I am a statistical ecologist interested in the development and application of hierarchical Bayesian models for wildlife and natural resource management and conservation. I currently work as a postdoctoral research associate in the Department of Integrative Biology at Michigan State University in the Zipkin Quantitative Ecology Lab. I received my PhD in Forestry and Ecology, Evolution, and Behavior at Michigan State University in the Geospatial Lab of Dr. Andrew Finley. My research interests lie in the development of Bayesian hierarchical models for environmental monitoring and decision making. More specifically, I am interested in the development and application of statistical models for methods of monitoring wildlife populations across large spatio-temporal regions by leveraging a variety of data sources, including citizen science data and acoustic recordings.
PhD Forestry and Ecology, Evolution, and Behavior, 2022
Michigan State University
MS Applied Statistics, 2021
Michigan State University
BS Mathematics and Biology, 2018
State University of New York at Geneseo
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Understanding the drivers of species distributions and biodiversity at macroscales is complicated by a variety of ecological and observational complexities, such as spatial autocorrelation, nonstationarity in species-environment relationships, and species interactions. In my work, I account for these complexities to provide a more complete understanding of macroscale biodiversity and inform effective monitoring and conservation approaches across spatial scales.
Effective wildlife and natural resource management requires user-friendly software that makes state-of-the-art statistical tools accessible to natural resource managers and conservation practitioners. A key pillar of my research is developing computationally-efficient and accessible software to understand the ecological and anthropogenic drivers of species distributions, population dynamics, and biodiversity patterns.
Autonomous monitoring systems such as acoustic recording units, camera traps, and remote sensing methods (e.g., LiDAR) can provide massive amounts of data to inform biodiversity conservation, yet analyzing these data presents novel computational complexities. In my work, I develop quantitative approaches to leverage these complex data to inform a variety of wildlife and natural resource management objectives.