Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape

Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.DouglasFireRData file of data used for analysis in Ecological Applications paper of 2013 Douglas Complex, Oregon USA. RData file contains two dataframes (douglas.fire.progression.rdnbdr.wx, blm.pi.sample.allvars.xy.df) douglas.fire.progression.rdnbdr.wx contains data on daily hectares burned, daily mean Relative difference Normalized Burn Ratio (RdNBR), and daily fire weather variables for Douglas Complex. blm.pi.sample.allvars.xy.df contains plot data of RdNBR, ownwrship class, topographic variables, fire weather, and pre-fire forest biomass.DouglasFireR script for 2013 Douglas Complex analyses, Oregon USA. Uses DouglasFire.RData file which contains two dataframes (douglas.fire.progression.rdnbdr.wx, blm.pi.sample.allvars.xy.df) douglas.fire.progression.rdnbdr.wx contains data on daily hectares burned, daily mean Relative difference Normalized Burn Ratio (RdNBR), and daily fire weather variables for Douglas Complex. blm.pi.sample.allvars.xy.df contains plot data of RdNBR, ownwrship class, topographic variables, fire weather, and pre-fire forest biomass. DouglasFire.R is R script for conducting the following statistical analyses on the two dataframes: 1. regressions of daily mean RdNBR in relation to individual fire weather (Wx) variables. 2. summary stats (mean and standard deviation) of response and predictor variables by ownership class. 3. Mann-Whitney-Wilcoxon Test of differences in distributions of response and predictor variables between ownership classes. 4. Random forest models of RdNBR in relation to predictor variables 5. Generalized least squares model of RdNBR in relation to predictor variable