The dataset includes several variables sampled across 54 recently burned aspen sites, with data collected in 2014 and 2015. Additionally, the dataset includes mean 30-year (1980-2008) and mean six year ‘fire-regen period’ (i.e., fire year and five years after fire) climate values for each site. The climate data presented here were used to calculate explanatory variables used in analysis, as outlined in the publication. Plots were located across a regional climate gradient spanning from the north-central Great Basin to the northeastern portion of the Greater Yellowstone Ecosystem (USA). Several attributes (e.g. tree characteristics, evidence of animal herbivory, shrub cover) were sampled at each plot. All trees present within a plot were counted to determine density. All large trees (stems greater than/equal to 10 cm diameter) were sampled as were all small trees (stems less than 10 cm diameter). Small trees were also divided into two height classes (less than 2 m or greater than/equal to 2 m tall). All trees were identified to species, and all conifers were grouped for analyses.

This dataset contains two phases of research. The first dataset includes several variables that were sampled across aspen stands in the Santa Rosa, Ruby, and Jarbidge mountain ranges (Great Basin, Northern Nevada, USA) in 2010 and 2011. Across 101 aspen sites, several plot-level attributes were collected (e.g. elevation, slope, aspen stand type). For each plot, data describing live trees (both those less than 7.5 cm diameter and those greater than/equal to 7.5 cm) are included, such as species, diameter, and age. The data set also includes information for dead trees greater than/equal to 7.5 cm diameter (e.g. species, location, diameter). The second dataset includes tree ring measurements (for live trees greater than/equal to 7.5 cm diameter) and monthly climate data for a subset of sites (n = 20) that were included in the first phase. For this subset of 20 sites, we analyzed the relationship between tree ring width measurements and climate variables. The climate variables represent monthly total precipitation, average temperature, and climatic moisture index values by year for the period of record.

This dataset contains two phases of research. The first dataset includes several variables that were sampled across aspen stands in the Santa Rosa, Ruby, and Jarbidge mountain ranges (Great Basin, Northern Nevada, USA) in 2010 and 2011. Across 101 aspen sites, several plot-level attributes were collected (e.g. elevation, slope, aspen stand type). For each plot, data describing live trees (both those less than 7.5 cm diameter and those greater than/equal to 7.5 cm) are included, such as species, diameter, and age. The data set also includes information for dead trees greater than/equal to 7.5 cm diameter (e.g. species, location, diameter). The second dataset includes tree ring measurements (for live trees greater than/equal to 7.5 cm diameter) and monthly climate data for a subset of sites (n = 20) that were included in the first phase. For this subset of 20 sites, we analyzed the relationship between tree ring width measurements and climate variables. The climate variables represent monthly total precipitation, average temperature, and climatic moisture index values by year for the period of record.

This dataset contains two phases of research. The first dataset includes several variables that were sampled across aspen stands in the Santa Rosa, Ruby, and Jarbidge mountain ranges (Great Basin, Northern Nevada, USA) in 2010 and 2011. Across 101 aspen sites, several plot-level attributes were collected (e.g. elevation, slope, aspen stand type). For each plot, data describing live trees (both those less than 7.5 cm diameter and those greater than/equal to 7.5 cm) are included, such as species, diameter, and age. The data set also includes information for dead trees greater than/equal to 7.5 cm diameter (e.g. species, location, diameter). The second dataset includes tree ring measurements (for live trees greater than/equal to 7.5 cm diameter) and monthly climate data for a subset of sites (n = 20) that were included in the first phase. For this subset of 20 sites, we analyzed the relationship between tree ring width measurements and climate variables. The climate variables represent monthly total precipitation, average temperature, and climatic moisture index values by year for the period of record.

Land management treatments in sagebrush steppe are an important opportunity to break the annual-grass fire cycle, provided they offer long-lasting resistance to annual-grass invasion and do not burn. However, for BLM areas seeded as part of the Emergency Stabilization and Rehabilitation (ESR) program, one of the largest programs for land management treatments, about 1/4 have at least partially reburned over the last 30 years, according to a recent study. Reburning of treatments can cause a loss of investment if fire-intolerant perennials do not recover and/or significant invasions occur, in which case the risks of wildfire are compounded by increased potential for ecological degradation. Alternatively, recovery of fire-tolerant perennials occurs naturally or due to treatments would represent a significant return on prior investment and the occurrence of fire would thus pose reduced ecological hazard risks. Fire risks are highly variable across sagebrush landscapes, owing to variability in fuel loading, ignition potential, and fire transmission. Information is needed on predicting future risks related to reburning - including post-fire hazards related to ecological degradation - for past land management investments to a) identify protection measures that could be applied now, and b) help design and positioning of future treatment investments to minimize their risk of reburning in ways that cause ecological degradation. This dataset was compiled in order to predict reburn risk to areas that had previously burned and were retreated.

This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

The downloadable ZIP file contains an Esri ArcInfo Coverage. This data set reflects large fires (approximately 200 acre minimum size) for 1900-1997 for Boise National Forest, Idaho. These data are intended to assist efforts surrounding broad-scale forest planning for analysis in fire-recovery: soil stabilization, reestablishing vegetation, and protection of other resources such as wildlife, riparian habitat, water quality, fisheries and timber.,

This dataset is comprised of four different zip files. Zip File 1: A combined wildfire polygon dataset ranging in years from 1878-2019 (142 years) that was created by merging and dissolving fire information from 12 different original wildfire datasets to create one of the most comprehensive wildfire datasets available. Attributes describing fires that were reported in the various source data, including fire name, fire code, ignition date, controlled date, containment date, and fire cause, were included in this product’s attribute table. Zip Files 2-4: The fire polygons were turned into 30 meter rasters with the values representing area burned in each year (128 yearly rasters total, as some years in the 1800s had no fires recorded). Three rasters were calculated from the yearly rasters: (a) their yearly values were turned to 1 and these values were summed to create a count of the number of times burned, (b) the first time a pixel burned was selected to create a first year burned raster, and (c) the last time a pixel burned was selected to create a most recent year burned raster. These calculations were done for the contiguous US, Alaska, and Hawaii separately in order to reduce file size and download time. Each of the zipped files contains the three rasters for one of the following locations: contiguous US, Alaska, and Hawaii.