Repeated landmass reformation limits diversification in the widespread littoral zone mosquito...

Repeated landmass reformation limits diversification inthe widespread littoral zone mosquito Anophelessundaicus sensu lato in the Indo-Oriental Region

MAGDALENA ZAROWIECKI , * YVONNE-MARIE LINTON,† ‡ RORY J . POST,§MICHAEL J . BANGS,¶ PE THAN HTUN,** THAUNG HLAING,** CHANG MOH SENG,† †

VISUT BAIMAI ,‡ ‡ TRUNG HO DING,§§ THO SOCHANTHA¶¶ and CATHERINE WALTON***

*Parasite Genomics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA,

UK, †Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, 4210 Silverhill Road, Suitland, MD

20746, USA, ‡Walter Reed Army Institute of Research, Entomology Branch, 503 Robert Grant Avenue, Silver Spring, MD

20910-7500, USA, §School of Natural Sciences and Psychology, Liverpool John Moores University, Byrom Street, Liverpool L3

3AF, UK, ¶Public Health & Malaria Control, International SOS, Jl. Kertajasa, Kuala Kencana, Papua 99920, Indonesia,

**National Malaria Control Programme, Department of Health, Ministry of Health, Nay Pyi Taw, Myanmar, ††World Health

Organization Cambodia, #177-179 Pasteur Street (51), Phnom Penh, Cambodia, ‡‡Department of Biology, Faculty of Science,

Mahidol University, Rama VI Road, Bangkok 10400, Thailand, §§National Institute of Malariology, Parasitology & Entomology,

Luong The Vinh Street, BC 10.200, Tu Liem, Hanoi, Vietnam, ¶¶National Center for Malaria Control, Parasitology &

Entomology, 372 Monivong Blvd (Corner St 322), Phnom Penh, Cambodia, ***Faculty of Life Sciences, The University of

Manchester, Michael Simon Building, Oxford Road, Manchester M13 9PL, UK

Abstract

Southeast Asia harbours abundant biodiversity, hypothesized to have been generated

by Pliocene and Pleistocene climatic and environmental change. Vicariance between

the island of Borneo, the remaining Indonesian archipelago and mainland Southeast

Asia caused by elevated sea levels during interglacial periods has been proposed to

lead to diversification in the littoral zone mosquito Anopheles (Cellia) sundaicus(Rodenwaldt) sensu lato. To test this biogeographical hypothesis, we inferred the pop-

ulation history and assessed gene flow of A. sundaicus s.l. sampled from 18 popula-

tions across its pan-Asian species range, using sequences from mitochondrial

cytochrome c oxidase subunit 1 (CO1), the internal transcribed spacer 2 (ITS2) and the

mannose phosphate isomerase (Mpi) gene. A hypothesis of ecological speciation for

A. sundaicus involving divergent adaptation to brackish and freshwater larval habitats

was also previously proposed, based on a deficiency of heterozygotes for Mpi allozyme

alleles in sympatry. This hypothesis was not supported by Mpi sequence data, which

exhibited no fixed differences between brackish and freshwater larval habitats. Mpiand CO1 supported the presence of up to eight genetically distinct population group-

ings. Counter to the hypothesis of three allopatric species, divergence was often no

greater between Borneo, Sumatra/Java and the Southeast Asian mainland than it was

between genetic groupings within these landmasses. An isolation-with-migration (IM)

model indicates recurrent gene flow between the current major landmasses. Such gene

flow would have been possible during glacial periods when the current landmasses

merged, presenting opportunities for dispersal along expanding and contracting coast-

lines. Consequently, Pleistocene climatic variation has proved a homogenizing, rather

than diversifying, force for A. sundaicus diversity.

Correspondence: Dr Magdalena Zarowiecki,

Fax: +44 1223-494919; E-mail: [email protected]

© 2014 John Wiley & Sons Ltd

Molecular Ecology (2014) 23, 2573–2589 doi: 10.1111/mec.12761

Keywords: Anopheles epiroticus, biogeography, isolation with migration, mannose phosphate

isomerase (Mpi), speciation, vicariance

Received 19 January 2012; revision received 14 April 2014; accepted 16 April 2014

Introduction

The biogeographical regions of Indo-Burma and Sunda-

land in Southeast Asia are two of the largest and richest

biodiversity hotspots in the world (Myers et al. 2000).

Their high biodiversity has in part been attributed to

the complex geological history of this region involving

rapid changes in tectonics, topography, climate and

land and sea distributions that are expected to facilitate

speciation by a combination of vicariance and dispersal

(Hall 1998; Woodruff 2010). Sundaland comprises the

Malay Peninsula, the major islands of Borneo, Sumatra

and Java as well as numerous smaller islands which all

lie on the large and shallow continental Sunda Shelf

(Fig. 5). Throughout the Miocene, Borneo and mainland

Southeast Asia formed a single landmass. As they

uplifted, they became connected to Sumatra and Java

from 10–5 Ma (reviewed in Lohman et al. 2011), facili-

tating terrestrial dispersal (Heaney 1991). Mainland

Southeast Asia, Borneo, Sumatra and Java were subse-

quently separated during the Pliocene by elevated sea

levels, which reached their maximum height by 3 Ma

(Zhong et al. 2004). In the Pleistocene, these landmasses

were again connected periodically during the long gla-

cial periods when lowered sea levels exposed the Sunda

Shelf (Fig. 5; Voris 2000).

The extreme climatic changes of the Pleistocene led to

the much-debated Pleistocene pump hypothesis, origi-

nally in the context of the Neotropics (Haffer 1987).

This proposed that speciation rates should be increased

in the Pleistocene as populations diverged in allopatric

rainforest refugia during the cool, arid glacial periods

that restricted rainforest growth. Counter to this,

numerous studies have revealed that divergence

between closely related species on the different South-

east Asian landmasses dates predominantly to the Plio-

cene, for example in murine rodents (Gorog et al. 2004),

palm civets (Patou et al. 2010), Rafflesia (Bendiksby et al.

2010) and Asian barbets (den Tex & Leonard 2013). This

has been largely attributed to the substantial sea barri-

ers present during the Pliocene (see above). Although

there is some Pleistocene divergence of closely related

species, for example, in freshwater crabs (Klaus et al.

2013), most Pleistocene diversification between the land-

masses occurs at the level of subspecies or distinct

genetic lineages, for example clouded leopards (Wilting

et al. 2011) and yellow-vented bulbuls (Lohman et al.

2011).

In the case of rainforest species, Pleistocene diversifi-

cation among landmasses has been attributed to isola-

tion in glacial rainforest refugia restricted to mountain

ranges in northwest Borneo, Sumatra and peninsular

Malaysia (e.g. Gathorne-Hardy et al. 2002; Quek et al.

2007; Lim et al. 2011). This is consistent with the vegeta-

tion of the exposed Sunda Shelf during glacial periods

being dominated by savannah (Hope et al. 2004; Bird

et al. 2005). However, more recent palaeoclimatic mod-

els indicate, to the contrary, that during Pleistocene gla-

cial periods, the lowland evergreen rainforests in this

region expanded to cover much of the large landmass

of exposed Sundaland (Cannon et al. 2009). In support

of this, resumed gene flow during the Pleistocene has

been suggested for some species, such as rainforest

trees (Ohtani et al. 2013) and Tephritid fruit flies

(Schutze et al. 2012). The extent and causes of Pleisto-

cene divergence in Southeast Asia therefore remain con-

troversial. A contributing factor to this is the inability to

separate the effects of land and sea barriers during

glacial and interglacial periods (Lohman et al. 2011;

Klaus et al. 2013).

The mosquito taxon Anopheles sundaicus (Rodenwaldt

1925) s.l. has a predominantly coastal distribution and

is distributed across the mainland and islands of SE

Asia and India (Reid 1968). This makes it an excellent

system for studying the effect of Pleistocene interglacial

period sea barriers on diversification in the Indo-Burma

and Sundaic biodiversity hotspots, without the potential

confounding effects of factors relating to habitat. Dus-

four et al. (2007) have previously hypothesized that

Pleistocene climate change has driven speciation in this

taxon resulting in distinct species on mainland South-

east Asia, Borneo and Sumatra/Java. Their estimates

from mitochondrial sequences date species divergence

to the early to mid-Pleistocene, 1.8–0.78 Ma. Here, we

test this hypothesis based on the expectations of early

to mid-Pleistocene-dated divergence between landmas-

ses and lack of recent or ongoing gene flow, despite the

potential for this to occur during glacial periods. Alter-

natively, if historical high sea levels only intermittently

interrupted gene flow across these landmasses, we

would expect divergence times to be much shorter than

if isolation had been complete throughout Pleistocene,

and/or recent dispersal between landmasses dating up

to even the most recent last glacial maximum (LGM).

Anopheles sundaicus s.l. is a primary malaria vector

(Sinka et al. 2011), so studies of diversification within


2574 M. ZAROWIECKI ET AL.

this taxon are also needed to inform epidemiological

studies (Morgan et al. 2013). Although there is no mor-

phological differentiation within the taxon, it has long

been suspected to comprise a species complex due to

its wide distribution (Fig. 1), and reported differences

in host preference and larval habitat (Reid 1968). Two

formally named species were described based on

genetic evidence (fixed differences in the second inter-

nal transcribed spacer (ITS2) of ribosomal DNA and the

mitochondrial cytochrome c oxidase subunit 1 (CO1)

gene): A. sundaicus s.s. from Sarawak, northern Borneo

(Rodenwaldt 1925; Linton et al. 2001), and Anopheles epi-

roticus Linton & Harbach, from mainland Southeast

Asia (Linton et al. 2005). Two other informally desig-

nated species were subsequently proposed: A. sundaicus

species E in Indonesia (Sumatra, Java and Lesser Sunda

islands) based on mitochondrial sequences (Dusfour

et al. 2004, 2007) and A. sundaicus species F in the And-

aman and Nicobar islands of India based on chromo-

somal forms and ITS2 sequences (Nanda et al. 2004;

Alam et al. 2006).

A potentially confounding effect in testing the above

vicariance-based hypothesis is the proposed presence of

three putative sympatric species within A. sundaicus s.l.

in Sumatra. These were described based on three chro-

mosomal forms (A, B and C), with two slight differ-

ences in their polytene chromosomes (Sukowati &

Baimai 1996). A subsequent study discovered the fourth

and final combination of the two characters and

described that as an additional species, A. sundaicus F

(Nanda et al. 2004). An alternative interpretation would

be that these characters represent alternative sorting of

ancestral polymorphism, which may not necessarily be

accompanied by reproductive isolation. The case for

reproductive isolation between chromosomal forms was

strengthened by a survey of 15 allozymes, in which

chromosomal forms A and B were found to have an

almost fixed difference in mannose phosphate isomer-

ase (Mpi), when sampled in sympatry in Asahan, north-

ern Sumatra (Sukowati et al. 1999). Furthermore, form

A is reportedly found almost exclusively in brackish

water sites, whereas form B is only in freshwater sites

(Sukowati & Baimai 1996; Sukowati et al. 1999).

Divergent selection to freshwater and saltwater habi-

tats has often led to ecological speciation (Schluter &

Conte 2009; Seidel et al. 2010). Chromosomal forms A

and B could therefore have resulted from ecological

speciation due to divergent selection to brackish and

Courtsey of 2000 The Field MuseumH.K. Voris; drawn by C.R. Simpson

0 500 km

Cam-C

Mal-E

Ind-S

Mya-W

Thai-N

Vie-SE

Vie-S

Type locality An. sundaicus

Type locality An. epiroticus

Thai-S

Y

C

G K T

28

27 41

123

27

T 136 6

0

0

459518

Mal-W

Ind-C

Ind-N

Ind-W

Mya-S

(B)

(U) (F)

i

i

i indel- gapped at site 583

Fig. 1 Frequency distribution of ITS2 poly-

morphism for 393 specimens from 18

populations across the range of Anopheles

sundaicus s.l. The circle size is propor-

tional to the sample size. The circle pat-

tern denotes the base frequency at sites

459 and 518, and a fixed insertion at site

583 in a population is indicated with an

i. Solid black represents undetermined

bases. Specimens from Ind-N are sepa-

rated by larval habitat; brackish (B), fresh

(F) and unknown (U).


INCOMPLETE SPECIATION IN A. SUNDAICUS S .L . 2575

fresh water, respectively, acting on either Mpi directly

or a region of the genome close to Mpi. An alternative

hypothesis is that chromosomal forms A and B do not

correspond to species and that Mpi is under balancing

selection, as has been proposed in barnacles (Schmidt

et al. 2000; Schmidt & Rand 2001) and periwinkles

(Schmidt et al. 2007), where Mpi genotypes differ in fit-

ness between habitats with different thermal and desic-

cation stress. Dusfour et al. (2007) found no population

structure between freshwater and brackish populations.

However, this does not disprove ecological speciation

because only those regions directly involved in diver-

gent selection will differentiate during the early stages

of the speciation process (Wu & Ting 2004). Here, we

use the Mpi gene to enable us to first test the hypothesis

of ecological differentiation within Sumatra. Having

eliminated the potential confounding effects of ecologi-

cal differentiation, we go on to use Mpi, CO1 and ITS2

sequences to test the above Pleistocene vicariance-based

hypothesis of Dusfour et al. (2007) of distinct species

among different landmasses. We predict we will have

greater power than Dusfour et al. (2007) to test the

hypothesis of allopatric speciation as we use a much

larger sample size (388 vs. 88 individuals) and employ

three loci rather than two.

Materials and methods

Samples used

Adult and immature mosquitoes were collected from 16

sites in six countries across the reported range of

Anopheles sundaicus s.l. (Fig 1, Table 1), including speci-

mens from the neotype series of A. sundaicus s.s.

(Malaysian Borneo, Mal-W) (Linton et al. 2001) and the

type series of Anopheles epiroticus (Vietnam, Vie-SE)

(Linton et al. 2005). In Asahan, larval specimens from

brackish (Ind-N-B) and fresh water (Ind-N-F) were col-

lected from habitats within a 2-km radius. Water salini-

ties of ≥3 ppm (parts per million) were classed as

brackish, and those ≤0.5 ppm as fresh. Anopheles subpic-

tus (Vietnam, Ho Chi Minh, n = 2) and Anopheles litoral-

is (Philippines, Luzon, Moron Bataan, n = 13) were

used as outgroup taxa. Sample sizes, exact localities

and GenBank accession numbers are given in Table S1

(Supporting Information).

DNA extraction, amplification and sequencing

DNA was extracted using either the phenol–chloroform

procedure of Linton et al. (2001) or the QIAGEN DNeasy

Tissue Kit (QIAGEN, Crawley, England). Amplification of

CO1 (442 bp) and ITS2 (619–620 bp) was conducted fol-

lowing Linton et al. (2001). Only those samples verified as

A. sundaicus s.l. by ITS2 sequence data were used. TheMpi

gene in A. sundaicus was identified by homology with

Anopheles gambiae (Ensembl ref. AGAP000053). A sequence

of 848 bp was generated for the Mpi gene from two over-

lapping PCR fragments (see Appendix S1, Supporting

Information for primer details).

PCR products were purified using either the QIA-

quick PCR Purification Kit (QIAGEN, Crawley, UK) or

ExoSAP-IT (USB, Cleveland, Ohio, USA) prior to bidi-

rectional sequencing using the Big Dye Terminator Kit

on an ABI 3730 automated sequencer (PE Applied Bio-

Systems, Foster City, CA, USA). Sequence chromato-

grams were assembled and edited manually in

Sequencher version 4.8 (Gene Codes Corporation, Ann

Arbor, Michigan, USA). Alignments were made in CLU-

STALW (Thompson et al. 1994) and checked manually in

MacClade version 4.06 (Maddison & Maddison 1992).

Data management and assembly

Despite high heterozygosity for Mpi in adult females

(114 heterozygotes from a total of 172 individuals), no

heterozygotes were encountered in the 20 adult males.

This complete lack of heterozygotes in males and the

mapping of Mpi to the X-chromosome in A. gambiae

(Holt et al. 2002) indicate that Mpi is located on the

X-chromosome in A. sundaicus s.l.Because there are no

morphological differences or genetic markers to deter-

mine the sex of larval mosquitoes, a sex had to be

assigned for the 14 larval homozygotes we found. Their

sexes were assigned based on the levels of heterozygous

vs. homozygous females in the adult population, in

order to minimize any errors in the estimated haplotype

frequencies of the populations.

Three different algorithms were used to infer the Mpi

haplotypes of heterozygous females from genotypic

sequences (n = 114): (i) a pseudo-Bayesian approach

(ELB; Excoffier, Laval, Balding) (Excoffier et al. 2003), as

implemented in Arlequin version 3.11 (Excoffier et al.

2005), (ii) the expectation–maximization (EM) method

fastPHASE (Scheet & Stephens 2006) and (iii) the simple

inference method in Beagle version 3.0 (Browning &

Browning 2009). The ELB solution was derived follow-

ing three independent runs generating 2000 sets of

inferred haplotypes, sampled with an interval of 5000,

heterozygous site influence zone 5, and Dirichlet prior,

gamma and epsilon value of 0.1. One million samples

were discarded as burn-in. Although the phase infer-

ence methods assume Hardy–Weinberg equilibrium

(HWE), deviation from HWE does not appear to greatly

affect the performance of these methods (Stephens &

Scheet 2005; Scheet & Stephens 2006).

In fastPHASE and Beagle, homozygotes were used to

guide the solutions, while the Arlequin ELB algorithm



Table

1Nucleo

tidediversity,Tajim

a’sD

andFu’s

Fsfor

SAMOVAgroupings.

Alltestsarehap

lotypeindep

enden

texceptforFu’s

Fs,

butas

theFu’s

Fsresu

ltsaresimilar

forall

Mpi

hap

lotypesolutions,

only

resu

ltsforthefastPHASEMpi

solutionaresh

own

SAMOVAgrouping

Populations

ITS2†

CO1

Mpi

Code

Salinity

No

(hap

lotypes)†

No

u�

SD

T’s

DFu’s

Fs

No

p�

SD

T’s

DFu’s

Fs

Ind-N

Ind-N

–73

44(YK),21

(CK),4(C

G),

2(YG),1(C

T),1(TK)

720.007�

0.001

�1.7*

�13.6***

––

––

Ind-N

(B)

Brackish

2220

(YK),1(C

K),1(YG)

220.006�

0.001

�1.3

�3.3***

410.007�

0.001

�0.9

�18.8***

Ind-N

(F)

Fresh

1716

(YK),1(TK)

140.006�

0.001

�0.2

�0.4**

170.008�

0.001

�0.1

�4.8**

Ind-C

Ind-C

Fresh

5833

(YK),25

(YG)

590.003�

0.000

�0.7

4.6

430.01

�0.000

2.2**

2.9

Ind-W

Ind-W

Brackish

2323

(CG)

210.007�

0.002

�0.8

�2.7***

320.006�

0.001

�0.7

�6.0**

Ind-S

Ind-S

Brackish

1919

(CK)

190.007�

0.001

�0.5

0.2**

290.005�

0.001

�1.3

�2.7**

Malay

sia

Mal-W

Brackish

1211

(CT-),1(u.d.)

80.005�

0.001

�0.3

�0.3**

150.004�

0.001

�0.4

0.4

Mal-E

Fresh

1515

(CT-)

130.002�

0.001

�1.1

0.5*

140.005�

0.001

�0.3

�0.2

Myan

mar

Mya-S

Brackish

2828

(TG)

280.008�

0.003

�0.6

3.8

440.003�

0.001

�0.1

2.8

Mya-W

Brackish

2525

(TG)

240.003�

0.001

�1.8*

�3.1***

340.002�

0.000

�1.3

�2.0**

Thailandan

dCam

-WBrackish

1514

(TG),1(TK)

150.012�

0.001

00.8*

160.004�

0.001

�0.3

�0.2

Cam

bodia

Thai-S

Brackish

3635

(TG),1(YK)

330.016�

0.001

�0.1

�3.0***

200.005�

0.000

1.1

�2.5**

Thai-E

Brackish

117(TG),4(u.d.)

110.012�

0.001

�0.3

�2.2**

120.004�

0.001

0.6

0.2

Vietnam

Vie-S

Brackish

2412

(TG),9(YK),3(TK)

210.002�

0.000

�0.9

�1.4**

250.002�

0.001

�1.4

�1.7**

Vie-SE

Brackish

1515

(TG)

140.002�

0.001

�1.6*

�3.5***

220.003�

0.001

�10.2

AllAnophelessundaicussp

ecim

ens

393

374

0.017�

0.000

�1.0

364

0.00

�0.000

�1.1

*0.10>P>0.05,**P<0.05,***P

<0.001.

†ITS2variable

sites(459

&518)

inbrackets,

–indicates

adeletionat

base583,

u.d.indicates

specim

ensforwhichITS2could

beunam

biguouslyiden

tified

asA.sundaicuss.l.,but

theexactITS2variationat

bases

459an

d518could

notbedetermined

.



did not allow for their incorporation. fastPHASE was

run with 30 random starts, 100 iterations and 200 sam-

ples from the posterior distribution. For each Beagle

run, four samples were taken per individual and 10

iterations performed, following the author’s recommen-

dations. Each calculation was repeated three times in

both fastPHASE and Beagle. Consistently resolved indi-

viduals in all three iterations were included in a new

input file as known haplotypes, and the calculations

were repeated a further three times. Solutions for any

remaining ambiguous individuals were deduced from

the consensus solution of the three estimations. To

assess the level of agreement both within and between

inference methods, resultant haplotypes were counted

and compared using individual resolving discrepancy

(IRD) statistics (Huang et al. 2008). All subsequent

analyses were performed using the optimal solutions

from each of the haplotype inference methods, unless

otherwise stated.

Models of sequence evolution were tested in jModel-

test version 0.1.1 using the Akaike information criterion

(Posada 2008). The resulting optimal substitution model

for the CO1 and both Mpi data sets was TIM + I + Γ.For the CO1 data, invariable sites (I) = 0.762 and

gamma (Γ) distribution shape parameter a = 0.936. For

alternative Mpi haplotype inferences, the corresponding

values were similar: ELB (I) = 0.834, a = 0.544; Beagle

(I) = 0.851, a = 0.647; fastPHASE (I) = 0.853, a = 0.655.

The ti/tv ratio was 4.4 for CO1 and 2.29 (ELB), 2.51

(Beagle) and 2.59 (fastPHASE) for Mpi.

Testing for divergent adaptation/balancing selection tofreshwater and brackish water habitats

To determine to what extent Mpi, or its genomic region,

is involved in divergent adaptation or balancing selec-

tion to freshwater and brackish water habitats, we con-

ducted an analysis of molecular variance (AMOVA) in

Arlequin version 3.11, with individuals grouped accord-

ing to their origin from brackish or freshwater larval

sites. Significance was tested using 10 000 permutations.

This analysis was only performed for Asahan (Ind-N),

as this is the only site containing sympatric brackish

and freshwater habitats. For this analysis, we increased

the length of the Mpi gene to comprise a contiguous

sequence of 1202 bp, almost the entire gene (excluding

only primer binding sites; 42 bp at the 50 end and 36 bp

at the 30 end). (See Appendix S1, Supporting Informa-

tion for primer details.) We refer to this as the extended

Mpi data set. The length of Mpi was increased for this

analysis as long-term balancing selection can generate

short-length linkage disequilibrium, which might result

in loss of the signal in the shorter fragment. Divergent

ecological adaptation (or balancing selection) was also

assessed by testing for heterozygote deficiency in Mpi

sequences from the female specimens from Asahan, the

site containing sympatric brackish and freshwater habi-

tats.

Population structure, polymorphism and haplotypegenealogy

As four of the small samples (n < 10) from southern

Indonesia (Java Island and Legundi Island, southern-

most Sumatra) were genetically very similar at all three

markers, they were merged into one population (Ind-S)

(detailed in Table S1, Supporting Information). Pairwise

differentiation between all populations was assessed

using analysis of molecular variance (AMOVA) and signif-

icance tested using 1000 permutations in Arlequin ver-

sion 3.11. Significance levels were corrected for multiple

testing using sequential Bonferroni correction (Holm

1979). Spatial analysis of molecular variance (SAMOVA)

(Dupanloup et al. 2002) was used to identify genetically

distinct groups of populations in which populations

were minimally differentiated within groups and

groups maximally genetically differentiated from each

other. Population divergence was visualized using a

principle components analysis (PCA) scatterplot gener-

ated using the R-package adegenet version 1.3.8 (Jom-

bart 2008). Nucleotide diversity (p) and Tajima’s D

were calculated in DnaSP version 4.90 (Rozas et al.

2003). Arlequin version 3.11 was used to estimate Fu’s

Fs (10 000 permutations) and to construct the mini-

mum-spanning haplotype networks (Excoffier et al.

1992).

Estimating divergence, gene flow and populationgrowth

To estimate the time of divergence and assess gene flow

between the three allopatric species previously pro-

posed within our study region (Dusfour et al. 2007), an

isolation-with-migration (IM) model was applied using

IMa (Hey & Nielsen 2004). Upper bounds for the prior

distribution were estimated from initial runs, and the

final Markov Chain Monte Carlo (MCMC) length was

approximately 5 000 000 million generations, sampling

every 200th generation and discarding a burn-in of

1 000 000 steps. IMa was only run with the Mpi fast-

PHASE solution (see Results). Analysis of the three

populations simultaneously was attempted using multi-

population IM, but the computations did not converge,

as the number of loci was too few to estimate this much

larger set of parameters. Confidence intervals were cal-

culated as the lower and upper bounds of the estimated

90% highest posterior density (HPD) intervals. In

order to get approximations of the divergence rates to



compare to those previously reported (Dusfour et al.

2007), we used the same mutation rate for CO1, based

on the estimated rate of arthropod mitochondrial diver-

gence of 2.3% per Myr (Brower 1994). LAMARC 2.0

(Kuhner 2006) was also used to estimate gene flow and

effective population size, generating 100 000 samples,

sampling every 100 generations, from four heated

chains.

Results

Summary of ITS2, CO1 and Mpi data sets

ITS2 (619–620 bp) was sequenced from 393 Anopheles

sundaicus s.l. specimens (Fig. 1, Table S1, Supporting

Information), including all specimens sequenced for

Mpi and CO1. Three sites in ITS2 were variable: 459,

518 and 583 (Table 1, Fig. 1). The Borneo populations

(Mal-W, Mal-E) were most distinctive in having fixed

differences at sites 459, 518 and a deletion at site 583

relative to all other populations (Fig. 1). The mainland

populations are distinct from Borneo in having exclu-

sively or predominantly T and G at sites 459 and 518,

whereas the Ind-W population is fixed for C (459) and

G (518). However, we observed high levels of intraindi-

vidual variation at these bases in all Indonesian popula-

tions (except Ind-W) and in Vie-S. So while our results

are consistent with the ITS2 sequences reported previ-

ously for each species described from the mainland,

Borneo and Sumatra (Linton et al. 2001, 2005; Alam

et al. 2006; Dusfour et al. 2007; Surendran et al. 2010),

they also show that the distinction between them is less

marked than previously noted.

Haplotypes were inferred statistically for the main Mpi

data set (sequences of 848 bp) for use in population and

phylogeographical analysis. Haplotype inference was

most consistent between runs using the ELB method,

with an IRD of 0.04. The IRD values for Beagle and fast-

PHASE were 0.39 and 0.32, respectively, which improved

after the second round of solutions to 0.13 and 0.05. The

levels of discrepancy between optimal solutions from dif-

ferent methods are only slightly greater than those

between solutions of the same method; IRD = 0.13

between ELB and fastPHASE and IRD = 0.15 between

Beagle and ELB and between Beagle and fastPHASE.

This consistency between the haplotype inference meth-

ods was reflected in the similar numbers of unique haplo-

types inferred (147 unique haplotypes from ELB, 149

from fastPHASE and 146 from Beagle) with all the most

frequent haplotypes being recovered in similar numbers

in all three solutions (results not shown). The most

common phase-solving uncertainties were due either to

spurious placement of unique singleton mutations or to

the genuine difficulty of Beagle and fastPHASE to deal

consistently with individuals carrying two highly diver-

gent alleles (>10 polymorphic sites). All analyses (except

IMa) were conducted using the optimal solution for each

of the phase-solving methods, and no significant differ-

ences were observed. Consequently, we typically only

show the results obtained from the fastPHASE data set as

this had the lowest IRD value.

The main Mpi data set comprised 365 sequences

inferred from 192 sequenced specimens of A. sundaicus

(20 males and 172 females). The 848-bp Mpi fragment

had an average of 4.3 polymorphic sites within hetero-

zygote individuals (max = 16, min = 1). For a subset of

samples from northern Sumatra, an extended Mpi frag-

ment data set (see Materials and Methods) was gener-

ated; for Ind-N (F) n = 23; Ind-N (B) n = 34; and Ind-C

(F) n = 31 (Table S1, Supporting Information). The

extended Mpi fragment has 54 unique haplotypes with

4.4% variable sites. The CO1 sequences (442 bp) from

374 specimens of A. sundaicus s.l. display 116 haplo-

types and 14% variable sites. An additional 10 haplo-

types were found in 14 outgroup sequences (Table S1,

Supporting Information).

Testing for differentiation between larval habitats

We tested the hypothesis that Mpi is either directly

involved in divergent selection or linked to such a

region, by testing whether Mpi sequences display

greater genetic differentiation among habitats from the

same site than between geographically distant sites of

the same habitat. Neutral unlinked markers, that is,

CO1, may show the same pattern if there is some repro-

ductive isolation. Contrary to these expectations, AMOVA

using freshwater and brackish water samples (Ind-F

and Ind-B) from the same village (Asahan, northern

Sumatra) as groups revealed that genetic variation did

not segregate according to larval habitat; between-group

genetic variance is �0.06% (P = 0.18) for the extended

Mpi data set and 2.59% for CO1, which was also non-

significant (P = 0.31). However, the comparison of the

total Ind-N sample (i.e. individuals from both fresh and

brackish water) to the freshwater population, Ind-C (F)

~200 km away, revealed that a significant proportion of

variation was explained by geographical differentiation

(10.21%, P < 0.001 for Mpi; 37.74%, P = 0.02 for CO1).

Further, there was no evidence for heterozygote defi-

ciency in Mpi in the combined freshwater and brackish

water samples from Asahan (Ind-N) (Fig. S1, Support-

ing Information). This directly contradicts a previous

report of lack of HWE for Mpi allozyme genotypes from

this same location (Sukowati & Baimai 1996).

The lack of genetic differentiation at Mpi between

habitats does not support the involvement of this geno-

mic region in ecological differentiation, either related to



speciation or to balancing selection within species.

Given this lack of evidence for selection on Mpi, we

therefore used the main Mpi data set to test the vicari-

ance-based hypothesis of A. sundaicus s.l. differentiation

throughout its known distribution.

Genetic diversity of CO1 and Mpi

There was a tendency for the peripheral populations to

have lower nucleotide diversity (p) (i.e. Vie-S, Vie-SE

and Mya-W for both CO1 and Mpi; Mya-S for Mpi

alone; and Mal-E for CO1 alone), whereas the popula-

tions that are more central in the current species distri-

bution (Cam-W, Thai-S and Thai-E) have higher genetic

diversity for CO1 (although not for Mpi) (Table 1). For

the majority of populations for both markers, Tajima’s

D and Fu’s Fs are negative, although only the values

for Fu’s Fs are significantly different from 0. Because

both CO1 and Mpi show this pattern, it suggests a sig-

nal of population expansion (rather than background

selection or a selective sweep of mtDNA as can com-

monly occur) (Bazin et al. 2006). The signal of expansion

in Fu’s Fs is strongest in the Ind-N population. Ind-C is

unusual in having positive values for Fu’s Fs for both

markers and a significant and positive value of Tajima’s

D for Mpi, which may indicate genetic admixture in this

population (Table 1).

Population structure

If in our data set there are three allopatric species

each confined to different landmasses as hypothesized,

the SAMOVA is expected to detect three maximally

differentiated groups of populations (i.e. k = 3), which

correspond to the species’ distributions. This expectation

is not met by either of the markers. Although there is no

sharp transition at any value of k, the maximal level of

differentiation between population groups and the mini-

mum level of differentiation among populations within

groups are attained by k = 6 for CO1 and k = 5 for Mpi

(Fig. S2, Supporting Information). For the CO1 data,

although the mainland and insular populations are dis-

tinguished at k = 2, at k = 3 the mainland is subdivided

into east and west mainland populations (east: Cam-W,

Vie-S, Vie-SE, Thai-E, Thai-S and west: Mya-S, Mya-W)

although these are all presumptively Anopheles epiroticus

(Fig. 2). The predicted split of the Borneo populations,

that is, A. sundaicus s.s., comes only at k = 4. For Mpi,

the SAMOVA identifies single populations as being the

most genetically distinct: Ind-S at k = 2; Mal-W at k = 3;

and Cam-C at k = 4 (Fig. 2, complete results in Fig. S2,

Supporting Information). PCA scatterplots further illus-

trate the lack of clear structuring according to landmass

(Fig. S3, Supporting Information).

Despite the differences, there is some concordance

between the two markers in how populations are

grouped by SAMOVA (Fig. 2). These findings were there-

fore used to identify groups comprising minimally dif-

ferentiated populations for downstream analyses. We

grouped the two Myanmar populations, Mya-W and

Mya-S (grouped at k = 2 for CO1 and k = 5 for Mpi);

the two Bornean populations, Mal-W and Mal-E (k = 4

and k = 5); the two Vietnam populations, Vie-S and

Vie-SE (k = 5 and k = 8); and Thai and Cambodian pop-

ulations, Thai-E, Thai-S and Cam-W (k = 5 and k = 8).

These groupings are supported by the lack of significant

pairwise FST values for all within-SAMOVA-group com-

parisons (Appendix S2). (The sole exception to this is

CO1

k = 4

Mpi

k = 5 k = 6 k = 7 k = 8k = 2 k = 3

Fig. 2 Optimal SAMOVA groups for 2–8 groups of populations for CO1 and Mpi. The SAMOVA grouping structures were identical for the

three haplotype solutions of Mpi so only those for the ELB solution are shown (Fig. 2).



the high FST value for the Borneo populations for Mpi

but we retained Borneo as a single group due to their

geographical proximity and lack of differentiation at

CO1.) The remaining populations, Ind-N, Ind-C, Ind-W

and Ind-S, were retained separately as they were not

grouped by the SAMOVA. Among these eight groups,

pairwise differentiation is generally very high with

good agreement between the CO1 and Mpi markers

(Table 2), further validating the choice of these eight

groupings for downstream analyses.

CO1 and Mpi networks

The minimum-spanning networks for the CO1 and the

Mpi genes are shown in Fig. 3. The multiple star-like

clusters in both the CO1 and the Mpi networks concur

with the neutrality tests in indicating demographic

expansion. In the Mpi network, the haplotypes from a

given SAMOVA group tend to occur at two or more places

around the network which, given the contrast with the

CO1 haplotype network, are most likely due to incom-

plete lineage sorting at the nuclear gene, as expected

for its larger effective population size. The relationships

between population groupings in the CO1 and Mpi net-

works are also similar. For both genes, the outgroup

haplotypes connect to the network via northern Suma-

tra (Ind-N) with the other Indonesian groupings, Ind-C,

Ind-W and Ind-S, all being derived from Ind-N. The

Borneo SAMOVA group is also derived from Ind-N for the

CO1 gene, although the Mpi haplotypes are distributed

across the network. In both cases, the Vietnam haplo-

types are more central in the network, whereas the

Myanmar, and Thailand & Cambodia haplotypes are

more external (Fig. 3), indicating that these latter popu-

lations may be the result of colonization from Vietnam.

Divergence and gene flow

If vicariance has generated three allopatric species in

this study region during the Pleistocene, as proposed

(Dusfour et al. 2007), we expect to observe Pleistocene-

dated divergence among landmasses. If merging coast-

lines during glacial periods facilitate dispersal among

landmasses, we expect to detect gene flow during the

Pleistocene. Divergence and gene flow were explored

using IMa and LAMARC analyses of the combined

CO1 and Mpi data sets. IMa was applied to the three

proposed species as a set of pairwise comparisons as

the application of multipopulation IMa to three popula-

tions failed to converge. As LAMARC is better suited to

the analysis of gene flow among multiple populations,

we were able to compare gene flow within and among

landmasses by applying LAMARC to the SAMOVA

groups.

Analysis using LAMARC showed that only a few

pairs of population groups had a minimum number of

migrants/generation significantly higher than zero

(Table 3). Most instances of gene flow were bidirec-

tional and between the geographically closest popula-

tions (Table 3), but LAMARC also detected migration

across the proposed allopatric species boundaries,

between Indonesia (Ind-N) (corresponding to A. sundai-

cus E) and the mainland populations of Thailand and

Cambodia and Vietnam (corresponding to A. epiroticus)

(Table 3, Fig. 5). IMa supports LAMARC’s finding of

bidirectional gene flow between the mainland and Indo-

nesia (Sumatra/Java) and dates this to the late Pleisto-

cene (Fig. 4e,h). IMa also detects unidirectional

migration from Borneo to the mainland (Fig. 4f), dating

to the late Pleistocene (but with wide confidence inter-

vals, Fig. 4h). Vicariance between Indonesia and Borneo

is estimated to be more recent, 0.26 Ma (H.P.D. 90%

0.19–0.37) (Fig. 4g), with no direct migration inferred

between Indonesia and Borneo (Fig. 4d). Splits between

putative species are estimated by IMa to occur during

the mid-Pleistocene (Fig. 4g); divergence times are 0.79

Ma (H.P.D. 90% 0.45–1.42) between the mainland

(Myanmar, Thailand and Cambodia, Vietnam) and Bor-

neo and 0.66 Ma (H.P.D. 90% 0.46–0.86) between the

mainland and Indonesia (Ind-C, Ind-N, Ind-W, Ind-S).

Table 2 Pairwise differentiation (FST) for SAMOVA grouping structure. CO1 below and Mpi above the diagonal

SAMOVA grouping Ind-N Ind-C Ind-W Ind-S Borneo Myanmar Thai & Cam Vietnam

Ind-N — 0.20 0.09 0.61 0.35 0.51 0.24 0.16

Ind-C 0.29 — 0.26 0.36 0.30 0.49 0.35 0.37

Ind-W 0.55 0.76 — 0.71 0.36 0.56 0.29 0.23

Ind-S 0.13 0.16 0.61 — 0.74 0.86 0.78 0.82

Borneo 0.58 0.79 0.66 0.64 — 0.46 0.38 0.51

Myanmar 0.71 0.81 0.78 0.74 0.78 — 0.53 0.64

Thailand and Cambodia 0.55 0.65 0.57 0.53 0.52 0.53 — 0.16

Vietnam 0.68 0.87 0.81 0.80 0.80 0.73 0.39 —

Mpi values shown are calculated from the ELB phase solution.

No value is nonsignificant after Holm’s Bonferroni correction, CO1 P (max) = 0.0046.



56483430242219121110

987654321

Ind-NInd-CInd-SInd-W

BorneoVietnamThailand & CambodiaMyanmar

6.0

An. subpictus

SAMOVA-groups Haplotype frequency

Connection length

Fig. 3 Inferred minimum-spanning network of CO1 (above, N = 388) and Mpi (below, N = 384). Each circle represents a unique hap-

lotype, and the size of the circle is proportional to the frequency of the haplotype. The colour of the haplotype corresponds to the

SAMOVA group.



Effective population sizes and population growth

From the combined CO1 andMpi data sets, IMa estimates

that Indonesia has the highest effective population size

(Fig. 4a,b), with the mainland being intermediate in size

(Fig. 4b,c) and Borneo the lowest (Fig. 4a,c). Examining

this in further detail, LAMARC identified the popula-

tions in the centre of the current distribution, Thailand

and Cambodia and Ind-N, as having the largest current

effective population sizes (Fig. 4). Ind-N is also the only

population identified by the LAMARC analysis of the

Mpi data as having significant population growth.

Discussion

Ecological barriers to gene flow

The strong association between larval habitat, chromo-

somal form and Mpi allele type – together with a defi-

ciency of chromosomal and Mpi heterozygotes – was

previously taken to indicate the presence of reproduc-

tive isolation within A. sundaicus in northern Sumatra

(Sukowati & Baimai 1996; Sukowati et al. 1999). In

contrast to this, we found no genetic differentiation

between the specimens from freshwater or brackish

water habitats in northern Sumatra (Ind-N) at any of the

three markers used here, including Mpi. There is also no

support for balancing selection acting on Mpi within

species, as not a single polymorphic site in the extended

Mpi sequences differed significantly in frequency

between habitats (Fig. S1).

Our inability to reproduce the earlier Mpi findings is

surprising given that other studies comparing DNA

sequences and allozymes have given congruent results,

for example (Nachman et al. 2003; Malausa et al. 2007).

An explanation that would reconcile our findings with

those from earlier studies (Sukowati & Baimai 1996;

Sukowati et al. 1999) is environmentally dependent

alternative splicing of Mpi, or post-translational modifi-

cation of the gene product. A hypothesis of divergent

ecological adaptation could still be valid, but selection

would have to be acting at an unlinked locus that alters

Mpi expression rather than on Mpi itself. Alternatively,

Mpi expression could be controlled by environmental

cues, that is, differences in phenotype without a corre-

sponding difference in genotype (Whitman & Agrawal

2009). Indeed, a plastic response to salinity in a change-

able environment could be one ecological factor that

inhibits ecological speciation (Nosil et al. 2009).

Table 3 Inferred migrants/generation. Values for CO1 below the diagonal, Mpi above the diagonal, for all haplotype solutions; ELB

(top), Beagle (middle), fastPHASE (bottom). Populations from which the minimum inferred migration is 1 or more migrant/genera-

tion are marked in boldface

Migrated into

Population of origin

Ind-N Ind-C Ind-W Ind-S Borneo Myanmar Thai & Camb Vietnam

Ind-N — 11 (4–51) 10 (3–38) 10 (2–44) 3 (0–19) 1 (0–11) 5 (1–23) 3 (1–22)

— 16 (4–58) 11 (3–52) 14 (5–70) 4 (1–25) 2 (0–14) 4 (1–26) 4 (1–25)— 20 (6–73) 24 (6–70) 7 (2–50) 3 (0–22) 1 (0–13) 5 (1–23) 3 (0–24)

Ind-C 5 (1–29) — 1 (0–13) 7 (2–38) 1 (0–8) 0 (0–6) 1 (0–10) 1 (0–7)5 (1–29) — 2 (0–13) 5 (1–33) 1 (0–11) 1 (0–7) 1 (0–10) 1 (0–9)

7 (2–33) — 1 (0–10) 5 (1–23) 1 (0–7) 0 (0–5) 1 (0–9) 0 (0–7)Ind-W 5 (1–50) 2 (0–31) — 2 (0–25) 2 (0–31) 1 (0–18) 2 (0–27) 2 (0–25)

7 (2–57) 3 (0–26) — 3 (0–28) 3 (0–28) 2 (0–19) 2 (0–22) 2 (0–21)17 (4–103) 1 (0–33) — 3 (0–41) 2 (0–31) 1 (0–20) 3 (0–32) 2 (0–27)

Ind-S 4 (1–47) 12 (3–75) 2 (0–27) — 1 (0–18) 1 (0–14) 1 (0–18) 1 (0–17)9 (2–59) 10 (2–56) 2 (0–28) — 1 (0–19) 1 (0–13) 2 (0–19) 1 (0–13)

4 (0–48) 12 (3–77) 4 (0–31) — 1 (0–19) 1 (0–16) 1 (0–19) 1 (0–19)Borneo 1 (0–25) 1 (0–20) 2 (0–30) 1 (0–20) — 1 (0–18) 3 (0–35) 1 (0–22)

1 (0–18) 1 (0–16) 1 (0–18) 1 (0–15) — 0 (0–12) 1 (0–24) 1 (0–18)2 (0–19) 1 (0–15) 1 (0–19) 1 (0–14) — 1 (0–13) 2 (0–23) 1 (0–15)

Myanmar 1 (0–15) 1 (0–13) 1 (0–17) 1 (0–13) 1 (0–15) — 5 (1–38) 1 (0–17)1 (0–26) 3 (0–25) 3 (0–26) 1 (0–25) 2 (0–28) — 15 (1–80) 4 (0–31)

1 (0–19) 1 (0–20) 1 (0–19) 1 (0–17) 1 (0–22) — 7 (1–58) 1 (0–21)Thailand and Cambodia 3 (0–44) 4 (0–34) 4 (0–35) 4 (0–28) 5 (0–47) 10 (1–66) — 173 (14–533)

3 (0–36) 5 (0–37) 4 (0–34) 3 (0–34) 3 (0–48) 12 (2–74) — 102 (1–518)4 (0–27) 3 (0–30) 3 (0–35) 3 (0–24) 5 (0–38) 12 (1–54) — 128 (3–451)

Vietnam 3 (0–64) 2 (0–42) 2 (0–48) 1 (0–40) 1 (0–45) 2 (0–41) 63 (2–531) —1 (0–46) 0 (0–33) 0 (0–34) 0 (0–25) 0 (0–38) 0 (0–33) 2 (0–104) —

1 (0–56) 0 (0–38) 0 (0–39) 0 (0–34) 0 (0–39) 0 (0–33) 16 (1–475) —



0

0.04

0.08

0.12

0.16

0.20

Borneo

Indonesia

Ancestral population

Effective population size (N) x 10–6

0

0.02

0.04

0.06

0.08 Mainland

Indonesia


0

0.04

0.08

0.12

0.16 Borneo

Mainland


0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6 Borneo – Indonesia

Indonesia – Mainland

Mainland – Borneo

Time of divergence (Ma)

0

5

10

15

20

25

30

35

Indonesia to Mainland

Mainland to Indonesia

02468

1012141618

Population migration rate (Nm) migrants/generation

Borneo to Mainland

Mainland to Borneo

0

10

20

30

40

50

60

70

80

Indonesia to Borneo

Borneo to Indonesia

0 2.0 4.0 6.0 8.0 10.0 0 1.0 2.0 3.0 4.0 5.0

0 2.0 4.0 6.0 8.0 10.0 0 1.0 2.0 3.0 4.0 5.0

0 2.0

0.4 0.8 1.0 1.4 1.6 1000 10.000 100.000 1.000.000

4.0 6.0 8.0 10.0 0 1.0 2.0 3.0 4.0 5.0

Time since migration (year log)

0.10

0.08

0.06

0.04

0.02

0

CO1 Indonesia to MainlandCO1 Mainland to IndonesiaMpi Indonesia to MainlandMpi Mainland to IndonesiaCO1 Borneo to MainlandMpi Borneo to Mainland

MigrationPopulation size

Time since migrationDivergence time

Pro

babi

lity

Pro

babi

lity

Pro

babi

lity

Pro

babi

lity

00

(a)

(b)

(c)

(d)

(e)

(f)

(g) (h)

Fig. 4 The marginal posterior probability distributions for the IM model parameters (scaled by the neutral mutation rate). Curves

show estimated effective population and migration based on pairwise comparisons of populations from the three putative species:

Borneo and Indonesia (a, d), Indonesia and mainland (b, e) and Borneo and mainland (c, f). Divergence time is estimated for all three

comparisons (g) along with the time since migration (h), for all significant migration events. Some curves are flat because the data

sets do not have enough information to produce a reliable estimation.



Timing of Allopatric Diversification

While the divergence rate of 2.3% per Myr we applied

was estimated for the CO1 gene and for arthropod taxa

(Brower 1994), the accuracy of this rate in this specific

taxon, and of molecular clocks generally, is not known.

The divergence times estimated here should therefore be

interpreted with caution. However, because the diver-

gence rate used here is the same as that employed by

Dusfour et al. (2007), our estimates are directly compara-

ble to theirs. Dusfour et al. (2007) estimated a divergence

time for the three proposed allopatric species of 1.8–

0.78 Ma, which overlaps with our estimate for diver-

gence between the mainland and Borneo of 0.45–1.42 Ma.

By contrast, our estimates of divergence between the

mainland and Indonesia (0.46–0.86 Ma) and between

Indonesia and Borneo (0.19–0.37 Ma) are substantially

more recent. Even allowing for some error in the diver-

gence rate we have used, it seems highly likely that the

divergences and dispersals we discuss below date to the

Pleistocene and are therefore related to changes in land-

mass and sea configuration during this period.

Centre of origin and dispersal history

Reconstructing the population history of A. sundaicus

s.l. could help to determine the constraints to its dis-

persal. We found that the north Indonesian populations

are notable in having high molecular genetic diversity

at all three markers, Mpi, CO1 and ITS2, corresponding

to the high effective population sizes estimated by IMa,

as well as a greater diversity of chromosomal forms

(Sukowati & Baimai 1996). This, coupled with the posi-

tion of these populations at the core of both the CO1

and Mpi haplotype networks linked directly to the out-

group haplotypes (Fig. 3), indicates that the populations

in northern Sumatra retain more ancestral variation

than all other investigated extant populations. Popula-

tions on Borneo, Java and the mainland are therefore

likely to have been populated by dispersal from ances-

tral populations on Sumatra (Fig. 4).

As a predominantly littoral taxon, A. sundaicus s.l.

has most likely spread along the changing coastlines as

sea levels fluctuated during the Pleistocene. The dis-

persal of A. sundaicus s.l. from Sumatra to the mainland

could thus have occurred via a western coastal route; to

western Thailand and then Myanmar, or by an eastern

coastal route; in which the Sumatran coastline would

have expanded across the Gulf of Thailand ultimately

reaching Vietnam (Voris 2000). Initial spread from

Sumatra via this eastern coastal route to Vietnam is

indicated by the connection of Vietnamese haplotypes

to the northern Sumatran (Ind-N and Ind-C) haplotypes

at the core of both the CO1 and the Mpi networks

(Fig. 3) and by the shared ITS2 diversity (Fig. 1). In

both haplotype networks, the Thai and Myanmar haplo-

types are largely derived from the Vietnamese haplo-

types. This, together with the high migration rates from

Vietnam to Thailand and Cambodia inferred by LAM-

ARC (Table 3), indicates that the mainland was colo-

nized predominantly from Vietnam. As the Thai-S

population forms part of the Thailand and Cambodia

grouping yet is on the west, rather than the east coast,

there must have been some gene flow across the Thai

Peninsula. Such gene flow may be associated with inter-

glacial high stands when elevated sea levels would

have inundated much of the Isthmus of Kra, the low-

lying land spanning the peninsula ~200 km north of

population Thai-S (Woodruff & Turner 2009). Dispersal

from the mainland westwards into Myanmar is also

likely to have occurred via this route.

Gene flow across land and sea

Anopheles sundaicus s.l. has been inferred to be a species

complex comprising at least three allopatric species

resulting from vicariance due to a combination of cycli-

cal island and refugium creation due to Pleistocene sea

level changes (Dusfour et al. 2007). Our results agree

that there is some differentiation between the three allo-

patric lineages described by Dusfour et al. (2007), but

our more extensive sampling and use of additional

markers identified eight significantly divergent groups

of A. sundaicus s.l., no less differentiated than the three

purported species (Fig. 2, Table 2) and evidence of

migration between them (Fig. 5, Table 3). Overall, the

LAMARC analysis revealed greater gene flow within

landmasses than between them, indicating that for

A. sundaicus the sea is normally a substantial barrier to

dispersal (Table 3). Despite this, the LAMARC and IMa

analyses indicate significant bidirectional gene flow

between Sumatra and the mainland, between the geo-

graphically most proximal population groups of Thai-

Cambodia and Ind-N. This could reflect the periodic

merging of these coastlines during Pleistocene glacia-

tions and/or be the result of ongoing dispersal because

the Malacca strait dividing these landmasses is cur-

rently only 2.7 km across its narrowest point.

Relative to the divergence of the mainland popula-

tions, the divergence of Borneo from the ancestral Indo-

nesian populations is more recent. However, both the

LAMARC and IMa analyses indicate no subsequent gene

flow between Borneo and Indonesia. IMa analysis indi-

cates gene flow from Borneo to the mainland (Fig. 4f),

but this appears inconsistent with the haplotype net-

works that show several instances of Bornean haplotypes

being external to, and therefore derived from, mainland

haplotypes. The result may instead be due to the absence



of the Sumatran/Javan sequences in the IMa Borneo–

mainland pairwise comparison. Overall, however, the

Bornean populations are the least connected to the others

by gene flow and they have smaller estimated popula-

tion sizes. These factors are expected to contribute to the

fixation of unique mutations in ITS2. Overall therefore,

Borneo is currently the most isolated and genetically dis-

tinct grouping of A. sundaicus s.l., even though it is the

most recently formed. This is consistent with the high

endemism of many mammalian taxa on Borneo that

dates to the Pleistocene (Meijaard & van der Zon 2003;

Earl of Cranbrook 2010).

The western mainland populations (Myanmar) on the

Andaman coast were identified by the SAMOVA and the

pairwise FST comparisons to be genetically very distinct

from the eastern mainland populations (predominantly

bordering the Gulf of Thailand) (Fig. 2). Several marine

and littoral taxa [e.g. mangrove sea snakes (Alfaro et al.

2004), intertidal Echinolittorina snails (Reid et al. 2006)

and seahorses (Lourie et al. 2005)] also show genetic dis-

continuities between the Andaman Sea and the Gulf of

Thailand. Previous studies of A. sundaicus biogeography

have not allowed for east–west comparison (Dusfour

et al. 2004, 2007), but we can now conclude that there is

predominantly east–west differentiation in A. sundaicus

s.l. across mainland Southeast Asia, most likely as a

result of an interglacial landmass barrier to gene flow.

Conclusion

The formation of both land and sea barriers during

Pleistocene interglacials, as well as intrinsic limits to

species dispersal, has allowed some allopatric diversifi-

cation in this littoral taxon. This has previously been

interpreted as resulting in allopatric speciation generat-

ing at least three species. Our results cannot confirm that

conclusion, as we detect eight genetic and geographical

groupings within this study region and also evidence

for recent gene flow between them in all three genetic

markers, when Pleistocene sea level changes have so

allowed. Gene flow is generally considered to impede

speciation (Smadja & Butlin 2011), but speciation under

a vicariant scenario can proceed when there are small

amounts of gene flow, for example (Runemark et al.

2012). When there is gene flow, allopatric speciation is

expected to take longer to occur (Yamaguchi & Iwasa

0

Myanmar

Vietnam

Borneo

Thailand & Cambodia

Ind-S

Ind-W

Ind-N

Ind-C

Sea-level 75 m below present

Present sea-level

Migration event inferred by IM

Bi-directional migration inferred by Lamarc

Malacca strait

Isthmus of Kra

Sample locations

Uni-directional migration inferred by Lamarc

500 km

Fig. 5 Summary of the inferred migration

and population history of Anopheles sun-

daicus. The circles/ovals indicate each

SAMOVA group. The bold circles mark

where groups were inferred to have

undergone recent population expansion

by LAMARC and Bayesian skyline plots.

The arrows indicate inferred migration

events.



2013), which is incompatible with our observation that

none of the genetic divergences, even among the land-

masses, are larger than those previously reported within

species between the mainland and Indonesian islands

(Zarowiecki et al. 2011). At present, the most genetically

isolated (but not the most divergent) Borneo populations

are most likely to be the best candidates for an indepen-

dently evolving lineage (De Queiroz 1999). However,

the dynamic Southeast Asian landmass reformation is

likely to continue to provide opportunities for recurrent

dispersal, which may delay, although not necessarily

ultimately prevent, diversification in this taxon. The

greater appreciation of genetic structure within A. sun-

daicus s.l. (both within and between landmasses) has

important implications for malaria control. It remains

important to determine whether there are any differ-

ences among genetically distinct groupings, which

should include the eight reported here and not only the

three purported species, in attributes relevant to malaria

epidemiology or vector control.

Acknowledgements

The authors wish to thank the Natural History Museum

(NHM), London, for funding the PhD study of MZZ. Most of

the work was carried out while MZZ, YML and RP were at

NHM. We would like to thank U Sein Minh, U Thi Ha and

other staff at the Department of Medical Research in Lower

Myanmar for help with fieldwork and Dr. Erling Pedersen for

providing specimens from Simeulue. This manuscript was pre-

pared in part while YML held a National Research Council

Research Associateship Award at the Walter Reed Army Insti-

tute of Research. The material to be published reflects the

views of the authors and should not be construed to represent

those of the US Department of the Army or the US Department

of Defence.

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M.Z., Y.-M.L., R.P. and C.W. conceived and designed

the study and wrote the manuscript. M.Z., Y.-M.L.,

C.W., M.J.B., P.T.H., T.H., C.M.S., V.B., T.H.D. and T.S.

conducted field research. M.Z. performed the laboratory

work and analysis.

Data accessibility

All sequence data have been submitted to GenBank; CO1

haplotypes with Accession nos. GQ395814–GQ395927,

Mpi as PopSet 262386140, Mpi (long) as PopSet 261599516

and ITS2 variants as PopSet 261599508. Alignments and

haplotype networks are available at Data Dryad doi:10.

5061/dryad.55qg0. Additional details on populations are

available in Table S1 (Supporting Information).

Supporting information

Additional supporting information may be found in the online ver-

sion of this article.

Table S1 This file contains exact locations and geographical

coordinates for all sampled populations.

Appendix S1 Mpi primers.

Fig. S1 Comparison of base frequencies at variable sites in Mpi

between larval specimens collected in fresh and brackish water

in Asahan, northern Sumatra (Indonesia), compared to Mpi al-

lozyme allele frequencies (Sukowati et al. 1999) for chromo-

somal forms A and B, from the same location.

Fig. S2 Complete SAMOVA results.

Fig. S3 PCA plots of population differentiation in Mpi and

CO1.

Appendix S2 Full FST values between all populations.



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