Molecular Ecology (2009) 18, 3268–3282 doi: 10.1111/j.1365-294X.2009.04265.x

Evidence for a discrete evolutionary lineage withinEquatorial Guinea suggests that the tsetse fly Glossinapalpalis palpalis exists as a species complex

N. A. DYER,* A. FURTADO,† J . CANO,‡ F. FERREIRA,§ M. ODETE AFONSO,–

N. NDONG-MABALE,** P . NDONG-ASUMU,†† S . CENTENO-LIMA,§ A. BENITO,‡

D. WEETMAN,* M. J . DONNELLY* and J . PINTO†

*Vector Group, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK, †Centro de Malaria e outras

Doencas Tropicais – LA, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Rua da Junqueira 96, 1349-008

Lisbon, Portugal, ‡Centro Nacional de Medicina Tropical, Instituto de Salud Carlos III, Sinesio Delgado, 4 pabellon 13, 28029

Madrid, Spain, §Unidade de Clınica das Doencas Tropicais and Centro de Malaria e Outras Doencas Tropicais – LA, Instituto

de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Rua da Junqueira, 96, 1349-008 Lisbon, Portugal, –Unidade de

Entomologia Medica ⁄ UPMM, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Rua da Junqueira 96,

1349-008 Lisbon, Portugal, **Centro de Referencia para el Control de Endemias, Instituto de Salud Carlos III, Malabo,

Equatorial Guinea, ††Programa Nacional de Control de la Tripanosomiasis, Ministerio de Sanidad y Bienestar Social,

Bata, Equatorial Guinea

Corresponde

E-mail: ndye

Abstract

Tsetse flies of the palpalis group are major vectors of Human African Trypanosomiasis in

Africa. Accurate knowledge of species identity is essential for vector control. Here, we

combine ribosomal internal transcribed spacer 1 (ITS1), mitochondrial CytochromeOxidase 1 (COI) and microsatellites to determine the population structure and

phylogenetic relations of Glossina p. palpalis in Equatorial Guinea. CO1 sequence data

suggest that G. p. palpalis in Equatorial Guinea is a distinct subspecies from previously

described G. p. palpalis in West Africa and Democratic Republic of Congo. Glossinap. palpalis in Equatorial Guinea and DRC share a common ancestor which diverged from

West African G. p. palpalis around 1.9 Ma. Previous ITS1 length polymorphism data

suggested the possible presence of hybrids in Equatorial Guinea. However, ITS1 showed

incomplete lineage sorting compared with clearly defined COI groups, and data from 12

unlinked microsatellites provided no evidence of hybridization. Microsatellite data

indicated moderate but significant differentiation between the populations analysed

(Rio Campo, Mbini and Kogo). Moreover, unlike previous studies of G. p. palpalis, there

was no evidence for heterozygote deficiency, presence of migrants or cryptic population

structure. Variance effective population size at Rio Campo was estimated at 501–731

assuming eight generations per year. This study of the population genetics of G. p.palpalis in central Africa provides the first estimate of genetic differentiation between

geographically separated G. p. palpalis populations.

Keywords: Allopatric speciation, caliginea, central Africa, Glossina palpalis, hybrids

Received 19 March 2009; revision received 28 April 2009; accepted 5 May 2009

Introduction

Tsetse flies (Glossina) are the only extant genus of the

family Glossinidae in the super family Hippoboscoidea.

nce: Naomi Dyer, Fax: +44 151 7053369;

r@liv.ac.uk

Tsetse flies of the Glossina palpalis group are major vec-

tors of Human African Trypanosomiasis (HAT) in sub-

Saharan Africa. Allopatric speciation in times of habitat

contraction is thought to have driven the speciation of

many of the existing tsetse species. Within the taxon

G. palpalis s.l. there are two subspecies, G. p. palpalis

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GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 26 9

and G. p. gambiensis, which show high levels of mito-

chondrial sequence divergence and postzygotic isola-

tion, with F1 males of intersubspecific crosses being

sterile (Gooding 1997). The subspecies are almost

entirely allopatric, with G. p. palpalis having a more

southerly distribution. Challier et al. (1983) proposed

that the subspecies diverged from an ancestral palpalis

species following the contraction of their riverine forest

habitat during a dry period at the last glacial maximum

around 19 000–13 000 years ago. Mating experiments

between colonies of G. p. palpalis originating in Nigeria

and the Democratic Republic of the Congo (DRC)

yielded sterile F1 hybrid males, suggesting that G. p.

palpalis itself may consist of more than one subspecies

(Gooding et al. 2004). A molecular phylogenetic study

of the palpalis group tsetse produced results consistent

with this hypothesis, as G. p. palpalis from DRC and

West Africa had divergent Cytochrome Oxidase 1 (CO1)

gene sequences and ribosomal internal transcribed spacer

1 (ITS1) lengths (Dyer et al. 2008).

Intriguingly, G. p. palpalis from Equatorial Guinea can

have ITS1 length polymorphism, containing copies of

ITS1 of the same size as both West African and DRC

flies, suggesting the possible presence of hybrids in this

population (Dyer et al. 2008). Intraspecific and intrain-

dividual length polymorphism due to insertions and

deletions of large sections or microsatellites in ITS1 has

been observed in many other insects including tiger

beetles (Cicindela dorsalis), anopheline mosquitoes and

black flies (Simuliidae) (Paskewitz et al. 1993; Vogler &

DeSalle 1994; Tang et al. 1996). The tandem repeats of

the ribosomal DNA region are homogenized by con-

certed evolution, a process of molecular drive involving

gene conversion events and unequal crossing over,

although this process is less effective between arrays

located on different chromosomes (Dover 1982; Ohta &

Dover 1984; Schlotterer & Tautz 1994; Vogler & DeSalle

1994). The frequent observation of heterogeneous ITS1

copies in individuals from complexes of sibling species

suggests that interbreeding might partially counteract

the homogenizing effects of concerted evolution, allow-

ing the introduction of new ITS1 alleles into species

(Tang et al. 1996).

Vector control is central to the control of HAT, espe-

cially the rhodesiense form, as there is no vaccine and

treatment of late stages of the disease can be problem-

atic. Vector control measures include the use of insecti-

cide-treated traps, screens or cattle, aerial sequential

spraying with pyrethroids and sterile insect releases.

The dispersal capacity of an insect vector species is a

key factor for vector control strategies as it determines

the extent to which vector control measures need to

be implemented, and the spread of genes of interest,

such as insecticide resistance genes. In combination

� 2009 Blackwell Publishing Ltd

with geographical information, population genetics can

define migration rates of reproducing migrants and bar-

riers to tsetse movement. For example, the elimination

of G. p. gambiensis from the Loos islands, Guinea was

predicated on molecular data (Camara et al. 2006; So-

lano et al. 2009). There was a high level of genetic dif-

ferentiation from the mainland (30 km distant),

suggesting that reproducing immigrants are extremely

rare. Thus, repopulation of the islands from the main-

land was deemed unlikely. Whether isolated tsetse pop-

ulations exist in mainland Africa is less clear. In Cote

d’Ivoire G. p. gambiensis populations showed evidence

for isolation by distance (IBD) along a 216-km stretch of

the Mouhoun River suggesting that habitat fragmenta-

tion via destruction of gallery forests limits distribution

and gene flow (Bouyer et al. 2007b). The levels of differ-

entiation are far lower than in the island vs. mainland

difference in Guinea, reflecting either recent population

fragmentation (that has not yet dissipated IBD) or less

complete separation of the populations (that maintains

IBD). Populations of the palpalis group species Glossina

fuscipes fuscipes, are highly differentiated north and

south of Lake Kyoga in Uganda, but show low levels of

differentiation within northern or southern populations

up to 125 km apart but not separated by the lake. This

suggests that Lake Kyoga may form an ecological bar-

rier to genetic exchange between Ugandan G. f. fuscipes

populations (Abila et al. 2008).

In Equatorial Guinea HAT is endemic, with four

known foci (WHO 2006), although, in the Bioko island

focus, trypanosomiasis has not been detected since

1995, following a sustained control campaign (Simarro

et al. 2006). In continental Equatorial Guinea, there are

three active foci: Rio Campo in the north; Mbini in the

centre and Kogo in the south. Rio Campo is at the

mouth of the river Ntem, at the northern border with

Cameroon. Mbini is drained by the river Wele and trib-

utaries, and Kogo is drained by several rivers which

converge at the Muni estuary. Since 2002 a combination

of monopyramidal traps and deltamethrin-impregnated

screens have been used to control tsetse flies in Mbini

and Kogo together with concomitant surveys of Glossina

distribution to monitor the impact of the control mea-

sures (Cano et al. 2007a, b). The area around Rio

Campo is described by Simo et al. (2008) as ‘a mix of

equatorial rainforest with farmland, marshes and

swampy areas along the coast’. Glossina p. palpalis is the

most common species (67.2% of tsetse caught), with G.

pallicera, G. caliginea and G. nigrofusca also present (Simo

et al. 2008). Glossina p. palpalis is considered the most

important HAT vector species in Equatorial Guinea. Lit-

tle data are available on the vector status of G. caliginea,

which has a limited distribution in the coastal man-

groves and forests of western Ghana to the Congo,

3270 N. A. DYER ET AL.

although it has also been reported to bite humans and

to transmit T. b. brucei and T. congolense, agents of Ani-

mal African Trypanosomiasis (Nash 1969; Fraga de

Azevedo 1970; Gouteux et al. 1987).

The aim of this study was to investigate the taxo-

nomic status of G. palpalis from Equatorial Guinea and

DRC with respect to West African G. p. palpalis. We

tested the hypothesis that ITS1 length polymorphism in

Equatorial Guinea was due to hybridization using mito-

chondrial and microsatellite markers. Samples were

analysed from the three mainland foci, spanning the

country from north to south, as this more extensive

sample might reveal a previously overlooked popula-

tion structure. Microsatellite data provided a first

glimpse of the population structure of this previously

uncharacterized G. palpalis subspecies in Equatorial Gui-

nea, and an estimate of the variance effective popula-

tion size of the northern (Rio Campo) population. ITS1

and COI were also genotyped for G. caliginea; the first

molecular data available for this species.

Materials and methods

Sample collection

Tsetse fly collections in Equatorial Guinea were carried

out between December 2002 and July 2005 (Fig. 1) using

monopyramidal traps (Gouteux & Lancien 1986).

Samples from Rio Campo (2003), Rio Campo (2005) and

Mbini (2005) were preserved dried in individual tubes

filled with silica gel. Samples from Kogo (2002) were

preserved in 80% ethanol. Specimens identified as

G. palpalis palpalis were shipped to Lisbon, where mor-

phological identification was checked for a subsample of

flies based on the morphological keys of Pollock (1982)

and Brunhes et al. (1994). Other collections of G. p. palpal-

is and G. p, gambiensis, G. fuscipes, G. tachinoides, G. palli-

cera and G. m. morsitans, that were used to infer

phylogenetic relationships, have been described previ-

20 km

Kogo

Mbini

Rio Campo Cameroon

Equatorial Guinea

Gabon

ously (Dyer et al. 2008; Solano et al. 2009). Glossina p.

gambiensis specimens from Senegal were collected from

June to July 2007.

DNA was extracted from individual flies using either

a boiling ⁄ phenol–chloroform (Ferreira et al. 2008) or a

Chelex method (Solano et al. 2000). To avoid microsat-

ellite hemizygotes on the X chromosome, and

amplification of Y chromosomal rDNA, only female

samples were used for DNA extraction and subsequent

analysis.

Microsatellite genotyping

Twelve microsatellites with dinucleotide (Pgp13, Pgp17,

Pgp35, Pgp8, Pgp22, Pgp11, Pgp28 and Pgp1) (Luna

et al. 2001), Gmm8 (Baker & Krafsur 2001) or trinucleo-

tide repeats (GmsCAG02, GpCAG133 and Gms-

CAG29B) (Baker & Krafsur 2001) were amplified using

published primers (primers and conditions are shown

in Table S1). Loci Pgp11 and Pgp13 are on the X chro-

mosome in G. p. gambiensis (Bouyer et al. 2007b). Micro-

satellite genotyping was performed using PCR using

fluorescently labelled forward primers (6-FAM, HEX

and NED). Reactions took place in a 20-lL PCR mix

containing 1· PCR buffer (Promega) 1.5 mM MgCl2,

200 lM of an equimolar mixture of dNTPs, 125–500 nM

of each forward and reverse primer, 0.5 U of Taq DNA

polymerase and 1 lL of a 1:10 dilution of DNA from

stock. Cycling conditions included an initial denatur-

ation at 94 �C for 3 min followed by 35 cycles each with

denaturation at 94 �C for 30 s, annealing at 50–56 �C

(locus dependent) for 30 s and extension at 72 �C for

1 min, followed by 10 min at 72 �C. Amplified frag-

ments were separated by capillary electrophoresis in an

automatic sequencer (ABI 3730, Applied Biosystems).

Fragment sizes were scored using the software Gene-

Marker (SoftGenetics, USA). PCR and electrophoresis of

all negative and ambiguous genotypes were repeated

once.

Fig. 1 Location of sampling sites.

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GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 27 1

Microsatellite analysis

Linkage disequilibrium between each pair of microsatel-

lite loci in each population was assessed using exact

tests on contingency tables using GENEPOP version 4.0

(Raymond & Rousset 1995).

FIS was calculated for each locus and sample

according to Nei (1987). The significance of FIS esti-

mates was assessed by randomization tests (1000 repli-

cates) using FSTAT (Goudet 1995, 2001). Each locus and

sample was tested for large allele drop-out (short

allele dominance) using MICROCHECKER (Van Oosterhout

et al. 2004). Null allele frequencies for each locus and

sample were calculated using the Expectation Maximi-

zation (EM) algorithm (Dempster et al. 1977), imple-

mented by FREENA software (Chapuis & Estoup 2007),

and by Brookfield’s method 1 (Brookfield 1996) using

MICROCHECKER (Van Oosterhout et al. 2004). Population

and individual inbreeding models were also used to

estimate the inbreeding coefficient (F) and null allele

frequency simultaneously from microsatellite data

using INEST software (Yasuda 1968; Chybicki &

Burczyk 2009).

FST was calculated using the ENA method, which

corrects for the positive bias of null alleles on FST esti-

mates (Chapuis & Estoup 2007). FST values were boot-

strapped 10 000 times over loci, and the 95%

confidence intervals taken as the 2.5% and 97.5% per-

centiles. The significance of the variation of allele fre-

quencies between populations was tested by exact

tests of homogeneity of allele frequency distribution

(genic test) between populations using GENEPOP version

4.0 (Raymond & Rousset 1995). FST values cannot be

higher than the homozygosity level, 1 ) HS (Hedrick

2005b). F0ST, an estimator unconstrained by genetic

diversity, was therefore calculated as FST ⁄ 1 ) HS. HS,

the unbiased estimate of genetic diversity was calcu-

lated following Nei (1987) using FSTAT version 2.9.3

(Goudet 1995, 2001).

Individual-based Bayesian approaches were imple-

mented to search for cryptic population structure. The

technique minimizes the departure from HWE and link-

age disequilibrium resulting from individuals originat-

ing from K randomly mating populations being

grouped into one population. STRUCTURE was used to

search for population structure without prior informa-

tion on sample locations (Pritchard et al. 2000; Falush

et al. 2003). Ten replicates of the calculations were per-

formed for K = 1–10, and the log likelihood of each

inferred structure recorded. For each calculation 60 000

iterations were performed, with the first 10 000 dis-

carded as burn-in. Optimum values of K were inferred

using the DeltaK method of Evanno et al. (2005). Bayes-

ian Analysis of Population Structure (BAPS) software

� 2009 Blackwell Publishing Ltd

version 5.2 (Corander et al. 2003, 2004) was also used to

search for cryptic population structure. Initially, we ran

BAPS with K set to maxima of K = 5, 10, 15 or 20, repli-

cated five times (such that all values of K = 2 to

K = maximum were investigated), followed by 10 repli-

cate runs each with fixed K of K = 2–15.

Population size estimates

Current effective population size (Ne) was estimated

based on allele frequency changes between two samples

from Rio Campo, collected in August 2003 and July

2005. Two methods were used: the moment-based (NeF)

method of (Waples 1989) and the likelihood-based (NeL)

method of Berthier et al. (2002). Both approaches

assume that allele frequency change is solely attribut-

able to genetic drift. Both also assume independent

sampling of individuals and independent (unlinked)

loci.

For moment-based NeF estimates, a sampling plan in

which individuals are taken before reproduction and

not replaced was assumed (Plan II, Waples 1989). Sam-

ples used in this study were not replaced, but it is not

known whether samples were taken before or after

reproduction. Calculations were performed using the

software NEESTIMATOR 1.3 (Peel et al. 2004).

The likelihood-based method relies on the assump-

tion that gene genealogies tend to be coalescent as pop-

ulation size increases. To calculate likelihood-based NeL,

the coalescent-based approach developed by Berthier

et al. (2002) was implemented by the TM3 program,

within NEESTIMATOR 1.3 with 20 000 iterations and a

NeMAX of 5000 (Peel et al. 2004). An appropriate value

for NeMAX was determined empirically by running anal-

ysis with NeMAX of 500, 1000 and 5000 for all-locus esti-

mates and observing likelihood posterior distribution

plots (Fig. S3). Both NeF and NeL methods require infor-

mation on the number of generations between time

point samples. Glossina generation times vary with the

species and environmental conditions (mainly tempera-

ture). Based on the duration of the different life stages,

the generation length in days varies between 38 and

120. At 25 �C, generation time is around 43 days (Har-

grove 2005). Taking the time interval between collec-

tions (2 years or approximately 730 days) and the

tropical humid climate with mean annual temperatures

around 25 �C in Equatorial Guinea, we predict about 16

generations between temporal samples in Rio Campo.

This gives roughly eight generations per year which

was the value used to estimate NeF for Glossina pallidipes

in southwestern Kenya (Ouma et al. 2006). Estimates

were calculated assuming this value and a minimum

and maximum of 6 (i.e. three per year) and 20 (i.e. 10

per year) generations respectively.

3272 N. A. DYER ET AL.

Simulation studies have shown that estimates of NeF

are skewed by allele frequencies approaching 0 or 1

(very rare or very common alleles) (Waples 1989; Ber-

thier et al. 2002). Rare alleles tend to lead to overestima-

tion of NeF. Therefore, alleles with frequency <2% in

both samples were pooled into a single class to calcu-

late NeF and NeL. Conversely, the precision of NeL is

increased with a higher number of independent alleles.

Therefore, NeL was also calculated using the original

database.

PCR and sequencing of COI and ITS1

PCR was used to amplify 880 bp of the 3¢-region of COI

using the primers CI-J-2195 TTGATTTTTTGGTCATC-

CAGAAGT (Simon et al. 1994) and CULR TGAAGCT-

TAAATTCATTGCACTAATC as described previously

(Dyer et al. 2008). A portion of ITS1 was amplified

using the primers Diagfor (TGGACTTCGGATTAAGT-

ACAACA) and Diagrev (TCATTATGCGCTATTAAGG-

TAAGC) as described previously (Dyer et al. 2008).

PCR products were electrophoresed on 3% agarose gels

labelled with ethidium bromide, and viewed using a

UV transilluminator. Fragment size was determined by

naked eye with respect to Hyperladder 5 (Bioline) and

control products: G. p. palpalis from DRC and G. p. palp-

alis from Cameroon. All ITS1 PCRs were repeated and

scored twice.

To determine the sequence of the different ITS1

length variants, where possible, a larger portion of ITS1

was amplified using either the Diagfor ⁄ Diagrev primer

pair or 5.8SCAS5p8sB1dshort TGCGTTCAAAATGTC-

GATGTTCA and TCAS18sF1shorter CACACCGCCCGT

CGCTACTA as described previously (Ji et al. 2003;

Dyer et al. 2008).

For sequencing, PCR products were purified using

Sureclean reagent (Bioline). COI PCR products were

sequenced directly using an ABI 3730XL (Life Technolo-

gies Corporation, Carlsbad, CA, USA) capillary sequen-

cer by Macrogen (Macrogen Inc., Seoul, South Korea).

ITS1 PCR products were excised from agarose gel,

cloned into the PGEM-TEASY vector (Promega) and

sequenced using the SP6 and T7 promoter primers.

For each PCR product or plasmid sequenced, forward

and reverse sequences were aligned and traces exam-

ined using CodonCode Aligner version 2.0.6 (Codon-

Code Corporation, Dedham, MA, USA).

Phylogenetic analysis

COI sequences were aligned and trimmed using the

CLUSTALW algorithm in MEGA version 4 resulting in an

alignment of 622 nucleotides. ITS1 sequences were

aligned with published Glossina ITS1 data (GenBank

accession no. EU591930–EU591942) using PRANKSTER

alignment software, which is more effective at recovering

phylogenetically meaningful alignments of sequences

containing many indels than CLUSTALW (Loytynoja &

Goldman 2005, 2008).

COI sequences of 14 individuals with an ITS1 length

of 240–250 or 240–250 plus 330 bp (one from Rio

Campo, seven from Mbini and six from Kogo), as well

as four of the individuals with the short (164 bp) ITS1

(KO138, KO144, RC136 and RC167) were aligned with

published Glossina COI sequences (Dyer et al. 2008:

GenBank accession no. EU591824, EU591826–EU591823,

EU591836, EU591838–EU591844, EU591846–EU591848,

EU591850–EU591865, EU591870–EU591871, EU591876–

EU591877) and used to construct phylogenetic trees.

Duplicate COI haplotypes were removed from the

alignment for tree building. Maximum parsimony trees

were generated using MEGA version 4, using the close

neighbour interchange algorithm with search level 3

(Nei & Kumar 2000). Initial trees were obtained (100

replicates) by random order of sequence addition.

MODELTEST (Posada & Crandall 1998) was used to choose

the model of substitution to use for calculation of dis-

tances and tree building. The model favoured by Ak-

aike Information Criterion 1 was the TRN + I + Cmodel of evolution, with the proportion of invariable

sites (I) = 0.7180 and the parameter describing the

shape of the gamma distribution a = 1.2229 (Tamura &

Nei 1993). The distance-based, neighbour-joining trees

were produced using PAUP version 4.0 using heuristic

tree searching (Swofford 2002). The maximum likeli-

hood tree was generated using PHYML online, allowing

four C categories (Guindon & Gascuel 2003; Guindon

et al. 2005). In all cases, a consensus tree was generated

from bootstrap replicates (2000 for maximum parsi-

mony and distance and 1000 for maximum likelihood).

Two genetic distances were calculated: DXY, the aver-

age number of nucleotide substitutions between haplo-

types from the two populations, and DA, the net

number of nucleotide substitutions per site between

haplotypes from pairs of populations (Nei 1987). The

standard error of DXY and DA due to sampling was cal-

culated using a bootstrap method with 2000 bootstraps

using MEGA4 (Tamura et al. 2007). Estimates were com-

puted for 16 individuals from Equatorial Guinea (14

individuals from this study plus two samples from

Kogo from a previous study (EU591825 and EU591865),

10 individuals from DRC (EU591840–EU591842) from a

previous study plus seven additional specimens from

this study (FJ767870–FJ767871). West Africa was repre-

sented by 14 individuals from Cameroon (EU591829–

EU591831, EU591860, EU591865), two from Togo

(EU591838–EU591839), seven from Cote d’Ivoire

(EU591846–EU591850) and five from Liberia

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GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 27 3

(EU591856–EU591859) (Dyer et al. 2008). For the G. p.

palpalis–G. p. gambiensis comparison, all the above

sequences were used to represent G. p. palpalis and a set

of 55 G. p. gambiensis sequences were used, representing

individuals from across the species range from Senegal

(FJ787504, FJ787506–FJ787511), Guinea (FJ387505–

FJ387524), the Gambia, Liberia and Burkina Faso

(EU591849–EU591855). The 95% confidence intervals of

DA were taken to be DA ) 1.96(SE) to DA + 1.96(SE).

The minimum divergence time = DA ⁄ divergence rate.

The ‘divergence rate’ is twice the substitution rate. To

test the molecular clock, a tree was constructed using

52 sequences: the G. p. palpalis and G. p. gambiensis

sequences above, without duplicate haplotypes, and an

outgroup (G. fuscipes EU591876). The HKY + I + Cmodel was defined by hierarchical likelihood ratio tests

to be appropriate for this data set using MODELTEST

(Posada & Crandall 1998). This model of evolution was

used to estimate pairwise sequence distances to make a

neighbour-joining tree using PAUP version 4.0 b10 (Swof-

ford 2002) with 2000 bootstrap replicates. For the likeli-

hood ratio test, the likelihood of this tree assuming the

HKY substitution model either assuming clocklike evo-

lution at the same rate throughout the tree (ln L1) or

without assuming the molecular clock (ln L0) was calcu-

lated using the BASEML programme in the PAML package

(Yang 1997). 2(ln L0 ) ln L1) is approximately chi-

squared distributed, with n ) 2 degrees of freedom,

where n is the number of sequences. The two-cluster

test tests the hypothesis that the average substitution

rate in sister groups in the tree is the same. The lintre

Table 1 ITS1 size variation

Population

Sample size

(G. p. palpalis) No amplification

Rio Campo 2003 41 (37) 4 (0.108)

Rio Campo 2005 33 (32) 5 (0.156)

Mbini 2005 27 (27) 0 (0.000)

Kogo 2002 32 (28) 8 (0.286)

The numbers of individuals having different ITS1 forms are indicated

each population.

Fig. 2 PCR products of primers Diagfor and Diagrev were electropho

ethidium bromide. ITS1 PCR products of G. p. palpalis from DRC and

dual from, Kogo, and RC073 were both scored as having two ITS1 f

were scored as only having the 240–250 bp form. RC075 has the 164bp

� 2009 Blackwell Publishing Ltd

package was used to implement the two-cluster test on

the same data set as the likelihood ratio test (Takezaki

et al. 1995).

Results

Internal transcribed spacer size polymorphism

The ITS1 PCR product sizes for all individuals are sum-

marized in Table 1 and Fig. 2. The phenotypes

observed were either no amplification, small (240–

250 bp) or large plus small (240–250 plus 330–333 bp).

The relative intensity of these bands was variable,

whereas the scoring system was discrete (Fig. 2). It is

therefore possible that some individuals harbouring a

small number of copies of the 330–333-bp ITS1 would

have been scored as lacking that form. In addition, a

total of nine individuals had an ITS1 size of 164 bp. A

subset of these individuals was identified on the basis

of genital morphology and abdominal second tergite

pigmentation to be G. caliginea. Mitochondrial and mi-

crosatellite data were obtained for these individuals

and are discussed below.

Ribosomal DNA, which consists of multiple tandem

repeats, has been mapped to autosome L1 and the Y

chromosome in G. p. palpalis (Willhoeft 1997). Due to

the possibility of repeat size variation within the tan-

dem array of each chromosome, we treated the ITS1

scores as phenotypic rather than diploid markers. The

distribution of ITS1 forms between populations was

tested by Fisher’s exact test on the contingency table of

ITS1 240–250

+ 330–333 bp ITS1 240–250 bp ITS1 164 bp

1 (0.027) 32 (0.865) 4

0 (0.000) 27 (0.844) 1

5 (0.185) 22 (0.815) 0

13 (0.464) 7 (0.250) 4

. The number in parenthesis is the frequency of that form in

resed with a size marker on 3% agarose gels and labelled with

Cameroon (Cam) were used as size markers. KO01: an indivi-

orms, whereas RC064 from Rio Campo and MB44 from Mbini

form.

Table 2 FIS values for each locus and sample

Rio Campo

2003

Rio Campo

2005 Mbini 2005 Kogo 2002

Pgp1 0.170 0.102 0.326 0.083

Pgp11 )0.014 0.191 0.075 0.140

Pgp13 0.018 0.021 )0.045 0.162

Pgp17 0.567 0.728 0.139 0.260

Pgp35 0.107 )0.042 0.043 0.173

Gms02 0.012 )0.208 )0.209 0.028

Gmm8 0.158 0.017 0.161 0.063

Gp133 0.188 0.016 0.022 0.103

Gms29 0.022 0.017 0.044 0.236

Pgp8 )0.003 0.134 0.372 )0.042

Pgp22 0.177 0.277 )0.028 0.125

Pgp28 0.068 0.152 )0.012 0.028

All 0.142 0.139 0.086 0.122

F (SE) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.001)

FIS values in bold face have uncorrected P < 0.05 based on a

randomization test. F is the inbreeding coefficient estimated

using the PIM method. The numbers in parenthesis are the

standard error of F.

3274 N. A. DYER ET AL.

repeat size frequencies in each population. Non-amplifi-

cation was probably due to human error or poor DNA

quality rather than mutations in the primer binding site,

as the primers amplify ITS1 from a broad range of Glos-

sina species. Therefore, cases of non-amplification were

not included in the table for testing. Fisher’s exact test

rejected the null hypothesis of random distribution of

ITS forms between populations with high confidence

(P > 0.001). This is due to the high frequency of indi-

viduals with two ITS forms in Kogo, as repeating the

tests following exclusion of this population but not the

other populations from the data set meant that the null

hypothesis could not be rejected.

A portion of ITS1 was cloned from PCR products

from five individuals from DRC and six individuals

showing length polymorphism from Equatorial Guinea

(two from Mbini and four from Kogo). To determine

the position of insertions leading to length variation in

ITS1, the resulting sequences were aligned with

previously published ITS1 sequence data (Dyer et al.

2008) (Fig. S1). The position of the insertion leading to

the 330 bp ITS1 in DRC and Equatorial Guinea is the

same.

No evidence for hybrid forms in microsatellite data

The presence of ITS1 length polymorphism in Equato-

rial Guinea including alleles found in both West Africa

and DRC might imply that the population in Equatorial

Guinea included hybrids between West African and

DRC forms. We sought to investigate the possibility of

hybrid G. p. palpalis in Equatorial Guinea using micro-

satellites. G. p. palpalis females (n = 124) were geno-

typed at 12 microsatellite loci. The mean expected

heterozygosity over loci ranged 0.726–0.773 and the

mean number of alleles per locus ranged 9.9–12.3. No

pair of microsatellites was in significant linkage disequi-

librium in any population.

Most single-locus FIS values were positive indicating

an excess of homozygotes, with 14 of 48 estimates sig-

nificantly different from zero (Table 2). Short allele

dominance and null alleles can bias FIS values upwards

(Wattier et al. 1998). Short allele dominance was not

detected at any locus in any population, indicating that

short allele dominance was not a contributor to

observed FIS values.

Null allele frequency for each locus and population

was calculated. Dempster et al.’s (1977) EM method

and Brookfield’s (1996) method 1 gave very similar

estimates (Table 3). The estimated frequency (EM) was

less than 10% for all loci except for Pgp1 (1–12%)

Pgp17 (6–34%) and Pgp8 (0–13%) (Table 3). If high

FIS values are due to null alleles, one would expect FIS

to be variable amongst loci, with high FIS values for

loci with high null allele frequencies, whereas if high

FIS values were caused by deviation from Hardy–

Weinberg equilibrium genotype frequencies, then high

FIS should be observed across loci. Concordantly, com-

bined estimates of the inbreeding coefficient, F, and

null allele frequencies resulted in estimates of F not

significantly greater than zero in any of the studied

populations (Table 2).

Cluster analysis of 12 loci microsatellite genotypes

from 133 individuals (124 G. p. palpalis plus nine puta-

tive G. caliginea included as an outgroup) was used to

investigate population structure. An optimum of two

populations was identified using STRUCTURE using the

DELTAK method (Fig. S2) (Evanno et al. 2005). In seven

of the 10 replicate runs with K = 2, the G. p. palpalis

(124) individuals, including one individual from the

Kogo population, with an ITS1 of 164 bp were in one

cluster, with the remaining eight G. caliginea individuals

in a second cluster. These eight individuals all had a

small ITS1 size of 164 bp. Two stable clusters were

found using BAPS, with highly confident assignment of

all individuals. The second cluster contained the same

eight individuals as the second STRUCTURE cluster. Solu-

tions suggesting greater levels of subdivision comprised

of clusters in which many individuals were assigned

with low levels of confidence.

The ENA method, which corrects for the positive bias

of null alleles, was used to calculate FST (Table 4). Esti-

mates were corrected for the constraint imposed by the

genetic diversity HS (Hedrick 2005b). The presence of

null alleles did not bias FST substantially. Per locus FST

� 2009 Blackwell Publishing Ltd

Table 3 Estimated null allele frequencies

Locus

Rio Campo 2003 Rio Campo 2005 Mbini 2005 Kogo 2002

EM Br1 PIM EM Br1 PIM EM Br1 PIM EM Br1 PIM

Pgp1 0.07 0.07 0.068 0.01 0.04 0.012 0.12 0.12 0.115 0.04 0.03 0.038

Pgp11 0.00 )0.01 0.000 0.07 0.08 0.073 0.04 0.03 0.039 0.05 0.05 0.050

Gpg 13 0.01 0.00 0.011 0.00 0.00 0.000 0.00 )0.03 0.000 0.05 0.06 0.054

Pgp17 0.27 0.27 0.267 0.34 0.34 0.338 0.06 0.06 0.058 0.11 0.11 0.105

Pgp35 0.01 0.04 0.013 0.00 )0.03 0.000 0.04 0.01 0.035 0.05 0.07 0.053

Gms02 0.00 0.00 0.002 0.00 )0.07 0.000 0.00 )0.05 0.000 0.00 0.00 0.005

Gmm8 0.06 0.07 0.061 0.00 0.00 0.000 0.05 0.07 0.052 0.01 0.02 0.010

Gpp133 0.04 0.07 0.043 0.00 0.00 0.000 0.00 0.00 0.000 0.03 0.04 0.030

Gms29 0.00 0.00 0.000 0.02 0.00 0.023 0.00 0.01 0.001 0.07 0.07 0.068

Pgp8 0.03 )0.01 0.034 0.03 0.05 0.026 0.13 0.14 0.133 0.00 )0.02 0.000

Pgp22 0.08 0.08 0.075 0.13 0.12 0.128 0.00 )0.02 0.000 0.04 0.05 0.039

Pgp28 0.02 0.02 0.023 0.00 0.05 0.006 0.00 )0.01 0.000 0.00 0.00 0.000

EM: null allele frequency using EM method of (Dempster et al. 1977). Br1: null allele frequency estimated using Brookfield (1996)

method 1. PIM: null allele frequency estimated using the Population inbreeding model (Yasuda 1968; Chybicki & Burczyk 2009).

Values in bold indicate that null alleles are expected by MICROCHECKER.

Table 4 Pairwise FST and genetic diversities

Sample pair FST FST (ENA)

HS (uncorrected

dataset)

HS (corrected

for null alleles) F0ST

F0ST

(ENA)

Rio Campo 2003 and Rio

Campo 2005

0.0068** [0.001, 0.013] 0.0070 NS [0.001, 0.013] 0.756 0.757 0.0278 0.0287

Rio Campo 2003

and Mbini 2005

0.0103** [0.002, 0.018] 0.0095* [0.003, 0.016] 0.739 0.748 0.0394 0.0379

Rio Campo 2005

and Mbini 2005

0.0127*** [)0.002, 0.032] 0.0108*** [)0.002, 0.026] 0.758 0.763 0.0526 0.0455

Rio Campo 2003

and Kogo 2002

0.0349*** [0.019, 0.051] 0.0333*** [0.019, 0.047] 0.733 0.741 0.1308 0.1288

Rio Campo 2005

and Kogo 2002

0.0303*** [0.016, 0.048] 0.0294*** [0.015, 0.046] 0.751 0.756 0.1217 0.1205

Mbini 2005 and

Kogo 2002

0.0257*** [0.010, 0.044] 0.0235*** [0.009, 0.040] 0.734 0.746 0.0966 0.0926

FST values in bold had the lower 95% confidence interval (as determined by bootstrapping genotypes among loci) greater than 0.

95% confidence intervals for FST values are given in brackets. FST(ENA): FST corrected for the bias induced by null alleles using the

ENA (excluding null alleles) method (Chapuis & Estoup 2007). HS: genetic diversity ***P < 0.0001; **P < 0.01; *P < 0.05: probability

of the chi-squared value calculated by permuting of alleles between populations to test the null hypothesis of no genic differentiation

between populations. The sequential Bonferroni correction for multiple tests was applied.

GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 27 5

estimates indicated that the differentiation is genome

wide and not restricted to a few loci (Table S2), sug-

gesting that the FST values are due to differentiation

between populations rather than null alleles. The great-

est differentiation was observed between Kogo (2002)

and Rio Campo (2003), with Mbini (2005) also showing

significant differentiation from Kogo (2002). Mbini

(2005) was not highly differentiated from Rio Campo

(2005), despite the greater geographical separation of

these two sample sites.

� 2009 Blackwell Publishing Ltd

Mitochondrial data do not correlate with ITS1genotypes but define Glossina p. palpalis inEquatorial Guinea to be distinct from West Africanand DRC populations

We sought to confirm whether ITS1 phenotype divi-

sions were corroborated by the mitochondrial DNA,

which has a different inheritance pattern and effective

population size. The COI sequence of 14 individuals

with an ITS1 length of 240–250 or 240–250 plus 330 bp

Fig. 3 Strict consensus of maximum likelihood, distance based and maximum parsimony trees for a 622 nucleotide portion of COI.

Support values for common branches are shown at the nodes. The first number at the node is for the maximum likelihood tree (as a

percent of 1000 bootstrap replications; TRN + I + C model of evolution), the second for the distance based tree (as a percent of 2000

bootstrap replications; TRN + I + C model of evolution), and the third number is for the maximum parsimony tree (as a percentage

of 2000 bootstrap replications). The length of the ITS1 PCR product for G. p. palpalis and G. caliginea haplotypes is indicated. Clades

of G. p. palpalis from West Africa (15 haplotypes), G. p. gambiensis (8 haplotypes), G. fuscipes (6 haplotypes), G. pallicera (5 haplotypes)

and G. tachinoides (2 haplotypes) have been collapsed for clarity.

3276 N. A. DYER ET AL.

(one from Rio Campo, seven from Mbini and six from

Kogo), as well as four G. caliginea individuals short

(164 bp) ITS1 (KO138, KO144, RC136 and RC167) were

aligned with published Glossina COI sequences (Dyer

et al. 2008) and used to construct phylogenetic trees

(Fig. 3). The 14 individuals sequenced had six haplo-

types. Glossina p. palpalis COI sequences from Equatorial

Guinea formed a well-supported clade (90% bootstrap

support in maximum likelihood tree) regardless of ITS1

type. Furthermore, the Equatorial Guinea and DRC

Table 5 Average and net substitutions per nucleotide of COI betwee

Population 1 (n) Population 2 (n) DXY (SE)

Equatorial Guinea (16) DRC (10) 0.0403 (0.0072)

West Africa (28) DRC + Equatorial

Guinea (26)

0.0453 (0.0065)

Cameroon (14) Liberia, Togo and

Cote d’Ivoire (14)

0.0111 (0.0024)

G. p. palpalis (54) G. p. gambiensis (54) 0.0683 (0.0087)

n is the number of individuals sampled. DXY is the average number o

two populations. DA is the net number of nucleotide substitutions per

95% confidence intervals for divergence times are given in brackets.

specimens form a clade which is the sister group of the

remainder of G. p. palpalis (bootstrap support for sepa-

ration 88% in the maximum likelihood tree) The COI

data for the specimens identified as G. caliginea on the

basis of morphology and ITS1 phenotype formed a clus-

ter that did not group with palpalis but as a sister taxon

to the fuscipes and palpalis species. The different tree-

building methods inferred different relationships of

G. caliginea to G. pallicera and G. tachinoides but with

low bootstrap support (Fig. 3).

n West African, DRC and Equatorial Guinea Glossina p. palpalis

DA (SE)

Time of divergence

(rate: 1.5% per Myr)

Time of divergence

(rate: 2.8% per Myr)

0.0357 (0.0067) 2.4 [1.5, 3.3] 1.3 [0.8, 1.7]

0.0289 (0.0052) 1.9 [1.2, 2.6] 1.0 [0.7, 1.4]

0.0019 (0.0006) 0.1 [0.0, 0.2] 0.1 [0.0, 1.1]

0.0480 (0.0078) 3.2 [2.2, 4.2] 1.7 [1.2, 2.3]

f nucleotide substitutions per site between haplotypes from the

site between haplotypes from the two populations.

� 2009 Blackwell Publishing Ltd

GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 27 7

We used the net substitutions between populations in

COI to estimate the time of divergence of G. p. palpalis

from DRC, Equatorial Guinea and West Africa, and the

divergence of G. p. palpalis and G. p. gambiensis (Table 5).

The molecular clock hypothesis could not be rejected for

G. p. gambiensis and G. p. palpalis COI sequences, as tested

by a likelihood ratio test (P = 0.1222, using HKY85

model of evolution) and a two-cluster test (P > 0.05)

(Takezaki et al. 1995; Yang 1996). We could not define

confidently any suitable calibration point for the molecu-

lar clock for Glossina. Therefore, we used a range of sub-

stitution rates from 1.5% to 2.8% per Myr previously

estimated for COI in insects (Buckley et al. 2001; Farrell

2001). The most recent estimate for the divergence of G.

p. palpalis and G. gambiensis was 1.2 Myr, with subse-

quent splits between West African and DRC G. p. palpalis

occurring no more recently than 0.7 Ma.

Estimating the variance effective population size ofRio Campo

Temporal-based estimates of Ne were made for the Rio

Campo population based on allele frequency changes

between the two samples collected in August 2003 and

July 2005. Sixteen generations are expected to have

elapsed between the sampling dates, corresponding to

mean over-loci estimates of 501 for NeF and 731 for NeL

(Table 6). Estimates obtained by NeL were consistently

higher than those from NeF, although 95% confidence

intervals overlapped on all occasions.

Discussion

Phylogenetic analysis of COI sequences suggests that

G. p. palpalis populations from DRC and Equatorial Gui-

nea form a sister group to West African G. p. palpalis.

Consistent with this, the position of the insertion leading

to the longer 330-bp ITS1 in DRC and Equatorial Guinea

is the same. If large indels in ITS1 occur infrequently,

ITS1 forms in DRC and Equatorial Guinea are probably

identical by descent. The average percentage divergence

in COI between Equatorial Guinea and West African G.

p. palpalis was 4.6%, suggesting a high level of genetic

differentiation between these widely spaced, allopatric

populations. This level of sequence divergence is over

Table 6 Temporal estimates of current Ne for Glossina palpalis from R

Method t = 20

Moment-based NeFk (rare alleles binned) 626 [334, 15

Likelihood-based NeL (rare alleles binned) 1141 [585, ¥]

Likelihood-based NeL 1410 [703, 46

T is the number of generations between time points. 95% confidence

� 2009 Blackwell Publishing Ltd

the 2% threshold which has been applied for distin-

guishing species using COI bar coding (Hebert et al.

2003a, b). Whilst assigning a species definition to an

arbitrary level of sequence divergence is far from infalli-

ble, breeding experiments conducted on G. p. palpalis

colonies originating in DRC and Nigeria showed hybrid

male sterility, suggestive of cryptic species (Gooding

et al. 2004). Although no sequence data are currently

available for Nigerian G. p. palpalis, samples from Cam-

eroon, which lies between Equatorial Guinea and Nige-

ria, are not very divergent from those sampled further

west.

The estimates of divergence times within G. p. palpalis

in West Africa, DRC and Equatorial Guinea date the

split between western and central (DRC plus Equatorial

Guinea) no more recently than 0.7 Myr, and the split

between G. p. palpalis and G. p. gambiensis no more

recently than 1.2 Myr. This is considerably more ancient

than the 19 000–13 000 G. p. palpalis–G. p. gambiensis

split proposed by Challier et al. (1983). Although the

contraction of forest habitats at the last glacial maxi-

mum (ca. 19 000 years ago) does not coincide with any

G. palpalis divergence events, climate change and conse-

quent habitat separation might still underlie allopatric

speciation in the G. palpalis. With the broad time ranges

estimated in the current study for the divergence

events, it is not possible to assign a particular historical

event to either divergence, as during the estimated time

spans there have been multiple climate and vegetation

changes (Leroy & Dupont 1994; Dupont et al. 2001;

Schefuss et al. 2003). The estimated time of the diver-

gence between DRC and Equatorial Guinea and West

African G. p. palpalis largely overlaps that for the diver-

gence of DRC from Equatorial Guinean G. p. palpalis,

although the phylogeny suggests that the split with

West African G. p. palpalis took place first. Given the

large geographical scale of the G. p. palpalis species and

the relatively recent time of these splits, retention of

ancestral polymorphism may have confounded the phy-

logenetic analysis to some extent. Future efforts to eluci-

date the status of G. p. palpalis species should be

extended to nuclear markers other than ITS.

If there are multiple species within G. p. palpalis, they

may have started to diverge in behaviour. Will studies

into odorant attractants and trap or target design for

io Campo

t = 16 t = 6

73] 501 [267, 1258] 188 [100, 472]

731 [384, 2564] 354 [147, 1450]

42] 1204 [656, 4453] 470 [197, 2374]

intervals are given in brackets.

3278 N. A. DYER ET AL.

G. p. palpalis performed in West Africa be applicable to

G. p. palpalis southeast of Cameroon? Without breeding

experiments to confirm the specific status and the num-

ber of the various putative cryptic species, control mea-

sures developed in West Africa should be tested for

effectiveness in a field context in countries such as

Equatorial Guinea and DRC.

Although the ITS1 data suggested that hybrid forms

of G. p. palpalis might exist in Equatorial Guinea, we

did not find significant evidence for population subdivi-

sion using microsatellite markers. Nor did the mito-

chondrial COI data correlate with ITS1 length

polymorphism in Equatorial Guinea. Based on the COI

data, a more parsimonious explanation would be that

the insertion leading to the 330 bp ITS1 may have

occurred in the common ancestor of G. p. palpalis in

DRC and Equatorial Guinea after it diverged from West

African G. p. palpalis. The insertion subsequently has

become fixed or at very high frequency in DRC,

whereas in Equatorial Guinea both ITS1 forms persist.

Ohta & Dover (1984) modelled the population variance

in copy number of new variant repeats during the

homogenization of repeats in multigene families. This

relative variance is expected to increase with decreasing

interchromosomal recombination rate, increasing gene

conversion rates and increasing population size. High

relative variance in ITS1 variants has been attributed to

a high intrachromosomal gene conversion rate in the

meadow grasshopper subspecies Chorthippus parallelus

parallelus and Chorthippus parallelus erythropus, which

diverged around 0.5 Ma (Parker & Butlin 2004). The

maintenance of at least two variants of the ITS1 of ribo-

somal DNA in G. p. palpalis in Equatorial Guinea might

indicate that the process of homogenization of ribo-

somal DNA tandem repeats is relatively slow compared

with the divergence time of G. p. palpalis populations in

Equatorial Guinea and DRC. The number of copies of

the ribosomal DNA locus in G. p. palpalis must be found

before further inferences can be made regarding the

variance in repeat number of ITS1 variants and inter-

chromosomal recombination rates.

Simultaneous estimation of null allele frequency and

inbreeding suggested that the departure from Hardy–

Weinberg equilibrium frequencies within populations

was more likely due to null alleles than to a Wahlund

effect (subdivided populations) or immigrants. Consis-

tently, analysis of population structure using BAPS or

STRUCTURE did not reveal the presence of multiple popu-

lations. High FIS values have previously been recorded

for G. p. palpalis around the recently described Bonon

focus of Cote d’Ivoire (Ravel et al. 2007). In Bonon, the

high FIS values were not entirely explained by null

alleles or short allele dominance, and assignment into

25 BAPS clusters led to a significant decrease in FIS,

leading to the conclusion that the captured flies origi-

nated from multiple cryptic populations. In the current

study, null alleles may have reduced the sensitivity of

the population assignment tests in STRUCTURE and BAPS,

but low values of the inbreeding coefficient after correc-

tion for null alleles imply that sub-structuring is not as

strong in the Equatorial Guinea HAT foci as in the

more recently defined Bonon focus. Cano et al. (2007a)

noted that recolonization of treated zones from non-

treated zones might be occurring at Kogo. The low lev-

els of genetic differentiation detected between three

physically separated sites in this study make detection

of immigrant flies from more closely separated areas in

Equatorial Guinea or neighbouring Gabon and Camer-

oon unlikely. To use assignment tests successfully in

Equatorial Guinea, it will be necessary to either extend

the set of markers to more than 12 microsatellites, or to

redesign more of the current primers specifically for

use on the species present in Equatorial Guinea.

Despite the high level of divergence over the large

geographical separation between G. p. palpalis in DRC

and Equatorial Guinea, the population structure does

not imply strong geographical isolation at a small scale,

within Equatorial Guinea itself. The FST values between

the geographically separated G. p. palpalis populations

in Equatorial Guinea were small but significant. If ITS1

length polymorphism does not imply the presence of

inter-sub-specific hybrid flies, it could just be consid-

ered another marker of within-population polymor-

phism. In that case, ITS1 length polymorphism

frequency data simply suggest that Kogo was the most

divergent of the three sites, consistent with the

observed FST estimates. However, frequency variation

of ITS1 length variants between the populations sug-

gests larger expected FST values between Kogo and the

other sites than observed with microsatellites. This dis-

crepancy may be due to concerted evolution accelerat-

ing the spread of new ITS1 variants across the genome

and thus accelerating their spread through populations.

Alternatively, the ribosomal DNA locus may be linked

to a locus under selection. Overall, the data on the level

of genetic differentiation of G. p. palpalis populations at

the three HAT foci in Equatorial Guinea do not suggest

that any of them is strongly isolated. Therefore, vector

control operations will be most effective when carried

out over a large geographical scale, as suggested by

(Cano et al. 2007a). FST estimates are similar to those

obtained using microsatellite markers for G. p. gambien-

sis, on a similar geographical scale along the Mouhoun

River, Burkina Faso, although in Equatorial Guinea the

sample sites were not along one river. Much higher lev-

els of genetic differentiation between populations (FST

0.1–0.3) have been previously recorded for various

tsetse fly species over equivalent geographic scales

� 2009 Blackwell Publishing Ltd

GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 27 9

(Solano et al. 1999; Krafsur & Endsley 2002; Ouma et al.

2006, 2007; Abila et al. 2008). Is the dispersal capability

of G. p. palpalis particularly high, or is this low diver-

gence due to a more continuous habitat in Equatorial

Guinea than in other studied countries? A mark–

release–recapture experiment on G. p. gambiensis in a

tributary of the Mouhoun River, Burkina Faso, was

used to estimate the dispersal characteristics of that spe-

cies along its linear (one-dimensional) gallery forest

habitat (Cuisance et al. 1985; Bouyer et al. 2007a). The

one-dimensional diffusion coefficient a was estimated at

0.46 km2 ⁄ day, assuming that traps, positioned along the

main river but not its tributaries only allowed a partial

observation of the dispersal process due to missing flies

on the tributaries (Bouyer et al. 2007a). The palpalis

group species G. f. fuscipes has an estimated diffusion

coefficient in two dimensional habitat of 0.029 km2 ⁄ day,

much lower than the estimate for G. p. gambiensis (Rog-

ers 1977). The coastal mangrove habitat of Equatorial

Guinea is not a strict gallery forest with savannah

on either side, and so movement between rivers in a

two-dimensional dispersal pattern seems more likely

than in Burkina Faso.

The variance effective population size of G. p. palpalis

in Rio Campo, mainland northern Equatorial Guinea,

was estimated at around 500 individuals, much larger

than the moment-based estimate of the Loos Islands G.

p. gambiensis population of <50 (Solano et al. 2009). The

variance effective population size was similar to that

estimated using the moment-based method for the mors-

itans group species G. pallidipes in the Nguruman and

Lambwe valleys in Kenya (Ouma et al. 2006). Ne is

likely to be much smaller than the census population

size (N) that would be measured by methods such as

mark–release–recapture: the median Ne ⁄ N ratio from 83

studies of a wide taxonomic range or organisms using

the temporal method was only 0.14 (Palstra & Ruzzante

2008). Apart from being affected by population bottle-

necks Ne ⁄ N is particularly small when there is a large

variation in reproductive success (Hauser et al. 2002;

Hedrick 2005a). In tsetse, there is likely to be variation

in male reproductive success but little variation in

female reproductive success (Solano et al. 2009). The

use of both ecological and genetic methods on the same

tsetse populations could be used to estimate the Ne ⁄ Nratio, and its variation among populations. In the

future, analysis of population samples before and after

control measures could be considered for assessing their

success in reducing the population size.

Acknowledgements

We acknowledge the support by AS Gracio (UEI Entomologia

Medica ⁄ IHMT), JL Vicente (CMDT.LA ⁄ IHMT), VE Rosario

� 2009 Blackwell Publishing Ltd

(CMDT.LA ⁄ IHMT) and the National Sleeping Sickness Con-

trol Program, Ministry of Health and Social Welfare of the

Republic of Equatorial Guinea. Field work in Equatorial Gui-

nea was funded by the Spanish Ministry of Public Health

and the Instituto de Salud Carlos III within the Network of

Tropical Diseases Research (RICET RD06 ⁄ 0021 ⁄ 0000) by a

grant from Centro Nacional de Medicina Tropical, Instituto

de Salud Carlos III and the Spanish Agency of International

Cooperation for Development (AECID). Molecular work at

LSTM was funded by an EU INCO grant. We thank Jeremy

Bouyer, Baba Sall and Momar Seck, with technical support

from IAEA TC project SEN5029, for the collection of G. p.

gambiensis from Senegal.

References

Abila PP, Slotman MA, Parmakelis A et al. (2008) High levels

of genetic differentiation between Ugandan Glossina fuscipes

fuscipes populations separated by Lake Kyoga. PLoS Neglected

Tropical Diseases, 2, e242.

Baker MD, Krafsur ES (2001) Identification and properties of

microsatellite markers in tsetse flies Glossina morsitans sensu

lato (Diptera: Glossinidae). Molecular Ecology Notes, 1, 234–236.

Berthier P, Beaumont MA, Cornuet JM, Luikart G (2002)

Likelihood-based estimation of the effective population size

using temporal changes in allele frequencies: a genealogical

approach. Genetics, 160, 741–751.

Bouyer J, Sibert A, Desquesnes M, Cuisance D, Roque SdL

(2007a) A model of diffusion of Glossina palpalis gambiensis

(Diptera: Glossinidae) in Burkina Faso. In: Area-wide Control

of Insect Pests. From Research to Field Implementation (eds

Vreysen MJB, Robinson AS, Hendrichs J), pp. 221–228.

Springer, Dordrecht, The Netherlands.

Bouyer J, Ravel S, Dujardin JP et al. (2007b) Population

structuring of Glossina palpalis gambiensis (Diptera:

Glossinidae) according to landscape fragmentation in the

Mouhoun river, Burkina Faso. Journal of Medical Entomology,

44, 788–795.

Brookfield JF (1996) A simple new method for estimating null

allele frequency from heterozygote deficiency. Molecular

Ecology, 5, 453–455.

Brunhes J, Cuisance D, Geoffroy B, Hervy JP, Lebbe J (1994)

Les glossines ou mouches tse-tse. Logiciel d’identification et

d’enseignement, ORSTROM, Coll. Didactiques.

Buckley TR, Simon C, Chambers GK (2001) Phylogeography of

the New Zealand cicada Maoricicada campbelli based on

mitochondrial DNA sequences: ancient clades associated

with cenozoic environmental change. Evolution, 55, 1395–

1407.

Camara M, Caro-Riano H, Ravel S et al. (2006) Genetic and

morphometric evidence for population isolation of Glossina

palpalis gambiensis (Diptera : Glossinidae) on the Loos

islands, Guinea. Journal of Medical Entomology, 43, 853–860.

Cano J, Descalzo MA, Ndong-Mabale N et al. (2007a) Predicted

distribution and movement of Glossina palpalis palpalis

(Diptera: Glossinidae) in the wet and dry seasons in the

Kogo trypanosomiasis focus (Equatorial Guinea). Journal of

Vector Ecology, 32, 218–225.

Cano J, Descalzo MA, Ndong-Mabale N et al. (2007b) Spatial

and temporal variability of the Glossina palpalis palpalis

3280 N. A. DYER ET AL.

population in the Mbini focus (Equatorial Guinea).

International Journal of Health Geographics, 6, 36, doi: 10.1186/

1476-072X-6-36.

Challier A, Gouteux J, Coosemans M (1983) La limite

geographique entre les sous-especes Glossina palpalis palpalis

(Rob.-Desv.) et Glossina palpalis gambiensis Vanderplanck en

Afrique occidentale. Cahiers – ORSTOM. Entomologie Medicale

et Parasitologie, 21, 207–220.

Chapuis MP, Estoup A (2007) Microsatellite null alleles and

estimation of population differentiation. Molecular Biology

and Evolution, 24, 621–631.

Chybicki IJ, Burczyk J (2009) Simultaneous estimation of null

alleles and inbreeding coefficients. Journal of Heredity, 100,

106–113.

Corander J, Waldmann P, Sillanpaa MJ (2003) Bayesian

analysis of genetic differentiation between populations.

Genetics, 163, 367–374.

Corander J, Waldmann P, Marttinen P, Sillanpaa MJ (2004)

BAPS 2: enhanced possibilities for the analysis of genetic

population structure. Bioinformatics, 20, 2363–2369.

Cuisance D, Fevrier J, Dejardin J, Filledier J (1985) Dispersion

line¢aire de Glossina palpalis gambiensis et G. tachinoides dans

une galerie forestiere en zone soudano-guineenne (Burkina

Faso). Revue d’ Elevage et de Medecine Veterinaire des Pays

Tropicaux, 38, 153–172.

Dempster AP, Laird NM, Rubin DB (1977) Maximum

likelihood from incomplete data via em algorithm. Journal of

the Royal Statistical Society Series B-Methodological, 39, 1–38.

Dover G (1982) Molecular drive: a cohesive model of species

evolution. Nature, 299, 111–117.

Dupont LM, Bonner B, Schneider R, Wefer G (2001) Mid-

Pleistocene environmental change in tropical Africa began as

early as 1.05 Ma. Geology, 29, 195–198.

Dyer NA, Lawton SP, Ravel S et al. (2008) Molecular

phylogenetics of tsetse flies (Diptera: Glossinidae) based on

mitochondrial (COI, 16S, ND2) and nuclear ribosomal DNA

sequences, with an emphasis on the palpalis group.

Molecular Phylogenetics and Evolution, 49, 227–239.

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of

clusters of individuals using the software STRUCTURE: a

simulation study. Molecular Ecology, 14, 2611–2620.

Falush D, Stephens M, Pritchard JK (2003) Inference of

population structure using multilocus genotype data: linked

loci and correlated allele frequencies. Genetics, 164, 1567–

1587.

Farrell BD (2001) Evolutionary assembly of the milkweed

fauna: cytochrome oxidase I and the age of Tetraopes

beetles. Molecular Phylogenetics and Evolution, 18, 467–478.

Ferreira F, Cano J, Furtado A et al. (2008) An alternative

approach to detect Trypanosoma in Glossina (Diptera,

Glossinidae) without dissection. The Journal of Infection in

Developing Countries, 2, 63–67.

Fraga de Azevedo J (1970) An attempt for the distribution of

research work on tsetse fly breeding. In: Tsetse Fly Breeding

under Laboratory Conditions and its Practical Application (ed.

Fraga de Azevedo J), pp. 487–496. Junta de investigacoes de

ultramar, Lisboa.

Gooding RH (1997) Genetic analysis of hybrid sterility in

crosses of the tsetse flies Glossina palpalis palpalis and Glossina

palpalis gambiensis (Diptera: Glossinidae). Canadian Journal of

Zoology-Revue Canadienne De Zoologie, 75, 1109–1117.

Gooding RH, Solano P, Ravel S (2004) X-chromosome mapping

experiments suggest occurrence of cryptic species in the

tsetse fly Glossina palpalis palpalis. Canadian Journal of Zoology-

Revue Canadienne De Zoologie, 82, 1902–1909.

Goudet J (1995) FSTAT (version 1.2): a computer program to

calculate F-statistics. Journal of Heredity, 86, 485–486.

Goudet J (2001) FSTAT, a program to estimate and test gene

diversities and fixation indices (version 2.9.3), available from

http://www.unil.ch/izea/softwares/fstat.html, updated

from Goudet et al. (2005).

Gouteux JP, Lancien J (1986) The pyramidal trap for sampling and

controlling tsetse-flies (Diptera-Glossinidae) – comparative

trials and description of new collecting techniques. Tropical

Medicine and Parasitology, 37, 61–66.

Gouteux JP, Noireau F, Frezil JL, Louembet MT (1987)

Morphological comparison between Glossina caliginea Austen,

1911 (Diptera, Muscidae) from Cameroon and Congo.

Bulletin De La Societe De Pathologie Exotique, 80, 261–265.

Guindon S, Gascuel O (2003) A simple, fast, and accurate

algorithm to estimate large phylogenies by maximum

likelihood. Systematic Biology, 52, 696–704.

Guindon S, Lethiec F, Duroux P, Gascuel O (2005) PHYML

Online – a web server for fast maximum likelihood-based

phylogenetic inference. Nucleic Acids Research, 33, W557–

W559.

Hargrove JW (2005) Extinction probabilities and times to

extinction for populations of tsetse flies Glossina spp.

(Diptera: Glossinidae) subjected to various control measures.

Bulletin of Entomological Research, 95, 13–21.

Hauser L, Adcock GJ, Smith PJ, Ramirez JHB, Carvalho GR

(2002) Loss of microsatellite diversity and low effective

population size in an overexploited population of New

Zealand snapper (Pagrus auratus). Proceedings of the National

Academy of Sciences of the United States of America, 99, 11742–

11747.

Hebert PD, Cywinska A, Ball SL, deWaard JR (2003a)

Biological identifications through DNA barcodes. Proceedings.

Biological Sciences ⁄ The Royal Society, 270, 313–321.

Hebert PD, Ratnasingham S, deWaard JR (2003b) Barcoding

animal life: cytochrome c oxidase subunit 1 divergences

among closely related species. Proceedings. Biological

Sciences ⁄ The Royal Society, 270(Suppl. 1), S96–S99.

Hedrick P (2005a) Large variance in reproductive success and

the Ne ⁄ N ratio. Evolution, 59, 1596–1599.

Hedrick PW (2005b) A standardized genetic differentiation

measure. Evolution, 59, 1633–1638.

Ji YJ, Zhang DX, He LJ (2003) Evolutionary conservation and

versatility of a new set of primers for amplifying the

ribosomal internal transcribed spacer regions in insects and

other invertebrates. Molecular Ecology Notes, 3, 581–585.

Krafsur ES, Endsley MA (2002) Microsatellite diversities and

gene flow in the tsetse fly, Glossina morsitans s.l. Medical and

Veterinary Entomology, 16, 292–300.

Leroy S, Dupont L (1994) Development of vegetation and

continental aridity in northwestern Africa during the Late

Pliocene – the pollen record of Odp Site-658. Palaeogeography

Palaeoclimatology Palaeoecology, 109, 295–316.

Loytynoja A, Goldman N (2005) An algorithm for progressive

multiple alignment of sequences with insertions. Proceedings

of the National Academy of Sciences of the United States of

America, 102, 10557–10562.

� 2009 Blackwell Publishing Ltd

GLOSSINA P. PALPALIS IN EQUATORIAL G UINEA 3 28 1

Loytynoja A, Goldman N (2008) Phylogeny-aware gap

placement prevents errors in sequence alignment and

evolutionary analysis. Science, 320, 1632–1635.

Luna C, Bonizzoni M, Cheng QY et al. (2001) Microsatellite

polymorphism in tsetse flies (Diptera : Glossinidae). Journal

of Medical Entomology, 38, 376–381.

Nash TAM (1969) Africa’s Bane: The Tsetse Fly. Collins, London.

Nei M (1987) Molecular Evolutionary Genetics. Columbia

University Press, New York.

Nei M, Kumar S (2000) Molecular Evolution and Phylogenetics

Oxford University Press, New York.

Ohta T, Dover GA (1984) The cohesive population genetics of

molecular drive. Genetics, 108, 501–521.

Ouma JO, Marquez JG, Krafsur ES (2006) Microgeographical

breeding structure of the tsetse fly, Glossina pallidipes in

south-western Kenya. Medical and Veterinary Entomology, 20,

138–149.

Ouma JO, Marquez JG, Krafsur ES (2007) Patterns of genetic

diversity and differentiation in the tsetse fly Glossina

morsitans morsitans Westwood populations in East and

southern Africa. Genetica, 130, 139–151.

Palstra FP, Ruzzante DE (2008) Genetic estimates of

contemporary effective population size: what can they tell us

about the importance of genetic stochasticity for wild

population persistence? Molecular Ecology, 17, 3428–3447.

Parker EJ, Butlin RK (2004) Within and between-individual

sequence variation among ITS1 copies in the meadow

grasshopper Chorthippus parallelus indicates frequent

intrachromosomal gene conversion. Molecular Biology and

Evolution, 21, 1595–1601.

Paskewitz SM, Wesson DM, Collins FH (1993) The internal

transcribed spacers of ribosomal DNA in five members of

the Anopheles gambiae species complex. Insect Molecular

Biology, 2, 247–257.

Peel D, Ovenden JR, Peel SL (2004) NEESTIMATOR: Software for

Estimating Effective Population Size. Queensland Government,

Department of Primary Industries and Fisheries, QLD,

Australia.

Pollock ME (1982) Description and keys for the identification

of Glossina species in Training manual for tsetse control

personnel. FAO, 1, 156–201.

Posada D, Crandall KA (1998) MODELTEST: testing the model of

DNA substitution. Bioinformatics, 14, 817–818.

Pritchard JK, Stephens M, Donnelly P (2000) Inference of

population structure using multilocus genotype data.

Genetics, 155, 945–959.

Ravel S, de Meeus T, Dujardin JP et al. (2007) The tsetse fly

Glossina palpalis palpalis is composed of several genetically

differentiated small populations in the sleeping sickness

focus of Bonon, Cote d’Ivoire. Infection, Genetics and

Evolution, 7, 116–125.

Raymond M, Rousset F (1995) GENEPOP (version-1.2) –

population-genetics software for exact tests and

ecumenicism. Journal of Heredity, 86, 248–249.

Rogers D (1977) Study of a natural-population of Glossina

fuscipes fuscipes Newstead and a model of fly movement.

Journal of Animal Ecology, 46, 309–330.

Schefuss E, Schouten S, Jansen JHF, Damste JSS (2003)

African vegetation controlled by tropical sea surface

temperatures in the mid-Pleistocene period. Nature, 422,

418–421.

� 2009 Blackwell Publishing Ltd

Schlotterer C, Tautz D (1994) Chromosomal homogeneity of

Drosophila ribosomal DNA arrays suggests intrachromosomal

exchanges drive concerted evolution. Current Biology, 4, 777–

783.

Simarro PP, Franco JR, Ndongo P et al. (2006) The elimination

of Trypanosoma brucei gambiense sleeping sickness in the

focus of Luba, Bioko Island, Equatorial Guinea. Tropical

Medicine & International Health, 11, 636–646.

Simo G, Njiokou F, Mbida JAM et al. (2008) Tsetse fly host

preference from sleeping sickness foci in Cameroon:

epidemiological implications. Infection, Genetics and Evolution,

8, 34–39.

Simon C, Frati F, Bechenbach A, Crespi B, Liu H, Flook P

(1994) Evolution, weighting, and phylogenetic utility of

mitochondrial gene sequences and a compilation of

conserved polymerase chain reaction primers. Annals of the

Entomological Society of America, 87, 651–701.

Solano P, de La Rocque S, Cuisance D et al. (1999) Intraspecific

variability in natural populations of Glossina palpalis

gambiensis from West Africa, revealed by genetic and

morphometric analyses. Medical and Veterinary Entomology,

13, 401–407.

Solano P, De La Rocque S, De Meeus T et al. (2000)

Microsatellite DNA markers reveal genetic differentiation

among populations of Glossina palpalis gambiensis collected in

the agro-pastoral zone of Sideradougou, Burkina Faso. Insect

Molecular Biology, 9, 433–439.

Solano P, Ravel S, Bouyer J et al. (2009) The population

structure of Glossina palpalis gambiensis from island and

continental locations in coastal Guinea. PLoS Neglected

Tropical Diseases, 3, e392.

Swofford DL (2002) PAUP*: Phylogenetic Analysis using Parsimony

(* and other Methods). Sinauer Associates, Sunderland,

Massachusetts.

Takezaki N, Rzhetsky A, Nei M (1995) Phylogenetic test of the

molecular clock and linearized trees. Molecular Biology and

Evolution, 12, 823–833.

Tamura K, Nei M (1993) Estimation of the number of

nucleotide substitutions in the control region of

mitochondrial DNA in humans and chimpanzees. Molecular

Biology and Evolution, 10, 512–526.

Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular

evolutionary genetics analysis (MEGA) software version 4.0.

Molecular Biology and Evolution, 24, 1596–1599.

Tang J, Toe L, Back C, Unnasch TR (1996) Intra-specific

heterogeneity of the rDNA internal transcribed spacer in the

Simulium damnosum (Diptera: Simuliidae) complex.

Molecular Biology and Evolution, 13, 244–252.

Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P

(2004) MICROCHECKER: software for identifying and correcting

genotyping errors in microsatellite data. Molecular Ecology

Notes, 4, 535–538.

Vogler AP, DeSalle R (1994) Evolution and phylogenetic

information content of the ITS-1 region in the tiger

beetle Cicindela dorsalis. Molecular Biology and Evolution, 11,

393–405.

Waples RS (1989) A generalized approach for estimating

effective population size from temporal changes in allele

frequency. Genetics, 121, 379–391.

Wattier R, Engel CR, Saumitou-Laprade P, Valero M (1998)

Short allele dominance as a source of heterozygote

3282 N. A. DYER ET AL.

deficiency at microsatellite loci: experimental evidence at the

dinucleotide locus Gv1CT in Gracilaria gracilis (Rhodophyta).

Molecular Ecology, 7, 1569–1573.

WHO WER (2006) Human African trypanosomiasis (sleeping

sickness): epidemiological update. Weekly Epidemiological

Record, 81, 71–80.

Willhoeft U (1997) Fluorescence in situ hybridization of

ribosomal DNA to mitotic chromosomes of tsetse flies

(Diptera: Glossinidae: Glossina). Chromosome Research, 5, 262–

267.

Yang Z (1996) Maximum-likelihood models for combined

analyses of multiple sequence data. Journal of Molecular

Evolution, 42, 587–596.

Yang Z (1997) PAML: a program package for phylogenetic

analysis by maximum likelihood. Computer applications in the

biosciences, 13, 555–556.

Yasuda N (1968) Estimation of the inbreeding coefficient from

phenotype frequencies by a method of maximum likelihood

scoring. Biometrics, 24, 915–935.

The research interests of the authors include the use of ecologi-

cal and population genetics methods to control insect vectors

of tropical diseases.

Supporting information

Additional Supporting Information may be found in the online

version of this article:

Fig. S1 ITS1 alignment. Alignment of the portion of ITS1

between Diagfor to Diagrev primer-binding sites for palpalis

group flies. The position of the insertion in Glossina p. palpalis

from Equatorial Guinea (palpalis_EqGui) is the same as in Glossi-

na p. palpalis from DRC (palpalis_DRC), from position 194 to 382.

Fig. S2 Population structure inferred using STRUCTURE.

Fig. S3 Posterior distribution (likelihood curves) for NeL of

Glossina palpalis from Rio campo.

Table S1 PCR conditions for the microsatellite loci analysed

Table S2 Pairwise FST for each locus

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