Current Zoology
58 (2): 211220, 2012
Genetic variation and population dispersal of Yangtze voles
Microtus fortis calamorum in the Dongting Lake region
Zongming GUO1,3, Pengfei SONG1, Cong GUO1, Zhaobin SONG1, Yong WANG2*,
Bo LI2, Meiwen ZHANG2, Jianghong RAN1
1
College of Life Sciences, Sichuan University, Chengdu 610064, Sichuan, China
Key laboratory agro-ecological processes of Subtropical region, Institute of Subtropical Agriculture, Chinese Academy of
Sciences, Changsha 410125, China
3
Yantai Nanshan University, Yantai 265713, Shandong, China
2
Abstract To understand genetic variation and population dispersal in the Yangtze vole Microtus fortis calamorum distributed in
the Dongting Lake region, 144 individuals were collected from six habitat patches. The mitochondrial DNA control region was
sequenced and 17 haplotypes were observed. Of the six investigated populations, haplotype and nucleotide diversities of those
from larger patches were higher than those from smaller patches. Nonparametric correlation analysis showed that patch size had a
positive correlation with haplotype diversity (r = 0.943, P < 0.01). A neighbour-joining tree of the 17 haplotypes showed no geographic genetic structure among the six populations. Analysis of isolation by distance showed that genetic differentiation among
the six populations was not positively related to geographic distance. Analysis of mismatch distribution indicated that the voles
had passed through a population expansion. The pattern of haplotype distribution in the Changsha population suggests that the
population was established by a founder effect [Current Zoology 58 (2): 211–220, 2012].
Keywords
Microtus fortis calamorum, mitochondrial DNA control region, genetic structure, Dongting Lake, Habitat patch
Dongting Lake (28°44′–29°35′ N, 111°53′– 113°05′ E)
is a typical wetland landscape located in Hunan, China.
It has experienced a complicated evolution since the
Pleistocene and changed from small to large, then from
large to small (Tong, 2003). Dongting Lake changed
rapidly in the last several hundred years (Fig. 1) because
of natural events (floods and sedimentation of the lake)
and human activities (anthropogenic infilling, settlements and farming). Not only was the area of the lake
drastically reduced from 6,300 km2 in 1825 (Zhang et al.,
1982; Wang, 1989) to 2,691 km2 in 1983 (Wang et al.,
1989), but it was also changed into complex river networks and the region around Dongting Lake partitioned
into various patches.
Alteration of habitat patches has had a great influence on the Yangtze vole, Microtus fortis calamorum, a
rodent pest in the Dongting Lake region since the 1950s
(Shou, 1962). Many patches have not been suitable
habitat for the vole due to urbanization and the expansion of agricultural land, but a few large patches with
vast lowlands are optimal habitats. For example, the
reed and Amur silvergrass patch, Carex cinerascens and
Carex breviculinis patch are good habitats for the vole,
Received Dec. 21, 2010; accepted July 22, 2011.
 Corresponding author. E-mail: [email protected]
© 2012 Current Zoology
whereas highland and cropland patches are not (Wu et
al., 1996; Guo et al., 1997). Many studies have been
done on the biology, population dynamics and environmental factors causing vole outbreaks (Chen et al., 1995;
Guo et al., 1997; Zou et al., 2000; Wang et al., 2004;
Zhang et al., 2007), but to date, reports on genetic
structure among regional populations, population
dispersal, population demography and potential underlying genetic factors are absent from the literature. In
addition, interestingly, the vole was observed in the
1980s and its population threatened the woods in the
summer of 2003 (Li et al., 2005) in Changsha, the capital city of Hunan. It was suggested that the voles may
have migrated from Dongting Lake along the banks of
the Xiangjiang River; however, there was no evidence
to support this scenario.
Mitochondrial DNA exhibits several peculiar features
such as maternal inheritance, the presence of singlecopy orthologous genes, lack of recombination and
a high mutation rate (Pesole et al., 1999; Larizza et al.,
2002). In particular the mtDNA control region (D-loop)
is a noncoding segment and the most variable part of the
molecule, which evolves 3–5 times more rapidly than
212
Fig. 1
Current Zoology
Vol. 58 No. 2
Evolution of Dongting Lake (1644–1958)
the coding region (Cann et al., 1984; Wenink et al.,
1994).
Alteration of habitat patches has affected the distribution and population dynamics of this vole, but has
fragmentation of the Dongting Lake region affected
gene flow and lead to genetic differentiation among
populations? Further, what is the population history of
the vole and was the Changsha population established
by a founder event? To resolve these questions we employed mtDNA control region sequence analyses.
1 Materials and Methods
1.1 Sampling
We trapped Yangtze voles from six different habitat
patches (P1, P2, P3, P4, P5 and P6, Fig. 2) using clamps
baited with sunflower seeds. Patch size (km2) was calculated using GIS (Table 1). We obtained 144 samples
and the sample size from each patch was: 24 from P1,
26 from P2, 25 from P3, 20 from P4, 26 from P5 and 23
from P6. Vole populations from P1, P2, P3, P4, P5 and
P6 are referred to as Chunfeng, Datonghu, Nanzui,
Miluo, Changsha and Junshan populations.
1.2 Tissue sampling and DNA extraction
Animals were killed and muscle tissue from the legs
of each individual was sampled and immediately preserved in absolute ethanol, and finally stored at - 20℃
in the laboratory. We extracted genomic DNA from a
small section of the muscles for all individuals follow-
Guo ZM et al.: Genetic variation and population dispersal of Yangtze voles
Fig. 2
213
Sampling localities of Microtus fortis calamorum
Table 1 The variable sites of haplotypes, haplotype diversity (h), nucleotide diversity (π), and haplotype distribution of
Microtus fortis calamorum based on the mtDNA control region sequence, sampling number (n) and patch size (s, km2).
Haplotype
H1
Chunfeng
Changsha
Datonghu
Junshan
Nanzui
Miluo
The variable sites
n = 24
n = 26
n = 26
n = 23
n = 25
n = 20
1123335556666666677
h = 0.736
h= 0.369
h = 0.692
h = 0.656
h = 0.363
h = 0.563
66990254890091123355949
π = 1.68%
π = 1.21%
π = 3.59%
π = 1.97%
π = 0.088%
π = 1.58%
38358685622301934557912
S = 53.67
S = 0.32
S = 35.20
S = 18.75
S = 7.52
S = 13.01
9
6
8
7
2
8
12
12
20
11
AATTTTGCCTTCTAAGTTGTCTC
H2
．．．．．．A ．．．．．．．．．．．．．．T ．．
9
H3
．．．．．．A ．．．．T ．．．．．．．．T ．．
2
H4
．．．．．．A ．．．．．C ．．．．．．．T ．．
1
H5
．．．．．CA ．．．．．．．．．．．．．．T ．．
1
H6
．．．．．．．．．．．．．．．．．．．．．A．．．．
1
H7
．．．．．．．．．．．．．．．．．．．．．．C．．．
1
H8
．．．．．．．．．T．．．GGG．．C．．．CT
H9
．．．C C ．．．．．．．．G G ．．．．．．．．
1
H 10
．．．．．．A ．．．．．．．．C ．．．．T ．．
1
H 11
．G．．．．A．．．A．．．．．．．．．T．．
H 12
G ．．．．．．．．．．．．．．．．．．．．．．
1
H 13
．．．．．．．．．．．．．G G ．．．．．．．．
1
H 14
G ．．．．．．．．．．．．．．．．．．．．．．．
1
H 15
．．C ．．．．．．．．．．．．．．．．．．．．
1
H 16
．．．．．．A ．．C ．．．．．．．．．．T ．．
1
H 17
．．．C ．．．A．．．．．GG ．．．．．．．．
ing the standard phenol-chloroform method (Sambrook
et al., 1989). The remaining muscle tissues were stored
at – 20℃.
1.3 PCR and Sequencing
A 1200 bp fragment of mtDNA was amplified using

20
4
1
1
1
primers L15135: 5-GAGGACAACCAGTTGAATACC
C-3and H91: 5-ATAAGGCCAGGACCAAACCT-3
(Yang, 20071). The fragment includes partial sequence
of cytochrome b gene, the complete sequence of
tRNA-Phr, control region, tRNA-Pro gene, tRNA-Phe
Yang LC, 2007. Study on the polymorphism and phylogency of several zokors inferred from mtDNA D-loop sequences. Gansu Agricultural University,
Lanzhou, China.
214
Current Zoology
gene and partial sequence of 12 S RNA. Polymerase
chain reaction (PCR) amplification was carried out using DNA thermal cyclers (Eppendorf) with 25 μl of the
reaction mixture containing 0.25 mM dNTP, 0.4 mM of
each primer, 50 mM KCl, 10 mM Tris-HCl (pH 8.3), 1.5
mM MgCl2, 1.25 units Taq DNA polymerase (Tiangen
Biotech 2× Taq PCR MasterMix), and ~200 ng of DNA
template. The PCR protocol was an initial 4 min denaturation at 94℃ followed by 35 cycles of 40 s at 94℃,
50 s at 56℃, 1 min at 72℃ and a final extension for 10
min at 72℃ before cooling to 22℃. PCR products were
separated by electrophoresis in a 1% agarose gel in 1×
TAE buffer and stained with ethidium bromide. For
each sample, after electrophoresis, a band was observed
at ~1200 bp. This band was excised from the gel and
DNA was purified from the agarose with a DNA purification kit (Tiangen, Beijing, China) following the
manufacturer’s instructions.
The purified mtDNA fragments were directly sequenced using an ABI3730XL automatic sequencer (Invitrogen Biotechnology Limited Company, Shanghai,
China). Because there are special structures (poly G or
C) in the control region of the vole there are overlapping
peaks in the sequence. Each sample was sequenced
twice utilizing the PCR primers and one newly designed
internal primer (W1F: AATCTCGCGCAGTTGGTATC)
in order to avoid base call errors.
1.4 DNA sequence alignment and analysis
We aligned nucleotide sequences using Clustal X
Version 1.83 (Thompson et al., 1997) and further refined them manually. To avoid possible base call errors
for both ends of the fragment we only utilized the complete control region in subsequent analyses. Gaps resulting from the alignment were excluded from population genetic analysis. Nucleotide polymorphic sites in
the mtDNA control region were explored using MEGA
Version 3.1 (Kumar et al., 2004), in which genetic distances within and between populations were computed.
We constructed a neighbour-joining tree (Saitou and Nei,
1987) based on haplotypes according to Kimura’s
2-parameter model (Kimura, 1980), with M. rossiaemeridionalis and M. kikuchii (GenBank accession
numbers: DQ015676 and NC003041) as outgroups.
Haplotype (h) and nucleotide diversities (π; Nei, 1987)
were determined by DnaSP 4.0 (Rozas et al., 2003). To
determine the population genetic structure within and
among populations we performed an analysis of molecular variance (AMOVA, Excoffier et al., 1992) using
Arlequin Version 3.0 (Excoffier et al., 2005). Fixation
Vol. 58 No. 2
indices (Fst, Excoffier et al., 1992) were calculated to
assess overall genetic divergence and between paired
populations. Estimates of gene flow (Nm) were derived
using the equation: Nm = [(1/Fst)-1]/2 (Slatkin, 1987;
Hudson et al., 1992). The statistical significance of the
total and pairwise fixation indices was estimated by
comparing the observed distribution with a null distribution generated by 10,000 permutations.
We conducted nonparametric correlation analysis
between patch size and population genetic diversity
using SPSS 16.0 (SPSS Inc., Chicago, USA). In order to
test for isolation by distance (IBD) we performed a linear regression of estimates of Fst/(1-Fst) between all
pairs of populations against the logarithms of interpopulation geographical distances following Rousset
(1997) and Ramirez and Haakonsen (1999). The significance of regression was tested by the Mantel test,
performing 5,000 randomizations for the analysis. All of
these were finished by using the isolation by distance
web service version 3.15 (www.bio.sdsu.edu/pub/andy/
IBD.html). Additionally, we constructed a median-joining
haplotype network (Bandelt et al., 1999) using Network
4.2.0.1 (http:// www.fluxus-engineering.com) to determine the spatial distribution of haplotypes. We characterized the historical demography of the vole population
by examining the observed mismatch distribution (Slatkin and Hudson, 1991) of pairwise differences between
sequences, as implemented in Arlequin Version 3.0
(Excoffier et al. 2005) and DnaSP 4.0 (Rozas et al.
2003). Populations that have passed through demographic expansions and genetic bottlenecks are predicted to have a unimodal wave in samples, whereas
bimodal or multimodal distributions of sharp peaks are
often found in populations that have been constant over
time (Rogers and Harpending, 1992; Schneider and Excoffier, 1999). To test the goodness-of-fit of distributions with the expected distributions using a model of
population expansion we calculated the sum of squared
deviations (SSD) and raggedness index (r). To test
whether the sequences conformed to the expectations of
neutrality of evolution, we used Tajima’s D statistics
(Tajima, 1989) and Fu’s FS (Fu, 1997) as two tests of
neutrality in DnaSP 4.0 (Rozas et al., 2003).
2
Results
2.1 Molecular characteristics of the vole mitochondrial control region
We obtained the entire sequence (914–917 bp) of the
mtDNA control region, which defined 17 haplotypes
among 144 samples (Table 1). We have deposited all
Guo ZM et al.: Genetic variation and population dispersal of Yangtze voles
sequences in GenBank with accession numbers FJ597650FJ597731 and GU474450-GU474511. After alignment,
we adopted a 912 bp sequence of the mtDNA control
region to analyze genetic variation in the vole. Analysis
of the 912 bp sequence of the mitochondrial control
region revealed 23 polymorphic sites, consisting of 11
singleton variable sites and 12 parsimony informative
sites.
2.2 Different levels of interpopulation variation
Total nucleotide diversity was 0.0021 and haplotype
diversity was 0.707. The haplotype and nucleotide diversities of populations in larger patches were higher
than that of those in smaller patches (Table 1). There
was a positive correlation (r = 0.943, P < 0.01) between
patch sizes and haplotype diversities of populations.
2.3 Population genetic structure and haplotype analysis
As the AMOVA of the six investigated populations
shows, most molecular variance was observed within
populations (85.37%), and variance among populations
were 14.63% and significant (Fst = 0.146, P < 0.001)
(Table 2). Pairwise Fst indicated significant differentiation (P < 0.01) between Changsha and the other five
populations (Table 3). Pairwise comparisons between
Nanzui and each of the five populations (Chunfeng,
Changsha, Datonghu, Junshan and Miluo) were significant (P < 0.05). The gene flow estimate between the
Changsha and Nanzui populations was the lowest (Nm =
0.841) among all gene flow estimates.
As shown in Fig. 3, analysis of genetic distance [Fst
/(1- Fst)] and the logarithm of geographic distance did
215
not yield a positive correlation, and the Mantel test also
supported this result with nonsignificant r2 values (r2 =
0.47, P > 0.05). After excluding the Changsha population, we reexamined IBD with the dataset. The result
was consistent with the previous one (Mantel test, r2 =
0.287, P > 0.05). It appears that genetic differentiation
in the studied populations was not positively related to
physical distance.
Based on Kimura’s 2-parameter genetic distances, a
neighbour-joining tree of the 17 haplotypes (Fig. 4) was
constructed to gain insight into the evolutionary genetics of the voles. The tree showed no geographic genetic
structure among the six populations because of promiscuously distributed haplotypes. The median-joining
network (Fig. 5) not only showed no geographic structure, but showed H1 and H2 as the predominant haplo
types. The short branch lengths and starlike distribution
suggested that divergence among the six populations
Table 2 AMOVA analysis of the genetic structure of the Microtus
fortis calamorum population
Structure tested
Variance
Sum of
Variance
% total
Among populatons
0.143
21.35
14.63
Within populations
0.836
115.35
85.37
Fst
P
0.146 0.000 0
Fig. 3 Differentiation among Microtus fortis calamorum
populations. Estimates of Fst/(1-Fst) of all pairs of populations are plotted against logarithms of interpopulation
geographical distances
Table 3 Values for the fixation index (below diagonal), associated P-values generated through a permutation test with 10 000 iterations (in
parenthesis) and gene flow estimates (migrants per generation) (above diagonal) among different populations of Microtus fortis calamorum.
Population
Chunfeng
Chunfeng
Changsha
Datonghu
Junshan
Nanzui
Miluo
1.105
11.063
﹡
3.870
﹡
1.498
1.115
0.841
0.974
25.514
2.928
20.785
5.016
﹡
Changsha
0.312(0.000)
Datonghu
0.043(0.108)
0.250(0.000)
Junshan
0.019(0.628)
0.310(0.000)
0.019(0.219)
Nanzui
0.114(0.013)
0.373(0.000)
0.146(0.003)
0.091(0.039)
Miluo
0.031(0.838)
0.339(0.000)
0.023(0.218)
0.040(0.999)
﹡could not be estimated because of negative Fst values
4.037
0.110(0.043)
216
Current Zoology
Fig. 4
Neighbor-joining tree based on haplotypes of Microtus fortis calamorum
Fig. 5
Median-joining network of mtDNA haplotypes
Vol. 58 No. 2
The size of each disc and sector indicates the relative frequency of the corresponding haplotype in the whole data set.
occurred recently. Interestingly, the Changsha population not only lacked H2, but also lacked unique haplotypes. In addition, the Changsha population shared H4
only with the Chunfeng population (Fig. 5). The pattern
of haplotype distribution in the Changsha population
suggests that the population may have been established
by a founder effect.
2.4 Historical demography
Both Tajima’s D and Fu’s FS negative values supported the hypothesis that the whole sample had passed
through a population expansion (Table 4). In addition,
the observed mismatch distribution of the whole sample
was nearly unimodal, although there was an additional
weak peak (Fig. 6a). Both sum of squared deviation (SSD)
and Harpending’s raggedness index (r) suggested goodness
of fit between the observed and the expected distributions,
which further supports population expansion. Among the
six populations, the observed mismatch distributions of
Chunfeng yielded a unimodal pattern with one peak (Fig.
6b), suggesting that the Chunfeng population has experienced a population expansion. The model of population
expansion was also supported by non-significant SSD and
r values (Table 4). In contrast, the five other populations
have been under demographic equilibrium because the
shapes of the mismatch distributions were all ragged and
multimodal or bimodal (Fig. 6 c–g).
Guo ZM et al.: Genetic variation and population dispersal of Yangtze voles
217
Table 4 Statistical tests of neutrality and demographic parameter estimates for Microtus fortis calamorum populations and their associated
P-value (in parenthesis)
Chunfeng
Changsha
Datonghu
Junshan
Nanzui
Miluo
Whole
Tajima’D
0.58(0.34)
1.00(0.84)
0.14(0.61)
0.88(0.22)
1.13(0.13)
0.47(0.37)
1.53(0.04)
Fs
1.69(0.14)
3.53(0.94)
2.85(0.90)
0.34(0.45)
1.42(0.12)
2.17(0.88)
5.87(0.04)
SSD
0.017(0.31)
0.18(0.047)
0.086(0.19)
0.105(0.09)
0.038(0.20)
0.168(0.09)
0.014(0.41)
r
0.06(0.61)
0.67(0.34)
0.28(0.07)
0.39(0.03)
0.33(0.50)
0.63(0.06)
0.05(0.65)
Goodness of fit tests
Demographic parameters
Fig. 6 Mismatch distribution for each population of Microtus fortis calamorum. (a), (b),
(c), (d), (e), (f) and (g) represent mismatch
distributions for the whole sample set, the
Chunfeng, Changsha, Datonghu, Junshan,
Nanzui and Miluo populations, respectively
Dashed lines represent the observed distributions and
solid lines represent expected ones (under the population growth model).
218
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Current Zoology
Discussion
3.1 Genetic variation
Habitat fragmentation in landscapes have effects on
species’ genetic variation and population demography
(Gaines et al., 1997; Bender et al., 1998; Gibbs, 2001;
Pergams et al., 2003). Dongting Lake has been changing
since the Pleistocene. In the modern age the most notable features of Dongting Lake are the drastic reduction
in area, complex river networks and various habitat
patches. Patch size has been shown to have potential
effects on various populations. Here, when compared to
smaller patches (P3, P4, P5), larger patches (P1, P2, P6)
are associated with higher genetic diversity (Table 1).
Nonparametric correlation analysis indicated that patch
size had a positive correlation (r = 0.943, P < 0.01) with
haplotype diversity of populations. Many studies have
demonstrated that both isolated populations and island
populations have low genetic variation and were a
greater risk than mainland or large island populations
(Frankham, 1997; Hinten et al., 2003; Wroblewska et al.,
2003; Neumann et al., 2004; Ohnishi et al., 2007).
Frankham (1996) demonstrated that genetic variation
within species was positively related to island size and
to population size, and that island populations have less
genetic variation than mainland populations.
3.2 Population genetic structure
We found no evidence of geographic genetic structure (Fig.4). Possible explanations may be as follows:
The AMOVA showed that the variation occurred mainly
within populations, not among populations (Table 2).
Haplotypes from different populations were promiscuously distributed (Fig.4). H1 and H2 were the common
and widespread haplotypes in the vole population
(Fig.5). There was no obvious clustering of haplotypes
of the studied populations. In addition, pairwise Fst between any two of the four populations (Chunfeng, Datonghu, Junshan and Miluo) were not significant. Correspondingly, estimates of gene flow between any two
of the four populations were very high (Table 3). It
seems that there was no genetic differentiation among
the four populations because of closer physical distance.
Interestingly, there was genetic differentiation between
Nanzui and Datongtu despite the closer physical distance. A lack of an isolation-by-distance relationship
(Fig. 3) among these populations suggests that genetic
differentiation was associated with the latest changes to
the Dongting Lake. Wright’s theory of isolated distance
has been proved by many studies (Goossens et al., 2001,
Floyd et al., 2005, Trizio et al., 2005), but genetic barri-
Vol. 58 No. 2
ers such as the anthropogenic obstructions (roads and
clear cuts), may exist even among geographically close
populations (Selonen and Hanski 2004, Selonen et al.,
2005, Trizio et al., 2005, Lampila et al., 2009). Local
landscape factors conducted by the anthropogenic obstructions affect gene flow and determine differentiation
(Trizio et al., 2005). The Yangtze vole prefers lower
lowland and remains far away from areas of human activity (Guo et al., 1997). Human activities such as extensive road networks and the expansion of agricultural
land and urbanization have exacerbated the fragmentation of the Dongting Lake region. Fragmentation caused
by anthropogenic obstructions has affected gene flow in
these voles and led to population differentiation, despite
the closer physical distance.
3.3 Population dispersal and historical demography
The Changsha population lacked unique haplotypes
and shared H4 (Table 1, Fig. 5) with the Chunfeng
population. The pattern of haplotype distribution in the
Changsha population can be explained by genetic drift.
Colonizing populations are usually composed of a subset of the genetic diversity present in the source population and spatial expansion can lead to bottleneck and
founder effects (Provan and Bennett, 2008). For example, the deer mouse, Peromyscus maniculatus, has undergone likely colonization events from the mainland to
islands (Ashley and Wills, 1987). Similarly, the Changsha population was composed of a subset of the genetic
diversity present in the Dongting Lake populations. Although there was genetic differentiation between the
Changsha and Chunfeng populations, gene flow estimation showed 1.1 migrants per generation (Table 3). This
situation indicates there is lower dispersal between the
Chunfeng population and the Changsha population.
Taking an ecological view, the Xiangjiang River is a
part of the river network and could be the path by which
some voles migrated from Dongting Lake. Our results
suggest that the Changsha population could have been
established by spatial expansion of the voles distributed
around Dongting Lake. On the other hand, Tajima’s D
and Fu’s FS negative value (Table 4) and mismatch distribution (Fig. 6a, Fig. 6b) of the vole population indicate that the voles experienced population expansion.
Due to the peak in the mismatch distribution close to the
Y axis, population expansion likely occurred more recently. The pattern of haplotype distribution in the
Changsha population and population expansion of the
Chunfeng population reminded of us that population
expansion of the vole may be related to events related to
Guo ZM et al.: Genetic variation and population dispersal of Yangtze voles
the Changsha population. To better understand this
complex connection more work on these animals is
needed.
219
variation than mainland populations? Heredity 78: 311–327.
Gaines MS, Diffendorfer JE, Tamarin RH, Whittam TS, 1997.
The effects of habitat fragmentation on the genetic structure
of small mammal populations. J. Hered. 88: 294–304.
Acknowledgements This study was supported by the National Basic Research Program of China (973 Program)
(2007CB109106), KSCX-EW-N-05，KSCX2-YW-N-06 of the
Chinese Academy of Sciences and National Natural Science Foundation of China (30870402). We are grateful to
Tingjie Xin and Jian Chen for help with sample collection. We
thank Chenghe Wu and his colleagues for help with sample
collection in Datonghu. We thank Jiurong Wang, Chunyong Li,
Boxiang Luo and Shufeng Song for assistance in the lab. We
would like to thank Weicai Chen for providing some data
processing software, and Jianrong Fu and Xiangkun Qi for
help with GIS. Emily H. King provided comments on an earlier version of this manuscript and Alan R. Rogers and Rafael
Zardoya assisted with analyzing the mismatch distribution.
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