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Genetic diversity and structure of mongolian gazelle (Procapra gutturosa) populations in fragmented habitats

BMC Genomics volume 24, Article number: 507 (2023) Cite this article

Abstract

Background

The Mongolian gazelle (Procapra gutturosa) population has shown a considerable range of contractions and local extinctions over the last century, owing to habitat fragmentation and poaching. A thorough understanding of the genetic diversity and structure of Mongolian gazelle populations in fragmented habitats is critical for planning effective conservation strategies.

Result

In this study, we used eight microsatellite loci and mitochondrial cytochrome b (Cytb) to compare the levels of genetic diversity and genetic structure of Mongolian gazelle populations in the Hulun Lake National Nature Reserve (HLH) with those in the China-Mongolia border area (BJ). The results showed that the nucleotide diversity and observed heterozygosity of the HLH population were lower than those of the BJ population. Moreover, the HLH and BJ populations showed genetic differentiation. We concluded that the HLH population had lower genetic diversity and a distinct genetic structure compared with the BJ population.

Conclusion

The genetic diversity of fragmented Mongolian gazelle populations, can be improved by protecting these populations while reinforcing their gene exchange with other populations. For example, attempts can be made to introduce new individuals with higher genetic diversity from other populations to reduce inbreeding.

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Introduction

Globally, severe human disturbances can easily lead to fragmentation of natural landscapes, resulting in fragmentation of the distribution of many animals [1]. Small populations in fragmented habitats are susceptible to factors such as inbreeding and genetic drift, and are often characterized by high extinction rates, low genetic diversity, and declining numbers [2,3,4,5]. Therefore, to effectively protect and manage animal populations in fragmented habitats, additional population genetics studies are needed to assess the genetic factors associated with extinction risk [6].

Mongolian gazelles are one of the largest existing wild animal populations in Asia. Over the past 50 years, their distribution area has decreased by approximately 190,000 km2, owing to habitat destruction caused by human disturbance and other factors [7, 8]. The present range of the Mongolian gazelle is limited to Mongolia and the adjacent areas of north-eastern China and Russia. High mobility is characteristic of Mongolian gazelle. In BJ, they aggregate throughout the year. The populations of Mongolian gazelle vary in size seasonally due to mating and calving. The recent population estimate is 1,100,000 individuals within a 275,000 km2 portion. At present, population size of Mongolian gazelle is approximately 1,000 individuals in China. According to population decline rate and extent of occurrence, Mongolian gazelle is listed as Critically Endangered in China. Protection Category of Mongolian gazelle was listed as a Category I species in the China’s Red List of Biodiversity: Vertebrates [9]. Previous studies on the Mongolian gazelle have mainly focused on macroscopic aspects, such as migration, behavioral characteristics, and feeding habits, and studies on its population genetics are relatively lacking [10,11,12]. However, an understanding of the genetic diversity of the Mongolian gazelle is important to achieve the optimal conservation of this animal.

The Mongolian gazelle population in HLH is the largest surviving wild population in China. The recent population estimate is 90 individuals within a 14 km2 portion. The presence of man-made facilities such as grassland fences precludes gene flow of this Mongolian gazelle population with the outside population. In contrast, the Mongolian gazelle BJ population occupies a contiguous habitat. Therefore, we speculate that the genetic diversity of the fragmented HLH habitat is low, and that it is has genetically differentiated from other populations. This study aimed to explore the genetic diversity and structure of Mongolian gazelle populations in fragmented habitats using microsatellite DNA and Cytb. These results will provide a scientific basis for formulating Mongolian gazelle protection strategies.

Materials and methods

Sample collection and DNA extraction

Fecal samples were collected from the HLH and BJ areas (Fig. 1). The sampling area was grassland covered by snow. Total of 146 samples were collected before consolidation of duplicate animals. Individuals were identified using eight microsatellite loci (OArFCB304, SPS115, TGLA68, IOBT395, PZE114, MNS72, BM1341, and MB066) [13,14,15,16,17,18,19]. If all microsatellite sites in the fecal samples had one allele mismatch or all alleles were identical to the genotype, we determined that these fecal samples were from the same Mongolian gazelle [20]. Fecal samples were collected from 54 different individuals after removal of duplicates (Table 1). 27 fecal samples represent 30% of the Mongolian gazelle population in HLH, which is sufficient to represent the overall diversity. According to the China Meteorological Data Service Center (http://data.cma.cn/), the daily average ambient temperature was approximately − 30℃ during the collection time (Table 1) to ensure the quality of DNA. The Mongolian gazelle sample tubes were stored at − 80℃ until DNA extraction. Total genomic DNA was extracted using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany), in accordance with the manufacturer’s recommendations.

Fig. 1
figure 1

Sampling locations

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Table 1 Detailed information for all samples
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Microsatellite genotyping and data analysis

The primers were labeled with FAM or HEX fluorescent tags. Polymerase chain reaction (PCR) amplification was performed in a 50 µL solution consisting of 0.3 µL of Taq polymerase, 5.0 µL of 10 × buffer, 4.0 µL of dNTP, 0.3 µL of bovine serum albumin solution, 1.2 µL of each primer, and 6 µL of DNA. Then, H2O was added to the PCR mixture to make a final volume of 50 µL. The reactions were performed in a Veriti thermal cycler (Applied Biosystems) with an initial denaturation (95 °C for 5 min); 35 cycles of denaturation (94 °C for 30 s), annealing (50–63 °C for 30 s) and extension (72 °C for 35 s); followed by final extension (72 °C for 7 min). The PCR products were analyzed using an ABI 3730 XL DNA analyzer and GeneMapper (Applied Biosystems). We calculated the number of alleles (Na), number of effective alleles (Ne), observed heterozygosity (Ho), and expected heterozygosity (He) using GenAlEx 6.5 to assess the levels of genetic diversity among populations [21]. We also explored the genetic structures of Mongolian gazelles using the STRUCTURE software [22]. The appropriate number of population clusters (K) was calculated using the STRUCTURE HARVESTE [23]. Finally, we calculated the differentiation among and within populations using analysis of molecular variance (AMOVA) and principal coordinate analysis (PCoA).

Mitochondrial DNA amplification and data analysis

Due to the fecal quality and other factors, we amplified Cytb sequences (1140 bp) of 34 individuals (HLH, 17; BJ, 17) using the primers F- CCCATAGATAGGTGAAGGT and R- CAGGGAATAGTTTAAGCAG. Primers were designed according to the mitochondrial genome sequence of Procapra przewalskii (GenBank: MG674218.1) using Primer software [24]. PCR amplification was performed in a 50 µL solution consisting of 0.5 µL of Taq polymerase, 0.5 µL of bovine serum albumin solution, 5.0 µL of 10 × buffer, 4.0 µL of dNTP, 2.0 µL each primer, and 4 µL of DNA. H2O was then added to the PCR mixture to make a final volume of 50 µL. The reactions were performed in a Veriti thermal cycler (Applied Biosystems) with an initial denaturation (95 °C for 5 min); 40 cycles of denaturation (94 °C for 45 s), annealing (50 °C for 30 s) and extension (72 °C for 90 s); and a final extension (72 °C for 10 min). PCR products were sequenced using an ABI 3730 XL DNA analyzer. Alignments of the Cytb sequences in Mongolian gazelles were performed using MEGA software [25]. DnaSP software was used to analyze genetic diversity indexes, including the number of individuals (N), number of haplotypes (H), haplotype diversity (h), and nucleotide diversity (π) [26]. Finally, haplotype networks of Mongolian gazelles were constructed using the median-joining algorithm in Popart software [27].

Result

Microsatellite genetic diversity

Analyses of eight microsatellite loci among the two Mongolian gazelle populations showed a higher mean number of alleles (NaBJ = 7.625 [SD = 3.773]) and observed heterozygosity (HoBJ = 0.593 [SD = 0.224]) in the samples from BJ than in those from HLH (NaHLH = 5.000 [SD = 1.803]; HoHLH = 0.523 [SD = 0.171]). The highest number of effective alleles (Ne = 7.010) and expected heterozygosity (He = 0.857) were found at the OArFCB304 locus in the BJ population (Table 2). However, there was no significant difference between HLH and BJ in these values (Na, Ho, Ne, and He). In general, the BJ population showed higher genetic diversity than the HLH population.

Table 2 Genetic diversity parameters inferred from 8 microsatellite loci
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Mitochondrial genetic diversity

The median-joining (MJ) network based on Cytb sequences (1140 bp) revealed 28 haplotypes (Fig. 2). Fifteen haplotypes were identified in 17 individuals from BJ, whereas 13 haplotypes were identified in 17 individuals from HLH. Overall, haplotype and nucleotide diversities were high for the BJ population (h = 0.985, π = 0.00992) but lower for the HLH population (h = 0.926, π = 0.00401) (Table 3).

Fig. 2
figure 2

Haplotype network based on Cytb sequences. The size of circles represents the number of haplotypes. Different colors represent different populations. The hash lines represent the numbers of mutational steps

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Table 3 Genetic diversity parameters inferred from the mitochondrial Cytb gene
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Population genetic structure

In the STRUCTURE analysis, the delta K values were highest when K = 2, indicating two genetically heterogeneous clusters (Fig. 3). We further plotted the STRUCTURE plots for K = 2, 3, and 4. The findings clearly suggest that the sampled Mongolian gazelle belonged to two large genetic groups: HLH and BJ (Fig. 4).

Fig. 3
figure 3

Delta K results. The maximum value was obtained at K = 2

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Fig. 4
figure 4

Estimated cluster proportion using STRUCTURE for Procapra gutturosa. Each individual is represented by a vertical line

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Consistent with the results of the STRUCTURE analysis, PCoA clustering demonstrated a clear separation between the two populations (Fig. 5). The AMOVA results indicated that most of the genetic variation (81%) occurred within the populations, with only 19% of the variation occurring between populations (Table 4).

Fig. 5
figure 5

Graph representing Principal Coordinate Analysis (PCoA) of genetic differences among 54 individuals of Procapra gutturosa

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Table 4 The results of an analysis of molecular variance in genetic partitioning for Procapra gutturosa using 8 microsatellite loci
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Discussion

Genetic diversity affects the adaptation of populations to environmental change [28]. Small populations generally exhibit low genetic variation owing to natural and anthropogenic factors [29]. However, low genetic diversity may increase the vulnerability of small isolated populations to external disturbances, making them more vulnerable to the adverse effects of climate change, disease, and human activities [30]. Thus, low genetic diversity could reduce the survival rate of a species [31]. Therefore, evaluation of the genetic variability of Mongolian gazelle populations in fragmented habitats is important for planning conservation strategies. To assess the genetic diversity and structure of Mongolian gazelles in fragmented habitats, we used microsatellite markers and Cytb.

Microsatellite and Cytb analyses revealed low levels of genetic diversity in the HLH population, whereas the BJ population exhibited higher levels of genetic diversity. The nucleotide diversity in the HLH population was 0.00401, which was lower than that of the Mongolian gazelle population in a previous study (0.05000) [32]. When the nucleotide diversity of Mongolian gazelles in fragmented habitats was compared with that of other Procapra species (Procapra przewalskii and Procapra picticaudata), it was found to be lower than that of P. przewalskii around Qinghai Lake in China (0.01500) and P. picticaudata in Tibet (0.08100) [33,34,35]. In 2015, researchers used mitochondrial and microsatellite markers to study the genetic structure of Mongolian gazelles along the international railroad in Mongolia. These results indicated that the observed heterozygosity of Mongolian gazelles was 0.849 (SD = 0.076) in Mongolia [36]. However, our results indicated that the observed heterozygosity in HLH was 0.523 (SD = 0.171), which was lower than that of the Mongolian gazelle population reported in a previous study. The nucleotide diversity and observed heterozygosity of BJ were 0.00992 and 0.593 (SD = 0.224), respectively, which were higher than those of Mongolian gazelle populations in fragmented habitats, but lower than those in previous studies. These differences may be due to the relatively higher levels of inbreeding in BJ and HLH. Compared to Bovidae species, such as Moschus moschiferus (h = 0.970, π = 0.0265) and Cervus nippon (h = 0.932, π = 0.0106), the HLH population showed a low level of genetic diversity (h = 0.926, π = 0.00401) [37, 38]. The HLH population also exhibited lower nucleotide diversity than other ungulate species (Antilope cervicapra, π = 0.00704; Cervus elaphus hanglu, π = 0.008); however, the BJ population exhibited higher nucleotide diversity (h = 0.985, π = 0.00992) than other ungulate species [39, 40]. As such, our results provide strong evidence that the Mongolian gazelle populations in fragmented habitats (HLH) suffer from a loss of genetic diversity. The relatively high level of genetic diversity in the BJ population may be due to gene flow, whereas the HLH population lost its connectivity with other Mongolian gazelle populations. For example, the major Indian Axis porcinus population may have been due to historical gene flow, which guarantees a high level of genetic diversity [41]. The low genetic diversity of Mongolian gazelle populations in fragmented habitats may be due to their small population size and the impact of inbreeding. Inbreeding small populations can cause morphological defects in organisms that can lead to the death of these individuals. For example, the inbreeding of Isle Royale wolves has led to some individual skeletal deformities, and more seriously, led this population to the verge of extinction [42]. The ability of a species to maintain its genetic diversity is often essential for ensuring long-term persistence [43]. Populations that lack genetic diversity may show a heightened risk of extinction, owing to their reduced adaptive capacity [44, 45]. Therefore, the establishment of regulations and management schemes for Mongolian gazelle populations in fragmented habitats is urgently required.

In addition to the reduced genetic diversity, our findings showed evidence of genetic differentiation between the HLH and BJ populations. Both PCoA and STRUCTURE analyses demonstrated a clear delineation between the two Mongolian gazelle populations on the basis of allelic composition, implying genetic isolation. Previous studies have shown that natural landscapes, such as mountains, rivers and deserts can act as ecological barriers to gene flow between populations [46,47,48]. In addition, human activities can affect gene flow between animals [49,50,51,52]. In summary, both natural landscapes and human activities have important effects on the genetic structure of populations. For example, the North Chinese leopard populations on the Loess Plateau shows obvious genetic differentiation due to the influence of geographical factors such as the Loess Plateau [53]. We also speculate that the genetic differences may be attributable to a lack of connectivity between Mongolian gazelle populations, small population sizes, and limited dispersal. For example, Isle Royale Moose lacks connectivity with the outside world, leading to genetic differentiation from other moose populations [54]. The AMOVA results indicated that most of the genetic variation was attributed to populations, which further indicated that habitat fragmentation caused by human and natural factors resulted in low genetic diversity within the Mongolian gazelle population [55]. However, as is the case for other species (white-tailed eagles, black rhinoceros, and greater one-horned rhinoceros), considerable genetic diversity is retained within small populations [56,57,58,59], indicating the need to strengthen the protection of Mongolian gazelle populations in fragmented habitats.

Conclusions

We estimated the genetic diversity and structure of Mongolian gazelle populations in fragmented habitats by using microsatellites and Cytb. In comparison with the BJ population, the HLH population showed lower genetic diversity and a distinct genetic structure. Low genetic diversity and small population sizes increase the risk of extinction of Mongolian gazelle populations in fragmented habitats. Therefore, increasing the population size and preventing inbreeding are crucial for protection of Mongolian gazelles. Our findings indicate the need for long-term program to monitor the dynamics of the Mongolian gazelle. In addition, for long-term survival of the HLH population, a certain number of Mongolian gazelles should be introduced from other populations.

Data Availability

The mitochondrial Cytb genes are available at GenBank repository (https://www.ncbi.nlm.nih.gov/), under the accession number: OP204753–OP204786. The raw data of microsatellite loci is available in Table S1.

References

  1. Newbold T, Hudson LN, Hill SLL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, et al. Global effects of land use on local terrestrial biodiversity. Nature. 2015;520(7545):45–50.

    Article  CAS  PubMed  Google Scholar 

  2. Peery MZ, Hall LA, Sellas A, Beissinger SR, Moritz C, Bérubé M, Raphael MG, Nelson SK, Golightly RT, McFarlane-Tranquilla L, et al. Genetic analyses of historic and modern marbled murrelets suggest decoupling of migration and gene flow after habitat fragmentation. P ROY SOC B-BIOL SCI. 2010;277(1682):697–706.

    CAS  Google Scholar 

  3. Westemeier RL, Brawn JD, Simpson SA, Esker TL, Jansen RW, Walk JW, Kershner EL, Bouzat JL, Paige KN. Tracking the long-term decline and recovery of an isolated population. Science. 1998;282(5394):1695–8.

    Article  CAS  PubMed  Google Scholar 

  4. Fagan WF, Holmes EE. Quantifying the extinction vortex. ECOL LETT. 2006;9(1):51–60.

    Article  PubMed  Google Scholar 

  5. Evans SR, Sheldon BC. Interspecific patterns of genetic diversity in birds: correlations with extinction risk. CONSERV BIOL. 2008;22(4):1016–25.

    Article  PubMed  Google Scholar 

  6. Uphyrkina O, O’Brien SJ. Applying molecular genetic tools to the conservation and action plan for the critically endangered far eastern leopard (Panthera pardus orientalis). CR BIOL. 2003;326(Suppl 1):93–S97.

    Article  Google Scholar 

  7. Lhagvasuren B, Milner-Gulland EJ. The status and management of the mongolian gazelle Procapra gutturosa population. ORYX. 1997;31(2):127–34.

    Article  Google Scholar 

  8. Milner Gulland EJ, Lhagvasuren B. Population Dynamics of the Mongolian Gazelle Procapra gutturosa: an historical analysis. J Appl Ecol. 1998;35(2):240–51.

    Article  Google Scholar 

  9. Jiang Z, Wu Y, Liu S, Jiang X, Zhou K, Hu H. China’s Red List of Biodiversity: vertebrates, volume I. Mammals: China’s Red List of Biodiversity: Vertebrates, Volume I, Mammals;; 2021.

    Google Scholar 

  10. Junsheng L, Jianping W, Zhaowen J. Food habits and selective grazing of mongolian gazelle (Procapra gutturosa) in Hulunber Grassland. J FORESTRY RES. 1999;10(3):187–90.

    Article  Google Scholar 

  11. Leimgruber P, McShea WJ, Brookes CJ, Bolor-Erdene L, Wemmer C, Larson C. Spatial patterns in relative primary productivity and gazelle migration in the Eastern Steppes of Mongolia. BIOL CONSERV. 2001;102(2):205–12.

    Article  Google Scholar 

  12. Odonkhuu D, Olson KA, Schaller GB, Ginsberg JR, Fuller TK. Activity, movements, and sociality of newborn mongolian gazelle calves in the Eastern Steppe. Acta Theriol. 2009;54(4):357–62.

    Article  Google Scholar 

  13. Buchanan FC, Crawford AM. Ovine microsatellites at the OarFCB11, OarFCB128, OarFCB193, OarFCB266 and OarFCB304 loci. ANIM GENET. 1993;24(2):145.

    Article  CAS  PubMed  Google Scholar 

  14. Reed KM, Beattie CW. Isolation of 105 microsatellite loci from an ovine genomic library enriched for microsatellites. ANIM BIOTECHNOL. 2001;12(1):77.

    Article  CAS  PubMed  Google Scholar 

  15. Chu M, Wang J, Wang A, Li N, Fu J. Genetic polymorphisms of five microsatellite loci in small tail Han sheep. Yi Chuan Xue Bao. 2002;29(6):502–6.

    CAS  PubMed  Google Scholar 

  16. Motavalian M, Rando A, Urciuoli M, Senese C, Gregorio PD, Masina P. PZE46 and PZE114: two bovine polymorphic microsatellite loci isolated from a placenta cDNA library. ANIM GENET. 2002;33(2):159–60.

    Article  CAS  PubMed  Google Scholar 

  17. Lv S, Yang Y, Hou G, MA Y, Geng S, Li H, Pu Y. Analysis of genetic diversity of Chinese Indigenous Sheep breeds by microsatellite markers. Acta Veterinariaet Zootechnica Sinica. 2008;39(7):858–65.

    Google Scholar 

  18. Feng D, Zhao H, Luo Y, Han J. Species specific alleles at three microsatellite loci in yak and cattle. J Gansu Agricultural Univ. 2010;45(4):22–7.

    Google Scholar 

  19. Nijman IJ, Lenstra JA, Schwerin M, Olsaker I. Polymorphisms and physical locations of three bovine microsatellite loci: IOBT395, IOBT528, IOBT1401. ANIM GENET. 1996;27(3):221–2.

    Article  CAS  PubMed  Google Scholar 

  20. Ning Y, Kostyria AV, Ma J, Chayka MI, Guskov VY, Qi J, Sheremetyeva IN, Wang M, Jiang G. Dispersal of Amur tiger from spatial distribution and genetics within the eastern Changbai mountain of China. ECOL EVOL. 2019;9(5):2415–24.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics. 2012;28(19):2537–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pritchard JK, Stephens M, Donnelly P. Inference of Population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Earl DA, VonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. CONSERV GENET RESOUR. 2012;4(2):359–61.

    Article  Google Scholar 

  24. Triinu K, Maarja L, Lauris K, Kairi R, Reidar A, Maido R. Primer3_masker: integrating masking of template sequence with primer design software. Bioinformatics. 2018;34(11):1937–8.

    Article  Google Scholar 

  25. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Molecular Biology & Evolution. 2013;30(12):2725–9.

    Article  CAS  Google Scholar 

  26. Librado P, Rozas J. DnaSP v5: a Software for Comprehensive Analysis of DNA Polymorphism Data. Bioinformatics. 2009;25(11):1451–2.

    Article  CAS  PubMed  Google Scholar 

  27. Leigh JW, Bryant D. POPART: full-feature software for haplotype network construction. 2015, 6(9):1110–6.

  28. Hughes AR, Inouye BD, Johnson MT, Underwood N, Vellend M. Ecological consequences of genetic diversity. ECOL LETT. 2008;11(6):609–23.

    Article  PubMed  Google Scholar 

  29. Banks SC, Cary GJ, Smith AL, Davies ID, Driscoll DA, Gill AM, Lindenmayer DB, Peakall R. How does ecological disturbance influence genetic diversity. TRENDS IN ECOLOGY AND EVOLUTION. 2013;28(11):670–9.

    Article  Google Scholar 

  30. Nunes F, Norris RD, Knowlton N. Implications of isolation and low genetic diversity in peripheral populations of an amphi-atlantic coral. MOL ECOL. 2009;18(20):4283–97.

    Article  CAS  PubMed  Google Scholar 

  31. Martinez-Cruz B, Godoy JA, Negro JJ. Population genetics after fragmentation: the case of the endangered spanish imperial eagle (Aquila adalberti). MOL ECOL. 2004;13(8):2243–55.

    Article  CAS  PubMed  Google Scholar 

  32. Ayumi O, Takehiko YI, Bayarbaatar B, Badamjav L, Atsushi T. Genetic structure of Mongolian Gazelle (Procapra gutturosa): the Effect of Railroad and demographic change. Mongolian J Biol Sci. 2012;10(1–2):59–66.

    Google Scholar 

  33. Lei R, Hu Z, Jiang Z, Yang W. Phylogeography and genetic diversity of the critically endangered Przewalski’s gazelle. 2003, 6(4):361–7.

  34. Zhang F, Jiang Z. Mitochondrial phylogeography and genetic diversity of tibetan gazelle (Procapra picticaudata): implications for conservation. MOL PHYLOGENET EVOL. 2006;41(2):313–21.

    Article  CAS  PubMed  Google Scholar 

  35. Leslie DM. Procapra picticaudata (Artiodactyla: Bovidae). Mammalian Species. 2010;42(861):138–48.

    Article  Google Scholar 

  36. Okada A, Ito TY, Buuveibaatar B, Lhagvasuren B, Tsunekawa A. Genetic structure in mongolian gazelles based on mitochondrial and microsatellite markers. MAMM BIOL. 2015;80(4):303–11.

    Article  Google Scholar 

  37. Kholodova MV, Prikhodko VI. Molecular genetic diversity of musk deer Moschus moschiferus L., 1758 (Ruminantia, Artiodactyla) from the northern subspecies group. RUSS J GENET +. 2006;42(7):783–9.

    Article  CAS  Google Scholar 

  38. Wu H, Wan Q, Fang S. Two genetically distinct units of the chinese sika deer (cervus nippon): analyses of mitochondrial DNA variation. BIOL CONSERV. 2004;119(2):183–90.

    Article  Google Scholar 

  39. Shukla MA, Joshi BD, Kumar VP, Mehta AK, Goyal SP. Investigating the genetic diversity and presence of forensically informative nucleotide sequences in indian antelope (Antilope cervicapra) using multiple genes of the mitochondrial genome. MOL BIOL REP. 2019;46(6):6187–95.

    Article  CAS  PubMed  Google Scholar 

  40. Mukesh, Kumar VP, Sharma LK, Shukla M, Sathyakumar S. Pragmatic perspective on conservation genetics and demographic history of the last surviving population of Kashmir red deer (Cervus elaphus hanglu) in India. PLoS ONE. 2015;10(2):e117069.

    Google Scholar 

  41. Gupta SK, Kumar A, Angom S, Singh B, Ghazi M, Tuboi C, Hussain SA. Genetic analysis of endangered hog deer (Axis porcinus) reveals two distinct lineages from the indian subcontinent. SCI REP-UK. 2018;8(1):16308.

    Article  Google Scholar 

  42. Robinson JA, Räikkönen J, Vucetich LM, Vucetich JA, Peterson RO, Lohmueller KE, Wayne RK. Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. SCI ADV. 2019;5(5):u757.

    Article  Google Scholar 

  43. Frankham R, Ballou J, Briscoe D, McInnes K. Introduction to Conservation Genetics. Cambridge University Press; 2002.

  44. Frankham R. Genetics and extinction. BIOL CONSERV. 2005;126(2):131–40.

    Article  Google Scholar 

  45. Altermatt F, Pajunen VI, Ebert D. Climate change affects colonization dynamics in a metacommunity of three Daphnia species. GLOBAL CHANGE BIOL. 2008;14(6):1209–20.

    Article  Google Scholar 

  46. Zhou Y, Tian J, Lu J. Genetic structure and recent population demographic history of Taihangshan macaque (Macaca mulatta tcheliensis), North China. Integr Zool 2022.

  47. Díaz-Muñoz SL. Role of recent and old riverine barriers in fine-scale population genetic structure of Geoffroy’s tamarin (Saguinus geoffroyi) in the Panama Canal watershed. ECOL EVOL. 2012;2(2):298–309.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Chambers JL, Garant D. Determinants of population genetic structure in eastern Chipmunks (Tamias striatus): the role of landscape barriers and sex-biased dispersal. J HERED. 2010;101(4):413–22.

    Article  CAS  PubMed  Google Scholar 

  49. Sharma S, Dutta T, Maldonado JE, Wood TC, Panwar HS, Seidensticker J. Spatial genetic analysis reveals high connectivity of tiger (Panthera tigris) populations in the Satpura-Maikal landscape of Central India. ECOL EVOL. 2012;3(1):48–60.

    Article  PubMed  Google Scholar 

  50. Zhu L, Zhang S, Gu X, Wei F. Significant genetic boundaries and spatial dynamics of giant pandas occupying fragmented habitat across southwest China. MOL ECOL. 2011;20(6):1122–32.

    Article  PubMed  Google Scholar 

  51. Wang X, Shang Y, Wu X, Wei Q, Zhou S, Sun G, Mei X, Dong Y, Sha W, Zhang H. Divergent evolution of mitogenomics in Cetartiodactyla niche adaptation. ORG DIVERS EVOL. 2023;23(1):243–59.

    Article  CAS  Google Scholar 

  52. Wang X, Zhou S, Wu X, Wei Q, Shang Y, Sun G, Mei X, Dong Y, Sha W, Zhang H. High-altitude adaptation in vertebrates as revealed by mitochondrial genome analyses. ECOL EVOL. 2021;11(21):15077–84.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Yin Q, Ren Z, Wen X, Liu B, Song D, Zhang K, Dou H. Assessment of population genetic diversity and genetic structure of the North Chinese leopard (Panthera pardus japonensis) in fragmented habitats of the Loess Plateau, China. GLOB ECOL CONSERV. 2023;42:e2416.

    Google Scholar 

  54. Kyriazis CC, Beichman AC, Brzeski KE, Hoy SR, Peterson RO, Vucetich JA, Vucetich LM, Lohmueller KE, Wayne RK. Genomic underpinnings of Population Persistence in Isle Royale Moose. MOL BIOL EVOL 2023, 40(2).

  55. Warghat AR, Bajpai PK, Srivastava RB, Chaurasia OP, Sood H. Population genetic structure and conservation of small fragmented locations of Dactylorhiza hatagirea in Ladakh region of India. SCI HORTIC-AMSTERDAM. 2013;164:448–54.

    Article  CAS  Google Scholar 

  56. Hailer F, Helander B, Folkestad AO, Ganusevich SA, Garstad S, Hauff P, Koren C, Nygård T, Volke V, Vilà C, et al. Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline. BIOL Lett. 2006;2(2):316–9.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Langguth T, Honnen A, Hailer F, Mizera T, Skoric S, Väli Ü, Zachos FE. Genetic structure and phylogeography of a european flagship species, the white-tailed sea eagleHaliaeetus albicilla. J AVIAN BIOL. 2013;44(3):263–71.

    Article  Google Scholar 

  58. Swart MK, Ferguson JW, du Toit R, Flamand JR. Substantial genetic variation in southern african black rhinoceros (Diceros bicornis). J HERED. 1994;85(4):261–6.

    Article  CAS  PubMed  Google Scholar 

  59. DINERSTEIN E, MCCRACKEN GF. Endangered Greater one-horned Rhinoceros carry high levels of genetic variation. CONSERV BIOL. 1990;4(4):417–22.

    Article  Google Scholar 

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Acknowledgements

We thank staff of Hulun Lake National Nature Reserve and World Wide Fund for Nature for helping with sample collection.

Funding

This work was supported by the National Natural Science Foundation of China (32070405, 32270444, 32200349).

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Author notes

  1. Lupeng Shi and Xiufeng Yang contributed equally to this work.

Authors and Affiliations

  1. College of Life Sciences, Qufu Normal University, Qufu, Shandong Province, China

    Lupeng Shi, Xiufeng Yang, Tianshu Lyu, Lidong Wang, Shengyang Zhou, Yuehuan Dong & Honghai Zhang

  2. Hulunbuir Academy of Inland Lakes in Northern Cold & Arid Areas, Hulunbuir, China

    Muha Cha & Huashan Dou

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  1. Lupeng Shi
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  9. Honghai Zhang
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Contributions

HZ, XY and LS conceived and designed the study. LS, XY, MC, TL, LW, SZ, YD and HD performed the research. LS and XY analyzed the data. LS and XY prepared the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Honghai Zhang.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All sample procedures and experimental methods were approved by the Qufu Normal University Institutional Animal Care and Use Committee (No. 2021090), Qufu, China. The collection of fecal sample of Mongolian gazelle was approved by Hulunbuir Academy of Inland Lakes in Northern Cold & Arid Areas.

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Shi, L., Yang, X., Cha, M. et al. Genetic diversity and structure of mongolian gazelle (Procapra gutturosa) populations in fragmented habitats. BMC Genomics 24, 507 (2023). https://doi.org/10.1186/s12864-023-09574-0

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Keywords

BMC Genomics

ISSN: 1471-2164

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