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/
Science and Products
Developing online integrated data visualization tools for WNS and NABat
Wetland State-and-Transition Model Project
North American Bat Monitoring Program (NABat)
Integrating Climate and Biological Data into Management Decisions for the Greater Sage-Grouse and their Habitats
Using a Collaborative Modeling Approach to Explore Climate and Landscape Change in the Northern Rockies and Inform Adaptive Management
Science and Products
- Science
Developing online integrated data visualization tools for WNS and NABat
Bat Research Research collaboration: Brian Reichert (FORT), Anne Ballmann (NWHC), Jeremy Coleman (USFWS), Paul Cryan (FORT), Wayne Thogmartin (UMESC), and Katherine Irvine (NOROCK) White-nose syndrome is caused by the fungal pathogen Pseudogymnoascus destructans (Pd), which has decimated hibernating bat populations across North America since it emerged 10 years ago in New York. While diagnostic...Wetland State-and-Transition Model Project
The Wetland STM project is creating a state-and-transition model to inform management of semi-permanently flooded wetlands in the Intermountain West and western Prairie Pothole Region, as well as designing a monitoring scheme to allow determination of current wetland condition.North American Bat Monitoring Program (NABat)
North American bats face unprecedented threats including habitat loss and fragmentation, white-nose syndrome, wind energy development, and climate change. However, it is difficult to evaluate the impacts of these threats due to a lack of basic information about the distribution and abundance of bats across the continent. Although bat monitoring has long been conducted in individual areas and for...Integrating Climate and Biological Data into Management Decisions for the Greater Sage-Grouse and their Habitats
Climate affects both the demographics of the Greater sage-grouse bird and the condition and long-term viability of their habitats, including sage-steppe communities. This project builds on collaboration among federal land managers, state wildlife biologists, scientists, and other organizations to create a long-term framework for implementing adaptive management for the sage-grouse. The study examiUsing a Collaborative Modeling Approach to Explore Climate and Landscape Change in the Northern Rockies and Inform Adaptive Management
Federal land managers need an adaptive management framework to accommodate changing conditions and that allows them to effectively link the appropriate science to natural resource management decision-making across jurisdictional boundaries. FRAME-SIMPPLLE is a collaborative modeling process designed to accomplish this goal by coupling the adaptive capabilities of the SIMPPLLE modeling system with - Data
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