Data represent predicted cheatgrass (Bromus tectorum) cover from a quantile regression model. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios.The model was developed using cheatgrass cover data collected by the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, paired with covariates representing climate, weather, fire history, and disturbance. Quantile regression estimates different coefficients for each predictor variable at each quantile of interest, allowing a given environmental variable to be more or less important at the high end of the response distribution. The predictions at each statistical quantile of interest can be interpreted as invasion scenarios, as they correspond to low, medium, and high cheatgrass cover for a given set of environmental conditions. This metadata file describes three raster files, which share a geographic extent and resolution and which represent predictions from different quantiles of the same quantile regression model.

Data represent predicted cheatgrass (Bromus tectorum) cover from a quantile regression model. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios.The model was developed using cheatgrass cover data collected by the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, paired with covariates representing climate, weather, fire history, and disturbance. Quantile regression estimates different coefficients for each predictor variable at each quantile of interest, allowing a given environmental variable to be more or less important at the high end of the response distribution. The predictions at each statistical quantile of interest can be interpreted as invasion scenarios, as they correspond to low, medium, and high cheatgrass cover for a given set of environmental conditions. This metadata file describes three raster files, which share a geographic extent and resolution and which represent predictions from different quantiles of the same quantile regression model.

This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The dataset was generated by integrating eMODIS NDVI satellite data with independent variables that influence cheatgrass germination and growth into a regression-tree model. Individual pixel values range from 0 to 100 with an overall mean value of 9.85 and a standard deviation of 12.78. A mask covers areas not classified as shrub/scrub or grass/herbaceous by the 2001 National Land Cover Database. The mask also covers areas higher than 2000 meters in elevation because cheatgrass is unlikely to exist at more than 2% cover above this threshold. Cheatgrass is an invasive grass that has invaded much of the Great Basin. It grows from seed, usually early in spring, and rapidly matures, produces seed, and dies. Its presence can deplete early-season moisture reserves and put native vegetation at a competitive disadvantage. In addition, it contributes fine fuels that facilitate fire ignition and fire spread. Rangeland fires are often stand replacing events in sagebrush communities, commuities which historically dominated much of the northern Great Basin. Increasing fire return intervals, increasing fire intensities, land management practices, and development have all contributed to the fragmentation of sagebrush ecosystems, which are critical for greater sagegrouse survival.

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.

This data release includes data and metadata describing 1) the rule set used to create vegetation type categories for the Great Basin; 2) estimation and validation data used to fit models of cheatgrass (Bromus tectorum) cover; and 3) mapped predictions of potential cheatgrass abundance.

This dataset consists of phenology observations of red brome (Bromus rubens) and cheatgrass (Bromus tectorum) collected at long-term monitoring sites and using daily timelapse camera imagery in the western United States. These observations include the location and day of year that flowering or senescence was observed per species. For timelapse camera images, 'flowering' observations denote the date that 75% of individual plants in the camera viewshed had open, mature flowers, and 'senescence' observations denote dates when 75% of individuals in the camera viewshed exhibited color change of leaves.

This dataset consists of phenology observations of red brome (Bromus rubens) and cheatgrass (Bromus tectorum) collected at long-term monitoring sites and using daily timelapse camera imagery in the western United States. These observations include the location and day of year that flowering or senescence was observed per species. For timelapse camera images, 'flowering' observations denote the date that 75% of individual plants in the camera viewshed had open, mature flowers, and 'senescence' observations denote dates when 75% of individuals in the camera viewshed exhibited color change of leaves.

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.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic and anthropogenic features.

Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensive and accurate geospatial data that is accessible and interpretable by those charged with managing landscapes across the sagebrush biome. The past decade has seen a rapid growth in these products, yet researchers and managers both report a persistent research-implementation gap between the availability of products and their application. To address this problem, we first conducted a systematic literature review to inventory spatial products released over the past decade that map cheatgrass, medusahead, and Ventenata within the western U.S. at regional and national scales. We then developed a series of informational data resources to guide land managers in understanding and selecting the best available spatial data for their management needs. This Excel-readable .xlsx file version database product represents a searchable, filterable, and sortable collection of summary information for each IAG spatial data product, published from January 2010 to February 2021, we summarized as part of our review. An additional, machine-readable .csv file version of the database is also available for users.