Skip to main content
PeerJ logoLink to PeerJ
. 2021 Mar 16;9:e10850. doi: 10.7717/peerj.10850

Phylogeny and evolution of Lasiopodomys in subfamily Arvivolinae based on mitochondrial genomics

Luye Shi 1, Likuan Liu 2, Xiujuan Li 1, Yue Wu 1, Xiangyu Tian 1,, Yuhua Shi 1,, Zhenlong Wang 1
Editor: Tony Robillard
PMCID: PMC7977381  PMID: 33777513

Abstract

The species of Lasiopodomys Lataste 1887 with their related genera remains undetermined owing to inconsistent morphological characteristics and molecular phylogeny. To investigate the phylogenetic relationship and speciation among species of the genus Lasiopodomys, we sequenced and annotated the whole mitochondrial genomes of three individual species, namely Lasiopodomys brandtii Radde 1861, L. mandarinus Milne-Edwards 1871, and Neodon (Lasiopodomys) fuscus Büchner 1889. The nucleotide sequences of the circular mitogenomes were identical for each individual species of L. brandtii, L. mandarinus, and N. fuscus. Each species contained 13 protein-coding genes (PCGs), 22 transfer RNAs, and 2 ribosomal RNAs, with mitochondrial genome lengths of 16,557 bp, 16,562 bp, and 16,324 bp, respectively. The mitogenomes and PCGs showed positive AT skew and negative GC skew. Mitogenomic phylogenetic analyses suggested that L. brandtii, L. mandarinus, and L. gregalis Pallas 1779 belong to the genus Lasiopodomys, whereas N. fuscus belongs to the genus Neodon grouped with N. irene. Lasiopodomys showed the closest relationship with Microtus fortis Büchner 1889 and M. kikuchii Kuroda 1920, which are considered as the paraphyletic species of genera Microtus. TMRCA and niche model analysis revealed that Lasiopodomys may have first appeared during the early Pleistocene epoch. Further, L. gregalis separated from others over 1.53 million years ago (Ma) and then diverged into L. brandtii and L. mandarinus 0.76 Ma. The relative contribution of climatic fluctuations to speciation and selection in this group requires further research.

Keywords: Lasiopodomys, Mitochondrial genomes, Phylogenetic analysis, Arvivolinae

Introduction

Although taxonomical and molecular systematics have led to some progress in the relationship between the genus Lasiopodomys and its related genera, numerous uncertainties remain unelucidated. The species belonging to this genus was first described by Lataste in 1887 as part of the Arvivolinae Gray 1821 (Cricetidae Fischer 1817) subfamily, which includes the genera Phaiomys Blyth 1863, Microtus Schrank 1798, and Neodon Horsfield 1841 (Allen, 1940; Corbet, 1978; Gromov & Polyakov, 1978; Liu et al., 2013; Wang, 2003). The genus Lasiopodomys includes three species from different colonial habitats of life—subterranean (L. mandarinus Milne-Edwards 1871), aboveground (L. brandtii Radde 1861), and plateau (L. fuscus Büchner 1889) by Wilson & Reeder (2005)—with relatively short tail and densely furred plantar surfaces. However, their generic taxonomy is not universally accepted, specifically in relation to Phaiomys, Microtus, and Neodon. Molecular data have revealed that the narrow-headed vole Microtus gregalis Pallas 1779 (formerly included in subgenus Stenocranius Katschenko 1901) is closely related to the species belonging to the genus Lasiopodomys (Abramson & Lissovsky, 2012). Morphological characteristics, such as karyotype (Gladkikh et al., 2016) and mating behavior (Zorenko & Atanasov, 2017), supported its current taxonomic status as L. gregalis. On the other hand, L. fuscus is nested in the genus Neodon Hodgson 1849 clade based on the longer length of ear and tail and greater number of inner angles in M1 and M3 compared with the genus Lasiopodomys (Liu et al., 2012a); moreover, CLOCK, BMA1, and Cytb gene sequences and their complete mitochondrial genomes supported this taxonomical status (Abramson et al., 2009a; Bannikova et al., 2010; Li, Lu & Wang, 2016a; Li et al., 2019; Liu et al., 2017). Recent studies have typically recognized Lasiopodomys as a separate genus that includes the species L. mandarinus and L. brandtii; L. gregalis was not widely accepted, whereas L. fuscus has been transferred to the genus Neodon and named Neodon fuscus.

According to fossils and molecular data, the genus Lasiopodomys originated and speciated during the Pleistocene epoch (∼2.58–0.012 million years ago (Ma)) when quaternary glaciations occurred in this period. Nuclear and mitochondrial phylogenetic estimates have shown that Lasiopodomys originated ∼2.4 Ma, whereas the division between L. gregalis and Lasiopodomys has been estimated to have occurred 1.8 Ma and that between L. mandarinus and L. brandtii was estimated at 0.5–0.95 Ma (Abramson et al., 2009b; Petrova et al., 2015; Li et al., 2017). However, chromosome analysis has shown that karyotype evolution has occurred between L. mandarinus and L. brandtii at ∼2.4 Ma, between Lasiopodomys and L. gregalis at 2.4 Ma, and between other Microtus species at 3 Ma (Gladkikh et al., 2016).

The species in the genus Lasiopodomys inhabit subterranean and aboveground environments and have recently become model species for comparative hypoxia adaptation (Dong et al., 2018; Sun et al., 2018). Species’ adaptation to low oxygen has been reported in numerous studies (Childress & Seibel, 1998; Dong et al., 2018; Nevo, 2013; Witt & Huerta-Sánchez, 2019), and most research has focused on animal models in an artificial environment or has compared them with subterranean rats to reveal the mechanisms of hypoxia (Ashur-Fabian et al., 2004; Malik et al., 2012; Malik et al., 2016). The differences in the environmental adaptability of proximal species are closely related to the historical events experienced during evolution, which play a key role in our understanding of the causes of current differences in life history among these species. However, the historical event that caused the Lasiopodomys species to adapt to a different environment has rarely been mentioned (Dong et al., 2018; Dong, Wang & Jiang, 2020).

Mitochondrial DNA are widely used to study the molecular ecology of animals because it is convenient and economical (Ballard & Rand, 2005; de Freitas et al., 2018; Kenechukwu, Li & An, 2018; Zhang et al., 2018). However, several studies have reported the limitations of mitochondrial DNA use (Galtier et al., 2009), such as recurrent horizontal transfer (Bergthorsson et al., 2003) and adaptive evolution (Bazin, Glémin & Galtier, 2006). The mitochondrial genome is involved in respiratory functions, which are closely associated with oxygen availability (Jain et al., 2016; Santore et al., 2002; Solaini et al., 2010).

In the present study, we sequenced the whole mitochondrial genomes of L. mandarinus, L. brandtii, and N. fuscus, which are species with three repeat individuals, using high-throughput sequencing technology and used the complete mitochondrial genomes of related species from the National Center for Biotechnology Information database to clarify the generic taxonomy of Lasiopodomys and evolutionary history of adaptation on aboveground and subsurface life. The findings of this research provide evolutionary information regarding the hypoxia adaptation of Lasiopodomys.

Materials and Methods

Material preparation and DNA sequencing

Total genomic DNA were extracted from the specimens of L. mandarinus (collected from 34°52′N, 113°85′E; Specimen No. LM023), L. brandtii (collected from 40°53′N, 116°38′E; Specimen No. LB003), and N. fuscus (collected from 34°9′N, 100°2′E; Specimen No. LF010) using the TIANamp Genomic DNA Extraction Kit (TIANGEN, DP304). All specimens were stored at the Animal Museum of Zhengzhou University. The Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA) platform was used for sequencing the samples with a short-insert of 150 bp at ORI-GENE Company, Beijing (https://www.origene.com/).

Genome assembly and annotation

NOVOPlasty 3.6 was used for de novo assembly using the mitochondrial genome of L. mandarinus (GenBank no. JX014233) as a reference (Dierckxsens, Mardulyn & Smits, 2017). All mitochondrial genomes were annotated using GeSeq (Tillich et al., 2017), OGDRAW (Lohse et al., 2013), and GB2sequin (Lehwark & Greiner, 2019) in the MPI-MP CHLOROBOX integrated web tool (https://www.mpimp-golm.mpg.de/chlorobox), which contains the function of the HMMER package for protein-coding genes (PCGs) and ribosomal RNA (rRNA) (Finn, Clements & Edd, 2011), and tRNAscan-SE v2.0.3 for transfer RNAs (tRNAs) (Lowe & Eddy, 1997). Adenine–thymine (AT) skew was calculated as AT skew = (A − T) / (A + T), whereas guanine–cytosine (GC) skew was calculated as GC skew = (G − C)/(G + C). Circular maps were drawn using the CGView Server V 1.0 web tool (http://stothard.afns.ualberta.ca/cgview_server/) for L. mandarinus, L. brandtii, L. gregalis (GenBank no. MN199169), and N. fuscus (Grant & Stothard, 2008).

Molecular phylogenetic analysis and divergence time estimation

Phylogenetic analyses were performed on the whole mitochondrial genome sequences (Appendix S1). Besides the nine mitochondrial genomes that were acquired for the present study, five previously published mitochondrial genomes from L. mandarinus, L. gregalis, and N. fuscus were included; therefore, overall, 37 complete mitochondrial genome sequences from 23 species from the subfamily Arvivolinae were considered for phylogenetic analysis. Moreover, three species from Cricetulus Milne-Edwards 1867 were chosen as the outgroup. All these sequences were aligned using MAFFT v7.450 (Katoh & Standley, 2013). The nucleotide diversity of the PCGs of Lasiopodomys and Arvivolinae was determined using the DNASP v6.12.03 software (Rozas et al., 2017), and the best nucleotide substitution models were constructed using jMODELTEST 2.1.7 and selected using the Akaike information criterion (Darriba et al., 2012).

The phylogenetic relationships of the two different matrices as well as the whole mitochondrial genomes and PCG sequence matrices were constructed using the maximum likelihood (ML) approach in IQ-TREE v1.6.12 (Nguyen et al., 2015) and Bayesian analysis (BI) in the BEAST v1.8.4 program (Drummond & Rambaut, 2007). We conducted analysis using 5000 ultrafast bootstrap replicates and the best-fit model in the IQ-TREE software. To determine the maximum clade credibility trees of two different matrices, BEAST analyses were performed using the GTR+G+I substitution models identified above and the uncorrelated relaxed clocks for clock type (Drummond et al., 2006), Yule process for tree prior (Gernhard, 2008), and other default parameters. Each Markov chain Monte Carlo of 20,000,000 generations was sampled in every 10,000 generations. The effective sample sizes were estimated using Tracer v1.7 for all parameters more than 200 (Rambaut et al., 2018). Maximum clade credibility trees were constructed using TreeAnnotator v1.8.4 with a burn-in of the first 20% of the sampled trees (Drummond & Rambaut, 2007). Positive selection in all 13 PCGs was determined using branch models and branch-site models via phylogenetic analysis using ML (PAML4.7) programs (Yang, 2007). Branch models were used with the one-ratio model, i.e., all the species had the same ω ratio, and the ω = 1 model, with all species in natural selection. Based on the phylogenetic tree, we estimated the ω values of each PCG. The branch-site models used all Lasiopodomys species as the foreground branches, and the likelihood ratio test (LRT) was conducted to assess the statistical significance of positive selection.

The molecular divergence time was estimated using the Yule and birth–death processes for trees before implementing phylogeny construction using BEAST v1.8.4 (Gernhard, 2008; Heath, Huelsenbeck & Stadler, 2014). Marginal likelihood estimation for path sampling and stepping-stone sampling (Xie et al., 2011) using 5,000,000 in chain lengths of 500 path steps was used to sample the likelihood of every 5,000 chains (Baele et al., 2012; Baele et al., 2013). We applied three constraints to calibrate the tree at three prior nodes: (1) the divergence time of the Taiwan vole, Microtus kikuchii Kuroda 1920, and the reed vole Microtus fortis, of which the split between the subgenus Alexandromys Ognev 1914 and Pallasiimus Schrank 1798 was estimated via molecular clock analysis at ∼1.19 ± 0.19 Ma (Bannikova et al., 2010; Gao et al., 2017), (2) the earliest known fossil of Eothenomys Allen 1924 at 2.0 Ma (Liu et al., 2012a; Kohli et al., 2014), and (3) the oldest fossil of Arvicola, which was estimated at 3.0–3.5 Ma (Abramson et al., 2009a; Chen et al., 2012); we used the mean value of 3.25 Ma.

Ecological niche modeling

The maximum entropy (Maxent) method was used to predict the current potential geographic distributions of L. mandarinus, L. brandtii, L. gregalis, and N. fuscus as well as their suitable distributions during the mid-Holocene, 6,000 years ago (kya), Last Glacial Maximum (LGM; 22 kya), and Last Interglacial (LIG; 120–140 kya) epochs (Phillips, Anderson & Schapire, 2006; Elith et al., 2011). Presence records were obtained for all four species according to the GBIF database and published papers (Appendix S2). Climatic variables with 19 bioclimatic layers were obtained from the database WorldClim version 1.4 at a resolution of 2.5 arc-minute grid format (Hijmans et al., 2005). The potential distributions of the species during the LGM and Holocene periods were predicted using both MIROC-ESM and CCSM4 models (Watanabe et al., 2011; Shields et al., 2012). Strongly correlated bioclimatic layers (r > 0.9) as determined using Pearson’s correlation analysis in R 3.6.2 (Appendix S3) (R Development Core Team, 2013) were excluded. Moreover, Maxent was independently performed among these species using area under the receiver operating characteristic curve (AUC) prediction model evaluation (DeLong, DeLong & Clarke-Pearson, 1988; Fawcett, 2006).

Results

The whole mitochondrial genome length of L. mandarinus was 16,562 bp, with the same sequences among repeated individuals. The mitochondrial genome length of L. brandtii was only 5 bp shorter than that of L. mandarinus, whereas that of N. fuscus was 220 bp shorter than that of L. mandarinus (Fig. 1). On the other hand, L. mandarinus was found to be 234 bp longer than the former sequenced mitogenomes (GenBank no. KF819832& JX014233). All sequences of the three species were longer than those of L. gregalis, a species previously in the genus Microtus, with sequence lengths of 16,292 bp (GenBank no. MN199169) and 16,294 bp (GenBank no. MN199170). All the three mitogenomes were assembled into a typical circular map with 13 PCGs, 22 tRNAs, 2 rRNAs (rrn12 and rrn16), and a D-loop region (Fig. 1, Table 1). Five types of start codons—ATA, ATC, ATG, ATT, and GTG—were identified among the PCGs, whereas three types of stop codons were identified for these species.

Figure 1. The complete mitochondrial genome map and GC skew of Neodon fuscus, Lasiopodomys brandtii, L. mandarinus, and L. gregalis.

Figure 1

Table 1. Characteristics of the mitochondrial genome of Neodon fuscus, Lasiopodomys brandtii, L. mandarinus, and L. gregalis.

Genes Position (bp) Strat/stop codon
L. brabdtii L. mandarinus L. gregalis Neodon fuscus L. brabdtii L. mandarinus L. gregalis Neodon fuscus
trnF-GAA 1-66 1-66 1-66 1-66
rrn12 69–1017 69–1018 69–1017 69–1015
trnV-UAC 1019–1087 1019–1088 1018–1087 1016–1086
rrn16 1088–2641 1089–2652 1088–2649 1087–2648
trnL-UAA 2650–2724 2655–2729 2651–2725 2650–2724
ND1 2710–3681 2715–3686 2726–3680 2725–3679 GTG/TAG GTG/TAG GTG/TAG GTG/TAG
trnI-GAU 3680–3748 3685–3752 3681–3748 3680–3747
trnQ-UUG 3746–3817 3750–3821 3746–3817 3745–3816
trnM-CAU 3820–3888 3823–3891 3820–3888 3818–3886
ND2 3889–4923 3865–4926 3889–4923 3887–4921 ATC/TAA ATC/TAA ATT/TAA ATC/TAA
trnW-UCA 4925–4991 4928–4994 4925–4991 4923–4989
trnA-UGC 4993–5061 4996–5064 4993–5061 4991–5059
trnN-GUU 5064–5133 5067–5136 5064–5133 5062–5131
trnC-GCA 5168–5235 5171–5237 5167–5234 5163–5230
trnY-GUA 5236–5302 5238–5303 5235–5301 5231–5297
COX1 5268–6848 5296–6849 5303–6847 5299–6843 ATG/TAA ATG/TAA ATG/TAA ATG/TAA
trnS-UGA 6846–6914 6847–6915 6845–6913 6841–6909
trnD-GUC 6918–6985 6919–6986 6918–6985 6913–6980
COX2 6978–7670 6979–7671 6987–7670 6982–7665 ATG/TAA ATG/TAA ATA/TAG ATG/TAA
trnK-UUU 7674–7737 7675–7738 7674–7738 7669–7732
ATP8 7738–7941 7739–7942 7739–7942 7733–7936 ATG/TAA ATG/TAA ATG/TAA ATG/TAA
ATP6 7899–8579 7900–8580 7900–8580 7894–8574 ATG/TAA ATG/TAA ATG/TAA ATG/TAA
COX3 8474–9412 8508–9413 8580–9363 8574–9357 ATG/TAG ATG/TAG ATG/TAG ATG/TAG
trnG-UCC 9363–9430 9364–9431 9364–9431 9358–9426
ND3 9431–9778 9432–9779 9432–9779 9427–9774 ATT/TAA ATT/TAA ATT/TAA GTG/TAA
trnR-UCG 9780–9846 9781–9847 9781–9847 9776–9842
ND4L 9849–10145 9851–10147 9850–10146 9844–10140 ATG/TAA ATG/TAA ATG/TAA ATG/TAA
ND4 9962–11521 10141–11523 10140–11517 10134–11511 ATG/TTA ATG/TTA ATG/TTA ATG/TTA
trnH-GUG 11517–11583 11519–11584 11518–11585 11512–11579
trnS-UCU 11584–11642 11585–11643 11586–11644 11580–11638
trnL-UAG 11642–11711 11643–11712 11644–11713 11638–11707
ND5 11691–13523 11692–13524 11714–13525 11708–13519 ATT/TAA ATT/TAA ATA/TAA ATA/TAA
ND6 13520–14104 13521–14147 13522–14046 13516–14040 ATG/TTA ATG/TTA ATG/TTA ATG/TTA
trnE-UUC 14042–14110 14046–14114 14047–14115 14041–14109
Cytb 14113–15258 14117–15262 14121–15263 14115–15257 ATG/TAA ATG/TAA ATG/TAA ATG/TAA
trnT-UGU 15260–15326 15265–15331 15265–15331 15260–15327
trnP-UGG 15566–15633 15522–15589 15332–15399 15328–15395

The nucleotide composition of L. brandtii, L. mandarinus, and N. fuscus was biased for A+T by 59.5%, 59.5%, and 58.4%, respectively. All these mitogenomes showed a positive AT skew of 0.08 for L. brandtii, 0.09 for L. mandarinus, and 0.09 for N. fuscus. However, these species showed a negative GC skew ranging from −0.30 for L. brandtii to −0.34 for L. mandarinus (Fig. 1, Table 2). L. gregalis showed higher AT skew (0.10) and GC skew (−0.30) compared with the other three species. Among the 13 PCGs in these 4 species, nucleotide composition ranged from −0.69 in ATP8 to −0.16 in ND4L for L. mandarinus, with a GC skew ranging from −0.14 in ND4L for L. brandtii to 0.33 in ND6 for L. mandarinus. Similarly, all 13 PCGs exhibited a negative GC skew; however, COX1, ND4L in all species, COX3 in L. brandtii and L. mandarinus, and ND3 in N. fuscus showed a negative AT skew and ND3 in L. brandtii and L. mandarinus had an AT skew of 0 (Table 2).

Table 2. Nucleotide composition data for the PCGs and whole mitochondrial genomes of Neodon fuscus, Lasiopodomys brandtii, L. mandarinus, and L. gregalis.

Species contents T C A G GC skew AT skew
L. brabdtii whole 27.4 26.4 32.1 14.1 −0.30 0.08
ATP6 28.3 29.8 31 10.9 −0.46 0.05
ATP8 26 27 37.7 9.3 −0.49 0.18
COX1 29.5 25.5 27.1 18 −0.17 −0.04
COX2 26.3 27.7 31.6 14.4 −0.32 0.09
COX3 29.3 26.8 28.6 15.4 −0.27 −0.01
cytB 27 29.1 30.5 13.4 −0.37 0.06
ND1 28.5 28.9 30.7 11.9 −0.42 0.04
ND2 26.7 31 33.9 8.4 −0.57 0.12
ND3 30.7 26.1 30.5 12.6 −0.35 0.00
ND4 27.8 28.3 31 12.9 −0.37 0.05
ND4L 31.3 30.3 23.6 14.8 −0.34 −0.14
ND5 28 27.8 32.4 11.8 −0.40 0.07
ND6 20.6 30.9 38.6 9.9 −0.51 0.30
L. gregalis whole 26.5 27.2 32.1 14.2 −0.31 0.10
ATP6 17.6 30.5 29.8 12 −0.44 0.26
ATP8 24.5 29.4 38.7 7.4 −0.60 0.22
COX1 28.7 26.5 26.9 18 −0.19 −0.03
COX2 28.3 26.1 30 15.6 −0.25 0.03
COX3 27.6 28.4 29.1 14.9 −0.31 0.03
cytB 26.5 29.7 30.3 13.5 −0.38 0.07
ND1 25.9 31.5 30 12.6 −0.43 0.07
ND2 26.1 29.7 35 9.3 −0.52 0.15
ND3 27.3 29.9 30.7 12.1 −0.42 0.06
ND4 27.1 29.4 31.9 11.4 −0.44 0.08
ND4L 30.6 31.3 26.6 11.4 −0.47 −0.07
ND5 25.9 30.4 31.5 12.3 −0.42 0.10
ND6 22.4 29.4 39.6 8.7 −0.54 0.28
L. mandarinus whole 27.1 27.1 32.4 13.4 −0.34 0.09
ATP6 29.8 29.2 30.7 10.3 −0.48 0.01
ATP8 27.9 28.9 37.7 5.4 −0.69 0.15
COX1 28.7 26.5 27.7 17.1 −0.22 −0.02
COX2 27 27.1 32.5 13.4 −0.34 0.09
COX3 29 28.4 28 14.6 −0.32 −0.02
cytB 26.5 30.7 30.6 12.1 −0.43 0.07
ND1 28.4 29.1 30.2 12.2 −0.41 0.03
ND2 26.9 30.9 33.9 8.3 −0.58 0.12
ND3 32.2 24.1 32.2 11.5 −0.35 0.00
ND4 28 28.5 32.3 11.2 −0.44 0.07
ND4L 32 29.3 26.6 21.1 −0.16 −0.09
ND5 27 28.9 33 11.2 −0.44 0.10
ND6 20 30.7 40.1 9.2 −0.54 0.33
Neodon fuscus whole 26.5 27.2 31.9 14.4 −0.31 0.09
ATP6 27.6 31.3 28.8 12.3 −0.44 0.02
ATP8 27 27 37.7 8.3 −0.53 0.17
COX1 29 26.4 26.6 18 −0.19 −0.04
COX2 26.8 26.8 31.5 14.9 −0.29 0.08
COX3 27.1 29.5 28 15.4 −0.31 0.02
cytB 25.8 31.3 28.8 14 −0.38 0.05
ND1 25.9 30.7 31 12.4 −0.42 0.09
ND2 25.7 30.7 35 8.6 −0.56 0.15
ND3 29.6 28.2 28.2 14.1 −0.33 −0.02
ND4 27 29.1 31 12.9 −0.39 0.07
ND4L 29.2 30.2 26.8 13.8 −0.37 −0.04
ND5 26.2 29.8 31.5 12.5 −0.41 0.09
ND6 21.8 28.8 39.9 9.4 −0.51 0.29

The nucleotide diversity among the published Arvicolinae mitogenome sequences and our study species was 0.1429 ±  0.0001, whereas the nucleotide diversity of the mitogenomes of Lasiopodomys was 0.0836 ± 0.0155 (Fig. 2). The total nucleotide diversity in all 13 PCGs of Arvicolinae and the genus Lasiopodomys was 0.1603 ± 0.0027 and 0.0953 ±  0.0180, respectively (Fig. 2). In Arvicolinae, nucleotide diversity ranged from 0.1378 ±  0.0049 in Cytb to 0.1977 ± 0.0077 in ND3, whereas for Lasiopodomys, it ranged from 0.0829 ±  0.0157 in COX3 to 0.1256 ± 0.021 in ND4L.

Figure 2. Nucleotide diversity of each protein-coding gene (PCG), concatenate PCG, and whole mitochondrial genomes of Microtinae (blue) and Lasiopodomys (orange).

Figure 2

The results of the ML and Bayesian approaches were applied to the datasets of the whole mitogenomes, and the 13 PCG matrices inferred the same topology of the phylogenetic tree structure (Fig. 3). Our results supported that Lasiopodomys, Microtus, and Neodon have close relationships with the basal group of Proedromys Thomas 1911. Furthermore, the phylogenetic tree suggested that L. brandtii, L. mandarinus, and L. gregalis formed the genus of Lasiopodomys, whereas N. fuscus showed a close relationship with N. irene, belonging to the genus Neodon. Microtus was subdivided into two groups: one containing M. fortis and M. kikuchii, which were strongly supported as the sister group to Lasiopodomys, and the other was the basal group of the above species.

Figure 3. Divergence time for Lasiopodomys with whole mitochondrial genomes. The numbers on each node are posterior probabilities and bootstrap values.

Figure 3

Blue bars show 95% highest posterior density intervals of node heights. Three red circles were fossil time. The genus of Cricetulus was used as an outgroup.

In the branch models, the one-ratio model was determined as superior to the ω = 1 model (df = 1, p <  0.01), suggesting that all the PCGs in the mitogenomes of Lasiopodomys undergo purifying selection (Table 3). In the branch-site model, only the ATP6 gene was present in some positive selection sites (60I 0.987, p < 0.01) in Lasiopodomys (Table 3). Moreover, positive selection sites were predicted in Cox1, Cox3, Cytb, ND2, ND3, and ND5. However, the LRTs were not significant.

Table 3. Likelihood ratio tests of branch models and branch-site models examining the proteincoding genes of the genus Lasiopodomys.

Gene Model lnL Models compared Parameter Estimates LRT ( P-value)
ATP6 Branch-model A:One-ratio −6321.793406 ω= 0.02625 p < 0.01
B:Omega = 1 −7802.578602 B vs A ω=1
Branch-site model Null −6298.662308 null vs A 7 A 0.578 P<0.01
Model A −6295.340396 60 I 0.987*
ATP8 Branch-model A:One-ratio −2156.664937 ω=0.16120 p < 0.01
B:Omega = 1 −2304.668614 B vs A ω=1
Branch-site model Null −2092.811906 1
Model A −2092.811906 null vs A NA
Cox1 Branch-model A:One-ratio −12806.04872 B vs A ω=0.00534 p < 0.01
B:Omega = 1 −17316.74494 ω=1
Branch-site model Null −12712.24034 null vs A 57 I 0.779 0.077
Model A −12710.67689 487 T 0.965*
Cox2 Branch-model A:One-ratio −5779.531238 ω=0.01386 p < 0.01
B:Omega = 1 −7512.338542 B vs A ω=1
Branch-site model Null −5711.411586 1
Model A −5711.411586 null vs A NA
Cox3 Branch-model A:One-ratio −6864.103188 ω=0.01989 p < 0.01
B:Omega = 1 −8741.926003 B vs A ω=1
Branch-site model Null −6757.123306
Model A −6757.116417 null vs A 50 N 0.642 0.9065
62 V 0.517
203 F 0.593
Cytb Branch-model A:One-ratio −10097.89327 ω=0.02761
B:Omega = 1 −12504.74705 B vs A ω=1 p < 0.01
Branch-site model Null −10010.08824
Model A −10009.07827 null vs A 4 M 0.976* 0.1552
7 K 0.892
116 I 0.567
242 V 0.522
315 I 0.516
ND1 Branch-model A:One-ratio −9200.160474 ω=0.02426
B:Omega = 1 −11391.13236 B vs A ω=1 p < 0.01
Branch-site model Null −9015.80101
Model A −9015.745075 null vs A NA 0.738
ND2 Branch-model A:One-ratio −11468.97809 ω=0.06165
B:Omega = 1 −13190.51757 B vs A ω=1 p < 0.01
Branch-site model Null −11268.21175
Model A −11268.21175 null vs A 11 F 0.747 1
14 F 0.816
31 I 0.845
95 T 0.837
122 I 0.856
207 I 0.845
220 H 0.867
228 K 0.847
235 N 0.860
241 L 0.858
ND3 Branch-model A:One-ratio −4086.367921 ω=0.06686
B:Omega = 1 −4686.550566 B vs A ω=1 p < 0.01
Branch-site model Null −3969.046821
Model A −3969.013478 null vs A 6 A 0.811 0.7962
14 S 0.790
20 V 0.861
108 Q 0.849
ND4 Branch-model A:One-ratio −15050.32692 ω=0.04173
B:Omega = 1 −17886.34941 B vs A ω=1 p < 0.01
Branch-site model Null −14856.89331
Model A −14856.89325 null vs A NA 0.992
ND4L Branch-model A:One-ratio −3210.084127 ω=0.05007
B:Omega = 1 −3775.223753 B vs A ω=1 p < 0.01
Branch-site model Null −3151.8857
Model A −3151.8857 null vs A NA 1
ND5 Branch-model A:One-ratio −19894.46685 ω=0.04666
B:Omega = 1 −23436.47839 B vs A ω=1 p < 0.01
Branch-site model Null −19737.31375
Model A −19737.31225 null vs A 194 E 0.512 0.9563
575 K 0.969*
ND6 Branch-model A:One-ratio −5081.893461 ω=0.06927
B:Omega = 1 −5814.462234 B vs A ω=1 p < 0.01
Branch-site model Null −4971.821282
Model A −4971.821283 null vs A NA 1

The species divergence time among the Lasiopodomys species and related genera was calculated using the uncorrelated relaxed molecular clock model, which was calibrated with three prior divergence times of Arvicolinae (Fig. 3). The results suggested that the origin of Lasiopodomys was no earlier than the early Pleistocene epoch (∼0.781–2.58 Ma), with a possible most common ancestor of Lasiopodomys at ∼1.79 Ma (95% HPD values: ∼1.52–2.09 Ma). The split between L. brandtii and L. mandarinus was dated to the early Pleistocene period at ∼0.76 Ma (95% HPD values: ∼0.58–0.98 Ma), whereas the separation of both from L. gregalis was dated to the early Pleistocene epoch at 1.53 Ma (95% HPD values: ∼1.26–1.81 Ma). The estimated divergence event of N. fuscus and N. irene was found to be during the early Pleistocene epoch at 1.44 Ma (95% HPD Interval: ∼1.12–1.75 Ma).

The high AUC values determined via ecological niche modeling (ENM) indicated the good performance of the model predictions of this study (Appendix S4). During the periods from the LIG to present, all species of Lasiopodomys showed no evident loss of a suitable habitat. A western expansion of L. brandtii has been predicted in Northeast China, Inner Mongolia, and South Siberia, whereas a weak fragment was predicted for L. gregalis among the Eurasia regions (Fig. 4). Moreover, suitable areas were predicted in highly suitable habitat regions during the LGM in these species. More northern suitable areas were predicted during the LIG, and a northern expansion was predicted during the transition from the Holocene period to the present (Fig. 4). In addition, highly suitable habitats were observed for N. fuscus in the Hengduan Mountains during all periods, whereas more eastern distributions were predicted during the LGM (Fig. 4).

Figure 4. Ecological niche modeling of Lasiopodomys and Neodon.

Figure 4

Lasiopodomys brandtii (A–F), L. mandarinus (G–L), L. gregalis (M–R), and Neodon fuscus (S–X) under the current climate and three periods in the past: the mid-Holocene, the Last Glacial Maximum (LGM), and the Last Interglacial Maxima (LIG).

Discussion

Structural features of the whole mitochondrial genome of Lasiopodomys

Among the nine complete mitochondrial sequences, all the species showed same sequences in the three repeated individuals, thereby supporting the accuracy and low intraspecific variation of our studies (Brown & Simpson, 1981). Although N. fuscus showed similar characteristics to previously sequenced mitogenomes (GenBank no. MG833880), L. mandarinus exhibited a longer sequence than that previously reported (Cong et al., 2016; Li, Lu & Wang, 2016a; Li et al., 2016b; Li et al., 2019). This difference may be due to nucleotide errors, particularly in tandem repeats, caused by different sequencing technologies: Sanger sequencing versus high-throughput sequencing (Pfeiffer et al., 2018). All these differences occurred in the intergenic region, with little impact on subsequent analysis. Therefore, we reserved both types of sequence data in the subsequent analysis.

All the PCGs of these species, similar to the other Arvicolinae mitogenomes, had an incomplete stop codon that was automatically filled during the transcription process in the mitogenomes of animals, with no effect on translation (Ojala, Montoya & Attardi, 1981). Similar to previous studies, the nucleotide diversity of all the PCGs in both Lasiopodomys and Arvicolinae typically showed the highest divergence in the NADH dehydrogenase complex and the lowest divergence in the cytochrome c oxidase subunit complex and cytochrome B gene (Huang et al., 2019; Ramos et al., 2018). The nucleotide sequence diversity of the NADH dehydrogenase gene groups may be affected by variations in the historical environment (Ramos et al., 2018; Mueller, 2006). Similar to previously published mitogenomes, the AT skew of Lasiopodomys and N. fuscus was consistent with that of vertebrates (Zhang, Cheng & Ge, 2019; Martin, 1995), further indicating evolutionary pressure related to the mechanism of DNA replication (Charneski et al., 2011; Dai & Holland, 2019).

Phylogenetic relationships of Lasiopodomys

Our molecular phylogenetic analysis results were highly consistent those of previous studies. In our study, the subfamily Arvicolinae was supported as a monophyletic group based on the molecular data of Cytb, COX1, GHR, CLOCK, and BMAL1 (Abramson et al., 2009b; Buzan et al., 2008; Liu et al., 2017; Martin et al., 2000; Sun et al., 2018). Our results suggest that N. (Lasiopodomys) fuscus within the genus Neodon forms a sister relationship with N. irene, consistent with the results reported by Chen et al. (2012) and Li et al. (2019). The stable clustering of L. brandtii, L. mandarinus, and L. gregalis into one group confirms the systematic positions of Lasiopodomys. This topology was consistent with that of other phylogenetic studies based on nuclear genes (Sun et al., 2018), mitochondrial DNA (Abramson et al., 2009a; Liu et al., 2012b; Martínková & Moravec, 2012; Petrova et al., 2016), and whole genomes (Li, Lu & Wang, 2016a; Li et al., 2019; Tian et al., 2020). However, it contradicts with the systematic position based on the morphological characteristics of these species (Allen, 1940; Corbet, 1978; Wilson & Reeder , 2005). Further, L. brandtii and L. mandarinus have consistently presented as a sister group in molecular phylogenetic studies, with seldom distinguished morphological characteristics but different aboveground and underground habitats, suggesting a mechanism of environmental adaptation during rapid speciation (Alexeeva, Erbajeva & Khenzykhenova, 2015; Dong et al., 2018; Li et al., 2017). Other species of Microtus and Neodon were not found in the monophyletic group (Liu et al., 2012a); M. kikuchii and M. fortis were grouped as sister lineages within the Lasiopodomys clades and were considered belonging to the subgenus Alexandromys based on phylogenetic research (Mezhzherin, Zykov & Morozov-Leonov, 1993), allozymes, and Cytb (Bannikova et al., 2010). All these genera form a “Microtus s. l.,” which could be the “core Arvicolinae” (Baca et al., 2019).

Evolution and demographic history of Lasiopodomys

When inferring the divergence time of Lasiopodomys and related genera, both the Yule process and birth–death process speciation models were required with multiple fossil calibration nodes employed in phylogenetic analysis to develop more robust estimates (Drummond & Rambaut, 2007; Humphreys et al., 2016). Based on complete genomes and PCG phylogenetic trees, both models presented similar estimates of a relatively recent origin and divergence time for Microtus s. l. during the early Pleistocene epoch. The oldest reported fossil of Microtus s. l. was during the early Pleistocene epoch (Chaline et al., 1999). An arid and cold environment raised species dispersal and speciation in response to Pleistocene climatic fluctuations (Vasconcellos et al., 2019). Our study supported the first appearance of Lasiopodomys in the late early Pleistocene epoch from the Transbaikal area (Alexeeva, Erbajeva & Khenzykhenova, 2015; Li et al., 2017) at ∼1.52–2.09 Ma (Petrova et al., 2016) but later than that estimated by chromosomes at 3 Ma (Gladkikh et al., 2016). At ∼1.28–1.81 Ma, the morphological characters of L. gregalis proposed the earliest clades of modern Lasiopodomys, as indicated by molecular data and fossils (Abramson et al., 2009a; Chaline et al., 1999; Petrova et al., 2016). Thereafter, the clades separated into L. brandtii and L. mandarinus at ∼0.58–0.98 Ma in our study, which is similar to inferences from Cytb and D-loop sequences (Li et al., 2017; Petrova et al., 2015) but less similar to the inferences from molecular cytogenetic analyses at ∼1.8 Ma (Gladkikh et al., 2016).

ENM indicated a considerably wider distribution area of Lasiopodomys in the past than in the present, which conforms to the fossils from the Pleistocene period (Alexeeva, Erbajeva & Khenzykhenova, 2015). During the early Pleistocene period, continuous cooling formed an arid climate in the high latitudes of the Northern Hemisphere (Guo et al., 2008). Climatic changes seldom shifted the suitable habitat of Lasiopodomys during the LIG and LGM periods. It is possible to infer that migration events occurred during the extremely cold and dry conditions, with a trend of continuous distribution farther to the northeast during the Pleistocene period until the Holocene period (Alexeeva, Erbajeva & Khenzykhenova, 2015; Prost et al., 2013). The appearance of N. fuscus, which is adapted to plateau climates, was later than the Qinghai-Tibet Plateau uplift (Wang et al., 2008), with no significant distributed shifts. All ancient species of Lasiopodomys may have been distributed as per their current distribution areas with a radiation evolution (Abramson et al., 2009b; Bannikova et al., 2010) before the interglacial and glacial periods based on ENM and fossil reports (Alexeeva, Erbajeva & Khenzykhenova, 2015; Petrova et al., 2015). Considering the lower sensitivity to climatic changes and adaptation to habitat areas, the Lasiopodomys species could colonize in north regions; moreover, the evolution of characteristics, such as teeth and densely furred plantar surfaces, further enabled their survival in cooler, drier conditions.

Despite precipitation and temperature fluctuations, a decline in atmospheric O2also occurred during the past 0.8 Ma (Stolper et al., 2016). Environmental stress caused a major driving on evolutionary process (Parsons, 2005). In the species of rodents, limited oxygen availability resulted in evolutionary adaptation and appearance of various strategies (Pamenter et al., 2020), such as different colonial habitats of life—subterranean (L. mandarinus) and plateau (L. fuscus); these strategies formed unique physiological and molecular adaptations to hypoxia (Jiang et al., 2020; Dong, Wang & Jiang, 2020). Our study supports a history of rapid population expansion under positive selection via mitogenome sequences such as the ATP6 gene, which uses oxygen to create adenosine triphosphate. However, further research using integrated phylogeographic analyses of the genus Lasiopodomys (Li et al., 2017; Petrova et al., 2015) is warranted to determine the adaptation of L. brandtii and L. mandarinus to factors including precipitation, temperature, and chronic hypoxia.

Supplemental Information

Supplemental Information 1. List of species used in this study and their accession numbers in GenBank.
DOI: 10.7717/peerj.10850/supp-1
Supplemental Information 2. All presence records among N. fuscus, L. brandtii, L. mandarinus, and L. gregalis.
DOI: 10.7717/peerj.10850/supp-2
Supplemental Information 3. Correlated bioclimatic values using Pearson’s correlation analysis.
DOI: 10.7717/peerj.10850/supp-3
Supplemental Information 4. Receiver operating characteristic curve (ROC) values for N. fuscus, L. brandtii, L. mandarinus, and L. gregalis under ecological niche models.
DOI: 10.7717/peerj.10850/supp-4
Supplemental Information 5. Original data on the mitochondrial genome of nine samples.
DOI: 10.7717/peerj.10850/supp-5

Acknowledgments

We would like to thank Yifeng Zhang and Xinrui Wang for their help in feeding the experimental animals.

Funding Statement

This work was supported by the National Natural Science Foundation of China (grant no. 31372193) and the Key scientific research projects of Henan Higher Education Institutions (grant no. 18A180007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Contributor Information

Xiangyu Tian, Email: 201531200038@mail.bnu.edu.cn.

Yuhua Shi, Email: 201631200032@mail.bnu.edu.cn.

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Luye Shi conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

Likuan Liu, Xiujuan Li and Yue Wu analyzed the data, prepared figures and/or tables, and approved the final draft.

Xiangyu Tian conceived and designed the experiments, performed the experiments, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Yuhua Shi analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

Zhenlong Wang conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

Sequences are available at GenBank: MT614214 to MT614219.

Sequences are also available in the Supplementary Files.

References

  • Abramson et al. (2009a).Abramson NI, Lebedev VS, Bannikova AA, Tesakov AS. Radiation events in the subfamily Arvicolinae (Rodentia): evidence from nuclear genes. Doklady Biological Sciences: Proceedings of the Academy of Sciences of the USSR, Biological Sciences Sections. 2009a;428:458–461. doi: 10.1134/S0012496609050196. [DOI] [PubMed] [Google Scholar]
  • Abramson et al. (2009b).Abramson NI, Lebedev VS, Tesakov AS, Bannikova AA. Supraspecies relationships in the subfamily Arvicolinae (Rodentia, Cricetidae): an unexpected result of nuclear gene analysis. Molecular Biology. 2009b;43:834–46. doi: 10.1134/S0026893309050148. [DOI] [PubMed] [Google Scholar]
  • Abramson & Lissovsky (2012).Abramson NI, Lissovsky AA. The mammals of Russia: a taxonomic and geographic reference. Archive of Zoological Museum of MSU. KMK Scientific Press; Moscow: 2012. Subfamily arvicolinae; pp. 220–276. [Google Scholar]
  • Alexeeva, Erbajeva & Khenzykhenova (2015).Alexeeva N, Erbajeva M, Khenzykhenova F. Lasiopodomys brandti in Pleistocene of Transbaiklia and adjacent territories: distribution area, evolutionary development in context of global and regional events. Quaternary International. 2015;355:11–17. doi: 10.1016/j.quaint.2014.08.017. [DOI] [Google Scholar]
  • Allen (1940).Allen GM. The mammals of China and Mongolia In: Granger W, ed. Natural History of Central Asia. Central Asiatic Expeditions of the American Museum of Natural History; New York: 1940. [Google Scholar]
  • Ashur-Fabian et al. (2004).Ashur-Fabian O, Avivi A, Trakhtenbrot L, Adamsky K, Cohen M, Kajakaro G, Joel A, Amariglio N, Nevo E, Rechavi G. Evolution of p53 in hypoxia-stressed Spalax mimics human tumor mutation. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:12236–12241. doi: 10.1073/pnas.0404998101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Baca et al. (2019).Baca M, Popović D, Lemanik A, Baca K, Horáček I, Nadachowski A. Highly divergent lineage of narrow-headed vole from the Late Pleistocene Europe. Scientific Reports. 2019;9:17799. doi: 10.1038/s41598-019-53937-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Baele et al. (2012).Baele G, Lemey P, Bedford T, Rambaut A, Suchard MA, Alekseyenko AV. Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. Molecular Biology and Evolution. 2012;29:2157–2167. doi: 10.1093/molbev/mss084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Baele et al. (2013).Baele G, Li WLS, Drummond AJ, Suchard MA, Lemey P. Accurate model selection of relaxed molecular clocks in Bayesian phylogenetics. Molecular Biology and Evolution. 2013;30:239–243. doi: 10.1093/molbev/mss243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Ballard & Rand (2005).Ballard JWO, Rand DM. The population biology of mitochondrial DNA and its phylogenetic implications. Annual Review of Ecology, Evolution, and Systematics. 2005;36:621–642. doi: 10.1146/annurev.ecolsys.36.091704.175513. [DOI] [Google Scholar]
  • Bannikova et al. (2010).Bannikova AA, Lebedev VS, Lissovsky AA, Matrosova V, Abramson NI, Obolenskaya EV, Tesakov AS. Molecular phylogeny and evolution of the Asian lineage of vole genus Microtus (Rodentia: Arvicolinae) inferred from mitochondrial cytochrome b sequence. Biological Journal of the Linnean Society. 2010;99:595–613. doi: 10.1111/j.1095-8312.2009.01378.x. [DOI] [Google Scholar]
  • Bazin, Glémin & Galtier (2006).Bazin E, Glémin S, Galtier N. Population size does not influence mitochondrial genetic diversity in animals. Science. 2006;312:570–572. doi: 10.1126/science.1122033. [DOI] [PubMed] [Google Scholar]
  • Bergthorsson et al. (2003).Bergthorsson U, Adams KL, Thomason B, Palmer JD. Widespread horizontal transfer of mitochondrial genes in flowering plants. Nature. 2003;424:197–201. doi: 10.1038/nature01743. [DOI] [PubMed] [Google Scholar]
  • Brown & Simpson (1981).Brown GG, Simpson MV. Intra- and interspecific variation of the mitochondrial genome in Rattus norvegicus and Rattus rattus: restriction enzyme analysis of variant mitochondrial DNA molecules and their evolutionary relationships. Genetics. 1981;97:125–143. doi: 10.1093/genetics/97.1.125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Buzan et al. (2008).Buzan EV, Krystufek B, Hänfling B, Hutchinson WF. Mitochondrial phylogeny of Arvicolinae using comprehensive taxonomic sampling yields new insights. Biological Journal of the Linnean Society. 2008;94:825–835. doi: 10.1111/j.1095-8312.2008.01024.x. [DOI] [Google Scholar]
  • Chaline et al. (1999).Chaline J, Brunet-Lecomte P, Montuire S, Viriot L, Courant F. Anatomy of the arvicoline radiation (Rodentia): palaeogeographical, palaeoecological history and evolutionary data. Annales Zoologici Fennici. 1999;36:239–267. [Google Scholar]
  • Charneski et al. (2011).Charneski CA, Honti F, Bryant JM, Hurst LD, Feil EJ. Atypical AT skew in firmicute genomes results from selection and not from mutation. PLOS Genetics. 2011;7:e1002283. doi: 10.1371/journal.pgen.1002283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Chen et al. (2012).Chen W, Hao H, Sun Z, Liu Y, Liu S, Yue B. Phylogenetic position of the genus Proedromys (Arvicolinae, Rodentia): evidence from nuclear and mitochondrial DNA. Biochemical Systematics and Ecology. 2012;42:59–68. doi: 10.1016/j.bse.2012.01.002. [DOI] [Google Scholar]
  • Childress & Seibel (1998).Childress JJ, Seibel BA. Life at stable low oxygen levels: adaptations of animals to oceanic oxygen minimum layers. The Journal of Experimental Biology. 1998;201:1223–1232. doi: 10.1242/jeb.201.8.1223. [DOI] [PubMed] [Google Scholar]
  • Cong et al. (2016).Cong H, Kong LM, Liu ZX, Wu Y, Motokawa M, Harada M, Li Y. Complete mitochondrial genome of the mandarin vole Lasiopodomys mandarinus (Rodentia: Cricetidae), Mitochondrial DNA. Part A, DNA Mapping, Sequencing, and Analysis. 2016;27:760–761. doi: 10.3109/19401736.2014.915528. [DOI] [PubMed] [Google Scholar]
  • Corbet (1978).Corbet GB. The mammals of the Palaearctic region: a taxonomic review. Natural History. British Museum; London: 1978. [Google Scholar]
  • Dai & Holland (2019).Dai Y, Holland PWH. The interaction of natural selection and GC Skew may drive the fast evolution of a sand rat homeobox gene. Molecular Biology and Evolution. 2019;36:1473–1480. doi: 10.1093/molbev/msz080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Darriba et al. (2012).Darriba D, Taboada GL, Doallo R, Posada D. JModelTest 2: more models, new heuristics and parallel computing. Nature Methods. 2012;9:772. doi: 10.1038/nmeth.2109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • De Freitas et al. (2018).De Freitas PD, Mendez FL, Chávez-Congrains K, Galetti PM, Coutinho LL, Pissinatti A, Bustamante CD. Next-generation sequencing of the complete mitochondrial genome of the endangered species black lion Tamarin Leontopithecus chrysopygus (Primates) and mitogenomic phylogeny focusing on the Callitrichidae family. G3. 2018;8:1985–1991. doi: 10.1534/g3.118.200153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • DeLong, DeLong & Clarke-Pearson (1988).DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845. doi: 10.2307/2531595. [DOI] [PubMed] [Google Scholar]
  • Dierckxsens, Mardulyn & Smits (2017).Dierckxsens N, Mardulyn P, Smits G. NOVOPlasty: de novo assembly of organelle genomes from whole genome data. Nucleic Acids Research. 2017;45:e18. doi: 10.1093/nar/gkw1060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Dong et al. (2018).Dong Q, Shi L, Li Y, Jiang M, Sun H, Wang B, Cheng H, Zhang Y, Shao T, Shi Y, Wang Z. Differential responses of Lasiopodomys mandarinus and Lasiopodomys brandtii to chronic hypoxia: a cross-species brain transcriptome analysis. BMC Genomics. 2018;19:901. doi: 10.1186/s12864-018-5318-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Dong, Wang & Jiang (2020).Dong Q, Wang Z, Jiang M, Sun H, Wang X, Li Y, Zhang Y, Cheng H, Chai Y, Shao T, Shi L. Transcriptome analysis of the response provided by Lasiopodomys mandarinus to severe hypoxia includes enhancing DNA repair and damage prevention. Frontiers in Zoology. 2020;17 doi: 10.1186/s12983-020-00356-y. Article 9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Drummond et al. (2006).Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLOS Biology. 2006;4:e88. doi: 10.1371/journal.pbio.0040088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Drummond & Rambaut (2007).Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology. 2007;7:214. doi: 10.1186/1471-2148-7-214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Elith et al. (2011).Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 2011;17:43–57. doi: 10.1111/j.1472-4642.2010.00725.x. [DOI] [Google Scholar]
  • Fawcett (2006).Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006;27:861–874. doi: 10.1016/j.patrec.2005.10.010. [DOI] [Google Scholar]
  • Finn, Clements & Edd (2011).Finn RD, Clements J, Edd Eddy, SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Research. 2011;39:W29–W37. doi: 10.1093/nar/gkr367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Galtier et al. (2009).Galtier N, Nabholz B, Glémin S, Hurst GDD. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Molecular Ecology. 2009;18:4541–4550. doi: 10.1111/j.1365-294X.2009.04380.x. [DOI] [PubMed] [Google Scholar]
  • Gao et al. (2017).Gao J, Yue LL, Jiang X, Ni L, Ashraf MA, Zhou Y, Li K, Xiao J. Phylogeographic patterns of Microtus fortis (Arvicolinae: Rodentia) in China based on mitochondrial DNA sequences. Pakistan Journal of Zoology. 2017;49:1185–1195. doi: 10.17582/journal.pjz/2017.49.4.1185.1195. [DOI] [Google Scholar]
  • Gernhard (2008).Gernhard T. The conditioned reconstructed process. Journal of Theoretical Biology. 2008;253:769–778. doi: 10.1016/j.jtbi.2008.04.005. [DOI] [PubMed] [Google Scholar]
  • Gladkikh et al. (2016).Gladkikh OL, Romanenko SA, Lemskaya NA, Serdyukova NA, O’Brien PC, Kovalskaya JM, Smorkatcheva AV, Golenishchev FN, Perelman PL, Trifonov VA, Graphodatsky AS. Rapid karyotype evolution in Lasiopodomys involved at least two autosome –sex chromosome translocations. PLOS ONE. 2016;11:e0167653. doi: 10.1371/journal.pone.0167653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Grant & Stothard (2008).Grant JR, Stothard P. The CGView Server: a comparative genomics tool for circular genomes. Nucleic Acids Research. 2008;36:W181–W184. doi: 10.1093/nar/gkn179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Gromov & Polyakov (1978).Gromov IM, Polyakov IY. Fauna of the USSSR, mammals In: Voles (Microtinae) Smithsonian Inst. Libraries and the National Science Foundation; New Delhi: 1978. [Google Scholar]
  • Guo et al. (2008).Guo ZT, Sun B, Zhang ZS, Peng SZ, Xiao GQ, Ge JY, Hao QZ, Qiao YS, Liang MY, Liu JF, Yin QZ, Wei JJ. A major reorganization of Asian climate by the Early Miocene. Climate of the Past. 2008;4:153–174. doi: 10.5194/cp-4-153-2008. [DOI] [Google Scholar]
  • Heath, Huelsenbeck & Stadler (2014).Heath TA, Huelsenbeck JP, Stadler T. The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences of the United States of America. 2014;111:E2957–E2966. doi: 10.1073/pnas.1319091111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Hijmans et al. (2005).Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 2005;25:1965–1978. doi: 10.1002/joc.1276. [DOI] [Google Scholar]
  • Huang et al. (2019).Huang P, Carpenter JM, Chen B, Li TJ. The first divergence time estimation of the subfamily Stenogastrinae (Hymenoptera: Vespidae) based on mitochondrial phylogenomics. International Journal of Biological Macromolecules. 2019;137:767–773. doi: 10.1016/j.ijbiomac.2019.06.239. [DOI] [PubMed] [Google Scholar]
  • Humphreys et al. (2016).Humphreys AM, Rydin C, Jønsson KA, Alsop D, Callender-Crowe LM, Barraclough TG. Detecting evolutionarily significant units above the species level using the generalised mixed Yule coalescent method. Methods in Ecology and Evolution. 2016;7:1366–1375. doi: 10.1111/2041-210X.12603. [DOI] [Google Scholar]
  • Jain et al. (2016).Jain IH, Zazzeron L, Goli R, Alexa K, Schatzman-Bone S, Dhillon H, Goldberger O, Peng J, Shalem O, Sanjana NE, Zhang F, Mootha VK. Hypoxia as a therapy for mitochondrial disease. Science. 2016;352:54–61. doi: 10.1126/science.aad9642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Jiang et al. (2020).Jiang M, Shi L, Li X, Dong Q, Sun H, Du Y, Zhang YF, Shao T, Cheng H, Chen WH, Wang Z. Genome-wide adaptive evolution to underground stresses in subterranean mammals: Hypoxia adaption, immunity promotion, and sensory specialization. Ecology and Evolution. 10:7377–7388. doi: 10.1002/ece3.6462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Katoh & Standley (2013).Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution. 2013;30:772–780. doi: 10.1093/molbev/mst010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Kenechukwu, Li & An (2018).Kenechukwu NA, Li M, An L, Cui M, Wang C, Wang A, Chen Y, Du S, Feng C, Zhong S, Gao Y, Qi D. Comparative analysis of the complete mitochondrial genomes for development application. Frontiers in Genetics. 2018;9:651. doi: 10.3389/fgene.2018.00651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Kohli et al. (2014).Kohli BA, Speer KA, Kilpatrick CW, Batsaikhan N, Damdinbazar D, Cook JA. Multilocus systematics and non-punctuated evolution of Holarctic myodini (Rodentia: Arvicolinae) Molecular Phylogenetics and Evolution. 2014;76:18–29. doi: 10.1016/j.ympev.2014.02.019. [DOI] [PubMed] [Google Scholar]
  • Lehwark & Greiner (2019).Lehwark P, Greiner S. GB2sequin - a file converter preparing custom GenBank files for database submission. Genomics. 2019;111:759–761. doi: 10.1016/j.ygeno.2018.05.003. [DOI] [PubMed] [Google Scholar]
  • Li et al. (2019).Li JQ, Li L, Fu BQ, Yan HB, Jia WZ. Complete mitochondrial genomes confirm the generic placement of the plateau vole, Neodon fuscus. Bioscience Reports. 2019;39(8):BSR20182349. doi: 10.1042/BSR20182349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Li et al. (2017).Li K, Kohn MH, Zhang S, Wan X, Shi DZ, Wang D. The colonization and divergence patterns of Brandt’s vole (Lasiopodomys brandtii) populations reveal evidence of genetic surfing. BMC Evolutionary Biology. 2017;17:145. doi: 10.1186/s12862-017-0995-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Li, Lu & Wang (2016a).Li YK, Lu JQ, Wang ZL. Complete mitochondrial genome of Lasiopodomys mandarinus mandarinus (Arvicolinae, Rodentia) Mitochondrial DNA. Part A, DNA Mapping, Sequencing, and Analysis. 2016a;27:1459–1460. doi: 10.3109/19401736.2014.953092. [DOI] [PubMed] [Google Scholar]
  • Li et al. (2016b).Li Y, Shi Y, Lu JQ, Ji W, Wang ZL. Sequence and phylogenetic analysis of the complete mitochondrial genome of Lasiopodomys mandarinus mandarinus (Arvicolinae, Rodentia) Gene. 2016b;593:302–307. doi: 10.1016/j.gene.2016.08.035. [DOI] [PubMed] [Google Scholar]
  • Liu et al. (2013).Liu M, Shi HX, Gao S, Song MJ. The introduction of Brandt’s vole and its changes in names and systematic classification. Chinese Journal of Comparative Medicine. 2013;23:53–57. [Google Scholar]
  • Liu et al. (2017).Liu S, Jin W, Liu Y, Murphy RW, Lv B, Hao H, Liao R, Sun Z, Tang M, Chen W, Fu J. Taxonomic position of Chinese voles of the tribe Arvicolini and the description of 2 new species from Xizang, China. Journal of Mammalogy. 2017;98:166–182. doi: 10.1093/jmammal/gyw170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Liu et al. (2012a).Liu S, Liu Y, Guo P, Sun Z, Murphy RW, Fan Z, Fu J, Zhang Y. Phylogeny of oriental voles (Rodentia: Muridae: Arvicolinae): molecular and morphological evidence (Rodentia: Muridae: Arvicolinae) Zoological Science. 2012a;29:610–622. doi: 10.2108/zsj.29.610. [DOI] [PubMed] [Google Scholar]
  • Liu et al. (2012b).Liu SY, Sun ZY, Liu Y, Wang H, Guo P, Murphy RW. A new vole from Xizang, China and the molecular phylogeny of the genus Neodon (Cricetidae: Arvicolinae) Zootaxa. 2012b;3235:1–22. [Google Scholar]
  • Lohse et al. (2013).Lohse M, Drechsel O, Kahlau S, Bock R. OrganellarGenomeDRAW—a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets. Nucleic Acids Research. 2013;41:W575–W581. doi: 10.1093/nar/gkt289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Lowe & Eddy (1997).Lowe TM, Eddy SR. TRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Research. 1997;25:955–964. doi: 10.1093/nar/25.5.955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Malik et al. (2016).Malik A, Domankevich V, Lijuan H, Xiaodong F, Korol A, Avivi A, Shams I. Genome maintenance and bioenergetics of the long-lived hypoxia-tolerant and cancer-resistant blind mole rat, Spalax: a cross-species analysis of brain transcriptome. Scientific Reports. 2016;6:38624. doi: 10.1038/srep38624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Malik et al. (2012).Malik A, Korol A, Weber M, Hankeln T, Avivi A, Band M. Transcriptome analysis of the Spalax hypoxia survival response includes suppression of apoptosis and tight control of angiogenesis. BMC Genomics. 2012;13:615. doi: 10.1186/1471-2164-13-615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Martin (1995).Martin A. Metabolic rate and directional nucleotide substitution in animal mitochondrial DNA. Molecular Biology and Evolution. 1995;12:1124–1131. doi: 10.1093/oxfordjournals.molbev.a040286. [DOI] [PubMed] [Google Scholar]
  • Martin et al. (2000).Martin Y, Gerlach G, Schlötterer C, Meyer A. Molecular phylogeny of European muroid rodents based on complete cytochrome b sequences. Molecular Phylogenetics and Evolution. 2000;16:37–47. doi: 10.1006/mpev.1999.0760. [DOI] [PubMed] [Google Scholar]
  • Martínková & Moravec (2012).Martínková N, Moravec J. Multilocus phylogeny of arvicoline voles (Arvicolini, Rodentia) shows small tree terrace size. Folia Zoologica. 2012;61:254–267. doi: 10.25225/fozo.v61.i3.a10.2012. [DOI] [Google Scholar]
  • Mezhzherin, Zykov & Morozov-Leonov (1993).Mezhzherin SV, Zykov AE, Morozov-Leonov SY. Biochemical variation and genetic divergence of palearctic voles (Arvicolidae), Meadow voles Microtus Schrank, 1798, snow voles, Chionomys Miller, 1908, water voles, Arvicola Lacepede, 1799. Genetica. 1993;29:28–41. [Google Scholar]
  • Mueller (2006).Mueller RL. Evolutionary rates, divergence dates, and the performance of mitochondrial genes in Bayesian phylogenetic analysis. Systematic Biology. 2006;55:289–300. doi: 10.1080/10635150500541672. [DOI] [PubMed] [Google Scholar]
  • Nevo (2013).Nevo E. Stress, adaptation, and speciation in the evolution of the blind mole rat, Spalax, in Israel. Molecular Phylogenetics and Evolution. 2013;66:515–525. doi: 10.1016/j.ympev.2012.09.008. [DOI] [PubMed] [Google Scholar]
  • Nguyen et al. (2015).Nguyen LT, Schmidt HA, Haeseler Avon, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular Biology and Evolution. 2015;32:268–274. doi: 10.1093/molbev/msu300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Ojala, Montoya & Attardi (1981).Ojala D, Montoya J, Attardi G. TRNA punctuation model of RNA processing in human mitochondria. Nature. 1981;290:470–474. doi: 10.1038/290470a0. [DOI] [PubMed] [Google Scholar]
  • Pamenter et al. (2020).Pamenter ME, Hall JE, Tanabe Y, Simonson TS. Cross-species insights into genomic adaptations to hypoxia. Frontiers in Henetics. 2020;11 doi: 10.3389/fgene.2020.00743. Article 743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Parsons (2005).Parsons PA. Environments and evolution: interactions between stress, resource inadequacy and energetic efficiency. Biological Reviews. 2005;80(4):589–610. doi: 10.1017/S1464793105006822. [DOI] [PubMed] [Google Scholar]
  • Petrova et al. (2016).Petrova TV, Tesakov AS, Kowalskaya YM, Abramson NI. Cryptic speciation in the narrow-headed vole Lasiopodomys (Stenocranius) gregalis (Rodentia: Cricetidae) Zoologica Scripta. 2016;45:618–629. doi: 10.1111/zsc.12176. [DOI] [Google Scholar]
  • Petrova et al. (2015).Petrova TV, Zakharov ES, Samiya R, Abramson NI. Phylogeography of the narrow-headed vole Lasiopodomys (Stenocranius) gregalis (Cricetidae, Rodentia) inferred from mitochondrial cytochrome b sequences: an echo of Pleistocene prosperity. Journal of Zoological Systematics and Evolutionary Research. 2015;53:97–108. doi: 10.1111/jzs.12082. [DOI] [Google Scholar]
  • Pfeiffer et al. (2018).Pfeiffer F, Gröber C, Blank M, Händler K, Beyer M, Schultze JL, Mayer G. Systematic evaluation of error rates and causes in short samples in next-generation sequencing. Scientific Reports. 2018;8:10950. doi: 10.1038/s41598-018-29325-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Phillips, Anderson & Schapire (2006).Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006;190:231–259. doi: 10.1016/j.ecolmodel.2005.03.026. [DOI] [Google Scholar]
  • Prost et al. (2013).Prost S, Guralnick RP, Waltari E, Fedorov VB, Kuzmina E, Smirnov N, Kolfschoten VT, Hofreiter K, Vrieling K. Losing ground: past history and future fate of A rctic small mammals in a changing climate. Global Change Biology. 2013;19(6):1854–1864. doi: 10.1111/gcb.12157. [DOI] [PubMed] [Google Scholar]
  • Rambaut et al. (2018).Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Systematic Biology. 2018;67:901–904. doi: 10.1093/sysbio/syy032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Ramos et al. (2018).Ramos B, González-Acuña D, Loyola DE, Johnson WE, Parker PG, Massaro M, Dantas GP, Miranda MD, Vianna JA. Landscape genomics: natural selection drives the evolution of mitogenome in penguins. BMC Genomics. 2018;19:53. doi: 10.1186/s12864-017-4424-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • R Development Core Team (2013).R Development Core Team . R Foundation for Statistical Computing; Vienna: 2013. [Google Scholar]
  • Rozas et al. (2017).Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, Sánchez-Gracia A. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular Biology and Evolution. 2017;34:3299–3302. doi: 10.1093/molbev/msx248. [DOI] [PubMed] [Google Scholar]
  • Santore et al. (2002).Santore MT, McClintock DS, Lee VY, Budinger GRS, Chandel NS. Anoxia-induced apoptosis occurs through a mitochondria-dependent pathway in lung epithelial cells. American Journal of Physiology. Lung Cellular and Molecular Physiology. 2002;282:L727–L734. doi: 10.1152/ajplung.00281.2001. [DOI] [PubMed] [Google Scholar]
  • Shields et al. (2012).Shields CA, Bailey DA, Danabasoglu G, Jochum M, Kiehl JT, Levis S, Park S. The low-resolution CCSM4. Journal of Climate. 2012;25:3993–4014. doi: 10.1175/JCLI-D-11-00260.1. [DOI] [Google Scholar]
  • Solaini et al. (2010).Solaini G, Baracca A, Lenaz G, Sgarbi G. Hypoxia and mitochondrial oxidative metabolism. Biochimica et Biophysica Acta. 2010;1797:1171–1177. doi: 10.1016/j.bbabio.2010.02.011. [DOI] [PubMed] [Google Scholar]
  • Stolper et al. (2016).Stolper DA, Bender ML, Dreyfus GB, Yan Y, Higgins JA. A Pleistocene ice core record of atmospheric O2 concentrations. Science. 2016;353:1427–1430. doi: 10.1126/science.aaf5445. [DOI] [PubMed] [Google Scholar]
  • Sun et al. (2018).Sun H, Zhang Y, Shi Y, Li Y, Li W, Wang Z. Evolution of the CLOCK and BMAL1 genes in a subterranean rodent species (Lasiopodomys mandarinus) International Journal of Biological Macromolecules. 2018;109:932–940. doi: 10.1016/j.ijbiomac.2017.11.076. [DOI] [PubMed] [Google Scholar]
  • Tian et al. (2020).Tian XY, Gu SM, Pan D, Shi LY, Wang ZL. The complete mitochondrial genome of Lasiopodomys brandtii. Mitochondrial DNA Part B. 2020;5:364–365. doi: 10.1080/23802359.2019.1703567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Tillich et al. (2017).Tillich M, Lehwark P, Pellizzer T, Ulbricht-Jones ES, Fischer A, Bock R, Greiner S. GeSeq –versatile and accurate annotation of organelle genomes. Nucleic Acids Research. 2017;45:W6–W11. doi: 10.1093/nar/gkx391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Vasconcellos et al. (2019).Vasconcellos MM, Colli GR, Weber JN, Ortiz EM, Rodrigues MT, Cannatella DC. Isolation by instability: historical climate change shapes population structure and genomic divergence of treefrogs in the Neotropical Cerrado savanna. Molecular Ecology. 2019;28:1748–1764. doi: 10.1111/mec.15045. [DOI] [PubMed] [Google Scholar]
  • Wang et al. (2008).Wang C, Zhao X, Liu Z, Lippert PC, Graham SA, Coe RS, Yi H, Zhu L, Liu S, Li Y. Constraints on the early uplift history of the Tibetan Plateau. Proceedings of the National Academy of Sciences of the United States of America. 2008;105:4987–4992. doi: 10.1073/pnas.0703595105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Wang (2003).Wang YX. A complete checklist of mammal species and subspecies in China: a taxonomic and geographic reference. China Forestry Publishing House; Beijing: 2003. [Google Scholar]
  • Watanabe et al. (2011).Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Kawamiya M. Miroc-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development. 2011;4:845–872. doi: 10.5194/gmd-4-845-2011. [DOI] [Google Scholar]
  • Wilson & Reeder (2005).Wilson DE, Reeder DM. Mammal species of the world: a taxonomic and geographic reference. 3rd edition Baltimore: Johns Hopkins University Press; 2005. [Google Scholar]
  • Witt & Huerta-Sánchez (2019).Witt KE, Huerta-Sánchez E. Convergent evolution in human and domesticate adaptation to high-altitude environments. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences. 2019;374:20180235. doi: 10.1098/rstb.2018.0235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Xie et al. (2011).Xie W, Lewis PO, Fan Y, Kuo L, Chen MH. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Systematic Biology. 2011;60:150–160. doi: 10.1093/sysbio/syq085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Yang (2007).Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution. 2007;24:1586–1591. doi: 10.1093/molbev/msm088. [DOI] [PubMed] [Google Scholar]
  • Zhang, Cheng & Ge (2019).Zhang Z, Cheng QQ, Ge Y. The complete mitochondrial genome of Rhynchocypris oxycephalus (Teleostei: Cyprinidae) and its phylogenetic implications. Ecology and Evolution. 2019;9:7819–7837. doi: 10.1002/ece3.5369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Zhang et al. (2018).Zhang JY, Zhang LP, Yu DN, Storey KB, Zheng RQ. Complete mitochondrial genomes of Nanorana taihangnica and N. yunnanensis (Anura: Dicroglossidae) with novel gene arrangements and phylogenetic relationship of Dicroglossidae. BMC Evolutionary Biology. 2018;18:26. doi: 10.1186/s12862-018-1140-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • Zorenko & Atanasov (2017).Zorenko T, Atanasov N. Patterns of behaviour as an evidence for the taxonomical status of Lasiopodomys (Stenocranius) gregalis (Pallas, 1779) (Rodentia: Arvicolinae) Acta Zoologica Bulgarica. Supplement. 2017;8:189–197. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Information 1. List of species used in this study and their accession numbers in GenBank.
DOI: 10.7717/peerj.10850/supp-1
Supplemental Information 2. All presence records among N. fuscus, L. brandtii, L. mandarinus, and L. gregalis.
DOI: 10.7717/peerj.10850/supp-2
Supplemental Information 3. Correlated bioclimatic values using Pearson’s correlation analysis.
DOI: 10.7717/peerj.10850/supp-3
Supplemental Information 4. Receiver operating characteristic curve (ROC) values for N. fuscus, L. brandtii, L. mandarinus, and L. gregalis under ecological niche models.
DOI: 10.7717/peerj.10850/supp-4
Supplemental Information 5. Original data on the mitochondrial genome of nine samples.
DOI: 10.7717/peerj.10850/supp-5

Data Availability Statement

The following information was supplied regarding data availability:

Sequences are available at GenBank: MT614214 to MT614219.

Sequences are also available in the Supplementary Files.


Articles from PeerJ are provided here courtesy of PeerJ, Inc

RESOURCES