Distributions, Drivers, and Risks of Wildlife Infectious Diseases Across Africa: Using Geospatial Analyses to Elucidate Disease Occurrence in Biodiversity Hotspots

Paul R. Sesink Clee

doi:10.17918/etd-7586

This dissertation focuses on wildlife infectious diseases in Africa with a goal of using geospatial analyses to better understand their distributions, drivers, and risks. Using widespread data from simian foamy virus (SFV) infections in humans, I identified bushmeat hunting and proximity to active commercial logging sites as the largest drivers of crossover from non-human primates. I created a risk map for humans to compare to models of SFV, simian immunodeficiency virus (SIV), and Ebola Virus Disease (EVD) occurrence in wild populations of non-human primates across the Congo Basin. A hotspot with the highest predicted zoonotic crossover risk to humans appears in northern Gabon and neighboring portions of Cameroon and Republic of the Congo where disease occurrence, bushmeat hunting intensity, and logging activities are high. Efforts to reduce demand for bushmeat in urban centers along with education of the risks of bushmeat consumption may help reduce the chance of future zoonotic outbreaks. In order to evaluate the roles that current and future climatic conditions may play in shaping wildlife infectious disease occurrence, I investigated SIV and SFV infections in chimpanzees. Using ensemble niche modeling techniques, I found that the occurrence of SIVs and SFVs infecting three chimpanzee subspecies across Central Africa remain divergent under both present and future climatic conditions across Central Africa. The geographic separation of these two pathogens within chimpanzee subspecies where they are both present may be explained by viral competition within hosts, localized pockets of immunity due to previous outbreaks, range restrictions due to the ecological niches of chimpanzee hosts, or geographic restriction of infected chimpanzees due to geographic or behavioral boundaries. I investigated how biodiversity may drive wildlife infectious disease occurrence by focusing on EVD infecting chimpanzees and gorillas in western equatorial Africa. I tested two hypotheses: the Dilution Effect, which states that disease occurrence is inversely correlated with biodiversity, and the Amplification Effect, which states that disease occurrence is directly correlated with biodiversity. In the case of African apes, low levels of primate species (host taxa) richness and high levels of bat species (potential natural reservoir taxa) richness appear to drive EVD occurrence. Frugivorous bats are a putative natural reservoir for Ebola virus strains in Africa, and studies suggest that multiple species may act as equally competent reservoirs for the virus, where areas of high bat species richness are likely to coincide with EVD occurrence in sympatric non-human primates. Lastly, I developed two new products to further the field of distribution modeling: TERRA6-AR5 and Biomod2EZ. TERRA6-AR5 is a multi-model ensemble of current climate change model projections at a global scale including the four representative concentration pathways defined by the International Panel on Climate Change for years 2030, 2050, and 2080 at a high spatial resolution (~1km2). Additionally, Biomod2EZ increases interpretation and accessibility of results compared to the original Biomod2 package by including a report generation feature to save results in an organized fashion, export maps for use in external mapping software, and simplifying the overall modeling process with annotated scripts and tutorial datasets.

Distributions, Drivers, and Risks of Wildlife Infectious Diseases Across Africa: Using Geospatial Analyses to Elucidate Disease Occurrence in Biodiversity Hotspots

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Distributions, Drivers, and Risks of Wildlife Infectious Diseases Across Africa: Using Geospatial Analyses to Elucidate Disease Occurrence in Biodiversity Hotspots

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