These data are a habitat restoration index based on the intersection of loss of habitat selected by sage-grouse and loss of habitat contributions to nest survival following wildfire.

We developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit Greater Sage-Grouse (Centrocercus urophasianus; hereafter sage-grouse) populations through time. Specifically, we estimated sagebrush (Artemisia spp.) recovery following wildfire and risk of non-native annual grass invasion under three scenarios: passive recovery, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

We evaluated nest site selection and nest survival both before and after a fire disturbance occurred. We then combined those surfaces to determine the areas which were most heavily impacted by the fire.

A raster identifying areas that met the criteria to be priority habitat for Greater sage-grouse before a fire disturbance occurred. This file is binary, a value of 1 indicates the pixel represents pre-fire priority habitat, a value of 0 indicates the pixel did not meet the criteria of selection, survival, and space-use to be considered pre-fire priority habitat.

These data document surface fuels data for a prescribed burning study with unburned controls on the Malheur National Forest in the southern Blue Mountains of Oregon. The original prescribed fires were conducted in the fall of 1997 and spring of 1998 and were repeated at two intervals, five and fifteen years. Five year interval reburns have been repeated three times (four burns total) and the fifteen year interval a single time (two burns total). These data document fuels prior to (2012) and following the last reburns including 1-hour (0 to 0.64 centimeter [cm] diameter), 10-hour (0.64 to 2.54 cm diameter), 100-hour (2.54 to 7.62 cm diameter) and 1000-hour fuels (> 7.62 cm diameter); average combined litter and duff depth; and surface fuel height.

Evaluating factors that affect recovery of canopy-forming, foundational species is needed to guide effective treatment implementation aimed at mitigating their loss due to the changing fire regimes being experienced in semi-arid shrub-steppe of the Western USA. Most inferences on factors influencing recovery are based on one-time measurements taken as a snapshot in time, usually focused on the short-term initial establishment phase or outcomes observed decades after. We measured factors associated with the secondary establishment of big sagebrush in nearly 2000 plots across a heterogeneous landscape five years after a megafire (115,000 ha) and the diverse mosaic of restoration treatments implemented and compare these findings to previously published inferences on initial, first-year germination patterns observed on the same plots.

Wildfire events are becoming more frequent and severe on a global scale. Rising temperatures, prolonged drought, and the presence of pyrophytic invasive grasses are contributing to the degradation of native vegetation communities. Within the Great Basin region of the Western United States, increasing wildfire frequency is transforming the ecosystem toward a higher degree of homogeneity, one dominated by invasive annual grasses and declining landscape productivity. Greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) are a species of conservation concern that rely on large tracts of structurally and functionally diverse sagebrush (Artemisia spp.) communities. Using a 12-year (2008-2019) telemetry dataset, we documented immediate impacts of wildfire on demographic rates of a population of sage-grouse that were exposed to two large wildfire events (Virginia Mountains Fire Complex - 2016; Long Valley Fire - 2017) near the border of California and Nevada. Spatiotemporal heterogeneity in demographic rates were accounted for using a Before-After Control-Impact Paired Series (BACIPS) study design.

LANDFIRE's (LF) Remap Vegetation Condition Class (VCC) is a reclassification and categorization of the LF Remap Vegetation Departure (VDep) product. VCC indicates the general level to which current vegetation is different from the simulated historical reference condition. Therefore, VCC is a derivative of VDep; the VDep product indicates how different current vegetation is compared to the estimated historical reference condition, and is based on change to species composition, structure, and canopy closure. To learn more about VCC and VDep go to https://www.landfire.gov/fireregime.php. Condition classes for VCC are defined in two ways; the original 3 category system from Fire Regime Condition Class Guidebook (FRCC Guidebook), and a newer 6 category system that provides additional precision. For the original 3 category system, the VDep value is reclassified as: Condition Class I: VDep value from 0 to 33 (Low Departure), Class II: VDep value between 34 - 66 (Moderate Departure), and Condition Class III: VDep value from 67 to 100 (High Departure). The 6 category system provides more resolution to VCC and is collapsible to the 3 category system. The 6 VCC categories are defined as: Condition Class I.A: VDep between 0 and 16 (Very Low Departure), Condition Class I.B: VDep between 17 and 33 (Low to Moderate Departure); Condition Class II.A: VDep between 34 and 50 (Moderate to Low Departure); Condition Class II.B: VDep between 51 and 66 (Moderate to High Departure); Condition Class III.A: VDep between 67 and 83 (High to Moderate Departure), and Condition Class III.B: VDep between 84 and 100 (High Departure).

LANDFIRE's (LF) Remap Vegetation Condition Class (VCC) is a reclassification and categorization of the LF Remap Vegetation Departure (VDep) product. VCC indicates the general level to which current vegetation is different from the simulated historical reference condition. Therefore, VCC is a derivative of VDep; the VDep product indicates how different current vegetation is compared to the estimated historical reference condition, and is based on change to species composition, structure, and canopy closure. To learn more about VCC and VDep go to https://www.landfire.gov/fireregime.php. Condition classes for VCC are defined in two ways; the original 3 category system from Fire Regime Condition Class Guidebook (FRCC Guidebook), and a newer 6 category system that provides additional precision. For the original 3 category system, the VDep value is reclassified as: Condition Class I: VDep value from 0 to 33 (Low Departure), Class II: VDep value between 34 - 66 (Moderate Departure), and Condition Class III: VDep value from 67 to 100 (High Departure). The 6 category system provides more resolution to VCC and is collapsible to the 3 category system. The 6 VCC categories are defined as: Condition Class I.A: VDep between 0 and 16 (Very Low Departure), Condition Class I.B: VDep between 17 and 33 (Low to Moderate Departure); Condition Class II.A: VDep between 34 and 50 (Moderate to Low Departure); Condition Class II.B: VDep between 51 and 66 (Moderate to High Departure); Condition Class III.A: VDep between 67 and 83 (High to Moderate Departure), and Condition Class III.B: VDep between 84 and 100 (High Departure).

The Existing Vegetation Cover (EVC) product depicts percent canopy cover by life form and is an important input to other LANDFIRE mapping efforts. EVC is generated separately for tree, shrub and herbaceous life forms using training data and a series of geospatial predictor layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (https://www.fia.fs.usda.gov/) were used as the training data for tree canopy cover mapping, with canopy cover of the plots estimated from stem-mapped tree data and calibrated with line intercept field measurements of canopy cover (Toney and others 2009). Shrub and herbaceous canopy cover training data were also derived from plot-level, ground-based visual assessments. More information regarding contributors of field plot data can be found at http://www.landfire.gov/participate_acknowledgements.php. Regression tree models were developed separately for each life form using the training data and a combination of multitemporal Landsat data, terrain data from a digital elevation model, and biophysical gradient data layers. Cubist software was used for modeling. The derived regression tree equations were then applied to the geospatial predictor data to create 30-m resolution, life form specific data layers (i.e., separate data layers are generated for tree, shrub and herbaceous vegetation cover). Each of the derived data layers (tree, shrub, herbaceous) has a potential range of 0-100 percent canopy cover. Tree, shrub and herbaceous values were binned into discrete classes (up to 10 bins at 10 percent intervals for tree, shrub and herbaceous canopy cover). The final EVC layer was evaluated and rectified through a series of QA/QC measures to ensure that the life form of the canopy cover code matched the life form of the LANDFIRE Existing Vegetation Type (EVT) layer. EVC is used in the development of subsequent LANDFIRE data layers. LF 2014 (lf_1.4.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs. Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data.