Background

Tsetse flies (Diptera: Glossina) remain to be insects of economic and medical importance in sub-Saharan Africa. They transmit pathogenic trypanosomes that cause sleeping sickness to humans and nagana to livestock [1, 2]. The occurrence of the disease in humans and livestock has greatly limited the development of agriculture and human health in the region [3]. Estimates by FAO show, Africa loses over 3 million cattle and other domestic livestock due to trypanosomiasis every year [4]. The annual losses, in terms of reduced meat and milk production and in terms of the costs related to treatment and controlling the disease, have been estimated at US $1.2 billion [5]. No vaccine has been developed for the disease to date both for humans and cattle due to the ability of trypanosome parasites to change their surface proteins by antigenic variation [6, 7]. Drugs which are used to treat cattle have been used for long time; as a result drug resistance is increasing rapidly which in the long run will seriously affect the use of these drugs for animal trypanosomiasis control. Similarly, drugs which are used to treat humans are toxic, expensive and difficult to administer at village settings and also have bad side effects [1, 8]. Therefore, tsetse control remains the most effective method of reducing trypanosome infections in animals and human in sub-Saharan countries [6,7,8,9].

Glossina fuscipes fuscipes is one of the most important tsetse species in the Palpalis group, subgenus Nermohina Robineau-Desvoidy [10]. In Tanzania the species is widely distributed along the shores of Lake Victoria supported by vegetation growing close to the water [11]. The species is found at the eastern margin in Uganda which extends further east along the shores of Lake Victoria in Western Kenya. The fly is also found in southern Sudan, Chad, the Central African Republic, the Democratic Republic of Congo (DRC) and Angola [12, 13]. Various methods have been employed in tsetse control in different areas in Africa, but the success of these methods varied. Some of these interventions used in the past included bush clearing (destroying vector habitat), elimination of wild animals (reservoir host of the parasites), insecticide ground spraying, live bait technology and the use of baited traps and targets. Despite the fact that tsetse fly densities were temporarily reduced, some of the methods used in the past are not used today because of environment reason and also they are against animal conservation [2, 5, 6]. Sterile Insect Technique (SIT) and Sequential Aerial Technique (SAT) have become promising methods which have been successful used to eradicate tsetse flies in some parts of Africa. SIT was successfully used to eradicate Glossina austeni in Unguja Island in Tanzania [2, 14] and SAT has been successfully applied in the Okavango Delta in northern Botswana in the eradication of Glossina morsitans centralis [5]. The success of these control interventions depend much on the biogeographical limits of the target tsetse species in such a way that maximum benefit is gained from the natural barriers to reinvasion of the previous controlled areas [5]. One factor that can improve the application of various control methods is knowledge on tsetse population genetics in addition to ecological information [9]. Studying the genetic differentiation between different populations of G. f. fuscipes will lead to novel insights into the relationships between genetically distinct populations, including geographical distribution, hybridization and migration patterns. This knowledge can then be used to inform ongoing or planned vector control programs across a target geographical region to identify the most suitable areas to target control to avoid re-infestation of cleared habitats. Studies on the population structure of G. f. fuscipes have been carried out in some parts of East Africa, particularly along Lake Victoria shores in Uganda [8, 15,16,17,18], and reported high genetic structuring of the species in the area. All authors confirmed the presence of two distinct lineages of G. f. fuscipes in Uganda using mtDNA and microsatellites markers except author 18. The isolation of northern and southern G. f. fuscipes populations which were thought to occur as a result of fragmentation during extreme drought in East Africa warrants these populations to be treated separately during eradication. Likewise, population study of another tsetse riverine species G. palpalis gambiensis in Senegal also indicated the species was sufficiently isolated and currently eradication is underway [9, 19]. However, population structure of G. p. palpalis in Burkina Faso and G. p. gambiensis in Equatorial Guinea indicated high gene flow compared to other riverine tsetse species [20, 21]. Similarly, population structure study on the same species in Cameroon indicated the species had heterozygote deficit suggesting suppression to be the best option to control the tsetse species in the area [7].

Little is known on the population structure and gene flow of G. f. fuscipes in the Lake Victoria basin in Kenya and Tanzania. The information will be useful in the planning of effective regional control of the tsetse species in the Lake Victoria basin (the Lake Victoria basin includes parts of Uganda, Tanzania and Kenya) under Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC) initiative [22]. Lake Victoria basin is one among important regions which have been identified for tsetse eradication by PATTEC. This report describes patterns of genetic differentiation among G. f. fuscipes sampled in Tanzania and Kenya and relates them to the recommendations for guiding future vector control efforts in the region.

Methods

Study sites and data collection

Glossina fuscipes fuscipes were sampled at 8 localities around Lake Victoria, with 2 sites in Kenya (KIS and MAN) and 6 sites in Tanzania. These sites include 4 locations on the border of Tanzania and Kenya in the Rorya district (MAS, TOB, RAS and KIR), one in the Bukoba district at the border between Uganda and Tanzania (BUK), and one approximately in between these two areas in the Ukerewe district (UKE; Fig. 1). The average distance among the four sampling sites in the Rorya district ranged from 2 to 10 km, the two sampling sites from Kenya (KIS and MAN) are 35 km apart, ~65 km from the Rorya district samples, and ~200 km away from the other two Tanzanian samples from the southern and western shores of Lake Victoria (BUK and UKE), which are also similarly distant from each other (187 km). Overall, 275 individual tsetse flies were collected using biconical and pyramid traps and preserved in cryotubes containing 90% ethanol. Flies from the Tanzanian sites were collected in 2011 while the ones from the two Kenyan sites were collected in 2009. All samples were collected during the dry season. All study sites experience a bimodal rainfall pattern, short rains fall between October and December and long rains from March to May. The average annual rainfall in the basin is estimated to be 1,015 mm [23]. The vegetation of the study sites was bushy vegetation and was the same in all study sites (Fig. 2).

Fig. 1
figure 1

Map showing sampling sites. Location of 8 sampling sites noted by red dots and a three letters code to identify sampling sites (BUK, Bukoba; MAN, Manga; KIR, Kirongwe; UKE, Ukerewe; KIS, Kisasi; RAS, Rasi Nyabero; TOB, River Tobwe; MAS, Masonga). The inset shows the location of the four sampling sites in the Rorya district. Lake Victoria is shown in light blue, as well as other major lakes in Uganda (Lake Kyoga and Lake Albert). Lines depict countries borders and the River Nile

Full size image
Fig. 2
figure 2

Bushy vegetation in Rasi Nyabero village in Rorya district. A photo showing the vegetation along lake shores in Rorya District in Rasi Nyabero village where flies were trapped. All sites had the same vegetation

Full size image

DNA extraction and PCR analysis

DNA extraction from tsetse legs was conducted using a Qiagen Micro Amp extraction kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. PCR and genotyping were conducted for microsatellite markers at 19 previously identified loci that are distributed across the genome [8]. PCR conditions used were in accordance with previously described methods [15]. Microsatellite peaks were scored for each individual sample using GeneMarker software [24]. Table 1 reports the number of flies genetically analyzed for each sampling site.

Table 1 Summary information on sampling sites and summary statistics for each samplings site and the patterns of genetic variation at 19 microsatellite loci for G. f. fuscipes tsetse flies from eight sampling sites along the Kenyan and Tanzania shores of Lake Victoria
Full size table

Genetic analyses

The program Genepop 4.2 [25] was used to test for deviations from Hardy-Weinberg equilibrium, estimate allelic richness (AR), calculate expected and observed heterozygosities (He and Ho), and evaluate levels of inbreeding (Fis). Pairwise FST values between all sampling sites were calculated in Arlequin v 3.5 [26]. Significance was tested using 10,000 permutations. Genetic differentiation among tsetse sampling sites was analyzed using a model-based Bayesian clustering method implemented in Structure 2.3.3 [27]. The optimal number of clusters in the data was calculated using the ad hoc statistic ΔK in STRUCUTURE HARVESTER [28]. The STRUCTURE analysis was run for k = 1 to 8, with 10 replicates of each k value, for a million generations and a ‘burn-in’ period lasting the first 200,000 generations. Afterwards, the results from the STRUCTURE analysis were summarized across the 10 replicates using the Greedy method of the software CLUMPP [29], and distruct package was used to plot the results. We also used discriminant analysis of principal components (DAPC) [30] conducted with the R adegenet package [31] to determine genetic structure. In addition, we used the Bayesian Information Criterion (BIC) to determine the most likely number of clusters in our data [32].

To investigate patterns of migration and identify any probable migrants within our dataset, we used GeneClass2 [33]. GeneClass2 computes the probability of an individual’s observed multilocus genotype belonging to a given population. As such, the program can detect whether an individual is a resident of the sampled population or a first generation migrant. Effective population sizes (Ne) for the clustered populations were estimated under the linkage disequilibrium model with random mating included in the program NeEstimator v2 [34]. All alleles were taken into account regardless of frequency. Jackknife on loci calculations was implemented to generate 95% confidence bounds.

Results and discussion

In the present study, a total of 275 tsetse flies were genotyped at 19 microsatellite loci. According to inbreeding coefficient values for each population, no loci deviated significantly from Hardy-Weinberg equilibrium (Table 1, Additional file 1: Table S1). The number of alleles, a measure of genetic diversity, varied from 4.1 in Rasi Nyabero (RAS) to 5.6 in Manga (MAN). The number of alleles per locus varied from 2 in D101 to 15 in GmA06 with an average of 9.4. Mean allelic richness (AR) ranged from 2.89 to 4.94 across the sites. No significant differences were observed between observed and expected heterozygosities (HE ranged from 5.07 to 19.85 per site, and HO ranged from 3.50 to 20.53 per site). These values are similar to the ones reported for G. f. fuscipes populations around Lake Victoria shores in Uganda [6, 19] and suggest that G. f. fuscipes flies in this region harbor a wealth of genetic diversity and thus have the long term potential to adapt to ecological changes in the area.

FST values revealed significant genetic differentiation among sampling sites over 60–65 km apart, with notable exceptions being the significant FST values found between two close sampling sites in the Rorya district (KIR and RAS, < 6 km apart, Additional file 1: Table S2) and the lack of significance for FST values between samples from KIS and either RAS or MAN, located ~ 68–71 km from KIS (Additional file 1: Table S2). When the population structure was investigated using Bayesian clustering and multivariate analyses, the data was most consistent with 4 distinct population clusters (Fig. 3; Additional file 1: Figure S1, and Additional file 1: Figure S2), with FST values between clusters being all statistically significant and ranging from 0.231 to 0.341 (Additional file 1: Tables S3 and S4). Clusters 1 and 2 group flies from BUK and UK in Bukoba and Ukerewe districts, respectively. Cluster 3 comprises flies from all the Rorya district sampling sites (Rasi Nyabero, RAS; Kirongwe, KIR; Masonga, MAS; and River Tobwe, TOB). Cluster 4 includes the two Kenyan locations (Kisasi, KIS and Manga, MAN). Individual flies collected in sampling sites within these clusters assign mostly to their respective clusters with only a few individuals showing signs of genetic admixture with another cluster. The high genetic differentiation (FST = 0.341; FST = 0.308 and FST = 0.286) recorded in this study is consistent with earlier reports on the same species in other areas of East Africa and is likely a consequence of genetic drift. In the riverine and lacustrine tsetse species, such as G. f. fuscipes, wet season dispersion from dry season refugia is thought to establish demes in which genetic drift leads to differentiation [8, 16, 18]. Similar results have been reported also for other riverine tsetse species in West Africa which include G. tachinoides in Ghana [35, 36], G. p. gambiensis in Niayes-Senegal [9, 37] and G. p. palpalis in Cote d’Ivore [38]. However different results of similar species (G. p. palpalis) in Cameroon indicated the species formed a large panmixia population suggesting suppression to be the best option for the vector control [7]. In contrast to other riverine tsetse species, G. swynnertoni, a savannah species from northern Tanzania, have been reported to have the high level of gene flow [39]. Geographical distance and ecological differences are factors that are involved in these genetic differences of flies [40].

Fig. 3
figure 3

Bayesian clustering obtained using STRUCTURE (27) based on 19 microsatellite loci and 8 sampling sites. Each vertical bar represents the probability of assignment (Y-axis) of one individual (X-axis) to each of the 4 clusters identified by STRUCTURE [27]. Different colors represent different genetic clusters: Red, Cluster 1 (BUK); Aqua, Cluster 2 (UKE); Green, Cluster 3 (KIR, RAS, MAS and TOB); Purple, Cluster 4 (KIS and MAN). Vertical bars with multiple colors denote admixed individuals, which are assigned to more than one cluster

Full size image

The presence of admixed genotypes within clusters 2 and 3 (Fig. 3) suggests ongoing or recent gene flow among sampling locales. This is confirmed by the GeneClass2 analysis aimed to detect first generation migrants, which identified nine likely migrants: three between clusters 1 and 3, two between clusters 2 and 3, and four between clusters 3 and 4 (Fig. 4). However, given the lack of data for intermediate locations, it is not possible to evaluate if this mixing is due to gene flow from intermediate locations or long-range dispersal. These patterns of genetic discontinuities mirror the ones obtained for the same species along the Uganda coast of Lake Victoria, where among similarly spaced samples genetic discontinuities were identified using microsatellite loci [16].

Fig. 4
figure 4

Patterns of migration and effective population size ranges as determined in GeneClass2 and NeEstimator v2, respectively [33, 34]. The gray arrows indicate the direction of migration. Line weights are proportional to the number of migrants. The values adjacent to the arrows are the number of migrants identified within our data set. Distances between clusters are not proportional to actual distances between sample sites. Cluster 1: BUK; Cluster 2: UKE; Cluster 3: RAS, KIR, MAS and TOB; Cluster 4: KIS and MAN

Full size image

Although estimates of effective population size (Ne) were not uniform among sampling sites, and given the confidence intervals for clusters 3 and 4 (Fig. 4), the Ne estimates may not be very reliable. The high genetic diversity (FST = 0.341; FST = 0.308 and FST = 0.286) recovered at some sampling sites (Table 1) points to large resident populations. Recent reports indicated high and varied G. f. fuscipes densities in the sampling sites analyzed in this study [11]. These data indirectly seem to support the existence of local large pockets of tsetse flies at these sampling sites. Further screening of tsetse flies at these and neighboring sites, including temporal collections to capture seasonal variations in fly population sizes and densities, would shed additional light on local tsetse population dynamics, which is of critical relevance for monitoring and control.

Conclusions

From a control perspective these results suggest that, given the genetic homogeneity among sampling sites from the Rorya district, these sites should be controlled and monitored together to guard against reinvasion from neighboring untreated areas. A similar strategy should be adopted for the region including the two Kenyan sampling sites. However, given that the microsatellite data show that Kenya and the Rorya district samples are genetically distinct even though they are only ~65–80 km apart, control and monitoring effort may not necessarily need to include large geographical areas. Future studies should include a more uniform geographical representation of tsetse infested areas than in the current study to quantify the extent and spatial pattern of the genetic discontinuities found in this study among sampling sites located at varying geographical distances.