The primary weakness in our current ability to evaluate future landscapes in terms of wildlife lies in the lack of quantitative models linking wildlife to forest stand conditions, including fuels treatments. This project focuses on 1) developing statistical wildlife habitat relationships models (WHR) utilizing Forest Inventory and Analysis (FIA) and National Vegetation Pilot data augmented with specific wildlife sampling at plot locations, and 2) to collect matching data in areas where fuels manipulation and reduction treatments have been applied. Coupled with forest growth models, statistical WHR are being used to estimate habitat occupancy on future landscapes that include extensive fuels treatments. The statistical nature of these models will allow the power of derived cumulative effects understandings to be evaluated. As an additional benefit, for select species we will directly relate abundance-based metrics with presence/absence data to determine the efficacy of using presence/absence data to assess abundance.