Yellow sweet clover (Melilotus officinalis; clover hereafter) is a biennial legume native to Eurasia that is now present in all 50 states. Clover can grow 2 m tall and achieve high densities across large areas in the Northern Great Plains when conditions are conducive, such as in 2019. Clover is highly efficient at fixing nitrogen in soils which reduces the abundance of native grasses, while simultaneously facilitating invasion of non-native grasses, which may alter fire regimes. In contrast, clover provides considerable forage for ungulates, attracts a wide variety of insects that, along with clover seeds, are important to waterfowl, gamebirds, and songbirds, and supports numerous pollinators. Little is known about the extent of clover in central Montana and northwest South Dakota and this study represents the first known attempt to map clover in these regions. In 2019, the Bureau of Land Management conducted Assessment, Inventory, and Monitoring (AIM) surveys at 10 sites in central Montana (defined as the approximate geographic extent of Musselshell County) and 24 sites in northwest South Dakota (defined as the approximate geographic extent of Butte and Harding Counties). Concurrent Unmanned Aerial Vehicle (UAV) flights were conducted at 22 sites: 6 in Montana and 16 in South Dakota. We created orthoimages from the 22 UAV surveys as well as clover maps for the 19 sites with clover. Percent clover cover from the UAV-derived clover maps closely matched percent cover from AIM data along surveyed transects. The UAV clover map with the greatest percent cover in each region was then used to identify pixels comprised of clover in National Agricultural Imagery Program (NAIP) imagery: 5,000 pixels in Montana and 2,500 pixels in South Dakota. We used separate MaxEnt models to classify clover across 1 NAIP tile in central Montana and 2 NAIP tiles in northwest South Dakota. Next, for each region, we calculated the percent of classified NAIP pixels within each Sentinel-2 pixel and selected 1,000 pixels from each of 2 fractional cover (FC) bins representing 20% increments from 10-50% cover and 1,000 pixels from each of 5 fractional cover (FC) bins representing 10% increments from 55-95% cover. We also selected 1,000 pixels in each region from dense clover strands visible in Sentinel-2 imagery representing pure (i.e., 100% cover) clover areas. Separate MaxEnt models were run in each region for the pure clover areas and each FC class. We fixed the pure clover area for each region and added fractional coverage components outside this consistent pure clover area by thresholding each of the 5 FC models using 5 common MaxEnt thresholds and merging results using 3 classification approaches for pixels classified by multiple FC models: minimum, mean, and maximum cover predicted. Accuracy of the 15 FC maps were validated by comparison to AIM survey data (30 m buffer from AIM plot center) and UAV-derived clover maps (300 x 300 m grid of 900 Sentinel-2 pixels centered on AIM plot centers). Datasets in this release include the following items in associated zipped folders: 22 UAV orthoimages of which 19 have embedded clover maps aligned to Sentinel-2 imagery (MT1-6_Sentinel_proj and SD1-16_Sentinel_proj). 2 Classified NAIP images aligned to Sentinel-2 imagery. (MT/SD_NAIP_Sweet_Clover_Sentinel_proj) 15 Fractional cover maps for both central Montana and northwest South Dakota (MT/SD_Sweet_Clover_Fractional_Cover_Maps). 2 Point shapefiles of AIM plot centers and 2 polygon shapefiles for Sentinel-2 to UAV comparison extents (MT/SD_Sweet_Clover_Shapefiles). Seven .csv files (Sweet_Clover_csv) that contain 1) Green Leaf Index reclassification values for UAV clover classifications (S1_GLI_Reclass.csv); 2) Clover cover from AIM surveys and UAV-derived clover maps along AIM transects and sites (S2_UAV_AIM_Comparisons.csv); 3) Center locations for all pixels used to classify clover with MaxEnt models (S3_Training.csv); 4) MaxEnt variable permutation importance

Yellow sweet clover (Melilotus officinalis; clover hereafter) is a biennial legume native to Eurasia that is now present in all 50 states. Clover can grow 2 m tall and achieve high densities across large areas in the Northern Great Plains when conditions are conducive, such as in 2019. Clover is highly efficient at fixing nitrogen in soils which reduces the abundance of native grasses, while simultaneously facilitating invasion of non-native grasses, which may alter fire regimes. In contrast, clover provides considerable forage for ungulates, attracts a wide variety of insects that, along with clover seeds, are important to waterfowl, gamebirds, and songbirds, and supports numerous pollinators. Little is known about the extent of clover in central Montana and northwest South Dakota and this study represents the first known attempt to map clover in these regions. In 2019, the Bureau of Land Management conducted Assessment, Inventory, and Monitoring (AIM) surveys at 10 sites in central Montana (defined as the approximate geographic extent of Musselshell County) and 24 sites in northwest South Dakota (defined as the approximate geographic extent of Butte and Harding Counties). Concurrent Unmanned Aerial Vehicle (UAV) flights were conducted at 22 sites: 6 in Montana and 16 in South Dakota. We created orthoimages from the 22 UAV surveys as well as clover maps for the 19 sites with clover. Percent clover cover from the UAV-derived clover maps closely matched percent cover from AIM data along surveyed transects. The UAV clover map with the greatest percent cover in each region was then used to identify pixels comprised of clover in National Agricultural Imagery Program (NAIP) imagery: 5,000 pixels in Montana and 2,500 pixels in South Dakota. We used separate MaxEnt models to classify clover across 1 NAIP tile in central Montana and 2 NAIP tiles in northwest South Dakota. Next, for each region, we calculated the percent of classified NAIP pixels within each Sentinel-2 pixel and selected 1,000 pixels from each of 2 fractional cover (FC) bins representing 20% increments from 10-50% cover and 1,000 pixels from each of 5 fractional cover (FC) bins representing 10% increments from 55-95% cover. We also selected 1,000 pixels in each region from dense clover strands visible in Sentinel-2 imagery representing pure (i.e., 100% cover) clover areas. Separate MaxEnt models were run in each region for the pure clover areas and each FC class. We fixed the pure clover area for each region and added fractional coverage components outside this consistent pure clover area by thresholding each of the 5 FC models using 5 common MaxEnt thresholds and merging results using 3 classification approaches for pixels classified by multiple FC models: minimum, mean, and maximum cover predicted. Accuracy of the 15 FC maps were validated by comparison to AIM survey data (30 m buffer from AIM plot center) and UAV-derived clover maps (300 x 300 m grid of 900 Sentinel-2 pixels centered on AIM plot centers). Datasets in this release include the following items in associated zipped folders: 22 UAV orthoimages of which 19 have embedded clover maps aligned to Sentinel-2 imagery (MT1-6_Sentinel_proj and SD1-16_Sentinel_proj). 2 Classified NAIP images aligned to Sentinel-2 imagery. (MT/SD_NAIP_Sweet_Clover_Sentinel_proj) 15 Fractional cover maps for both central Montana and northwest South Dakota (MT/SD_Sweet_Clover_Fractional_Cover_Maps). 2 Point shapefiles of AIM plot centers and 2 polygon shapefiles for Sentinel-2 to UAV comparison extents (MT/SD_Sweet_Clover_Shapefiles). Seven .csv files (Sweet_Clover_csv) that contain 1) Green Leaf Index reclassification values for UAV clover classifications (S1_GLI_Reclass.csv); 2) Clover cover from AIM surveys and UAV-derived clover maps along AIM transects and sites (S2_UAV_AIM_Comparisons.csv); 3) Center locations for all pixels used to classify clover with MaxEnt models (S3_Training.csv); 4) MaxEnt variable permutation importance

Yellow sweetclover (Melilotus officinalis; YSC), an invasive biennial legume, bloomed throughout the Northern Great Plains (NGP) following greater-than-average precipitation during 2018-2019. YSC can increase nitrogen (N) levels and potentially cause broad changes in the composition of native plant species communities. There is little knowledge of the drivers behind its spatiotemporal variability, including conditions causing significant widespread blooms across western South Dakota (SD). We aimed to develop a generalized prediction model to map the relative abundance of YSC in suitable habitats across rangelands of western SD for the recent sweet clover year 2019. The following research questions were asked: 1. What is the spatial extent of YSC across western SD? 2. Which model can accurately predict the habitat and percent cover of YSC? and 3. What environmental drivers affect its presence across western SD? We trained machine learning models with in-situ data (2016-2021), Sentinel 2A-derived surface reflectance and indices (10m and 20m) and site-specific variables (e.g., climate, topography, land cover, and edaphic factors) to optimize model estimates. Our study identified the most suitable drivers to explain the variability in YSC presence and its percent cover through data dimensionality reduction techniques. Our research demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of invasive rangeland plant species. We found major YSC hotspots in Butte and Meade counties of SD. The floodplains of major rivers in SD, such as the Cheyenne, White, and Bad Rivers, also showed a higher occurrence probability and percent cover range. These prediction maps could aid land managers in devising strategies for regions that are prone to YSC overruns. This management workflow can serve as a prototype for mapping other invasive plant species worldwide.

,This ongoing data package, which began in 1992, contains monthly plant phenological observations by species at the 15 Net Primary Production (NPP) study sites at the Jornada Experimental Range (JER) or Chihuahuan Desert Rangeland Research Center (CDRRC) in Dona Ana County, New Mexico, USA. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. A transect at each site is monitored monthly to assess the following phenological stages for each species: dormancy, non-reproductive status, budding, flowering, and fruiting. Sampling methods in the first years of the study monitored all plant species present but have been changed to focus on specific species at each site.,,

This data publication contains 2012 flowering data for the 52 populations of big sagebrush (Artemisia tridentata) grown in 3 garden locations: Majors Flat and Ephraim in Idaho, as well as Orchard, Idaho. Data include geographical details, subspecies, julian date of flowering, and population climate variable information.

This data set contains atmospheric nitrogen deposition rates and herbaceous plant species richness values across sample sites in the USA, originally used for the publication by Simkin et al. (2016. Conditional vulnerability of plant diversity to atmospheric nitrogen deposition across the United States. Proceedings of the National Academy of Sciences of the United States of America 113: 4086-4091. A portion of this data set was used for example quantile regression analyses in the book chapter Cade (2017. Quantile regression applications in ecology and the environmental sciences. R. Koenker, et al., eds. Handbooks of Modern Statistical Methods: Handbook of Quantile Regression. Chapman & Hall/CRC. The data set contains the herbaceous species counts used as the response variable and the various environmental predictors, including nitrogen deposition rate, used in the statistical models.

This data set contains atmospheric nitrogen deposition rates and herbaceous plant species richness values across sample sites in the USA, originally used for the publication by Simkin et al. (2016. Conditional vulnerability of plant diversity to atmospheric nitrogen deposition across the United States. Proceedings of the National Academy of Sciences of the United States of America 113: 4086-4091. A portion of this data set was used for example quantile regression analyses in the book chapter Cade (2017. Quantile regression applications in ecology and the environmental sciences. R. Koenker, et al., eds. Handbooks of Modern Statistical Methods: Handbook of Quantile Regression. Chapman & Hall/CRC. The data set contains the herbaceous species counts used as the response variable and the various environmental predictors, including nitrogen deposition rate, used in the statistical models.