microsatellite variability reveals high genetic diversity ... · microsatellite variability reveals...

YANG et al: Giant panda genetic diversity

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UNCORRECTED PROOF

Microsatellite variability reveals high genetic diversity and low

genetic differentiation in a critical giant panda population

Jiandong YANG1, 2, Zhihe ZHANG2, Fujun SHEN2, Xuyu YANG3, Liang ZHANG2, Limin

CHEN4, Wenping ZHANG2, Qing ZHU1, Rong HOU2*

1 College of Animal Science, Sichuan Agricultural University, Ya’an 625014, China 2 The Key Laboratory for Conservation Biology of Endangered Wildlife, Sichuan Province, Chengdu Research Base of Giant Panda Breeding,

Chengdu 610081, China 3 Wildlife Conservation Division, Sichuan Forestry Department, Chengdu 610082, China 4 Tangjiahe National Nature Reserve, Guangyuan 628109, Sichuan, China

Abstract Understanding present patterns of genetic diversity is critical in order to design effective conservation and management strategies

for endangered species. Tangjiahe Nature Reserve (NR) is one of the most important national reserves for giant pandas (Ailuropoda

melanoleuca) in China. Previous studies have shown that giant pandas in Tangjiahe NR may be threatened by population decline and

fragmentation. Here we used 10 microsatellite DNA markers to assess the genetic variability in the Tangjiahe population. The results indicate

a low level of genetic differentiation between the Hongshihe and Motianling subpopulations in the reserve. Assignment tests using the

Bayesian clustering method in STRUCTURE identified one genetic cluster from 42 individuals of the two subpopulations. All individuals

from the same subpopulation were assigned to one cluster. This indicates high gene flow between subpopulations. F statistic analyses

revealed a low FIS-value of 0.024 in the total population and implies a randomly mating population in Tangjiahe NR. Additionally, our data

show a high level of genetic diversity for the Tangjiahe population. Mean allele number (A), Allelic richness (AR) and mean expected

heterozygosity (HE) for the Tangjiahe population was 5.9, 5.173 and 0.703, respectively. This wild giant panda population can be restored

through concerted effort [Current Zoology 57 (6): – , 2011].

Key words Ailuropoda melanoleuca, Microsatellite, Genetic diversity, Genetic differentiation, Tangjiahe Nature Reserve

The loss and fragmentation of natural habitat is one of the greatest threats to the persistence of natural populations

of species (Young and Clark, 2000; Goossens et al., 2005). The fragmentation of a population may have important

consequences for population genetic diversity and structure due to the effects of genetic drift and reduced gene flow

(Pimm and Raven, 2000; Martinez–cruz et al., 2007). Historically, the giant panda (Ailuropoda melanoleuca) was

distributed across southwestern China (Schaller et al., 1985; Hu, 2001; Pan et al., 2001), but is now restricted to 1,600

animals across six isolated mountain ranges along the eastern edge of the Tibetan Plateau (State Forestry

Administration, 2006). At present, its habitat has been fragmented into more than 30 isolated areas and the population

divided into an estimated 24 groups, most with less than 50 individuals (O’Brien et al., 1994; Loucks et al., 2001; Lü et Received Nov. 30, 2010; accepted Apr. 13, 2011 Corresponding authors. E-mail: [email protected], [email protected]

© 2011 Current Zoology


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al., 2001).

Understanding patterns of genetic diversity is critical for the effective conservation and management of

endangered species (Beaumont and Bruford, 1999). Protein electrophoresis (Su et al.,1994), mitochondrial DNA (Zhang

et al., 1997; Lü et al., 2001; Zhang et al., 2007 ), minisatellite DNA (Fang et al., 1997; Wan et al., 2005) and

microsatellite loci analysis (Lü et al., 2001; Zhan et al., 2006; Zhang et al., 2007; He et al., 2008; Shen et al., 2009; Hu

et al., 2010) have been used to assess the amount and distribution of genetic variability present in wild giant pandas.

Some researchers assumed that wild populations might have low genetic variability (Su et al., 1994; Zhang et al., 1997;

Fang et al., 1997). However, Lü et al. (2001) and Zhang et al. (2007) concluded that wild populations might maintain

high genetic variation based on mtDNA and microsatellite loci analysis. Differences between these studies shows that

additional systematic studies are needed to clarify the genetic status of the wild giant panda populations.

The Minshan area is a key zone for conserving giant pandas and accounts for approximately 37% of giant panda

habitat and 46% of animals (Hu, 2001; State Forestry Administration, 2006). Minshan has been split into three

segments named Minshan A, B and C habitats (Hu, 2001). Tangjiahe Nature Reserve (NR) lies within Minshan A and is

the only giant panda reserve never to have had human inhabitants (Zhang et al., 2002; Hu, 2005; Wan et al., 2005).

Habitat within the reserve was previously destroyed but has since been restored and habitat conditions have improved

significantly (Wan et al., 2005). Nevertheless, rapid population decline of giant pandas in Tangjiahe NR has not been

avoided in the last 30 years (Hu et al., 2003, State Forestry Administration, 2006). Previous studies have shown that

giant pandas in the reserve may be threatened by population fragmentation (Zhang et al. 2002; Wan et al. 2005). Zhang

et al. (2002) reported that the Tangjiahe population was divided into three subpopulations, Hongshihe, Motianling and

Luoyigou. Using minisatellite DNA, Wan et al. (2005) found that most adult individuals dispersed only a short distance

and formed three clusters in Tangjihe NR. However, their results seem to be incongruous with the availability of quality

habitat across the reserve.

Microsatellite markers are widely applied in genetic studies into genetic variation in giant pandas and other

endangered species because of the high levels of variations, small amount of DNA required and noninvasive sampling

techniques (Jarne and Lagoda, 1996; Beaumont and Bruford, 1999). However, few in-depth studies have assessed the

level of genetic differentiation in the Tangjiahe population using microsatellite markers. Here, 10 microsatellite DNA

markers were used to determine the amount and distribution of genetic variability present in two large subpopulations

(Hongshihe and Motianling) in Tangjiahe NR. The aim of this study was to evaluate patterns of genetic diversity of

giant pandas in this reserve and develop an effective management strategy.

1 Materials and Methods

1.1 Study area and sample collection

Tangjiahe NR (104° E, 32° N) covers 40,000 hectares in the northwestern part of the Minshan Mountains

and adjoins Baishuijiang NR to the north. This reserve focuses on the protection of alpine ecosystems and species

such as giant pandas and other rare species which share the habitat (Hu et al., 2005). Giant panda fecal and hair

samples were collected from the reserve following Zhan et al. (2006). Most samples were <15 days old as

determined from the status of the outer mucosa layer of feces such as presence and sheen of a mucous coating,

color and odor. The size of bamboo stem fragments in each scat were measured, and the degree to which they

were chewed was detected based on intactness (Hu, 2001). Sampling locations were GPS recorded and mapped in

Arcview 3.2a (Fig. 1). Up to 5 g of fecal matter was extracted from the outer layer and stored in 100% ethanol.

Hair samples were collected with sterile forceps from marking trees and other natural objects giant pandas use for


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scratching or scent marking and stored in small paper envelopes. We collected 185 fecal and 16 hair samples

from Tangjiahe NR (113 from Hongshihe, 85 from Motianling and 3 from Luoyigou) during 2009 and 2010.

1.2 DNA preparation and quality verification

DNA was extracted from fecal and hair samples using the QIAamp Stool Mini Kit and QIAamp DNA Mini

Kit (Qiagen Inc., Valencia, USA) according to the manufacturer’s instructions. The quality of DNA samples was

verified by polymerase chain reaction (PCR) amplifications using three microsatellite loci: Panda-22, gp901 and

Ame-μ21. If they were successfully amplified at least once they were considered eligible for further microsatellite

analysis.

1.3 Microsatellite genotyping and sexing

A total of 10 microsatellite loci (Ame-μ14, Ame-μ15, Ame-μ21, Ame-μ25, Panda-05, Panda-22, Panda-44,

Gp001, Gp008, and Gp901) designed for giant pandas were amplified to analyze genetic structure of the

Tangjiahe population (Zhang et al., 1995; Lü et al., 2001; Shen et al., 2007). PCR products were separated by

capillary electrophoresis using POP4 gel on an ABI 3100 automated sequencer (Applied Biosystems Inc., Foster

City, CA, USA). Alleles were sized using Genscan-500 (ROX) and the size standard within the GenoTyper

Analysis Software version 3.7 (Applied Biosystems Inc., Foster City, CA, USA). We obtained reliable genotypes

according to the multitubes approach of Zhan et al. (2006) as follows: first, we amplified each extract twice

simultaneously, and then accepted loci that exhibited the same heterozygous genotype twice. Otherwise, we

performed a third repeat, and accepted some genotypes from three positive PCRs. If the genotype could not be

determined, four additional amplifications were performed. One pair of primers to co-amplify homologous

fragments with size polymorphism located at amelogenin (AMEL) exon 5 was used to identify the sex of each

sample under previously published conditions (Xu et al., 2008).

1.4 Data analysis

Microsatellite markers are very useful tools to distinguish individuals in conservation biology. However, low

quality and/or quantity of DNA in noninvasive samples can lead to genotyping errors (Taberlet et al., 1996;

Pompanon et al., 2005; Zhan et al., 2010). Micro-Checker (Van Oosterhout et al., 2004) was utilized to assess

data quality and estimate the presence of null alleles, large allele dropout or stuttering null allele of the data sets.

We followed the multi-tube genotyping approach to minimize genotyping error in the final individual

identification results (Zhan et al., 2006; Zhang et al., 2007). We followed Zhan et al. (2006) and Xu et al. (2008)

based on microsatellite genotype and sex to perform individual identification. We used GIMLET (Valière, 2002)

to explore the discrimination power of the microsatellite locus combination, and estimated the probability of

full-sib or unrelated pairs of pandas bearing an identical multilocus genotype.

We used Cervus 3.0 to estimate genetic polymorphism for each population as the number of alleles per locus

(A), observed heterozygosity (HO), expected heterozygosity (HE), and polymorphic information content (PIC)

(Marshall et al., 1998). We utilized the software Convert 1.31 to analyze allele frequency and private alleles

(Glaubitz, 2004). Allelic richness (AR) was calculated with the FSTAT 2.9.3 program package (Goudet, 2001).

Markov chain tests of Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were conducted

with GenePop 3.4 (Raymond and Rousset, 1995). All probability tests were based on the Markov chain method

using 1,000 dememorization steps, 100 batches and 1,000 iterations per batch.

Weir and Cockerham’s version of Wright’s F-statistic were estimated with the use of GDA 1.1 (Lewis and

Zaykin, 2002), the FSTAT 2.9.3 program package (Goudet, 2001), and Arlequin 3.1 (Excoffier et al., 2005).


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Wright’s (1978) method was used to interpret FST values in light of suggested qualitative guidelines of FST values

(FST = 0–0.05 indicates little population differentiation, 0.05–0.15 indicates moderate differentiation, 0.15–0.25

indicates strong differentiation, and >0.25 indicates very strong differentiation). A Bayesian clustering method

(STRUCTURE) was used to infer population structure (Pritchard et al., 2000). Ten independent runs of K = 1 to

10 were performed at 1,000,000 Markov chain Monte Carlo (MCMC) repetitions with a 1,000,000 burn-in period

using no prior information and assuming correlated allele frequencies and admixture. The optimum K-value was

identified using the maximal values of LnP(D) (the posterior probability of the data for a given K) returned by

structure (Pritchard et al., 2000). In addition, △K-values based on the rate of change in the log probability of data

between successive K-values, was also estimated (Evanno et al. 2005). STRUCTURE HARVESTER (Earl, 2011)

was used to generate graphs for LnP(D) (mean±SD).

2 Results

2.1 Genetic diversity

A total of 201 samples from Tangjiahe NR were collected (Fig. 1). The quality of the fecal DNA from

Luoyigou was too poor to analyze. Forty fecal and six hair samples (25 from Hongshihe and 21 from Motianling)

were considered suitable for further microsatellite analysis based on quality verification. Ten microsatellite loci

could be amplified from 46 samples (seven loci with approximately 90% success, two with approximately 80%

success, one with approximately 70% success). These 46 DNA samples included 42 unique genotypes, which

indicated there were at least 42 different individuals in the data set (22 from Hongshihe and 20 from Motianling).

Micro-Checker showed that null alleles might be present, but there was no evidence for large allele dropout, false

alleles (FA) and multiple alleles (MA) in the data set. Based on these results, we know our data were sufficient

for further analyses. Analysis using GIMLET showed that the combination of the ten chosen loci would only

produce the identical genotypes of full siblings by chance with a probability of 0.00024% (Fig. 2). Molecular

sexing identified 22 male and 19 female giant pandas; we failed to successfully sex one individual.

A total of 59 alleles were obtained from the 10 microsatellite loci in the 42 samples (Table 1), with a mean allele

number per locus of 5.9. The mean allelic richness (AR) at each locus was 5.173 for the total population (range from

2.000 to 7.059). The Hongshihe subpopulation preserved the higher allelic diversity (AR = 5.240) compared to the

Motianling subpopulation (AR = 5.026). The mean expected heterozygosity (HE) was 0.703 for the total population

(range: 0.453–0.814), 0.699 for the Hongshihe subpopulation (range: 0.512–0.826), and 0.674 for the Motianling

subpopulation ( range: 0.273–0.836).

2.2 Linkage disequilibrium (LD) and HWE heterozygosity

Following Bonferroni correction highly significant LD tests were not shown for any pair of loci within each of the

subpopulations. Hardy–Weinberg equilibrium tests of the total population in Tangjiahe NR indicated that almost all

investigated loci did not deviate from Hardy–Weinberg equilibrium except one locus (Panda-22) showing highly

significant heterozygote excess (P<0.01, Table 1). In global tests of HWE (across loci), the Tangjiahe population

conformed to Hardy–Weinberg equilibrium, and significant heterozygote excess was not found (P = 0.089). For the two

subpopulations only one locus (Ame-μ21) showed a highly significant departure from Hardy–Weinberg equilibrium (P

< 0.01). Overall loci for the two subpopulations were in Hardy–Weinberg equilibrium. The 95% confidence intervals

for FIS revealed a low inbreeding level (FIS = 0.024, range: -0.093–0.083). The low FIS-value implied that mating is

random within the Tangjiahe subpopulations.

2.3 Population differentiation


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In the genetic differentiation analysis, the 95% confidence intervals for FST values (FST = 0.044, range:

0.013–0.082, P = 0.05) reflected a slight population differentiation (0 < FST < 0.05) between these two subpopulations

in Tangjiahe NR. Assignment tests following Pritchard et al. (2000) revealed that the probability of the data in the Ln

probability was the highest when K = 1 (Ln P(D) = - 1101.4, Fig. 3). However, the △K method (Evanno et al., 2005)

only involves K = 2 to K = n－1, and this method cannot detect △K for K = 1. Therefore, the △K approach was not

applied in our study. Bayesian population genetic algorithms to STRUCTURE variation showed that all individuals

from the two subpopulations (Hongshihe or Motianling ) were assigned to one cluster. This revealed low genetic

subdivision and high gene flow between the two subpopulations within Tangjiahe NR.

3 Discussion

Noninvasive genetic sampling for genetic analyses are being increasingly applied to ecological and behavioral

studies of giant pandas (Lü et al., 2001; Zhan et al., 2006; Zhang et al., 2007). The use of noninvasive samples such as

feces, hair and urine can provide useful information for population monitoring and the estimation of important genetic

parameters (Taberlet et al., 1996). In our study, 201 noninvasive samples (113 from Hongshihe, 85 from Motianling and

three from Luoyigou) were collected from Tangjiahe NR, with 46 good quality DNA samples (40 fecal and six hair

samples) being suitable for further microsatellite amplification. The low success rate of DNA extraction was mostly due

to DNA long-term degradation affected by many factors in the environment (Taberlet et al., 1996; He et al., 2008).

Additionally, it was difficult to search for fresh feces and available hair samples in the field. In the Luoyigou area,

traces of giant pandas were rarely found and only three ineffective fecal samples were obtained. Hence, the Luoyigou

panda subpopulation was removed from further genetic analyses.

Analysis of microsatellite DNA revealed low genetic subdivision and high gene flow between the Hongshihe and

Motianling subpopulations. Assignment tests using the Bayesian clustering method in STRUCTURE identified one

genetic cluster from 42 individuals of the two subpopulations in Tangjiahe NR (Fig. 3). These results suggest good

habitat connectivity across Tangjiahe NR. Our results are inconsistent with the results of Zhang et al. (2002) whom

inferred that the Tangjiahe population was divided into three subpopulations (Hongshihe, Motianling and Luoyigou)

according to data from the third national survey of giant pandas using bamboo bite sizes in feces. These methods have

proven poor at identifying individuals, resulting in questionable estimates (Zhan et al., 2006). Moreover, their results

seem to contradict the availability of suitable habitat in the reserve. Although Wan et al. (2005) stressed the existence of

three reproductive clusters with formalin-fixed feces and oligonucleotide fingerprinting methods, they showed frequent

pairings and migrations between the two studied subpopulations. Therefore, the results of Wan et al. (2005) are

probably enough to explain the low differentiation observed in this study, at the level of allele frequencies.

In the last 30 years, three national giant panda surveys have shown a rapid population decline (86 individuals in the

1970s, 44 individuals in the 1980s and 38 individuals in the 1990s) using bamboo bite sizes in feces (Hu et al. 2003;

State Forestry Administration 2006). However, our data show a minimum of 42 giant pandas in the reserve and there

may be other giant pandas we did not sample. This finding indicates a population increase comparable to that of the

third (1990’s) survey (State Forestry Administration, 2006). This is supported by extensive field data in which humans

have increasingly seen individual giant pandas in the wild. For example, six individuals were observed in less than one

year from 2008 to 2009 in Tangjiahe NR. This indicates suitable habitat for giant pandas in Tangjiahe NR. Recently,

microsatellite analysis using fecal DNA has proven effective in estimating population size of other elusive animals such

as brown bears (Ursus arctos) (Bellemain et al., 2005; Zhan et al., 2006). Cronin et al. (2009) used 14 microsatellite

DNA loci to estimate effective population sizes (Ne) of polar bears (Ursus maritimus) in the southern Beaufort Sea,


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Alaska. We were unable to further estimate Ne in our study because of a lack of knowledge about the reproductive

patterns and population information of giant pandas in the field. We can only make a rough comparison of population

size (N) because of the change of population census methods in each national survey.

Despite population decline in the last 30 years (Fu et al., 2003; Wan et al., 2005; State Forestry Administration,

2006), our data indicate a high level of genetic diversity for the Tangjiahe population compared with that of previous

studies in other nature reserves (Lü et al., 2001; Zhang et al., 2007; He et al., 2008; Shen et al., 2009). Zhan et al. (2006)

reported that values of numbers of alleles (A) and expected heterozygosity (HE) were 4.8, 0.670, respectively for the

Huanglong NR population, 2.9, 0.633 for the Wujiao NR population, and 5.4, 0.609 for the Wanglang NR population

with nine microsatellite loci. Zhang et al. (2007) found that A = 3.5–5.3 and HE = 0.366–0.642 for all five extant

mountain populations (Qinling, Minshan, Qionglai, Liangshan, and Xiaoxiangling) using 11 microsatellite loci. Zhu et

al. (2010) tested the average A = 3.2–4.6, He = 0.56–0.63 for the Xiaoxiangling and Daxiangliang populations. Other

studies of giant pandas also found that A = 6.2, HE = 0.680 for the Wanglang NR population, and A = 7.6, HE = 0.819

for the Baoxing region population, and A = 4.0, HE = 0.592 for the Liangshan region population (He et al., 2008; Hu et

al., 2010). Therefore, population decline appears to have not affected the level of population genetic diversity, which

may be because of good habitat conditions and effective management measures in Tangjiahe NR. Zhang et al. (2007)

concluded that present wild populations still comprise a rich gene pool and we posit that current wild giant panda

populations have the potential to be restored through careful effort as a result of the higher level of genetic diversity

reported here. However, the giant panda population in Tangjiahe NR is still small with less than 50 giant pandas and

faces overall loss of genetic diversity and high inbreeding. Evolution in small populations (Ne < 200) is determined

primarily by genetic drift rather than adaptation through natural selection (Frankham et al., 2002).

Frankham et al. (2002) put forward that a 0.25 level of inbreeding would be high enough to directly reduce fitness

even in the absence of detectable genetic abnormalities. Our data show a low value (FIS = 0.024) that is far from

dangerous (FIS < 0.25). This indicates a random mating population in Tangjiahe NR. Zhang et al. (2007) found that the

FIS value ranged from - 0.124 to 0.040 for extant five mountain populations (Qinling, Minshan, Qionglai, Liangshan,

and Xiaoxiangling). He et al. (2008), however, reported high inbreeding for giant panda populations in Wanglang (FIS =

0.258) and Baoxing (FIS = 0.407). Although some populations may respond to inbreeding differently, mixing

populations to maintain genetic diversity and minimize potential inbreeding should be considered. Additionally, animals

from the study population may be good source animals for other areas.

Microsatellite variability reveals high genetic diversity and low genetic differentiation in the Tangjiahe population,

indicative of suitable habitat and effective conservation strategies. Suitable habitat provides giant pandas with key

resources (Schaller et al.,1985; Wei et al., 2000; Hu, 2001; Pan et al., 2001). Previous research suggests that abundant

bamboo, suitable birth dens and old growth forests may be important survival factors for wild giant pandas (Yong et al.,

2004; Zhan et al., 2007). Since the reserve’s establishment in the 1970s, the State Forestry Administration took effective

measures to enforce farmer migration out of the reserve and ban intra-reserve human activities such as logging, farming,

poaching, grazing and mining (Hu, 2005). Habitat and biodiversity have undergone restoration strategies (Zhang et al.,

2002; Hu, 2005; Wan et al., 2005). The large population of takin (Budorcas taxicolor) in Tangjiahe NR is also evidence

for the reserve’s habitat suitability (Hu, 2005).

While our findings are promising, the zone surrounding Tangjiahe is still threatened by human activity. Although

Zhang et al. (2002) reported 10 giant pandas in the Luoyigou region, it was very difficult to find traces of these animals

in our study because of long-term granite mining in the area. National giant panda national surveys have revealed a

reduction in giant panda habitat and population decline in Qingchuan County within the last 30 years (Hu, 2001; Hu et


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al., 2003). Historically, giant pandas were distributed in Tangjiahe NR and the adjacent seven townships including

Qiaozhuang, Kongxi, Dashi, Qingxi, Sanguo, Haoxi and Yaodu within Qingchuan County in the 1970s (Hu et al. 2003).

They survived in Tangjiahe NR and the neighboring region of Sanguo and Haoxi in the 1980s. As a result, giant panda

habitat has contracted 30 km westward from the 1970s to the 1980s in this county (Hu, 2001; Hu et al., 2003; State

Forestry Administration, 2006). Increasing human disturbance has caused a decline in the extent and quality of

surrounding giant panda habitat (Liu et al., 2001; Loucks et al., 2001; Yan et al., 2005) and we believe it is necessary to

strengthen habitat conservation and management in this area. For example, we propose that the government should ban

mining projects that result in environmental issues including erosion and the formation of sinkholes. We further suggest

that Dongyanggou NR be incorporated into Tangjiahe NR as this will increase the effective size of these protected

areas.

Acknowledgements This research was supported by the National Basic Research Program of China (973 Project:

2009CB426311), National Natural Science Foundation of China (C031202), Sichuan Youth Science and Technology

Foundation (08ZQ026-082), Chengdu Giant Panda Breeding Research Fund (CPF08001), Sichuan Agricultural

University Research Start Fund for Introduced Talented Person (00221200), and the Sichuan Youth Science and

Technology Foundation (09ZQ026-042). Thanks to the Sichuan Forestry Bureau for providing tremendous assistance

with this project, staff of Tangjiahe NR and Chengdu Research Base of Giant Panda Breeding for sample collection, and

Sarah Bexell, Hu Jinchu and Ran Jianghong for useful suggestions on an earlier version of this manuscript.

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Fig. 1 Study area with sampling locations of giant pandas and the distribution of three giant panda subpopulations in

Tangjiahe Nature Reserve

Inset shows the current giant panda distribution across China.


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Fig. 2 Analysis using gimlet showed decrease in the probability of identify, P(ID), for giant panda genotypes as more

microsatllite loci are added in order of increasing values

Fig. 3 Results from Bayesian Clustering Analyses of giant pandas in Tangjiahe Nature Reserve using STRUCTURE,

Admixture model, and burn-in and replication values set at 1,000,000 and 1,000,000, respectively

Plot displays mean log-likelihood values (LnP(D)) for 10 independent runs for each value of K for K =1–10. The highest value was at K = 1,

indicating one genetic cluster in the Tangjiahe population.

microsatellite variability reveals high genetic diversity ... · microsatellite variability reveals...

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