More than one-fifth of vertebrate species are threatened with extinction. Despite global efforts to conserve biodiversity, species abundance and diversity continues to decline across taxonomic groups and geographic regions. Achieving conservation goals relies on accurate biodiversity monitoring and a deep understanding of how natural and anthropogenic factors influence individual species and communities as a unit. A key theme of my research is advancing the application of quantitative methods to determine the status and persistence of wildlife by maximizing the use of available data. To that end, my dissertation work aims to build knowledge that will help sustain biodiversity in a rapidly changing world. In chapter one, I develop a modified hierarchical distance-sampling model to estimate the abundance of hard-to-detect chimpanzees and elephants in a dense tropical forest within the Albertine Rift ecoregion in east-central Africa. The Albertine-Rift is one of the most biodiverse places in the world, supporting more than half of Africa’s bird species and 40% of mammals. In situations where visibility is limited, indirect measures of species (e.g., nests, dung) serve as proxies for counts of individuals. Current approaches to estimate population abundance using indirect sign data do not adequately account for variations in sign production and spatial patterns of animal density. My model reveals a significant decline in chimpanzees, and an increase in elephants between 2007 and 2021 within the region. My modelling approach produces more precise estimates of covariate effects on animal density by maximizing the use of all the available data to account for long-term and recent variations in abundance. In chapter two, I apply a community distance sampling model to evaluate niche overlap of ecologically similar bird species within a montane tropical forest in the Albertine Rift, where elevation strongly correlates with environmental conditions (e.g., climate, forest type). Although hundreds of bird species live within the region, the underlying mechanisms that facilitate coexistence of many competing species are poorly understood. My model shows that bird species coexistence is determined primarily by abiotic factors (i.e., the environmental elevation gradient) and secondarily by biotic factors (i.e., within-habitat segregation across horizontal space and vertical forest strata). Quantifying niche overlap indices along multiple dimensions provides deep insights into community structuring and a foundation to predict species distributions in response to ongoing environmental change. In chapter three, I assess the impacts of anthropogenic threats across bird and mammal functional groups using global data collected by the International Union for Conservation of Nature. Anthropogenic threats vary in how they affect individual species, taxonomic groups, and biodiversity as a whole. My analysis identified the anthropogenic threats with the highest impacts on bird and mammal functional groups and the functional groups that are most threatened overall. Across different anthropogenic threats, I found that the most vulnerable functional groups were vertivores, aquatic predators, frugivores, and herbivores for birds, and vertivores, aquatic predators, and frugivores/nectivores/granivores for mammals. My results reveal that anthropogenic threats vary in how they affect functional diversity, offering valuable baseline information for how best to target conservation actions for vulnerable species groups. The work in my dissertation offers valuable insights on how to monitor, estimate, and predict wildlife population distributions in response to natural environmental variation and anthropogenic threats. Tracking biodiversity trends over time and space is critical to teasing apart the interacting factors that drive species abundance patterns and determining the consequences of changing environmental conditions. The models and methods in my research are transferable to other systems and taxonomic groups, offering a path forward to improved conservation planning.
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