Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs

Abstract

Ongoing forest restoration on public lands in the western US is a concerted effort to counter the growing incidence of uncharacteristic wildfire in fire‐adapted ecosystems. Restoration projects cover 725,000 ha annually, and include thinning and underburning to remove ladder and surface fuel, and seeding of fire‐adapted native grasses and shrubs. The backlog of areas in need of restoration combined with limited budgets requires that projects are implemented according to a prioritization system. The current system uses a stand‐scale metric that measures ecological departure from pre‐settlement conditions. Although conceptually appealing, the approach does not consider important spatial factors that influence both the efficiency and feasibility of managing future fire in the post‐treatment landscape. To address this gap, we developed a spatial model that can be used to explore different landscape treatment configurations and identify optimal project parameters that maximize restoration goals. We tested the model on a 245,000 ha forest and analyzed tradeoffs among treatment strategies as defined by fire behavior thresholds, total area treated, and the proportion of the project area treated. We assumed the primary goal as the protection and conservation of old growth ponderosa pine trees from potential wildfire loss. The model located optimal project areas for restoration and identified treatment areas within them, although the location was dependent on assumptions about acceptable fire intensity within restored landscapes, and the total treated area per project. When a high percentage of stands was treated (e.g., >80%), the resulting project area was relatively small, leaving the surrounding landscape at risk for fire. Conversely, treating only a few stands with extreme fire behavior (<20%) created larger projects, but substantial old growth forests remained susceptible to wildfire mortality within the project area. Intermediate treatment densities (35%) were optimal in terms of the overall reduction in the potential wildfire mortality of old growth. The current work expands the application in spatial optimization to the problem of dry forest restoration, and demonstrates a decision support protocol to prioritize landscapes and specific areas to treat within them. The concepts and model can be applied to similar problems in spatial ecology.