Geographic distribution data were collected based on county level occurrences (or converted from point occurrences to county level occurrences) within the five focal states (Minnesota, North Dakota, South Dakota, Nebraska & Iowa) and each U.S. state or Canadian province bordering those focal states (Wisconsin, Illinois, Missouri, Kansas, Wyoming, & Montana in the USA and Saskatchewan, Ontario & Manitoba in Canada).

This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st percentile, 10th percentile, and maximum of sensitivity-specificity ). This data bundle contains a single file of occurrence data with abundance information (Nonnative_plants_US.csv) and a subfolder for each species that contains the two raster files associated with the species. Each of the two rasters represent the following: species_code for current climate and species_code.2c for predictions under a +2C climate change scenario. The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) Nonnative_plants_US.csv is the occurrence and abundance data. 3) XX.tif where XX is the species code with current climatic conditions and species code with '2c' appended for habitat suitability predictions with +2C of climate change.

This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st percentile, 10th percentile, and maximum of sensitivity-specificity ). This data bundle contains a single file of occurrence data with abundance information (Nonnative_plants_US.csv) and a subfolder for each species that contains the two raster files associated with the species. Each of the two rasters represent the following: species_code for current climate and species_code.2c for predictions under a +2C climate change scenario. The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) Nonnative_plants_US.csv is the occurrence and abundance data. 3) XX.tif where XX is the species code with current climatic conditions and species code with '2c' appended for habitat suitability predictions with +2C of climate change.

Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to develop a new regional vision for invasive annual grass management across the West. Foundational to implementing this new vision is the creation of a common spatial map to guide strategic actions. The WGA cheatgrass working group sought to develop a 30-m base map of annual herbaceous cover to support a common spatial strategy for tackling invasive annual grasses across the western U.S. Here, we leverage three large-scale datasets to provide land managers with a product estimating the recent extent (2016-2018) of annuals across western rangelands. Input annual herbaceous datasets include Rangeland Analysis Platform (Jones et al. 2018), US Geological Survey (USGS) Harmonized Landsat and Sentinel (Pastick et al. 2020, Pastick et al. in prep) and USGS National Land Cover Database (NLCD) (Rigge et al. 2020). These three datasets are combined using a weighted mean approach to generate the final annual herbaceous mean cover product across the sagebrush biome (Jeffries and Finn 2019). References: Jeffries, M.I., and Finn, S.P. 2019. The Sagebrush Biome Range Extent, as Derived from Classified Landsat Imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P950H8HS. Jones, M.O., Allred, B.W., Naugle, D.E., Maestas, J.D., Donnelly, P., Metz, L.J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J.D., McIver, J.D. 2018. Innovation in rangeland monitoring: annual, 30m, plant functional type percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9, e02430. https://doi.org/10.1002/ecs2.2430. Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., Wu, Z. 2020. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sens. 12, 725. Pastick, N.J., Dahal, D., Wylie, B.K., Rigge, M.B., Jones, M.O, Allred, B.W., Boyte, S.P., Parajuli, S., and Wu, Z. In prep. Rapid monitoring of the occurrence and spread of exotic annual grasses in the western United States using remote sensing and machine learning. Global Change Biology. Reeves, M., and Mitchell, J. 2011. Extent of Coterminous US Rangelands: Quantifying Implications of Differing Agency Perspectives. Rangeland Ecology and Management 64: 585-597. Rigge, M., Shi, H., Homer, C., Danielson, P., Granneman, B. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere: e02762. Rigge, M., Homer, C., Cleeves, L., Meyer, D., Bunde, B., Shi, H., Xian, G., Bobo, M. 2020. Quantifying Western U.S. Rangelands as Fractional Components with Landsat. Remote Sensing. 12: 412.

Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to develop a new regional vision for invasive annual grass management across the West. Foundational to implementing this new vision is the creation of a common spatial map to guide strategic actions. The WGA cheatgrass working group sought to develop a 30-m base map of annual herbaceous cover to support a common spatial strategy for tackling invasive annual grasses across the western U.S. Here, we leverage three large-scale datasets to provide land managers with a product estimating the recent extent (2016-2018) of annuals across western rangelands. Input annual herbaceous datasets include Rangeland Analysis Platform (Jones et al. 2018), US Geological Survey (USGS) Harmonized Landsat and Sentinel (Pastick et al. 2020, Pastick et al. in prep) and USGS National Land Cover Database (NLCD) (Rigge et al. 2020). These three datasets are combined using a weighted mean approach to generate the final annual herbaceous mean cover product across the sagebrush biome (Jeffries and Finn 2019). References: Jeffries, M.I., and Finn, S.P. 2019. The Sagebrush Biome Range Extent, as Derived from Classified Landsat Imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P950H8HS. Jones, M.O., Allred, B.W., Naugle, D.E., Maestas, J.D., Donnelly, P., Metz, L.J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J.D., McIver, J.D. 2018. Innovation in rangeland monitoring: annual, 30m, plant functional type percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9, e02430. https://doi.org/10.1002/ecs2.2430. Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., Wu, Z. 2020. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sens. 12, 725. Pastick, N.J., Dahal, D., Wylie, B.K., Rigge, M.B., Jones, M.O, Allred, B.W., Boyte, S.P., Parajuli, S., and Wu, Z. In prep. Rapid monitoring of the occurrence and spread of exotic annual grasses in the western United States using remote sensing and machine learning. Global Change Biology. Reeves, M., and Mitchell, J. 2011. Extent of Coterminous US Rangelands: Quantifying Implications of Differing Agency Perspectives. Rangeland Ecology and Management 64: 585-597. Rigge, M., Shi, H., Homer, C., Danielson, P., Granneman, B. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere: e02762. Rigge, M., Homer, C., Cleeves, L., Meyer, D., Bunde, B., Shi, H., Xian, G., Bobo, M. 2020. Quantifying Western U.S. Rangelands as Fractional Components with Landsat. Remote Sensing. 12: 412.

The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution for 2016 to 2019. A total of 10,906 AIM plots from years 2016 - 2019 were used to train an ensemble of regression tree models (n=5). Besides cheatgrass (Bromus tectorum), other species such as Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus mardritensis L.,Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., Taeniatherum caput-medusae were included in the study. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas.

The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution for 2016 to 2019. A total of 10,906 AIM plots from years 2016 - 2019 were used to train an ensemble of regression tree models (n=5). Besides cheatgrass (Bromus tectorum), other species such as Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus mardritensis L.,Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., Taeniatherum caput-medusae were included in the study. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas.

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD). 2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD). 2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD). 2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.