Abstract. Wildfire simulation modelling was used to examine whether fuel reduction treatments can potentially reduce future wildfire emissions and provide carbon benefits. In contrast to previous reports, the current study modelled landscape scale effects of fuel treatments on fire spread and intensity, and used a probabilistic framework to quantify wildfire effects on carbon pools to account for stochastic wildfire occurrence. The study area was a 68 474 ha watershed located on the Fremont-Winema National Forest in southeastern Oregon, USA. Fuel reduction treatments were simulated on 10% of the watershed (19% of federal forestland). We simulated 30 000 wildfires with random ignition locations under both treated and untreated landscapes to estimate the change in burn probability by flame length class resulting from the treatments. Carbon loss functions were then calculated with the Forest Vegetation Simulator for each stand in the study area to quantify change in carbon as a function of flame length. We then calculated the expected change in carbon from a random ignition and wildfire as the sum of the product of the carbon loss and the burn probabilities by flame length class. The expected carbon difference between the nontreatment and treatment scenarios was then calculated to quantify the effect of fuel treatments. Overall, the results show that the carbon loss from implementing fuel reduction treatments exceeded the expected carbon benefit associated with lowered burn probabilities and reduced fire severity on the treated landscape. Thus, fuel management activities Correspondence to: A. A. Ager (aager@fs.fed.us) resulted in an expected net loss of carbon immediately after treatment. However, the findings represent a point in time estimate (wildfire immediately after treatments), and a temporal analysis with a probabilistic framework used here is needed to model carbon dynamics over the life cycle of the fuel treatments. Of particular importance is the longterm balance between emissions from the decay of dead trees killed by fire and carbon sequestration by forest regeneration following wildfire.

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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.

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The physiological model STAND-BGC was linked to the forest vegetation simulator (FVS) as a system extension. With the linked model, an FVS user can invoke STAND-BGC to obtain tree-and stand-level physiological output in addition to standard FVS mensurational output. An FVS user may choose to have increments in diameter, height, crown ratio, and mortality from STAND-BGC replace those generated by FVS. This option essentially replaces the empirical growth engine of FVS with the physiological engine from STAND-BGC. Physiological and mensurational outputs were generated for an existing, fully stocked, Pinus contorta Dougl. ex Loud. stand, with and without thinning, using the hybrid model. The STAND-BGC engine produced results similar to FVS for the unthinned stand but predicted more rapid tree growth than FVS following thinning. Simulations for a newly regenerated stand using the linked model allowed assessment of the predicted effects of grass competition and drought on stand production. Comparisons of model predictions to remeasured permanent plot data showed the empirical and process growth engines had similar precision, but that STAND-BGC substantially overpredicted growth, while FVS slightly underpredicted growth. The need for model calibration and opportunities for more sophisticated communication between models is discussed. Résumé :Le modèle physiologique STAND-BGC a été joint au Simulateur de végétation forestière (SVF) en tant qu'extension du système. A l'aide du modèle auxiliaire, un utilisateur du SVF peut faire appel à STAND-BGC pour obtenir des résultats physiologiques à l'échelle du peuplement et de l'arbre en plus des résultats dendrométriques standard de SVF. Un utilisateur de SVF peut choisir d'utiliser les accroissements en diamètre, hauteur, proportion de cime et mortalité produits par STAND-BGC à la place de ceux générés par SVF. Dans les faits, cette option substitue le moteur de croissance empirique de SVF par le moteur physiologique de STAND-BGC. Des résultats physiologiques et dendrométriques ont été générés à l'aide du modèle hybride pour un peuplement dense existant de Pinus contorta Dougl. ex Loud., avec et sans éclaircie. Le moteur de STAND-BGC a produit des résultats comparables à ceux de SVF pour le peuplement non éclairci mais a prédit une croissance plus rapide que celle prédite par SVF suite à l'éclaircie. Des simulations effectuées à l'aide du modèle auxiliaire pour un peuplement récemment régénéré ont permis d'évaluer les effets prévus de la compétition herbacée et de la sécheresse sur la production du peuplement. Une comparaison des prédictions du modèle avec les données de placettes permanentes remesurées a montré que les moteurs empiriques et de processus ont une précision similaire. Toutefois, STAND-BGC surestime substantiellement la croissance alors que SVF la sous-estime légèrement. La nécessité de calibrer le modèle et de développer des liens plus sophistiqués entre les modèles est abordée.[Traduit par la Rédaction] Milner et al. 479