Kathi Irvine, Ph.D.
I am a Research Statistician with the U.S. Geological Survey at the Northern Rocky Mountain Science Center in Bozeman, Montana.
Research Interest
Prior to finding my home in the federal system in 2011, I was an assistant professor at Montana State University (2008-2010). Since receiving my PhD in Statistics from Oregon State University in 2007, I have collaborated with ecologists and biologists charged with monitoring natural resources on federal and state lands. My team provides statistical support for monitoring programs led by the National Park Service, Fish and Wildlife Service, and state agencies. Our work involves development of survey design and analysis strategies for a variety of plants, animals, and other indicators. We currently support monitoring of whitebark pine in the Greater Yellowstone Ecosystem, upland plant communities throughout the Western US, and bats across North America.
My applied statistical research involves developing analytical approaches for ordinal data and bat acoustic surveys that better link the ecological and observation process within a Bayesian framework, applications of causal analysis, investigating spatial sampling designs, and model-assisted methods for status and trend analyses. I mentor statistics students and support graduate research assistants at Montana State University (MSU). Several of my students have participated in writing peer-reviewed papers during their time at MSU. I encourage students interested in ecological statistics to contact me for possible graduate research assistantships, paid summer work, and other opportunities.
Related Projects:
https://www.whitenosesyndrome.org/
Education and Certifications
PhD. Statistics. Oregon State University
MS. Statistics. Oregon State University; MS. Ecology and Environmental Sciences. University of Maine
BS. Biology. University of North Carolina at Chapel Hill
Science and Products
Power analysis and trend detection for water quality monitoring data. An application for the Greater Yellowstone Inventory and Monitoring Network Power analysis and trend detection for water quality monitoring data. An application for the Greater Yellowstone Inventory and Monitoring Network
Monitoring direct and indirect climate effects on whitebark pine ecosystems at Crater Lake National park Monitoring direct and indirect climate effects on whitebark pine ecosystems at Crater Lake National park
Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration
Spatial design and strength of spatial signal: Effects on covariance estimation Spatial design and strength of spatial signal: Effects on covariance estimation
Evaluating shading bias in malaise and intercept traps Evaluating shading bias in malaise and intercept traps
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Power analysis and trend detection for water quality monitoring data. An application for the Greater Yellowstone Inventory and Monitoring Network Power analysis and trend detection for water quality monitoring data. An application for the Greater Yellowstone Inventory and Monitoring Network
Monitoring direct and indirect climate effects on whitebark pine ecosystems at Crater Lake National park Monitoring direct and indirect climate effects on whitebark pine ecosystems at Crater Lake National park
Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration
Spatial design and strength of spatial signal: Effects on covariance estimation Spatial design and strength of spatial signal: Effects on covariance estimation
Evaluating shading bias in malaise and intercept traps Evaluating shading bias in malaise and intercept traps
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.