analysis corresponded to that of PCoA analysis (Fig. 3). The
first two axes accounted for 65.6 % of the total variation. The
first axis reflected Yakushima Island, other Pacific Ocean side
populations and Japan Sea side populations. The second axis
reflected northern Tohoku populations and other populations
on the Japan Sea side (Fig. 3).
ABC analysis
A comparison of posterior probabilities of the six scenarios
using local linear regression indicated that Scenario 6 had the
highest support, with a probability of 0.811 (Table 2). Scenario
6 had the smallest number of marginal summary statistics
(Supplementary Data Table S4). ABC analysis provided good
support for Scenario 6, with the same time divergence of four
gene pools and no bottleneck (Supplementary Data Fig. S1, the
type I error rate ¼ 0.22).
The median value of the divergence time (t2) was estimated as
507 generations (Supplementary Data Figs S1 and S5; 95 %
CI ¼ 161– 1170). For Scenario 6, the median values of effective
population size of NB1, NB2, NB3, NB4 and NA before population
divergence were 4520, 8040, 8840, 5330 and 2630, respectively
(Table 3). Posterior distributions are shown in full in Table 3.
Current and past (LGM) distributions of C. japonica
For C. japonica, the AUC test (mean + s.d.) for SDM was
high (0.821 + 0.003). Although the current distributional predictions were good representations of the extant distribution of
species (Fig. 4A), we also found some potential habitats where
the species does not occur at present, probably due to human activity (e.g. low land on the Japan Sea side; Fig. 4A). In contrast,
the distribution model for the LGM (Fig. 4B) differed
0·5
J
PCoA axis 2 (27·9 %)
0·4
0·3
J
J
J
0·2
0·1
0
–0·1
J
J
J J
J
J
J
J
J
–0·2
–0·3
J
J
J J
J
J
J J
J
JJ
J
J
J
J
J
–0·4
–0·6 –0·5 –0·4 –0·3 –0·2 –0·1
0
PCoA axis 1 (37·7 %)
J
D IS C US S IO N
Genetic structure of C. japonica
The results of PCoA analysis and Bayesian clustering analysis indicated clear genetic divergence of two gene pools
(A1 and A2; Supplementary Data Fig. S2) and four gene pools
(B1– B4; Fig. 2). Using the delta K (K ¼ 2) indicates two
major gene pools of C. japonica: one distributed along the
Japan Sea side (Japan Sea lineage, A1) and one along the
Pacific Ocean side (Pacific Ocean lineage, A2). Our results
were consistent with previous studies using CAPS markers
(Tsumura et al., 2007) and SNP markers (Tsumura et al.,
2012). Using 148 CAPS markers, Bayesian analysis of the
genetic structure defined eight groups for 29 natural populations
(Tsumura et al., 2007). Tsumura et al. (2012) also found seven
groups (highest log-likelihood K) and two groups (delta K) for
14 natural populations, according to structure analysis based on
results from 1023 SNPs, which provided a much clearer
genetic structure than that from 148 CAPS markers. In this
study, we used only 20 loci but highly polymorphic markers
with a total of 311 alleles corresponding to those of Tsumura
et al. (2007) in terms of the number of alleles, which may be sufficient to detect the genetic structure in this species.
The genetic structures show that two major groups (i.e. the
Pacific Ocean side and the Japan Sea side) have also been
reported in other tree species (e.g. Fagus crenata, Hiraoka and
Tomaru, 2009; Kalopanax septemlobus, Sakaguchi et al.,
2011). The Japan Sea side of the country has heavy snowfall
during the winter season, whereas the Pacific Ocean side is
quite dry during the winter. The contrasting climatic conditions,
especially the difference in amount of snowfall, have strongly
TA B L E 2. Posterior probability of each scenario and its 95 %
confidence interval based on the logistic estimate according to
DIY ABC
J
J J
J
J
0·1
substantially from the current distribution, indicating a general
southward range shift. Predicted suitable habitats for this
species at the LGM were restricted to lowland areas, such as
Oga Peninsula, Sakata (near pop 9), Toyama Bay, Oki Island,
Kii Peninsula and Yakushima Island, indicating an even
smaller geographic range than at present (Figs 1 and 4B).
The relative importance of variables (IOV) indicated that
winter precipitation (Bio19, 50.2 %) was the most influential
factor for C. japonica, followed by summer precipitation
(Bio18, 27.2 %), mean temperature of summer (Bio10, 14.9 %)
and the mean daily minimum temperature of winter (Bio6,
7.7 %). The regional variation of coefficient value was different
among current climatic variables (Supplementary Data Fig. S6).
Simulation
0·2
0·3
F I G . 3. Genetic similarity among 37 populations, as depicted from principal coordinate analysis. Pie charts represent the proportion of cluster memberships in
each population using STRUCTURE analysis with K ¼ 4 (see Fig. 2). Gene
pool B1 ¼ blue, gene pool B2 ¼ sky blue, gene pool B3 ¼ orange, gene pool
B4 ¼ red.
First set
Second set
Scenario
Posterior probability
95 % CI (lower–upper)
1
2
3
4
5
2
6
0.180
0.934
0.005
0.022
0.020
0.189
0.811
0.138–0.022
0.924–0.945
0.004–0.007
0.017–0.027
0.016–0.025
0.158–0.221
0.779–0.842
Kimura et al. — Cryptic refugia in Cryptomeria japonica
1695
TA B L E 3. Demographic parameters obtained by DIY ABC
Parameter
Effective population
size
Time scale in
generations
Mean mutation rate
Mean
Median
Mode
Quantile
2.5 %
Quantile
5.0 %
Quantile
95.0 %
Quantile
97.5 %
NB1
NB2
NB3
NB4
NA
t2
4760
7780
8590
5420
2980
567
4520
8040
8840
5330
2630
507
3770
8620
9100
4900
1560
455
1380
4350
5800
1780
290
126
1730
4990
6490
2210
505
161
8660
9710
9840
9060
6790
1170
9240
9860
9920
9510
7750
1360
Mean mutation
rate_SSR
Mean P*
Mean mutation
rate_SNI
2.00 × 10 – 4
1.60 × 10 – 4
1.18 × 10 – 4
1.00 × 10 – 4
1.04 × 10 – 4
4.36 × 10 – 4
5.57 × 10 – 4
0.116
8.93 × 10 – 5
0.106
9.12 × 10 – 5
0.100
9.34 × 10 – 5
0.100
6.84 × 10 – 5
0.100
7.29 × 10 – 5
0.170
9.91 × 10 – 5
0.207
9.96 × 10 – 5
*The parameter of the geometric distribution to generate multiple stepwise mutation.
A
influenced the characteristics of tree species, resulting in differences between the two sides of the country. Geographical variation in C. japonica was also observed in morphological traits
(Murai, 1947), diterpene components (Yasue et al., 1987) and
clonal reproduction (Kimura et al., 2013) between the Japan
Sea side and Pacific Ocean side. Each gene pool has probably
been adapted to local environmental conditions, leading to the
current genetic structure of this species, including neutral and
adaptive divergence between populations. In a previous study,
several candidate SNPs for local adaptation were detected, especially between populations of the Japan Sea side and Pacific
Ocean side (Tsumura et al., 2012).
Current
Prediction of divergence time using ABC analysis
B
LGM MIROC
0–0·041
0·041–0·1
0·1–0·2
0·2–0·3
0·3–0·4
0·4–0·5
0·5–0·6
F I G . 4. Potential distributions as probability of occurrence (A) at present and (B)
at the last glacial maximum (LGM, 21 000 years BP) for Cryptomeria japonica.
The ABC analysis using 14 SSR loci supported Scenario 6, in
which four gene pools separated at the same time, rather than
gradual divergence scenarios. The time of divergence (t2) was
predicted as 507 generations. These results suggested that four
ancestral populations were present before approx. 500 generations. This divergence time is dependent on the generation
time of this species, but the generation time of woody species
can be much longer than their maturation age (Petit and
Hampe, 2006). As C. japonica begins to produce cones at
20 years of age in artificial open habitats (e.g. Endo et al.,
2012), we therefore assumed a generation time for this species
of approx. 150 years under natural conditions. If we assume
the generation time to be 150 + 50 years, the divergence time
for the four gene pools could be 76 050 + 25 305 years BP.
Although the estimated divergence time also changes by the
mutation rate, the posterior distribution of model parameters
under the most likely scenario was used to make inferences
about the timing of divergence before the LGM. The four gene
pools of C. japonica probably indicated refugia populations at
the LGM.
Climatic conditions controlling C. japonica distribution
The distribution of the potential habitats for C. japonica was
predicted on the basis of the GAM using the present distribution
1696
Kimura et al. — Cryptic refugia in Cryptomeria japonica
data of C. japonica and a current climatic database. The prediction accuracy of the potential habitats was evaluated as ‘good’
based on the AUC. Species distribution modelling showed that
the ecological niche of C. japonica is constrained primarily by
winter precipitation (Bio19, 50.2 %). The distribution probability was low in Kanto and Hokkaido districts (Fig. 4). In
these areas, climatic variables also showed low coefficient
values (mean temperature of summer and winter precipitation
in Kanto, mean daily minimum temperature of winter and
summer precipitation in Hokkaido; Supplementary Data
Fig. S6). Tsukada (1982) noted that the probable glacial
refugia in Cryptomeria can be characterized by climatic variables: annual precipitation of at least 1200 mm, annual temperature .5.0 8C, minimum mean January temperature of – 7.0 to
– 8.2 8C and mean July temperature of 20.0 – 25.0 8C. This climatic regime is almost equivalent to that at the northern limit
of planted Cryptomeria forest in Hokkaido today (Tsukada,
1980). Our results were consistent with the report by Tsukada
(1982). In addition, the current distribution of the predicted
potential habitats covered the natural distribution map of
C. japonica by Hayashi (1960). In this model, past human activity, such as logging of natural populations, was not considered;
therefore, the predicted current potential habitat area would be
wider, especially in the lowlands, if we took past human activity
into consideration, but no contradiction occurred with the current
positions of natural populations.
Comparing pollen data with predicted potential habitats for
the LGM, the MIROC simulation was suggested to be more realistic than CCSM in the case of Japan (Supplementary Data
Fig.S3). Calculating the distances between C. japonica pollen
data and the projected potential habitat during the LGM
showed that the projected potential habitat on the MIROC is reasonably accurate. Potential glacial refugia of C. japonica were
predicted not only in western Japan, e.g. around Oki Island, Kii
Peninsula and Yakushima Island, but also in lowlands of
Tohoku district, such as Oga Peninsula. These results suggested
the presence of putative refugia in the coastal areas of Tohoku
district.
Many suitable habitats for this species were predicted in
Kyushu district at the LGM; however, these natural populations
were thought to have been destroyed by volcanic eruptions.
For example, a large-scale eruption of Mt. Aso occurred
90 000 years ago and the Kikai caldera erupted violently in the
southern Kyushu district 7300 years ago (Machida and Arai,
1992), causing catastrophic damage in these areas where plant
life was thought to have been virtually annihilated. Although
the small old forests of C. japonica distributed on Kyushu
Island, this population might be established by the immigration
from Chugoku populations, such as Azouji and others, after the
extinction of natural forest on Kyushu Island (Takahara, 1998).
The supporting result was obtained by the genetic analysis
using SNP markers (Tsumura et al., 2012).
Evidence of cryptic refugia of C. japonica in northern Japan
Bayesian clustering analysis with the highest log-likelihood
values also divided into four gene pools: northern Tohoku district (B1); from Chubu to Chugoku districts (B2); from
Tohoku to Shikoku district on the Pacific Ocean side (B3); and
the Yakushima Island population (B4). The results of PCoA
analysis also supported these gene pools. Axis 1 (37.7 %) represents genetic differences among Yakushima Island populations,
the other Pacific Ocean side populations and Japan Sea side
populations, and axis 2 (27.9 %) represents genetic difference
between the northern Tohoku and the other Japan Sea side populations (Fig. 3).
Relatively high levels of within-population gene diversity
were detected in the populations of Tohoku district (HE:
average 0.604, range 0.581– 0.642) in comparison with the
other districts (HE: average 0.614, range 0.570– 0.643). In addition, no evidence of a recent bottleneck was observed in
Tohoku district (except the Oga population). These results
suggest the potential of northern cryptic refugia and/or the potential of admixture events from several refugia between populations in Tohoku district. The results of STRUCTURE analysis
in K ¼ 4 indicated the relatively purity of gene pool B1 in northern Tohoku populations (Fig. 2). We also found that some
populations in northern Tohoku possessed private alleles and/
or rare alleles. This genetic structure suggested the presence of
cryptic refugia in northern Japan at the LGM. Recent phylogeographic studies on temperate trees and animals in Asia suggested
that small populations may have survived in the northernmost
part or high elevation mountain area and separated from the
southern large glacial refugia (Tsuda and Ide, 2005; Tsumura
et al., 2007; Ohnishi et al., 2009; Opgenoorth et al., 2010).
Tsukada (1982) predicted probable refugia at Toyama Bay
using environmental data, such as annual precipitation and temperature, and fossil pollen records. In this case, the C. japonica
forest had to expand at a rate of 130 m per year from Toyama
Bay (near population no. 14) to Akita Prefecture (near population
no. 6) after the LGM to establish the current distribution
(Tsukada 1980). In contrast, a few studies found a low frequency
(approx. 10 %) of fossil pollen of C. japonica 12 000 years ago
at Oga Peninsula (Yoshida and Takeuti, 2009). Moreover the
fossil pollen of C. japonica was found in northern Tohoku district
at least 5000– 6000 years ago (e.g. Shimokita Peninsula;
Kawamura, 1979; Yamanaka et al., 1990). These results correspond to the consideration of Takahara (1998) that scattered and
limited populations could have survived in geographically isolated pockets of favourable habitat within a forest mosaic dominated by boreal taxa in northern Japan during the LGM. Our
SDM also predicted that this species could have been distributed
in northern Tohoku district (the coastal side from population
no. 9 and Oga Peninsula) during the LGM, although population
sizes in these areas were limited. Tsukada (1982) suggested that
small isolated refugial populations could hardly persist in the
harsh sub-arctic climate, such as the northern region of Japan,
during the full glacial period. However, the effective population
size of gene pool B1 was sufficient to maintain populations and
genetic diversity (median 4650, 95 % CI 1640 – 8630), although
this number was the smallest compared with other gene pools.
Frequent layering from trunks and/or branches occurs in this
species, especially in snowy regions (Kimura et al., 2013). As
there are large amounts of snowfall over the mountainous
regions on the Japan Sea side, the main regeneration system for
C. japonica in these areas is generally accepted to be layering,
which occurs when a flexible branch or stem is bent to the
ground by snow pressure (Shimizu et al., 2002; Hirayama and
Sakimoto, 2008). Several studies using genetic markers have
shown a higher frequency of layering in regions with heavy
Kimura et al. — Cryptic refugia in Cryptomeria japonica
snowfall (Taira et al., 1997; Moriguchi et al., 2001; Hirayama
and Sakimoto, 2008; Kimura et al., 2013). The reproductive
strategy of C. japonica was found to be strongly affected by
genetic factors and snow depth (Kimura et al., 2013). These
results suggest that clonal reproduction allows persistence
under harsh conditions, such as in the LGM, where establishment
of small individuals, e.g. seedlings, is disturbed by heavy snow.
Clonal reproduction as a reproductive strategy in C. japonica
contributes to maintenance of genetic variation by increasing
the life span of rare genotypes with unique alleles and by preserving the few seedlings that are able to establish under conditions of
frequent disturbance. Such a reproductive strategy may explain
the persistence of the small refugial populations in northern
Tohoku district.
Potential of anagenetic divergence on Yakushima Island
The results of STRUCTURE analysis in K ¼ 4 also indicated
the specificity of gene pool B4 in Yakushima Island populations
(Fig. 2). Abundant Cryptomeria pollen occurrence (.40 %) was
recorded during the Pliocene on southern Kyushu Island, although sediments derived from the full- and late-glacial
periods have yielded neither Cryptomeria pollen nor its microscopic remains, suggesting that it became extinct in Kyushu
Island before the LGM (Tsukada, 1980, 1982a). The main
island (Honshu), Shikoku and Kyushu of the Japanese
Archipelago were connected and formed one large island
during the LGM. However, the Kikai caldera near Yakushima
Island erupted violently 7300 years ago (Machida and Arai,
1992), causing catastrophic damage to the forests in Kyushu district and Yakushima Island (Kimura et al., 1996). Fossil pollen
data indicated that Yakushima Island was one of the main
refugia at the LGM (Tsukada, 1982a). Therefore, Yakushima
Island may have been isolated from the other C. japonica populations for a long time.
Anagenesis (also known as phyletic speciation) has attracted
attention as a speciation process, especially on oceanic islands,
in which the initial founder populations simply diverge over
time without further specific differentiation (Stuessy et al.,
1990, 2006; Whittaker et al., 2008; Takayama et al., 2012; Zou
et al., 2013). The clear genetic distinctions and similar genetic
diversity were found between common species distributed on
the Asian continent and endemic species distributed on islands
as evidence of anagenetic speciation (Takayama et al., 2012;
Zou et al., 2013). Bayesian clustering analyses showed a relatively high F-value in gene pool B4, suggesting an episode of genetic
drift and/or selection during colonization. No clear evidence of a
bottleneck was detected based on allelic frequency distribution
and excess of observed heterozygosity; indeed, genetic diversity
was high compared with other populations. Both PA and RAwere
also found in Yakushima Island populations. The old stumps
have remained in extremely fresh condition in spite of the high
levels of precipitation and humidity on the island due to their
high resin contents (Toda and Sato, 1969; Takahashi et al.,
2008). This result seems to show the difference in wood traits, although the effect of environmental condition was not avoided.
Needle shape is also different compared with those of trees
from other regions (M. K. Kimura, unpubl. data). The needles
of individuals from Yakushima Island are short, opened and
hard regardless of planted environment. These results probably
1697
suggest an anagenetic process on Yakushima Island. In fact,
many endemic species on Yakushima Island are found in
several taxa (Yahara et al., 1987).
Admixture-like structure of C. japonica
High admixture of several gene pools was found in Kyoto,
Toyama Prefecture and southern Tohoku district (population nos
8, 9, 14–16, 22). This result suggeststhat multiple refugia and/oradmixture events, with an ambiguous genetic cline along latitudes,
would be found in C. japonica. We found potential cryptic
refugia in Tohoku district (B1), further north than previous pollen
records (Tsukada, 1982a). On the main Island of Japan (Honshu),
the other two clear gene pools (B2 and B3) were also found from
Chubu to Chugoku district on the Japan Sea side and from
Tohoku to Shikoku district on the Pacific Ocean side, reflecting a
refugial area (Wakasa Bay, Kii Peninsula and Izu Peninsula)
based on fossil pollen analysis (Tsukada, 1982a). Species distribution modelling also predicted that these areas would be suitable with
high probability for C. japonica at the LGM. It has numerous geographical barriers (e.g. high mountain ranges, valleys and seaways),
which are expected to have influenced the distribution patterns and
past migration of tree species in Japan. For example, the high mountain range divides the main island of Honshu into the Pacific Ocean
and the Japan Sea sides. However, it is thought that secondary contacts could easily have occurred in some areas of lower mountains,
because the surroundings of the locations of populations showing
admixture have no geographical barrier such as higher mountains.
Evidence of multiple refugia, such as high genetic diversity in
marginal populations or contact populations, has been reported
in many species (e.g. Hewitt, 2000; Petit et al., 2003). The existence of multiple refugia may have played important roles in
maintaining the genetic diversity in these areas. Admixture of
genetically differentiated populations would result in high
genetic diversity in the newly established populations comparable with other populations. In admixed zones, elevated
genetic diversity has been reported in several tree species, e.g.
Fagus sylvatica, Fraxinus excelsior, Pinus resinosa and
Kalopanax septemlobus (Comps et al., 2001; Walter and
Epperson, 2001; Petit et al., 2003; Heuertz et al., 2004;
Sakaguchi et al., 2011).
Conclusions
The combined evidence from nuclear microsatellites and SDM
clearly indicated that climate-driven range fluctuations have
shaped population genetic diversity and structure of C. japonica
in the Japanese Archipelago. The gene pool detected in northern
Tohoku district is likely to have been generated by cryptic northern
refugia in the coastal landscapes on the Japan Sea side. The characteristic gene pool on Yakushima Island could probably be
explained simply by isolation from the other gene pools since the
LGM, e.g. by anagenetic divergence processes. An ambiguous
genetic diversity cline along elevations would be predicted by
the distribution pattern and accessibility of refugial populations
after the LGM, suggesting that multiple refugia and/or admixture
events affected the overall structure of genetic diversity. These
results were consistent with those of SDM and predicted divergence time using ABC analysis.
1698
Kimura et al. — Cryptic refugia in Cryptomeria japonica
S UP P L E M E NTA RY DATA
Supplementary data are available online at www.aob.oxford
journals.org and consist of the following. Table S1: genetic diversity measures estimated at each of 20 microsatellite loci
across the 37 C. japonica populations. Table S2: genetic diversity
measures estimated using 20 microsatellite loci in each of the 37
populations. Table S3: prior distributions of the parameters used
in DIYABC. Table S4: model checking for introduction scenarios.
Figure S1: the four scenarios tested in DIY ABC. Figure S2: distribution of cluster memberships at individual and population levels
in the 37 populations obtained by STRUCTURE analysis. Figure
S3: potential distributions as probability of occurrence at the last
glacial maximum. Figure S4: values of the log likelihood of the
data, ln P(X|K), as a function of the number of clusters, K, resulting
from the simulation in the STRUCTURE method. Figure S5: the
prior and posterior distributions for each parameter obtained
using DIY ABC. Figure S6: spatial distribution of the standardized
coefficients of four explanatory variables.
AC KN OW LED GEMEN T S
The authors are grateful to Dr T. Nagamitsu for valuable discussions. We also thank Y. Taguchi, M. Koshiba and Y. Komatsu for
their help in DNA analyses. This study was partly supported by a
grant from the Forest Agency of Ministry of Agriculture, Forestry
and Fishery, and by the Programme for the Promotion of Basic
and Applied Research for Innovations in Bio-oriented Industry.
M.K.K., K.U., Y.M. and Y.T. conceived and designed the experiments. M.K.K. and L.S.J.M. performed molecular analyses and
statistical analyses. K.N. estimated current and past distributions
by species distribution modelling. M.K.K. and Y.T. wrote the
manuscript; the other authors provided editorial advice.
L I T E R AT U R E CI T E D
Antao T, Lopes A, Lopes R, Beja-Pereira A, Luikart G. 2008. LOSITAN: a
workbench to detect molecular adaptation based on a Fst-outlier method.
BMC Bioinformatics 9: 323.
Beaumont MA, Nichols RA. 1996. Evaluating loci for use in the genetic analysis
of population structure. Proceedings of the Royal Society B: Biological
Sciences 263: 1619–1626.
Beaumont MA, Zhang W, Balding DJ. 2002. Approximate Bayesian computation in population genetics. Genetics 162: 2025–2035.
Burnham KP, Anderson DR. 2002. Model selection and multi-model inference:
a practical information-theoretic approach. Berlin: Springer.
Collins WD, Bitz CM, Blackmon ML, et al. 2006. The Community Climate
System Model Version 3 (CCSM3). Journal of Climate 19: 2122–2143.
Comps B, Gömöry D, Letouzey J, Thiébaut B, Petit R. 2001. Diverging trends
between heterozygosity and allelic richness during postglacial colonization
in the European beech. Genetics 157: 389– 397.
Cornuet J-M, Santos F, Beaumont MA, et al. 2008. Inferring population history
with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics 24: 2713– 2719.
Cornuet J-M, Ravigné V, Estoup A. 2010. Inference on population history and
model checking using DNA sequence and microsatellite data with the software DIYABC (v1. 0). BMC Bioinformatics 11: 401.
Dorken M, Eckert C. 2001. Severely reduced sexual reproduction in northern
populations of a clonal plant, Decodon verticillatus (Lythraceae). Journal
of Ecology 89: 339– 350.
Eckert C. 2002. The loss of sex in clonal plants. Evolutionary Ecology 15:
501–520.
Elith J, Leathwick JR. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology,
Evolution, and Systematics 40: 677 –697.
Elith J, Graham C, Anderson R, et al. 2006. Novel methods improve
prediction of species’ distributions from occurrence data. Ecography 29:
129– 151.
El Mousadik A, Petit RJ. 1996. High level of genetic differentiation for allelic
richness among populations of the argan tree [Argania spinosa (L.) Skeels]
endemic to Morocco. Theoretical and Applied Genetics 92: 832–839.
Endo R, Fukushima S, Takeyama F, Kobayashi S, Iwasawa M. 2012. Change
of mast year male flower setting of Japan cedar (Cryptomeria japonica
D. Don) plus tree clones accompanying aging from 26 to 42-years-old (subtitle in Japanese). Journal of the Japanese Forest Society 94: 252–256.
Eriksson O. 1989. Seedling dynamics and life histories in clonal plants. Oikos
55: 231–238.
Eriksson O. 1996. Regional dynamics of plants: a review of evidence for
remnant, source–sink and metapopulations. Oikos 77: 248–258.
Estoup A, Jarne P, Cornuet JM. 2002. Homoplasy and mutation model at
microsatellite loci and their consequences for population genetics analysis.
Molecular Ecology 11: 1591–1604.
Evanno G, Regnaut S, Goudet J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular
Ecology 14: 2611–2620.
Falush D, Stephens M, Pritchard JK. 2003a. Inference of population structure
using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567– 1587.
Falush D, Wirth T, Linz B, et al. 2003b. Traces of human migrations in
Helicobacter pylori populations. Science 299: 1582–1585.
Goudet J. 2002. FSTAT, a program to estimate and test gene diversities and
fixation indices (version 2.9.3.2). Available from: http://www2.unil.ch/
popgen/softwares/fstat.htm.
Hayashi Y. 1960. Taxonomical and phytogeographical study of Japanese conifers. Tokyo: Norin-Shuppan.
Heuertz M, Hausman JF, Hardy OJ, Vendramin GG, Frascaria-Lacoste N,
Vekemans X. 2004. Nuclear microsatellites reveal contrasting patterns of
genetic structure between western and southeastern European populations
of the common ash (Fraxinus excelsior L.). Evolution 58: 976–988.
Hewitt G. 2000. The genetic legacy of the Quaternary ice ages. Nature 405:
907– 913.
Hijmans R, Cameron S, Parra J, Jones P, Jarvis A. 2005. WorldClim, version
1.3. University of California, Berkeley. http://biogeo.berkeley.edu/world
clim/worldclim.htm.
Hiraoka K, Tomaru N. 2009. Genetic divergence in nuclear genomes between
populations of Fagus crenata along the Japan Sea and Pacific sides of Japan.
Journal of Plant Research 122: 269– 282.
Hirayama K, Sakimoto M. 2008. Clonal structure and diversity of Cryptomeria
japonica along a slope in a cool-temperate, old-growth mixed forest in the
snowy region of Japan. Canadian Journal of Forest Research-Revue
Canadienne de Recherche Forestière 38: 2804– 2813.
Huntley B, Birks HJB. 1983. An atlas of past and present pollen maps for
Europe: 0 –13,000 years ago. Cambridge: Cambridge University Press.
Jakobsson M, Rosenberg NA. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23: 1801–1806.
K-1 Model Developers. 2004. K-1 coupled GCM (MIROC) description. K-1
Technical Report 1: 1 –34.
Kawamura T. 1979. Special reference to the distribution of Cryptomeria japonica. Quaternary Research 18: 79– 88.
Kimura K, Ooi N, Suzuki S. 1996. Evidence of vegetation recovery on
Yakushima Island after the major Holocene eruption at the Kikai caldera,
as revealed by the pollen record of buried soils under the old-growth
Cryptomeria japonica forest. Japanese Journal of Historical Botany 4:
13–23.
Kimura MK, Kabeya D, Saito T, et al. 2013. Effects of genetic and environmental factors on clonal reproduction in old-growth natural populations of
Cryptomeria japonica. Forest Ecology and Management 304: 10–19.
Kondo T, Tsumura Y, Kawahara T, Okamura M. 1998. Paternal inheritance of
chloroplast and mitochondrial DNA in interspecific hybrids of
Chamaecyparis spp. Breeding Science 48: 177–180.
Langella O. 1999. Populations, a population genetic software (version 1.2.30).
Available from: http://bioinformatics.org/~tryphon/populations/.
Machida H, Arai F. 1992. Atlas of tephra in and around Japan. Tokyo:
University of Tokyo Press (in Japanese).
Maiorano L, Cheddadi R, Zimmermann N, et al. 2012. Building the niche
through time: using 13,000 years of data to predict the effects of climate
Kimura et al. — Cryptic refugia in Cryptomeria japonica
change on three tree species in Europe. Global Ecology and Biogeography
22: 302–317.
McCauley D. 1995. The use of chloroplast DNA polymorphism in studies of gene
flow in plants. Trends in Ecology and Evolution 10: 198– 202.
McDonald GM. 1993. Fossil pollen analysis and the reconstruction of plant invasions. Advances in Ecological Research 24: 67–110.
McLachlan JS, Clark JS. 2004. Reconstructing historical ranges with fossil data
at continental scales. Forest Ecology and Management 197: 139 –147.
Metz C. 1978. Basic principles of ROC analysis. Seminars in Nuclear Medicine
8: 283–298.
Miyake N, Nakamura J, Yamanaka M, Nakagawa T, Miyake M. 2011. Spatial
changes in the distribution of Cryptomeria japonica since the last interstade
in Shikoku Island, southwestern Japan (Metasequoia: the legacy of Dr.
Shigeru Miki: proceedings of the third International Metasequoia
Symposium). Japanese Journal of Historical Botany 19: 61– 68.
Moriguchi Y, Matsumoto A, Saito M, Tsumura Y, Taira H. 2001. DNA analysis of clonal structure of an old growth, isolated forest of Cryptomeria
japonica in a snowy region. Canadian Journal of Forest Research-Revue
Canadienne de Recherche Forestière 31: 377–383.
Moriguchi Y, Iwata H, Ujino-Ihara T, Yoshimura K, Taira H, Tsumura Y.
2003. Development and characterization of microsatellite markers for
Cryptomeria japonica D.Don. Theoretical and Applied Genetics 106:
751– 758.
Murai S. 1947. Major forestry tree species in the Tohoku region and their
varietal problems. In: Kokudo Saiken Zourin Gijutsu Kouenshu, Aomoririnyukai, 131– 151 (in Japanese).
Nei M. 1987. Molecular evolutionary genetics. New York: Columbia University
Press.
Ohba K, Iwakawa M, Okada Y, Murai M. 1971. Paternal transmission of a
plastid anomaly in some reciprocal crosses of sugi, Cryptomeria japonica
D. Don. Silvae Genetica 20: 101–107.
Ohnishi N, Uno R, Ishibashi Y, Tamate H, Oi T. 2009. The influence of climatic
oscillations during the Quaternary Era on the genetic structure of Asian
black bears in Japan. Heredity 102: 579– 589.
Opgenoorth L, Vendramin GG, Mao K, et al. 2010. Tree endurance on the
Tibetan Plateau marks the world’s highest known tree line of the Last
Glacial Maximum. New Phytologist 185: 332–342.
Parducci L, Jørgensen T, Tollefsrud MM, et al. 2012. Glacial survival of boreal
trees in Northern Scandinavia. Science 335: 1083–1086.
Peakall R, Smouse PE. 2006. GENALEX 6: genetic analysis in Excel.
Population genetic software for teaching and research. Molecular Ecology
Notes 6: 288–295.
Pearson R, Dawson T. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology
and Biogeography 12: 361 –371.
Petit RJ, Hampe A. 2006. Some evolutionary consequences of being a tree.
Annual Review of Ecology, Evolution, and Systematics 37: 187– 214.
Petit RJ, Aguinagalde I, de Beaulieu JL, et al. 2003. Glacial refugia: hotspots
but not melting pots of genetic diversity. Science 300: 1563–1565.
Piry S, Luikart G, Cornuet JM. 1999. Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using
allele frequency data. Journal of Heredity 90: 502–503.
Pornon A, Escaravage N, Till-Bottraud I, Doche B. 1997. Variation of reproductive traits in Rhododendron ferrugineum L. (Ericaceae) populations
along a successional gradient. Plant Ecology 130: 1– 11.
Pornon A, Escaravage N, Thomas P, Taberlet P. 2000. Dynamics of genotypic
structure in clonal Rhododendron ferrugineum (Ericaceae) populations.
Molecular Ecology 9: 1099–1111.
Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure
using multilocus genotype data. Genetics 155: 945–959.
Provan J, Bennett K. 2008. Phylogeographic insights into cryptic glacial
refugia. Trends in Ecology and Evolution 23: 564– 571.
Qi XS, Chen C, Comes HP, et al. 2012. Molecular data and ecological niche
modelling reveal a highly dynamic evolutionary history of the East Asian
Tertiary relict Cercidiphyllum (Cercidiphyllaceae). New Phytologist 196:
617– 630.
R Development Core Team. 2012., R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Sakaguchi S, Sakurai S, Yamasaki M, Isagi Y. 2010. How did the exposed seafloor function in postglacial northward range expansion of Kalopanax septemlobus? Evidence from ecological niche modelling. Ecological Research
25: 1183– 1195.
1699
Sakaguchi S, Takeuchi Y, Yamasaki M, Sakurai S, Isagi Y. 2011. Lineage admixture during postglacial range expansion is responsible for the increased
gene diversity of Kalopanax septemlobus in a recently colonised territory.
Heredity 107: 338– 348.
Savolainen O, Bokma F, Garcı́a-Gil R, Komulainen P, Repo T. 2004. Genetic
variation in cessation of growth and frost hardiness and consequences for
adaptation of Pinus sylvestris to climatic changes. Forest Ecology and
Management 197: 79–89.
Shimizu Y, Ando M, Sakai F. 2002. Clonal structure of natural populations
of Cryptomeria japonica growing at different positions on slopes,
detected using RAPD markers. Biochemical Systematics and Ecology
30: 733 – 748.
Stewart JR, Lister AM. 2001. Cryptic northern refugia and the origins of the
modern biota. Trends in Ecology and Evolution 16: 608– 613.
Stuessy TF, Crawford DJ, Marticorena C. 1990. Patterns of phylogeny in the
endemic vascular flora of the Juan Fernandez Islands, Chile. Systematic
Botany 15: 338– 346.
Stuessy TF, Jakubowsky G, Gómez RS, et al. 2006. Anagenetic evolution in
island plants. Journal of Biogeography 33: 1259– 1265.
Taira H, Tsumura Y, Tomaru N, Ohba K. 1997. Regeneration system and
genetic diversity of Cryptomeria japonica growing at different altitudes.
Canadian Journal of Forest Research-Revue Canadienne de Recherche
Forestière 27: 447–452.
Takahara H. 1998. Distribution history of Cryptomeria forest. In: Yasuda Y,
Miyoushi N, eds. Nippon Rettou Syokusei-si. Tokyo: Asakura-Shoten,
207–223 (in Japanese).
Takahashi T, Tani N, Taira H, Tsumura Y. 2005. Microsatellite markers reveal
high allelic variation in natural populations of Cryptomeria japonica near
refugial areas of the last glacial period. Journal of Plant Research 118:
83–90.
Takahashi T, Tani N, Niiyama K, Yoshida S, Taira H, Tsumura T. 2008.
Genetic succession and spatial genetic structure in a natural old growth
Cryptomeria japonica forest revealed by nuclear and chloroplast microsatellite markers. Forest Ecology and Management 255: 2820– 2828.
Takayama K, Sun B-Y, Stuessy TF. 2012. Genetic consequences of anagenetic
speciation in Acer okamotoanum (Sapindaceae) on Ullung Island, Korea.
Annals of Botany 109: 321 –330.
Tanaka N, Nakao K, Tsuyama I, Higa M, Nakazono E, Matsui T. 2012.
Predicting the impact of climate change on potential habitats of fir (Abies)
species in Japan and on the East Asian continent. Procedia Environmental
Sciences 13: 455 –466.
Tani N, Takahashi T, Ujino-Ihara T, Iwata H, Yoshimura K, Tsumura Y.
2004. Development and characteristics of microsatellite markers for sugi
(Cryptomeria japonica D. Don) derived from microsatellite-enriched libraries. Annals of Forest Science 61: 569– 575.
Thuiller W, Araujo M, Lavorel S. 2003. Generalized models vs. classification
tree analysis: predicting spatial distributions of plant species at different
scales. Journal of Vegetation Science 14: 669–680.
Toda R, Sato T. 1969. Variety, breeding and the expression of the characteristics
of the variety of Cryptomeria japonica. In: Sakaguchi M, ed. Sugi no subete.
Tokyo: Zenkoku Ringyo Fukyu Kyokai, 64– 70 (in Japanese).
Tsuda Y, Ide Y. 2005. Wide-range analysis of genetic structure of Betula maximowicziana, a long-lived pioneer tree species and noble hardwood in the
cool temperate zone of Japan. Molecular Ecology 14: 3929– 3941.
Tsukada M. 1980. The history of Cryptomeria japonica (sugi): the last
15000 years. Kagaku 50: 538– 546.(in Japanese).
Tsukada M. 1982a. Cryptomeria japonica: glacial refugia and late-glacial and
postglacial migration. Ecology 63: 1091–1105.
Tsukada M. 1982b. Late-Quaternary development of the Fagus forest in the
Japanease archipelago. Japanease Journal of Ecology 32: 113– 118.
Tsumura Y, Yoshimura K, Tomaru N, Ohba K. 1995. Molecular phylogeny of
conifers using RFLP analysis of PCR-amplified specific chloroplast genes.
Theoretical and Applied Genetics 91: 1222–1236.
Tsumura Y, Kado T, Takahashi T, Tani N, Ujino-Ihara T, Iwata H. 2007.
Genome scan to detect genetic structure and adaptive genes of natural populations of Cryptomeria japonica. Genetics 176: 2393–2403.
Tsumura Y, Uchiyama K, Moriguchi Y, Ueno S, Ihara-Ujino T. 2012.
Genome scanning for detecting adaptive genes along environmental gradients inthe Japanese conifer, Cryptomeria japonica. Heredity 109:
349 – 360.
Tsuyama I, Nakao K, Higa M, Matsui T, Tanaka N. 2014. What controls the
global distribution of Japanese endemic hemlock, Tsuga diversifolia?
1700
Kimura et al. — Cryptic refugia in Cryptomeria japonica
Footprint of climate in the glacial periods on the current habitat occupancy.
Journal of Forest Research 19: 154–165.
Ueno S, Moriguchi Y, Uchiyama K, et al. 2012. A second generation framework
for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica. BMC
Genomics 13: 136.
Wakeley J. 2008. Coalescent theory: an introduction. Greenwood Village:
Roberts & Company Publishers.
Walter R, Epperson B. 2001. Geographic pattern of genetic variation in Pinus
resinosa: area of greatest diversity is not the origin of postglacial populations. Molecular Ecology 10: 103– 111.
Weir BS, Cockerham CC. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1358– 1370.
Whittaker RJ, Triantis KA, Ladle RJ. 2008. A general dynamic theory of
oceanic island biogeography. Journal of Biogeography 35: 977– 994.
Widmer A, Lexer C. 2001. Glacial refugia: sanctuaries for allelic richness, but
not for gene diversity. Trends in Ecology and Evolution 16: 267–269.
Worth J, Sakaguchi S, Tanaka N, Yamasaki M, Isagi Y. 2013. Northern richness and southern poverty: contrasting genetic footprints of glacial refugia in
the relictual tree Sciadopitys verticillata (Coniferales: Sciadopityaceae).
Biological Journal of the Linnean Society 108: 263–277.
Worth J, Yokogawa M, Pérez-Figueroa A, et al. 2014. Conflict in outcomes for
conservation based on population genetic diversity and genetic divergence
approaches: a case study in the Japanese relictual conifer Sciadopitys verticillata (Sciadopityaceae). Conservation Genetics (in press).
Yahara T, Ohba H, Murata J, Iwatsuki K. 1987. Taxonomic review of vascular
plants endemic to Yakushima Island. Journal of the Faculty of Science, the
University of Tokyo Selection III 14: 69– 119.
Yamanaka M, Ishikawa S, Sugawara K. 1990. Palynological studies of quaternary sediments in northeast Japan. VII. Shiriya-zaki moor in Shimokita
Peninsula. Ecological Review 22: 33–47.
Yasue M, Ogiyama K, Suto S, Tsukahara H, Miyahara F, Ohba K. 1987.
Geographical differentiation of natural Cryptomeria stands analyzed by diterpene hydrocarbon constituents of individual trees. Journal of the
Japanese Forestry Society 69: 152–156.
Yoshida A, Takeuti S. 2009. Quantitative reconstruction of palaeoclimate from
pollen profiles in northeastern Japan and the timing of a cold reversal event
during the Last Termination. Journal of Quaternary Science 24: 1006–1015.
Yun JH, Nakao K, Tsuyama I, et al. 2014. Does future climate change
facilitate expansion of evergreen broad-leaved tree species in humandisturbed landscape of the Korean Peninsula? Implication for monitoring design of the impact assessment. Journal of Forest Research 19:
174– 183.
Zou J, Sun Y, Li L, et al. 2013. Population genetic evidence for speciation pattern
and gene flow between Picea wilsonii, P. morrisonicola and P. neoveitchii.
Annals of Botany 112: 1829– 1844.