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Uganda is the only country where the chronic and acute forms of human African Trypanosomiasis (HAT) or sleeping sickness both occur and are separated by < 100 km in areas north of Lake Kyoga. In Uganda, Glossina fuscipes fuscipes is the main vector of the Trypanosoma parasites responsible for these diseases as well for the animal African Trypanosomiasis (AAT), or Nagana. We used highly polymorphic microsatellite loci and a mitochondrial DNA (mtDNA) marker to provide fine scale spatial resolution of genetic structure of G. f. fuscipes from 42 sampling sites from the northern region of Uganda where a merger of the two disease belts is feared. Based on microsatellite analyses, we found that G. f. fuscipes in northern Uganda are structured into three distinct genetic clusters with varying degrees of interconnectivity among them. Based on genetic assignment and spatial location, we grouped the sampling sites into four genetic units corresponding to northwestern Uganda in the Albert Nile drainage, northeastern Uganda in the Lake Kyoga drainage, western Uganda in the Victoria Nile drainage, and a transition zone between the two northern genetic clusters characterized by high level of genetic admixture. An analysis using HYBRIDLAB supported a hybrid swarm model as most consistent with tsetse genotypes in these admixed samples. Results of mtDNA analyses revealed the presence of 30 haplotypes representing three main haplogroups, whose location broadly overlaps with the microsatellite defined clusters. Migration analyses based on microsatellites point to moderate migration among the northern units located in the Albert Nile, Achwa River, Okole River, and Lake Kyoga drainages, but not between the northern units and the Victoria Nile drainage in the west. Effective population size estimates were variable with low to moderate sizes in most populations and with evidence of recent population bottlenecks, especially in the northeast unit of the Lake Kyoga drainage. Our microsatellite and mtDNA based analyses indicate that G. f. fuscipes movement along the Achwa and Okole rivers may facilitate northwest expansion of the Rhodesiense disease belt in Uganda. We identified tsetse migration corridors and recommend a rolling carpet approach from south of Lake Kyoga northward to minimize disease dispersal and prevent vector re-colonization. Additionally, our findings highlight the need for continuing tsetse monitoring efforts during and after control.
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RESEARCH ARTICLE
Genetic diversity and population structure of
the tsetse fly Glossina fuscipes fuscipes
(Diptera: Glossinidae) in Northern Uganda:
Implications for vector control
Robert Opiro
1
*, Norah P. Saarman
2
*, Richard Echodu
1
, Elizabeth A. Opiyo
1
,
Kirstin Dion
2
, Alexis Halyard
2
, Augustine W. Dunn
3
, Serap Aksoy
4
, Adalgisa Caccone
2
1Department of Biology, Faculty of Science, Gulu University, Gulu, Uganda, 2Department of Ecology and
Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America, 3Division of
Genetics and Genomics, Boston Children’s Hospital, Boston, Massachusetts, United States of America,
4Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut,
United States of America
These authors contributed equally to this work.
*robopiro@gu.ac.ug (RO); norah.saarman@yale.edu (NPS)
Abstract
Uganda is the only country where the chronic and acute forms of human African Trypanoso-
miasis (HAT) or sleeping sickness both occur and are separated by <100 km in areas north
of Lake Kyoga. In Uganda, Glossina fuscipes fuscipes is the main vector of the Trypano-
soma parasites responsible for these diseases as well for the animal African Trypanosomia-
sis (AAT), or Nagana. We used highly polymorphic microsatellite loci and a mitochondrial
DNA (mtDNA) marker to provide fine scale spatial resolution of genetic structure of G.f.fus-
cipes from 42 sampling sites from the northern region of Uganda where a merger of the two
disease belts is feared. Based on microsatellite analyses, we found that G.f.fuscipes in
northern Uganda are structured into three distinct genetic clusters with varying degrees
of interconnectivity among them. Based on genetic assignment and spatial location, we
grouped the sampling sites into four genetic units corresponding to northwestern Uganda in
the Albert Nile drainage, northeastern Uganda in the Lake Kyoga drainage, western Uganda
in the Victoria Nile drainage, and a transition zone between the two northern genetic clusters
characterized by high level of genetic admixture. An analysis using HYBRIDLAB supported
ahybrid swarm model as most consistent with tsetse genotypes in these admixed samples.
Results of mtDNA analyses revealed the presence of 30 haplotypes representing three
main haplogroups, whose location broadly overlaps with the microsatellite defined clusters.
Migration analyses based on microsatellites point to moderate migration among the north-
ern units located in the Albert Nile, Achwa River, Okole River, and Lake Kyoga drainages,
but not between the northern units and the Victoria Nile drainage in the west. Effective
population size estimates were variable with low to moderate sizes in most populations and
with evidence of recent population bottlenecks, especially in the northeast unit of the Lake
Kyoga drainage. Our microsatellite and mtDNA based analyses indicate that G.f.fuscipes
movement along the Achwa and Okole rivers may facilitate northwest expansion of the
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0005485 April 28, 2017 1 / 29
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OPEN ACCESS
Citation: Opiro R, Saarman NP, Echodu R, Opiyo
EA, Dion K, Halyard A, et al. (2017) Genetic
diversity and population structure of the tsetse fly
Glossina fuscipes fuscipes (Diptera: Glossinidae) in
Northern Uganda: Implications for vector control.
PLoS Negl Trop Dis 11(4): e0005485. https://doi.
org/10.1371/journal.pntd.0005485
Editor: Philippe Solano, Institut de recherche pour
le developpement, FRANCE
Received: November 1, 2016
Accepted: March 12, 2017
Published: April 28, 2017
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This work received financial support from
Fogarty Global Infectious Diseases Training Grant
D43TW007391 to SA, and NIH R01 awards
AI068932 and 5T32AI007404-24 to SA. The
research was accomplished while RO was a
Fogarty Research Fellow at Yale University. The
funders had no role in study design, data collection
Rhodesiense disease belt in Uganda. We identified tsetse migration corridors and recom-
mend a rolling carpet approach from south of Lake Kyoga northward to minimize disease
dispersal and prevent vector re-colonization. Additionally, our findings highlight the need for
continuing tsetse monitoring efforts during and after control.
Author summary
Northern Uganda is an epidemiologically important region affected by human African
trypanosomiasis (HAT) because it harbors both forms of the HAT disease (T.b.gambiense
and T.b.rhodesiense). The geographic location of this region creates the risk that these
distinct foci could merge, which would complicate diagnosis and treatment, and may
result in recombination between the two parasite strains with as yet unknown conse-
quences. Both strains require a tsetse fly vector for transmission, and in Uganda, G.f.fus-
cipes is the major vector of HAT. Controlling the vector remains one of the most effective
strategies for controlling trypanosome parasites. However, vector control efforts may not
be sustainable in terms of long term reduction in G.f.fuscipes populations due to popula-
tion rebounds. Population genetics data can allow us to determine the likely source of
population rebounds and to establish a more robust control strategy. In this study, we
build on a previous broad spatial survey of G.f.fuscipes genetic structure in Uganda by
adding more than 30 novel sampling sites that are strategically spaced across a region of
northern Uganda that, for historical and political reasons, was severely understudied and
faces particularly high disease risk. We identify natural population breaks, migration cor-
ridors and a hybrid zone with evidence of free interbreeding of G.f.fuscipes across the
geographic region that spans the two HAT disease foci. We also find evidence of low effec-
tive population sizes and population bottlenecks in some areas that have been subjects of
past control but remain regions of high tsetse density, which stresses the risk of population
rebounds if monitoring is not explicitly incorporated into the control strategy. We use
these results to make suggestions that will enhance the design and implementation of con-
trol activities in northern Uganda.
Introduction
The tsetse fly (genus Glossina) is the major vector of human African trypanosomiasis (HAT)
and animal African trypanosomiasis (AAT). The diseases occur throughout sub-Saharan
Africa, causing extensive morbidity and mortality in humans and livestock [1][2]. Human dis-
ease is caused by two different subspecies of the flagellated protozoa Trypanosoma brucei;T.b.
rhodesiense in eastern and southern Africa, and T.b.gambiense in west and central Africa. The
two HAT diseases are separated geographically more or less along the line of the Great Rift
Valley [3]. Although the animal disease (or Nagana) is caused by different trypanosome sub-
species; T.b.brucei,T.congolense and T.vivax, animals are also known to be reservoirs of the
human infective T.b.rhodesiense. Thus, while AAT is a problem in its own right because of
economic losses and reduced availability of nutrients [4][5][6], the animals also act as impor-
tant reservoirs for human infective T.b.rhodesiense.
Although the human diseases have been on a decline[7], they still put 60 million people at
risk in 37 countries covering about 40% of Africa [8][9]. The human disease T.b.gambiense is
near elimination while control of T.b.rhodesiense remains more complicated because of
Northern Uganda G.f.fuscipes genetic diversity
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0005485 April 28, 2017 2 / 29
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
animal reservoirs. For both T.b.gambiense and T.b.rhodesiense, there are no prophylactic
drugs or vaccines available. Furthermore, the drugs for treatment are expensive, can cause
severe side effects, and are difficult to administer in remote villages [10][11]. Although AAT
can be prevented with prophylactic drugs and effectively treated with trypanocidal drugs,
progress towards elimination of the animal disease has been slow because of the high cost of
drug administration and repeated emergence of drug resistance [12]. Thus, AAT instances
remain high and continue to burden livestock farmers [13] and provide animal reservoirs of T.
b.rhodesiense. As a consequence, the most effective way to control both AAT and HAT is con-
trol of the tsetse vector [14].
Uganda is in the precarious position of being the only country that harbors both forms of
HAT, with T.b.gambiense present in the northwestern corner of the country and T.b.rhode-
siense found in the center and southeast [15]. There is a significant risk that the two sleeping
sickness subspecies will merge in the north-central districts of Uganda, a region already bur-
dened by political and social instability [16]. Merging of the two disease belts would complicate
treatment and diagnosis [17], and may lead to the emergence of unforeseen pathologies if there
is recombination between the T.b.gambiense and T.b.rhodesiense trypanosomes [18][19][20].
Glossina fuscipes fuscipes is a member of the palpalis group of tsetse and is the main vector
implicated in the transmission of both AAT and HAT in Uganda. The vector is distributed
over vast regions of sub-Saharan Africa (Fig 1), where it occupies discrete patches of riverine
and lacustrine habitats distributed among pasture and agricultural land. Assessing the popula-
tion structure and the extent to which these apparently discrete populations are connected by
dispersal and migration patterns is central to defining the most effective scale for vector con-
trol [21][22]. For example, the major challenge that faces most control efforts is tsetse rebound
following short-term control efforts. The source of rebounding populations could be residual
pockets of surviving individuals or migrant flies coming from neighboring untreated regions,
or both [23]. Increased knowledge of vector population dynamics through application of pop-
ulation genetics can help in assessing the suitability of the operational units selected for vector
control and result in more effective tsetse control.
Although previous studies have made great strides towards understanding the population
biology of G.f.fuscipes in Uganda [24][25][26][27][28], regions north of Lake Kyoga remain a
high priority for additional study. Northern Uganda harbors both forms of HAT in close geo-
graphic proximity [7][20]. Identifying the precise extent of the two disease belts and possible
risk of merger has been difficult until recently because of social and political upheaval experi-
enced in these regions [29][30]. Our previous population genetic studies have identified three
major genetic units present in north and south of Lake Kyoga and in western Uganda [24][25]
[31]. Each of these units consists of genetically distinct populations with high differentiation
between sampling sites and evidence of further sub-structuring [22][25][31][24].
A more detailed understanding of the genetic structure and population dynamics of G.f.
fuscipes in northern Uganda will help estimate the likelihood of the merger of the two HAT
disease forms, and identify the best tsetse control strategies for the region. In this study,
we comprehensively sampled G.f.fuscipes from 42 sites in areas north of Lake Kyoga and
assessed variation in 16 nuclear microsatellites and over a 570 bp region of mitochondrial
DNA (mtDNA) to understand both short time scale resolution of demographic events [32]
[33] and inference of phylogeographic events dating further back in time [34][35]. We com-
pared this new knowledge of fine scale population structure, migration patterns and popula-
tion dynamics in northern Uganda with previous studies that concentrated on the southern
and central regions of the country. This comparative approach allowed us deeper insights into
the evolutionary forces at play and enriched our ability to make recommendations for G.f.fus-
cipes control strategies in northern Uganda.
Northern Uganda G.f.fuscipes genetic diversity
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Materials and methods
Study area and tsetse samples
The map in Fig 1 shows the sampling sites. We used biconical traps [36] to collect from 30
sites between January 2014 and April 2015, and also included 12 collections from a previous
study between January 2008 and January 2012 [31][25]. Sampling sites were chosen to detect
fine spatial scale population structure. To do this, we collected from multiple sites separated
by just over 5 km, which is the smallest unit area for which genetic differentiation has been
observed in G.f.fuscipes in Uganda [25]. At each site, we placed an average of 6 traps at least
100 m from each other and collected an average of 18 flies per trap over a period of 3–4 days.
Flies were stored individually in 95% ethanol and information on sex, collection date, trap
number, and geographic coordinates of each trap was recorded. The genotypic data included
from previous studies [25][29] were separated from our samples by a time span of 3–7 years
(approx. 22–52 generations), which opened up the possibility of genetic change. However, a
previous study showed no evidence of large demographic changes between the temporal col-
lections [37], justifying the combined analyses of 42 sampling sites spanning these time points
(details in S1 Table).
Fig 1. Map of Uganda showing sampling area. Markers indicate sampling sites for the major drainage basins; Albert Nile sites are
marked with triangles, Achwa River sites are marked with squares, Okole River sites are marked with diamonds, Lake Kyoga sites are
marked with circles, and Victoria Nile sites are marked with stars (see Table 1 for details). Vertical and horizontal stripes indicate the
approximate distribution of the two Trypanosoma (T.brucei gambiense [Tbg] and T.brucei rhodesiense [Tbr]) responsible for the two
sleeping sickness disease forms in Uganda. The major water bodies (lakes and rivers) are identified with a dark shade of gray.
https://doi.org/10.1371/journal.pntd.0005485.g001
Northern Uganda G.f.fuscipes genetic diversity
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We extensively sampled in areas north of Lake Kyoga, which includes tsetse flies that in a
previous study were grouped into two genetic clusters [25], with an effort to sample major
water drainage systems. [25] described one genetic cluster north of the Lake Kyoga and the
Victoria Nile, and one in western Uganda. In the northern genetic cluster, we sampled the
Albert Nile, the Achwa River, the Okole River, and Lake Kyoga drainages (see Table 1). The
Albert Nile basin is in the far northwest corner of Uganda, a region known to be an active
focus of T.b.gambiense sleeping sickness [20][38]. The Albert Nile is bordered to the east and
eventually joined by the Achwa River, and both drainage systems generally consist of patchy
habitat suitable for G.f.fuscipes, characterized by lowland woodland near semi-permanent
water bodies [2]. Habitat patches are surrounded by unsuitable savannah, agricultural and pas-
toral lands. Although the district of Arua was recently included in a pilot vector control pro-
gram in 2011–2013 [[39]], our samples from 2014 (DUK, AIN and GAN) that may have been
impacted did not overlap spatially with the program. Further south, we sampled the Okole
River and Lake Kyoga basins (Fig 1,Table 1). These regions form vast areas of marsh and
swampland, and are the most northerly geographical extent of the T.b.rhodesiense HAT dis-
ease belt [40]. Some districts in the Lake Kyoga drainage, such as Dokolo and K’maido, were
targets of the Stamp Out Sleeping Sickness (SOS) campaign of 2006–2009 [41], which may
have impacted our samples from this region from 2009 (OC) and 2014 (OCU, AMI and
KAG). Finally, in the western genetic cluster described by [25], we collected samples along
and south of the Victoria Nile (Fig 1,Table 1), which flows northwest from Lake Kyoga into
Lake Albert on the edge of the western rift valley. Here we sampled sites along and on minor
tributaries of the Victoria Nile in the districts of Masindi and Kiryandongo (Table 1). This
region is characterized by lowland woodland and the Uganda Wildlife Authority protects
much of the region as part of the Murchison Falls National Park.
DNA extraction and microsatellite genotyping
DNA was extracted from two to three legs per sample, using PrepGEM Insect DNA Extraction
kit (ZyGEM New Zealand, 2013), following the manufacturer’s protocols and stored at -20˚C.
We collected genotypic data from 16 microsatellite loci (details in S2 Table). Amplifications
were performed with fluorescently labeled forward primers (6-FAM, HEX and NED) using a
touchdown PCR (10 cycles of annealing at progressively lower temperatures from 60˚C to
51˚C, followed by 35 cycles at 50˚C) in 13.0μl reaction volumes containing 2.6 μl of 5X PCR
buffer, 1.1 μl of 10 mM dNTPs, 1.1 μl of 25mM MgCl
2
and 0.1 μl of 5 units/μl GoTaq (Pro-
mega, USA), 0.1 μl of 100X BSA (New England Biolabs, USA), 0.5 μl of 10 μM fluorescently-
labeled M13 primer, 0.5 of μl 10 μM reverse primer, and 0.3 μl of 2 μM M13-tailed forward
primer. For loci C7b and GmL11, 0.5 units of Taq Gold polymerase (Life Technologies, USA)
were used instead of Promega GoTaq. PCR products were multiplexed in groups of two or
three and genotyped on an ABI 3730xL Automated Sequencer (Life Technologies, USA) at the
DNA Analysis Facility on Science Hill at Yale University (http://dna-analysis.yale.edu/). Alleles
were scored using the program GENEMARKER v2.4.0 (Soft Genetics, State College, PA, USA)
with manual editing of the automatically scored peaks.
mtDNA amplification and sequencing
We followed the protocol designed by [25] to sequence a 570 bp fragment of mtDNA that
spans the COI and COII genes. Briefly, we used primers COIF1 (5’–CCT CAA CAC TTT TTA
GGT TTA G– 3’) and COIIR1 (5’–GGT TCT CTA ATT TCA TCA AGT A– 3’) to amplify 570
bp with an initial denaturation step at 95˚C for 5 min, followed by 40 cycles of annealing at
50˚C, and a final extension step at 72˚C for 20 min. We used a reaction volume of 13.0 μl
Northern Uganda G.f.fuscipes genetic diversity
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Table 1. Sampling localities and microsatellite and mtDNA assignment.
General information Microsatellites mtDNA Genetic unit
assignment
Population Closest
Village
District Drainage
Basin
N Cluster-1
average q
Cluster-2
average q
Cluster-3
average q
N
sequences
Count
A
Count
B
Count
C
%
mismatched
Northwest: Overall statistics 311 0.90 0.05 0.04 173 130 43 0 26.2% Northwest
DUK Duku Arua Albert N. 25 0.89 0.04 0.07 13 13 0 0 0.0% Northwest
AIN Aina Arua Albert N. 19 0.91 0.04 0.05 10 9 1 0 0.0% Northwest
GAN Gangu Arua Albert N. 20 0.88 0.06 0.06 11 11 0 0 0.0% Northwest
*OM Omugo Arua Albert N. 15 0.87 0.07 0.06 15 15 0 0 0.0% Northwest
OSG Osugo Moyo Albert N. 20 0.84 0.06 0.09 12 12 0 0 0.0% Northwest
BLA Belameling Moyo Albert N. 10 0.93 0.05 0.02 9 8 1 0 11.1% Northwest
LEA Lea Moyo Albert N. 8 0.91 0.05 0.04 6 3 3 0 50.0% Northwest
ORB Orubakulemi Moyo Albert N. 20 0.93 0.03 0.04 11 8 3 0 27.3% Northwest
*MY Moyo Adjumani Albert N. 15 0.87 0.08 0.05 17 15 2 0 16.7% Northwest
OLO Olobo Adjumani Albert N. 24 0.88 0.07 0.05 10 7 3 0 20.0% Northwest
OYA Oringya Adjumani Albert N. 9 0.94 0.02 0.04 8 6 2 0 25.0% Northwest
PAG Pagirinya Adjumani Albert N. 20 0.92 0.04 0.04 9 5 4 0 44.4% Northwest
OKS Okidi Amuru Albert N. 26 0.83 0.15 0.02 11 8 3 0 27.3% Northwest
GOR Gorodona Amuru Albert N. 25 0.89 0.09 0.02 12 8 4 0 16.7% Northwest
NGO Ngomoromo Lamwo Achwa R. 25 0.96 0.02 0.02 10 2 8 0 80.0% Northwest
PAW Pawor Lamwo Achwa R. 13 0.95 0.02 0.03 0 0 0 0 N/A Northwest
LAG Lagwel Lamwo Achwa R. 17 0.95 0.03 0.03 9 0 9 0 100% Northwest
Transition Zone: Overall statistics 310 0.54 0.43 0.03 150 36 114 0 19.9% Transition
BOL Bola Kitgum Achwa R. 25 0.80 0.18 0.01 10 3 7 0 10.0% Transition
TUM Tumangu Kitgum Achwa R. 20 0.76 0.21 0.03 10 3 7 0 10.0% Transition
KTC Kitgum Kitgum Achwa R. 20 0.76 0.22 0.02 9 1 8 0 33.3% Transition
*KT Kitgum Kitgum Achwa R. 17 0.87 0.08 0.04 9 2 7 0 55.6% Transition
OMI Omido Pader Achwa R. 15 0.66 0.32 0.02 9 2 7 0 22.2% Transition
*PD Pader Pader Achwa R. 13 0.63 0.34 0.03 10 1 9 0 30.0% Transition
KIL Kilak Pader Achwa R. 21 0.37 0.56 0.06 9 2 7 0 22.2% Transition
CHU Chua Pader Achwa R. 25 0.25 0.73 0.03 9 5 4 0 11.1% Transition
OCA Ocala Oyam Okole R. 20 0.51 0.46 0.03 9 3 6 0 22.2% Transition
AKA Akayo-debe Oyam Okole R. 26 0.39 0.57 0.04 9 1 8 0 11.1% Transition
*KO Kole Oyam Okole R. 15 0.45 0.52 0.03 9 2 7 0 22.2% Transition
OLE Olepo Kole Okole R. 24 0.37 0.61 0.02 9 3 6 0 11.1% Transition
ACA Acanikoma Kole Okole R. 25 0.64 0.31 0.06 11 3 8 0 9.1% Transition
APU Aputu-Lwaa Apac Okole R. 29 0.19 0.79 0.02 13 1 12 0 7.7% Transition
*AP Apac Apac Okole R. 15 0.44 0.53 0.04 15 4 11 0 20.0% Transition
Northeast: Overall statistics 184 0.03 0.95 0.01 80 3 77 0 2.9% Northeast
*UGT Kaberamaido Dokolo L. Kyoga 64 0.02 0.97 0.01 20 2 18 0 10.0% Northeast
*AM Aminakwach Dokolo L. Kyoga 30 0.02 0.97 0.01 16 0 16 0 0.0% Northeast
AMI Aminakwach Dokolo L. Kyoga 25 0.02 0.98 0.01 10 0 10 0 0.0% Northeast
(Continued)
Northern Uganda G.f.fuscipes genetic diversity
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Table 1. (Continued)
General information Microsatellites mtDNA Genetic unit
assignment
Population Closest
Village
District Drainage
Basin
N Cluster-1
average q
Cluster-2
average q
Cluster-3
average q
N
sequences
Count
A
Count
B
Count
C
%
mismatched
*OC Oculoi K’maido L. Kyoga 20 0.05 0.94 0.02 14 1 13 0 7.1% Northeast
OCU Oculoi K’maido L. Kyoga 25 0.04 0.95 0.01 11 0 11 0 0.0% Northeast
KAG Kangai K’maido L. Kyoga 20 0.07 0.92 0.01 9 0 9 0 0.0% Northeast
West: Overall statistics 149 0.02 0.02 0.96 78 58 0 20 N/A West
UWA Uganda WA K’dongo Victoria N. 25 0.02 0.01 0.97 11 7 0 4 N/A West
*KR Karuma K’dongo Victoria N. 60 0.04 0.02 0.94 9 7 0 2 N/A West
*KF Kafu K’dongo Victoria N. 34 0.01 0.01 0.98 3 3 0 0 N/A West
*MS Masindi Masindi Victoria N. 30 0.02 0.02 0.96 55 41 0 14 N/A West
Sampling localities ordered from north to south: General information includes population, closest village, district, drainage basin, and number of samples included (N). Microsatellite
results include average probability of assignment (q-value) to STRUCTURE defined clusters (1–3) based on 16 microsatellites. mtDNA results include number of sequences analyzed
(N sequences), counts of each mtDNA haplogroup (A-C) and % mismatch with Microsatellite cluster assignment on an individual basis. The last column indicates genetic unit
assignment. Genetic units were assigned based on each populations’ mean STRUCTURE assignment; populations with >0.8 average q were assigned to pure genetic units
(Northwest, Northeast, and West), and populations with <0.8 average q were assigned to the Transition Zone.
*indicates samples collected prior to 2014.
https://doi.org/10.1371/journal.pntd.0005485.t001
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containing 1 μl of template genomic DNA, 2.6 μl of 5X PCR buffer, 1.1 μl of 10 mM dNTPs,
0.5 μl of 10mM primers, 1.1 μl of 25 mM MgCl2, and 0.1 μl (U/μL) of GoTaq polymerase (Pro-
mega, USA). The PCR products were purified using ExoSAP-IT (Affymetrix Inc., USA) as per
the manufacturer’s protocol. Sequencing was carried out for both forward and reverse strands
on the ABI 3730xL automated sequencer at the DNA Analysis Facility on Science Hill at Yale
University (http://dna-analysis.research.yale.edu/). Sequence chromatograms were inspected
by eye and sequences trimmed to remove poor quality data using GENEIOUS v6.1.8 (Biomat-
ters, New Zealand). The forward and reverse strands were used to create a consensus sequence
for each sample, and the sequences trimmed to a length of 490 bp. Only a subset of the samples
screened for microsatellite variation was also sequenced at the mtDNA locus (Table 1).
Microsatellite marker validation
For nuclear microsatellite marker validation, we tested for neutrality and independence with
GENEPOP v4.2 [42]. We tested for departures from Hardy-Weinberg (HW) proportions in
each sample and microsatellite locus using an approximation of an exact test based on a Mar-
kov chain iteration (10,000 dememorization steps, 1000 batches, 10,000 iterations per batch in
the Markov chain); significance values were obtained following the Fisher’s method that com-
bines probabilities of exact tests [43]. We tested for genotypic linkage disequilibrium (LD)
among each pair of loci using the Guo and Thompson method [44]. To correct for false assign-
ments of significance by chance alone for all simultaneous statistical tests and comparisons, we
used the Benjamini-Hochberg False Discovery Rate (FDR) method [45]as opposed to the Bon-
ferroni correction, because of lower incidence of false negatives[45][46].
Microsatellite genetic diversity and population structure
For nuclear microsatellite data, we assigned individuals to genetic units without prior informa-
tion on sampling locality with STRUCTURE v2.3.4 [47][48]. STRUCTURE simultaneously
identifies unique genetic units and provides a probability of assignment (q-value, ranging
from 0 to 1) for each individual. Twenty independent replicate runs for each K = 1–10 were
carried out with an admixture model, independent allele frequencies, and a burn-in value of
50,000 steps followed by 250,000 iterations. The optimal value of K was determined using
STRUCTURE HARVESTER v0.6 [49] to calculate the ad hoc statistic “ΔK” [50], and indepen-
dent replicates were aligned with CLUMPP v1.1.2 [51].
In addition to STRUCTURE, we performed Discriminant Analysis of Principal Compo-
nents (DAPC) with the "adegenet" package v1.4–2 [52] in the R version 3.0.2 environment
[53]. The DAPC is a multivariate, model-free method that makes no assumptions about devia-
tions from Hardy Weinberg and linkage disequilibrium, designed to describe patterns of
genetic clustering among groups of individuals [54]. In this analysis, we grouped samples by
their site of origin and used the cross-validation formula available to choose number of princi-
pal components (PCs) to retain. To understand the partitioning of microsatellite variance
within and between genetic units, we performed an analysis of molecular variance (AMOVA)
in ARLEQUIN v3.5 [55].
Genetic diversity indices including observed heterozygosity (H
O
), expected heterozygosity
(H
E
), number of alleles, allelic richness (A
R
) and [56] estimator of inbreeding coefficient (F
IS
)
were calculated in GENALEX v6.501 [57]. Pairwise differentiations at different hierarchical
levels were estimated with two F-statistics. For comparison with previous G.f.fuscipes studies,
we calculated Wright’s F-statistics [58], following the variance method developed by [56] using
10,000 permutations in ARLEQUIN. For accuracy with highly polymorphic markers [59], we
estimated Jost’s D
EST
with the R package DEMEtics [60][53], where p-values and confidence
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intervals were calculated based on 1000 bootstrap resamplings. With the resulting F-statistics,
we tested for isolation by distance (IBD) using Rousset’s procedure [61] implemented in the
“isolation by distance” v3.23 web service [62]. Geographic distances were generated using the
web-based “geographic matrix generator” v1.2.3 [63]. The significance of the regression was
tested by a Mantel test with 10,000 randomizations [64].
Effective population size (Ne) and bottleneck analysis
Using microsatellite data, we estimated effective population size (Ne) for each sampling site
independently. We did not group sites based on assignment to genetic clusters because strong
evidence of substructure within clusters would violate assumptions. We estimated Ne using
two methods implemented in N
E
ESTIMATOR v2.01 [65]: the modified two-sample temporal
method [66] based on [67] for sites with multiple temporal samples, and the one-sample link-
age disequilibrium (LD) method [68] for all 42 sites. We used two methods to estimate Ne
because they each have different strengths and weaknesses [66,6971]. The two-sample tempo-
ral method [64] is useful because it is robust when there are overlapping generations [71], but
only provides an average estimate across two time points assuming a closed population, so
cannot be used to assess the impact of control efforts. On the other hand, the LD method [66]
is useful because it can provide an estimate for each sampling point and employs the bias cor-
rections by [72], but is influenced by bias associated with non-overlapping generations and is
not powerful enough to distinguish from infinite population sizes when there are insufficient
polymorphisms and numbers of markers to detect patterns of LD [67,71].
We tested for population bottlenecks using two methods implemented in the program
BOTTLENECK v1.2.02 [73]. The first method tested for an excess of heterozygosity relative to
observed allelic diversity [74]. We used the two-phase mutation model (TPM), the most appro-
priate for microsatellites [75], with 70% single-step mutations and 30% of multiple-step muta-
tion. Significance was assessed using Wilcoxon’s signed rank test, as is recommended when
fewer than 20 loci are used [73]. The second method tested for a bottleneck-induced mode
shift in allele frequency distributions that is usually evident in recently bottlenecked popula-
tions [76].
Hybrid zone analyses
We investigated the mixed ancestry suggested by STRUCTURE analysis in the Achwa and
Okole River regions. These sampling sites displayed an average probability of assignment (q-val-
ues) of less than 0.8 (See Table 1), which could either be caused by methodological shortcom-
ings (i.e. low genetic distance and inability of the markers to distinguish clear genetic clusters),
or by accurate detection of interbreeding of two distinct lineages. Following [77], we tested for
accurate detection of interbreeding by comparing observed admixture data against two alterna-
tive admixture models; a pure mechanical mixing model representing a scenario of strong repro-
ductive barriers and free migration, and a hybrid swarm model representing a scenario of free
hybridization and admixture using HYBRIDLAB v1.0 [78]. For the mechanical mixing model,
we simulated individual admixture proportions by randomly drawing alleles from the observed
allele frequency distribution of ’pure’ samples where the average probability of assignment (q-
values) were greater than 0.8 to a single STRUCTURE cluster (Table 1). For the hybrid swarm
model, we simulated individual admixture proportions from the observed allele frequency dis-
tribution of ’admixed’ samples where the average probability of assignment (q-values) were less
than 0.8 to any single STRUCTURE cluster (Table 1). We chose localities from the geographic
extremes of the northwest and northeast units to represent ’pure’ samples, and regions with
generally uncertain assignment from the Achwa and Okole Rivers to represent ’admixed’
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samples. Then, we used STRUCTURE to estimate individual probability of assignment (q-
value) with all three datasets; the true observed genotypes, the simulated genotypes under a
mechanical mixing model, and the simulated genotypes under a hybrid swarm model. Finally,
we used a Wilcoxon signed rank test to assess differences between observed and simulated dis-
tributions. We interpret significant differences between simulated mechanical mixing and hybrid
swarm datasets as evidence that uncertain STRUCTURE assignments do not represent a meth-
odological shortcoming. Likewise, we interpret non-significant differences between the
observed data and the simulated hybrid swarm data as evidence for interbreeding.
Relatedness and migration
Using microsatellite data, we determined if patterns of observed genetic structure could be
attributable to sampling related individuals, by testing for relatedness between individuals
using the program ML-Relate [79]. We assigned pairwise relationships within each genetic
unit into one of four relationship categories: unrelated (U), half siblings (HS), full siblings (FS)
or Parents/offspring (PO).
Detection of first generation migrants and progeny of successful mating of very recent
migrants between genetic regions was done using GENECLASS v2.0 [80], and using FLOCK
v3.1 [81], a program that provides accurate assignment of individuals to genetic units of origin
even in the absence of pure genotypes. In GENECLASS, we used the "detect migrant function",
which calculates the likelihood of finding an individual in the locality in which it was sampled
(Lh), the greatest likelihood among all sampled localities (L
max
), and their ratio (Lh/max) to
identify migrants. To distinguish true from statistical migrants (type I error), we selected the
Rannala and Mountain criterion [82], and the Monte Carlo resampling algorithm of [83]
(n = 1000) to determine the critical value of the test statistics, Lh/Lmax. Individuals were con-
sidered immigrants when the probability of being assigned to the reference population was
lower than 0.05. In FLOCK, we used a K value of 4, starting partitions chosen by location of
origin, ran 500 iterations and used a log-likelihood difference threshold (LLOD) value of 1.
mtDNA genetic diversity and population structure
For the mtDNA sequence data, all statistical parameters and tests were calculated using the
program ARLEQUIN [55]. Genetic diversity within populations was estimated by computing
haplotype diversity (H
d
) and nucleotide diversity (N
d
) [84] in DnaSP v5.0 [85]. Relationships
among haplotype lineages were inferred by constructing a parsimony network using TCS [86]
implemented in the free, open source population genetics software PopART (http://otago.ac.
nz). We used nucleotide diversity to estimate genetic differentiation (F
ST
) and performed an
analysis of molecular variance (AMOVA) in ARLEQUIN. We tested for IBD with the same
methods described above for the microsatellites.
Finally, we compared mtDNA haplogroup assignment with the Microsatellite STRUC-
TURE assignment, and tallied the percent mismatch. Individuals were considered a mismatch
if they displayed a high q-value (probability of assignment) score to one microsatellite based
cluster but low frequencies of the haplogroup generally found in the same geographic region
as the microsatellite based cluster.
Results
Microsatellite marker validation
Of the 17 microsatellite markers considered, most were under HW equilibrium in the majority
of populations with the exception of pg17, which was dropped from the analyses, as it showed
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significant departures from HWE at P<0.05. All remaining loci were polymorphic in all popu-
lations analyzed except D101, which was monomorphic in one population (OC). The most
polymorphic locus was GpB20b (24 alleles), while the least polymorphic was B05 (5 alleles;
details in S3 Table). None of the LD tests on pairs of microsatellite loci gave a significant
result after the Benjamini-Hochberg correction, confirming neutrality and independence of
markers.
Microsatellite population structure and defining geographic units
Fig 2A shows the results from STRUCTURE analyses. In this analysis, individuals fell into
three genetic clusters with clear geographic variation in probability of assignment (q-value)
(Table 1,S4 Table). The DAPC multivariate analysis (S1 Fig) corroborated the results of
STRUCTURE.
Based on the results of the STRUCTURE and DAPC analyses and their geographic loca-
tions, we grouped sampling sites into four units: West, Northwest, Transition Zone, and
Northeast. Sampling sites west of lake Kyoga along the Victoria Nile (i.e. UWA, KR, KF, MS)
had average q-values >0.8 to a single cluster (blue in Fig 2). The samples north of Lake Kyoga
(Fig 1) belong to two genetic clusters (gray and orange, Fig 2) and were grouped into three
units. The “Northwest” unit comprises samples from the Albert River drainage (e.g. DUK,
GAN, and AIN) and the most northerly Achwa River sites (i.e. NGO, LAG and PAW) with
Fig 2. Patterns of genetic differentiation and phylogenetic relationship based on microsatellites and
mtDNA markers for G.f.fuscipes in northern Uganda. (A) Bayesian clustering plots based on microsatellite
data showing genetic membership of 954 flies to three genetic clusters from STRUCTURE v2.3.4 [47]. Y-axis
shows the probability of assignment (q-values) for each individual (bars). Colors within each bar represent the
probability of assignment to one of the three genetic clusters. Sampling sites and the genetic unit assigned for each
individual are reported below the X-axis, using the same abbreviations as in Fig 1 and Table 1. Criteria for
assignment of each sampling site to a Genetic Unit are given in the text and in Table 1.(B) Haplotype network
depicting the mtDNA evolutionary relationships inferred by using the TCS method (Clement et al 2002) as
implemented in PopART (http://popart.otago.ac.nz). Haplotypes are represented by circles, sized proportional to
their frequency in the sample. Dashes have been used to represent mutational steps when haplotypes were
separated by more than one step. Haplogroups are identified by different colors; gray (Haplogroup A), orange
(Haplogroup B) and blue (Haplogroup C). (C) Distribution of the microsatellite clusters (rectangles) and the mtDNA
haplogroup frequencies (circles) within the four Genetic Units. Colors are as in panels A and B.
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average q-values >0.8 to a single cluster (gray in Fig 2). The “Northeast unit” comprises sam-
ples from north of Lake Kyoga (e.g. KAG, AM, OCU and AMI, Table 1) with average q-
values >0.8 to one cluster (orange in Fig 2). Sampling sites between the Northwest and North-
east units in the Achwa and Okole River basins (Fig 1) had a much lower average q-value
(0.54) than the other three units, and moving west to east, probability of assignment to one
cluster (gray in Fig 2) progressively decreased while it increased for the other cluster (orange
in Fig 2). We refer to this region between the Northwest and Northeast units as the "Transition
Zone".
Microsatellite based F
ST
between sampling sites either within or between the STRUCTURE-
defined clusters ranged from 0 to 0.229 with most comparisons being statistically significant
(S5A Table). Table 2 reports average F
ST
between the four units (Northwest, Transition Zone,
Northeast, and West). The West unit is the most genetically distinct from the other three
(F
ST
= 0.162, 0,163, and 0.218 for Northwest, Transition Zone, and Northeast, respectively).
Lower but still statistically significant F
ST
values were estimated between the Northwest and
Northeast units (F
ST
= 0.064) and even lower values between these units and the Transition
Zone (F
ST
= 0.021 and 0.035, respectively). D
EST
values showed the same trend as F
ST
, except
with overall higher estimates (S6 Table). Isolation by distance (IBD) analyses (S7 Table) showed
a significant correlation between genetic distance and geographic distance for all sampling sites
combined (R
2
= 0.438, p = 0.0001) and for sampling sites within the Northwest (R
2
= 0.259,
p = 0.00) and Transition Zone (R
2
= 0.216; p = 0.001). No significant IBD was detected among
sampling sites in the Northeast and West units.
AMOVA results using microsatellites showed that most of the variation was between indi-
viduals within sampling sites (89.63%) but differences were statistically significant at all levels
of comparison, including between sampling sites and among the four units (Table 3).
Microsatellite genetic diversity
Overall, all sites showed moderate to high levels of genetic variability (S1 Table). H
O
ranged
from 0.461 in LAG to 0.690 in OM and H
E
ranged from 0.537 in KAG to 0.678. For most of
the sites, H
O
and H
E
microsatellite diversities were similar, indicating random mating within
sites. Averaged over all samples and loci, the inbreeding coefficient (F
IS
) were generally low
with an overall grand mean of 0.048±0.008, and with significant heterozygote excess in 7 out
of 42 populations (S1 Table). Allelic richness ranged from a high of 7.785 in KR to a low of
4.188 in AMI, with an overall mean of 5.186 (S1 Table). Generally, microsatellite diversity was
highest in flies sampled in the Northwest and the Transition Zone sites (Table 1;S1 Table) and
lowest in flies from the Northeast (e.g. in sites KAG, AM, OCU and AMI). The trend of decline
in diversity from the Northwest to the Northeast is apparent and significant when allelic rich-
ness values were linear-regressed over longitude (R
2
= 0.121; p = 0.032; S2 Fig). Flies in the
West unit had microsatellite diversity values similar to or on par with the Northwest unit.
Table 2. Average estimates of genetic differentiation among the Northwest (NW), Transition Zone
(TZ), Northeast (NE) and West (W) units based on microsatellite F
ST
and mtDNA Φ
ST
estimates.
Microsatellites mtDNA
Northwest Transition Northeast Northwest Transition Northeast
Transition 0.021 Transition 0.041
Northeast 0.064 0.035 Northeast 0.080 0.051
West 0.162 0.163 0.218 West 0.018 0.190 0.313
Significant values at p<0.05 are indicated in bold. F
ST
and Φ
ST
were calculated in ARLEQUIN v3.5.
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Effective population size (Ne) and bottleneck analysis
S8 Table shows the results of the Ne estimates based on microsatellite data using the LD and
the temporal methods. Estimates using the heterozygote excess method were infinite for all
sites tested. Using the one-sample LD method, Ne estimates ranged widely from 101.6
(36.4-infinite 95% confidence interval [CI]) in OC to 1685.7 (234.2-infinite CI) in UGT and
were all bound by a CI that included infinity (S8 Table). Ne estimates using the two-sample
temporal method ranged from 103 (73–138 CI) in KTC to 962 (669–1309 CI) in OCU (S8
Table). Where a comparison between the two methods was possible, estimates were largely
congruent except for one site (OCU), where Ne using the temporal method was 962 (669–
1309 CI), and using the LD method was 112 (47.7-infinite CI; S8 Table). Results based on the
TPM model indicate a genetic bottleneck in 5 sampling sites (NGO from the Northwest, OCA
from the Transition zone, AMI and OC from the Northeast, and MS from the West; S8 Table).
Results based on allele frequency distributions showed a genetic bottleneck in only one sample
(AMI from the Northeast; S8 Table).
Hybrid zone analyses
Fig 3 shows the results of the HYBRIDLAB analyses. The distribution of STRUCTURE assign-
ments from the simulated hybrid swarm and mechanical mixing datasets are clearly distinct
(Fig 3) with a Wilcoxon two-tailed p-value of 0.002 (S9 Table). This indicates power to detect
interbreeding in the transition zone and thus evidence of hybridization. Comparisons of these
models with the observed data (S9 Table) indicate that the observed data (Fig 3A) matches
most closely with the hybrid swarm model (Fig 3B) than the mechanical mixing (Fig 3C) from
which it is statistically distinct.
Relatedness and migration
Relatedness analyses showed that the majority (>86%) of the individuals in all units are unre-
lated (Table 4). The percentage of individuals that were full siblings was very low, ranging
Table 3. Analysis of molecular variance analysis (AMOVA) based on microsatellite and mtDNA data.
Microsatellites
Source of variation d.f. Sum of
squares
Var.
components
Percentage of
variation
P-
values
Among units 3 578.062 0.385 7.26 0.000
Among sampling sites within
units
38 454.009 0.165 3.11 0.000
Within sampling sites 1866 8855.638 4.746 89.63 0.000
Total 1907 9887.709 5.295
mtDNA
Source of variation d.f. Sum of
squares
Var.
components
Percentage of
variation
P-
values
Among units 3 69.464 0.316 32.112 0.000
Among sampling sites within
units
37 34.452 0.079 8.048 0.000
Within sampling sites 441 180.280 0.589 59.840 0.000
Total 481 284.196 0.984
Results of AMOVA within and between the genetic units. Computations were carried out in ARLEQUIN.
v3.5.
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between 0.33% and 0.91% for all units. An even lower number of individuals had parent-off-
spring relationships ranging from 0% in the Transition Zone to 1.04% in the Northeast.
Microsatellite-based migrant detection using GENECLASS and FLOCK showed a higher
number of migrants between the Northwest, the Transition Zone, and the Northeast than
between these areas and the West (Fig 4). GENECLASS indicated slight asymmetry in migra-
tion into the Northwest. There are 20 migrants from the Transition Zone into the Northwest
and 10 migrants in the reverse direction, with both sexes almost equally represented (10 and 2
male migrants vs. 10 and 6 female migrants; S10 Table). We also detected two first generation
female migrants from the Northeast to the Northwest. In contrast, migration between the
Transition Zone and the Northeast is symmetrical with 8 migrants from the Northeast into the
Transition Zone and 9 migrants in the opposite direction. Both sexes are moving in both direc-
tions, although the low sample sizes (2 and 3 male migrants vs. 5 and 1 female migrants; S10
Table) precludes any strong conclusion. We also detect two migrants between the Northwest
Fig 3. Hybrid zone analysis. Distribution of probability of assignment (q-values) and 95% posterior probability intervals
among individuals estimated using STRUCTURE for; (A) the true observed genotypes, (B) the simulated genotypes under
amechanical mixing model, and (C) the simulated genotypes under a hybrid swarm model. Highly significant difference (p-
value <0.0001) are marked by ***, significant difference (p-value <0.05) are marked by *, and non-significance is
indicated as "NS".
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Table 4. Relatedness category for G.f.fuscipes samples within the four units and combined.
Northwest Transition Northeast West Overall
Unrelated 87.93 90.33 82.90 86.04 86.93
Half siblings 11.51 9.33 15.15 13.09 12.35
Full siblings 0.49 0.33 0.91 0.67 0.61
Parent-Offspring 0.07 0.00 1.04 0.20 0.11
Percent of pairwise comparisons of individuals that fell into each relatedness category as calculated in
ML-Relate [79]. Comparisons were made among the 3 units Northwest, Northeast, West) and the Transition
zone; and among all samples combined (Overall).
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and West, one in each direction. FLOCK analysis provided similar migration rates between
regions (Fig 4), but showed less asymmetry from the Transition Zone into the Northwest (23
from the Transition Zone into the Northwest, and 17 in the opposite direction), and more
asymmetry from the Northeast into the Transition Zone (13 from Northeast into the Transi-
tion Zone, two in the opposite direction; S10 Table). FLOCK showed no migration between
the West and any other region (S10 Table).
mtDNA genetic diversity and population structure
The mtDNA dataset consisted of 481 sequences (490 bp long), which included 289 sequences
from individuals sampled for this study (a subset of the ones screened for microsatellite loci
variation, Table 1) plus 192 sequences from individuals from previous ones [25][31], Table 1).
Sequences could be grouped into 30 haplotypes (S11 Table), displayed as a TCS network (Fig
2B). There are three major haplogroups (groups of related haplotypes); Haplogroup A, Hap-
logroup B, and Haplogroup C (Fig 2B). Table 1 reports haplogroup frequencies for each site
and for the four units. Haplogroup A occurs in all studied regions, but is most frequent in the
Northwest and West units (75.1% and 74.4%, respectively). It occurs less commonly in the
Transition Zone (24.0%) and only rarely in the Northeast (4.8%). Haplogroup B occurs most
commonly in the Northeast unit (95.2%) and it is less common going from Northeast unit to
the Transition Zone (76.0%) and to the Northwest unit (24.9%). Haplogroup B does not occur
in the West unit and Haplogroup C occurs only in this unit (25.6%, Table 1,Fig 2C).
The number of haplotypes at each sampling site ranged from 1 to 6 (S1 Table). Haplotype 1
is the most frequent (186 individuals) and occurs in all units except the West, and falls into
Haplogroup B (S11 Table). Haplotype 2 from Haplogroup A is the second most common (140
Fig 4. Migration patterns. Arrows summarize the direction of movement of first generation migrants and
progeny of successful mating of very recent migrants obtained using the program GENECLASS [80] and
FLOCK [81] respectively, separated by a slash. Their thickness reflects the relative amount of migrants with
actual estimates reported above each arrow.
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individuals) and it is found in all units (S11 Table). The third and fourth most common haplo-
types fall in Haplogroup A and C, and only occur in the West (41 individuals and 19 individu-
als, respectively). Thirteen haplotypes were singletons (observed once in the sample) and fall
into a mix of haplogroups, eight of which were from the Northwest, four from the Transition
Zone and one from the West (S11 Table). Nucleotide diversity averaged 0.002 and ranged
from 0 (OSG and KF) to 0.008 (UWA; S1 Table). Likewise, average haplotype diversity was
0.757, and ranged from 0 (KF and OSG) to 0.836 (UWA; S1 Table). There was no apparent dif-
ference in haplotype diversity from Northwest to Northeast units.
S6B Table shows estimates of genetic differentiation (F
ST
) between sampling sites. F
ST
ran-
ged from 0 to 1; with some sampling sites showing no evidence of differentiation (e.g. PD in
the Transition Zone vs AMI in the Northeast), while reached 1 for pairs that did not share
haplotypes at all (e.g. OSG in the Northwest vs KF in the West). S7 Table shows the results
of the IBD analyses using mtDNA-based F
ST.
Like in the microsatellite-based test, the correla-
tion between genetic distance and geographic distance was significant for all sampling sites
combined (R
2
= 0.490, p = 0.001) and for samples within the Northwest unit (R
2
= 0.425,
p = 0.001), but non-significant for the Northeast and West units. Unlike in the microsatellite-
based IBD tests, the correlation between geographic and genetic distance in the Transition
Zone was non-significant (R
2
= 0.002; p = 0.374).
AMOVA results based on mtDNA agree with the Microsatellite (Table 3), with most of the
variation between individuals within sites (59.84%) and significant values at all levels of com-
parison, including between the four units (Northwest, Transition Zone, Northeast, and West;
Table 3).
To evaluate the possible role of differential introgression of nuclear vs mitochondrial mark-
ers we assessed levels of mismatches by comparing individual assignments for each marker
type (S4 Table), and calculated percent individuals with discordant nuclear vs mitochondrial
assignment (Table 1). This analysis could only be done for the three northern units because
the common microsatellite based cluster (blue in Fig 2) in the West was not clearly associated
with a single mitochondrial haplogroup, as both Haplogroup A and Haplogroup C occur
there. On the contrary, the Northeast and Northwest were clearly associated each with a single
haplogroup, so we scored any individual from the north with a microsatellite-based q-value
greater than 0.9 as a “match” if both nuclear and mitochondrial assignments were associated
with the same region (grey/grey or orange/orange in Fig 2), or a “mismatch” if assignments
were associated with different regions (grey/orange or orange/grey in Fig 2). The highest per-
centage of mismatches were found in the Northwest unit (22.8%), followed by the Transition
Zone (20.03%), and then the Northeast unit (4.0%) (Table 1).
Discussion
We evaluated the fine scale genetic structure of G.f.fuscipes populations north of Lake Kyoga
in Uganda, a region that is of special interest due to the impending risk of merger of the two
forms of HAT disease that G.f.fuscipes transmits in Uganda. Our sampling scheme targeted
the fine spatial scale resolution of genetic structure so as to provide the most accurate informa-
tion available on genetic connectivity and population dynamics in the region spanning the two
HAT disease foci. This kind of information is necessary to inform vector control program
design [8791]. Findings indicate two strong genetic breaks in northern Uganda and deter-
mine that hybridization is occurring freely across the contact zone between the Northwest and
Northeast. We explored underlying mechanisms of population dynamics in northern Uganda
and found that large influence has been imposed by (i) sustained connectivity of the Northwest
with the rest of the G.f.fuscipes species range, (ii) past geologic events associated with the
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opening of the great rift valley during the last ~35 ka, and (iii) vector control programs that
have caused population bottlenecks but have not always sustainably controlled tsetse popula-
tions. We also identified a general pattern of isolation by distance and moderate migration
within interconnected regions. Findings suggest that population rebounds may have occurred
from very close by populations soon after vector control efforts, and that interbreeding across
a hybrid zone that spans the two disease foci could promote recolonization from different
genetic units across further distances. These results support the need for long-term monitoring
and a design that mitigates recolonization from neighboring regions, especially within the
hybrid zone that spans the two disease foci.
Patterns of genetic diversity and population dynamics
Genetic diversity at both microsatellite and mtDNA markers (Table 1) were generally low
compared to many Diptera and Coleoptera species, which is consistent with reproductive lim-
its imposed by the tsetse’s viviparous life history [92]. The mtDNA haplotype network (Fig 2B)
was congruent with the network published by [24] with more haplotypes because of the higher
spatial resolution of this study. Levels of diversity in both markers (Table 1) were similar to
previous estimates for sampling sites north of Lake Kyoga [25][31], but higher than estimates
of southern Uganda populations [27]. We found a subtle decline in genetic diversity from west
to east (S1 Fig) in northern Uganda similar to the pattern previously observed in central and
southern Uganda [25]. [25] suggested that this gradient reflected sequential founder events
originating from the main tsetse belt in the Northwest and moving eastward. Conversely
though, our results for northern Uganda are not consistent with a single genetic origin from
the main tsetse belt because we found two distinct genetic backgrounds (Fig 2A) and two dis-
tinct mtDNA haplogroups (Fig 2B). This apparent inconsistence between past and current
results could be due to the inability of previous studies to pick up the spatial differentiation
and admixture patterns that we detected because of their much sparser sampling than in this
study. Rather than sequential founder events pushing for a northwest to northeast range
expansion, our results suggest that sustained connectivity to the greater G.f.fuscipes distribu-
tion and recent human induced population processes, such as vector control and habitat
destruction, may account for the higher genetic diversity in the Northwest vs the Northeast.
The G.f.fuscipes range extends continuously westward as far as Cameroon and Gabon (Fig
1; [93][94]) and has been sustained since the last glacial maximum ~15–20 ka [95][2][96], with
the Uganda sites being at the extreme northeast of G.f.fuscipes’ contiguous distribution. The
size of this range and its temporal stability suggest that populations from the main part of its
distribution are likely to be interconnected and old enough to harbor the high levels of genetic
diversity characteristic of large and stable populations. This may have facilitated intermittent
gene flow and can be a factor in explaining the higher genetic diversity in the Northwest than
in Northeast of Uganda. In contrast, populations to the east and south of Lake Kyoga are bor-
dered by unsuitable habitat to the east [93], and have experienced recent arid periods during
the last glacial maximum ~15–20 ka, and again during the latest Pleistocene ~14 ka, when the
lakes in Uganda completely desiccated multiple times [97][95]. These climate events could
have led to contractions and expansions of populations, accentuating the effects of genetic
drift and creating isolated populations with low genetic diversity such as that found in UGT,
AMI, AM, OC, KAG, and OCU (Table 1;S2 Fig). Despite high genetic diversity in some locali-
ties in the West such as UWA (Table 1;S2 Fig), which conforms with the general pattern of
high to low diversity from west to east (S2 Fig), connectivity with the rest of the G.f.fuscipes
distribution in the West is limited by Lake Albert and the less suitable habitat in the bordering
Blue Mountains. Thus, we suggest that the high allelic richness and haplotype diversity in the
Northern Uganda G.f.fuscipes genetic diversity
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West was created by contact between the distinct genetic lineages at the Victoria Nile with a
small amount of asymmetrical introgression (see below) rather than through connectivity with
the central part of the species range.
As expected, the gradient from higher to lower effective population size estimates (Ne)
from the northwest to the southeast parallels the results on genetic diversity, and is likely
caused by similar evolutionary forces, as Ne calculations are based on diversity estimates. Our
interpretation of Ne was somewhat limited because we were only able to draw inference from
the two-sample temporal method. As expected, the one-sample LD based Ne estimates yielded
high confidence intervals that overlapped with infinity (S8 Table). Improved Ne estimates
based on a larger number of nuclear markers will be an important focus of future research
using Single Nucleotide Polymorphisms (SNPs). Despite uncertainty in Ne estimates from the
LD method [67], the temporal method [64] provided estimates that ranged from 100 to 1000,
had low confidence intervals (S8 Table), and showed higher estimates in the Northwest. This
result is in line with the distinct life-history traits of tsetse flies such as lower population sizes,
reproductive outputs, and longer generation times than other insects. Ne and genetic diversity
results that we report for the Northwest were similar to what has been reported in G.f.fuscipes
sampled from northern Uganda [31][25] and in G.palpalis, another riverine species [1]. How-
ever, estimates were higher than reported in populations from southern Uganda [27]. This
suggests that Northwest populations are influenced by either high connectivity with the rest of
the G.f.fuscipes range, or by lower levels of vector control in the Northwest as compared to
regions impacted by the SOS campaign in the Northeast.
Detection of recent bottlenecks (S8 Table) provides further evidence that Ne has been influ-
enced by vector control campaigns. The bottleneck analysis we used can detect extreme reduc-
tions in population sizes that occurred more recently than 2–4 Ne generations [67][98], which
corresponds to 25–500 years in G.f.fuscipes, depending on the exact Ne and generation time
of the population in question [99][25][31][27]. Signals of bottlenecks from these short time
scales can be due to natural or human induced changes in population size. Both of these causes
may be at play given G.f.fuscipes’ patchy distribution, unique life history traits, and history as
the target of intense, even if somewhat irregular, vector control campaigns. Bottlenecks in OC
and AMI can be attributed to the SOS campaign of 2009 [100][41]. Similar tsetse control proj-
ects, like the Farming in Tsetse Controlled Areas (FITCA) in southeastern Uganda in places
not included in this study like Okame, Otuboi, and Bunghazi, resulted in detection of bottle-
necks in these areas in previous studies [26][25]. On the other hand, we found no evidence of
bottlenecks in the 2014 samples most proximal to the location of the pilot vector control pro-
gram conducted by [39] in the district of Arua (DUK, AIN and GAN). This may have been
because the location of sampling was too spatially distant (minimum of ~20 km) to influence
the population sampled, or because the time of sampling was too temporally near (~6 months)
for a genetic signal to propagate. Survey data indicates that some control efforts resulted in
long-term reduction in tsetse census [26,31], while other control efforts such as the SOS cam-
paign in the Northeast resulted in only short-term population size reductions. Population
rebound is evidenced by the similar number of flies caught per trap at sites in the SOS region
and at sites where no control activities have been carried out. For example, during sampling in
2014, traps set at two sites in the SOS region (OCU and AMI) caught an average of 67 and 14
flies per trap, which are numbers that are similar to or higher than the average catch of 18 flies
per trap. This underscores the importance of long-term control and monitoring campaigns
following tsetse control. A population bottleneck in GOR (S8 Table) remains unexplained by
known vector control campaigns, which may indicate that some bottlenecks are induced by
natural evolutionary processes such as weather events or changes in ecological interactions.
Thus, there is evidence that the joint influence of natural processes such as the greater
Northern Uganda G.f.fuscipes genetic diversity
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connectivity to the rest of the G.f.fuscipes distribution in the Northwest, the dramatic climate
change including arid periods in central and southern Uganda in the last ~35 ka years, and
recent vector control programs have determined the west to east gradient in G.f.fuscipes
genetic diversity and population dynamics in northern Uganda.
Patterns of genetic differentiation
Clustering (Fig 2A) and multivariate (S1 Fig) analyses detected three distinct genetic clusters
each composed of multiple sampling sites and broadly corresponding to the Northwest,
Northeast, and West units (Fig 2C). mtDNA haplotypes also clustered into three hap-
logroups (Fig 2B), which approximately correspond to these same three regions (Fig 2C).
Two of these genetic lineages have been described by previous research as Northern and
Western clusters [24][25][31], and we find evidence of a previously undescribed divergence
in the Northern cluster, which is now partitioned into Northeast, Transition Zone, and
Northwest units.
Our data confirm deep genetic divergence between the G.f.fuscipes nuclear lineages
found at the Victoria Nile, which harbor a mix of mtDNA haplotypes of both northern and
southern associated lineages (Fig 2). STRUCTURE clustering showed close geographic prox-
imity of distinct clusters at the confluence of the Okole River and the Victoria Nile (Fig 2).
This stark genetic break in the nuclear genetic make-up may be due in part to insufficient
sampling between UWA in the West and AKA and OLE in the North for accurate descrip-
tion of the shape and geographic span of the genetic divergence between these regions.
Future sampling efforts should encompass detailed sampling in this region to determine if
there is indeed another transition zone between the West and units identified in this study
(i.e. Northwest, Transition Zone, and Northeast), and between the West and previously
described units [24][25][31]. Divergence between the North and West is thought to have
originated during past allopatry more than 100,000 years ago [25]. Subsequent changes in
the river systems associated with the opening of the great rift valley 13,000–35,000 years ago
[95] created the modern outflow from Lake Victoria into Lake Kyoga and the reversal of the
Kafu river to meet the Victoria Nile before flowing into Lake Albert [97]. These changes may
have shifted the range of the Western G.f.fuscipes populations into contact with the North-
ern units at the Victoria Nile.
We find mixed mtDNA ancestry in the West, which [24] described and suggested indicates
recent rare female dispersal from the north and chance amplification of northern haplotypes
by drift. Another possible explanation is the preferential introgression of organelle DNA from
the resident population into the colonizing genetic background when two divergent lineages
come into secondary contact during range expansion [101]. This scenario is supported by
changes in the river systems and multiple drying cycles of the lakebeds [85] that would have
promoted repeated retraction and expansion of G.f.fuscipes in central Uganda. If northern lin-
eages had recolonized central Uganda before a northward shift of the southwestern lineage,
the result would be a large number of northern mtDNA haplotypes in a Western nuclear
genetic background. There are also possible ecological interactions at play because this region
is unique in the co-distribution of other tsetse species, G.morsitans submorsitans and G.palli-
dipes [2] especially in the large protected area of the Murchison Falls National Park (Fig 1).
Thus, evidence supports that strong evolutionary and ecological forces maintain genetic dis-
tinctiveness between the West and the other genetic units, but the details remain unclear and
an important focus for future work. More fine scale sampling of all tsetse species across the
North/West genetic break as well as experiments that test mating compatibility would help
shed light on the mechanism(s) that maintain genetic discontinuity.
Northern Uganda G.f.fuscipes genetic diversity
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Both microsatellite based F
ST
and mtDNA based F
ST
showed significant differentiation
despite our fine scale sampling effort, which aligns with previous studies that have found sig-
nificant differentiation across small geographic scales of as little as 1–5 km
2
in Uganda [102]
[27][25]. Tsetse flies are known to be sensitive to environmental conditions and exist in dis-
crete patches [2]. We suggest that low connectivity between adjacent habitat patches coupled
with small Ne has allowed genetic drift to create significant differentiation at small spatial
scales in G.f.fuscipes in northern Uganda. High signals of isolation by distance we detected in
both microsatellites and mtDNA (S7 Table) further support the idea that population structure
is maintained by the dual action of migration and genetic drift.
Levels of genetic admixture
The genetic break between the Northwest and Northeast forms a broad region of mixed micro-
satellite and mtDNA assignment along the Achwa and Okole rivers, in what we call the Transi-
tion Zone. The genetic break between the Northwest and Northeast and the one between the
broad northern and southern clusters described by [25] are both characterized by what we
think are secondary contact with admixed individuals and introgression of mtDNA haplo-
types. However, the width of the transition zone, the level of differentiation, and the patterns
and levels of admixture, is different across these two contact zones, with a broader, less differ-
entiated, and more gradual pattern of admixture in the Transition Zone than in the North/
South contact. The Transition Zone extends more than 200 km (Fig 2), while the secondary
contact zone between the North and South clusters extends less than 75 km [25][31]. This
difference in width may have been facilitated by colonization patterns and the distinct geo-
graphical break imposed by the swampy upper reaches (southern extent) of Lake Kyoga at the
contact zone between the North and South, while less conspicuous physical breaks only par-
tially limit movement of flies to and from the Transition Zone (Fig 1). The Transition Zone is
characterized by uninterrupted suitable habitat along the entire length of the Achwa River,
with only short distances of less than 15 km between the Achwa and Okole Rivers and neigh-
boring drainage basins of Lake Kyoga and the Albert Nile (Fig 1).
The levels of differentiation are also different between these two contact zones. In the Tran-
sition Zone, microsatellite-based F
ST
estimates are lower (average F
ST
= 0.064, Table 2) than
the comparable values for the North and South clusters (average F
ST
= 0.236; [25]). This pat-
tern was even more extreme in mtDNA F
ST
estimates, with an average F
ST
of 0.080 between
the Northwest and Northeast (Table 2) and 0.535 between the North and South [25][103][31].
Similarly, the patterns of admixture are distinct between the two contact zones. In the
North/South contact zone, there is a dramatic increase in mismatched individuals that assign
with high frequency (>90%) to one nuclear based genetic cluster but with mtDNA haplotypes
found in another [31] at the zone of contact, with 16.98% in the contact zone vs 0–2% on either
side. The pattern of mismatch in the North/South contact zone suggest differential introgres-
sion of mtDNA and nuclear loci, which could be due to Wolbachia infections [25][104][103]
[31], given its maternal inheritance and ability to induce cytoplasmic incompatibility in G.
morsitans [105] and other insects [106]. In contrast, in northern Uganda, the Transition Zone
does not display an increase in mismatches, with 19.9% in the contact zone vs 26.2% to the
north, which leaves no evidence of differential levels of introgressions of the two markers
(Table 1) or asymmetrical introgression. The match of observed data with the hybrid swarm
model in the HYBRIDLAB analysis (Fig 3) provides further evidence of relatively free and sym-
metrical interbreeding in the Transition Zone over multiple generations.
Taken together, our results suggest that for the northern secondary contact area, isolation
by distance and genetic drift are the two most likely processes that have shaped the distribution
Northern Uganda G.f.fuscipes genetic diversity
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0005485 April 28, 2017 20 / 29
of the nuclear and mtDNA polymorphisms, rather than Wolbachia infections. Nonetheless,
symmetrical interbreeding in the Transition Zone of this study remains tentative without the
ability to classify hybrid classes because of wide and overlapping 95% confidence intervals
around expected q-values (Fig 3A). Further genetic characterization of the northern hybrid
zone as well as characterization of the circulating Wolbachia strain(s) in the North would
improve understanding of the forces shaping the genetic cline that lies between the disease
belts of the two forms of HAT in northern Uganda.
Migration patterns
The methods we used to detect migrants reflects both first generation migrants and progeny of
successful mating of very recent migrants rather than dispersal, and thus allowed us to assess
recent gene flow across the full geographic range of our study [82][81]. We detect compara-
tively high migration rates among the northern clusters and low migration between these units
and the West. High gene flow between the three northern units supports the assertion by [91]
and others that waterways, in this case the Achwa River, maintain connectivity in tsetse popu-
lations. The vast majority of the migrants were a result of short-range dispersal from geograph-
ically proximate sampling sites connected by rivers. GENECLASS detected only two long-
range migrants from the Northwest into the Northeast, which would not be expected with
available ecological and physiological data that indicate tsetse cannot disperse over long dis-
tances [107]. Thus, it is likely these long-range migrants are offspring of assortative mating
between first generation migrants found in geographically intermediate locations rather than
first generation migrants.
The overall direction of migration we detected was slightly asymmetrical towards the
Northwest from the Northeast. However, we found no evidence of sex-bias (S10 Table). These
findings agree with previous studies which detected similar movement rates for the two sexes
for G.f.fuscipes from the southeast into the northeast [25][31]. [25] suggested that movement
along riverine habitats might be linked to passive dispersal of pupae via seasonally flooded
river systems. Transportation of adults and pupae downstream may also be aided by large
floating islands with dry substrate that form in backwaters and eddies and move northwards
for sometimes hundreds of km along the major rivers in the region such as the Nile and its
tributaries, and potentially, the Achwa river [108] [109]. Nonetheless, this hypothesis remains
to be tested, and alternatives include the movement of flies with livestock [3][110], shifting dis-
tribution of suitable habitat with human activities, and ongoing migration along corridors of
suitable habitat that connect the north and south of Uganda.
Implications for vector control and future directions
The observations from this study have important implications on the epidemiology of the two
HAT diseases, as well as on future vector control and monitoring efforts in this region. A
dense sampling scheme across a relatively small geographic area allowed an unprecedented
spatial resolution of genetic structure in this region. Our results point to the presence of four
genetic units, three of which have high levels of gene flow among them. The genetic distinc-
tiveness of the West from the other three units suggests that this unit could be treated as a
separate entity from the Northern ones. However, when planning control and monitoring
strategies, it is opportune to look at the patterns and levels of genetic discontinuities between
West vs. South and West vs. North genetic backgrounds in more detail to more precisely
define the boundaries of each genetic unit at a country-wide scale. Given the results of this
work, for sampling sites North of Lake Kyoga, control efforts undertaken at the unit level
are unlikely to produce long-lasting results due to re-invasion from adjacent units, unless
Northern Uganda G.f.fuscipes genetic diversity
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physical barriers are incorporated to avoid re-invasion from adjacent units. The best strategy
would be a “rolling-carpet” initiative where control is initiated from the Northeast through the
Transition Zone into the Northwest followed by intense monitoring and additional control to
manage fly migration from previously cleared sites due to population recrudescence after
control.
Our results suggest that ecological and geographic features, especially the river systems in
northern Uganda, play a major role in keeping G.f.fuscipes populations connected–a fact that
should be taken advantage of when designing control. The genetic connectivity we found
along waterways provides further support for a vector control strategy that incorporates targets
along waterways and barriers to recolonization from adjacent stretches of riverbanks. This
idea is also supported by a recent study that comprehensively evaluated a “tiny targets” vector
control strategy along riverine savannah and found that a target density of 20 per linear km
can achieve >90% tsetse control [39].
Our data also suggest that there is current movement of flies from the Northeast and North-
west into the Transition Zone but with a slight asymmetry towards the Northwest. Given that
previous studies also demonstrated northward migration from the east [25][31], it is possible
that tsetse, besides livestock movement, is contributing to the northwards expansion of the T.
b.rhodesiense sleeping sickness.
Of major relevance for disease control is the finding of high levels of genetic intermixing
and gene flow in the Transition Zone, which implies that a fusion of the two diseases (T.b.
gambiense and T.b.rhodesiense) is unlikely to be prevented by an incompatibility between vec-
tor populations in the region of contact. Given the extent of connectivity in the three northern
genetic units and the apparent genetic stability of G.f.fuscipes populations in the region [37],
ongoing monitoring following control would be paramount if interventions are to be sustain-
able. Monitoring programs should involve a combination of both ecological and genetic sur-
veys to check on changes in population density and re-emergence either from residual pockets
of tsetse or dispersal from proximal locations. For example, our results from the Northeast
highlight the risk of population rebound following control. In this region, we found strong evi-
dence of genetic bottlenecks indicating initial success of the SOS campaign in reducing tsetse
density. However, our 2014–2015 surveys in the same sampling sites returned some of the
highest tsetse trap densities. It appears, therefore, that when control activities were relaxed at
the end of the SOS campaign, tsetse populations recovered to high densities. Focused monitor-
ing could provide early detection of such population rebound and allow for identification of
the source and proper mitigation of the recolonizing tsetse.
Supporting information
S1 Fig. Discriminant Analysis of Principal Components (DAPC) based on genetic diversity
at 16 microsatellite loci in 42 populations and obtained using the adegenet package [52] in
R [53]. Two linear discriminants (LD1 and LD2) were used, following selection of principal
components using a-score optimization, to plot G.f.fuscipes genotypes. Color codes are the
same as in Fig 3. Letter codes represent sampling locations. Dots represent individual geno-
types and the groups belonging to a sampling site as ellipses. Upper and bottom left insets
show eigen values of principle components in relative magnitude. Black bars of eigen values
show the proportion of principal components retained.
(PDF)
S2 Fig. Linear regression of allelic richness (microsatellite loci) and haplotype diversity
(mtDNA) over longitude produced using JMP V11.0 (SAS Institute Inc., Cary, NC, USA,
1989–2007). Triangles, diamonds, and squares identify sampling sites within the Northwest,
Northern Uganda G.f.fuscipes genetic diversity
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0005485 April 28, 2017 22 / 29
Transition Zone, Northeast genetic units, respectively.
(PDF)
S1 Table. Sample information and molecular diversity indices. Sample geographic locations,
sample sizes, and genetic diversity statistics for 16 microsatellite loci and for a 490bp mtDNA
COI-COII gene fragment in 42 populations of G.f.fuscipes.Indicates samples collected
prior to 2014, N = number of individuals analyzed, AR = Allelic richness, Ho = Observed het-
erozygosity, He-Expected heterozygosity, Fis = inbreeding coefficient.
(XLSX)
S2 Table. Microsatellite loci information. The table reports loci names followed by the for-
ward (F) and reverse (R) primers names and sequences. The last column reports its source.
M13 tails are marked with an asterisk ().
(DOCX)
S3 Table. Total number of microsatellite alleles by locus.
(DOCX)
S4 Table. Table shows the probability of assignment (q-values) of individuals to each of
the 3 genetic units, individual mtDNA haplotype, home region of individual, and if it’s a
migrant the origin of migration, as well as comparison between microsatellite and mtDNA
genetic assignment.
(XLSX)
S5 Table. Pairwise F
ST
and F
ST
comparisons for microsatellites (A) and mtDNA (B) respec-
tively. F
ST
values are reported in the lower diagonal. Since most values are significant, we high-
light those that are non-significant in bold. All computations were done in ARLEQUIN.
Significance was calculated based on a P<0.05.
(XLSX)
S6 Table. Pairwise D
EST
for 42 populations averaged over 16 loci. The first two columns
show the sampling site pairs, while the third and fourth columns report their mean D
EST
values
and the Benjamini-Hochberg corrected significance p-values, respectively. Estimates were
made in the R package DEMEtics [60].
(XLSX)
S7 Table. Results of tests for isolation by distance where geographic distance between sam-
pling sites (km) were linear-regressed over nuclear microsatellites based genetic distance
(F
ST
/(1-F
ST
)) and mtDNA sequence based genetic distance (F
ST
/(1-F
ST
)). Results for the
Northwest, Transition Zone, Northeast, West units, and all samples combined (Overall) are
shown separately. Genetic group, root mean square values (R
2
), p-value for the Mantel test (p),
and slope and intercept of the linear regressions are shown. Significant correlations are indi-
cated in bold (p<0.05).
(DOCX)
S8 Table. Effective population size and bottleneck tests. Estimates of effective population
size (Ne) were computed for each of the 42 sampling sites across the geographic regions in
northern Uganda using three methods: LD, modified temporal method of Waples [66] based
on [67] and heterozygote excess method. Estimates are provided together with their 95% CI.
Bottleneck tests were carried out using the two methods implemented in the program BOT-
TLENECK [73]. Significance of tests for population bottlenecks assumed TPM model and it is
displayed as a p-value based on 1-tailed Wilcoxon’s test (P<0.05).
(XLSX)
Northern Uganda G.f.fuscipes genetic diversity
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0005485 April 28, 2017 23 / 29
S9 Table. Wilcoxon signed rank test results comparing STRUCTURE results from the real
data to the hybrid swarm model and the mechanical mixing model.
(XLSX)
S10 Table. Table showing the total number of migrants based on GENECLASS and
FLOCK. General information is displayed (population, closest village, drainage basin). Total,
female, and male migrants are shown as counts from each unit based on both analyses.
(XLSX)
S11 Table. Table showing list and frequency of distribution of haplotypes recovered from
the G.f.fuscipes samples mtDNA sequences analyzed from northern Uganda.
(XLSX)
Acknowledgments
We are grateful to the Gulu University tsetse field team; Alfonse Okello, Calvin Owora and
Constant Khizza, for their help with sample collection. We wish to also thank all those District
Vector Control officers and the various individuals who worked with us during tsetse collec-
tion excursions. Lastly, we acknowledge and appreciate Carol Mariani and Mary Burak of the
Yale Caccone laboratory for help with sample processing and laboratory analysis.
Author Contributions
Conceptualization: AC EAO RE SA RO.
Formal analysis: RO NPS.
Funding acquisition: AC SA.
Investigation: RO AH KD AWD.
Methodology: AC EAO SA RO RE NPS.
Project administration: EAO AC RE SA.
Resources: AC SA.
Supervision: AC EAO NPS RE SA.
Visualization: RO NPS.
Writing – original draft: RO NPS AC.
Writing – review & editing: RO NPS AC.
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Supplementary resources (13)

... Over 70% (140,000 km 2 ) of Uganda's land area is estimated to be infested by tsetse flies, but with varying levels of prevalence [5], with Glossina fuscipes fuscipes being the most predominant [6]. Recent population genetic studies on G. f. fuscipes in northern Uganda identified a vector "genetic transition zone" where different lineages freely mix and interbreed [7]. This zone also coincides with a previously known HAT disease-free belt in northern Uganda, where the two forms of HAT are likely to merge [7,8]. ...
... Recent population genetic studies on G. f. fuscipes in northern Uganda identified a vector "genetic transition zone" where different lineages freely mix and interbreed [7]. This zone also coincides with a previously known HAT disease-free belt in northern Uganda, where the two forms of HAT are likely to merge [7,8]. The zone, therefore, presents an epidemiologically significant area due to the existence of the two forms of humaninfective trypanosomes (Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense), parasite reservoirs, vectors, and susceptible hosts. ...
... Study Area. The study was conducted in Oyam (2.2776°N, 32.4467°E) and Otuke (2.4444°N, 33.5053°E) districts, located within the vector genetic transition zone in northern Uganda [7] (Figure 1). Flat grasslands and seasonally flooded swamps characterize both districts. ...
Article
Full-text available
Background: Tsetse flies are vectors of the genus Trypanosoma that cause African trypanosomiasis, a serious parasitic disease of people and animals. Reliable data on the vector distribution and the trypanosome species they carry is pertinent for planning sustainable control strategies. This study was carried out to estimate the spatial distribution, apparent density, and trypanosome infection rates of tsetse flies in two districts that fall within a vector genetic transition zone in northern Uganda. Materials and methods: Capturing of tsetse flies was done using biconical traps deployed in eight villages in Oyam and Otuke, two districts that fall within the vector genetic transition zone in northern Uganda. Trapped tsetse flies were sexed and morphologically identified to species level and subsequently analyzed for detection of trypanosome DNA. Trypanosome DNA was detected using a nested PCR protocol based on primers amplifying the internal transcribed spacer (ITS) region of ribosomal DNA. Results: A total of 717 flies (406 females; 311 males) were caught, all belonging to the Glossina fuscipes fuscipes species. The overall average flies/trap/day (FTD) was 2.20 ± 0.3527 (mean ± SE). Out of the 477 (201 male; 276 females) flies analyzed, 7.13% (34/477) were positive for one or more trypanosome species. Three species of bovine trypanosomes were detected, namely, Trypanosoma vivax, 61.76% (21/34), T. congolense, 26.47% (9/34), and T. brucei brucei, 5.88% (2/34), and two cases of mixed infection of T. congolense and T. brucei brucei, 5.88% (2/34). The infection rate was not significantly associated with the sex of the fly (generalized linear model (GLM), χ 2 = 0.051, p = 0.821, df = 1, n = 477) and district of origin (χ 2 = 0.611, p = 0.434, df = 1, n = 477). However, trypanosome infection was highly significantly associated with the fly's age based on wing fray category (χ 2 = 7.56, p = 0.006, df = 1, n = 477), being higher among the very old than the young. Conclusion: The relatively high tsetse density and trypanosome infection rate indicate that the transition zone is a high-risk area for perpetuating animal trypanosomiasis. Therefore, appropriate mitigation measures should be instituted targeting tsetse and other biting flies that may play a role as disease vectors, given the predominance of T. vivax in the tsetse samples.
... Using critical assumptions about gene flow, a model developed by Rousset [1], and analyses of trap samples of tsetse flies (Glossina spp), de Meeûs et al. [2] claimed to have found strong support for the hypothesis that the dispersal distance per generation, in tsetse, increases as a power function of decreasing population density. The claim was based on genetic analyses of material from five different species of tsetse, sampled using traps in ten studies in six different countries in West and East Africa [3][4][5][6][7][8][9][10][11]. De Meeûs et al. concluded that negative densitydependent dispersal (NDDD) probably applied to all tsetse species [2]. ...
... Thus, if investigators deployed traps in different patterns within the same population, while following the rules for estimating S [2], the resulting estimates of δ should be the same, regardless of the trap distribution adopted-subject only to experimental errors in estimating b and N e . We investigate whether this is true, using data from studies carried out in Tanzania and Uganda [2,7,11]. ...
... In the study of G. fuscipes fuscipes in Uganda, six traps were deployed at each of 42 sites, spread across an area of about 4000 km 2 [11]. Traps at each site were separated by a distance of at least 100 m. ...
Article
Full-text available
Published analysis of genetic material from field-collected tsetse ( Glossina spp, primarily from the Palpalis group) has been used to predict that the distance ( δ ) dispersed per generation increases as effective population densities ( D e ) decrease, displaying negative density-dependent dispersal (NDDD). Using the published data we show this result is an artefact arising primarily from errors in estimates of S , the area occupied by a subpopulation, and thereby in D e . The errors arise from the assumption that S can be estimated as the area ( S ^ ) regarded as being covered by traps. We use modelling to show that such errors result in anomalously high correlations between δ ^ and S ^ and the appearance of NDDD, with a slope of -0.5 for the regressions of log( δ ^ ) on log( D ^ e ), even in simulations where we specifically assume density-independent dispersal (DID). A complementary mathematical analysis confirms our findings. Modelling of field results shows, similarly, that the false signal of NDDD can be produced by varying trap deployment patterns. Errors in the estimates of δ in the published analysis were magnified because variation in estimates of S were greater than for all other variables measured, and accounted for the greatest proportion of variation in δ ^ . Errors in census population estimates result from an erroneous understanding of the relationship between trap placement and expected tsetse catch, exacerbated through failure to adjust for variations in trapping intensity, trap performance, and in capture probabilities between geographical situations and between tsetse species. Claims of support in the literature for NDDD are spurious. There is no suggested explanation for how NDDD might have evolved. We reject the NDDD hypothesis and caution that the idea should not be allowed to influence policy on tsetse and trypanosomiasis control.
... These population fluctuations make it difficult to identify the extent of the distribution with trapping efforts, as a negative result does not necessarily mean low density at all times of year. These challenges have prompted extensive efforts by KENTTEC and others to collect across multiple seasons and years for the full distribution of G. pallidipes in the region (Bateta et al., 2020;Cecchi et al., 2008;Ngari et al., 2020;Okeyo et al., 2017Okeyo et al., , 2018Opiro et al., 2017). Nonetheless, copyright of much of the sampling efforts by the Kenyan government makes these data unavailable to the scientific community (Ngari et al., 2020), leaving urgent need for a publicly available up-to-date suitability model that is based on environmental conditions and is well integrated with knowledge of tsetse dispersal patterns. ...
... If true, this suggests that migration often occurs over several generations along corridors of high connectivity. This suggestion has been made to explain the much longer migration distances retrieved in genetic studies that consider several generations than migration distances found in ecological field studies that track a single individual (Bateta et al., 2020;Okeyo et al., 2018;Opiro et al., 2017). ...
Article
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Vector control is an effective strategy for reducing vector‐borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub‐Saharan Africa. We used random forest regression to: (i) Build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania, and (ii) provide novel vector control recommendations. Inputs for the models included field‐survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data, and exhibited high accuracy based on cross‐validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and inform vector control recommendations. Post‐hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g. suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin, and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.
... Mitochondrial genes have a faster evolution rate than nuclear DNA, a predominantly maternal inheritance, a lack of genetic recombination, a relatively high mutation rate, and high levels of polymorphism and divergence due to their inherent sensitivity. Thus, they are extremely useful as molecular markers (Kang et al., 2015;Opiro et al., 2017). Particularly, there have been many studies using COI and ND5 as markers to determine whether a species has been introduced or to determine the genetic diversity of a population (Kamgang et al., 2013;Ž itko et al., 2011). ...
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In high abundance, females of the genus Mansonia (Blanchard) can be a nuisance to humans and animals because they are voraciously hematophagous and feed on the blood of a myriad of vertebrates. The spatial-temporal distribution pattern of Mansonia species is associated with the presence of their host plants, usually Eichhornia crassipes, E. azurea, Ceratopteris pteridoides, Limnobium laevigatum, Pistia stratiotes, and Salvinia sp. Despite their importance, there is a lack of investigation on the dispersion and population genetics of Mansonia species. Such studies are pivotal to evaluating the genetic structuring, which ultimately reflects populational expansion-retraction patterns and dispersal dynamics of the mosquito, particularly in areas with a history of recent introduction and establishment. The knowledge obtained could lead to better understanding of how anthropogenic changes to the environment can modulate the population structure of Mansonia species, which in turn impacts mosquito population density, disturbance to humans and domestic animals, and putative vector-borne disease transmission patterns. In this study, we present an Illumina NGS sequencing protocol to obtain whole-mitogenome sequences of Mansonia spp. to assess the microgeographic genetic diversity and dispersion of field-collected adults. The specimens were collected in rural environments in the vicinities of the Santo Antônio Energia (SAE) hydroelectric reservoir on the Madeira River.
... This is a crucial and important aspect as the frequent movement can play an addition role in disease transmission (Migchelsen et al. 2011, Büscher et al. 2017, Wamboga et al. 2017). The predominant Glossina species in the West Nile region is Glossina fuscipes fuscipes of the Palpalis group (Echodu et al. 2011, Opiro et al. 2017. The West Nile region is characterized by riverine forest along the tributaries of the White Nile. ...
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Introduction: Trypanosomiasis is a parasitic infection caused by the protozoa Trypanosoma. It is exclusively associated with Glossina species habitats and, therefore, restricted to specific geographical settings. It affects a wide range of hosts, including humans. Animals may carry different Trypanosoma spp. while being asymptomatic. They are, therefore, potentially important in unpremeditated disease transmission. Aim: The aim of this study was to study the potential impact of the government tsetse fly control program, and to elucidate the role of pigs in the Trypanosoma epidemiology in the West Nile region in Uganda. Methods: A historically important human African trypanosomiasis (HAT) hotspot was selected, with sampling in sites with and without a government tsetse fly control program. Pigs were screened for infection with Trypanosoma and tsetse traps were deployed to monitor vector occurrence, followed by tsetse fly dissection and microscopy to establish infection rates with Trypanosoma. Pig blood samples were further analyzed to identify possible Trypanosoma infections using internal transcribed spacer (ITS)-PCR. Results: Using microscopy, Trypanosoma was detected in 0.56% (7/1262) of the sampled pigs. Using ITS-PCR, 114 of 341 (33.4%) pig samples were shown to be Trypanosoma vivax positive. Of the 360 dissected tsetse flies, 13 (3.8%) were positive for Trypanosoma under the microscope. The difference in captured tsetse flies in the government intervention sites in comparison with the control sites was significant (p < 0.05). Seasonality did not play a substantial role in the tsetse fly density (p > 0.05). Conclusion: This study illustrated the impact of a government control program with low vector abundance in a historical HAT hotspot in Uganda. The study could not verify that pigs in the area were carriers for the causative agent for HAT, but showed a high prevalence of the animal infectious agent T. vivax.
... The T. b. gambiense form occurs in the northwestern corner of the country (where this study was conducted) while T. b. rhodesiense is in the Eastern and Southern part of Uganda. Evidence already point to a danger of merger of the two HAT belts, fueled by animal movements [7,8], and vector migration northwards [9][10][11]. This underscores the need for research geared towards providing information to support sustainable control in the country. ...
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Background: African trypanosomiasis, caused by protozoa of the genus Trypanosoma and transmitted by the tsetse fly, is a serious parasitic disease of humans and animals. Reliable data on the vector distribution, feeding preference and the trypanosome species they carry is pertinent to planning sustainable control strategies. Methodology: We deployed 109 biconical traps in 10 villages in two districts of northwestern Uganda to obtain information on the apparent density, trypanosome infection status and blood meal sources of tsetse flies. A subset (272) of the collected samples was analyzed for detection of trypanosomes species and sub-species using a nested PCR protocol based on primers amplifying the Internal Transcribed Spacer (ITS) region of ribosomal DNA. 34 blood-engorged adult tsetse midguts were analyzed for blood meal sources by sequencing of the mitochondrial cytochrome c oxidase 1 (COI) and cytochrome b (cytb) genes. Results: We captured a total of 622 Glossina fuscipes fuscipes tsetse flies (269 males and 353 females) in the two districts with apparent density (AD) ranging from 0.6 to 3.7 flies/trap/day (FTD). 10.7% (29/272) of the flies were infected with one or more trypanosome species. Infection rate was not significantly associated with district of origin (Generalized linear model (GLM), χ2 = 0.018, P = 0.895, df = 1, n = 272) and sex of the fly (χ2 = 1.723, P = 0.189, df = 1, n = 272). However, trypanosome infection was highly significantly associated with the fly's age based on wing fray category (χ2 = 22.374, P < 0.001, df = 1, n = 272), being higher among the very old than the young tsetse. Nested PCR revealed several species of trypanosomes: T. vivax (6.62%), T. congolense (2.57%), T. brucei and T. simiae each at 0.73%. Blood meal analyses revealed five principal vertebrate hosts, namely, cattle (Bos taurus), humans (Homo sapiens), Nile monitor lizard (Varanus niloticus), African mud turtle (Pelusios chapini) and the African Savanna elephant (Loxodonta africana). Conclusion: We found an infection rate of 10.8% in the tsetse sampled, with all infections attributed to trypanosome species that are causative agents for AAT. However, more verification of this finding using large-scale passive and active screening of human and tsetse samples should be done. Cattle and humans appear to be the most important tsetse hosts in the region and should be considered in the design of control interventions.
... Population genetic studies have several implications in vector control (Opiro et al., 2017;Saarman et al., 2018). Genetic structure and the connectivity among mosquito populations help to determine the rate as well as the route of mosquito dispersal. ...
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We investigated the genetic variability and differentiation among 12 A. aegypti populations collected within the Madurai city in Tamil Nadu state of Southern India. Genotyping of 12 microsatellite markers in 353 individual samples showed moderate levels of genetic diversity among 12 populations. UPGMA tree, hierarchical clustering, Bayesian clustering and Discriminant Analysis on Principal Components roughly divided these populations into two genetic clusters: main city populations and the populations located at the border of the corporation limit. Significant positive correlation between genetic and geographic distance was observed among 12 populations, however, the correlation was non-significant within each genetic cluster. Population assignment and DivMigrate graph depicted less migration between two groups. Overall, the findings of this study provided an overview of Ae. aegypti population structure within an urban settings in India that have implications in effective implementation of vector control in the city area.
... Mitochondrial genes are widely used in research on molecular evolution and population genetics of vector insects. Because they have a relatively high mutation rate and high levels of polymorphism and divergence due to their inherent sensitivity, they are highly useful as molecular markers [9][10][11][12]. Many vector studies have investigated where the population was introduced using mitochondrial genes [13,14]. ...
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