Modeling and Mapping White Pine Blister
Rust Infection in Whitebark Pine
Donald J.
1USDA
1
Helmbrecht ,
for Environmental Management, Missoula, MT
Blister Rust Life Cycle
•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.
and Katharine L.
2
Gray
Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory
2Systems
Background
Robert E.
1
Keane ,
Late Spring – Early
Summer
The aeciospores
produced in cankers
are released and
infect Ribes.
June – July
The fungus spreads from
the needles to the bark.
Moderate daytime
temperatures and moist
conditions allow
urediniospore formation
and spread among Ribes.
August – October
Continued cool, moist
conditions allow rust to
complete its life cycle and
produce air dispersed
basidiospores which
infect pine needles.
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.
Gradient Modeling
Methods
Map of predicted blister rust infection in whitebark pine overlaid
on wilderness and National Forest polygons.
•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.
Results
•Identify key environmental gradients known to govern rust
propagation and that are available as spatial data.
•869 plots from the Whitebark-Limber Pine Information System
database were usable as response variables.
•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.
•The model explained 58% of the variation in the rust infection
data.
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.
•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.