A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda |
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| Authors: | Norah Saarman Robert Opiro Chaz Hyseni Richard Echodu Kirstin Dion Elizabeth A Opiyo Augustine W Dunn Giuseppe Amatulli Serap Aksoy Adalgisa Caccone |
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| Affiliation: | 1. Department of Ecology and Evolutionary Biology, Yale University, New Haven, ConnecticutEqually contributing first authors.;2. Department of Biology, Faculty of Science, Gulu University, Gulu, Laroo Division, Uganda;3. Department of Biology, University of Mississippi, Oxford, Massachusetts;4. Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut;5. Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts;6. Department of GeoComputation and Spatial Science, Yale School of Forestry and Environmental Studies, New Haven, Connecticut;7. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut |
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| 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. |
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| Keywords: | landscape genetics maximum entropy model sleeping sickness spatial genetics tsetse fly vector control |
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