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.

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.

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.

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.

Soil data includes carbon (total, organic, mineral-associated, particulate-organic) and texture. Bulk density data includes bulk density values used to calculate carbon stocks. Plant data includes microsite coverage percents and sagebrush demographics. Data is from uninvaded and cheatgrass invaded points near Grand Junction, Colorado and Rock Springs, Wyoming in October and November 2023.

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.

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.