Skip to main content
PMC home page
Scientific Reports logoLink to Scientific Reports
. 2015 Jun 30;5:11698. doi: 10.1038/srep11698

Pleistocene glaciations, demographic expansion and subsequent isolation promoted morphological heterogeneity: A phylogeographic study of the alpine Rosa sericea complex (Rosaceae)

Yun-Dong Gao 1, Yu Zhang 1, Xin-Fen Gao 1,a, Zhang-Ming Zhu 1
PMCID: PMC5155592  PMID: 26123942

Abstract

While most temperate plants probably underwent glacial constriction to refugia and interglacial expansion, another type of interglacial refugia might have existed to maintain alpine plants during warm periods. To test this hypothesis, we applied phylogeographic methods to 763 individuals (62 populations) which belong to 7 taxonomically difficult species of the Rosa sericea complex distributed in alpine regions of the temperate and subtropical zones in eastern Asia. We used three chloroplast (cp) DNA fragments (trnL-trnF, ndhF-rpl32 and ndhJ-trnF) approximately 3,100 bp and nuclear microsatellite (nSSR) on eight sites to determine whether cold tolerant plants experienced expansion during the Pleistocene. The neutral test and mismatch distribution analysis (MDA) indicated that whole populations and major lineages of the Qinghai-Tibet Plateau (QTP) underwent expansion during the middle to late Pleistocene. Environmental niche modeling (ENM) indicates more suitable habitats during the Last Glacial Maximum (LGM) than at present. We concluded that the demographic history of R. sericea, which diverged in the middle Pleistocene, was mostly affected by climatic oscillations instead of by geographical barriers. The low genetic divergence, as well as the weak phylogenetic structure in the R. sericea complex both support treating this complex as a single taxon.

Paleoclimatic and paleogeological events have been identified as being the two major drivers of genetic variation in extant organisms. Geographic changes, such as the uplifting of the Qinghai-Tibet Plateau and Rocky mountain systems, result in habitat fragmentation that forms barriers to gene flow, further leading to genetic diversification, accelerated variation and even speciation1,2,3,4,5. On the other hand, climatic oscillations during the Quaternary also played a significant role in affecting the demographic history of many temperate plants, which experienced glacial retreat and postglacial expansion in their ranges (e.g., Qiu et al.6). Repeated glacial retreats and postglacial expansions of the ranges of many plants profoundly shaped their distribution patterns in eastern Asia and in many other regions of the Northern Hemisphere7,8. In most cases, plants that were not cold tolerant (temperate species) were restricted to relatively warmer places during glacial cycles where they occurred in shelters, now identified as refugia8. However, the effects of Quaternary climatic oscillations on the population dynamics of cold tolerant plants is not yet clear, especially for those plants in temperate zones.

Two theories, named the pioneer model and the phalanx model, have been advanced to address the consequences of population expansion on genetic legacy7,8. The ‘pioneer’ model proposed by Hewitt7 predicts that a small pioneer or edge populations expand from a refugium and should exhibit low genetic diversity as a result of the founder effect. Not entirely in contrast, the ‘phalanx’ model of spread7 predicts that large populations with pronounced genetic structure expand slowly from refugia and that sharp contact zones may occur at colonizer fronts. Compared to the pioneer model, which is well-documented, the phalanx model has received less attention despite its potential importance in the demographic history of montane (especially alpine) species. In particular, The phylogeographic structure of high elevation species may have been shaped by several cycles of interglacial allopatric fragmentation followed by glacial contiguous range expansion consistent with the phalanx hypothesis along an elevational gradient9,10,11,12. Compared to the former model that was well documented, the phalanx model was less consulted despite its potential importance in the demographic history of montane (especially alpine) species.

In recent years, phylogeographic research has shown that glaciation may not only force population contractions in plants, but also promote expansion or persistence of cold adapted or cold tolerant species. For example, studies in Dryas integrifolia13; Picea glauca14; Pinus kwangtungensis15; juniper16, and animals, such as lemmings17,18, rock ptarmigan (Lagopus muta)19, mountain sheep20 and Aura21 have shown cold adaption and demographic expansion during cold periods. Therefore, it seems likely that glacial expansions and interglacial contractions were a recurrent pattern for cold adapted species during the Late Quaternary.

Although recent phylogeographic studies have been conducted on various plant groups and many geographic regions21,22,23,24,25, few have concentrated on taxonomically complex groups. Phylogeographic studies on monophyletic groups, especially taxonomically difficult groups, may provide a comprehensive understanding of demographic history, potential interspecific gene flow and species delimitation in long controversial taxa. On the other hand, incorrect phylogeographic inference could be avoided with a more comprehensive sampling strategy. In taxonomically difficult taxa, focusing on a single species was not feasible. Instead, a monophyletic group should be favored26.

The Rosa sericea complex comprises a group of shrubs in section Pimpinellifoliae DC. Ser. Sericeae (Crep.) Yu et Ku27,28 of the family Rosaceae. The flowers have white or yellowish petals and four tepals. The complex represents a typical alpine shrub with a distribution in the Sino-Himalayan region extending eastward into low elevation montane areas of western Guizhou and Hubei provinces at elevations between 1,400 and 4,500 m. With deceasing elevation, the complex is absent in southeastern China, but with one taxon disjunctly distributed at high elevations in the central mountains of Taiwan (Fig. 1). According to recent phylogenetic studies of our research team29, the R. sericea complex is a monophyletic group most closely related to R. hugonis Hemsl. and contains seven species: R. sericea, R. omeiensis, R. zhongdianensis, R. sikangensis, R. morrisonensis, R. taronensis, R. mairei or infraspecific taxa30. Compared to other shrubs endemic to QTP, the R. sericea complex has a broader eastward distribution. The complex may have had a continuous distribution in southeastern China in the past, and is a good candidate for a demographic study of cold adapted species.

Figure 1. Geographical distribution of 37 chlorotypes (H1-H37) identified in 62 populations of Rosa sericea complex.

Figure 1

Open in a new tab

Dashed lines delimit five population groups (I-V) divided by geographic features by Wu et al.54. The map generated by ESRI ArcGIS 9.3 (ERSI co., USA).

In this study, we used DNA sequences and a phylogeographic approach as well as niche modeling to determine the mechanism of range expansion and disjunction within the R. sericea complex. In particular, we wanted to answer these questions: 1) Did range expansion exist in the demographic history of R. sericea? If it did, which factors (e.g., interglaciations, glaciations or others) contributed to this expansion? 2) How were southwestern China – Taiwan disjunctions formed? Did continuous distributions once exist in eastern China? If so, then which part served as a bridge between the two areas? In addition, we also aimed to determine suitable species delimitations for the R. sericea complex.

Results

Sequence characteristics and within-population genetic diversity

The three cpDNA-IGS surveyed across 691 individuals (belonging to 62 populations) were aligned. Of the 3057 bp, nucleotide substitutions occurred at 32 sites and 10 indels (from 1 to 24 bp) were present (Table S3). After indels were reduced and treated as single mutation events, the aligned length was 2972 bp, and contained 42 variable sites of which 32 sites were parsimony informative. A total of 37 chlorotypes were identified according to these combination polymophisms. Table 1 shows basic genetic data and statistics for all populations in the regions that we studied.

Table 1. Detected chlorotypes and the genetic diversity identified in this study.

Population code Taxon code* Regional group Line age Sample size Haplo types (no. of individuals) Hd (SD) π (SD)
    I: Qinghai–Xizang Plateau region       0.799(0.020) 0.00078(0.00007)
XAR RS, RO I A 13 H1 (4), H28 (9) 0.462(0.110) 0.00062(0.00015)
XBR RO I A 13 H1 (1), H15 (12) 0.154(0.126) 0.00005(0.00004)
XCN RO I A 8 H30 (8) 0.000 0.00000
XDJ RO I A 8 H31 (8) 0.000 0.00000
XMZ RO I A 6 H1 (4), H15 (1), H29 (1) 0.600(0.215) 0.00022(0.00009)
XJL RO I A 11 H1 (11) 0.000 0.00000
XNML RS, RO I A 8 H1 (4), H29 (4) 0.571(0.094) 0.00019(0.00003)
XYD RO I A 12 H30 (12) 0.000 0.00000
    II: Eastern Himalayas & Hengduan Mountains region     0.579(0.031) 0.00043(0.00004)
SBT RO II A 6 H1 (6) 0.000 0.00000
SDC1 RO II A 16 H1 (15), H24 (1) 0.125(0.106) 0.00025(0.00021)
SDC2 RM II A 20 H1 (20) 0.000 0.00000
SDC3 RK II A 8 H1 (8) 0.000 0.00000
SEM RO II A 13 H1 (11), H33 (2) 0.282(0.142) 0.00009(0.00005)
SKD1 RS II B 10 H7 (1), H8 (7), H9 (2) 0.511(0.164) 0.00038(0.00012)
SKD2 RO II B 10 H1 (1), H3 (9) 0.2(0.154) 0.00007(0.00005)
SLD1 RO II B 10 H9 (6), H11 (4) 0.533(0.095) 0.00018(0.00003)
SLD2 RS II B 6 H23 (6) 0.000 0.00000
SXC1 RM II A 9 H1 (7), H25 (2) 0.389(0.164) 0.00013(0.00006)
SXC2 RK II A 17 H1 (12), H25 (5) 0.441(0.098) 0.00015(0.00003)
SXJ1 RO II B 10 H36 (8), H37 (2) 0.356(0.159) 0.00024(0.00011)
SXJ2 RO II B 15 H3 (15) 0.000 0.00000
SYJ1 RS, RO II A&C 18 H1 (16), H10 (2) 0.209(0.116) 0.00007(0.00004)
SYJ2 RO II A&C 18 H1 (17), H10 (1) 0.111(0.096) 0.00004(0.00003)
XBM1 RO II A 10 H1 (8), H13 (2) 0.356(0.159) 0.00012(0.00005)
XBM2 RO II A 7 H1 (6), H14(1) 0.286(0.196) 0.00019(0.00013)
XBS1 RK II A 13 H1 (7), H13 (6) 0.538(0.060) 0.00018(0.00002)
XBS2 RO II A 7 H1 (7) 0.000 0.00000
XBS3 RK II A 10 H1 (9), H13 (1) 0.200(0.154) 0.00007(0.00005)
XCY RO II A 7 H1 (3), H12 (3), H13 (1) 0.286(0.196) 0.0001(0.00007)
XJD RO II A 13 H1 (11), H16 (2) 0.282(0.142) 0.00009(0.00005)
XLL1 RK II A 7 H1 (6), H15(1) 0.286(0.196) 0.0001(0.00007)
XLL2 RS II A 7 H1 (7) 0.000 0.00000
XLZ1 RO II A 16 H1 (1), H14 (5), H27 (12) 0.542(0.098) 0.00042(0.00010)
XLZ2 RM II A 16 H1 (14), H15 (1), H28 (1) 0.242(0.135) 0.00021(0.00015)
XMK1 RO II A 10 H1 (10) 0.000 0.00000
XMK2 RS II A 9 H1 (6), H12 (3) 0.5(0.128) 0.00084(0.00022)
XMK3 RK II A 9 H1 (8), H12 (1) 0.222(0.166) 0.00037(0.00028)
YFG RO, RT II A 15 H1 (8), H2 (7) 0.533(0.052) 0.00018(0.00002)
YZD1 RO II A 10 H1 (2), H26 (8) 0.356(0.159) 0.00024(0.00011)
    III: Yungui Plateau region       0.558(0.044) 0.00045(0.00004)
GUWN RS III B 12 H3 (10), H19 (2) 0.303(0.147) 0.0001(0.00005)
SJL RM III B 10 H3 (10) 0.000 0.00000
SMG RS III B 13 H3 (13) 0.000 0.00000
SMN1 RS III B 9 H3 (6), H4 (3) 0.500(0.128) 0.00017(0.00004)
SMN2 RS, RO III B 10 H3 (10) 0.000 0.00000
YDL RO III B 8 H1 (3), H5 (2), H6 (3) 0.536(0.123) 0.00018(0.00004)
YJD RS III B 12 H6 (12) 0.000 0.00000
YLQ RS III B 8 H1(1), H3 (2), H24 (1), H32 (4) 0.714(0.123) 0.00034(0.00007)
YTC RM III B 14 H3 (14) 0.000 0.00000
YWX RS III B 11 H3 (9), H19 (2) 0.327(0.153) 0.00011(0.00005)
YYD RO III B 11 H6 (1), H35 (10) 0.182(0.144) 0.00006(0.00005)
YZD2 RO, RZ III B 7 H3 (7) 0.000 0.00000
    IV: Qilian-Qin Mountains region       0.598(0.0051) 0.00047(0.00006)
GHZ RO IV A 8 H14 (5), H15 (3) 0.536(0.123) 0.00018(0.00004)
GJT RO IV A 14 H17 (9), H22 (5) 0.495(0.088) 0.00017(0.00003)
GYZ RO IV A 11 H16 (7), H17 (4) 0.509(0.101) 0.00017(0.00003)
GZL RO IV A 12 H17 (12) 0.000 0.00000
HXS RO II A 17 H1 (6), H20 (11) 0.485(0.079) 0.00016(0.00003)
QDT RO IV A 13 H17 (13) 0.000 0.00000
SHPL RO II A 7 H1 (7) 0.000 0.00000
SHXA RO IV C 12 H18 (8), H21 (4) 0.485(0.106) 0.00016(0.00004)
SJZ RO IV A 12 H6 (1), H7 (8), H17 (3) 0.530(0.136) 0.0003(0.00008)
SSD RO II A 14 H1 (2), H16 (4), H17 (7), H18 (1) 0.538(0.115) 0.00029(0.00012)
    V: Taiwan          
THL MO V C 15 H33 (15) 0.000 0.00000
Total           0.815(0.017) 0.00056(0.00003)
Open in a new tab

*RO = Rosa omeiensis, RS = R. sericea, MO = R. morrisonensis, RK = R. sikangensis, RT = R. taronensis, RM = R. mairei, RZ = R. zhongdianensis.

Total nucleotide (π) and haplotype (Hd) diversity across all populations was 0.00056 and 0.815. The QTP group exhibited the highest haplotype diversity (Hd = 0.779) while the largest group in Hengduan Mountains (region II in Fig. 1) group possessed the lowest (Hd = 0.522). Genetic diversity calculated for all populations (HT = 0.823) was higher than the average intra-population diversity (HS = 0.267). Relative high levels of genetic differentiation were observed (GST = 0.676, NST = 0.766). Permutation tests showed that NST was greater than GST in all groups and in the metapopulation (Table 2), suggesting the existence of phylogeographic structure (P < 0.01, Table 2).

Table 2. Genetic diversity parameters for all 62 populations of Rosa sericea complex and for four group separately.

  HS HT NST GST permutation test (P)
All 0.267 (0.0309) 0.823 (0.0352) 0.766 (0.0367) 0.676 (0.0371) NST > GST (P < 0.01)
Group I 0.223 (0.0967) 0.872 (0.0464) 0.834 (0.0672) 0.744 (0.1170) NST > GST (P > 0.05)
Group II 0.265 (0.0375) 0.599 (0.0823) 0.746 (0.0429) 0.558 (0.0651) NST > GST (P < 0.01)
Group III 0.264 (0.0824) 0.707 (0.1221) 0.720 (0.0725) 0.627 (0.0988) NST > GST (P > 0.05)
Group IV 0.373(0.0836) 0.814(0.0768) 0.746 (0.0813) 0.541 (0.0989) NST > GST (P < 0.05)
Open in a new tab

HS, average genetic diversity within populations; HT, total genetic diversity; NST, inter-population differentiation taking into account sequence difference; GST, inter-population differentiation.

Nuclear microsatellite genotyping

The 62 populations of the Rosa sericea complex (n = 595) revealed a total of 254 alleles across the eight nSSR loci surveyed, along with high mean per-locus estimates of allele and gene diversity (NA = 24.4; HS = 0.567; HT = 0.807; see Table S2). Population differentiation (FST) was significant at each locus (P< 0.05) and averaged 0.298 across all loci (Table S2).

For the entire nSSR dataset (62 populations, n = 763), STRUCTURE yielded the highest likelihood when samples were clustered into three groups (K = 3, data not shown). The pre-defined taxa and the recognized clusters in R. sericea complex showed no relativeness, but distinct geographic patterns can be detected (Fig. 2). Three mainly regions were recognized as Tibetan Plateau, Hengduan Mountains and eastern edge of QTP (divided by lines in Fig. 2). Contact among populations in the three regions was detected as heterozygosity (Fig. 2).

Figure 2. Geographic origin of the 62 R.sericea complex populations and their colour-coded grouping according to the STRUCTURE analysis.

Figure 2

Open in a new tab

Population codes are identified in Table 1. The bottom showed a histogram of the STRUCTURE assignment test for 62 populations (763 individuals) of R. sericea complex based on variation at eight nuclear microsatellite (nSSR) loci. Population codes and abbreviation for species are identified in Table 1. The map generated by ESRI ArcGIS 9.3 (ERSI co., USA).

Population genetic and phylogeographic structure

Spatial genetic analyses of cpDNA haplotypes in 62 populations using SAMOVA indicated that FCT increased with partitioning of species into 2 to 20 groups (Fig. S1). When the 62 populations were evenly subdivided into 20 groups, there was still increase variation among divided groups. Thus for AMOVA analysis, we divided the whole sampling area into five groups mainly based on geographic features (Table 1, Fig. 1). The results showed that 20.76% of the total vicariance was distributed within populations (FST = 0.822, p < 0.0001), and a large level of variation was due to differentiations between populations within geographic regions (56.82%, FSC = 0.761, p < 0.0001) and among geographical regions (PV = 25.33%, FCT = 0.253. P < 0.0001) (Table 3). The AMOVA analysis of groups of separated taxa showed a very low level of variation among the seven species traditionally recognized according to morphology (PV = 7.36%, FCT = 0.074, P < 0.05) (Table S4).

Table 3. Hierarchical analysis of molecular variation (AMOVA) based on data from the three chloroplast DNA spacers for all 62 populations of Rosa sericea complex and for five groups separated by geographic features.

Regional grouping of populations Source of variation d.f. SS VC PV F- statistics
All Among groups 4 160.148 0.280 25.33 FCT = 0.253**
  Among populations within groups 57 405.876 0.627 56.82 FSC = 0.761**
  Within populations 629 123.909 0.197 17.85 FST = 0.822**
Group I Among populations 7 75.118 1.074 82.96 FST = 0.830**
  Within populations 71 15.667 0.221 17.04  
Group II Among populations 30 190.115 0.554 73.16 FST = 0.732**
  Within populations 321 63.93 0.199 26.84  
Group III Among populations 11 72.735 0.616 74.10 FST = 0.741**
  Within populations 113 24.337 0.215 25.90  
Group IV Among populations 9 82.533 0.746 73.66 FST = 0.737**
  Within populations 110 29.35 0.267 26.34  
Group V Among populations / / / /  
  Within populations / / / /  
Total populations Among populations 61 591.005 0.848 77.33 FST = 0.773**
  Within populations 629 156.384 0.249 22.67  
Open in a new tab

d.f., degree of freedom; SS, sum of squares; VC, variance components; PV, percentage of variation

**P < 0.0001

Phylogenetic relationships of cpDNA haplotypes and divergence time estimates

The tree topologies generated by Neighbor-joining (NJ) and Bayesian inference (BI) were congruent in terms of major clades, thus only the NJ tree is presented in Figure S2. Overall, 37 chlorotypes were clustered into three major lineages: 1) northern Hengduan Mountains and QTP, 2) southern Hengduan Mountains and Yungui Plateau, and 3) eastern China, named CPA to CPC, respectively (Fig. S2). This was congruent with the parsimony network, which showed the 37 chlorotypes grouped into three major clades separated by only one mutational step (Fig. 3). We separated the three haplotype lineages for demographic analysis. The separation appears to be artificial since they did not receive strong support, although they demonstrated basic geographic information in such context. As the whole network showed, the Rosa sericea complex presented a radiation-shaped relationship, with a weak phylogeographic pattern (Fig. 3). We separated all chlorotypes into three lineages with the aim to obtain the maximum amount of information on demographic history.

Figure 3. A network of cpDNA haplotypes constructed by TCS 1.21.

Figure 3

Open in a new tab

Size of circles in network are proportional to observed frequencies of chlorotypes. Three lineages were identified as referencing the phylogenetic result in Fig. S2. The background dots showed the sample locations and gross distribution of these three lineages (CPA-CPC). Shade part indicated the LGM coastline extension which is −130 m compared to present. The map generated by ESRI ArcGIS 9.3 (ERSI co., USA).

Assuming substitution rates of 2.0 × 10−9 s/s/y, the divergence time for the Rosa sericea complex was estimated to be 0.482 Ma, with a 95% confidence interval (95% CI) of 0.281–0.722 Ma (Fig. S1). We used lower and upper bounds on the rates of nucleotide substitution of 1.1 × 10−9 s/s/y and 2.9×10−9 s/s/y, respectively, and estimated that the divergence time within this complex was 0.935 (95% CI 0.480–1.269 Ma) and 0.352 Ma (95% CI 0.175–0.469 Ma), also espectively. Overall, all our results indicate a mid-Pleistocene divergence of populations within this complex.

Demographic history

Since SAMOVA did not provide optimal K to define the group (Fig. S1), we used the lineages identified by phylogeny and network (see results above) for mismatch distribution analysis (MDA). We applied MDA analysis to the three lineages CPA-CPC and the entire chlorotypes of the Rosa sericea complex using a sudden demographic expansion model. The results showed that the whole populations and CPA lineage were unimodal, while lineages CPB and CPC were bimodal and multimodal, respectively. Accordingly, the two statistics –SSD and HRag – that assessed the fitness of the model only rejected the expansion model in lineage CPC. In neutrality tests, the whole group, lineage CPA and CPB showed negative values in Tajima’s D and Fu’s Fs test, which indicated population expansion. Only the whole lineage, however, showed significant negative value for Fu’s Fs (p < 0.0001, Table 4). The neutrality tests both supported a demographic expansion in the past in the CPA lineage, although not significantly (0.01 < p < 0.05, Table 4). This was consistent with the MDA results that indicated both of these two sets were unimodal while the rest were not. Despite the good fitness of the expansion model to lineage CPB with bimodal distributions in MDA, this is not taken as strong evidence for expansion at present, because multimodal distributions generally indicate either populations that are stable or shrinking in size or structure31,32. Thus, based on the corresponding τ value, and assuming minimum and maximum mutation rates of 1.1 × 10−9 s/s/y and 2.9 × 10−9 s/s/y, the expanding event for the whole group was dated as 425 k years and 112 k years BP, respectively (Table 4). Bayesian skyline plots suggested that effective population sizes increased slowly during the LGM (Fig. S4).

Table 4. Mismatch distribution analysis (MDA) of cpDNA sequence data for Rosa sericea complex from lineages as a whole and three lineages recognized by phylogenetic inferences.

Line ages Tajima’s D (p value) Fu’s FS (p value) τ tmin (Ma) tmax(Ma) MDA SSD (p value) HRag (p value)
Over all −1.6323(0.0140) −20.9098(0.0000) 0.734(0.609–1.125) 0.04255(0.03531–0.06522) 0.11218(0.09309–0.17194) Unimodal 0.1185(0.0000) 0.0234(1.0000)
CPA −1.5871(0.0250) −6.4842(0.0440) 1.052(0.000–3.436) 0.06099(0.00000–0.19920) 0.16079(0.00000–0.52516) Unimodal 0.0009(0.8740) 0.0322(0.9390)
CPB 0.1675 (0.6150) −1.8907(0.2550) 4.328(0.139–8.531) NC NC Multimodal 0.0173(0.5390) 0.0637(0.6130)
CPC 1.6769(0.9410) 2.7972(0.8860) 3.317(0.000–10.373) NC NC Bimodal 0.0855(0.0540) 0.2293(0.0090)
Open in a new tab

The values of the neutrality tests for each lineage were examined by Tajima’s D and Fu’s Fs statistics. Goodness of fit of observed-to-theoretical mismatch distributions under a sudden (stepwise) expansion model (Rogers and Harpending 1992) is test with the sum of squared deviations (SSD) and Harpending’s raggedness index (HRag). Upper and lower 95% confidence limits around estimates of τ and associated ranges of t (in million years before present, Ma) are in parentheses.

NC, Not calculate

Palaeodistribution modeling

The Maxent model had high predictive power [AUC = 0.986 ± 0.003 (mean ± SD), based on 10 replicates]. The model predicted suitable habitat for the species that was mostly coincident with its natural geographic distribution (Fig. 4a) except that it extended to Korea and Japan, which may not accommodate the species according to specimen records. The main distributional area predicted for the complex was concentrated in southwestern China, mainly at high elevations. To the southeast, the east-west trending Qinling-Dabie-Wuyi mountains showed great possibility for being occupied by the R. sericea complex, and this region is located west of Taiwan (Fig. 4b). Obviously, the elevational distribution during the LGM was much lower than at present, and the distribution of R. sericea was along the ranges in the east part of Asia which are now occupied by subtropical forests adapted to lower elevations and higher temperatures.

Figure 4. Predicted distribution probability (in logical value) is shown in each 2.5 arc-min pixle, based on the paleodistribution modeling (a) at present (0 BP) and (b) at the last glacial maximum (LGM, 21000 BP).

Figure 4

Open in a new tab

Distribution of major river systems on exposed East China Sea during LGM; from Sakaguchi et al.71. Occurrence records of R. sericea complex at present also plotted as black points on maps generated by ESRI ArcGIS 9.3 (ERSI co., USA).

Discussion

Generally, if deep divergence occurred through paleogeological events, then a phylogeographic study should detect strong geographic clustering that coincides with known geographic boundaries. This scenario appears in some relict plants or relatively old species (e.g., Cercidiphyllum33). A major finding of the present study was that the Rosa sericea complex comprises no distinct geographic lineages as inferred from the chlorotype network and phylogenetic analysis (Figs. 4 and 5), which provided only weak support to divide the complex into three lineages. The three lineages occur throughout the Hengduan Mountains and QTP region (Fig. 3). From the SAMOVA analysis (Fig. S1), the FCT increased as more groups were constructed, indicating very weak boundaries among presumed groups based on both geographic and molecular distance (Figs. 1 and 3). Though in nSSR, a weak geographic pattern was detected, however, it was along a continuous gradient from west to east with no distinct boundary (Fig. 2). Based on our results, we suspect physical barriers to past or contemporary dispersal (e.g., the Mekong-Salween divide in Hengduan Mountains region for Sinopodophyllum hexandrum34; the East China Sea/Tsushima-Korean straits for Croomia35; the Tokara Gap, a deep sea-barrier in the Ryukyu Islands, for Neolitsea sericea36) have had limited influence on the distributions of the R. sericea. In fact, the relative uniformity among geographic distributions of genotypes suggests sudden demographic expansion, and this type of pattern has been reported recently in Hippuris vulgaris (Plantaginaceae)37, an aquatic herb distributed on the QTP and in adjacent areas.

Rosa sericea emerged in the middle or late Pleistocene (ca. 0.78–0.01 Ma), and its major lineages diversified not earlier than the middle of Pleistocene (Fig. S3). By the time that R. sericea evolved, the major geographic features were already well-established. Therefore, the range expansion detected in the R. sericea complex is more likely the result of climate oscillations. The relationship between Pleistocene glaciation cycles and demography of organisms is well-documented in the Northern Hemisphere9,10, including in southern China (reviewed in Qiu et al.6 and animals38,39,40). Typically, rapid population expansion occurred during interglacial stages and shaped the current distributional patterns and genetic diversity. However, all populations that we sampled demonstrated a sudden radiation 28-15ka (Table 4), which is coincident with Last Glacial Maximum (LGM) of the Pleistocene rather than with an interglacial period. The R. sericea complex comprises cold tolerant plants that can survive in the periglacial zone. ENM supports the presence of extensive habitats for the R. sericea complex during the LGM (Fig. 4b).

The wide distribution of haplotype H1 and the fact that the expansion hypothesis was not rejected during the late Pleistocene indicate that colonization of most of the QTP and Hengduan Mountains region occurred recently through rapid population expansion (Fig. 1, Table 4). Additionally, the high FST values among populations (FST = 0.822, p < 0.0001, Table 3) indicate significant population diversity. These observations are consistent with the phalanx model of Hewitt7, who pointed out that large mountains acting as interglacial refugia may allow many alleles and genomes to survive.

Although the phalanx model may best explain the overall distributional patterns of the R. sericea complex, some population features of the group are consistent with the pioneer model. In our case, the pioneer model (edge effect) predicts rapid colonization of an area during glacial periods and fragmentation during interglacials accompanied by genetic drift and range expansions from interglacial refugia over short distances involving limited gene flow during glacial periods41. This should result in a clear but weak phylogeographic signature of isolation by distance and detectable interglacial refugia. We observed this in Group IV (chlorotypes H16, H17 and H22) (Figs. 1 and 4). Our genotype data are consistent with several interglacial refugia in populations YDL (H1, H5 and H6) and YLQ (H1, H3, H24 and H32) and in the Hengduan Mountains including SKD, SLD and SXJ (Table 1, Fig. 1). Additionally, range expansion during glacial periods and subsequent range fragmentation probably explains the predominance of the H3 haplotype throughout subtropical area of the Yungui Plateau and the demography of lineage CPA, in which haplotype H1 is predominant (Figs. 1 and 3). Moreover, missing intermediates from the root of H1 (H14, H27, H28 and H30, including H31 as descendant, Fig. 3) are indicative of fragmentation during interglacial periods.

The Rosa sericea complex is disjunct between southwest China and Taiwan (Fig. 4). We sampled one population of 15 individuals from Taiwan, and discovered a unique haplotype (H33, Table 1) that is most similar to a population from Xi’an, located in the Qinling Mountains (SHXA) (H18, H21). The Xi’an and Taiwan chlorotypes comprise the CPC lineage. The CPC lineage probably had a widespread distribution from the Qinling-Dabie-Wuyi mountain ranges (Fig. 3) through Taiwan during the LGM when the coastline was much lower (−130 m, ETOPO1) and a land connection existed between Taiwan and mainland China. Our ENM results support the presence of suitable, contiguous habitat for the complex extending from China to Taiwan. An alternative possibility is that the Taiwan lineage represents pre-Pleistocene vicariance associated with the formation of Taiwan island, which may have occurred 10-5 Ma42. However, our molecular divergence time estimates show that the CPC lineage diverged from its sister group ca. 0.4–0.7 Ma even when we applied the slowest, most conservative substitution rates (Fig. S3). Thus we reject the possibility of pre-Pleistocene divergence of lineage CPC, and conclude that the colonization of Taiwan is the result of Pleistocene climate oscillation. This finding supports a west to east migration route from southwest China to Taiwan through the Qinling-Dabie Mountains that was first proposed by

Hu43,44 and clarified by Wang45. To further understand this, nuclear SSR cluster analysis using STRUCTURE resolved the Taiwan population in green (cluster 2), which predominated in Hengduan Mountains region indicated a fact that the resident of Taiwan island should be a result of once widely spread R. sericea during the past (Fig. 2).

We attempted to use our phylogenetic and population genetic data to delimit species within the R. sericea complex. However, our phylogeny showed weakly supported clades and our network analysis resulted in a star-like pattern. Our results also showed low levels of variation among putative species (PV = 7.36%, FCT = 0.074, P < 0.05) (Table S4), compared to among population variation within such species (PV = 70.61%, FSC = 0.762, P < 0.0001) (Table S4). It is clear that the three clusters identified in present are not associated with the taxonomic identities. Our findings support the continuous variation that we observed in the field and in specimens in herbaria. We propose that the putative species should be treated as R. sericea.

In summary, our results show that the cold-tolerant R. sericea complex is comprised of a single species that underwent rapid geographic radiation during the LGM. The phylogeographic structure of modern populations is consistent with a phalanx model-type expansion during the LGM and subsequent pioneer model-type population histories. Our work supports the recognition of a single R. sericea species.

Methods

Plant sampling

Samples were collected from 763 individuals from 62 populations throughout the distribution range of the Rosa sericea complex during 2010–2011 (average 11.1 collections per population Fig. 1, Table S1). Leaves were collected and dried by silica gel in the wild, then stored at −80 °C until used. For each population, we sampled 10 or more individuals except that in several sites, because of the rarity of the species, less than 10 individuals were collected. However, in such cases most populations had more the 7 individuals (Table 1). Vouchers were deposited in the Herbarium of the Chengdu Institute of Biology, Chinese Academy of Sciences (CDBI).

DNA extraction, sequencing, and microsatellite genotyping

Total genomic DNA was extracted from the dried leaf tissue by a modified cetyltrimethyl ammonium bromide (CTAB) method46. In a preliminary cpDNA screen, twelve intergenic spacer (IGS) regions were surveyed in ten individuals of the R. sericea complex (data not show). Based on the results of amplify efficiency and amount of polymorphism, three IGS, ndhJ-trnF47, trnL-trnF48 and ndhF-rpl3247, were adopted for further analysis. PCRs were performed in 25 μL of ca. 50 ng of template DNA, 2.5 μL of 10 × PCR buffer, 2 μL of 2.5 mM dNTPs,0.5 μL of 10 μM each primer and 0.5 unit of TaKaRa ExTaq (TaKaRa). The PCR cycle for all three fragments was as follows: template denaturation at 94 °C for 3 min, followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 55 °C for 1 min and extension at 72 °C for 1.5 min; followed by a final extension of 72 °C for 10 min. Purified PCR products were sequenced by standard methods on an ABI PRISM 3730 (Applied Biosystems), using the same primers as those of the initial PCR. Forward and reverse sequences were edited manually and aligned by SeqMan Pro 7.1 (implement in DNASTAR, Lasergene). CLUSTALX was used to carry out sequence alignment49. All chlorotypes from Rosa sericea were deposited in GenBank under accession numbers KF850715 – KF850751, KF850756 – KF850792, KF851050 – KF851086, and for three chloroplast sequences for four outgroup accessions, i.e. R. hugonis (2 accessions), R. banksiae and R. cymosa (KF850752 – KF850755, KF850793 – KF850796, KF851087 – KF851090).

All DNA samples were genotyped at eight nSSR loci using primers and amplification protocols developed for Rosa50 (see Table S2). A pre-experiment was conducted using twenty pairs of EST primers, and based on the results these eight pairs showed significant polymorphism were chosen in present. PCR products were separated on a MegaBACE 1000 (GE Healthcare Biosciences, Sunnyvale, California, USA). Alleles were scored manually using GENETIC PROFILER (version 2.2; GE Healthcare Biosciences).

Population genetic and phylogeographic data analyses

To verify the existence of significant heterogeneity in the phylogenetic signal among the three cpDNA fragments, we carried out a partition-homogeneity test51 using the software PAUP* 4.0b. As no significant differences were detected, we used the combined cpDNA data set for all subsequent analyses. Indels were reduced and treated as single mutation events and coded as substitutions (A or T).

Sequence variation was analyzed using MEGA 4, whereas nucleotide (π) and haplotype (h) diversity52 was computed using the software DnaSP 5. We also compared population differentiation for phylogenetically ordered (NST) and unordered (GST) chlorotypes, both in the overall cpDNA data set and within population groups, using PERMUT1.0. The result of this program and the significant test by 1000 permutation were then employed to examine if NST > GST, which is a test for the presence of phylogeographic structure. The phylogenetic relationships among chlorotypes were investigated using the statistical parsimony procedure for phylogenetic network estimations implemented in TCS 1.21, with a 95% criterion for a parsimonious connection.

To identify population groups and genetic barriers, a spatial analysis of molecular variance (SAMOVA) was performed using SAMOVA 1.0. Based on a simulated annealing procedure, the SAMOVA algorithm iteratively seeks the composition of a user-defined number of groups (K) of geographically adjacent populations that maximizes the proportion of total genetic variance (FCT) as a result of differences between groups of populations. We set the number of initial condition to 100 with K = 2–20. To verify its consistency, we ran the analysis five times for each K value with 1000 independent annealing processes. Afterwards, analyses of the molecular variance (AMOVA)53 were carried out with the software ARLEQUIN 3.11, using available groupings suggested by SAMOVA and the best-fit models of sequence evolution as indicated by jModelTest. If SAMOVA did not provide suitable barriers, we subdivided groups by geography following Wu et al.54 with the aim to verify if these traditional boundaries contributed to lineage diversity in cold tolerant groups. Meanwhile, AMOVA was also carried out using subdivided groups based on taxa identified according to treatments in the Flora of China30 to check whether gene barriers exist among morphologically distinct individuals.

For nSSRs, measures of genetic diversity were assessed for each population without missing data, and across all loci, by calculating the total number of detected alleles (NA), allelic richness (RS), and gene diversity (HS). Differentiation between populations (individuals with missing data were excluded) was computed using FST55, and its significance (at each locus, and overall) was tested using 1000 bootstrap permutations. All these analyses were performed in GenALEx (version 6.501) and GENEPOP version 4.0. To characterize population structure, STRUCTURE version 2.3 was run on the entire nSSR dataset (i.e. 62 populations) for 100 000 Markov chain Monte Carlo (MCMC) cycles following 10 000 burn-in cycles, using the admixture model with independent allele frequencies. Ten replications were performed for each K, in the range K = 1–20, and the optimal K was estimated according to Evanno et al.56.

Phylogenetic relationship and Divergence time estimates

The phylogeny reconstruction based on haplotype was performed in both Neighbor-joining (NJ) and Bayesian inference (BI) using four accessions of three species, R. hugonis, R. cymosa and R. banksiae, as outgroups based on the framework of Rosa phylogeny29. The NJ analysis was conducted with MEGA version 4.0, incorporating Kimura’s 2-parameter model of DNA evolution57. To evaluate clades support, 1000 bootstrap replicates58 were performed using fast heuristic search and tree bisection-reconnection (TBR) branch swapping. BI analysis was conducted using MrBayes version 3.1.2. The best substitution model for BI analysis was identified using the Akaike Information Criterion (AIC)59, which is determined via jModelTest version 2.1.4. The Markov chain Monte Carlo (MCMC) algorithm was run for 50,000,000 generations, with four incrementally heated chains. The analysis involved starting from a random tree and sampling every 1000 generations. The first 20% of trees were treated as burn-in, and discarded. The remaining trees were used to construct a Bayesian consensus tree.

Using the same matrix as above, tree topologies and node ages were determined in BEAST version 1.61 using a GTR+I substitution model, selected by jModelTest, and an uncorrelated lognormal relaxed clock60. A Yule process was specified as tree prior. Although there are fossil records of Rosa61, the absolutely position or the nearest living relatives of these fossils are not easy to determine. Since, using remote calibrations might also not accurately determine terminal members, we adopted a substitution rate method. A likelihood ratio test in PAUP 4.10b suggested that the chloroplast dataset rejected a strict molecular clock (P < 0.01, data not shown), and therefore we used relaxed molecular clock approaches. In this study, as the lineage-specific cpDNA substitution rate for Rosa sericea is unknown, three different cpDNA substitution rates for angiosperms [the slowest (1.1 × 10−9 substitution/site/year), fastest (2.9 × 10−9 s/s/y) and mean (2.0 × 10−9 s/s/y)62,63 ]were employed to estimate divergence times. We ran two independent Markov chains for 50,000,000 generations each from a random starting tree. The chains were sampled every 1,000 generations, but the first 20% of these were eliminated as burn-in. All log and tree files from independent, simultaneous runs were combined using LogCombiner, and maximum credibility trees were generated in TreeAnnotator using the product method64.

Demographic analysis

For a single expanding subclade identified (see the Results section), demographic history was initially explored using mismatch distribution analysis (MDA), which represented the frequency distribution of pair-wise nucleotide differences among all chlorotypes in the population(s)31. A population in demographic equilibrium is characterized by multimodal mismatch distributions, whereas populations that have experienced recent demographic expansion should show smooth unimodal distributions. The distribution of the number of pairwise differences between chlorotypes with their theoretical distribution expected under a model of sudden (stepwise) demographic expansion. Goodness of fit was tested with the sum of squared deviations (SSD) between observed and expected mismatch distributions and Harpending’s raggedness index (HRag)65 using 1000 bootstrap replicates. We conducted this analysis in ARLEQUIN 3.1166 to test for demographic patterns in the major regional groups identified by phylogenetic analysis of TCS (see the Results section).

In such a context, two methods were used to estimate the time of expansion of the Rosa sericea complex. First, the expansion time was estimated directly from the mismatch distribution with the statistic τ (tau) and translated into absolute time in years (t), using the equation T = τ/2u (T, in number of generations), where u is the neutral mutation rate for the entire sequence per generation and is calculated as u = μkg, where μ is the substitution rate in substitutions per site yr−1 (s/s/y), k is the average sequence length of the cpDNA region under study (k = 2974 in this study), and g is the generation time in yr (i.e. age of first reproduction; approximated as 3 yr, as observed for Rosa cultivation by X. F. Gao). The substitution rate range of the three combined cpDNA-IGS regions was assumed as minimum and maximum mutation rates of 1.1 × 10−9 s/s/y and 2.9 × 10−9 s/s/y62,63. To obtain estimates of changes in demographic growth over the history of major areas, the historical demographic dynamics of the R. sericea complex were inferred from Bayesian skyline plot (BSP) analyses using BEAST 1.6.164. The BSP analyses are preferred because multiple loci are used to estimate effective population size through time. Linear and stepwise models were explored using an uncorrelated lognormal relaxed clock. Runs consisted of 50,000,000 generations, with trees sampled every 1000 generations. The BSP was visualized in the program Tracer version 1.4, which summarizes the posterior distribution of population size over time. In addition, we used Tajima’s D test67 and Fu’s FS test68 as implemented in ARLEQUIN version 3.1.1 to detect evidence of a recent demographic expansion within each inferred biogeographical region. The significance of deviation from value was tested with 10,000 bootstrap replicates.

Present and past distribution modeling

To reconstruct the past distribution of the R. sericea complex during cold periods, species distribution models for the R. sericea complex were generated using MAXENT 3.3.3k. MAXENT is a program for maximum entropy modeling of the geographical distributions of species; it combines presence only data with ecological climatic layers to predict species occurrence in areas where data points are unavailable. We used MAXENT to predict species occurrence under both present day and LGM conditions. Although potentially useful for this study, we avoided making predictions for the last interglacial phase (about 135 000–115 000BP), because of a lack of detailed bioclimatic data for this period. Information on the geographic distribution of the R. sericea complex was based on present collection and herbarium specimens from the herbaria A, BM, CDBI, E, HNWP, IBSC, K, KATH, KUN, PE, TUCH and WUK. In total, 317 presence records of members of the R. sericea complex were obtained from throughout the species range, after removal of duplicate records within each pixel (2.5 arc-min; ~5 km). Six bioclimatic variables (Hijmans et al. 2005) at 2.5 arc-min resolution were used to model the species’ niche: (i) annual mean temperature, (ii) mean temperature of the warmest quarter, (iii) mean temperature of the coldest quarter, (iv) annual precipitation, (v) precipitation of the wettest quarter and (vi) precipitation of the driest quarter. Model validation was performed using default settings with 100 replicates of cross-validation procedures with 25% of the data used for model testing. The area under the ROC curve (AUC) was then calculated for each run, which is an indicator of the accuracy of the model prediction69.

The established model was then projected onto the reconstructed climatic conditions during the LGM simulated by Community Climate System Model 3.070, downloaded from the WorldClim database website (http://www.worldclim.org). LGM paleo climate layers in 2.5 arc-min resolution were then prepared from these data following Sakaguchi et al.71. To estimate the paleo coastlines (−130 m than at present) and the paleo climate surfaces of the exposed seafloor area during the LGM, seafloor topography data (ETOPO1 bedrock) were downloaded from the National Geophysical Data Center of the National Oceanic and Atmospheric Administration (NOAA, USA) and used to adjust the coastline of LGM in ArcGIS 9.3 (ERSI co., USA).

Additional Information

How to cite this article: Gao, Y.-D. et al. Pleistocene glaciations, demographic expansion and subsequent isolation promoted morphological heterogeneity: A phylogeographic study of the alpine Rosa sericea complex (Rosaceae). Sci. Rep. 5, 11698; doi: 10.1038/srep11698 (2015).

Supplementary Material

Supplementary Figures 1-4
srep11698-s1.pdf (630.4KB, pdf)
Supplementary Table S1
srep11698-s2.xls (34KB, xls)
Supplementary Table S2
srep11698-s3.doc (32.5KB, doc)
Supplementary Table S3
srep11698-s4.xls (48.5KB, xls)
Supplementary Table S4
srep11698-s5.xls (33KB, xls)

Acknowledgments

Drs. Yu-Lan Peng, Bo Xu, Liang Zhang, Ms Xue-Li Zhao, and Mr. Wen-Bin Ju joined some of the field surveys. We thank Dr. Ching-I Peng from Academia Sinica and Drs. Shih-Wen Chung and Tsai-Wen Hsu from Taiwan Forestry Research Institute for their helps to our field works in Taiwan. We thank the curators and staffs of the herbaria A/GH, BM, CDBI, E, FI, HNWP, IBSC, K, KATH, KUN, PE, TUCH, and WUK for their help when X. F. Gao visited their herbarium. We thank Dr. David Boufford of Harvard University Herbaria and AJ Harris from Department of Botany, Oklahoma State University for their kindly help in English writing. The authors are also grateful for the help of Dr. Dong-Mei Chen of the School of Agriculture and Biology and Shanghai Jiao Tong University research Centre for Low Carbon Agriculture and Dr. Shota Sakaguchi from Kyoto University for helping with the ENM analysis. This research was supported by grants from The National Natural Science Foundation of China (NSFC grand nos. 31070173, 31110103911), Knowledge Innovation Program of the Chinese Academy of Sciences (grand nos. KSCX2-EW-J-22, KSCX2-EW-Z-1), the Ministry of Science and Technology (grand no. 2007FY110100), and Project of Platform Construction for Plant Resources of Sichuan Province to X. F. Gao, China Postdoctoral Science Foundation (grand no. 2013M542296) to Y. D. Gao.

Footnotes

Author Contributions Conceive and design the experiments: X.-F.G. Perform the experiments: Y.-D.G. and Y.Z. Analysis the data: Y.-D.G., Y.Z. and Z.-M.Z. Contributed reagents/materials/analysis tools: X.-F.G., Y.-D.G., Y.Z. and Z.-M.Z. Wrote the paper: Y.-D.G. and X.-F.G.

References

  1. DeChaine E. G. & Martin A. P. Historic cycles of fragmentation and expansion in Parnassius smintheus (papilionidae) inferred using mitochondrial DNA. Evolution 58, 113–127 (2004). [DOI] [PubMed] [Google Scholar]
  2. Liu J. Q., Wang Y. J., Wang A. L., Hideaki O. & Abbott R. J. Radiation and diversification within the Ligularia-Cremanthodium-Parasenecio complex (Asteraceae) triggered by uplift of the Qinghai-Tibetan Plateau. Mol. Phylogen. Evol. 38, 31–49 (2006). [DOI] [PubMed] [Google Scholar]
  3. Antonelli A., Nylander J. A. A., Persson C. & Sanmartin I. Tracing the impact of the Andean uplift on Neotropical plant evolution. Proc. Natl. Acad. Sci. USA 106, 9749–9754, 10.1073/pnas.0811421106 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Che J. et al. Spiny frogs (Paini) illuminate the history of the Himalayan region and Southeast Asia. Proc. Natl. Acad. Sci. USA 107, 13765–13770 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Li Z. J., Yu G. H., Rao D. Q. & Yang J. X. Phylogeography and Demographic History of Babina pleuraden (Anura, Ranidae) in Southwestern China. PLOS ONE 7, e34013 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Qiu Y. X., Fu C. X. & Comes H. P. Plant molecular phylogeography in China and adjacent regions, Tracing the genetic imprints of Quaternary climate and environmental change in the world’s most diverse temperate flora. Mol. Phylogen. Evol. 59, 225–244 (2011). [DOI] [PubMed] [Google Scholar]
  7. Hewitt G. M. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58, 247–276 (1996). [Google Scholar]
  8. Hewitt G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999). [Google Scholar]
  9. Hewitt G. M. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000). [DOI] [PubMed] [Google Scholar]
  10. Hewitt G. M. Biodiversity, a climate for colonization. Heredity (Edinb) 92, 1–2 (2004). [DOI] [PubMed] [Google Scholar]
  11. Jaramillo-Correa J. P. et al. Ancestry and divergence of subtropical montane forest isolates: molecular biogeography of the genus Abies (Pinaceae) in southern Mexico and Guatemala. Mol. Ecol. 17, 2476–2490, 10.1111/j.1365-294X.2008.03762.x (2008). [DOI] [PubMed] [Google Scholar]
  12. Petit R. J., Hu F. S. & Dick C. W. Forest of the past, a window to future changes. Science 320, 1450–1452 (2008). [DOI] [PubMed] [Google Scholar]
  13. Tremblay N. O. & Schoen D. J. Molecular phylogeography of Dryas integrifolia: glacial refugia and postglacial recolonization. Mol. Ecol. 8, 1187–1198 (1999). [DOI] [PubMed] [Google Scholar]
  14. Anderson L. L., Hu S. F., Nelson D. M., Petit R. J. & Paige K. N. Ice-age endurance: DNA evidence of a white spruce refugium in Alaska. Proc. Natl. Acad. Sci. USA 13, 12447–12450 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Tian S., Lopez-Pujol J., Wang H. W., Ge S. & Zhang Z. Y. Molecular evidence for glacial expansion and interglacial retreat during Quaternary climatic changes in a montane temperate pine (Pinus kwangtungensis Chun ex Tsiang) in southern China. Plant Syst. Evol. 284, 219–229, 10.1007/s00606-009-0246-9 (2010). [DOI] [Google Scholar]
  16. Opgenoorth L. et al. Tree endurance on the Tibetan Plateau marks the world’s highest known tree line of the Last Glacial Maximum. New Phytol. 185, 332–342 (2010). [DOI] [PubMed] [Google Scholar]
  17. Fedorov V. B. & Stenseth N. C. Glacial survival of the Norwegian lemming (Lemmus lemmus) in Scandinavia: inference from mitochondrial DNA variation. Proc. R. Soc. Lond. B Biol. Sci. 268, 809–814 (2001). [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fedorov V. B. & Stenseth N. C. Multiple glacial refu-gia in the North American Arctic: inference from phylogeography of the collared lemming (Dicrostonyx groenlandicus). Proc. R. Soc. Lond. B Biol. Sci. 269, 2071–2077 (2002). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Holder K., Montgomerie R. & Friesen V. L. A test of the glacial refugium hypothesis using patterns of mitochondrial and nuclear DNA variation in rock ptarmigan (Lagopus mutus). Evolution 53, 1936–1950 (1999). [DOI] [PubMed] [Google Scholar]
  20. Loehr J. et al. Evidence for cryptic glacial refugia from North American mountain sheep mitochondrial DNA. J. Evol. Biol. 19, 419–430 (2005). [DOI] [PubMed] [Google Scholar]
  21. Yan F. et al. Geological events play a larger role than Pleistocene climatic fluctuations in driving the genetic structure of Quasipaa boulengeri (Anura, Dicroglossidae). Mol. Ecol. 22, 1120–1133 (2013). [DOI] [PubMed] [Google Scholar]
  22. Wang J., Gao P., Kang M., Lowe A. J. & Huang H. Refugia within refugia, the case study of a canopy tree (Eurycorymbus cavaleriei) in subtropical China. J. Biogeogr. 36, 2156–2164 (2009). [Google Scholar]
  23. Yan H. F. et al. Population Expanding with the Phalanx Model and Lineages Split by Environmental Heterogeneity, A Case Study of Primula obconica in Subtropical China. PLos One 7, e41315 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Wang B., Jiang J. P., Xie F. & Li C. Phylogeographic Patterns of mtDNA Variation Revealed Multiple Glacial Refugia for the Frog Species Feirana taihangnica Endemic to the Qinling Mountains. J. Mol. Evol. 76, 112–128 (2013). [DOI] [PubMed] [Google Scholar]
  25. Fan D. M. et al. Phylogeography of Sophora davidii (Leguminosae) across the ‘Tanaka-Kaiyong Line’, an important phytogeographic boundary in Southwest China. Mol. Ecol. 22, 4270–4288, 10.1111/mec.12388 (2013). [DOI] [PubMed] [Google Scholar]
  26. Liu J. Q., Sun Y. S., Ge X. J., Gao L. M. & Qiu Y. X. Phylogeographic studies of plants in China: Advances in the past and directions in the future. J. Syst. Evol. 50, 267–275 (2012). [Google Scholar]
  27. Ku T. C. in Flora Reipublicae Popularis Sinicae (eds Yu T. T. & Ku T. C. ) 37 (Science Press, 1985). [Google Scholar]
  28. Yu T. T. & Ku T. C. Taxa nova Rosacearum Sinicarum (III). Bulletin of Botanical Research Harbin 1, 1–33 (1981). [Google Scholar]
  29. Fougere-Danezan M., Joly S., Bruneau A., Gao X. F. & Zhang L. B. Phylogeny and biogeography of wild roses with specific attention to polyploids. Ann. Bot. 115, 275–291, 10.1093/aob/mcu245 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Ku T. C.& Robertson K. R. in Flora of China Vol. 9 (eds Z.Y. & P.H. Raven ) 339–381 (Science Press; Missouri Botanical Garden Press, 2003). [Google Scholar]
  31. Rogers A. R. & Harpending H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992). [DOI] [PubMed] [Google Scholar]
  32. Ray N., Currat M. & Excoffier L. Intra-deme molecular diversity in spatially expanding populations. Mol. Biol. Evol. 20, 76–86, 10.1093/molbev/msg009 (2003). [DOI] [PubMed] [Google Scholar]
  33. Qi X. S. et al. Molecular data and ecological niche modelling reveal a highly dynamic evolutionary history of the East Asian Tertiary relict Cercidiphyllum (Cercidiphyllaceae). New Phytol. 196, 617–630, 10.1111/j.1469-8137.2012.04242.x (2012). [DOI] [PubMed] [Google Scholar]
  34. Li Y. et al. Glacial survival east and west of the ‘Mekong-Salween Divide’ in the Himalaya-Hengduan Mountains region as revealed by AFLPs and cpDNA sequence variation in Sinopodophyllum hexandrum (Berberidaceae). Mol. Phylogen. Evol. 59, 412–424 (2011). [DOI] [PubMed] [Google Scholar]
  35. Li E. X. et al. Phylogeography of two East Asian species in Croomia (Stemonaceae) inferred from chloroplast DNA and ISSR fingerprinting variation. Mol. Phylogen. Evol. 49, 702–714 (2008). [DOI] [PubMed] [Google Scholar]
  36. Zhai S. N., Comes H. P., Nakamura K., Yan H. F. & Qiu Y. X. Late Pleistocene lineage divergence among populations of Neolitsea sericea (Lauraceae) across a deep sea-barrier in the Ryukyu Islands. J. Biogeogr. 39, 1347–1360 (2012). [Google Scholar]
  37. Chen J. M., Du Z. Y., Sun S. S., Gituru R. W. & Wang Q. F. Chloroplast DNA phylogeography reveals repeated range expansion in a widespread aquatic herb Hippuris vulgaris in the Qinghai-Tibetan Plateau and adjacent areas. PLOS ONE 8, e60948 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li S. H. et al. Sailing through the Late Pleistocene: unusual historical demography of an East Asian endemic, the Chinese Hwamei (Leucodioptron canorum canorum), during the last glacial period. Mol. Ecol. 18, 622–633, 10.1111/j.1365-294X.2008.04028.x (2009). [DOI] [PubMed] [Google Scholar]
  39. Zhan A. & Fu J. Past and present, Phylogeography of the Bufo gargarizans species complex inferred from multi-loci allele sequence and frequency data. Mol. Phylogen. Evol. 61, 136–148 (2011). [DOI] [PubMed] [Google Scholar]
  40. Gao Y. et al. Quaternary palaeoenvironmental oscillations drove the evolution of the Eurasian Carassius auratus complex (Cypriniformes, Cyprinidae). J. Biogeogr. 39, 2264–2278, 10.1111/j.1365-2699.2012.02755.x (2012). [DOI] [Google Scholar]
  41. Johansen A. D. & Latta R. G. Mitochondrial haplotype distribution, seed dispersal and patterns of postglacial expansion of ponderosa pine. Mol. Ecol. 12, 293–298 (2003). [DOI] [PubMed] [Google Scholar]
  42. Chen D. et al. Phylogeography of Quercus variabilis based on chloroplast DNA sequence in East Asia, multiple glacial refugia and Mainland-migrated island populations. PLOS ONE 7, e47268 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hu H. H. A preliminary survey of the forest flora of southeastern China. Contr. Biol. Lab. Sci. Soc. China 2, 1–20 (1926). [Google Scholar]
  44. Hu H. H. The nature of the forest flora of southeastern China. Peking Soc. Nat. Hist. Bull. 4, 47–56 (1929-1930). [Google Scholar]
  45. Wang W. T. On some distribution patterns and some migration routes found in the Eastern Asiatic region. Acta Phytotaxonomica Sinica 30, 1–24 (1992). [Google Scholar]
  46. Doyle J. J. & Doyle J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin 19, 11–15 (1987). [Google Scholar]
  47. Shaw J., Lickey E. B., Schilling E. E. & Small R. L. Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms, the tortoise and the hare III. Am. J. Bot. 94, 275–288 (2007). [DOI] [PubMed] [Google Scholar]
  48. Taberlet P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35, e14 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Thompson J. D., Gibson T. J., Plewniak F., Jeanmougin F. & Higgins D. G. The CLUSTAL_X windows interface, Flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 25, 4876–4882 (1997). [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Park Y. H. et al. Rose (Rosa hybrida L.) EST-derived microsatellite markers and their transferability to strawberry (Fragaria spp.). Sci Hortic-Amsterdam 125, 733–739, 10.1016/j.scienta.2010.05.012 (2010). [DOI] [Google Scholar]
  51. Farris J. S., Kallersjo M., Kluge A. G. & Bult C. Testing Significance of Incongruence. Cladistics 10, 315–319 (1994). [Google Scholar]
  52. Nei M. Molecular Evolutionary Genetics. (Columbia University Press, 1987). [Google Scholar]
  53. Excoffier L., Smouse P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes, application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992). [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Wu Z. Y., Sun H., Zhou Z. K., Li D. Z. & Peng H. Floristics of seed plants from China. 1–57 (Science Press, 2011). [Google Scholar]
  55. Weir B. S. & Cockerham C. C. EstimatingF-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984). [DOI] [PubMed] [Google Scholar]
  56. Evanno G., Regnaut S. & Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620, 10.1111/j.1365-294X.2005.02553.x (2005). [DOI] [PubMed] [Google Scholar]
  57. Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980). [DOI] [PubMed] [Google Scholar]
  58. Felsenstein J. Confidence Limits on Phylogenies, An Approach Using the Bootstrap. Evolution 39, 783–791 (1985). [DOI] [PubMed] [Google Scholar]
  59. Kelchner S. A. & Thomas M. A. Model use in phylogenetics, nine key questions. Trends Ecol. Evol. 22, 87–94 (2007). [DOI] [PubMed] [Google Scholar]
  60. Drummond A. J., Ho S. Y. W., Phillips M. J. & Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Becker H. F. The Fossil Record of the Genus Rosa. Bulletin of the Torrey Botanical Club 90, 99–110 (1963). [Google Scholar]
  62. Wolfe K. H., Li W. H. & Sharp P. M. Rates of nucleotide substitution vary greatly among plant mitochondrial, chloroplast, and nuclear DNAs. Proc. Natl. Acad. Sci. USA 84, 9054–9058 (1987). [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Muse S. V. Examining rates and patterns of nucleotide substitution in plants. Plant Mol. Biol. 42, 25–43 (2000). [PubMed] [Google Scholar]
  64. Drummond A. J. & Rambaut A. BEAST, Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Harpending H. C. Signature of Ancient Population-Growth in a Low-Resolution Mitochondrial-DNA Mismatch Distribution. Hum. Biol. 66, 591–600 (1994). [PubMed] [Google Scholar]
  66. Excoffier L., Laval G. & Schneider S. ARLEQUIN (version 3.0), an integrated software package for population genetic data analysis. Evolutionary Bioinformatics Online 1, 47–50 (2005). [PMC free article] [PubMed] [Google Scholar]
  67. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989). [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Fu Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997). [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Fawcett T. An introduction to ROC analysis. Pattern Recog. Lett. 27, 861–874 (2006). [Google Scholar]
  70. Collins W. D. et al. The Community Climate System Model version 3 (CCSM3). J. Clim. 19, 2122–2143, 10.1175/Jcli3761.1 (2006). [DOI] [Google Scholar]
  71. Sakaguchi S., Sakurai S., Yamasaki M. & Isagi Y. How did the exposed seafloor function in postglacial northward range expansion of Kalopanax septemlobus? Evidence from ecological niche modelling. Ecol. Res. 25, 1183–1195 (2010). [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Figures 1-4
srep11698-s1.pdf (630.4KB, pdf)
Supplementary Table S1
srep11698-s2.xls (34KB, xls)
Supplementary Table S2
srep11698-s3.doc (32.5KB, doc)
Supplementary Table S3
srep11698-s4.xls (48.5KB, xls)
Supplementary Table S4
srep11698-s5.xls (33KB, xls)

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES