Mapping tsetse fly connectivity in Uganda with machine learning landscape genetics
Introduction - Tsetse flies (genus Glossina) are biting insects that transmit human and animal trypanosomiases across sub-Saharan Africa, and sustainable vector control depends on understanding dispersal barriers and reinvasion routes. Despite major progress toward elimination, Uganda remains at risk for both human forms of the disease (Trypanosoma brucei gambiense and T. b. rhodesiense) and…
Tsetse flies, carriers of human and animal trypanosomiases across sub-Saharan Africa, continue to pose a risk in Uganda, where both human forms of the disease persist. Planners lack reliable maps of tsetse movement and reinvasion risk, hindering sustainable vector control efforts. Researchers have now employed machine-learning landscape genetics and species distribution models to address this knowledge gap.
By integrating population genetic distance estimates from 11 microsatellite loci and geospatial environmental data, the team predicts and maps Glossina fuscipes fuscipes connectivity across Uganda and western Kenya. The random forest models outperformed distance-only models, indicating the efficacy of a machine-learning framework for connectivity inference in complex, heterogeneous landscapes.
Temperature and water availability emerged as the strongest predictors of genetic connectivity. Combining landscape genetics predictions with a species distribution model, the researchers identified regions with high habitat suitability but low connectivity, revealing priority zones for area-wide integrated pest management strategies.
These maps and quantitative uncertainty metrics can guide targeted tsetse control measures, while the analytical pipeline can be applied to model and map genetic connectivity in other species and landscapes.
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