Seven exclosures that were part of the original 28 Taylor Grazing Act exclosures across northern Nevada were surveyed for cover of biological soil crusts in May 2018. Surveys consisted of 15 quadrats both inside and outside of the exclosures. Quadrats were used to measure biocrust cover via point-intercept at 39 vertices within each quadrat. Cattle grazing outside of the exclosures was characterized by distance from the closest water source as well as permitted, suspended and active Animal Unit Months from the Rangeland Administration System. Abundance of cyanobacteria in the soils was assessed with the moistened soil method.

Fifteen fires from the Chronosequence dataset (see Knutson et al. 2014) were visited in 2012 and 2013 and surveyed for cover of lichens and mosses. Fires were selected to cover the range of average precipitation for each of three water years following fire, fire severity, time since fire, season of ignition, total acres burned and grazing intensity. Cattle grazing was characterized by distance from water sources for cattle, cow dung density counts and Animal Unit Months from the Rangeland Administration System of the Bureau of Land Management. Fire was characterized by whether or not a site burned, time since fire, the area burned, and an estimated amount of shrub cover consumed by the fire as compared to seemingly comparable unburned sites. In total, 99 plots were surveyed.

These data were compiled to assess the recovery of vegetation on reclaimed oil and gas sites. Objective(s) of our study were to assess patterns in reclamation outcomes relative to 1) soil attributes, climate, and time since 39 reclamation and 2) plant and soil reference benchmarks. These data represent observations of vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots. These data were collected on lands impacted by oil and gas development on the Colorado Plateau as well as Arizona and New Mexico Plateau of New Mexico, Colorado, and Utah. Data was collected from July- September of 2020 and May-September of 2021. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods. These data can be used to estimate plant community recovery on reclaimed oil and gas pads.

These data were compiled to assess the recovery of vegetation on reclaimed oil and gas sites. Objective(s) of our study were to assess patterns in reclamation outcomes relative to 1) soil attributes, climate, and time since 39 reclamation and 2) plant and soil reference benchmarks. These data represent observations of vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots. These data were collected on lands impacted by oil and gas development on the Colorado Plateau as well as Arizona and New Mexico Plateau of New Mexico, Colorado, and Utah. Data was collected from July- September of 2020 and May-September of 2021. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods. These data can be used to estimate plant community recovery on reclaimed oil and gas pads.

Sixty-eight monitoring plots within the Browns Park National Wildlife refuge in Northwest Colorado were surveyed in the Summer of 2007 and 2021 for vegetation-community changes after grazing cessation in 1986. Surveys consisted of line-point intercept measurements at 0.5m intervals along three 15-m transects arranged in a spoke around plot center at each plot location.

Sixty-eight monitoring plots within the Browns Park National Wildlife refuge in Northwest Colorado were surveyed in the Summer of 2007 and 2021 for vegetation-community changes after grazing cessation in 1986. Surveys consisted of line-point intercept measurements at 0.5m intervals along three 15-m transects arranged in a spoke around plot center at each plot location.

In 2004 U.S. Geological Survey biologists recorded vegetation and biological soil crust (BSC) cover by species as well as measured soil stability and compaction data along 85 transects at the Badger Wash study area, approximately 10 miles northwest of Mack in western Colorado. Soil samples were collected and processed for chemistry and texture. Using analysis of variance and nonmetric multidimensional scaling (NMDS) we assessed the cover of vegetation and BSC both grouped by plant physiognomy and dynamic soil properties (soil chemistry/nutrients and stability and compaction) as influenced by the effects of grazing history and soil group (which varied by slope, topographic wetness index and soil properties). Vegetation and BSC cover data were also compared to plant cover measurements collected in the same experimental watersheds by Lusby et al. in 1953, 1963, and 1972. Data used for all these analyses are contained within this data file. These data were compiled to accompany the publication “Insights from Long-term Ungrazed and Grazed Watersheds in a Salt Desert Colorado Plateau Ecosystem (Larger Work Citation).

In 2004 U.S. Geological Survey biologists recorded vegetation and biological soil crust (BSC) cover by species as well as measured soil stability and compaction data along 85 transects at the Badger Wash study area, approximately 10 miles northwest of Mack in western Colorado. Soil samples were collected and processed for chemistry and texture. Using analysis of variance and nonmetric multidimensional scaling (NMDS) we assessed the cover of vegetation and BSC both grouped by plant physiognomy and dynamic soil properties (soil chemistry/nutrients and stability and compaction) as influenced by the effects of grazing history and soil group (which varied by slope, topographic wetness index and soil properties). Vegetation and BSC cover data were also compared to plant cover measurements collected in the same experimental watersheds by Lusby et al. in 1953, 1963, and 1972. Data used for all these analyses are contained within this data file. These data were compiled to accompany the publication “Insights from Long-term Ungrazed and Grazed Watersheds in a Salt Desert Colorado Plateau Ecosystem (Larger Work Citation).

Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time. Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.

Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time. Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.