coalescence patterns of endemic tibetan species of stream ...labs.eeb.utoronto.ca/murphy/pdfs of...

Molecular Ecology (2012) 21, 3308–3324 doi: 10.1111/j.1365-294X.2012.05606.x

Coalescence patterns of endemic Tibetan speciesof stream salamanders (Hynobiidae: Batrachuperus)

BIN LU,*† YUCHI ZHENG,* ROBERT W. MURPHY‡§ and XIAOMAO ZENG*

*Department of Herpetology, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, Sichuan 610041, China,

†Graduate School of the Chinese Academy of Sciences, Beijing 100049, China, ‡Department of Natural History, Royal Ontario

Museum, 100 Queen’s Park, Toronto, ON M5S 2C6, Canada, §State Key Laboratory of Genetic Resources and Evolution,

Kunming Institute of Zoology, the Chinese Academy of Sciences, Kunming 650223, China

Corresponde

E-mail: zengx

Abstract

Orogenesis of topographically diverse montane regions often drives complex evolution-

ary histories of species. The extensive biodiversity of the eastern edge of the Tibetan

Plateau, which gradually decreases eastwardly, facilitates a comparison of historical

patterns. We use coalescence methods to compare species of stream salamanders

(Batrachuperus) that occur at high and low elevations. Coalescent simulations reveal that

closely related species are likely to have been influenced by different drivers of

diversification. Species living in the western high-elevation region with its northsouth

extending mountains appear to have experienced colonization via dispersal followed by

isolation and divergence. In contrast, species on the eastern low-elevation region, which

has many discontinuous mountain ranges, appear to have experienced fragmentation,

sometimes staged, of wide-ranging ancestral populations. The two groups of species

appear to have been affected differently by glaciation. High-elevation species, which are

more resistant to cooler temperatures, appear to have experienced population declines as

recently as the last glaciation (0.016–0.032 Ma). In contrast, salamanders dwelling in the

warmer and wetter habitats at low-elevation environs appear to have been affected less

by the relatively recent, milder glaciation, and more so by harsher, extensive glaciations

(0.5–0.175 Ma). Thus, elevation, topography and cold tolerance appear to drive evolu-

tionary patterns of diversification and demography even among closely related taxa. The

comparison of multiple species in genealogical analyses can lead to an understanding of

the evolutionary drivers.

Keywords: coalescent simulation, diversification drivers, ecological niche modelling, elevation

effects, historical demography, Tibetan uplift

Received 9 June 2011; revision received 28 February 2012; accepted 12 March 2012

Introduction

Tectonic movement, orogenesis and climatic change can

drive species diversification especially via allopatric

speciation. For terrestrial species, this can occur by the

formation of sky islands in which dry, hot, deep valleys

serve as barriers to gene flow (Knowles 2000), as well

as the height of mountains forming a barrier to dis-

persal for species that live in the valleys (Craw et al.

2008). For aquatic species, orogenesis can divide and

nce: Xiaomao Zeng, Fax: +86 028 85222753;

[email protected]

change the courses of rivers (Haffer 2008). Such drivers

are exemplified by one region, the Tibetan Plateau (Che

et al. 2010; Zhang et al. 2010). This region’s extensive

biodiversity and well-studied complex geology facilitate

investigations on the genetic consequence of orogenesis.

For example, the eastern edge of the Tibetan Plateau

contains major features that might have played impor-

tant roles in the divergence of some endemic verte-

brates (Yu et al. 2000; Luo et al. 2004; Peng et al. 2006).

However, the processes that shaped the patterns remain

to be confirmed. Are all extant populations derived

from the fragmentation of a widely distributed common

ancestral population, sequentially staged isolation

� 2012 Blackwell Publishing Ltd

PHYL OGEOGRAPHY OF TIBETAN SALAMANDERS 3309

events or colonization via a series of dispersals followed

by isolation and divergence?

Genetic patterns can identify historical events and the

associated driving processes. For example, the Euro-

pean alpine system is topographically complex, and the

resident species have correspondingly complex evolu-

tionary histories and patterns of divergence (Despres

et al. 2002; Kropf et al. 2003). Topography can drive

divergence patterns (Smith & Farrell 2005) and Pleisto-

cene climatic fluctuations associated with cyclical glacia-

tion events can also be involved, even in lower

latitudes (Bryson et al. 2011a,b). The eastern edge of the

Tibetan Plateau is extremely complicated geologically. It

is composed of several nonuniform landform assem-

blages, yet it can be defined as having two main topo-

graphical areas: the western highland and eastern

lowland. The highland area includes various northsouth

extending mountain ranges, such as the Shaluli and Da-

xue mountains. High-elevation montane taxa might

have dispersed either from north to south or vice versa

along the crest of the mountain ranges during orogene-

sis of the Tibetan Plateau and in conjunction with asso-

ciated climatic changes. In contrast, the lowland area is

comprised of many isolated mountains, such as Emei,

Luoji and Bailing mountains, that are separated by deep

valleys. The physiographic structure would probably

inhibit dispersal and promote fragmentation, especially

for taxa that have limited dispersal abilities. The more

continuous high-elevation habitat and ongoing orogene-

sis in the west would be expected to lead to a pattern

of sequential isolation when compared with a more uni-

form early fragmentation pattern in the east. Therefore,

the two topographical developments may be the drivers

of contrasting patterns of genetic divergence.

The effects of Pleistocene glacial cycling on popula-

tions depend on latitude and topography. Responses to

climatic oscillations appear to vary among species as a

function of cold tolerance (Hewitt 1996, 2004). Whereas

heavy ice is known to have covered high-latitude conti-

nental Europe and North America, glaciations occurred

discontinuously on high mountains of the eastern edge

of the Tibetan Plateau; low-elevation habitats are

thought to have been ice free (Shi et al. 1998; Zhou

et al. 2006). Climatic oscillations appear to have miti-

gated demographic stresses for species at lower relative

to higher elevations (Qu et al. 2010).

Orogenesis of the Tibetan Plateau is known to have

continued during the Late Pleistocene. A combination

of uplifted terrain and lowered snowlines during this

period may be responsible for previously low, glacier-

free regions becoming glaciated (Shi et al. 1997; Liu

et al. 1999; Zheng et al. 2002). Whereas climatic shifts in

montane regions can affect the distributions of temper-

ate species, some cold-tolerant taxa may persist in ice-


free areas (Crespi et al. 2003; Smith & Farrell 2005;

Shepard & Burbrink 2008). Variation in lineage-specific

cold tolerance complicates the analyses of elevational

patterns because all taxa do not respond equally to cli-

matic changes.

Species of Batrachuperus (family Hynobiidae) are pri-

marily restricted to isolated montane regions of the

Tibetan Plateau’s eastern edge. These salamanders have

weak dispersal abilities, and they are likely to have

experienced several orogenic events and glacial cycles

(Zhang et al. 2006). The well-established phylogeny (Fu

& Zeng 2008) provides a solid foundation for testing

hypotheses regarding the ecological and physiographic

drivers of genetic divergence. The species appear to be

parapatric, yet they are separated by elevation. Eleva-

tional differences between sister species provide an

opportunity to investigate responses to topographical

variation and Pleistocene glaciation, the two most

important factors influencing the current spatial distri-

butions of species occurring in the Tibetan Plateau and

places of similar topography (Hewitt 2000; Zhang &

Jiang 2006).

Recent methodological developments facilitate the

ability to infer historical processes. In particular,

hypothesis tests employing coalescent-based methods

allow for the statistical evaluation of the fit of diver-

gence timing to various historical scenarios while taking

into account the stochasticity of genealogical relation-

ships (Knowles & Maddison 2002; Knowles 2004, 2009).

Further, ecological modelling allows for the identifica-

tion of significant differences in the environments of

species and enables the prediction of suitable environ-

mental conditions (Carstens & Richards 2007; Shepard

& Burbrink 2008).

Herein, we compare the patterns of diversification

and demography in species of Batrachuperus living at

high and low elevations using coalescent-based meth-

ods and a combination of historical demography and

ecological niche modelling. Specifically, we test hypoth-

eses involved in the following questions: (i) How was

divergence driven in closely related, high- and low-

elevation species? (ii) How did these species respond to

glacial cycles as a result of elevation and cold-tolerant

physiology?

Material and methods

Species sampling and lab protocols

We selected five species of Batrachuperus distributed in

the montane eastern edge of the Tibetan Plateau (Fu &

Zeng 2008) including B. karlschmidti, B. londongensis,

B. pinchonii, B. yenyuanensis and an undescribed spe-

cies; the undescribed species, mainly distributed in the

3310 B . LU ET AL.

central and southern Shaluli Mountains, was referred to

herein as ‘Daocheng species’. It corresponded to Fu &

Zeng’s (2008) B. sp. 2. Batrachuperus karlschmidti and

Daocheng species occurred at western high elevations

(3500–4300 m) such as Shaluli and Daxue mountains,

while B. londongensis, B. pinchonii and B. yenyuanensis

were mainly in eastern lower reaches (1800–3200 m),

such as Emei, Luoji and Bailing mountains. A total of

418 specimens of Batrachuperus from 54 locations were

sampled between 1999 and 2009 from throughout the

species’ distributions (Fig. 1; Appendix S1, Supporting

information). We also downloaded previously pub-

lished sequences of Batrachuperus tibetanus (GenBank:

DQ333817), Pseudohynobius shuichengensis (GenBank:

FJ532060), Hynobius quelpaertensis (EF201847), Liua shihi

(DQ333810), Pachyhynobius shangchengensis (DQ333812),

Paradactylodon mustersi (DQ333821) and Ranodon sibiricus

(AJ419960) for the subsequent phylogenetic analysis

and age estimation.

Total genomic DNA was extracted using the high-salt

method (Aljanabi & Martinez 1997). Liver or muscle tis-

sue of alcohol-preserved specimens was minced, 20 lL

20 mg ⁄ mL Proteinase-K was added and then the mix-

ture was incubated at 56 �C for 6 h. Three mitochon-

drial gene fragments were sequenced: cytochrome b

(Cytb), cytochrome c oxidase subunit I (COI) and

NADH dehydrogenase 5 (ND5). Slightly modified prim-

ers Bacytb1F (5¢-GAACTAATGGCCCACCCAATTCG

AA-3¢) and Bacytb1R (5¢-AAYARAAAATATCAYTCYG

GTTGRAT-3¢) based on MVZ15 and MVZ16 were used

for amplifying Cytb (Mueller et al. 2004). Using con-

served portions of complete mitochondrial genomes of

stream salamanders (Zhang et al. 2006), we designed

two pairs of primers. Bacox1-F9 (5¢-CACTCGATGAC

TATTYTCAACTAATCATA-3¢) and Bacox1-R943 (5¢-GGATRGCAATAATTATTGTGGCGGA-3¢) were used

for amplifying COI, and Band5-F1 (5¢-ATGAATTC

AGTATTAATTTTTAATTC-3¢) and Band5-R939 (5¢-TAAYCCTAATTGGCTTGAAGTTGA-3¢) were used for

amplifying ND5. A reaction volume of 50 lL contained

2 mM MgCl2, 0.2 mM of each dNTP, 0.2 lM of each primer

and 2.5 U of Taq DNA polymerase (TianGen Biotech)

with 20–100 ng template DNA. PCR involved 5 min at

94 �C, then 35 cycles with 94 �C for 30 s, 50 �C (Cytb) or

55 �C (COI and ND5) for 40 s and 72 �C for 1 min, fol-

lowed by incubation for 10 min at 72 �C. The amplified

DNA products were purified, and automated DNA

sequencing was performed on an ABI3730 with an ABI

PRISM BigDye terminator Cycle Sequencing Ready Reac-

tion Kit (PerkinElmer Biosystems). The three fragments

were concatenated into one data set. All sequences were

aligned using BIOEDIT 7.0.5 with the CLUSTAL W option

and default multiple alignment parameters. Haplotype

identification, nucleotide diversity (p) and haplotype

diversity (Hd) per lineage were estimated in DNASP

5.10.01 (Librado & Rozas 2009) (Table 1).

Genealogical reconstruction

Bayesian inference analyses (BI) were performed using

MRBAYES 3.1.2 (Huelsenbeck & Ronquist 2001; Ronquist &

Huelsenbeck 2003). Three partitioning strategies were

applied to our data set to determine the optimal strategy.

First, we defined the concatenated data set as one parti-

tion. Second, we defined a separate partition for each

gene (three partitions). Third, we divided the data set into

nine partitions according to each codon position in each

gene. The multiple partitions controlled for heterogeneity

Fig. 1 Map showing collecting localities

for salamanders of the genus Batrachupe-

rus on the eastern edge of the Tibetan

Plateau, southwestern China (see

Appendix S1, Supporting information

for details).


Table 1 Genetic diversity and demographic statistics on salamanders of the genus Batrachuperus

Species Lineage n h p Hd SSD r

B. karlschmidti Daxue Mtn C 19 12 0.00507 0.924 0.03210 0.05475

Gan Zi 50 23 0.00529 0.932 0.01171 0.01581

Xin Long 18 7 0.00354 0.569 0.37703*** 0.12986

Shaluli Mtn C 7 6 0.00444 0.952 0.04567 0.05669

Shaluli Mtn S 11 6 0.00695 0.891 0.12104 0.12959

Total 105 54 0.01850 0.969 0.01070 0.00509

B. londongensis Emei Mtn 9 6 0.00136 0.722 0.07184 0.17284

Wawu Mtn 21 5 0.00039 0.724 0.01032 0.12000

Han Yuan 14 7 0.00162 0.670 0.16500* 0.40370**

Total 44 18 0.00952 0.871 0.07209** 0.06890**

B. pinchonii Qionglai Mtn NW 7 4 0.00154 0.810 0.07424 0.24490

Qionglai Mtn SE 39 17 0.01630 0.889 0.05308* 0.03988***

Emei Mtn 6 4 0.00108 0.600 0.50667*** 0.20444

Daxue Mtn S 25 15 0.00454 0.910 0.02718 0.06106*

Total 77 40 0.02893 0.959 0.00938 0.00911***

Daocheng species Shaluli Mtn SE 15 10 0.00201 0.933 0.00947 0.03619

Shaluli Mtn S 7 5 0.00629 0.857 0.87982*** 0.31066

Yulongxue Mtn 37 26 0.00938 0.979 0.01766 0.00919

Shaluli Mtn C 71 9 0.00073 0.560 0.08795 0.23995

Total 130 50 0.02320 0.867 0.04047* 0.02748***

B. yenyuanensis Daxue Mtn S 11 4 0.00184 0.745 0.15509 0.34116*

Bailing Mtn 22 18 0.00445 0.983 0.00651 0.01008

Luoji Mtn 29 17 0.00273 0.938 0.03614 0.03351

Total 62 39 0.01675 0.977 0.01440** 0.00432

n, number of individuals; h, number of haplotypes; p, nucleotide diversity; Hd, haplotype diversity; SSD, sum of square deviation

(goodness of fit to a simulated population expansion); r, raggedness index (*P < 0.05, **P < 0.01, ***P < 0.001).


across data set, such as variation in substitution rates. The

best-fitting substitution model for each partition was

selected using JMODELTEST 0.1.1 (Posada 2008) (Table 2).

Bayesian information criterion (BIC) was used to select a

model because of its high accuracy and precision (Luo

et al. 2010). For each partition strategy, Markov chains

were run for 10 million generations with two parallel

searches using one cold and three heated chains, each

starting with a random tree. Trees were sampled every

1000 generations using split frequencies <0.01 to indicate

convergence. TRACER 1.5 (Drummond & Rambaut 2007b)

was used to determine when the log likelihood (lnL) of

sampled trees reached a stationary distribution. In all

Bayesian analyses, apparent stationarity of Markov Chain

Monte Carlo (MCMC) runs was reached within 1 million

generations; we conservatively discarded the first 5 mil-

lion generations from each run as ‘burn-in’ and used the

sampled trees from the remaining 5 million generations

(5001 trees) to calculate the frequency of nodal resolution

in a 50% majority-rule consensus tree, termed Bayesian

posterior probabilities (BPPs). Three replicate analyses

were conducted to assess whether or not individual runs

failed to converge upon the optimal posterior distribution

and if likelihood values, branch lengths, tree topology

and posterior probabilities differed between runs. The rel-

ative fit of different partitioning strategies was evaluated


using Bayes factors (BF) in TRACER. A BF > 10 was consid-

ered strong evidence for using a more-partitioned model

(Kass & Raftery 1993). Maximum-likelihood (ML) analy-

ses were performed under the BF-preferred partitioning

strategy using the GTR+G model in the program RAXML

7.0.3 (Stamatakis 2006). Additional ML analyses were run

using the alternative partitioning schemes to check for

sensitivity to BF. A likelihood ratio test was performed to

verify the BF-preferred partitioning strategy. For estimat-

ing nodal support, nonparametric bootstrap proportions

(Felsenstein 1985) with 500 replicates were used. Maxi-

mum-likelihood bootstrap proportions (MLBs) ‡70% and

BPPs ‡0.95 were considered strong support (Hillis & Bull

1993; Erixon et al. 2003; Huelsenbeck & Rannala 2004).

The approach potentially assessed discordance among

trees based on the implemented method of inference and

provided a preferred topology for divergence estimates.

Divergence time estimation

Topology, divergence dates and substitution rates were

simultaneously estimated using BEAST 1.6.1 (Drummond

et al. 2002; Drummond & Rambaut 2007a). The best-

fitting substitution model for each data partition under

the BF was chosen as in the BI analyses. Application of

the optimal model avoided over-parameterization of the

Table 2 Nucleotide substitution models used in genealogical reconstruction, divergence date estimation (intra- and interspecific),

demographic history estimation and coalescent simulations. The estimation of h and Ne was also presented

Partition

Population level Species level

Best model Pinvar Gamma Best model Pinvar Gamma

ND5_1 GTR+G — 0.241 GTR+I+G 0.560 1.806

ND5_2 GTR+I+G 0.453 0.249 HKY+I 0.823 —

ND5_3 HKY+G — 1.427 HKY+G — 1.523

Cytb_1 SYM+G — 0.157 K80 + G — 0.088

Cytb_2 HKY+I 0.817 — HKY+G — 0.010

Cytb_3 GTR+G — 2.497 HKY+G — 2.095

COI_1 K80 + I+G 0.622 0.239 K80 + I+G 0.627 0.229

COI_2 F81 — — F81 — —

COI_3 GTR+G — 1.737 HKY+G — 1.945

BSPs and coalescent simulations

Best model Pinvar Gamma h (95% CI) Ne (95% CI)

Batrachuperus karlschmidti GTR+I+G 0.645 0.305 0.0240 (0.0126–0.0512) 993 789 (521 739–2 120 083)

Batrachuperus londongensis HKY — — 0.0039 (0.0014–0.0094) 161 491 (57 971–389 234)

Batrachuperus pinchonii GTR+G — 0.011 0.0181 (0.0104–0.0341) 749 482 (430 642–1 412 008)

Daocheng species HKY+I+G 0.536 0.305 0.0239 (0.0141–0.0452) 989 648 (583 851–1 871 636)

Batrachuperus yenyuanensis HKY+I+G 0.319 0.018 0.0150 (0.0085–0.0268) 621 118 (351 967–1 109 731)

BSP, Bayesian skyline plots; COI, cytochrome c oxidase subunit I; Pinvar, proportion of invariable sites; Gamma, gamma shape

parameter; CI, confidence interval.

3312 B . LU ET AL.

model and reduced the trade-off between bias and

unnecessary variance (Kelchner & Thomas 2007). Prior

to the BEAST analysis, we ran analysis using different

clock models (non-clock, strict clock and relaxed clock)

in MRBAYES to determine whether our data set was

evolving in a clocklike fashion and which clock model

was more appropriate for our data set. For each clock

model, Markov chains were run for 10 million genera-

tions. Trees were sampled every 1000 generations. BF

was used to select among the models and to test the

null hypothesis of a linear rate of change (Weisrock

et al. 2001). For the BEAST analysis, an initial MCMC was

run 20 million generations with an uncorrelated lognor-

mal clock model and a constant population size prior to

determine the optimal parameter in BEAST. TRACER was

used to examine the parameter ucld.stdev to test for

departures from the molecular clock. A parameter value

near zero suggested that our data evolved in a clocklike

manner. An additional BEAST analysis with an enforced

strict clock was performed to compare the result using

the relaxed clock approach. The final MCMC was run

for 20 million generations while sampling every 1000

generations. Five independent runs achieved the same

result. Burn-in and convergence of the chains were

determined with TRACER. Further, effective sample sizes

(ESS) were required to have values greater than 200.

Divergence times for Batrachuperus were estimated

using two different methods: a calibration method and

a mean substitution rate method. First, we used the fos-

sil record and previously established divergence times

among hynobiid genera (Zheng et al. 2011) to calibrate

the clock. The split between Ranodon and Paradactylodon

was constrained to be at least 2.6 Ma based on the fossil

Ranodon cf. sibiricus reported from the Upper Pliocene

(Averianov & Tjutkova 1995). We did not download

data for Andrias davidianus because the commonly used

cryptobranchid fossil (c. 145 Ma) (Gao & Shubin 2003;

Marjanovic & Laurin 2007; Zhang & Wake 2009; Roe-

lants et al. 2011; Zheng et al. 2011) was probably too

old to be directly applicable to our younger taxa (Zhang

et al. 2006), and it might have caused an overestimation

of the divergence dates (Hugall et al. 2007; Zheng et al.

2011). Slowly evolving nuclear exons have been sug-

gested to be appropriate to correct potential bias in esti-

mating divergence dates, especially for a large-timescale

phylogeny (Diego 2010; Zheng et al. 2011). Conse-

quently, we used the divergence dates recently reported

by Zheng et al. (2011) based on nuclear exons. Conser-

vatively, we estimated priors for the divergence

between Hynobius and Pseudohynobius-Liua-Batrachuperus

to be 30 ± 14 Ma (mean ± 95% CI), and among Batra-

chuperus to 13 ± 8 Ma, which followed normal distribu-

tions. We applied all three constraints in this analysis.

Both intra- and interspecific hierarchical levels were

included in our divergence time analysis. Rates of long-

term (species level) substitutions have been reported to



often strongly conflict with short-term (population

level) substitutions. Thus, results based on implied rates

of change across different hierarchical timescales have

been suggested to be invalid and often overestimated

(Ho et al. 2005). To investigate the possible effect this

phenomenon might have had on our results, we also

performed BEAST analyses using only species-level sam-

pling with calibration. The Yule process was used to

describe speciation under relaxed clock model. The

results were used as interspecific times of divergences

for Batrachuperus. The divergence dates and substitution

rates estimated using this calibration method were

applied to subsequent coalescent simulations.

To compare the results from the calibration methods,

we calculated divergence dates using an estimated mean

substitution rate (0.64% per MY per lineage) for our

combined sequence data. Yoshikawa et al. (2008)

reported a substitution rate of 0.68% per MY per lineage

for Cytb in another hynobiid (Onychodactylus japonicas).

This rate was similar to that estimated for other salaman-

ders, for example an average rate of 0.62% per MY per

lineage for plethodontids (Mueller 2006). The average

net p-distance for the combined sequences vs. Cytb

alone was 0.947. Therefore, the estimated mean substitu-

tion rate for the combined data was obtained by multi-

plying the Cytb rate of O. japonicas (0.68%) by the

p-distance ratio for Cytb alone (0.947).

Coalescent simulations

Coalescent simulations were conducted in MESQUITE

2.7.2 (Maddison & Maddison 2010). These were used to

both investigate the possible drivers of diversification

and test whether or not the timing of diversification

within each species of Batrachuperus was consistent with

the most recent uplift of the Tibetan Plateau and to test

the climatic shift hypotheses.

Three possible models of diversification were tested:

fragmented ancestor, staged fragmentation and coloni-

zation. Dates for hypotheses testing corresponded to the

approximate age estimated for each species in the BEAST

analysis, the most intensive and frequent uplift of the

Tibetan Plateau (Li et al. 1996; Li & Fang 1999; Wu

et al. 2001), and the initial Pleistocene climatic changes

(2.6–3.6 Ma). These absolute times (years) were con-

verted to coalescent times (generations) assuming a gen-

eration time of 3.5 years for the species of Batrachuperus

(Han & Lu 1999). ML estimations of Ne (inbreeding

effective population size) used h-values were calculated

in the program MIGRATE-N 3.2.1 (Beerli & Felsenstein

1999, 2001). Using the 0.69% per lineage substitution

rate produced in the BEAST analysis and a generation

time of 3.5 years, the h-value was then converted to Ne

using the formula h = Ne*l for mitochondrial DNA,


with l = 2.42 · 10)8 (3.5 · 0.69 · 10)8) substitutions per

site per generation (Table 2). For the coalescent simula-

tions, the overall Ne was set to empirically estimated

values. The Ne of the putative regional population was

constrained to a size proportional to the overall Ne at

any single point on a branch. DNA sequences were sim-

ulated under the same evolutionary model of the

empirical data, as selected by JMODELTEST 0.1.1 (Posada

2008) (Table 2). For each simulation, 100 coalescent

genealogies constrained within different hypotheses of

population history were simulated under a neutral coa-

lescent process. PAUP* 4.0b10 (Swofford 2002) was used

to construct trees from the simulated matrices and to

generate one consensus tree representing genes for each

simulation. We fit the simulated gene trees to each pos-

sible divergence model of population trees with differ-

ent date hypotheses and calculated the amount of

discordance between the gene trees and population

trees as measured by number of deep coalescences (DC)

(Maddison 1997). We then compared DC values from

the empirically reconstructed gene trees to those gener-

ated from the reconstructed gene trees from simulated

data. The null model was rejected when the observed

DC value fell below 95% of the distribution of the

expected values.

The area of origin for each species of Batrachuperus

was estimated from reconstructed ancestral states using

MESQUITE 2.7.2 (Maddison & Maddison 2010). Each indi-

vidual was assigned to its collecting location. Because

no prior knowledge of dispersal rate existed, the Mar-

kov k-state 1 parameter (Mk1) model with equal likeli-

hood the rate of change among different regions was

selected for estimating ancestral areas.

Historical demography

Past population dynamics of all lineages were estimated

using coalescent-based Bayesian skyline plots (BSP;

Drummond et al. 2005) as well as mismatch distribu-

tions (Rogers & Harpending 1992). BSPs were imple-

mented in BEAST. They were used to estimate the

dynamics of past populations through time without

requiring a prespecified parametric model of demo-

graphic history. Uncertainty in the genealogy was con-

trolled using a Bayesian approach under a coalescence

model. The best-fitting model (Table 2) with a relaxed

lognormal clock was selected to construct the BSP in

BEAST for each lineage. Chains were run for 10 million

generations, sampled every 1000 generations, and the

first 10% of the trees were discarded as burn-in. The

results were summarized using TRACER. Mismatch dis-

tributions were calculated using ARLEQUIN 3.5 (Excoffier

& Lischer 2010). Multimodal distributions were

expected in populations at demographic equilibrium or

3314 B . LU ET AL.

in decline, and unimodal distributions were anticipated

in populations having experienced a recent demo-

graphic expansion (Slatkin & Hudson 1991; Rogers &

Harpending 1992). The expected distributions were gen-

erated by bootstrap resampling (10 000 replicates) using

a model of sudden demographic expansion. The sum of

square deviations and raggedness index between the

observed and the expected mismatch were used as a

test statistic. P-values were calculated as the probability

of simulations producing a greater value than the

observed value.

Species habitat modelling

The relationship between species occurrences and the

corresponding environment was estimated from a dis-

tribution model using MAXENT 3.3.3a with default set-

tings (Phillips et al. 2006; Elith et al. 2011). The 19

bioenvironment-associated variables with 30 arc-sec res-

olution (�1 km2) were downloaded from the high-reso-

lution interpolated WORLDCLIM database (http://

www.worldclim.org/) (Hijmans et al. 2005). The vari-

ables were used in habitat modelling to examine for

potential factors that limited the species’ distributions.

Principal components analysis was used to convert a

set of possibly correlated environmental variables into a

set of values of independent variables. Principal compo-

nents with eigenvalues >1 that explained >10% of the

variation were retained. These factor scores were used

in a multivariate analysis of variance (MANOVA) as

implemented in SPSS version 19 to test for differences

among the five species of Batrachuperus, and the multi-

variate tests for each principal component were per-

formed to determine significant differences in

environmental conditions between species because these

may have been the drivers responsible for demographic

and divergence patterns.

Results

Topology and divergence dates

We resolved 764 bp of Cytb, 848 bp of COI and 856 bp

of ND5. The concatenated data set comprised 2468

aligned sites. Sequences were deposited in GenBank

under accession nos JQ303644–JQ304246. Both the BF

(>509) and likelihood ratio test (P < 0.001) strongly

favoured the nine-partitioning strategy against the one-

and three-partitioning strategies. Therefore, the nine-

partitioning strategy was used for all analyses of the

concatenated data using MRBAYES, RAXML and BEAST.

Bayesian inference and ML produced identical overall

topologies. Thus, only the ML tree [Fig. 2a] with support

values was presented. However, the topology among lin-

eages of B. pinchonii in the BEAST tree differed from those

in the MRBAYES and ML trees. In the BEAST tree, the lineage

Qionglai Mtn NW clustered with all other lineages of

B. pinchonii (BPPs, 1.0). In contrast, the lineage Qionglai

Mtn NW clustered with Qionglai Mtn SE 2 [Fig. 2a;

BPP = 0.96; MLB = 75%]. We then used BF comparisons

to test alternative topologies among lineages of B. pincho-

nii. BI incorporating a topological constraint was per-

formed with the same model. Markov chains were run

for 10 million generations. Trees were sampled every

1000 generations. BFs provided strong evidence

(BF > 14) for Qionglai Mtn NW and Qionglai Mtn SE 2

being sister lineages. Thus, the BI and ML topologies

were preferred, and all subsequent BEAST analyses used a

topological constraint. This disorder may have been

caused by being trapped in local optimum of MCMC

(Huelsenbeck & Ronquist 2001).

BF strongly favoured the relaxed clock model over

the nonclock model (BF > 268) and the strict clock

model (BF > 144), suggesting that the relaxed clock

model was more appropriate for our data. Thus, an un-

correlated lognormal clock model was imposed for

BEAST analyses. These estimated divergence times were

then employed to get a general idea of the timescale of

diversification as a reference for the subsequent coales-

cent simulations. However, a nearly zero ucld.stdev

parameter was estimated, and similar age estimations

were obtained for most nodes after running BEAST with

both the relaxed and strict clock models (Table 3).

Therefore, the analyses failed to reject the hypothesis of

a linear rate of change.

Time estimations based on calibration points indi-

cated that the earliest intraspecific divergences within

Batrachuperus occurred from the Late Miocene c. 7.2 Ma

(95% CI, 5.4–9.1 Ma) to the Early Pleistocene c. 2.0 Ma

(95% CI, 1.3–2.8 Ma; Fig. 2b; Table 3). These estimates

were younger than those obtained using a mean substi-

tution rate of 0.64% per MY per lineage. This rate was

inferred from Cytb in the Japanese clawed salamander,

and this application could not be validated indepen-

dently. Thus, we did not use dates estimated by the

mean rate method for subsequent coalescent simula-

tions. The BEAST analyses using only species-level sam-

pling estimated divergence times ranging from the

Middle Miocene c. 14.9 Ma (95% CI, 11.9–18.0 Ma) to

the Late Miocene c. 8.5 Ma (95% CI, 6.3–10.9 Ma). This

timescale was similar to that using both intra- and

interspecific hierarchical levels (Table 3). Thus, the dif-

ference between long- and short-term substitution rates

probably did not heavily affect our data set (Ho et al.

2005). The mean substitution rate produced by the cali-

bration method was 0.69% (95% CI, 0.58%–0.82%) per

MY per lineage. This rate was then used in subsequent

coalescent simulations.


(a)

(b)

62.46

98

88

99

95

98

46.46

98

69.87

75.96

65

83.99

Daxue Mtn C

Xin Long

Bt

Bk

Bp

Bl

By

DC

Gan Zi

Sll Mtn CSll Mtn S

Daxue Mtn SEmei MtnQl Mtn SE 1

Ql Mtn SE 2

Ql Mtn NWEmei MtnHan Yuan

Wawu Mtn

Daxue Mtn S

Bailing Mtn

Luoji Mtn

Sll Mtn SESll Mtn S

Ylx Mtn

Sll Mtn C

0.09

0.010.020.030.040.050.0

a

b

c

d

e

f

g

h

i

j

k

l

mno

pq

r

s

tu

v

w

xy

z

Bk

Bp

Bl

By

DC

RsPmPasHqPssLsBtDaxue Mtn C

Xin Long

Gan ZiSll Mtn C

Sll Mtn SDaxue Mtn SEmei Mtn

Ql Mtn SE 1

Ql Mtn SE 2Ql Mtn NW

Emei MtnHan Yuan

Wawu MtnDaxue Mtn S

Luoji MtnBailing Mtn

Sll Mtn SE

Sll Mtn S

Ylx Mtn

Sll Mtn C

Fig. 2 Topology and divergence dates for salamanders of the

genus Batrachuperus. (a) RAXML phylogram. Names of the non-

Batrachuperus out-group species are removed for clarity. The

branching order is the same as that in the BEAST chronogram.

Maximum-likelihood bootstrap proportions (MLBs) support is

shown above the node, with Bayesian posterior probabilities

(BPPs) shown below. Unmarked nodes have MLBs = 100%

and BPPs = 1.0. (b) Simplified BEAST chronogram produced by

calibration method under relaxed clock model. Blue bars repre-

sent 95% highest probability densities (HPD) interval. Num-

bers on scale bar are millions of years before present. The

mean age estimates and actual 95% HPD are shown in

Table 3. Species name abbreviations: Rs, Ranodon sibiricus; Pm,

Paradactylodon mustersi; Pas, Pachyhynobius shangchengensis; Hq,

Hynobius quelpaertensis; Pss, Pseudohynobius shuichengensis; Ls,

Liua shihi; Bt, B. tibetanus; Bk, B. karlschmidti; DC, Daocheng

species; Bp, B. pinchonii; Bl, B. londongensis; By, B. yenyuanen-

sis. Region name abbreviations: Sll, Shaluli; Ql, Qionglai; Ylx,

Yulongxue.



Hypotheses testing using coalescent simulations

Results from coalescent simulations with the corre-

sponding Ne failed to reject the colonization models for

B. karlschmidti (P = 0.188) and Daocheng species

(P = 0.190), the fragmented ancestor models for B. lon-

dongensis (P = 0.136) and B. yenyuanensis (P = 0.155),

and the staged fragmentation model for B. pinchonii

(P = 0.109). Thus, the diversification of these closely

related species seemed to be driven by three different

causes (Fig. 3).

The coalescent framework was also used to evaluate

whether or not the level of divergence within each

species of Batrachuperus corresponded with the most

recent orogenesis of the Tibetan Plateau and the corre-

sponding glacial-induced climatic shift hypothesis.

Thus, we adjusted the first divergence dates (tree

depth) to 2.6 and 3.6 Ma. These two time-points were

also close to or fell within the 95% CI obtained from

the BEAST dating estimates for most species (Table 3).

Coalescent simulations for corresponding diversifica-

tion models failed to reject the null hypotheses at

2.6 Ma for B. karlschmidti (P = 0.117), B. londongensis

(P = 0.159) and B. yenyuanensis (P = 0.101); diversifica-

tion appeared to be driven by the most recent orogen-

esis of the Tibetan Plateau and its concomitant glacial-

induced climatic shift. This hypothesis was rejected

when tested for B. pinchonii and Daocheng species

(P < 0.01), which had estimated divergence dates of

c. 5.0 Ma (95% CI, 3.7–6.3 Ma) and c. 7.2 Ma (95% CI,

5.4–9.1 Ma), respectively. Consequently, we assumed

the divergences occurred in the last 3.6 Ma, the begin-

ning of the most recent uplift of the Tibetan Plateau.

Coalescent simulations were then used to test these

null hypotheses. The DC values from reconstructed

Table 3 Age estimates using different methods. Letters for nodes correspond to Fig. 2b. The age unit is millions years ago

Node

Calibration method (95% HPD)

Mean rate method (95% HPD)Relaxed clock Strict clock Species-level sampling

a 40.8 (32.4–49.4) 37.9 (29.1–46.5) 38.7 (31.6–45.8) 44.2 (25.5–69.3)

b* 26.4 (17.9–35.9) 25.5 (20.5–31.2) 27.4 (20.9–34.0) 29.1 (13.9–49.4)

c 38.2 (30.6–46.5) 37.1 (28.6–45.9) 37.5 (30.7–44.2) 41.1 (23.3–64.3)

d* 34.7 (27.6–42.0) 35.2 (27.8–44.0) 35.3 (29.1–42.1) 37.3 (20.7–58.9)

e 28.7 (22.4–35.5) 30.8 (24.6–39.5) 30.5 (24.7–36.6) 30.9 (16.9–49.2)

f 23.4 (18.0–29.0) 26.0 (20.4–33.0) 25.7 (20.4–31.1) 25.1 (13.3–39.5)

g* 14.8 (11.7–18.0) 14.2 (11.3–17.9) 14.9 (11.9–18.0) 15.8 (9.0–24.5)

h 12.4 (9.5–15.4) 11.6 (8.9–15) 11.8 (9.1–14.9) 13.2 (7.5–21)

i 11.7 (8.9–14.4) 11.9 (9.3–15.2) 12.2 (9.5–14.9) 12.5 (7.2–19.7)

j 7.8 (5.6–10.1) 8.1 (6.5–10.4) 8.5 (6.3–10.9) 8.5 (4.5–13.3)

k 10.3 (7.7–13.0) 10.6 (8.0–13.2) 10.8 (8.4–13.7) 11.0 (6.1–17.3)

l 3.9 (2.8–5.1) 3.6 (2.7–4.8) — 4.2 (2.4–6.7)

m 2.2 (1.5–2.8) 1.9 (1.4–2.5) — 2.3 (1.3–3.7)

n 1.7 (1.2–2.2) 1.4 (1.1–1.9) — 1.8 (1.0–2.8)

o 1.3 (0.9–1.7) 1.2 (0.8–1.5) — 1.4 (0.8–2.2)

p 1.5 (1.0–2.1) 1.4 (0.9–1.9) — 1.6 (0.8–2.7)

q 3.8 (2.8–5.1) 3.6 (2.7–4.7) — 4.2 (2.3–6.8)

r 5.0 (3.7–6.3) 4.8 (3.6–6.2) — 5.5 (3.0–8.6)

s 4.5 (3.3–5.8) 4.3 (3.1–5.5) — 4.9 (2.7–7.8)

t 2.0 (1.3–2.8) 2.1 (1.6–2.6) — 2.1 (1.1–3.5)

u 1.0 (0.6–1.4) 1.0 (0.6–1.3) — 1.0 (0.4–1.7)

v 2.2 (1.4–2.9) 1.9 (1.4–2.5) — 2.4 (1.3–3.9)

w 3.0 (2.1–4.0) 2.6 (1.8–3.6) — 3.3 (1.8–5.3)

x 7.2 (5.4–9.1) 6.5 (5.0–8.2) — 7.6 (4.2–12.2)

y 5.7 (4.2–7.4) 5.4 (4.4–7.0) — 6.1 (3.3–9.8)

z 2.1 (1.4–2.8) 1.7 (1.3–2.1) — 2.2 (1.2–3.4)

HPD, highest probability densities.

*Calibration points.

The first divergence occurred within each species of Batrachuperus.

3316 B . LU ET AL.

gene trees for B. pinchonii (P = 0.352) and Daocheng

species (P = 0.236) were not significantly lower than

those values from the gene trees simulated by neutral

coalescence. Again, the null hypotheses of two main

divergence events could not be rejected.

Ancestral area reconstructions assigned the highest

probability for the origin of B. karlschmidti to the central

region of Daxue Mountains (proportional likelihoods,

PL = 0.61; Table 4), for Daocheng species, the south-

eastern region of Shaluli Mountains (PL = 0.60), and for

B. pinchonii, the southeastern region of Qionglai Moun-

tains (PL = 0.72). The probabilities of the three species’

origins for alternative regions were considerably lower

(highest PL = 0.23). Thus, a single ancestral area of ori-

gin was identified. These results were consistent with

the colonization models of B. karlschmidti and Daocheng

species and the staged fragmentation model of

B. pinchonii. In contrast, probabilities for all regions of

B. londongensis and B. yenyuanensis were low (highest

PL = 0.43). It seemed highly likely that multiple ances-

tral areas existed at the deeper nodes on the tree. This

discovery supported results from coalescent simulations

that diversification occurred via the fragmentation of a

wide-ranging common ancestor.

Historical demography

The mismatch distributions revealed distinct demo-

graphic histories for each species. Distributions of the

pairwise mutation differences were unimodal for B. kar-

lschmidti and B. yenyuanensis (Fig. S1, Supporting infor-

mation), and the sum of square deviations and

raggedness index suggested that the curves did not sig-

nificantly differ from the distributions expected under a

model of sudden demographic expansion (Table 1). In

contrast, the mismatch analyses for the remaining three

species of Batrachuperus showed a bimodal profile,

which might have resulted from either a constant or

declining demography. Further, the BSP analysis pro-

vided additional and sometimes different details. The

population size of the two high-elevation species

(B. karlschmidti and Daocheng species) appeared to


Table 4 Proportional likelihoods for the area of origin for the oldest ancestral node in the five species of Batrachuperus

Species The area of origin

B. karlschmidti Daxue Mtn C Gan Zi Xin Long Shaluli Mtn C Shaluli Mtn S

0.6118 0.0896 0.2341 0.0323 0.0323

B. londongensis Emei Mtn Wawu Mtn Han Yuan — —

0.3941 0.2812 0.3248 — —

B. pinchonii Qionglai Mtn NW Qionglai Mtn SE Emei Mtn Daxue Mtn S —

0.0838 0.7244 0.0904 0.1013 —

Daocheng species Shaluli Mtn SE Shaluli Mtn S Yulongxue Mtn Shaluli Mtn C —

0.5976 0.2008 0.1985 0.0031 —

B. yenyuanensis Daxue Mtn S Bailing Mtn Luoji Mtn — —

0.2554 0.3100 0.4346 — —

0.1792 0.1208 0.2583 0.3208 0.1208

0.4872 0.1282 0.3846

0.3400 0.5867 0.0733

0.1757 0.2008 0.5690 0.0544

0.5193 0.0883 0.0221 0.3702Daxue Mtn C Xin Long Gan Zi Shaluli Mtn C Shaluli Mtn S

Qionglai Mtn SE2 Qionglai Mtn NW Qionglai Mtn SE1 Emei&Daxue

Time in generations

(a) B. karlschmidti

Colonization

(c) B. pinchonii

(d) B. londongensis

(e) B. yenyuanensis

Staged Fragmentation

Fragmentation

Colonization

(b) Daocheng species

Han Yuan Wawu Mtn Emei Mtn

Luoji Mtn Bailing Mtn Daxue Mtn S

Fragmentation

Shaluli Mtn SE Shaluli Mtn S Yulongxue Mtn Shaluli Mtn C

Time in generations

Time in generations

Time in generations

Time in generations

Fig. 3 Illustration of the three biogeographic hypotheses of diversification within each species of Batrachuperus tested using coales-

cent simulations. Branch lengths are time in generations based on a 3.5-year generation time. Branch widths (Ne) are scaled based on

the proportion of the Total Ne that each mountain comprised (listed below mountain name). Internal branches on models were scaled

such that all branch widths summed to Total Ne at any single point in time. (c) Emei&Daxue, Emei Mtn and Daxue Mtn S.


remain relatively stable during the extensive glaciation

period (0.5–0.175 Ma), rapidly declined in the Late

Pleistocene, but increased sharply around the end of

the last glacial maximum (LGM) in the eastern edge of

the Tibetan Plateau (0.016–0.032 Ma; Fig. 4). The sud-

den expansion model for Daocheng species contradicted

the results from the mismatch distributions. The popu-

lation size of two low-elevation species (B. pinchonii


and B. londongensis) appeared to gradually decrease at

the beginning of the extensive glacial period (0.5–

0.175 Ma). Substantial population growth of B. yenyuan-

ensis started approximately 0.15 Ma, which coincided

with the ending of the extensive glaciation period. The

historical dynamics of the five species of Batrachuperus

were also supported by the BSP analyses for each regio-

nal population within each species.

Table 5 Nineteen biological environmental variables used in

habitat modelling and the principal component analysis on these

variables used in a comparison of climatic conditions between

occurrence locations for the five species of Batrachuperus

Bio-variable

Component

1 2

BIO1 = Annual Mean Temperature 0.786 0.612

BIO2 = Mean Diurnal Range )0.902 0.247

BIO3 = Isothermality )0.666 0.229

BIO4 = Temperature Seasonality )0.300 0.086

BIO5 = Max Temperature of Warmest Month 0.654 0.720

BIO6 = Min Temperature of Coldest Month 0.896 0.421

BIO7 = Temperature Annual Range )0.809 0.174

BIO8 = Mean Temperature of Wettest Quarter 0.768 0.629

BIO9 = Mean Temperature of Driest Quarter 0.771 0.599

BIO10 = Mean Temperature of Warmest Quarter 0.769 0.625

BIO11 = Mean Temperature of Coldest Quarter 0.796 0.562

BIO12 = Annual Precipitation 0.829 )0.514

BIO13 = Precipitation of Wettest Month 0.737 )0.520

BIO14 = Precipitation of Driest Month 0.850 )0.402

BIO15 = Precipitation Seasonality )0.502 0.051

BIO16 = Precipitation of Wettest Quarter 0.756 )0.548

BIO17 = Precipitation of Driest Quarter 0.849 )0.391

BIO18 = Precipitation of Warmest Quarter 0.742 )0.563

(a)

(c)

(d)

(e)

(b)

Fig. 4 Bayesian skyline plots showing

the demographic history of five species

of Batrachuperus. (a) B. karlschmidti, (b)

Daocheng species, (c) B. pinchonii, (d)

B. londongensis and (e) B. yenyuanensis.

Dark lines represent median values for

the log10 of the population size (Ne*s) (s,

generation time), blue lines mark the

95% highest probability density (HPD)

intervals in all panels.

3318 B . LU ET AL.

Ecological niche modelling

Ecological niche modelling projected that the distribu-

tion of each species of Batrachuperus was now restricted

to a specific montane habitat. The region of highly suit-

able habitat was limited to and consistent with the dis-

tributions of each species (Fig. S2, Supporting

information). For each distributional model, the AUC

value approached one (‡0.997) indicating a better than

random prediction (0.5 = random). Two principal com-

ponents among the 19 possibly correlated environmen-

tal variables explained 80.64% of the total variation

(Table 5). The first principal component represented

precipitation and temperature (the lower the value, the

colder and drier), whereas the second principal compo-

nent represented climatic variability (the lower the

value, the less variable). Habitats of high-elevation spe-

cies appeared to be cooler, drier and more variable than

those of low-elevation species (Fig. 5). The MANOVA

analysis indicated that the habitats of the five species of

Batrachuperus differed significantly (Wilk’s k = 0.281,

F = 15.07, P < 0.001). This result provided ecological

and physiographic explanations for the distinct demo-

graphic and divergence histories of each species.

BIO19 = Precipitation of Coldest Quarter 0.847 )0.378

Initial eigenvalues 11.028 4.293

% of variance 58.042 22.595
Discussion
The various drivers of species divergence associated with

orogenesis seem to play roles in the evolution of Batra-

chuperus. Closely related species of Batrachuperus seem to

have experienced different patterns of diversification dri-

ven by topography. They appear to have been affected

by different glacial periods as a result of variation in ele-

vation distributions and cold-tolerant physiology.


Fig. 5 Results from the comparison of environmental (means ± 95% CIs) conditions at sampling sites for Batrachuperus. Habitats

where high-elevation species (Bk, B. karlschmidti; DC, Daocheng species) occur are significantly cooler, drier and more variable than

those occupied by low-elevation species (Bp, B. pinchonii; Bl, B. londongensis; By, B. yenyuanensis) occupied.


Distinct divergence patterns

Topographically diverse montane regions usually have

a suite of species with differing and complex evolution-

ary histories and divergence patterns (Despres et al.

2002; Kropf et al. 2003). Closely related species of sala-

manders endemic to the eastern edge of the Tibetan

Plateau also have distinct patterns, and these are associ-

ated with high- and low-elevation topographies.

Coalescent simulations find that diversification in

western high-elevation species is more consistent with

a colonization model involving a series of dispersals

from one mountain to another followed closely by iso-

lation and divergence. Fragmentation of the range of a

widely distributed common ancestor is not suggested.

In contrast, diversification in eastern low-elevation spe-

cies appears to have been driven by either fragmenta-

tion or staged fragmentation of a once wide-ranging

species. Species of Batrachuperus generally occupy moist

habitats in forested areas. Consequently, their distribu-

tions can be strongly influenced by the occurrence of

mesic forests. Orogenesis of the Tibetan Plateau and

Pleistocene glacial cycling are responsible for climatic

and ecological shifts. The climate appears to have

become gradually drier and colder; the development of

glaciers in this region is associated with the contraction

of broadleaf deciduous forests and a dominance of

grasslands (Wu et al. 2001). Most temperate species

appear to have dispersed to lower latitudes in response

to Pleistocene climatic oscillations (Hewitt 2000; Rowe

et al. 2004).

Under a coalescent framework, the null hypotheses

states that the initial divergence within each species

occurred at c. 2.6–3.6 Ma, and it cannot be rejected. This

dating corresponds to the most intense uplift of the

Tibetan Plateau (Zheng et al. 2000; An et al. 2001) and

the beginning of the Pleistocene. Consequently, climatic

changes triggered by the two events may have driven

the divergence, although more evidence is needed.


The region’s northsouth tending mountain ranges

facilitate lower-latitude dispersal by providing favour-

able climate and habitats. The levels of population dif-

ferentiation reflect historical isolation events. Multiple

montane occurrences and drift, and not ongoing gene

flow, are suggested to be the driving force. The precise

colonization routes are likely to have been obscured by

the accumulation of genetic differences and coalescence

times. Eastern low-elevational species are highly likely

to have been restricted to mesic sky island forests iso-

lated by deep valleys with unfavourable environments.

Unlike contiguous mountains in the western area, val-

leys appear to serve as barriers to dispersal because lin-

eages are separated by unsuitable conditions (Wiens

2004; Wiens & Graham 2005; Wiens et al. 2006).

Ancestral area reconstructions provide additional

support for the distinction between divergence patterns

in high- and low-elevation species. High-elevation spe-

cies are associated with a single area of origin. Their

ancestors are from a relatively small area. Batrachuperus

karlschmidti seems to be derived from the central region

of Daxue Mountains (1–4), and subsequent colonization

appears to have occurred mainly along the Shaluli

Mountain range eventually reaching its southern region

(18, 19). Daocheng species appears to have its origin in

the southeastern Shaluli Mountains (19); a series of

short-distance colonizations restrict it to the central and

southern Shaluli Mountains. Interestingly, the common

ancestor of Daocheng species appears to have been

present in the southeastern Shaluli Mountains before

B. karlschmidti colonized that area. Thus, B. karlschmidti

seems to have invaded suitable habitats previously

dominated by Daocheng species. Niche modelling pre-

dicts that the two species occupy similar habitats, which

may facilitate competition, as is documented to occur in

other closely related species of salamanders (Hairston

1951; Crespi et al. 2003).

Our analyses cannot identify single ancestral areas of

origin for most low-elevation species that exhibit

3320 B . LU ET AL.

fragmentation patterns, especially B. londongensis and

B. yenyuanensis. This finding suggests the rapid frag-

mentation of wide-ranging ancestral populations into

several parts. The ancestral area reconstruction for

B. pinchonii assigns the highest probability to southeast-

ern Qionglai Mountains (28–32). The staged fragmenta-

tion pattern of this low-elevation species infers that its

common ancestor initially fragmented into two ances-

tral populations (northern and southern), and subse-

quently both of them fragmented. The first stage may

be associated with orogenesis of the Qionglai Moun-

tains, although more powerful evidence is needed to

support this scenario. Gongga Mountain, the highest

(7556 m) in the region, is surrounded by more than 20

other peaks that exceed 6000 m in the central Daxue

Mountains. The palaeomagnetic age of Gongga Moun-

tain is c. 2.48 Ma (Li et al. 1991), and the first coloniza-

tion event (c. 2.6 Ma) in B. karlschmidti is closely

associated with this origin. Batrachuperus karlschmidti

does not occur south of Gongga Mountain. Orogenesis

in the region may have restricted ancestral populations

to the north. The Late Mesozoic Yanshan movement is

responsible for the initial creation of Mount Emei, and

the most recent Tibetan Plateau movement (c. 3 Ma)

uplifted the mountain to its present height. Concor-

dance between dates for the fragmentation of B. lon-

dongensis and the uplifting of Mount Emei suggests that

the creation of deep valleys produced barriers to disper-

sion, thus reducing gene flow and promoting allopatric

divergence.

Contrasting demographic histories

Species at high and low elevations appear to have been

affected by different glacial periods. Responses to Pleis-

tocene glacial cycles are expected to vary among species

and geographical regions, in part because of differential

cold tolerance (Hewitt 1996, 2004).

Bayesian skyline plots show that high-elevation spe-

cies were suppressed by the last glaciation (0.016–

0.032 Ma) but not by the earlier, more extensive glacia-

tions (0.5–0.175 Ma; Shi 2002; Shi et al. 1995; Zhang

et al. 2000; Zheng et al. 2002), although the mismatch

distribution rejects the model of sudden demographic

expansion for Daocheng species. Methods estimating

population parameters based on the distribution of

pairwise differences do not make full use of DNA

sequence data, while methods based on coalescent the-

ory, by incorporating information from the genealogy,

may obtain better estimates of the demographic histo-

ries of populations (Felsenstein 1992; Pybus et al. 2000).

Only a few mountain heights extend above the Middle

Pleistocene snowline, and glaciation appears to have

been limited to areas such as Gongga Mountain (Shi

et al. 1990; Li et al. 1991). Glaciation is controlled by cli-

mate and geomorphology. Although the climate is doc-

umented to have been colder during the extensive

glacial period than the LGM (Shi et al. 1995), mountain

heights below the snowline could not be glaciated. Dur-

ing the LGM, if mountain heights extended above the

snowline, glaciers could form and became cold sources

acting as positive-feedback mechanisms through reflect-

ing more of the sun’s energy and absorbing less; oro-

genesis raised mountain heights and glaciers

contributed to cooler climates (Zheng et al. 2002). Cur-

rent snowlines in the eastern edge of the Tibetan Pla-

teau extend from 4200 to 5200 m (Shi et al. 1997; Liu

et al. 1999; Li et al. 2009). During the LGM, the snow-

line descended to approximately 3300 m in many

mountain ranges (Shi et al. 1997; Liu et al. 1999). The

two high-elevation species currently occur above

3500 m. Their suitable habitat seems to have been heav-

ily covered with ice during the LGM. If true, then sala-

manders must have dispersed to ice-free refugia, such

as lower elevations or adjacent areas, only to recolonize

the region via a population expansion during postgla-

cial times.

The population sizes of the three low-elevation spe-

cies seem to have been affected from the beginning of

the extensive glacial period. The low-elevation species

dwell below the snow line (1800–3200 m). Suitable hab-

itat might have not been covered by ice during both

the LGM and the extensive glacial period. Ecological

niche modelling suggests that the low-elevation species

are less cold-tolerant than the high-elevation species. If

true, then low-elevation species are more likely to suf-

fer from climatic cooling than high-elevation species.

From the beginning of the extensive glaciation period,

low-elevation species are predicted to have lowered

population sizes even in ice-free regions. In contrast,

high-elevation, cold-tolerant species are expected to be

affected only if their suitable habitat is heavily covered

with ice during the LGM. These predictions are consis-

tent with the hypothesis that variation in ecological

adaptations affects geographical patterns of genetic var-

iation (Gavrilets 2003; Hewitt 2004). Glacial advances

decrease temperatures while increasing aridity. These

changes may have contributed to declines in popula-

tion size in the less cold-tolerant species and resulted

in the use of refugia by many species during the LGM

(Hewitt 1996, 2000, 2004; Rowe et al. 2004). However,

B. yenyuanensis, which occupies a low-elevation and

low-latitude region, seems to have experienced a sub-

stantial amount of population growth after the exten-

sive glaciation period yet did not suffer losses during

the LGM. The climate during the extensive glaciation

period might have been much colder and drier than

that during the LGM (Shi et al. 1995; Zheng et al.



2002). This may be an important reason why glaciation

affects low-latitude species. In contrast, a cold climate

without ice cover is likely to have little negative effect

on more cold-tolerant species, such as B. karlschmidti

and Daocheng species. It remains unclear why the pop-

ulations of B. pinchonii and B. londongensis did not

expand after the glaciers retreated. Other factors appear

to be involved.

Our study on Tibetan Batrachuperus underscores the

various roles played by elevation, geological history

and animal physiology in shaping the patterns of

genetic diversity and demography among closely

related species. Further studies using the rigorous test-

ing of hypotheses are likely to yield an understanding

the drivers of evolutionary change.

Acknowledgements

We thank Jinzhong Fu for constructive comments on earlier

versions of this manuscript. We also thank Xianguang Guo, Li

Ding and Jiatang Li for valuable discussion during the revi-

sion. This project was supported by a National Natural Science

Foundation of China (NSFC grant no. 30870287) and a Chinese

Academy of Sciences (KSCX2-EW-J-22) to X.M. Zeng, and a

National Natural Sciences Foundation of China (NSFC-

30900134) and a Chinese Academy of Sciences (09C3011100,

KSCX2-YW-Z-0906) to Y.C. Zheng, and by a Visiting Professor-

ship for Senior International Scientists from the Chinese Acad-

emy of Sciences and a Natural Sciences and Engineering

Research Council Discovery Grant (A3148) to R.W. Murphy.

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B.L is interested in understanding the genetic and evolutionary

mechanisms that generate and maintain patterning variation

within and between species. Y.C.Z‘s research focuses on the

systematics, correlated evolution, and biogeography of amphi-

bians. R.W.M. is broadly interested in genetics, genomics, and

evolution. X.M.Z‘s general areas of interest are systematics and

evolutionary biology of amphibians and reptiles. Current

research activities focus on the mechanisms of speciation and

more specifically to understand the role of chromosomal rear-

rangements in speciation.

3324 B . LU ET AL.

Data accessibility

DNA sequences: GenBank accessions JQ303644–JQ304246.

Sampling information uploaded as online supplemental mate-

rial.

Phylogenetic data and DNA sequence alignment for all indi-

viduals: TreeBASE Study accession no. S12425.

Supporting information

Additional supporting information may be found in the online

version of this article.

Fig. S1 Mismatch distributions for five species of Batrachuperus.

(a) B. karlschmidti, (b) Daocheng species, (c) B. pinchonii, (d)

B. londongensis and (e) B. yenyuanensis.

Fig. S2 The ecological niche models of the current distribution

for five species of Batrachuperus. (a) B. karlschmidti, (b) Daoch-

eng species, (c) B. pinchonii, (d) B. londongensis and (e) B. yeny-

uanensis. Colours indicate predicted probability that conditions

are suitable, with values ranging from 0 to 1. High values indi-

cate high probability of suitable conditions for the species,

whereas low values indicate low predicted probability of suit-

able conditions.

Appendix S1 Specimens of Batrachuperus used in genealogical

and coalescent analyses. All vouchers are deposited in the

Chengdu Institute of Biology (CIB). Locality numbers corre-

spond to those on the map in Fig. 1.

Please note: Wiley-Blackwell is not responsible for the con-

tent or functionality of any supporting information supplied

by the authors. Any queries (other than missing material)

should be directed to the corresponding author for the

article.


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