Microsatellite and Skull Morphology Analysis of Tupaia belangeri chinensis in Yunnan Province

Yue Ren1, Ting Jia2, Hao Zhang1, Zhengkun Wang1 and Wanlong Zhu1*

1Key Laboratory of Ecological Adaptive Evolution and Conservation on Animals Plants in Southwest Mountain Ecosystem of Yunnan Province Higher Institutes College, School of Life Sciences, Yunnan Normal University, Kunming, 650500, China

2Yunnan College of Business Management, Kunming, 650106, China

ABSTRACT

Chinese tree shrew (Tupaia belangeri chinensis) is an experimental animal with a close affinity to primates. A lot of genetic marker selections and physiological adaptation studies of T. belangeri chinensis have been reported in detail. But genetic diversity and skull adaptation within T. belangeri chinensis populations from different habitats remain largely unknown or are controversial. In the present study focuing on T. belangeri chinensis that from Mengla, Puer, Jingdong, Kunmimng, Chuxiong, Dali, Pianma and Lijiang. Using microsatellite approaches, we observed considerable genetic variation and high heterozygosity in T. belangeri chinensis populations in different Yunnan province, and Chuxiong population and Lijiang population had relative low genetic diversity. Moreover, skull morphology changed for adapting to changing environment in T. belangeri chinensis. Finally, MengLa population had remarkable differences with the other populations both in genetic diversity and skull morphology. Our study provides genetic diversity and skull morphology knowledge of T. belangeri chinensis, their adaptation of genetic level and skull offers new insight into their classification and adaptation.

Article Information

Received 24 September 2021

Revised 08 October 2021

Accepted 27 October 2021

Available online 09 February 2022

(early access)

Published 19 October 2022

Authors’ Contribution

WLZ and ZKW conceived the study and participated in design and coordination. YR carried out the studies of microsatellite and skull, and drafted the manuscript. TJ and HZ made the skull specimens.

Key words

Tupaia belangeri chinensis, Genetic diversity, Microsatellite, Skull morphology, Fst

DOI: https://dx.doi.org/10.17582/journal.pjz/20210924080925

* Corresponding author: [email protected]

0030-9923/2023/0001-57 $ 9.00/0

Copyright 2023 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

INTRODUCTION

Genetic diversity protection, ecosystem diversity protection as well as species diversity protection were listed as three priority contents of global biodiversity conservation by International Union for Conservation of Nature (IUCN). Genetic diversity has been generally regarded as genetic variation between different populations and within population, and has been studied mainly from the phenotype, chromosome, protein, and DNA (Ellegren and Galtier, 2016). Genetic diversity both helps understand the population genetic structure and polymorphism, and provides valuable baseline information for species classification, utilization and protection, and the environment factors play an important role in species genetic diversity (Scheiner, 2003). Species genetic diversity in different environment with different altitude, temperature, latitude and longitude has been reported in detail, including in a variety of flora and fauna species (Slatking, 1987; Huang et al., 2005). Microsatellite, a kind of universal molecular marker, has been wildly used to explore the genetic variation, evolution and origin of mammals (Ellegren, 2004; Scibner and Pearce, 2000).

Environment factors effect can directly reflect in mammalian body traits and skull morphology (Caumul and Polly, 2005; Klingenberg, 2010). It is easy and quick to distinguish species and even subspecies based on the animals phenotypic polymorphism. For small mammal, skull morphology is an important phenotypic trait (Mu et al., 2015) that change for adapting to changing environment (Gao et al., 2017).

Tupaia belangeri chinensis (Tupaiidae, Scandentia) has a wide distribution cover a large variety of environments, ranging from South Asia, Southeast Asia and Southwest China (Wang, 1987; Roberts et al., 2011). Due to its unique characteristics, such as small body size, high encephalization quotient, short reproductive cycle or life span, T. belangeri chinensis has been proposed to be alternative experimental animal to primates in biomedical research (Yu et al., 2016; Lu et al., 2016; Pryce and Fuchs, 2017; Gawne et al., 2017). Previous studies have shown that there were significant differences in the phenotypic morphological characteristics of T. belangeri chinensis from different environments (Wu et al., 2013; Gao et al., 2017), and there were high diversity and microsatellite polymorphism within population from one region (He et al., 2009; Chen et al., 2011; Liu et al., 1989; Li et al., 2012), knowledge of classification and adaptation within T. belangeri chinensis populations is limited because of the lack of the comparison studies between multiple populations. The primary objective of this paper is to resolve this issue by combining microsatellite as well as skull morphology to explore the genetic variation and skull morphology of T. belangeri chinensis among the different populations in Yunnan province.

MATERIALS AND METHODS

Experimental animals

Adult healthy male tree shrews (T. belangeri chinensis) used in the present study were captured from MengLa (ML, n=7), PuEr (PE, n=26), JingDong (JD, n=6), KunMing (KM, n=12), ChuXiong (CX, n=7), DaLi (DL, n=15), PianMa (PM, n=6), LiJiang (LJ, n=15) from January, 2016 to January, 2017. All animals were captured using standard rat cages. Detailed sampling site information is given in Table I. Animals caught in wild were immediately sacrificed by anesthesia, and their livers were immediately dissected and frozen in liquid nitrogen. Samples were stored in dry ice and transported to the laboratory of Yunnan Normal University and stored at -80oC refrigerator until assayed. All procedures were licensed under and approved by Animal Care and Use Committee of School of Life Science, Yunnan Normal University. This study was approved by the Committee (13-0901-011). Total genomic DNA was extracted from the tissue samples of the animals using the phenol/chloroform method. DNA samples were detected by using agar-gel electrophoresis and stored at -20oC until used (Zhu et al., 2014).

The PCR amplification of microsatellite DNA

Eight pairs microsatellite DNA primers with easy amplification and high polymorphism of T. belangeri chinensis were screened from the 27 pairs of microsatellite primers which have been reported (Srikwan et al., 2002; Olson et al., 2004, 2005; Munshi-souyh and Wilkinson, 2006), and the detailed primer information is shown in the Table II. Eight loci were coamplified in a single reaction (15µl reaction volume) which consisted of 1.5μl10×PCR buffer, 2.5mM dNTPmixture, 2.5U Taq polymerase, 10pM of the reverse primers and 10pM of the forward primers, 10ng/μl DNA template and 2.5mM Mg2+. The normal cycle consisted of denaturation at 94°C for 8 min followed by 30 cycles each of 94°C for 1 min, 50~58°C for 1 min, and 72°C for 1 min, and final excision at 72°C for 6 min. 3μl of the PCR product was detected by using 2% agarose gel electrophoresis. And performed by capillary electrophoresis. Further, 1ul of the PCR product was mixed with 0.8μl of LIZ 500 internal standard and 8.0 μl of HI-DI formamide (Applied Biosystems), and the mixture was incubated at 95°C for 5min. The PCR product was detected using capillary electrophoresis by using ABl3730XL (Applied Biosystems). Finally, microsatellite fragments were read by using GeneMarkerV2.2.0 software (Soft Genetics LLC, State College, PA) (Yang et al., 2016). We deleted the microsatellite loci with less than 4 loci (Barker, 1994).

 

Table I. Sampling sites of Tupaia belangeri chinensis.

Population	Code	Latitude	Longitude	Climate	Altitude (m)	Annual precipitation (mm)	Average annual temperature (oC)	Sample size
Mengla	ML	N21°28′48″	E101°33′56″	Tropical monsoon climate	646	1540	21	7
Puer	PE	N22°47′9″	E100°58′20″	Subtropical monsoon climate	1320	1100-1780	15-20	26
Jingdong	JD	N24°26′44″	E100°50′53″	Subtropical monsoon climate	2450	1086	18.3	6
Kunming	KM	N24°52′7″	E100°50′53″	Subtropical monsoon climate	1895	1011	18.5	12
Chuxiong	CX	N25°06′0″	E101°01′48″	Subtropical and subhumid plateau monsoon	1857	838	14.6	7
Dali	DL	N25°41′26″	E100°12′41″	Low latitude plateau monsoon climate	1976	1056	15	15
Pianma	PM	N26°0′48″	E98°37′35″	Tropical monsoon climate of the Indian Ocean	1900	1200	14-16	6
Lijiang	LJ	N26°52′21″	E100°13′19″	Subtropical humid climate	2418	1000	12-20	15

 

Table II. Characteristics of microsatellite loci amplified from eight Tupaia belangeri chinensis geographic populations.

Primer	Primer sequence 5’ 3’	Locus repeat motif	Tm (oC)	Size	Fis	Fit	Fst
TBC1	F: AGGAGGTCCAAGCAATGAGA; R: AATAATCCAGCCACCAAGAA	(CA)19	48	376-384	-0.34	-0.17	0.13
TBC3	F: TGGCAGGATTTCCTTCATTC; R: TCATTGCACGAGAATTTCCA	(TG)3TATGC(GT)5	44	162	-0.18	-0.08	0.09
TBC5	F: CACTTGCCATTAGTCTTTGA; R: TTGGGAGAATTAGGTTTGAG	(AC)10GCAG(AC)4	48	147-172	-0.35	-0.19	0.12
TBC8	F: TCTGCTTTATGTCTGTATCTTT; R: CTGTGTCTCTCCGCAATGAG	(TC)11AGTCA(CT)5	51.5	240	0.16	0.40	0.28
TG4	F: TGAAAACTGGCAATTCATATGC; R: CAATCCTTTTTCGTTAGTTTTGTG	(CA)9TA(CA)2	57.5	150-152	0.05	0.25	0.22
TG19	F: ACCCCTCCCTAAAGGAACT; R: CGCCCTATAGAAACCTCTCC	(CA)7TAAA(CA)8	54	173-177	-	-	-
TG22	F: GTGAGTGCACTTGCCCTGTA; R: TCCTGAACCTGGTGGCTAAC	(CA)8A(CA)10	54.5	149-182	-0.15	0.04	0.16
JS22	F: CAATGTCCTGGTGGTTATGG; R: GAAGTGGTCACTCTGCAATCC	(GT)3GC(GT)14	55.2	139-201	-0.26	-0.12	0.11
Mean					-0.15	0.02	0.16

 

The detection of skull geometric morphology

We used method of using larva of Tenebrio molitor to manufacture skull specimen of small mammals (Gao et al., 2017). The damaged skull specimens were not performed following analysis. Finally, a total 81 complete, adult and healthy male individual skulls were examined in the morphological study, and skull morphological data were obtained by using landmark methods to digitally dispose each skull sample. The processes mainly including image acquisition, image digitization, de-morphing. First, digital SONY Camera (DSC-Y110), which was 10 cm from the skull, was used to obtain pictures of the dorsal cranium, lateral cranium, ventral cranium and lateral mandible of the skull. Secondly, the pictures were digitally disposed by using TPSDIG2 software (Rohlf, 1990). According to the method of marking the skull of beasts (Cardini and Higgins, 2004), we dotted and marked the pictures, and selected homologous points including 16 points in dorsal cranium, 18 points in lateral cranium, 22 points in ventral cranium, and 12 points in lateral mandible to obtain the coordinate data (Fig. 1). The left structure of skull samples were marked to avoid data repetition, and all markers were made by one person for repeating six time, and the average coordinate data were used in the following statistical analysis. Moreover, generalized procrustes analysis (GPA) was used to remove the non-morphological variations.

Data analyses

First, we converted data format by using CONVERT software (version 1.31) (Liu et al., 2018). Secondly, POPGENE software (version 1.32) (Yeh and Yang, 1999) was used to calculate genetic diversity indexes including the number of alleles (Na), effective number of allele (Ne), observed heterozygosity (Ho), expected heterozygosity (He), F-statistics (Fis, Fit, Fst), and polymorphism information content (PIC). To determine whether the pattern of differentiation among T. belangeri chinensis populations supported a hypothesis of isolation-by-distance, Mantel test of matrix correspondence (Mantel, 1967) as implemented in Ecodist package in R (version 3.6.3). This approach tested whether genetic distances between populations increased with greater geographic distances between their sites of origin. Point-to-point geographic distances were measured from topographic maps of the study area. Genetic distances were calculated as mean pairwise Fst between populations (Nei and Li, 1979).

Moreover, Hardy-Weinberg equilibrium (HWE) test of populations was performed by using GENEPOP software (version 4.2) (Raymond and Rousset, 1995). UPGMA cluster method was used to evaluate the reliabilities of construction of phylogenetic trees based on the microsatellite data from eight T. belangeri chinensis populations in Yunnan Province. Finally, factorial correspondence analysis (FCA) and the population structure were performed using Genetix software (version 4.05) (Pritchard et al., 2000).

Morphologika (version 2v2.5) (Zhang et al., 2019) was used to analyze skull lice spline. Moreover, SPSS software (version 22.0) (Jia et al., 2009) was used to analyze the skull multidimensional scaling and preform principal component analysis (PCA).

 

RESULTS

Genetic diversity of T. belangeri chinensis

Partial results of capillary electrophoresis were showed in Figure 2. Detection results of TBC1, TBC3, TBC8, TG4 and TG19 loci showed that TBC3 and TBC8 were homozygous microsatellite with 162 and 240bp size of fragments respectively, and TBC1, TG4 and TG19 were heterozygous microsatellite, and the fragment sizes of them were 376/384, 150/152 and 173/177, respectively. Total 84 alleles at eight microsatellite loci were detected, and the microsatellite loci with less than 4 loci were deleted, and finally total 82 alleles at seven microsatellite loci were used in the following analysis.

Indeed, high Na (mean: 4.04, min: 1, max: 9), Ho (mean: 0.58, min: 0.08, max: 0.96) and He (mean: 0.65, min: 0.08, max: 0.93) were observed for each of the microsatellite markers (Table III) in this study, which indicating that there was high genetic diversity among T. belangeri chinensis populations in Yunnan province. Moreover, PM population and PE population had the highest PIC value (0.68), followed by ML population (0.62), JD population (0.58), DL population (0.53), KM population (0.52), LJ population (0.48), and CX population (0.47). Hardy-Weinberg equilibrium test was further performed, and the result showed that most of the microsatellite loci were conform to the Hardy-Weinberg equilibrium (PHWE > 0.05), but few microsatellite loci were not as PHWE < 0.05.

Differentiation of T. belangeri chinensis populations

F-statistics results were shown in Table II. Five Fis counts of loci were less than zero, indicating that there are many heterozygotes within population, and the absolute value ranged from 0.05 to 0.35. Four Fit counts of loci were less than zero, and the absolute value ranged from 0.02 to 0.40. Pairwise Fst value ranged from 0.09 to 0.28 with average 0.16. Pairwise Fst that visualized genetic differentiation between the populations are shown in Table IV. Pairwise Fst ranged from 0.02 to 0.33 (average, 0.12). Mantel test between geographic distance and pairwise Fst was showed in Figure 3. This result reflected that there was no correlation between geographic distance and pairwise Fst and indicated that geographic distance may not effect T. belangeri chinensis distribution and diffusion (mantel r = 0.047, P = 0.812).

 

Table III. Genetic diversities and Hardy-Weinberg equilibrium test of eight microsatellite loci in the eight Tupaia belangeri chinensis geographic populations.

Population	Loci	Na	Ho	He	PIC	PHWE
ML	TBC3	5.00	0.43	0.51	0.45	0.21
	TBC5	4.00	0.52	0.82	0.70	0.01
	TBC8	5.00	0.40	0.76	0.64	0.05
	TG4	6.00	0.33	0.89	0.79	0.00
	TG22	1.00	0.64	0.76	0.64	0.81
	JS22	5.00	0.67	0.68	0.56	0.91
	TBC1	4.00	0.54	0.69	0.55	0.26
	Mean	4.29	0.47	0.73	0.62	-
PE	TBC3	3.00	0.23	0.21	0.19	0.96
	TBC5	8.00	0.96	0.83	0.79	0.08
	TBC8	6.00	0.81	0.75	0.69	0.51
	TG4	4.00	0.67	0.70	0.63	0.03
	TG22	1.00	0.90	0.87	0.83	0.65
	JS22	8.00	0.69	0.78	0.83	0.01
	TBC1	8.00	0.78	0.84	0.80	0.13
	Mean	5.43	0.71	0.71	0.68	-
JD	TBC3	3.00	0.29	0.38	0.33	0.11
	TBC5	5.00	0.67	0.78	0.68	0.70
	TBC8	4.00	0.38	0.52	0.44	0.60
	TG4	3.00	0.33	0.59	0.46	0.30
	TG22	2.00	0.74	0.89	0.80	0.17
	JS22	7.00	0.75	0.67	0.59	0.07
	TBC1	5.00	0.73	0.84	0.74	0.98
	Mean	4.14	0.53	0.67	0.58	-
KM	TBC3	2.00	0.80	0.51	0.36	0.05
	TBC5	4.00	0.80	0.60	0.51	0.70
	TBC8	1.00	0.00	0.00	-	-
	TG4	3.00	0.30	0.28	0.25	0.98
	TG22	2.00	0.60	0.75	0.67	0.65
	JS22	4.00	0.69	0.75	0.66	0.14
	TBC1	4.00	0.72	0.90	0.64	0.00
	Mean	2.86	0.54	0.54	0.52	-
CX	TBC3	2.00	0.71	0.49	0.35	0.20
	TBC5	3.00	0.86	0.69	0.57	0.03
	TBC8	3.00	0.29	0.27	0.24	0.99
	TG4	3.00	0.64	0.65	0.52	0.11
	TG22	2.00	0.65	0.69	0.57	0.30
	JS22	3.00	0.65	0.65	0.53	0.11
	TBC1	3.00	0.66	0.65	0.52	0.11
	Mean	2.71	0.64	0.58	0.47	-
Population	Loci	Na	Ho	He	PIC	PHWE
DL	TBC3	3.00	0.50	0.44	0.36	0.94
	TBC5	3.00	0.66	0.68	0.55	0.17
	TBC8	3.00	0.17	0.32	0.27	0.01
	TG4	5.00	0.50	0.67	0.58	0.34
	TG22	2.00	0.79	0.86	0.60	0.53
	JS22	5.00	0.65	0.71	0.60	0.54
	TBC1	4.00	0.83	0.85	0.75	0.65
	Mean	3.57	0.55	0.65	0.53	-
PM	TBC3	3.00	0.33	0.44	0.36	0.17
	TBC5	5.00	0.76	0.82	0.71	0.80
	TBC8	6.00	0.67	0.88	0.78	0.08
	TG4	5.00	0.67	0.67	0.58	0.85
	TG22	2.00	0.50	0.93	0.79	0.07
	JS22	6.00	0.67	0.91	0.81	0.10
	TBC1	7.00	0.78	0.86	0.76	0.58
	Mean	4.86	0.60	0.79	0.68	-
LJ	TBC3	3.00	0.25	0.24	0.21	0.98
	TBC5	6.00	0.75	0.59	0.54	1.00
	TBC8	2.00	0.08	0.08	0.08	1.00
	TG4	4.00	0.75	0.66	0.56	0.89
	TG22	2.00	0.75	0.57	0.49	0.96
	JS22	5.00	0.86	0.86	0.80	0.24
	TBC1	9.00	0.75	0.74	0.68	0.81
	Mean	4.43	0.60	0.53	0.48	-

 

Groups: ML, Mengla population; PE, Puer population; JD, Jingdong population; KM, Kunming population; CX, Chuxiong population; DL, Dali population; LJ, Lijiang population.

 

Table IV. Pairwise Fst between Tupaia belangeri chinensis populations.

	ML	PE	JD	KM	CX	DL	PM
ML	-
PE	0.04	-
JD	0.10	0.06	-
KM	0.33	0.21	0.14	-
CX	0.28	0.17	0.09	0.02	-
DL	0.15	0.11	0.06	0.13	0.12	-
PM	0.03	0.04	0.04	0.19	0.12	0.09	-
LJ	0.22	0.16	0.07	0.20	0.14	0.02	0.15

 

For details of the groups, see Table III.

 

Genetic structure of T. belangeri chinensis populations

UPGMA cluster method and FCA analysis were further used to evaluate the construction of cluster analysis tree and perform population structure (Fig. 4). The results showed that ML population and PM population respectively became a separate cluster. Moreover, the other six populations branch off in pairs, including JD population and PE population, DL population and LJ population, CX population and KM population.

 

 

Variations of skull morphology

The PCA result of dorsal cranium, lateral cranium, ventral cranium and lateral mandible were summarized in Table V. In the dorsal cranium, the eigenvalues for the first three principal components were 0.00963, 0.00213 and 0.00140, respectively, and accounted for 89.15% of the total variance. The first and second principal components were mixed together (Fig. 5A). In the lateral cranium, the eigenvalues for the first three principal components were 0.00120, 0.00107, 0.00057, respectively, and accounted for 73.23% of the total variance. The first two principal components were mixed together, except for the ML population, which had very little overlap that could be due to intraspecific individual variations (Fig. 5B). In the ventral cranium, the eigenvalues for the first three principal components were 0.00195, 0.00154, 0.00038, respectively, and accounted for 92.37% of the total variance. The first two principal components of JD and ML populations mixed together, and the other populations were mixed together (Fig. 5C). In the lateral mandible, the eigenvalues for the first three principal components were 0.00063, 0.00040, 0.00011, respectively, and accounted for 76.72% of the total variance. The first two principal components were mixed together, except for the KM population, which had very little overlap with the other population. Moreover, PE population were mixed with all of the other populations.

 

We combined the data of dorsal cranium, lateral cranium, ventral cranium and lateral mandible of animals from CX, JD, DL, KM, LJ, ML, PM, and PE populations, and used multidimensional scaling analysis analyze the combination data. The result showed that there were differences between eight populations, and there was great difference between ML population and the other seven populations (Fig. 6).

 

DISCUSSION

Heterozygosity is a measure of genetic variation within the population, and its value reflects the uniformity of individuals within the population. A high value indicates high genetic variation within the population, while a low value indicates low genetic variation within the population, mainly including Ho and He. Ho refers to the proportion of heterozygous individuals in the population. In the case of the same Na, the higher the proportion of heterozygous individuals in the population, the richer the genetic diversity of the population. He is the heterozygosity calculated on the premise that each gene locus conforms to HWE. Moreover, the average He can be used to measure the genetic variation of population. The higher the average of He level is, the richer the genetic diversity of the population becomes (Botstein et al., 1980). When He range from 0.5 to 0.7, the population has high genetic diversity and low inbreeding number which belong to the characteristics of a closed colony. When He is lower than 0.5, the genetic diversity is low and the inbreeding number is high; When He is greater than 0.7, the genetic heterozygosity is high and the individual differences of the population are great (Liu et al., 2018), neither of these cases belong to the characteristics of closed colony. In the present study, the high genetic diversity among T. belangeri chinensis populations in Yunnan province is consistent with the other studies (Liu and Yao, 2013; Zhu et al., 2014). The most He were higher than Ho, indicating that there may be heterozygosity among populations. The average He level of JD population, CX population, KM population, DL population and LJ population ranged from 0.5 to 0.7 reflecting a high genetic diversity which are corresponding to the characteristics of enclosed group (Li et al., 2011; Liu et al., 2018). Increased genetic diversity of a population means it has increased environmental adaptability (Ellegren and Galiter, 2016). While the He level of ML population, PE population and PM population revealed that the four populations did not corresponding to the characteristics of closed colony (Li et al., 2011; Liu et al., 2018).

 

Table V. Principal component, variance explained and cumulative variance explained of dorsal cranium, ventral cranium, lateral cranium and lateral mandible.

Skull/ Principal component	Eigenvalue	Variance explained (%)	Cumulative variance explained (%)
Dorsal cranium
PC1	0.00963	65.14	65.14
PC2	0.00213	14.41	79.54
PC3	0.00140	9.50	89.04
Ventral cranium
PC1	0.00120	81.26	81.26
PC2	0.00107	7.23	88.48
PC3	0.00057	3.88	92.37
Lateral cranium
PC1	0.00195	36.85	36.85
PC2	0.00154	29.11	65.96
PC3	0.00038	7.27	73.23
Lateral mandible
PC1	0.00063	42.33	42.33
PC2	0.00040	26.71	69.03
PC3	0.00011	7.68	76.72

 

We further compared genetic diversity between different Tupaia species which showed in Table VI. In the present study, the average He was 0.64 which is lower than Tupaia longipes (average 0.69) (Brunke et al., 2020), Tupaia belangeri in Guangxi (average as 0.70) (Tang et al., 2016; Yang et al., 2016), but is higher than T. belangeri yaoshanensis (average as 0.48) (Su et al., 2017), T. belangeri chinensis in laboratory (average as 0.52) (Zhang et al., 2015) and T. belangeri chinensis born in laboratory including F1, F2, F3 and F4 of T. belangeri chinensis (average as 0.62, 0.61, 0.60 and 0.60, respectively) (Yang et al., 2010; Liu et al., 2018), and is similar with Tupaia glis (average as 0.65) (Srikwan et al., 2002), Tupaia spp (average as 0.66) (Munshi-South et al., 2006), T. belangeri chinensis in Yunnan (average as 0.62) (Liu and Yao, 2013).

 

Table VI. Genetic diversity comparison between different Tupaia species.

Species	Ho	He	PIC	Reference
Tupaia glis	0.83	0.65	-	Srikwan et al., 2002
Tupaia spp.	0.67	0.66	-	Munshi-South et al., 2006
Tupaia longipes	0.78	0.69	-	Brunke et al., 2020
Tupaia belangeri yaoshanensis	0.40	0.48	0.44	Su et al., 2017
T. belangeri in Guangxi	0.48	0.70	0.73	Tang et al., 2016
T. belangeri in Guangxi	0.62	0.70	0.64	Yang et al., 2016
T. b. chinensis in laboratory	0.61	0.52	-	Zhang et al., 2015
T. b. chinensis born in laboratory	-	0.62	0.52	Yang et al., 2010
T. b. chinensis in Yunnan	0.57	0.62	0.58	Liu et al., 2012
T. belangeri F1	0.50	0.62	0.56	Liu et al., 2018
T. belangeri F2	0.56	0.61	0.54	Liu et al., 2018
T. belangeri F3	0.53	0.60	0.53	Liu et al., 2018
T. belangeri F4	0.48	0.60	0.47	Liu et al., 2018
T. b. chinensis in Mengla	0.47	0.73	0.62	Present study
T. b. chinensis in Puer	0.71	0.71	0.68	Present study
T. b. chinensis in Jingdong	0.53	0.67	0.58	Present study
T. b. chinensis in Kunming	0.54	0.48	0.52	Present study
T. b. chinensis in Chuxiong	0.64	0.58	0.47	Present study
T. b. chinensis in Dali	0.55	0.65	0.53	Present study
T. b. chinensis in Pianma	0.60	0.79	0.68	Present study
T. b. chinensis in Lijiang	0.60	0.53	0.48	Present study

 

The heterozygosity and PIC of microsatellites are optimal parameters for measuring genetic variations within population, and the high value reflect the high genetic diversity of population (Rousset, 1997). Botstein et al. (1980) suggested that population has high genetic polymorphism when PIC value is more than 0.5, and has moderate genetic polymorphism when PIC range from 0.25 to 0.5, and has low genetic polymorphism when PIC value is less than 0.25. Our data showed that CX population and LJ population had moderate genetic polymorphism, and the other populations had high genetic polymorphism.

In the present study, most of the microsatellite loci conformed to the Hardy-Weinberg equilibrium, but few microsatellite loci were not. It may be caused by the small sample size, however, in the study of genetic structure of wild specie populations, there are general phenomena of deviation from the Hardy-Weinberg equilibrium caused by invalid allele or inbreeding among populations (Lade et al., 1996).

The F-statistics results indicated that there are more heterozygotes in T. belangeri chinensis populations, and this may caused by different degrees of inbreeding, indicating that it is necessary to strengthen the selection to breed excellent varieties in the breeding process of T. belangeri chinensis in the future (Yang et al., 2016). The low pairwise Fst may reflected that there were high gene flow between T. belangeri chinensis populations in Yunnan province and genetic variation mainly comes from within the population. Because of geographical differences, the same species populations from different habitat exhibit different levels of genetic diversity (Ellegren and Galtier, 2016). In this study, the selected populations mainly belong to mountainous areas category, and the result showed that geographic distance may not effect T. belangeri chinensis distribution, so there are other factors to influence the genetic diversity among different populations, such as longitude, latitude, temperature and food recourse (Zhu et al., 2014).

The UPGMA cluster and FCA analysis indicate that T. belangeri chinensis populations in Yunnan province had obvious genetic structure, and they could be divided into five branches, which first divided ML population and PM population as a separate cluster. Moreover, the other six populations branch off in pairs, including JD population and PE population, DL population and LJ population, CX population and KM population. This is consistent with the previous result of D-loop gene sequence of T. belangeri chinensis in Yunnan province in our group (Zhu et al., 2014), and it may be caused by the geographical barrier of the Nu Jiang and Wuliang mountain (Zhu et al., 2014).

In general, species can survive from changing climates in three ways: migration, plasticity, and adaptive evolution (Williams et al., 2008; Chen et al., 2018). Animals can change phenotypic traits for adapting to the changing environment with different altitude, vegetation and climate (Caumul and Polly, 2005). Our geometric morphological analysis of skull revealed that there was difference in skull in different T. belangeri chinensis populations in Yunnan province, which consistent with the results shown in previous studies (Jia et al., 2009; Zhu et al., 2013; Gao et al., 2017). Interestingly, the ML population had more difference with the other populations by using multidimensional scaling analysis, while there were no difference between the other populations. These differences may be related to the habitat and/or environment. For example, ML population living in low altitude area with tropical monsoon climate, and the food (in terms of quality and quantity) is sufficient, so that environmental stress is small. As a result, there are difference between ML population with the other populations.

CONCLUSION

In conclusion, we characterized a set of eight polymorphic microsatellite markers and skull morphology for the T. belangeri chinensis in Yunnan province. Our data confirm the T. belangeri chinensis had a considerably high heterozygosity and genetic diversity, and CX population and LJ population have relatively lower genetic diversity and have characteristics of enclosed group. Moreover, skull morphology change for adapting to changing geographical environment in T. belangeri chinensis. Finally, ML population has remarkable differences with other population both in genetic diversity and skull morphology. We hope that these results will provide essential help for clarifying the classification and relationships of T. belangeri chinensis from different regions and advancing the inbreeding project of the T. belangeri chinensis.

ACKNOWLEDGEMENTS

We are grateful to all the members of Physiological Ecology Group in Yunnan Normal University for their help with conducting the capture of animals and discussing the results. This work was financially supported by the National Natural Scientific Foundation of China (Grant No. 31760118), Yunnan Ten Thousand Talents Plan Young & Elite Talents Project (YNWR-QNRC-2019-047), and Yunnan Provincial Middle-Young Academic and Technical Leader candidate (2019HB013).

Statement of conflict of interest

The authors have declared no conflict of interest.

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