Data includes head smut infection level (caused by the fungal pathogen, Ustilago bullata) on cheatgrass (Bromus tectorum) and cheatgrass cover for plots measured annually during the first four years after the 2015 Soda wildfire. Additional landscape and weather covariates that are hypothesized to influence infection and host density are included.

Comma-separated values (.csv) files containing data related to plant biomass and seed production responses of invasive Bromus tectorum (cheatgrass) and Taeniatherum caput-medusae (medusahead) to varying sucrose treatments.

Comma-separated values (.csv) files containing data related to plant biomass and seed production responses of invasive Bromus tectorum (cheatgrass) and Taeniatherum caput-medusae (medusahead) to varying sucrose treatments.

The exotic grass-fire cycle is degrading semiarid rangelands, such as the vast areas of shrub-steppe in North America now invaded by fire-promoting cheatgrass. Chemical- or bio-herbicides are sprayed onto soils to inhibit the invaders, but information on chemical- or bio-herbicide effects on plant communities is limited. We asked how the plant community responded to the bioherbicide Pseudomonas fluorescens strain ACK55 (Battalion Pro®) in comparison to the separate and combined effects of the most conventional pre-emergent chemical herbicide, imazapic (Plateau®), in two cheatgrass-invaded sagebrush-steppe sites. Plant community responses are compared with soil microbial community responses in the Larger Work, and soil microbial data are available in GenBank. Plant community responses are compared with soil microbial community responses in the Larger Work, and soil microbial sequence data were deposited to the NCBI Short Read Archive (BioProject PRJNA1254875).

We conducted a case studies testing effectiveness of a soil borne bacteria, Pseudomonas fluorescens strain D7, in controlling Bromus tectorum (cheatgrass) and in affecting the density of sown desirable seedlings. Response variables (foliar cover, aboveground biomass, and density of B. tectorum; density of sown native plants) were measured for three years after treatment.

We conducted a case studies testing effectiveness of a soil borne bacteria, Pseudomonas fluorescens strain D7, in controlling Bromus tectorum (cheatgrass) and in affecting the density of sown desirable seedlings. Response variables (foliar cover, aboveground biomass, and density of B. tectorum; density of sown native plants) were measured for three years after treatment.

Cheatgrass (Bromus tectorum) cover data were derived from the Bureau of Land Management’s Assessment Inventory, and Monitoring data and paired with geospatial data representing climate, weather, and disturbances. We derived covariates to capture both the climatic averages (1981-2010) that underlie long-term suitability, hereafter referred to as climate, and conditions during the year of observation, hereafter referred to as weather, that can drive annual variation in invasive grass cover (e.g., fall germination conditions were matched to cheatgrass cover sampled the following spring). Custom variables reflected cheatgrass natural history. Covariates describing geological context (e.g., aspect, elevation, soils), plant communities based on geophysical conditions and natural disturbance regimes, fire history (binary burned or unburned), human disturbance and infrastructure, and management history were used to represent processes that may limit or facilitate cheatgrass invasion

Cheatgrass (Bromus tectorum) cover data were derived from the Bureau of Land Management’s Assessment Inventory, and Monitoring data and paired with geospatial data representing climate, weather, and disturbances. We derived covariates to capture both the climatic averages (1981-2010) that underlie long-term suitability, hereafter referred to as climate, and conditions during the year of observation, hereafter referred to as weather, that can drive annual variation in invasive grass cover (e.g., fall germination conditions were matched to cheatgrass cover sampled the following spring). Custom variables reflected cheatgrass natural history. Covariates describing geological context (e.g., aspect, elevation, soils), plant communities based on geophysical conditions and natural disturbance regimes, fire history (binary burned or unburned), human disturbance and infrastructure, and management history were used to represent processes that may limit or facilitate cheatgrass invasion

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. Here, we integrated in situ observations, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables (e.g. soils and topography) and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution from 2016 to 2018. Comparisons with Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) field data (2016 and 2017) indicate good agreement between observed and mapped values (n = 1700; r = 0.83; mean absolute error [MAE] = 11), as constructed from an ensemble of regression tree models, with slightly lower agreement between mapped values and independent field observations (n = 112; r = 0.65; MAE =14). Geographic coverage of the study area includes portions of Oregon, California, Idaho, and Nevada.

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. Here, we integrated in situ observations, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables (e.g. soils and topography) and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution from 2016 to 2018. Comparisons with Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) field data (2016 and 2017) indicate good agreement between observed and mapped values (n = 1700; r = 0.83; mean absolute error [MAE] = 11), as constructed from an ensemble of regression tree models, with slightly lower agreement between mapped values and independent field observations (n = 112; r = 0.65; MAE =14). Geographic coverage of the study area includes portions of Oregon, California, Idaho, and Nevada.