Species delimitation and phylogeny in the genusNasutitermes (Termitidae: Nasutitermitinae) in FrenchGuiana

VIRGINIE ROY,* REGINALDO CONSTANTINO,† VINCENT CHASSANY,*1 STEPHANIE

GIUSTI-MILLER,* MICHEL DIOUF,* PHILIPPE MORA* and MYRIAM HARRY‡§*UMR 7618 Bioemco-Ibios, Universit�e Paris-Est Cr�eteil, 61 avenue du G�en�eral de Gaulle, 94010, Cr�eteil Cedex, France,

†Departamento de Zoologia, Universidade de Bras�ılia, 70910-970 Bras�ılia, DF, Brasil, ‡Laboratoire Evolution, G�enomes et

Sp�eciation, UPR 9034 CNRS, IRD, Universit�e Paris Sud-11, avenue de la Terrasse, Batiment 13, 91198 Gif sur Yvette, France,

§UFR de Sciences, Universit�e Paris Sud-11, 91400 Orsay, France

Abstract

Species delimitation and identification can be arduous for taxa whose morphologic

characters are easily confused, which can hamper global biodiversity assessments and

pest species management. Exploratory methods of species delimitation that use DNA

sequence as their primary information source to establish group membership and esti-

mate putative species boundaries are useful approaches, complementary to traditional

taxonomy. Termites of the genus Nasutitermes make interesting models for the appli-

cation of such methods. They are dominant in Neotropical primary forests but also

represent major agricultural and structural pests. Despite the prevalence, pivotal eco-

logical role and economical impact of this group, the taxonomy of Nasutitermes species

mainly depends on unreliable characters of soldier external morphology. Here, we gen-

erated robust species hypotheses for 79 Nasutitermes colonies sampled throughout

French Guiana without any a priori knowledge of species affiliation. Sequence analy-

sis of the mitochondrial cytochrome oxidase II gene was coupled with exploratory spe-

cies-delimitation tools, using the automatic barcode gap discovery method (ABGD)

and a generalized mixed Yule-coalescent model (GMYC) to propose primary species

hypotheses (PSHs). PSHs were revaluated using phylogenetic analyses of two more

loci (mitochondrial 16S rDNA and nuclear internal transcribed spacer 2) leading to 16

retained secondary species hypotheses (RSSH). Seven RSSHs, represented by 44/79 of

the sampled colonies, were morphologically affiliated to species recognized as pests in

the Neotropics, where they represent a real invasive pest potential in the context of

growing ecosystem anthropization. Multigenic phylogenies based on combined align-

ments (1426–1784 bp) were also reconstructed to identify ancestral ecological niches

and major-pest lineages, revealing that Guyanese pest species do not form monophy-

letic groups.

Keywords: 16S rDNA, automatic barcode gap discovery method, cytochrome oxidase II, general

mixed Yule-coalescent, internal transcribed spacer 2, Nasutitermes

Received 15 January 2013; revision received 5 December 2013; accepted 13 December 2013

Introduction

Diverse and taxonomically understudied taxa are par-

ticularly challenging in terms of species delimitation

and identification (Puillandre et al. 2012b). This taxo-

nomic hurdle represents a real constraint to biodiversity

Correspondence: Virginie Roy, Fax: +33145171505;

E-mail: roy@u-pec.fr1Present address: Universit�e Paris Diderot, 4 rue Lagroua

Weill-Hall�e, 75205 Paris cedex 13, France

© 2013 John Wiley & Sons Ltd

Molecular Ecology (2014) 23, 902–920 doi: 10.1111/mec.12641

assessment (Deca€ens et al. 2013) and can impede pest

management. In this domain, molecular data can be

useful and complementary to traditional expertise in

several ways. For example, they can help to rapidly

identify referenced pest species, to delimit genetic units

within large-scale collections of samples for subsequent

morphological identification and to identify phyloge-

netic relationships between potential sibling pests.

For the bulk of undescribed biodiversity, the single-

gene DNA barcoding approach may be used, not to

identify specimens, but to propose primary species

hypotheses (PSHs) for approximating species descrip-

tions (Goldstein & DeSalle 2011; Puillandre et al. 2012b).

Methods of species delimitation use DNA sequence

itself as the primary information source to establish

group membership and estimate putative species

boundaries. There are only two formalized methods,

referred to as exploratory methods, specifically

designed to delimit species from single-locus data with-

out taking into account any kind of a priori species or

population delimitation (Coissac et al. 2012): the auto-

matic barcode gap discovery (ABGD) (Puillandre et al.

2012a) and general mixed Yule coalescent (GMYC)

(Pons et al. 2006; Monaghan et al. 2009) procedures.

The use of barcodes and single-gene approaches to

species delimitation (especially those based on mtDNA)

has been a source of controversy (Hebert et al. 2003;

Moritz & Cicero 2004; Blaxter et al. 2005; Rubinoff &

Holland 2005), and some major pitfalls linked to the

use of a sole mitochondrial gene should be mentioned.

First, DNA barcoding can overestimate the number of

species when nuclear mitochondrial pseudogenes (num-

ts) are coamplified (Song et al. 2008). Second, introgres-

sion events and/or incomplete lineage sorting can

commonly affect mtDNA-based phylogenetic analyses,

resulting in paraphyletic and polyphyletic relationships

(Funk & Omland 2003). Finally, maternally inherited

endosymbionts, such as the proteobacteriae Wolbachia,

may cause linkage disequilibrium with mtDNA, result-

ing in a homogenization of mtDNA haplotypes (Hurst

& Jiggins 2005). One strategy that has been used to

avoid single-gene pitfalls is to increase the gene sam-

pling to two or more, preferably unlinked, genes

(Knowles 2009; Leach�e & Fujita 2010; O’Meara 2010;

Yang & Rannala 2010). Comparison of different sources

of data has been thus suggested as the most effective

way to understand the evolutionary history of a group

(Rubinoff & Holland 2005).

Among insects, termites (Blattodea: Termitoidae) rep-

resent interesting models for exploratory species-delimi-

tation methods for a number of reasons. In tropical and

subtropical ecosystems, termites are particularly abun-

dant, frequently exceeding 1000 individuals/m2 or

2000 mg/m2 (Engel et al. 2009). They play a major role

in the decomposition of organic matter, nutrient recy-

cling, aeration and drainage of soils (Garnier-Sillam &

Harry 1995; Lavelle et al. 1997) and are particularly sen-

sitive to ecosystem perturbations (Fonseca de Souza &

Brown 1994; Dupont et al. 2008). Additionally, about

10% of species are xylophagous or foraging species,

which are described as pests for constructions or cul-

tures. Until recently, species delimitation in termites

was essentially based on three types of character, which

were rarely used together: morphological observations,

analytical chemistry (chemotaxonomy) and molecular

phylogeny. Then, recently, Monaghan et al. (2009)

applied an exploratory method (GMYC) to delimitate

termite species from Madagascar. Their results indi-

cated that the GMYC model captures species bound-

aries that are comparable to those from traditional

methods, opening a new avenue for the exploration of

termite biodiversity.

The genus Nasutitermes Dudley, 1890 (Termitidae:

Nasutitermitinae) comprises 243 currently described

species, including approximately 71 Neotropical species

in which the soldier caste possesses a frontal projection

(nasus) and vestigial mandibles. Most species build

arboreal nests and are wood-feeders. They occur in a

variety of habitats, including primary and secondary

forests, cropland and urban areas. Some Nasutitermes

species play a major role in soil ecological processes,

consuming up to 3% of the annual production of wood

litter in Brazilian forests (Vasconcellos & Moura 2010).

However, several species of Nasutitermes are important

pests in South America (Constantino 2002), causing sig-

nificant damage in agriculture (e.g. on coffee, maize,

cotton, eucalyptus, fruit trees, rice and sugar cane) and

to wood and other cellulosic materials. For example,

Nasutitermes nigriceps, N. ephratae and N. surinamensis

are well known to ravage timber wood, and N. corniger

is a major urban pest in Brazil and Argentina (Constan-

tino 2002). In French Guiana, three pest species of tim-

ber wood, N. nigriceps, N. ephratae and N. surinamensis,

have been reported among the twenty species of Nasut-

itermes recorded from this region (Lefeuve 1990;

Constantino 2002; Ensaf et al. 2003; Ensaf & Eggleton

2004; Ensaf 2010).

As in many other termite genera, the taxonomy of the

genus Nasutitermes is extremely confused, making spe-

cies identification remains difficult. Specific identifica-

tion of soldiers within the genus Nasutitermes usually

depends upon differences in pilosity, shape of the head,

colour and size (Holmgren 1910; Emerson 1925). Few

studies have used information other than that on mor-

phology to address taxonomic or phylogenetic problems

in the genus Nasutitermes. Nevertheless, the use of cutic-

ular hydrocarbons made it possible to differentiate

very similar species such as Nasutitermes acajutlae and

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 903

N. nigriceps (Thorne et al. 1994) or N. corniger and

N. ephratae (Howard et al. 1988), and mitochondrial

markers have successfully demonstrated the synonymy

of N. corniger, N. costalis and N. polygynus (Scheffrahn

et al. 2005a,b). Finally, phylogenetic relationships

between Nasutitermes species are poorly known, and

molecular studies have rarely focused on Neotropical

species (but see Bergamaschi et al. 2007 and Miura et al.

2000 for Nasutitermes phylogenies from Australia and

the Pacific tropics, respectively).

The aims of the present study were to generate robust

species hypotheses for 79 Nasutitermes colonies sampled

throughout French Guiana, without a priori knowledge

of species affiliation, thus comparing ABGD and GMYC

results in termites for the first time, and to reconstruct

phylogenies for Guianese species in order to retrace the

history of Nasutitermes ecological niche evolution and

pest species emergence.

Materials and methods

Samples

Nasute samples were represented by 79 colonies and

collected from 16 sites in French Guiana referred as

Awala-Yalimapo (AWA), Bellevue (BEL), Cacao (CAC),

Counami (COU), Elah�e (ELA), Iles du Salut (IDS), Ilet la

M�ere (ILM), Marais de Yiyi (MDY), Maripasoula

(MAR), Matoury (MAT), Montjoly (MON), Nouragues

(NOU), Patagai (PAT), Rocoucoua (ROC), Route de St

Elie (RSE) and Sa€ul (SAU) (Table 1 and Fig. S1, Sup-

porting information). The sampling procedure for BEL,

CAC, PAT and ROC was detailed in Dupont et al. (2008).

Other sites were sampled quantitatively (1–2 days

per site). Samples were conserved in absolute etha-

nol until molecular analyses. Sample affiliation to the

Nasutitermes genus was checked using the soldier

description of Mathews (1977). Two colonies belonging

to Syntermitinae genera (Cyrilliotermes and Silvestriter-

mes) were chosen as outgroups for phylogenetic recon-

structions.

DNA extraction, sequencing and alignments

Total genomic DNA was extracted from one soldier per

colony (N = 79) by dissecting the head, thorax and legs,

and removing the abdomen. DNA extraction was real-

ized using the DNeasy Blood & Tissue Kit (Qiagen,

France) according to the manufacturer’s instructions.

The mitochondrial cytochrome oxidase II (COII) gene

was sequenced and applied to a species-delimitation

procedure. One additional mitochondrial gene, 16S

rDNA, and one nuclear sequence, internal transcribed

spacer 2 (ITS2), were sequenced to consolidate the

species-delimitation procedure and to concatenate loci

in phylogenetic analyses.

PCR amplifications were performed in 40 lL mixture

using Taq PCR Master Mix Kit (Qiagen) and GoTaq�

Flexi DNA Polymerase (Promega, France) following the

manufacturer protocol. The PCR primers and amplifica-

tion protocols are listed in Table S1 (Supporting infor-

mation). Amplification products were sent to Beckman

Coulter UK Ltd for sequencing. The sequences gener-

ated for this study were submitted to GenBank, and the

accession numbers are provided in Table 1.

The standard barcode gene, cytochrome oxidase I

(COI), was also tested for amplification and sequencing,

but returned only very partial results. It failed to

amplify or gave unusable sequences for numerous sam-

ples despite various modifications in the protocol,

including the use of Phusion High-Fidelity DNA Poly-

merase (NEB, France) and nested PCRs. Furthermore,

some COI sequences showed protein sequences that

were highly divergent and two stop codons in the read-

ing frame that could be attributed to parts of the mito-

chondrial genome that were transferred to the nucleus

(nuclear mitochondrial sequences or NUMTs). Conse-

quently, COI was not retained for further analyses.

For ten samples sequenced for ITS2 nuclear marker,

electropherograms showed convoluted DNA trace pro-

duced by direct sequencing of a template containing

heterozygous insertions/deletions. For these samples,

phase determination in length variant heterozygotes

was performed by direct sequencing of mixed PCR

products by combining for each individual the com-

plementary information contained in its forward and

reverse chromatograms (Flot et al., 2006) with the help

of Champuru v1.0 (Flot 2007, available online at

http://www.mnhn.fr/jfflot/champuru/). For all other

samples, heterozygous positions were identified by a

secondary peak in the electropherograms that reached

at least 50% of the intensity of the primary peak,

using CodonCode Aligner (CodonCode Corporation).

The low-sensitivity option was used when searching

for mutations to reduce false positives. Heterozygous

sites were coded using the standard IUPAC codes for

combinations of bases, and for all samples where two

or more heterozygous sites were found in a sequence,

we determined the gametic phase of alleles using the

program PHASE 2.1 as implemented in DNASP v.5

(Librado & Rozas 2009). PHASE uses a Bayesian

approach to infer haplotypes from diploid genotypic

data accounting for both recombination and linkage

disequilibrium.

An initial alignment for individual loci was produced

with the ClustalW2 algorithm (Thompson et al. 1994).

Manual adjustments and concatenation were made

using SEAVIEW v4.3.0 (Gouy et al. 2010).

© 2013 John Wiley & Sons Ltd

904 V. ROY ET AL.

Table

1Sam

ple

labels,

collectionlocalities,collectors,ecological

niches

andPSHsproposedforthe79

GuianeseNasutiterm

essamplesusedin

thesp

ecies-delim

itationan

alysis

Sam

ple

Collectionlocality

(collectors)

Ecological

niche

PSH

COIIGen

Ban

kID

(COIIhap

lotype)

16SGen

Ban

kID

ITS2

Gen

Ban

kID

AW

A1

Awala-Yalim

apo(V

C)

F1

KC63

0989

(hap

lotype1.1)

KF72

4731

(hap

lotype1.1)

KF72

4765

BEL2

Bellevue(V

R,MH)

C1

KC63

0989

(hap

lotype1.1)

KF72

4732

(hap

lotype1.2)

KF72

4766

CAC2

Cacao

(VR,MH)

F1

KC63

0989

(hap

lotype1.1)

MD

MD

ILM1

Ilet

laM� ere(V

C)

F1

KC63

0989

(hap

lotype1.1)

KF72

4733

(hap

lotype1.3)

KF72

4767

MAT1(10)

Matoury

(VC)

F1

KC63

0989

(hap

lotype1.1)

KF72

4731

(hap

lotype1.1)

KF72

4768

MON1

Montjoly

(VR,MH)

F1

KC630989

(hap

lotype1.1)

KF724734

(hap

lotype1.4)

KF724769

COU1

Counam

i(V

R,MH)

F1

KC63

0990

(hap

lotype1.2)

MD

MD

ELA2

Elah� e(V

R,MH)

C1

KC63

0991

(hap

lotype1.3)

MD

KF72

4770

BEL3

Bellevue(V

R,MH)

S2a

KC63

0992

(hap

lotype2a

.1)

KF72

4735

(hap

lotype2a

.1)

KF72

4771

RSE3

Route

StElie(V

R,MH)

S2a

KC63

0993

(hap

lotype2a

.2)

MD

KF72

4772

RSE2

Route

StElie(V

R,MH)

S2a

KC63

0993

(hap

lotype2a

.2)

KF72

4736

(hap

lotype2a

.2)

KF72

4773

MDY1(10)

MaraisdeYiyi(V

C)

S2b

KC63

0994

(hap

lotype2b

.1)

KF72

4737

(hap

lotype2b

.1)

KF72

4774

BEL4

Bellevue(V

R,MH)

F3

KC63

0995

(hap

lotype3.1)

MD

KF72

4775

ROC1

Rocoucoua(V

R,MH)

F3

KC63

0995

(hap

lotype3.1)

KF72

4738

(hap

lotype3.1)

KF72

4776

NIS2

F.GuianaNI(V

R,MH)

MD

3KC63

0996

(hap

lotype3.2)

KF72

4739

(hap

lotype3.2)

KF72

4777

NIS1

F.GuianaNI(V

R,MH)

MD

3KC63

0996

(hap

lotype3.2)

MD

KF72

4778

NOU1

Nouragues

(VC)

F3

KC63

0996

(hap

lotype3.2)

KF72

4740

(hap

lotype3.3)

KF72

4779

NOU2(10)

Nouragues

(VC)

F3

KC63

0996

(hap

lotype3.2)

KF72

4739

(hap

lotype3.2)

KF72

4780

NOU3

Nouragues

(VC)

F3

KC63

0996

(hap

lotype3.2)

KF72

4739

(hap

lotype3.2)

KF72

4781

PAT2

Patag

a€ ı(V

R,MH)

F3

KC63

0997

(hap

lotype3.3)

KF72

4741

(hap

lotype3.4)

KF72

4782

PAT3

Patag

a€ ı(V

R,MH)

F3

KC63

0998

(hap

lotype3.4)

KF72

4739

(hap

lotype3.2)

KF72

4783

PAT4

Patag

a€ ı(V

R,MH)

F3

KC63

0999

(hap

lotype3.5)

KF72

4738

(hap

lotype3.1)

KF72

4784

RSE1

Route

StElie(V

C)

F3

KC63

1000

(hap

lotype3.6)

KF72

4739

(hap

lotype3.2)

KF72

4785

NOU25(10)

Nouragues

(PM)

F4

KC63

1001

(hap

lotype4.1)

KF72

4742

(hap

lotype4.1)

KF72

4786

BEL1(10)

Bellevue(V

R,MH)

S5

KC63

1002

(hap

lotype5.1)

KF72

4743

(hap

lotype5.1)

KF72

4787

BEL5

Bellevue(V

R,MH)

C6

KC63

1003

(hap

lotype6.1)

MD

MD

ELA3

Elah� e(V

R,MH)

F6

KC63

1003

(hap

lotype6.1)

KF72

4744

(hap

lotype6.1)

KF72

4788

CAC3

Cacao

(VR,MH)

F6

KC63

1004

(hap

lotype6.2)

KF72

4745

(hap

lotype6.2)

KF72

4789

MAT2(10)

Matoury

(VC)

F6

KC63

1004

(hap

lotype6.2)

KF72

4745

(hap

lotype6.2)

KF72

4790

NOU26

Nouragues

(PM)

F6

KC63

1004

(hap

lotype6.2)

KF72

4745

(hap

lotype6.2)

KF72

4791

NOU4

Nouragues

(VC)

F6

KC63

1004

(hap

lotype6.2)

KF72

4745

(hap

lotype6.2)

KF72

4792

NOU5

Nouragues

(VC)

F6

KC63

1004

(hap

lotype6.2)

KF72

4746

(hap

lotype6.3)

KF72

4793

PAT5

Patag

a€ ı(V

R,MH)

F6

KC63

1004

(hap

lotype6.2)

KF72

4747

(hap

lotype6.4)

KF72

4794

PAT6

Patag

a€ ı(V

R,MH)

F6

KC63

1004

(hap

lotype6.2)

KF72

4747

(hap

lotype6.4)

KF72

4795

SAU1

Sa€ ul(V

C)

F6

KC63

1004

(hap

lotype6.2)

KF72

4745

(hap

lotype6.2)

KF72

4796

NOU6(10)

Nouragues

(VC)

F7

KC63

1005

(hap

lotype7.1)

KF72

4748

(hap

lotype7.1)

KF72

4797

NOU28

Nouragues

(PM)

F8

KC63

1006

(hap

lotype8.1)

KF72

4749

(hap

lotype8.1)

KF72

4798

NOU17

Nouragues

(VC)

F8

KC63

1006

(hap

lotype8.1)

KF72

4749

(hap

lotype8.1)

KF72

4799

NOU16

Nouragues

(VC)

F8

KC63

1006

(hap

lotype8.1)

KF72

4750

(hap

lotype8.2)

KF72

4800

NOU15(10)

Nouragues

(VC)

F8

KC63

1006

(hap

lotype8.1)

KF72

4749

(hap

lotype8.1)

KF72

4801

PAT7

Patag

a€ ı(V

R,MH)

F8

KC63

1006

(hap

lotype8.1)

MD

KF72

4802

NOU20

Nouragues

(VC)

F8

KC63

1007

(hap

lotype8.2)

KF72

4751

(hap

lotype8.3)

KF72

4803

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 905

Tab

le1

Continued

Sam

ple

Collectionlocality

(collectors)

Ecological

niche

PSH

COII

Gen

Ban

kID

(COIIhap

lotype)

16SGen

Ban

kID

ITS2

Gen

Ban

kID

NOU18

Nouragues

(VC)

F8

KC63

1007

(hap

lotype8.2)

KF72

4752

(hap

lotype8.4)

KF72

4804

NOU19

Nouragues

(VC)

F8

KC63

1007

(hap

lotype8.2)

KF72

4753

(hap

lotype8.5)

KF72

4805

NOU8

Nouragues

(VC)

F9

KC63

1008

(hap

lotype9.1)

KF72

4754

(hap

lotype9.1)

KF72

4806

NOU7(10)

Nouragues

(VC)

F9

KC63

1008

(hap

lotype9.1)

KF72

4754

(hap

lotype9.1)

KF72

4807

NOU9

Nouragues

(VC)

F9

KC63

1008

(hap

lotype9.1)

KF72

4754

(hap

lotype9.1)

KF72

4808

PAT8(2)

Patag

a€ ı(V

R,MH)

F10

KC63

1009

(hap

lotype10

.1)

KF72

4755

(hap

lotype10

.1)

KF72

4809

COU3(10)

Counam

i(V

R,MH)

F11

KC63

1010

(hap

lotype11

.1)

KF72

4756

(hap

lotype11

.1)

KF72

4810

NIS4

F.GuianaNI(V

R,MH)

MD

11KC63

1011

(hap

lotype11

.2)

MD

KF72

4811

NOU27

Nouragues

(PM)

F12

KC63

1012

(hap

lotype12

.1)

KF72

4757

(hap

lotype12

.1)

KF72

4812

NOU10

Nouragues

(VC)

F12

KC63

1012

(hap

lotype12

.1)

KF72

4757

(hap

lotype12

.1)

KF72

4813

NOU11

Nouragues

(VC)

F12

KC63

1012

(hap

lotype12

.1)

KF72

4757

(hap

lotype12

.1)

KF72

4814

NOU12

Nouragues

(VC)

F12

KC63

1012

(hap

lotype12

.1)

KF72

4757

(hap

lotype12

.1)

KF72

4815

NOU13(10)

Nouragues

(VC)

F12

KC63

1012

(hap

lotype12

.1)

KF72

4757

(hap

lotype12

.1)

KF72

4816

NOU14

Nouragues

(VC)

F12

KC63

1013

(hap

lotype12

.2)

KF72

4757

(hap

lotype12

.1)

KF72

4817

AWA2

Awala-Yalim

apo(V

C)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4818

CAC4

Cacao

(VR,MH)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4819

COU2

Counam

i(V

R,MH)

F13

KC63

1014

(hap

lotype13

.1)

MD

KF72

4820

NIS3

F.GuianaNI(V

R,MH)

MD

13KC63

1014

(hap

lotype13

.1)

MD

KF72

4821

IDS3

Iles

duSalut(V

C)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4822

ILM2

Ilet

laM� ere(V

C)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4823

ILM3

Ilet

laM� ere(V

C)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4824

NOU21

Nouragues

(VC)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4825

NOU22(10)

Nouragues

(VC)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4826

NOU23

Nouragues

(VC)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4827

NOU24

Nouragues

(VC)

F13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4828

ROC2

Rocoucoua(V

R,MH)

C13

KC63

1014

(hap

lotype13

.1)

KF72

4758

(hap

lotype13

.1)

KF72

4829

MAT3

Matoury

(VC)

F13

KC631015

(hap

lotype13.2)

KF724758

(hap

lotype13.1)

KF724830

SAU2

Sa€ ul(V

C)

F13

KC63

1016

(hap

lotype13

.3)

KF72

4758

(hap

lotype13

.1)

KF72

4831

ELA1

Elah� e(V

R,MH)

C14

KC63

1017

(hap

lotype14

.1)

KF72

4759

(hap

lotype14

.1)

KF72

4832

MAR1(10)

Maripasoula

(VR,MH)

C14

KC63

1017

(hap

lotype14

.1)

KF72

4759

(hap

lotype14

.1)

KF72

4833

PAT1

Patag

a€ ı(V

R,MH)

F14

KC63

1018

(hap

lotype14

.2)

MD

MD

CAC1(10)

Cacao

(VR,MH)

F15

KC63

1019

(hap

lotype15

.1)

KF72

4760

(hap

lotype15

.1)

KF72

4834

IDS4

Iles

duSalut(V

R,MH)

F15

KC63

1020

(hap

lotype15

.2)

MD

KF72

4835

IDS5

Iles

duSalut(V

R,MH)

F15

KC63

1020

(hap

lotype15

.2)

MD

KF72

4836

IDS6

Iles

duSalut(V

R,MH)

F15

KC63

1021

(hap

lotype15

.3)

MD

KF72

4837

IDS1

Iles

duSalut(V

C)

F15

KC63

1022

(hap

lotype15

.4)

KF72

4761

(hap

lotype15

.2)

KF72

4838

IDS2

Iles

duSalut(V

C)

F15

KC63

1022

(hap

lotype15

.4)

KF72

4762

(hap

lotype15

.3)

KF72

4839

Silvestriterm

essp

.Nouragues

(PM)

——

KC63

1023

KF72

4763

KF72

4840

Cyrilliotermes

sp.

Rocoucoua(V

R,MH)

——

KC63

1024

KF72

4764

KF72

4841

VC,V.Chassany;MH,M.Harry;PM,P.Mora;VR,V.Roy;F,forest;S,savan

nah

;C,cu

lture;NI,noniden

tified

site;MD,missingdata;

PSH,primarysp

ecieshypotheses.

Sam

plesin

bold

arethose

usedformorphological

speciesaffiliation,withnumbersofindividualsusedformorphological

analysesin

brackets.

Gen

Ban

kaccessionnumbers(A

N)

areindicated

forCOIIhap

lotypes,16SrD

NA

andITS2sequen

ces.

© 2013 John Wiley & Sons Ltd

906 V. ROY ET AL.

Species-delimitation procedure

Principle. A workflow diagram is presented as Fig. 1.

First, PSHs were constructed based on the pattern of

diversity of COII as a single gene. COII is not the stan-

dard barcode gene, but it was demonstrated to be suit-

able for a single-locus species-delimitation procedure

(Monaghan et al. 2009). Historically, COII has been pref-

erentially sequenced in termites, and about five times

more sequences are available for it in nucleotide databas-

es than for COI. PSHs were proposed using two explor-

atory methods for species delimitation: ABGD and

GMYC procedures applied to the COII gene. To consoli-

date PSHs, additional sequences, that is, mitochondrial

16S rDNA and nuclear ITS2 were phylogenetically analy-

sed (i.e. monophyly, with support and shared haplotypes

between PSHs set as criteria). This step made it possible

to choose the most likely option amongst alternate PSHs

proposed by ABGD and GMYC and to propose RSSHs

(Puillandre et al. 2012b). Finally, RSSHs were affiliated to

known species through to the detailed morphological

expertise of individuals belonging to the soldier caste.

ABGD method. The ABGD method (Puillandre et al.

2012a) uses several a priori thresholds to propose parti-

tioning of specimens into PSHs based on the distribu-

tion of pairwise genetic distances. In the distribution of

pairwise differences between sequences, one can

observe a gap between intraspecific diversity and

interspecific diversity; this gap has been named the

‘barcode gap’ and can be used as a threshold for delim-

iting primary species under the assumption that indi-

viduals within species are more similar than between

species. The COII alignment was used to compute

matrices of pairwise distances using the p-distance, the

Kimura 2-parameter (K2P) and the Tamura–Nei (TN)

models with MEGA5 (Tamura et al. 2011). Matrices

were then used as inputs on the ABGD webpage

(http://wwwabi.snv.jussieu.fr/public/abgd/abgdweb.

html), using the default settings search (except for the

relative gap width X which was set to 1 because only

one group was found for the default value X = 1.5,

prior maximal distance of 0.001) on a set of prior mini-

mum genetic distances ranging from 0.001 to 0.1.

GMYC procedure. The general mixed Yule-coalescent

delimitation procedure is based on a coalescent

approach and requires a chronometric phylogram in

which branch lengths are approximately proportional to

time. We applied the uncorrelated lognormal model

implemented in BEAST v1.7.3 (Drummond et al. 2012) to

the COII alignment. Three independent Markov chain

Monte Carlo (MCMC) analyses were run for 20 million

generations, sampling every 1000 with a 10% burn-in

under the substitution model determined using MRMOD-

ELTEST (GTR + G + I). Convergence and effective sample

size (ESS) of estimated parameters were inspected using

TRACER v1.5, and a maximum clade credibility tree was

reconstructed with the program TREEANNOTATOR v1.7.3

COII (N = 79)

COII (N = 79)

DELIMITATION

PSHs

CONSOLIDATION

Morphologicalidentification

Morphologicalidentification

Morphology

AFFILIATION

Species

ABGD, GMYC

RSSHsITS2

(N = 64)ITS2

(N = 64)

BI, ML (monophyly + support)/shared haplotypes

16S (N = 64)

16S (N = 64)

ITS2 (N = 75)

ITS2 (N = 75)

16S(N = 64)

16S(N = 64)

COII (N = 64)

COII (N = 64)

Haplo-network

RECONSTRUCTION OF ANCESTRAL ECOLOGICAL NICHES

PHYLOGENETIC RECONSTRUCTIONS

BI on total combined datasetBI on mit combined dataset

(a) (b)

Fig. 1 Workflow diagram for species delimitation, phylogeny and reconstruction of ancestral states. (a) Delimitation analyses: PSHs

were proposed using two methods for species delimitation (ABGD and GMYC procedures, applied to the COII data set). Phyloge-

netic analyses of COII, 16S rDNA and ITS2 data sets were used to choose the most likely option amongst alternate PSHs proposed

by ABGD and GMYC. RSSHs were then affiliated to known species by detailed morphological identification of individuals belonging

to the soldier caste. (b) Combined phylogenetic analyses: COII, 16S rDNA and ITS2 data sets were combined to reconstruct phyloge-

nies and ancestral ecological niches. COII: cytochrome oxidase II, ITS2: internal transcribed spacers 2, ABGD: automatic barcode gap

discovery method, GMYC: generalized mixed Yule-coalescent method, PSHs: primary species hypotheses, RSSHs: retained secondary

species hypotheses, BI: Bayesian inference, ML: maximum likelihood, N: number of colonies sequenced/locus.

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 907

with the default settings. The ultrametric phylogenies

recovered with BEAST were then subjected to GMYC

analysis.

The GMYC model was used to perform analyses of

species delimitation and propose PSHs (Pons et al.

2006). This method exploits the differences in the rate

of lineage branching at the level of species and popula-

tions that can be visualized as a switch between slow

and fast rates of branching events in a lineage-through-

time plot. A combined model that separately describes

population (a neutral coalescent model) and speciation

(a Yule model) processes is fitted on the ultrametric

tree. The method optimizes a threshold position of

switching from interspecific to intraspecific events such

that nodes older than the threshold are considered to

be diversification events and nodes younger than the

threshold reflect coalescence occurring within each spe-

cies. We generated a log-lineage-through-time plot to

identify a sharp increase in lineage accumulation that

represents the inferred threshold between speciation

and coalescent processes. A standard likelihood ratio

test (LRT) was used to assess whether the alternative

model (i.e. the simple-threshold GMYC model)

provided a better fit than the null model (i.e. a single

PSH). These methods were implemented in R software

v2.15.1 (R Development Core Team 2010), using the

splits (available at http://r-forge.r-project.org/projects/

splits/) and ape (Paradis et al. 2004) packages.

Consolidation of PSHs. As a first step, the monophyly

and support of PSHs were evaluated from Bayesian

inference (BI) and maximum likelihood (ML) based on

the COII, 16S rDNA and ITS2 data sets. The appropri-

ateness of partitioning the COII alignment by codon

position was determined using the Bayes factor (BF)

(Kass & Raftery 1995; Nylander 2004) on ‘gene nonpar-

titioned by codon’ and ‘gene partitioned by codon’

matrices. For this, we used the sump command in

MRBAYES to obtain the log-transformed harmonic means.

The BF can be calculated as the ratio of the harmonic

means of likelihoods of the two analyses being tested.

In this study, 2lnBF ≥ 10 was considered as very strong

evidence supporting the alternative hypothesis based

on hypothesized cut-off values (Kass & Raftery 1995).

For the ITS2 phylogenetic reconstructions, we used

sequences with IUPAC codes: all gaps and heterozy-

gous positions were coded as ambiguous characters

and were treated as missing data. The most appropriate

likelihood models were determined with MRMODELTEST

2.0 (Nylander 2004) using the Akaike information crite-

rion. Bayesian search was carried out with MRBAYES

v.3.1.2 (Huelsenbeck & Ronquist 2001) using four simul-

taneous Markov chains, 10 million generations and

sampling every 100 generations, resulting in 100 000

generations being saved. A burn-in of 20% was used.

We used TRACER v1.5 (Drummond et al. 2012) to ascer-

tain that our sampling of the posterior distribution had

an adequate ESS. ML analyses were performed using

PhyML (Guindon et al. 2010) with 1000 bootstraps.

As a second step, shared haplotypes between PSHs

were then identified using DNASP v.5 for COII and 16S

rDNA alignments. We explored relationships between

ITS2 haplotypes reconstructed by PHASE using a median

joining network obtained in NETWORK 4.6 (http://www.

fluxus-engineering.com) and identified shared ITS2

haplotypes.

Identification to species level. RSSHs were affiliated to

known species through to the detailed morphological

examination of individuals belonging to the soldier caste.

The 79 colonies could not all be analysed for reasons of

conservation and availability of the different castes. Fif-

teen colonies underwent a detailed morphological identi-

fication (Table 1): 142 individuals were examined using a

Leica M205C stereomicroscope under magnification up

to 1009, with both incident and transmitted light. Soldier

characters used for identification were shape of head

capsule; number, size and distribution of hairs on head

capsule, thoracic nota and tergites; gut morphology in

situ (observed by transparency); number of antennal arti-

cles and their relative length; colour and colour pattern;

and morphometric characters: length of head, width of

head and length of posterior tibia. These were then com-

pared with published descriptions (Holmgren 1910;

Banks 1918; Emerson 1925; Mathews 1977; Bandeira &

Fontes 1979) and with preserved specimens and images

of type specimens. The Appendix S1 (Supporting infor-

mation) presents more detailed information on the identi-

fication of each species.

Phylogenetic analyses

Phylogenetic analyses were performed with BI parti-

tioned by gene on mitochondrial combined (COII and

16s rDNA) and total combined alignments (COII, 16s

rDNA and ITS2). Samples with at least one missing

locus were discarded from the analyses, resulting in

data sets of 64 Nasutitermes and two outgroup samples.

The procedure for BI was identical to that described in

the ‘Consolidation of PSHs’ section.

Reconstruction of ancestral ecological niches

MESQUITE v. 2.75 (Maddison & Maddison 2011) was used

to reconstruct the ecological state of the ancestral nodes

(i.e. forest or savannah). The topology of the BI tree

obtained with the total combined alignment was used

as input in Mesquite. For each node, a likelihood

© 2013 John Wiley & Sons Ltd

908 V. ROY ET AL.

reconstruction method was used to find the state that

maximized the probability of arriving at the observed

states in the terminal taxa, given the model of evolu-

tion, and allowing the states at all other nodes to vary.

Results

Delimitation of mtDNA clusters with ABGD andGMYC methods

Genetic pairwise distances for COII gene were computed

using three different substitution models: the p-distance,

the K2P distance and the TN distance. The distribution of

genetic distances, whatever the substitution model used,

displayed three modes (Fig. 2a). The different ABGD a

priori thresholds led to partitions with 20, 17, 16 or 14

PSHs when extreme a priori thresholds were excluded

(Fig. 2c), but a first major barcode gap was evident at a

priori genetic distance threshold of 0.0046 (estimated dis-

tance p = 0.010) (Fig. 2b). Thus, this prior intraspecific

divergence value, supporting the presence of 16 PSHs,

was favoured in further discussion about ABGD species

delimitation. Five PSHs contained a single sequence, with

these singletons representing 31% of delineated ABGD

species (PSHs 2b, 4, 5, 7 and 10, longer black branches in

Fig. 2d).

A lineage-through-time plot based on the BEAST ultra-

metric tree revealed a sudden increase in branching rate

towards the present, likely corresponding to the switch

from interspecies to intraspecies branching events

(Fig. 3). A LRT favoured the simple-threshold GMYC

model over a null model of a single PSH (likelihood of

null model: 625.1238, likelihood of GMYC model:

632.7517, likelihood ratio: 16.00316, LR test: <0.005). Themodel identified 15 distinct PSHs (confidence limits:

12–17). Four PSHs contained a single sequence, with

these singletons representing 27% of delineated GMYC

species (PSHs 4, 5, 7 and 10, longer black branches in

Fig. 3).

We calculated mean intra- and inter-PSHs levels of

sequence variation considering the less conservative

partition proposed by the delimitation methods (i.e. 16

PSHs). Mean intra-PSH (i.e. putative intraspecific) levels

of sequence variation were low, ranging from 0 to

0.003 � 0.001 (Table S2, Supporting information). Pair-

wise PSHs comparisons were investigated for all COII

sequences (Table S3, Supporting information): the low-

est distance, calculated with a K2P model, was between

PSH 2a and PSH 2b (p = 0.013 � 0.004). The highest

distance was between PSH 2a and PSH 14

(0.135 � 0.016). Mean inter-PSH (i.e. putative interspe-

cific) distance for the COII gene was 0.087 � 0.026.

PSH consolidation and species affiliation

The COII alignment consisted of 79 Nasutitermes

sequences of 705 bp with 186 polymorphic sites. No stop

codon was observed. The best-fit models selected by

MRMODELTEST were GTR+I+G (nonpartitioned), F81 (1st

codon position), GTR+G (2nd codon position) and GTR+I(3rd codon position). The 2lnBF was �149.28 (harmonic

mean partitioned, �ln = 3240.13; harmonic mean non-

partitioned –ln = 3165.49), suggesting that assuming a

single model of evolution for the whole COII had a better

fit to the data than allowing the three codon positions of

the COII gene to have independent parameter values.

When at least two sequences per PSH were available,

each PSH defined on the COII alignment using ABGD

and GMYC methods was monophyletic and supported

by ML bootstrap (BT) values >70 and BI Bayesian poster-

ior probability (BPP) values >0.95, except PSH3 (BI BPP

<0.95 and ML BT <70) and PSH6 (BI BPP <0.95) (Figs. 4aand 5). No haplotype was shared between PSHs.

The 16S rDNA alignment comprised 721 bp, with 64

Nasutitermes sequences showing 142 polymorphic sites,

and two outgroups. The best-fit model selected by

MRMODELTEST was GTR+I+G. All PSHs were monophy-

letic using the 16S rDNA alignment and supported by

ML bootstrap (BT) values >70 (Fig. 4b). In three cases

(PSHs 1, 2a and 2ab), BI BPP values were <0.95. The

two PSH 2a sequences differed by only one indel, while

PSH 2a and PSH 2b sequences differed by five to six

substitutions/indels. No 16S rDNA haplotype was

shared between PSH 2a and PSH 2b (Figs 4b and 5).

The ITS2 alignment included 75 Nasutitermes

sequences of 358 bp showing 37 polymorphic sites. The

best-fit model selected by MRMODELTEST was HKY+G.

Six PSHs were found to be monophyletic, and five of

them were supported by ML BT values >70 and BI

BPP >0.95. PSHs 2a, 2b, 2ab, 3, 6, 7, 8 and 11 showed

relationships between members that were unresolved

(Figs 4c and 5). Relationships between reconstructed

Fig. 2 (a) Frequency distribution of pairwise sequence comparisons based on the K2P, TN and p-distances for the COII alignment,

(b) frequency distribution for the barcode gap range of distances only, (c) automatic barcode gap discovery method (ABGD) auto-

matic partition of the COII alignment: number of groups inside the partitions is indicated as a function of the prior limit between

intra- and interspecies divergence, (d) Nasutitermes BIONJ tree obtained with ABGD with a priori genetic distance threshold of 0.0046

[16 primary species hypotheses (PSHs)]. ABGD genetic groups recognized as PSHs are highlighted in red and separated by longer

black branches.

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 909

PSH 12

PSH 14

PSH 13

PSH 15

PSH 10

PSH 9

PSH 11

PSH 8

PSH 7

PSH 6

PSH 5PSH 4

PSH 3

PSH 2a

PSH 1

PSH 2b

BEL2ILM1MAT1AWA1MON1

ELA2COU1

CAC2RSE2

RSE3BEL3

MDY1PAT2

PAT4BEL4ROC1NOU1PAT3RSE1

NIS1NIS2NOU2NOU3

NOU25BEL1

CAC3BEL5

ELA3MAT2NOU26NOU4NOU5PAT5PAT6SAU1

NOU6NOU20NOU18NOU19NOU28NOU17NOU15NOU16PAT7

NOU7NOU8NOU9

PAT8COU3

NIS4NOU14

NOU13NOU12NOU27NOU10NOU11

SAU2NOU24NOU23NOU22NOU21

NIS3COU2

AWA2MAT3

CAC4ROC2ILM3ILM2IDS3

PAT1MAR1

ELA1IDS2IDS1

IDS6IDS4

IDS5CAC1

0.01

(d)

(c)

(b)

(a)

© 2013 John Wiley & Sons Ltd

910 V. ROY ET AL.

–0.10 –0.08 –0.06 –0.04 –0.02 0.00

12

510

2050

Time

N

–0.10 –0.08 –0.06 –0.04 –0.02 0.00

624

626

628

630

632

Time

likel

ihoo

d

PSH 12

PSH 14

PSH 13

PSH 15

PSH 10

PSH 9

PSH 11

PSH 8

PSH 7

PSH 6

PSH 5PSH 4

PSH 3

PSH 2ab

PSH 1

ELA2MAT1

BEL2ILM1AWA1MON1CAC2COU1RSE3RSE2BEL3MDY1PAT4PAT3RSE1NOU2NOU3NOU1NIS1NIS2PAT2BEL4ROC1BEL1NOU25ELA3BEL5NOU5PAT5MAT2SAU1CAC3PAT6NOU4NOU26NOU6NOU19NOU20NOU18NOU17NOU28PAT7NOU16NOU15COU3NIS4NOU9NOU7NOU8PAT8SAU2NOU23NOU22MAT3NIS3CAC4NOU21ILM3COU2IDS3NOU24AWA2ROC2ILM2NOU10NOU12NOU27NOU13NOU11NOU14MAR1PAT1ELA1IDS6IDS1IDS5IDS2IDS4CAC1

0.02

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 911

ITS2 haplotypes are shown on the haplo-network of

Fig. S2 (Supporting information). According to PHASE

allele reconstruction, the same ITS2 haplotype was

shared between PSH 2a, 2b and 3, between PSHs 6 and

8 and between PSHs 6, 7 and 8.

These additional data allowed the most likely

option to be selected amongst alternate PSHs pro-

posed by ABGD and GMYC. A PSH was converted

to retained secondary species hypotheses (RSSH)

when it met the following conditions: monophyly,

support by ML BT values >70 and/or BI BPP >0.95and no shared haplotypes with another PSH, for at

least two genes. Consequently, 16 PSHs were con-

verted into RSSHs (Fig. 5).

Based on the detailed morphological analysis,

RSSHs were linked to taxonomic names available in the

literature (Fig. 5). Each RSSH could be morphologically

analysed, except RSSH 2a, which was designated as

Nasutitermes sp., resulting in fourteen known species:

RSSH 1-N. corniger, RSSH 2b-N. coxipoensis, RSSH

3-N. ephratae, RSSH 4-N. callimorphus, RSSH 5-N. inter-

medius, RSSH 7-N. guayanae, RSSH 9-N. obscurus RSSH

10-N. unduliceps, RSSH 11-N. wheeleri, RSSH 12-N. octop-

ilis, RSSH 13-N. surinamensis, RSSH 14-N. acangussu and

RSSH 15-N. acajutlae. RSSH 6 and RSSH 8 were both

morphologically identified as N. similis.

Phylogenetic analyses

Phylogenetic reconstruction for the combined mitochon-

drial alignment (66 sequences, 1426 bp, BI partitioned by

gene, arithmetic mean –ln = 6245.64) is presented Fig. 6a.

AWA1ILM1MAT1BEL2

COU1CAC2

ELA2MON1

RSE2RSE3

BEL3MDY1

BEL4ROC1NIS1NIS2NOU1NOU2NOU3PAT3RSE1

PAT2PAT4

NOU25BEL1

BEL5CAC3ELA3MAT2NOU26NOU4NOU5PAT5PAT6SAU1

NOU6

NIS4COU3

PAT8

NOU19NOU18NOU20PAT7NOU16NOU15NOU17NOU28

NOU9NOU8NOU7

NOU14NOU13NOU12NOU11NOU10NOU27

IDS2IDS1

IDS6IDS4IDS5CAC1

AWA2CAC4COU2ROC2IDS3ILM2ILM3MAT3NIS3NOU21NOU22NOU23NOU24SAU2

PAT1MAR1ELA1

Cyrilliotermes sp.Silvestritermes sp.

1.00/97.2

1.00/100

1.00/99.91.00/87.2

0.97/<70

1.00/97.9

1.00/98.1

1.00/88

1.00/97.4

1.00/100

1.00/99.6

1.00/100

1.00/86.7

1.00/84.1

1.00/100

0.2

<0.95/78.6

<0.95/82.1

<0.95/99.9

<0.95/87.8

1.00/100

1.00/100

1.00/99.9

PSH 12

PSH 14

PSH 15

PSH 10PSH 9

PSH 11

PSH 8

PSH 7

PSH 6

PSH 5

PSH 3

PSH 4

PSH 2a

PSH 1

PSH 2b

PSH 13

(a) (b) (c)

BEL3RSE2MDY1ROC1NIS2NOU1NOU2NOU3PAT3RSE1

PAT2PAT4

NOU25BEL1

ELA3CAC3MAT2NOU26NOU4NOU5PAT5PAT6SAU1NOU6

COU3PAT8

NOU28NOU17NOU16NOU15NOU20NOU18NOU19NOU8NOU7NOU9

AWA2CAC4IDS3ILM2ILM3NOU21NOU22NOU23NOU24ROC2MAT3SAU2

AWA1BEL2ILM1MAT1

MON1

ELA1MAR1

CAC1IDS2IDS1

NOU14NOU13NOU12NOU11NOU10NOU27

Silvestritermes sp.Cyrilliotermes sp.

0.99/87.1

0.99/95.1

0.98/89.4

0.99/98.9

0.99/88.8

0.96/<70

0.98/<70

1.00/100

1.00/100

1.00/100

1.00/99.9

1.00/100

1.00/99.4

0.05

PSH 12

PSH 14

PSH 15

PSH 10

PSH 9

PSH 11PSH 7

PSH 8

PSH 6

PSH 5

PSH 3

PSH 4

PSH 2a

PSH 1

PSH 2b

PSH 13

<0.95/78.8

<0.95/93.1<0.95/95.7

<0.95/75.9

<0.95/78.1

ELA2MON1MAT1ILM1BEL2AWA1

NOU14NOU13NOU12NOU11NOU10NOU27

ELA3CAC3MAT2NOU26NOU4NOU5PAT5PAT6SAU1NOU6NOU28NOU17NOU16NOU15PAT7NOU20NOU18NOU19

COU3NIS4

NOU8NOU7NOU9

PAT8AWA2CAC4COU2NIS3IDS3ILM2ILM3NOU21NOU22NOU23NOU24ROC2MAT3SAU2

ELA1MAR1

CAC1IDS4IDS5IDS6IDS1IDS2

Cyrilliotermes sp.Silvestritermes sp.

0.97/<70

1.00/93.8

1.00/98.2

1.00/99.9

1.00/82.6

1.00/100

1.00/100

0.1

<0.95/85.2PSH 1

BEL1NOU25

RSE1PAT4PAT3

PAT2NOU3NOU2NOU1NIS1NIS2ROC1BEL4

MDY1

RSE2RSE3BEL3

PSH 2aPSH 2b

PSH 3

PSH 5PSH 4

PSHs6+7+8

PSH 10PSH 9

PSH 11

PSH 14

PSH 15

PSH 13

PSH 12

<0.95/77.9

<0.95/85.2

Fig. 4 Fifty per cent majority rule consensus trees obtained from the Bayesian inference analyses of individual loci, with support val-

ues of the two analyses plotted on the nodes: BPP (Bayesian inference) >0.95/BT>70% (Maximum Likelihood). (a) COII, (b) 16S

rDNA gene and (c) ITS2. Monophyletic primary species hypotheses (PSHs) are indicated by plain lines, while nonmonophyletic/

unresolved PSHs are indicated by dotted lines.

Fig. 3 Nasutitermes ultrametric tree obtained with BEAST using the COII alignment. Generalized mixed Yule-coalescent model genetic

clusters recognized as primary species hypotheses (PSHs) are highlighted in red and separated by longer black branches (singletons).

The vertical bars group all sequences within each significant cluster, labelled with their PSH number. Figure insets in the upper left-

hand corner are the corresponding log-lineages-through-time plot showing a sudden increase in branching rate towards the present,

likely corresponding to the switch from interspecies to intraspecies branching events, and likelihood surface plot.

© 2013 John Wiley & Sons Ltd

912 V. ROY ET AL.

N. octopilis

N. acangussu

N. surinamensis

N. acajutlae

N. undulicepsN. obscurus

N. wheeleri

N. similis2

N. guayanae

N. similis1

N. intermediusN. callimorphus

N. ephratae

N. corniger

N. coxipoensisNasutitermes sp.

PSHs

ITS

2

N/Y

--

N/N

Y/N-

Y/Y

Y/Y

Y/Y

Y/Y

AB

GD

on

CO

II

PSH 1

PSH 2aPSH 2b

PSH 3

PSH 4PSH 5

PSH 6

PSH 7

PSH 8

PSH 9PSH 10PSH 11

PSH 12

PSH 13

PSH 14

PSH 15

GM

YC

on

CO

II

PSH 1

PSH 3

PSH 4PSH 5

PSH 6

PSH 7

PSH 8

PSH 9PSH 10PSH 11

PSH 12

PSH 13

PSH 14

PSH 15

PSH 2ab

16S

rDN

A

Consolidation

RSSH 1

RSSH 2aRSSH 2b

RSSH 3

RSSH 4RSSH 5

RSSH 6

RSSH 7

RSSH 8

RSSH 9RSSH 10RSSH 11

RSSH 12

RSSH 13

RSSH 14

RSSH 15

RSSHs Affiliation

N = 8

N = 3

N = 1

N = 11

N = 1N = 1

N = 10

N = 1

N = 8

N = 3

N = 1N = 2

N = 6

N = 14

N = 3

N = 6

CO

IIY/Y

Y/Y

--

Y/Y-

Y/Y

Y/Y

Y/Y

Y/Y

Y/Y

N/Y

-

Y/Y

-Y/Y

N/Y

Y/Y

--

Y/Y--

Y/Y

Y/Y

Y/Y

Y/Y

Y/Y

-

Y/Y

-N/Y

N = 5

N = 2

N = 1

N = 9

N = 1N = 1

N = 9

N = 1

N = 7

N = 3

N = 1N = 1

N = 6

N = 12

N = 2

N = 3

N = 6

N =3

N = 1

N = 11

N = 1N = 1

N = 9

N = 1

N = 8

N = 3

N = 1N = 2

N = 6

N = 14

N = 2

N = 6

Fig. 5 Summary results of species delimitation, consolidation and identification analyses. From left to right: primary species hypothe-

ses (PSHs) drawn from the automatic barcode gap discovery method and generalized mixed Yule-coalescent model methods from

the COII data set, consolidation of the PSHs drawn from COII, 16S rDNA and ITS2 phylogenetic analyses (monophyly, statistical

support and shared haplotypes between PSHs), retained secondary species hypotheses and their morphological affiliation. Dotted

blocks represent uncertainty on the monophyly of the PSHs (polytomy). Y-N/Y-N represents BI BPP/ML BT statistical support for

PSHs, N: number of colonies sequenced/locus/PSH.

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 913

Eight internal nodes were supported by BPP > 0.95. Par-

ticularly, a cluster of five species (major cluster 1, MC1)

((((Nasutitermes sp. + N. coxipoensis) + N. ephratae) +

N. corniger) + N. callimorphus + N. intermedius) and a

cluster of six species (major cluster 2, MC2) ((((N. simi-

lis1 + N. guayanae) + N. wheeleri) + N. similis2 + N. obs-

curus) + N. unduliceps) were strongly supported by

mitochondrial data.

No incongruence between mitochondrial and nuclear

regions (p = 0.77) was detected with an incongruence

length difference test (ILD test or partition-homogeneity

test) under PAUP (Swofford 2003). The total combined

alignment (66 sequences, 1784 bp, BI partitioned by

gene, arithmetic mean –ln = 7346.36) (Fig. 6b) showed

11 internal nodes supported by BPP>0.95. A major sup-

plementary cluster of three species (major cluster 3,

MC3) (N. surinamensis + (N. acangussu + N. acajutlae))

supported by BPP > 0.95 was distinguished with the

total combined alignment.

Ancestral ecological niche reconstruction and peststatus

Mesquite reconstruction of ancestral characters indi-

cated that the ancestral ecological niche of Guianese

Nasutitermes was forest (proportional likelihood,

PL = 0.99) (Fig. 7). According to our data set, the colo-

nization of savannahs occurred independently in two

clades/lineages: (Nasutitermes sp. + N. coxipoensis) and

N. intermedius. The ancestral ecological niche of MC1, 2

and 3 was found to be forest, with relative likelihoods

suggesting no uncertainty in the character states

(PL = 0.99, 1.00 and 1.00, respectively). The seven spe-

cies reported as agricultural and structural pests in the

literature (Constantino 2002) do not form monophyletic

groups (Fig. 7), with no strict association with forest or

savannah ecological niches. Concerning our sam-

pling, only four Nasutitermes species (N. acangussu,

N. corniger, N. similis1 and N. surinamensis) showed

MON1MAT1ILM1BEL2AWA1BEL3RSE2MDY1ROC1NIS2NOU1NOU2NOU3PAT3RSE1PAT4PAT2NOU25BEL1ELA3CAC3MAT2NOU26NOU4NOU5PAT5PAT6SAU1NOU6

COU3NOU28NOU17NOU16NOU15NOU20NOU18NOU19

NOU8NOU7NOU9PAT8AWA2CAC4IDS3ILM2ILM3NOU21NOU22NOU23NOU24ROC2MAT3SAU2

ELA1MAR1

CAC1IDS2IDS1

NOU14NOU13NOU12NOU11NOU10NOU27 Cyrilliotermes sp.

Silvestritermes sp.

1.00

1.00

0.96

0.97

1.00

1.00

1.00

0.95

1.00

1.00

1.00

0.1

1.00

N. corniger

N. spN. coxipoensis

N. ephratae

N. callimorphus

N. similis2

N. similis1

N. guayanae

N. unduliceps

N. wheeleri

N. obscurus

N. surinamensis

N. acangussu

N. acajutlae

N. octopilis

N. intermedius

MC1

MC2

MC3

MON1MAT1ILM1BEL2AWA1

BEL3RSE2MDY1ROC1NIS2NOU1NOU2NOU3PAT3RSE1PAT4PAT2

BEL1NOU25

ELA3CAC3MAT2NOU26NOU4NOU5PAT5PAT6SAU1NOU6

COU3NOU28NOU17NOU16NOU15NOU20NOU18NOU19

NOU8NOU7NOU9PAT8AWA2CAC4IDS3ILM2ILM3NOU21NOU22NOU23NOU24ROC2MAT3SAU2

ELA1MAR1

CAC1IDS1IDS2

NOU14NOU13NOU12NOU11NOU10NOU27

1.00

1.00

1.00

1.00

1.00

0.96

1.00

1.00

1.00

0.05Cyrilliotermes sp.

Silvestritermes sp.

N. corniger

N. spN. coxipoensis

N. ephratae

N. callimorphus

N. similis2

N. similis1

N. guayanae

N. unduliceps

N. wheeleri

N. obscurus

N. surinamensis

N. acangussu

N. acajutlae

N. octopilis

N. intermedius

MC1

MC2

(a) (b)

Fig. 6 Fifty per cent majority rule consensus trees obtained from the Bayesian inference analyses of (a) the mitochondrial combined

alignment (COII/16S) and (b) total combined alignment (COII/16S/ITS2) with Bayesian posterior probabilities >0.95 plotted on the

nodes (intraspecific supports have been removed for clarity).

© 2013 John Wiley & Sons Ltd

914 V. ROY ET AL.

colonies established in cultures and no structural pests

were sampled.

Discussion

Species delimitation in the genus Nasutitermes

Delineating and identifying species are fundamental

aims to appreciate biodiversity, to understand biological

interactions and to define functions in ecosystems.

According to Dayrat (2005), there is a critical need for

rigorously delineated species, not only for producing

accurate species inventories but because most questions

in evolutionary biology, ecology or conservation biology

depend in part on such species inventories and our

knowledge of species. In the case of diversified taxa,

such as the Nasutitermes genus, approaches based on

molecular data can provide support to species delimita-

tion and identification and accelerate biodiversity inven-

tory procedures or identification processes of pest

species for applied purposes.

In a recent study, Monaghan et al. (2009) tested the

validity of species-delimitation procedures in termites,

using the GMYC approach on the single gene COII.

Authors argued for this choice by the fact that COII

was the most polymorphic mtDNA marker that could

be readily amplified in termites, based on previous

experience in their laboratory. Although COII is not the

most variable region of the termite mitochondrial gen-

ome, Cameron and Whiting’s study (2007) on Reticulit-

ermes genomes confirmed that it is one of the most

polymorphic. Monaghan et al. (2009) validated the

N. intermedius

Nasutitermes sp.

N. similis1

N. wheeleri

N. similis2

N. obscurus

N. unduliceps

N. acajutlae

N. corniger

N. coxipoensis

N. ephratae

N. callimorphus

N. guyanae

N. surinamensis

N. acangussu

N. octopilis

MC1

MC2

MC3

Nasute termites reported as pest species in South America (Constantino 2002)

Species Site of collection (this study)

Pest status (Constantino 2002)

N. acajutlae F

N. acangussu F+AA P

N. callimorphus F P

N. obscurus F

N. corniger F+AA P

N. coxipoensis S

N. ephratae F P

N. guayanae F P

N. intermedius S

N. octopilis F P

N. similis1 F+AA

N. similis2 F

N. surinamensis F+AA P

N. unduliceps F

N. wheeleri F

Nasutitermes sp. S

Forest

Savannah

Nasute termites found associated with humans in this study

Fig. 7 Sites of collection and pest status of the sampled species, shown with a reconstruction of ancestral ecological niches obtained

with Mesquite based on the topology of the total combined tree. The ancestral habitat reconstruction was only performed for forest

vs. savannah; pest status and human association were mapped onto the tree. The ancestral habitat reconstruction was only per-

formed for forest vs. savannah; pest status and human association were mapped onto the tree. AA: anthropized areas, S: savannah,

F: forest, P: pest, MC1, 2 and 3: major clades 1, 2 and 3.

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 915

approach on the termite model, justifying that GMYC

captures species boundaries comparable to those from

traditional methods (NPSHs GMYC = 23/24 and

Nmorpho = 22) and from an additional nuclear marker

(28S rDNA). In the current study, it was essentially for

practical reasons that COII was used instead of the

standardized barcode gene COI because PCR amplifica-

tion problems are recurrent for the COI gene, and

pseudogenes are strongly suspected to be encountered

when sequencing COI gene in termites. Indeed, as

underlined by Hausberger et al. (2011), the occurrence

of numts can result in misleading phylogenies and

overestimate the number of species delimited. Here, we

draw the attention on the fact that the COI gene should

be used with caution because of pseudogenes, but in no

case does this study discredit its potential utility in the

delimitation of termite species.

Automatic barcode gap discovery method (Puillandre

et al. 2012a) and GMYC (Pons et al. 2006) exploratory

procedures were applied to delimit PSHs among 79

Nasutitermes colonies collected throughout French Gui-

ana without a priori knowledge on species affiliation.

ABGD, which was applied here for the first time to

termite PSH delimitation, and GMYC retrieved similar

numbers of molecular clusters: ABGD made it possible

to delimit 16 PSHs and GMYC to delimit 15 PSHs.

ABGD and GMYC methods have been used together

and compared in a growing number of recent papers:

Puillandre et al. (2012b) and Pantaleoni & Badano

(2012) showed that GMYC and ABGD recovered similar

partitions, while Puillandre et al. (2012a) and Castelin

et al. (2012) showed contrasting results. The two meth-

ods were compared in detail in Puillandre et al. (2012b),

and the authors suggested that the two procedures

were complementary and should be used together to

increase the overall robustness of the final partition.

Exploratory single-gene methods of species delimita-

tion nevertheless have some limitations that need to be

discussed. First, as underlined by Puillandre et al.

(2012b), both ABGD and GYMC methods are problematic

when species are represented with only a few specimens.

Simulations showed that ABGD works when there are

more than 3–5 sequences per species (Puillandre et al.

2012a) and GMYC, when singletons account for up to

60% of delineated species (Monaghan et al. 2009). Our

COII data set comprised five sequences per species and

about 30% of singletons, thus exceeding these limits, but

some singleton PSHs remain difficult to discuss with

other characters and criteria. Second, the geographical

scale of sampling has been shown to affect intraspecific

genetic variability and genetic divergence from the clos-

est heterospecific (Bergsten et al. 2012). It is very likely

that our sampling scheme, which that was restricted to

French Guiana, overestimated the molecular distinctness

between species. Inversely, results may also be underesti-

mated if there is gene flow between species. We are

aware of the fact that this sampling design was not ideal

and that a bigger and more representative data set would

reduce bias induced by regionally restricted sampling.

Particularly, species with large distribution areas, such as

N. corniger and N. ephratae, merit further molecular and

morphological studies. However, results from this study

were quite clear, and morphological identification of

RSSHs confirmed that this kind of approach is applicable

to regional Nasutitermes data sets. Except for two RSSHs

(RSSH 6 and RSSH 8), which were both affiliated to

N. similis, each RSSH examined corresponded to a clearly

identified species. The absence of a clear morphological

distinction between RSSH 6 and RSSH 8 could be

explained either by close phylogenetic proximity or simi-

lar ecology. This phenomenon could be extended to

N. similis and N. guayanae because the two species are

hardly distinguishable, differing only in coloration

(Emerson 1935). Conservation in ethanol could alter this

character and confound identifications; therefore, it is

likely that the specimens identified here as N. similis1 are

actually N. guayanae.

Single-gene approaches such as ABGD and GMYC

have been demonstrated here to constitute informative

tools to detect genetic divergence even in cryptic spe-

cies such as N. similis and N. guayanae. Phylogenetic

analyses of additional genes to consolidate the single-

gene approach of delimitation showed no clear incon-

gruence in the delineation of putative species. This

result was clearly expected for mitochondrial 16S

rDNA, whose evolutionary history is linked to that of

COII, but needed to be confirmed with an unlinked

locus. A preliminary analysis on Nasutitermes species

based on partial nuclear 28S rDNA sequences showed

inappropriate variability at this evolutionary scale (data

not shown). Although ITS2 were shorter and less vari-

able sequences than mitochondrial markers, they were

suspected to offer a useful level of power for phyloge-

netic resolution at the interspecific scale (Jenkins et al.

2001; Uva et al. 2004; Roy et al. 2006). Information

obtained here from ITS2 unlinked region was not in

conflict with mitochondrial proposition, but was less

informative as it merged some of the PSHs. Indeed, it is

likely that the nuclear data set did not have enough

variable positions to resolve PSHs very closely.

Phylogenetic relationships

As underlined in the species-delimitation analysis, mito-

chondrial and nuclear data showed no conflict and pro-

vide phylogenetic information that converged towards

the same phylogenetic tree. Because single-gene

fragments were quite short (358–721 bp), combining

© 2013 John Wiley & Sons Ltd

916 V. ROY ET AL.

congruent data could increase the accuracy of the phy-

logenetic reconstruction by increasing the length of the

alignment. Part of the increase in accuracy afforded by

concatenating multiple genes is contributed because

individual-gene trees may have many multifurcating

internal branches. Adding genes to a data set by concat-

enation increases the absolute number of evolutionary

changes on such branches and makes it possible to infer

them with greater accuracy. An overall increase in

sequence length also would lead to smaller variances

for evolutionary distances and other parameters in

model-based methods (Gadagkar et al. 2005). Here,

combined alignments, particularly the total combined

alignment, produced a resolved tree, with high BPP

supports.

Phylogenetic reconstructions revealed a major con-

sensus cluster, MC1: ((N. corniger + ((Nasutitermes sp. +N. coxipoensis) + N. ephratae)) + N. callimorphus + N.

intermedius). N. corniger and N. ephratae have often been

described as phylogenetically close species (Miura et al.

2000; Scheffrahn et al. 2005a,b) but, as underlined by

Hartke and Rosengaus (2011), phylogenetic analyses to

date had not clarified whether or not N. corniger and

N. ephratae are sister species. The two species are char-

acterized by differences in nest architecture, morphol-

ogy and molecular data, but data from reproductive

behaviour experiments (Hartke & Rosengaus 2011),

defensive secretions (Prestwich 1983) and isozyme

analyses (Collet & Ruvolo-Takasusuki 2003) suggest

that reproductive isolation between the species is not

complete and that hybridization could periodically

occur in nature. Based on the results of the current

study, N. ephratae does not seem to be the sister clade

of N. corniger but that of the cluster formed by the two

savannah species, N. coxipoensis and Nasutitermes sp.

Another cluster included N. surinamensis, N. acajutlae

and N. acangussu. N. acajutlae and Nasutitermes nigriceps,

the last of which was not found in our Guianese sam-

pling, are part of the N. nigriceps complex of species.

The known distributions of N. acajutlae and N. nigriceps

are allopatric (Thorne et al. 1994). Previous inventories

in French Guiana indicated the presence of both species

(Lefeuve 1990; Ensaf 2010), suggesting either a problem

of identification for specimens affiliated to N. acajutlae

and N. nigriceps or a sympatric distribution of the two

species in this region. Constantino (2002) suggested that

the species identified as N. nigriceps in South America

was probably either N. acajutlae or N. macrocephalus,

supporting the first hypothesis.

Pest species delimitation and evolution

In a review of pest termite species in South America,

Constantino (2002) reported a total of 77 structural or

agricultural pest species: 40 species were reported as

structural pests, 53 species as agricultural pests and 15

species as both. In this study, we provide molecular

information that can be used to accelerate further

delimitation and identification of seven of these species:

N. acangussu (a structural pest in Brazil), N. callimorphus

(a structural pest in Brazil), N. corniger (a widespread

structural and agricultural pest), N. ephratae (a wide-

spread structural and agricultural pest), N. guayanae (a

structural pest in Brazil, Colombia, The Guianas, Trini-

dad and Venezuela), N. octopilis (an agricultural pest in

Guyana and Brazilian Amazonia) and N. surinamensis (a

structural and agricultural pest in Amazonia and The

Guianas) (Constantino 2002). Here, only three of them

have been clearly identified in areas of French Guiana

under human influence: N. acangussu in Amerindian

cultures of Maripasoula and Elah�e, and N. corniger and

N. surinamensis in various cultures. One more species,

N. similis1, has been observed in Amerindian cultures

of Bellevue, while N. similis is not referenced as a pest

species. The presence of N. similis1 in cultures could be

serendipitous and did not cause damage to crops (but

see Discussion about N. similis1 identification). The four

other pest species (N. callimorphus, N. ephratae, N. guay-

anae and N. octopilis) have only been recorded in forests

in this study. However, it is noteworthy that colonies

affiliated to the seven pest species described in the

literature represented more than 55% of the colonies

sampled throughout French Guiana (44/79 colonies),

that is, they represent a real invasive pest potential in

the context of growing anthropization of ecosystems. A

compelling question is the basis of the difference

between the pest and nonpest species. Pest status can

be attributed to several factors, including reproductive

potential, invasiveness or the range of host crops (Reitz

2009). All of these factors are related to the basic life

cycle and life history strategy of the species, which

urgently need to be studied and compared between

Nasutitermes species.

According to the position of pest species in the phy-

logeny and the reconstruction of ancestral ecological

niches, their pest status did not seem to emerge from a

single clade or ecological niche because pest species

were scattered throughout the tree. MC1 was the major-

pest lineage comprising three pest species and includ-

ing the most important pest of the genus, N. corniger

(Constantino 2002). MC2 and MC3 also included pest

species (N. guayanae, and N. surinamensis and N. acan-

gussu, respectively). For the pest species listed here,

despite the fact that our tree is restricted to Guyanese

species, it may be assumed that there were various

independent instances of the species becoming pests.

Phylogenetic relatedness is often cited as an important

component of invasive potential (Strauss et al. 2006). In

© 2013 John Wiley & Sons Ltd

SPECIES DELIMITATION IN NASUTITERMES SPECIES 917

their review on biological invasions, Le Roux and Wie-

czorek (2009) suggested that molecular phylogenies that

describe relatedness among closely related taxa that are

highly invasive, moderately invasive, minimally inva-

sive and noninvasive should reveal whether a correla-

tion exists between genetic relatedness and

invasiveness. They suggested that new and powerful

risk assessment and management protocols could be

developed if molecular phylogenies supported the cur-

rent views on the phylogenetic importance in invasive-

ness. An accurate recognition of pest species, including

the identification and characterization of cryptic species

complexes, coupled to the knowledge of evolutionary

history of ‘major-pest lineages’, is an ongoing challenge

for the application of specific control measures.

Acknowledgements

The authors are grateful to the staff of CIRAD and IRD

(Kourou, Cayenne) for field research logistic work, Valery

Gond, Corinne Rouland, Noureddine Bousserrhine, Patrick

Lavelle, Thibaud Deca€ens and Emmanuel Lapied for their sci-

entific field collaboration, Mrs Kouyouri, Mr Vanq and Mr So-

tak for agreeing to allow us to sample termites in Amerindian

and Hmongs cultural areas. The authors are grateful to Jerome

Chave and Philippe Gaucher for the use of sampling facilities

in the Nouragues station. We would also like to thank the edi-

tors of this journal and four anonymous reviewers for their

many helpful comments. This work was partially supported by

the MEDD (Ecosyst�emes Tropicaux Program, Minist�ere de

l’Environnement et du D�eveloppement Durable, France) and

by the 2010 CNRS Nouragues research grant.

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Banks N (1918) The termites of Panama and British Guiana.

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659–667.Bergamaschi S, Dawes-Gromadzki TZ, Luchetti A, Marini M,

Mantovani B (2007) Molecular taxonomy and phylogenetic

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V.R. and M.H. designed research, analysed data and

wrote the article with the help of M.D., V.C. and

R.C., and V.C., V.R., M.H. and P.M. participated in

the sampling. V.R. and S.G.-M. performed the

molecular analyses. R.C. performed the morphological

analyses.

Data accessibility

DNA sequences: GenBank accession numbers for all

unique DNA sequences: COII: KC630989–KC631024,

16S rDNA: KF724731–KF724764, ITS2: KF724765–

KF724841.

Morphological data set, total combined sequence

alignment and DNA sequences: Dryad entry doi:10.

5061/dryad.gh317 (Roy 2013).

Supporting information

Additional supporting information may be found in the online

version of this article.

Appendix S1 Material and methods.

Table S1 Sequences of primers and PCR profiles used.

Table S2 Mean corrected p-distances within each PSH (intra-

cluster p-distance) for the COII gene.

Table S3 Pairwise corrected p-distances between PSHs (inter-

cluster p-distance) for the COII gene (below diagonal) and

Standard Errors (above diagonal).

Fig. S1 Map of French Guiana, showing the 16 geographic sites

of termite sampling.

Fig. S2 Median joining network showing relationships between

reconstructed ITS2 haplotypes of Nasutitermes complete data

set (N = 75 9 2).

© 2013 John Wiley & Sons Ltd

920 V. ROY ET AL.