Data includes functional group cover of exotic annual grasses, deep rooted perennial grasses, and shallow rooted perennial grasses within the first five years after the 2015 Soda wildfire across different post-fire restoration treatments. Additional landscape and weather covariates hypothesized to influence treatment effectiveness are included.

Data includes cover and presence (within microsites and 13 m radius plots) of three exotic annual grass, Bromus tectorum, Taeniatherum caput-medusae, and Ventenata dubia and presence (within microsites) of four perennial bunchgrass species (Agropyron cristatum, Pseudoroegneria spicata, Poa secunda, Elymus elymoides) within the first five years after the 2015 Soda wildfire. Additional landscape and weather covariates hypothesized to influence landscape resistance to invasion are included.

Data includes cover and presence (within microsites and 13 m radius plots) of three exotic annual grass, Bromus tectorum, Taeniatherum caput-medusae, and Ventenata dubia and presence (within microsites) of four perennial bunchgrass species (Agropyron cristatum, Pseudoroegneria spicata, Poa secunda, Elymus elymoides) within the first five years after the 2015 Soda wildfire. Additional landscape and weather covariates hypothesized to influence landscape resistance to invasion are included.

The point data file ("Soda Fire Point and Pasture Data (2016).Point Data.csv") includes 2016 vegetative cover values of exotic annual grass and perennial grass measured within three different types of plots for 75 pastures in the Soda Fire, which burned in 2015: 6m² plot using a grid-point intercept photo software, SamplePoint (Booth et al. 2006), 1m² quadrat using an unguided rapid ocular estimate in the field, 531m² circular plot using an unguided rapid ocular estimate in the field. Smaller plots were nested within larger plots. The pasture data file ("Soda Fire Point and Pasture Data (2016).Pasture Data.csv") includes pasture level metrics of area, elevation, precipitation, slope, heatload, soils, and herbicide treatments within the 75 pastures. Booth, D.T., Cox, S.E., and Berryman, R.D., 2006, Point sampling digital imagery with ‘SamplePoint’, Environmental Monitoring and Assessment, 123 (1-3): 97-108, https://doi.org/10.1007/s10661-005-9164-7.

The point data file ("Soda Fire Point and Pasture Data (2016).Point Data.csv") includes 2016 vegetative cover values of exotic annual grass and perennial grass measured within three different types of plots for 75 pastures in the Soda Fire, which burned in 2015: 6m² plot using a grid-point intercept photo software, SamplePoint (Booth et al. 2006), 1m² quadrat using an unguided rapid ocular estimate in the field, 531m² circular plot using an unguided rapid ocular estimate in the field. Smaller plots were nested within larger plots. The pasture data file ("Soda Fire Point and Pasture Data (2016).Pasture Data.csv") includes pasture level metrics of area, elevation, precipitation, slope, heatload, soils, and herbicide treatments within the 75 pastures. Booth, D.T., Cox, S.E., and Berryman, R.D., 2006, Point sampling digital imagery with ‘SamplePoint’, Environmental Monitoring and Assessment, 123 (1-3): 97-108, https://doi.org/10.1007/s10661-005-9164-7.

Data includes functional group cover of exotic annual grasses and deep rooted perennial grasses within the first five years after the 2015 Soda wildfire across different post-fire restoration treatments. Additional landscape and restoration treatment covariates hypothesized to influence post-fire invasive annual grass and perennial grass cover are included.

Data includes functional group cover of exotic annual grasses and deep rooted perennial grasses within the first five years after the 2015 Soda wildfire across different post-fire restoration treatments. Additional landscape and restoration treatment covariates hypothesized to influence post-fire invasive annual grass and perennial grass cover are included.

Data includes functional group cover of exotic annual grasses, shallow rooted perennial grasses, and cover, basal diameter, height, and density of deep-rooted perennial grasses within the first five years after the 2015 Soda wildfire across post-fire drill-seeding treatments.

Data includes functional group cover of exotic annual grasses, shallow rooted perennial grasses, and cover, basal diameter, height, and density of deep-rooted perennial grasses within the first five years after the 2015 Soda wildfire across post-fire drill-seeding treatments.

This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through June 19, 2017. This is the second iteration of an early estimate of herbaceous annual cover for 2017 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/F7445JZ9). The pixel values for this most recent estimate ranged from 0 to100% with an overall mean value of 7.02% and a standard deviation of +/-9.08. The model's test mean error rate (n = 1664), based on nine different randomizations, equaled 5.2% with a standard deviation of +/- 0.09. Overall statistics between the May and June datasets were similar. However, some individual pixel differences can be considerable and are attributed to changing conditions on the ground that are reflected in the satellite data. These changes can influence how the models relate the dependent variable to the independent variables. Both datasets were generated by integrating ground-truth measurements of annual herbaceous percent cover with 250-m spatial resolution eMODIS NDVI satellite derived data and geophysical variables into regression-tree software. The geographic coverage includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2250-m elevation because annual grasses are unlikely to exist at substantial cover above this threshold. To target likely sagebrush ecosystems, the mask also covered pixels classified as something other than shrub or grassland/herbaceous by the 2011 National Land Cover Dataset (NLCD). The model was not trained on any masked pixels. Cheatgrass (Bromus tectorum) is the most common annual grass in the study area. It grows from seed, usually in spring, matures quickly, produces seed, and dies. After dying, cheatgrass contributes fine fuels that facilitate fire ignition and spread throughout sagebrush ecosystems. These fires remove sagebrush stands. Increasing fire frequencies, land management practices, and development have all contributed to the fragmentation of the once expansive sagebrush ecosystems. These ecosystems are critical for water quality, reduced fire threats, and the survival of sagebrush-dependent wildlife.