Introduction

Work to adapt the Forest Vegetation Simulator (FVS) for use in British Columbia (BC), Canada, has been in progress since 1997. The BC version is called PrognosisBC.

FVS was selected for use in the southeastern portion of British Columbia because of its ability to model multi-age, multi species stands, similar to those commonly found in southeastern BC.

Based on ecological similarities between northern Idaho and southeastern British Columbia, the Northern Idaho (NI) variant of the Forest Vegetation Simulator was adapted to form the basis for PrognosisBC.

Development of PrognosisBC has been coordinated by BC's Ministry of Forests, in conjunction with faculty, staff and students at the University of British Columbia (UBC). More information on the role of UBC in the ongoing development of the PrognosisBC model in found throughout this web site.

What PrognosisBC Does - and Doesn't Do

PrognosisBC forecasts future stand conditions based upon expected growth and mortality of individual trees in a stand (diameter growth, height growth, crown development, and mortality of individual trees). The model can simulate almost any form of harvesting, from partial cutting to clearcutting. It can simulate thinning from above, below, or by diameter class, and has been modified to simulate certain forest health events. Currently, PrognosisBC does not predict regeneration establishment.

General Issues in Adaptation of the FVS Model

1. Different measurement units and standards;
2. Conversion of US Habitat Type to BC's Biogeoclimatic Ecosystem Classification (BEC) is subjective;
3. Sub-model coefficients and model forms may not fit BC data;
4. Need for inclusion of hardwoods in the model; and
5. Insufficient ground data for some stand types in the application area.

The University of British Columbia's Role

Graduate students have been involved in calibration of the regeneration and small tree components of the PrognosisBC model. Faculty and research associates have been involved in a number of other areas, including calibrating components of the model and seeking innovative solutions for supplying inputs to the model.