Biochemical Systematics and Ecology 61 (2015) 161e168
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Biochemical Systematics and Ecology
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Molecular identification and allopatric divergence of the white pine species in China based on the cytoplasmic DNA variation
* Zhong-Hu Li a, b, c, , 1, Chen Yang b, 1, Kang-Shan Mao b, Ya-Zhen Ma b, Jie Liu c, Zhan-Lin Liu a, Tuan-Tuan Deng a, Gui-Fang Zhao a a Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi'an 710069, China b Molecular Ecology Group, State Key Laboratory of Grassland Agro-Ecosystem, College of Life Sciences, Lanzhou University, Lanzhou 730000, Gansu, China c Key Laboratory of Biodiversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China article info abstract
Article history: Molecular identification of plant species may be highly related to the geographic isolation Received 11 December 2014 and speciation stages among species. In this study, we examined these possibilities in a Received in revised form 15 May 2015 group of white pines in China. We sampled 449 individuals from 60 natural populations of Accepted 11 June 2015 seven species from sect. Quinquefoliae subsect. Strobus. We sequenced four chloroplast Available online xxx DNA regions (around 3100 bp in length) and two mitochondrial DNAs (around 1000 bp in length). We identified 21 chlorotypes and 10 mitotypes. Both chlorotypes and mitotypes Keywords: recovered from four species with long disjunction and restricted distributions in northern Allopatric divergence Geographic isolation or northwestern China, Pinus sibirica, Pinus koraiensis, Pinus wallichiana and Pinus pumila fi Speciation stage are species-speci c, suggesting that these cytoplasmic DNAs can distinguish them from the Species identification close relatives. Allopatric isolations should have contributed greatly to their genetic di- White pines vergences. However, both chlorotypes and mitotypes recovered for Pinus dabeshanensis and Pinus fenzeliana distributed in southeastern and southern China are shared or closely related to those found in the widely distributed Pinus armandii. These two species may have diverged or derived from P. armandii recently. All of our findings together suggest that the discrimination power of the molecular identifications based on the cytoplasmic DNA barcodes may show variable discriminability depending on geographic isolation and speciation stages among the sampled species. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Species identification of plant materials is critical in diverse fields, especially related to biodiversity. Traditional identi- fication depend exclusively on morphological traits. Both well-trained taxonomists and the presence of the key morpho- logical traits (e.g. flowers and seeds) are necessary in identifying plant samples into species. However, molecular
* Corresponding author. Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi'an 710069, China. E-mail address: [email protected] (Z.-H. Li). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.bse.2015.06.002 0305-1978/© 2015 Elsevier Ltd. All rights reserved. 162 Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168 identification based on the standard DNA barcodes and special DNA fragments provides rapid and accurate species identi- fication (Kress et al., 2005; Hollingsworth et al., 2009, 2011). Cytoplasmic DNA fragments, especially chloroplast (cp) DNA regions (e.g., rbcL and matK), are suggested as the suitable candidates for identifying plant species (Kress et al., 2005; CBOL Plant Working Group, 2009). However, the commonly used cpDNA fragments (e.g., matK and rbcL) failed to develop species- specific mutations in gymnosperm groups (Liu et al., 2012a). Although some nuclear DNA regions in conifers may be useful for identifying plant species, it is difficult to amplify and sequence these fragments. Therefore, other cpDNAs as well as the mitochondrial (mt) DNAs with moderate mutations, were suggested to identify species in conifers (Liu et al., 2012a; Hao et al., 2015). In addition to looking for candidate DNA regions in barcoding species of numerous families or a special group (CBOL Plant Working Group, 2009; Hollingsworth et al., 2009; China Plant BOL Group, 2011), the recommended barcodes were also used for diverse applications, for example, to construct biodiversity inventory (Lahaye et al., 2008) and identify cryptic species (Yassin et al., 2008; Ragupathy et al., 2009; Liu et al., 2011). However, few researches were designed to examine discrimi- nation powers of these barcodes in identifying the closely related species with different speciation ages and allopatric iso- lations. Here we conduct such an example study to address this question using seven species of the white pine species (sect. Quinquefoliae subsect. Strobus) as a system. Around 11 white pine species are recorded in China, Pinus armandii Franchet, Pinus bhutanica Grierson & al., Pinus dabeshanensis Cheng et Law, Pinus pumila (Pallas) Regel in Kuester & al., Pinus fenzeliana Handel-Mazzetti, Pinus koraiensis Siebold & Zuccarini, Pinus kwantungensis Chun & Tsiang, Pinus morrisonicola Hayata, Pinus sibirica Du Tour in Deterville, Pinus wallichiana A. B. Jackson and Pinus wangii Hu & W. C. Cheng (Fu et al., 1999; Eckert et al., 2013). Among them, P. armandii is widely distributed from western to southeast China with altitudes ranged from 1000 to 3300 m. Six other species, Pinus bhutanica, Pinus kwantungensis, Pinus fenzeliana, Pinus dabeshanensis, Pinus wangii and Pinus morrisonicola were closely related to P. armandii. All of these seven species show short-distance disjunctions to each another. The other five species occur in southern China and some of them are narrowly restricted to a small range, for example, P. morrisonicola endemic to Taiwan and P. dabeshanensis to the Dabieshan Mountains. Recent studies suggested that P. armandii shared genetic variant with the other six species at the examined loci, indicating no complete lineage sorting among them (Liu et al., 2014a; Hao et al., 2015). However, Pinus wallichiana is found at higher altitude (1600e3300 m) along temperate forests in the Himalaya while P. koraiensis has a wide distribution in northeastern China. The other two species, P. pumila and P. sibirica, are found in the northeastern and western China, respectively. These four species are closely related to each another than to P. armandii complex although they are distributed with long-distance disjunctions based on morphological and molecular evidences (Fu et al., 1999; Eckert et al., 2013). Furthermore, the sequence variations from the commonly used cpDNAs could not diagnose them effectively (Hernandez-Le on et al., 2013; Liu et al., 2014a; Hao et al., 2015). In this study, we sequenced four cpDNA fragments and two mtDNAs for 449 individuals from 60 natural populations of seven species from two tentative groups of sect. Quinquefoliae subsect. Strobus. One group comprises P. armandii, P. dabeshanensis and P. fenzeliana and distributional ranges between them are allopatric, but with short-distance. The other group consists of P. sibirica, P. koraiensis, P. wallichiana and P. pumila with long-distance allopatric distributions. We hypothesized that with the more cpDNA fragments sequenced, it is likely to distinguish these species. However, discrimination power for species identification based on sequence variations will change dependent on the geographic isolations and possible speciation ages. In addition to test this hypothesis, our range-wide sampling and two large population genetic datasets based on cytoplasmic DNA variation undoubtedly will aid to understand the interspecific relationship of these species and their response to the historical climate changes (Liu et al., 2012b, 2014b; Hao et al., 2015).
2. Materials and methods
2.1. Plant material and sampling design
We collected 449 individuals from 60 natural populations of Pinus armandi, P. dabeshanensis, P. fenzeliana, P. koraiensis, P. pumila, P. sibirica and P. wallichiana. For both P. armandi and P. dabeshanensis, our samples cover their entire distributional ranges. We collected samples for P. koraiensis, P. pumila, P. sibirica and P. wallichiana from most recorded counties in China. For most populations, 5e10 individuals were sampled and needle leaves were taken and preserved in silica gel until DNA extraction. The geographic information of each sampling site, which includes the latitude, longitude and altitude, were recorded by an Extrex GPS (Garmin, Olathe, USA). A population of Pinus tabulaeformis was sampled as outgroup (Table 1, Figs. 1 and 2).
2.2. DNA extraction, amplification and sequencing
We extracted total genomic DNA from needle leaves using the modified 2 cetyltrimethylammonium bromide (CTAB) procedure (Doyle and Doyle, 1987). For all sampled individuals, four cpDNA non-coding regions (trnL-F, trnS-fM, trnS-G, rpl16) and two mtDNA non-coding regions (nad5 intron1, nad7 intron1) were amplified and sequenced using universal primers (see primer sequences and references in Table 2). All PCR amplifications were conducted in a total volume of 25 mL on a Gene Amp PCR system 9700 thermal cycler (Applied Biosystems, Foster City, CA, USA), using 10e40 ng genomic DNA. The mixture also contains 0.2 mM each primer, 1.5 mM MgCl2, 0.5 mM dNTPs, 50 mM TriseHCl (pH 8.3), and 1.0 U Taq polymerase (TaKaRa, Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168 163
Table 1 Geographic information of all sampled populations used in this study.
Species ID Population n Latitude (N) Longitude (E) Altitude (m) Pinus koraiensis 1 Ning'an, HLa 10 44 11.7750 126 31.6170 900 2 Baishan, JL 10 41 56.3320 127 35.5720 829 3 Helong, JL 10 42 24.3840 128 45.3200 564 4 Tieli, HL 10 46 56.9220 128 52.9740 418 5 Anyang, JL 10 42 27.5000 128 09.9600 802 6 Muleng, HL 10 44 53.7220 130 33.060 277 7 Fusong, JL 10 42 33.5450 127 47.0020 769 8 Dunhua, JL 10 48 30.6530 128 35.5950 594 9 Anyang, JL 10 48 09.5200 129 02.0520 613 10 Dandong, LN 10 40 07.7400 124 22.2980 40 11 Fengcheng, LN 10 40 25.6120 124 04.1750 176 12 Huanren, LN 10 41 14.1420 125 23.0070 400 13 Fusong, JL 10 41 56.8380 127 34.9170 830 14 Anyang, JL 10 42 16.8100 128 11.6300 970 15 Jidong, HL 10 45 06.0180 131 15.5680 180 16 Yichun, HL 10 47 11.7300 128 56.9830 386 Pinus wallichiana 17 Gyirong, TB 10 28 30.6650 85 13.0320 3362 18 Yadong, TB 10 27 25.8380 88 54.2050 3390 Pinus pumila 19 Genhe, IM 10 52 21.9850 122 28.4160 862 20 Jagdaqi, HL 10 51 37.3540 123 59.9300 627 21 Genhe, IM 10 51 50.6370 122 02.2060 847 Pinus sibirica 22 Burqin, XJ 1 48 39.8400 87 06.0560 2088 23 Burqin, XJ 10 48 25.5120 86 06.0560 1822 24 Burqin, XJ 5 48 36.0960 87 02.9790 1321 25 Burqin, XJ 8 48 28.8130 87 20.3930 1384 Pinus armandii 26 Meixian, SX 10 33 59.4820 107 59.8270 1615 27 Fengxian, SX 10 33 52.9100 106 33.4200 1452 28 Bome, TB 10 29 45.6750 95 56.7400 2922 29 Bome, TB 10 29 57.6020 95 22.3430 2909 30 Bome, TB 10 29 37.2440 96 18.2100 3244 31 Mianning, SC 10 28 30.7090 102 12.5090 1932 32 Bome, TB 10 29 51.7420 94 46.2500 3101 33 Xunhua, QH 10 35 47.9780 102 41.3670 2327 34 Hanyuan, SC 10 29 38.5270 102 29.4020 2295 35 Lixian, SC 10 31 57.5120 102 18.2790 2760 36 Wenxian, GS 6 32 54.6270 104 38.3100 2321 37 Zhouzhi, SX 3 33 53.6070 107 48.6220 2025 38 Nanzhao, HN 10 33 34.2570 111 58.8770 727 39 Chang'an, SX 4 33 55.8950 109 02.3800 2242 40 Zhouzhi, SX 1 33 56.1320 108 11.0350 1795 41 Taibaishan, SX 10 34 03.2800 107 41.9800 2145 42 Taibaishan, SX 4 34 02.2070 107 42.9500 2832 43 Taibaishan, SX 2 34 03.1150 107 41.9870 2215 44 Taibaishan, SX 3 34 02.4350 107 42.4780 2424 45 Taibaishan, SX 8 34 01.1330 107 43.1080 2730 46 Taibaishan, SX 7 34 03.6630 107 41.9870 1878 47 Taibaishan, SX 4 34 00.5400 107 43.2900 2537 48 Taibaishan, SX 5 34 02.4470 107 42.4080 2356 49 Yuexi, AH 7 30 59.4630 116 05.3380 1341 50 Kangle, GS 1 34 57.1970 103 41.0390 2270 51 Zhouqu, GS 5 33 34.7440 104 30.8950 2047 52 Shangri-Lab,YN 7 27 22.0560 100 07.8610 3280 53 Mouli, SC 3 27 41.4830 100 57.3820 2918 54 Taibaishan, SX 3 33 59.9620 107 43.6520 2659 Pinus dabeshanensis 55 Yuexi, AH 2 30 59.3680 116 05.6980 1131 56 Yuexi, AH 10 30 44.8750 116 05.7950 1039 57 Yuexi, AH 6 30 43.2880 116 08.2500 1034 58 Yuexi, AH 1 30 53.1650 116 05.8300 1121 Pinus fenzeliana 59 Wuzhishan, HA 2 19 01.3600 109 31.8370 1333 60 Wuzhishan, HA 1 18 57.0470 109 21.9830 1232 Pinus tabulaeformisc 61 Baotou, IM 10 40 47.3020 110 18.5330 1510
a HL, Heilongjiang; JL, Jilin; LN, Liaoning; TB, Tibet (Xizang Autonomous Region); IM, Inner Mongolia (Nei Monggol Autonomous Region); XJ, Xinjiang Uighur Autonomous Region; SX, Shaanxi; SC, Sichuan; GS, Gansu; QH, Qinghai; HN, Henan; YN, Yunnan; AH, Anhui; HA, Hainan; n, number of individuals in each population. b Also known as name Zhongdian before 2001. c Used as outgroup in this study. 164 Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168
Dalian, China). Two cpDNA fragments, trnS-G and trnS-fM, were performed under the PCR programs as follows: initial denaturation at 94 C for 5 min, then 36 cycles of denaturation at 94 C for 1 min, annealing at 57 C for 45 s, extension at 72 C for 1.5 min, then a final extension at 72 C for 7 min. Another two cpDNA fragments, trnL-F and rpl16, were performed as initial denaturation at 94 C for 3 min, 37 cycles of denaturation at 94 C for 45 s, annealing at 60 C for 55 s, extension at 72 C for 75 s, and a final extension at 72 C for 7 min. For, the mtDNA fragments nad5 intron 1 and nad7 intron 1 were performed as initial denaturation at 94 C for 3 min, 30 cycles of denaturation at 94 C for 30 s, annealing at 55 C for 30 s, extension at 72 C for 2 min and a final extension at 72 C for 10 min. The amplification products were separated on 1.5% agarose gels and purified with a TIANquick Midi Purification Kit (Tiangen Biotech, Beijing, China) according to the manufacturer's instructions. Sequencing PCR reactions were performed using ABI Prism BigDye Terminator version 3.1 Cycle Sequencing Kit, and capillary electrophoresis was run on an ABI 3130 XL sequencer (PE Applied Biosystems, Foster City, CA, USA).
2.3. Data analysis
DNA sequences were aligned and manually adjusted using MEGA v. 5 (Tamura et al., 2011). Since both chloroplast genome and mitochondrial genome are single circular DNA molecule and each could be regarded as a single locus, we therefore concatenated four cpDNA and two mtDNA fragments together, respectively, to create the four-locus cpDNA dataset and the two-locus mtDNA dataset. DnaSP v. 5.10 (Librado and Rozas, 2009) was applied to identify cpDNA and mtDNA haplotypes, and all haplotype sequences generated in the present study were deposited in GenBank and their registered Num. is KT159776eKT159796. Phylogenetic relationships among haplotypes were reconstructed using Bayesian Inference (BI) in
Fig. 1. CpDNA variation in Pinus subsection Strobus. (a) Geographic distribution of the 21 chlorotypes identified. The range of the seven species is indicated by different colours of outer rings. (b) Inferred phylogenetic network among the 21 chlorotypes (see text for details). Each chlorotype is represented by a circle whose size is proportional to its frequency over all populations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168 165
Fig. 2. MtDNA variation in Pinus subsection Strobus. (a) Geographic distribution of the ten mitotypes identified. The range of the seven species is indicated by different colours of outer rings. (b) Inferred phylogenetic network among the ten mitotypes. Each mitotype is represented by a circle whose size is proportional to its frequency over all populations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
MrBayes v. 3.1.2 (Huelsenbeck and Ronquist, 2001). The MCMC algorithm was run for 5 000 000 generations with four incrementally heated chains, starting from random trees and sampling one out of every 500 generations. The first 2500 trees were discarded as burn-in, depending on when chains appeared to have become stationary. An examination of in- terrelationships among both cpDNA and mtDNA haplotypes was separately conducted using NETWORK v. 4.2.0.1 (Bandelt et al., 1999). We assumed that all coded indels and mutations evolved at an equal rate in these analysis.
3. Results
3.1. cpDNA variation and species identification
Alignments of DNA sequences that represent 449 individuals from 60 populations of seven pine species generated a 3178 bp cpDNA dataset. A total of 16 substitutions and 7 indels were identified, which resulted in 21 chlorotypes (C1eC21). Most of these chlorotypes are species-specific. However, C15 and C18 are shared between P. armandii and P. dabeshanensis or P. fenzeliana, respectively (Fig. 1). In addition, the widespread P. armandii harbored 11 chlorotypes (C6eC21). Two chlorotypes, C4 and C5 are fixed in P. sibirica. Meanwhile, P. koraiensis, P. wallichiana and P. pumila have only fixed one chlorotype C1, C2 and C3, respectively. Four species (P. sibirica, P. koraiensis, P. wallichiana and P. pumila) can be successfully identified by the combination of four chloroplast DNA regions. Network and Bayesian analyses showed chlorotypes recovered from P. kor- aiensis in northeast China and P. sibirica from western China are more closely related to each another comparing with those from P. koraiensis and P. pumila (see Supporting information, Fig. S1). 166 Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168
Table 2 Primers of the amplification fragments used in present study.
Fragments Primer pairs (50e30) PCR product size (bp) Primer source trnLeF trnL: GGTTCAAGTCCCTCTATCCC 900e1000 Taberlet et al., 1991 trnF: ATTTGAACTGGTGACACGAG Taberlet et al., 1991 trnSefM trnS: GAGAGAGAGGGATTCGAACC 700e800 Shaw et al., 2005 trnfM: CATAACCTTGAGGTCACGGG Shaw et al., 2005 trnSeG trnS: GCCGCTTTAGTCCACTCAGC 600e700 Hamilton, 1999 trnG: GAACGAATCACACTTTTACCAC Hamilton, 1999 rpl16 F71: GCTATGCTTAGTGTGTGACTCGTTG 700e800 Shaw et al., 2005 R15: CCCTTCATTCTTCCTCTATGTTG Shaw et al., 2005 nad5intron1 F: AGTCCAATAGGGACAGCACAC 700e800 Jaramillo-Correa et al., 2003 R: ATCTCCGGTAACCGAMATTG Du et al., 2009 nad7intron1 F: GGAACCGCATATTGGATCAC 400e500 Jaramillo-Correa et al., 2004 R: GTTGTACCGTAAACCTGCTC Jaramillo-Correa et al., 2004
F, forward; R, reverse.
3.2. MtDNA variation and species discrimination
The size of the two mtDNA markers was 729e731 (nad5 intron 1) and 379e608 bp (nad7 intron 1) in length, respectively. A total of 8 indels and 17 substitutions were identified for the concatenated mitochondrial DNA sequence, yielding 10 mitotypes (M1-M10) among the trees investigated. Most mitotypes are species-specific; however, M8 is shared between P. armandii and P. fenzeliana. The widespread P. armandii possessed four mitotypes (M6-M8, M10) while two mitotypes, M3 and M4 are fixed in P. pumila. Each of the remaining four miotypes (M1, M2, M5 and M9) is fixed for P. koraiensis, P. wallichiana, P. sibirica and P. dabeshanensis respectively. Therefore, five exampled species, P. sibirica, P. koraiensis, P. wallichiana, P. pumila and P. dabe- shanensis, can be successfully discriminated by the combination of two mtDNA fragments. Both network and Bayesian tree showed that the mitotypes (M1, M2 and M5) from P. sibirica, P. koraiensis, P. wallichiana clustered together with a moderate support (see Supporting information, Fig. S2).
4. Discussion
4.1. Interspecific relationships and allopatric divergence of the white pine species
Both sets of cytoplasmic DNA variations suggested that seven examined white pine species can be classified into two group. Both chlorotypes and mitotypes of P. dabeshanensis and P. fenzeliana were recovered from or closely related to those from P. armandii. However, those from P. sibirica, P. koraiensis, P. wallichiana and P. pumila are more closely related to one another than to those from P. armandii complex. This finding is basically consistent with the previous studies (Hernandez- Leon et al., 2013; Liu et al., 2014a; Hao et al., 2015). Both chlorotypes and mitotypes of P. dabeshanensis revealed before were also found in P. armandii by Liu et al. (2014a). However, in the present study, we surveyed each population of P. dabeshanensis and our findings suggested that the only mitotype of P. dabeshanensis distinctly difference from those found in P. armandii although the chlorotypes showed no distinct differentiation between the two species. These two species showed allopatric distribution patterns, but geological isolations are short. It is very likely that both species diverged recently or P. dabeshanensis might have derived from P. armandii, which leaded to the low genetic differentiation between them. Similarly, P. fenzeliana may have also diverged from P. armandii recently and therefore, both chlorotypes and mitotypes of P. fenzeliana were found in P. armandii as reported before (Liu et al., 2014a). It is interesting that populations from four regions of P. armandii from southeastern to western China showed great genetic differentiation, especially at the mtDNA marker, with totally different mitotypes fixed in each region (Fig. 2). Therefore, geographic isolations seem to have also contributed greatly to the intraspecfic differentiation of P. armandii. In addition, total different mitotypes and chlorotypes were fixed for each species of the other group, P. koraiensis, P. pumila, P. sibirica and P. wallichiana respectively. Because of the strong geographic isolation, these findings are not unexpected. P. sibirica and P. pumila are of sympatric distribution in southern Russia and P. koraiensis and P. pumila co-occur in southeastern Russia. Natural hybridization and gene flow between P. sibirica and P. pumila were detected in Russia (Politov et al., 1999; Vasilyeva and Semeriko, 2014). However, from the chlorotype network (Fig. 1), P. sibirica seems to be more closely related to P. koraiensis than to other two species. This is different from the interspecific relationship inferred before based on the other cpDNA sequence variations (Liu et al., 2014a). Their cpDNA phylogenetic tree suggested that P. sibirica clustered as one small clade with P. pumila while the mtDNA haplotype network showed that P. sibirica is closely related to P. wallichiana (see Supporting information, Fig. S1). Obviously, the interspecific relationships among these four species were need further in- vestigations based on more evidences in future. However, our current data together with those reported before (Liu et al., 2014a) both suggest that geographic isolations should have played an important role in driving allopatric divergences among these species. We did not calibrate these divergences due to the lack of the reliable fossils and mutation rate. However, numerous studies of species diversification for other genera suggested the rapid uplifts of the Qinghai-Tibet Plateau since the Z.-H. Li et al. / Biochemical Systematics and Ecology 61 (2015) 161e168 167
Miocene and the subsequent climate change should have caused habitat fragmentation across China, which had probably triggered allopatric speciation in numerous genera occurred there (Liu et al., 2002, 2006, 2012b, 2014b; Xu et al., 2010; Liu et al., 2013). In addition, all detected mitotypes or chlorotypes should have produced before the last glaciation maximum (LGM, around 18e21 Ka BP) of the Quaternary. Therefore, our findings based on a large scale of investigation of a group of white pine species also supported the multiple-refugia hypothesis for plants across China during the LGM (Liu et al., 2012b). However, as suggested before (e.g. Liu et al., 2012b; Zou et al., 2012), the Pleistocene climatic oscillations should have caused regional range retreat and expansion of these white pine species, which not only promoted allopatric diverge, but also caused their regional fixture of the monotypic mitotype (Fig. 2).
4.2. Species identification based on cytoplasmic DNA sequence variation
Both interspecific introgression and incomplete lineage sorting may have led to the shared cytoplasmic haplotypes be- tween species. Generally, introgression could be caused by successive backcross of hybrids with one of its parents, under the premise that fertile hybrids survive in the contact zones between two parental species. According to the recent simulation (Currat et al., 2008) and empirical studies (e.g. Du et al., 2009; Petit and Excoffier, 2009), genetic markers with the higher rate of gene flow can discriminate species better. In addition, the high rate of gene flow also contributes to the faster lineage sorting (into monophyly) at the species level, which may promote the maintenance of species and their cohesive evolution (Petit and Excoffier, 2009; Zhou et al., 2010; Ren et al., 2012). For conifers, cpDNA and mtDNA are inherited paternally (via pollens) and maternally (via seeds) respectively. Therefore, it is expected that the variations at the cpDNA with the higher rate of gene flow should differentiate pine species better than those from the mtDNA markers. However, our results suggested that mitotypes based on mtDNA sequence variations can diagnose the sampled species better than the chlorotypes from the cpDNA (Figs. 1 and 2). This may be due to the fact that geographic isolations between species may have promoted the faster fixture of the species-specific mitotypes during the range retreats and expansions within the historical climatic oscillations. The long-distance dispersal of pollens, however, continue to transfer chlorotypes between regions, slowing down the lineage sorting between regions and species. The genetic differentiations at both mtDNA and cpDNA markers between one group of species (P. koraiensis, P. pumila P. sibirica and P. wallichiana) with the stronger geographic isolations are much higher than those between the other group of species (P. armandii and either P. dabeshanensis or P. fenzeliana). For the latter group, it is likely that the recent speciation between them may also reduce the diagnosing powers of the cytoplasmic DNA regions. These findings suggested that species identification based on cytoplasmic DNAs may vary dependent on geographic isolations and speciation stages.
Acknowledgments
We are highly grateful for Professor Jian-Quan Liu for his direction on this work and for revising the manuscript. This study was financially co-supported by the National Natural Science Foundation of China (41101058), the Specialized Research Fund for the Doctoral Program of Higher Education (20126101120021) and the open fund of Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany of East Asia, the Chinese Academy of Sciences (KLBB201204).
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.bse.2015.06.002.
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