Gradient Modeling Spatial layers of environmental gradients (predictor variables) known to govern rust propagation were compared to percent rust infection (measured response variable) to build a predictive landscape model and map rust infection levels to the entire range of whitebark pine in the western U.S. DAYMET data are climatological summaries of meteorological observations interpolated to a 1 km² grid over the conterminous U.S. The Whitebark-Limber Pine Information System (WLIS) is a database of plot-level surveys on whitebark and limber pine distribution and condition developed by the USDA Forest Service. Map of predicted blister rust infection in whitebark pine overlaid on wilderness and National Forest polygons. Background Whitebark pine populations are rapidly declining across many parts of their range due to the combined effects of native mountain pine beetle (Dendroctonus ponderosae) epidemics, fire exclusion, and, most importantly, exotic white pine blister rust (Cronartium ribicola). White pine blister rust undergoes a complex life cycle and propagation is dependent on an interaction of climate and the spatial distribution of its hosts – shrubs of the genus Ribes and five-needled (white) pines. Gradient modeling identifies key environmental gradients (i.e. temperature, precipitation) that drive a response (i.e. rust infection), which are then mathematically represented in a statistical model to predict and map ecosystem characteristics across the landscape. Maps of blister rust infection levels are important for prioritizing restoration treatments on those lands that have the greatest rust infection and mortality. Modeling and Mapping White Pine Blister Rust Infection in Whitebark Pine Donald J. Helmbrecht 1, Robert E. Keane 1, and Katharine L. Gray 2 1 USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory 2 Systems for Environmental Management, Missoula, MT Objective Develop a 1 km² resolution spatial data layer of blister rust infection levels for the entire range of whitebark pine using gradient modeling techniques. Results 869 plots from the Whitebark-Limber Pine Information System database were usable as response variables. The model explained 58% of the variation in the rust infection data. 25% (3.5 million acres) of the whitebark pine potential habitat was modeled to have greater than 50% infection. What’s Next The rust infection maps produced in this study will be used in a Forest Service-wide whitebark pine restoration strategy. A field study is being set up to collect reference data for model validation. This study and the model validation results will be documented in a Rocky Mountain Research Station, Research Note. Blister Rust Life Cycle Methods Select records in the Whitebark-Limber Pine Information System database where both percent infection and spatial coordinates were recorded to build a point feature class of field plots. Identify key environmental gradients known to govern rust propagation and that are available as spatial data. Use sample function in ArcGIS to build a table of gradient values (predictor variables) and rust infection level (response variable) at each point. Use table of predictor and response variables to build a predictive model using the random forest method in the R statistical package. Map model results to the range of whitebark pine. June – July Moderate daytime temperatures and moist conditions allow urediniospore formation and spread among Ribes. Late Spring – Early Summer The aeciospores produced in cankers are released and infect Ribes. The fungus spreads from the needles to the bark. August – October Continued cool, moist conditions allow rust to complete its life cycle and produce air dispersed basidiospores which infect pine needles.