Debate over the influence of postwildfire management on future fire severity is occurring in the absence of empirical studies. We used satellite data, government agency records, and aerial photography to examine a forest landscape in southwest Oregon that burned in 1987 and then was subject, in part, to salvage-logging and conifer planting before it reburned during the 2002 Biscuit Fire. Areas that burned severely in 1987 tended to reburn at high severity in 2002, after controlling for the influence of several topographical and biophysical covariates. Areas unaffected by the initial fire tended to burn at the lowest severities in 2002. Areas that were salvage-logged and planted after the initial fire burned more severely than comparable unmanaged areas, suggesting that fuel conditions in conifer plantations can increase fire severity despite removal of large woody fuels.public land management ͉ salvage-logging ͉ Biscuit Fire ͉ Landsat ͉ landscape ecology

Although the rates and mechanisms of soil organic matter (SOM) stabilization are difficult to observe directly, radiocarbon has proven an effective tracer of soil C dynamics, particularly when coupled with practical fractionation schemes. To explore the rates of C cycling in temperate forest soils, we took advantage of a unique opportunity in the form of an inadvertent standlevel 14 C-labeling originating from a local industrial release. A simple density fractionation scheme separated SOM into interaggregate particulate organic matter (free light fraction, free LF), particulate organic matter occluded within aggregates (occluded LF), and organic matter that is complexed with minerals to form a dense fraction (dense fraction, DF). Minimal agitation and density separation was used to isolate the free LF. The remaining dense sediment was subjected to physical disruption and sonication followed by density separation to separate it into occluded LF and DF. The occluded LF had higher C concentrations and C:N ratios than the free LF, and the C concentration in both light fractions was ten times that of the DF. As a result, the light fractions together accounted for less than 4% of the soil by weight, but contained 40% of the soil C in the 0-15 cm soil increment. Likewise, the light fractions were less than 1% weight of the 15-30 cm increment, but contained more than 35% of the soil C. The degree of SOM protection in the fractions, as indicated by D 14 C, was different. In all cases the free LF had the shortest mean residence times. A significant depth by fraction interaction for 14 C indicates that the relative importance of aggregation versus organo-mineral interactions for overall C stabilization changes with depth. The rapid incorporation of 14 C label into the otherwise depleted DF shows that this organo-mineral fraction comprises highly stable material as well as more recent inputs. D

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Forest managers in the United States must respond to the need for climate-adaptive strategies in the face of observed and projected climatic changes. However, there is a lack of on-the-ground forest adaptation research to indicate what adaptation measures or tactics might be effective in preparing forest ecosystems to deal with climate change. Natural resource managers in many areas are also challenged by scant locally or regionally relevant information on climate projections and potential impacts. The Adaptive Silviculture for Climate Change (ASCC) project was designed to respond to these barriers to operationalizing climate adaptation strategies by providing a multiregion network of replicated operational-scale research sites testing ecosystem-specific climate change adaptation treatments across a gradient of adaptive approaches, and introducing conceptual tools and processes to integrate climate change considerations into management and silvicultural decisionmaking. Here we present the framework of the ASCC project, highlight the implementation process at two of the study sites, and discuss the contributions of this collaborative science-management partnership.

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed burns and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate and interpret logistic regression models; explanatory variables in logistic regression models; factors influencing scope of inference and model limitations; model validation; and management applications. Logistic regression is currently the most widely used and available technique for predicting post-fire tree mortality. Over 100 logistic regression models have been developed to predict post-fire tree mortality for 19 coniferous species following wild and prescribed fires. The most widely used explanatory variables in post-fire tree mortality logistic regression models have been measurements of crown (e.g. crown scorch) and stem (e.g. bole char) injury. Prediction of post-fire tree mortality improves when crown and stem variables are used collectively. Logistic regression models that predict post-fire tree mortality are the basis of simple field tools and contribute to larger fire-effects models. Future post-fire tree mortality prediction models should include consistent definition of model variables, model validation and direct incorporation of physiological responses that link to process modelling efforts.