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 planners still lack reliable maps of tsetse movement and reinvasion risk.

METHODS AND RESULTS: We address this gap with machine-learning landscape genetics and species distribution models, integrating estimates of population genetic distance and geospatial environmental data to predict and map Glossina fuscipes fuscipes connectivity across Uganda and western Kenya. Inputs included microsatellite genotypes from 11 loci genotyped in 2,736 flies sampled from 87 localities and remotely sensed environmental predictors summarized along least-cost paths. Random forest models predicted patterns of genetic differentiation better than distance-only models, supporting the use of a machine-learning framework for connectivity inference across complex heterogeneous landscapes, and identified variables related to temperature and water availability as the strongest predictors of genetic connectivity.

CONCLUSIONS: Combining landscape genetics predictions of connectivity with a species distribution model revealed regions with high habitat suitability but low connectivity that represent priority zones for area-wide integrated pest management strategies, including established riverine control tools such as tiny targets and other targeted interventions aimed at reducing reinvasion risk. These results provide biologically interpretable maps and quantitative uncertainty metrics that can guide targeted tsetse control, while providing a transferable analytical pipeline for modeling and mapping genetic connectivity across other species and landscapes.