A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

Abstract

Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub‐Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co‐occurrence of both forms of human‐infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field‐survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field‐survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well‐connected habitat in northwestern Uganda, and twenty‐four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model‐predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well‐connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.

The population genomics of multiple tsetse fly (Glossina fuscipes fuscipes) admixture zones in Uganda

“…We modelled the population history of the major genetic clusters found in Uganda (Figure ) with approximate Bayesian computation ( abc ) in diyabc version 2.0.4 (Cornuet et al, ) and with migraine version 0.5.2 software (http://kimura.univ-montp2.fr/~rousset/Migraine.htm; Peery et al, ; Supporting Information Appendix ) for comparison. The models focused on northern Uganda where alternative explanations for the patterns of divergence and diversity observed in each genetic cluster are in debate (Beadell et al, ; Echodu et al, ; Hyseni et al, ; Opiro et al, ; Saarman et al, ). Both abc and migraine simulations assume panmictic populations unless explicitly specified otherwise, so we used samples from the four watersheds that fell squarely within one of the four major genetic clusters (Figure ; Table ; Supporting Information Table ).…”

“…However, from the first step of the preliminary analysis, posterior estimates were unrealistically recent and truncated by zero. Thus, considering that all priors were interdependent, to make the models more realistic we performed two more steps of the preliminary analysis with increased specificity based on more detailed biogeographic scenarios that were built from independent records of geologic events (Supporting Information Figure ) and published population genetics data (Beadell et al, ; Opiro et al, ; Saarman et al, ). There were three major river reorganizations in the region that would have impacted tsetse distribution and connectivity: (a) Lake Victoria formed ~400 ka and reversed the direction of flow of the major river systems from west to north (Supporting Information Figure a; Williams, Adamson, Prescott, & Williams, ; Danley et al, ), (b) the outflow from Lake Victoria altered its course to connect through Kafu River to Lake Albert ~20–40 ka (Supporting Information Figure b; Bishop, ) and (c) the outflow from Lake Victoria altered its course a third time to bypass the Kafu River and to connect directly to Albert Nile through Murchison falls (Supporting Information Figure c; Talbot & Williams, ).…”

“…Scenario 2 modelled the possibility that divergence among the four genetic backgrounds in Uganda was created during geologic events ~400 (Figure and Supporting Information Figure a: t4) and ~20–40 ka (Figure and Supporting Information Figure b: t3) and that the admixture zone “b” represents ongoing divergence with gene flow or incomplete divergence between the northwest and northeast (Figure and Supporting Information Figure a: t1), which is an idea proposed by Saarman et al () and is based on theoretical considerations (Falush, VanDorp, & Lawson, ; Frantz, Cellina, Krier, Schley, & Burke, ; Meirmans, ).…”

“…ddRAD data provided a genome‐wide analysis of hybridization and introgression. Microsatellites offered continuity with previous studies and reliable models of recent evolutionary history because of their well‐documented mutation rates, high molecular diversity and high minor allele frequencies (Beadell et al, ; Echodu et al, ; Hyseni et al, ; Opiro et al, ; Saarman et al, ). mtDNA sequences supplied inference of maternal inheritance, which can help distinguish asymmetrical reproductive barriers (Coyne & Orr, ) and past colonization patterns in hybrid zones (Currat, Ruedi, Petit, & Excoffier, ).…”

“…One prediction of this hypothesis is the rare occurrence of stable long‐lasting hybrid zones (Gompert et al, ), which implies rarity of first‐generation hybrids as well as intermediate levels of introgression in nature. Here, we investigate this prediction with a comparison of introgression patterns found in G. f. fuscipes , a unique system with four admixture zones that exist within a ~40,000 km 2 region in Uganda (Beadell et al, ; Echodu et al, ; Opiro et al, ; Saarman et al, ). Our goal was to investigate the biogeographic context and patterns of introgression in this unique system (Figure ) to provide insight on the evolutionary and ecological processes important in diversification.…”