Spatial epidemiology in sub-Saharan African wildlife: schistosomes of Cape Buffalo

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Animal distributions, including disease vectors and parasites, are influenced by environmental features, which can either facilitate or restrict their movement across the landscape. Local adaptation, dispersal behaviour and successful reproduction leaves a genomic footprint, allowing organisms to be tracked through space and time.Such data allow us to determine the drivers of disease emergence and spread, which can translate into more effective preventative and/or control measures to mitigate outbreaks. Schistosomiasis is a neglected tropical disease of great medical and veterinary importance caused by a parasitic trematode. The parasites have a complex transmission cycle involving specific aquatic freshwater snails and a mammalian host.Focusing on the Cape buffalo (Syncerus caffer caffer), a keystone species critical to the health of savannah grassland ecosystems, this studentship will involve fieldwork, species identification (snail and parasites) and landscape genomics to identify the drivers of schistosomiasis dynamics in the iconic Kruger National Park (KNP), South Africa.You will:collect, identify and determine the distribution of Buffalo schistosome parasites within the Kruger National Park (WP1);identify and assess diversity of Bulinus snail intermediate hosts (WP2);assess the "genetic health and population structure of Cape buffalos (WP3);correlate landscape variables with genetic diversity (WP4).To achieve these aims, WP1-2 will involve morphological identification and molecular metabarcoding. For WP3, you will apply double-digest restriction-site associated DNA sequencing to obtain genome-wide SNP loci to resolve fine scale population structure in buffalo.WP4 will deploy a landscape genomics approach incorporating GIS resistance maps for each environmental variable. Using R statistical packages, resistance distance will be correlated with buffalo genetic distance in order to identify variables that are either positivity or negatively correlated with gene flow. Lastly, current maps will be generated in CIRCUITSCAPE to identify areas within the landscape of high and low connectivity that can inform disease spread and predictions of outbreaks.This project will provide a model for future landscape genomics of zoonotic disease vectors making a significant contribution to the field of molecular epidemiology.