Shawn O'neil
About Shawn O'neil
Shawn O'Neil is a Wildlife Biologist specializing in quantitative analysis at the U.S. Geological Survey, where he has worked since 2017. He holds a PhD in Forest Science and Wildlife Biology from Michigan Technological University and has a strong background in statistical modeling for wildlife habitat mapping.
Work at U.S. Geological Survey
Shawn O'Neil has been employed at the U.S. Geological Survey (USGS) since 2017, serving as a Wildlife Biologist, Quantitative. He is part of the Western Ecological Research Center, where he applies his expertise in wildlife biology to analyze long-term monitoring data. His work involves utilizing modern statistical modeling techniques to map wildlife habitats and distributions, contributing to the understanding of wildlife populations.
Education and Expertise
Shawn O'Neil earned a Doctor of Philosophy (PhD) in Forest Science and Wildlife Biology from Michigan Technological University, where he studied from 2012 to 2017. He also holds a Master of Environmental Management from the University of North Dakota, focusing on Earth System Science and Policy, which he completed from 2008 to 2010. Earlier, he obtained a Bachelor of Arts in Political Science from the University of Minnesota-Morris, studying there from 2002 to 2006.
Previous Experience
Before his role at USGS, Shawn O'Neil worked as a Graduate Research Assistant/Spatial Analyst at Michigan Technological University from 2012 to 2017. He also served as a Biological Science Aid at the U.S. Fish and Wildlife Service's Red Rock Lakes National Wildlife Refuge for seven months in 2010. These positions provided him with foundational experience in wildlife biology and spatial analysis.
Research Focus
Shawn O'Neil specializes in providing critical information regarding wildlife populations, particularly for species and ecosystems that are declining or at risk. His research emphasizes the use of statistical modeling to analyze long-term monitoring data, which aids in understanding and mapping wildlife habitats and distributions.