1. Extension of the forest ecosystem simulation model FORECAST: incorporating mountain pine beetle, fire, climate change, and wildlife Hamish Kimmins, Kim Scoular, Brad Seely, Clive Welham, Yueh-Hsin Lo, Brock Simons, Angelica Boldor Mule deer (Odocoileus hemionus) Photo © John Marriott, www.wildernessprints.com Pine marten (Martes americana) Photo © John Marriott, www.wildernessprints.com Feedback References 1.Ministry of Water, Land and Air Protection. 2002. Indicators of Climate Change for British Columbia. 2.Seely, B., P. Arp, and J.P. Kimmins (1997). A forest hydrology submodel for simulating the effect of stand management and climate change on water competition and stand water stress. In Amaro, A. and M. Tome (Eds.), Conference proceedings "Empirical and process-based models for forest, tree and stand growth simulation", 21-27 September, 1997, Oeiras, Portugal. 3.Shore, T.L., and L. Safranyik. 1992. Susceptibility and risk rating systems for the mountain pine beetle in lodgepole pine. Forestry Canada. Pacific Forestry Centre, Victoria BC. 4.Shore, T.L., L. Safranyik, J.P. Lemieux. 2000. Susceptibility of lodgepole pine stands to the mountain pine beetle: testing of a rating system. Abstract Over the last few years, the mountain pine beetle has shown once again that it is a powerful force of forest disturbance. Strategies to minimize the negative impacts of this natural disturbance must be developed and incorporated into forest management plans. However, experience has shown us that for management strategies to be robust they must be developed within an ecosystem context and projected over relevant time and spatial scales. FORECAST is a stand-level, hybrid ecosystem management simulation model that acts as the foundation for the landscape-level simulation programs PFF and LLEMS, and can be used as an ecosystem-based driver of timber supply (e.g. ATLAS/FPS) and wildlife habitat supply (e.g. SIMFOR) models. The predictive capacity of FORECAST is being increased through the addition of dynamic mountain pine beetle, fire, climate change, and wildlife habitat suitability components. Together, these additions will greatly increase the power of FORECAST as a tool for predicting possible effects of proposed forest management activities within the context of risks of natural disturbance and possible climate change. User-Defined Infestation Timing Loss Prediction Model (Shore et al. 2000) Mountain Pine Beetle Wildlife Stand-Level Habitat Variables AgeDensity Basal Area of Pine >15cm dbh User-Defined Location Total Basal Area >7.5cm dbh Shore/Safranyik Stand Susceptibility Index (Shore and Safranyik, 1992) Feedback Trees  Species  Density  Age  Height  Canopy Depth Plants  Species  Height  Edible Biomass Snags  Species  Decay State  DBH  Density Coarse Woody Debris  Species  Decay State  DBH  Density Habitat Variables User-Defined Habitat Suitability/ Population Demographic Equations Fuel Variables Tree  Foliage  Branches  Bark  Roots  CWD  Snags Plants  Above ground  Below ground LitterBryophytesDuff Fire SeverityFire Risk User-defined Climate Data  Precipitation  Relative Humidity  Wind (speed, direction)  Temperature Fire Module Fire Impact Figure 1 a) Change in Annual Temperature, 1895-1995 (Ministry of Water, Land and Air Protection, 2002). b) Change in seasonal precipitation, 1929-1998, % per decade (Ministry of Water, Land and Air Protection, 2002). a)b) Fire Climate Change Growth Rate Adjustment Decomposition Rate Adjustment Photo by Phil Maranda FORECAST User-Defined Temperature Change User-Defined Precipitation Pattern Transpiration Demand ForWaDy Hydrological Model (Seely et al. 1997) Actual Transpiration Belowground Vegetation Structure Understory Vegetation Structure Moisture in LFH Layers Overstory Vegetation Structure Soil Structure Transpiration Deficit Index Biomass Variables