British Columbia is currently experiencing the largest mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic on record. The spatial extent of this infestation highlights the need for large-area forest management. We explore the use of three large-area data sets for implementing a stand-scale model of forest susceptibility that quantifies the probability of loss of pine basal area because of attack by the mountain pine beetle. Using these data sets, we investigate the impact of surrogate variables, which is necessary when variables required for the susceptibility model are not present in a data set. The impact of the source data information content on the susceptibility model output is also analyzed. Results indicate that the susceptibility model is sensitive to both surrogate variables and data sources and suggest that landscape level application of the susceptibility model, which was developed using stand-scale relationships, is problematic. Of particular concern is the use of photointerpreted data sets for model parameterization. The information content in photointerpreted data sets is much different than data on similar forest characteristics collected in the field and provides an inadequate substitute for implementing the forest susceptibility model.
ASJC Scopus subject areas
- Global and Planetary Change