An ecological approach to developing a decision support system for forest planning

The fundamental premise behind the development of this ecological site quality model for lodgepole pine is the ecologically-based structured systems approach, which provides the structure for evaluating data and developing and applying the model. Forest productivity is evaluated indirectly through an assessment of various ecological parameters that characterize site quality-nutrient regime, moisture regime, geomorphology, soil, topography, and vegetation. This characterization occurs within an ecological classification framework that provides the support structure for interpreting the resulting information. One of the objectives of such an integrated ecological approach to forest management is to better predict ecological site quality to facilitate better silviculture decisions and estimates of volume and timber flow. The principal means of doing this is by developing an understanding of the ecological conditions that influence site quality and, ultimately, the growth of tree species on those sites (forest productivity). Applying this systems approach to lodgepole pine showed that there were considerable differences in the amount of variability explained by site index across the classes at each hierarchical level of ecological organization. Ecosite alone accounted for more than 48% of the variability in lodgepole pine site index in the Lower Foothills Subregion. However, combining ecosite and tree canopy (in the Lower Foothills) yielded the strongest relationship with nearly 70% of the variability in lodgepole pine site index being explained. These relationships were used as key input variables for the development of a productivity model to evaluate site quality in the study area. Through successive analysis of these ecological components at each hierarchical level, 25 unique productivity groupings were developed for lodgepole pine. This ecologically-based structured systems approach to the evaluation of ecological site quality enables resource managers to better anticipate and even predict the potential productivity of different sites and how they will respond to operational activities (harvesting, treatment, stand tending).