Kathi Irvine, Ph.D.
Biography
Education
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
Research Interest
I am a Research Statistician with the U.S. Geological Survey at the Northern Rocky Mountain Science Center in Bozeman, Montana. 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:
Science and Products
Developing online integrated data visualization tools for WNS and NABat
Research collaboration: Brian Reichert (FORT), Anne Ballmann (NWHC), Jeremy Coleman (USFWS), Paul Cryan (FORT), Wayne Thogmartin (UMESC), and Katherine Irvine...
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...
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...
Using 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...
NABat: A top-down, bottom-up solution to collaborative continental-scale monitoring
Collaborative monitoring over broad scales and levels of ecological organization can inform conservation efforts necessary to address the contemporary biodiversity crisis. An important challenge to collaborative monitoring is motivating local engagement with enough buy-in from stakeholders while providing adequate top-down direction for scientific...
Reichert, Brian E.; Bayless, Mylea L.; Cheng, Tina L.; Coleman, Jeremy T.H.; Francis, Charles M.; Frick, Winifred F.; Gotthold, Benjamin; Irvine, Kathryn; Lausen, Cori; Li, Han; Loeb, Susan C.; Reichard, Jonathan D.; Rodhouse, Thomas J.; Segers, Jordi L.; Siemers, Jeremy; Thogmartin, Wayne E.; Weller, TheodoreThe use of Bayesian priors in Ecology: The good, the bad and the not great
Bayesian data analysis (BDA) is a powerful tool for making inference from ecological data, but its full potential has yet to be realized. Despite a generally positive trajectory in research surrounding model development and assessment, far too little attention has been given to prior specification.Default priors, a sub‐class of non‐informative...
Banner, Katharine M.; Irvine, Kathryn M.; Rodhouse, Thomas J.Scientist’s guide to developing explanatory statistical models using causal analysis principles
Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models...
Grace, James B.; Irvine, KathrynEvidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors
Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported....
Thomas J. Rodhouse; Rogelio M. Rodriguez; Katharine M. Banner; Patricia C. Ormsbee; Jenny Barnett; Irvine, KathrynNorth American Bat Monitoring Program regional protocol for surveying with stationary deployments of echolocation recording devices: Narrative version 1.0, Pacific Northwestern US
The outbreak of white-nose syndrome (WNS) and the growing awareness of the risks to bats from wind power generating facilities have driven radical changes to North American bat conservation. Over the last decade, formerly common species such as the little brown myotis (Myotis lucifugus) and hoary bat (Lasiurus cinereus) have experienced...
Rogelio M. Rodriguez; Thomas J. Rodhouse; Jenny Barnett; Irvine, Kathryn; Katharine M. Banner; Jeff Lonneker; Patricia C. OrmsbeeClimatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem
Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. Understanding climatic correlates associated with spread is...
Thoma, David; Shanahan, Erin K.; Irvine, KathrynCohesive framework for modeling plant cover class data
The study of plant distribution and abundance is a fundamental pursuit in ecology and conservation biology. Measuring plant abundance by visually assessing percent cover and recording a cover class is a common field method that yields ordinal data. Statistical models for ordinal data exist but entail cumbersome interpretations and sometimes...
Irvine, Kathryn; Wright, Wilson J.; Shanahan, Erin K.; Rodhouse, Thomas J.Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program
Dynamic occupancy models provide a flexible framework for estimating and mapping species occupancy patterns over space and time for large-scale monitoring programs (e.g., the North American Bat Monitoring Program (NABat), the Amphibian Research and Monitoring Initiative). Challenges for designing surveys using the dynamic occupancy modeling...
Banner, Katherine; Irvine, Kathryn M.; Rodhouse, Tom J; Donner, Deahn M.; Litt, Andrea R.Using the beta distribution to analyze plant cover data
Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data.Commonly, plant abundance is quantified by...
Damgaard, Christian; Irvine, Kathryn M.Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?
Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the...
Wright, Wilson; Irvine, Kathryn M.; Higgs, Megan D.Wetland drying linked to variations in snowmelt runoff across Grand Teton and Yellowstone national parks
In Grand Teton and Yellowstone national parks wetlands offer critical habitat and play a key role in supporting biological diversity. The shallow depths and small size of many wetlands make them vulnerable to changes in climate compared with larger and deeper aquatic habitats. Here, we use a simple water balance model to generate estimates of...
Ray, Andrew M.; Sepulveda, Adam J.; Irvine, Kathryn M.; Wilmoth, Siri K.C.; Thoma, David P.; Patla, Debra A.Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes
Long-term ecological monitoring programs often use linear mixed models to estimate trend in an ecological indicator sampled across large landscapes. A linear mixed model is versatile for estimating a linear trend in time as well as components of spatial and temporal variationin the case of unbalanced data structures,...
Starcevich, Leigh Ann H.; Irvine, Kathryn M.; Heard, Andrea M.