Huebner, Sarah2023-03-272023-03-272023-01https://hdl.handle.net/11299/253422University of Minnesota Ph.D. dissertation. January 2023. Major: Conservation Biology. Advisor: Craig Packer. 1 computer file (PDF); vii, 85 pages.Due to alarming rates of wildlife decline throughout the world, ecological monitoring programs have become a critical component in evidence-based conservation planning. Continuous systematic monitoring using standardized camera trap grids helps to detect trends in wildlife population dynamics; however, the amount of data generated can be difficult to process in a timely manner. To mitigate this issue, citizen science and cutting-edge machine learning technologies can be combined to accelerate data collection, analysis, and reporting. In this dissertation, I will describe the creation, goals, and outcomes thus far of the Snapshot Safari project, an international, long-term ecological monitoring network using ~2000 camera traps and a hybrid data classification pipeline. Next, I’ll demonstrate the general utility of Relative Abundance Indices (RAIs) from camera trap data collected at five South African protected areas of varying sizes, management types, and herbivore assemblages by comparing them to RAIs from aerial surveys that were conducted during the same period. Finally, I will discuss the ‘elephant problem’ in South Africa and draw on three decades of long-term transect data on woody plant species combined with aerial surveys to examine the effects elephants exert on their ecological communities and, consequently, biodiversity at multiple trophic levels when they recolonize an area after extirpation.encamera trapselephantsSnapshot SafariThesis or Dissertation