This dataset includes three datasets collected in 2021 and 2022 to assess the potential effects of bison (Bison bison) herbivory and climate on grassland functional properties throughout semi-arid meadows in Grand Canyon National Park and Kaibab National Forest of northern Arizona. Bison herbivory offtake (utilization) and aboveground herbaceous production are demonstrated in the production offtake dataset titled 'BisonHerbivory_ANPP_Ot.csv'. This dataset includes the experimental treatment variables (Stratum and Treatment) and estimates for seasonal offtake, total annual offtake, total aboveground net primary production, and grazing intensity. Data on herbaceous nitrogen yield provides calculations of percent nitrogen and nitrogen yield for graminoid and forb samples collected from biomass clippings in areas of bison grazing (file titled 'BisonHerbivory_N_Yield.csv'). Meteorological data are presented for seasonal and total annual climate variables including precipitation (measured in mm) and temperature (measured in Growing Degree Days). Climate data is within the file titled 'BisonHerbivory_Climate.csv".

This data set includes the relative production scenarios for bufflaograss [0.72(Temp) - 0.12(Precip) - 0.04(Sand) + 3.08]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier scenario (ACCESS 1-0, RCP8.5, Collier and Uhe, 2012). The results were binned into 7 classes based on breaks in the data and comparison with field observations.Climate change has been identified as a high-priority threat to grasslands by the Great Plains Landscape Conservation Cooperative (GPLCC) and as a priority change agent for grasslands in the Southern Great Plains Rapid Ecoregional Assessment by the Bureau of Land Management. The area of interest includes four level III ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. To address this priority information need for multiple stakeholders, we evaluated the potential vulnerability of four grassland communities (shortgrass, mixed-grass, and tallgrass prairies, and semiarid grasslands) using four climate change scenarios (representing hotter-drier, hotter-wetter, warmer-drier, and warmer-wetter conditions, relative to contemporary conditions). We used relative above-ground productivity models (Epstein et al., 1998) to evaluate the potential for change in productivity for each grassland community using mean annual precipitation and temperature for the contemporary climate (1981-2010) and the four climate scenarios (2016-2045), and the percent of sand, silt, and clay from the dominant soils component from the Natural Resource Conservation Service (Earth System Science Center, 2008). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis) and buffalo grass (Bouteloua dactyloides); mixedgrass prairie: sideoats grama (Bouteloua curtipendula) and little bluestem (Schizachyrium scoparium); tallgrass prairie: big bluestem (Andropogon gerardii) and Indiangrass (Sorghastrum nutans); and semiarid grasslands: black grama (Bouteloua eriopoda) and tobosagrass (Pleuraphis mutica). For each indicator species, we evaluated the potential change in relative productivity for each climate scenario compared to the contemporary climate. We used standard deviations to classify the differences between predicted productivity relative to the contemporary predicted productivity to evaluate whether the distributions of the indicator species were expected to remain stable, decrease, or expand for each scenario.Spatial data representing the estimated relative productivity of grassland species in the Southern Great Plains are provided as a 1-square kilometer gridded surface (raster dataset). This information will help to address priority management questions for grassland conservation in the GPLCC and Southern Great Plains regions and can be used to inform other regional-level land management decisions.Collier, Mark, and Uhe, Peter, 2012, CMIP5 datasets from the ACCESS1.0 and ACCESS1.3 coupled climate models: Centre for Australian Weather and Climate Research Technical Report No. 059, 25 p.Earth System Science Center, 2008, Soil fraction data: College of Earth and Mineral Sciences at The Pennsylvania State University, accessed January 7, 2016, at http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&fract&datasets&alb.Epstein, H.E., Lauenroth, W.K., Burke, I.C., and Coffin, D.P., 1998, Regional productivities of plant species in the Great Plains of the United States: Plant

,Data were collected on the Central Plains Experimental Range (CPER) from 2014-2018, near Nunn, Colorado as part of the common experiments in grazinglands for the Long-Term Agroecosystem Research network. LTAR scientists seek to create new knowledge regarding sustainable management of grazinglands. This dataset on cattle foraging behavior and distribution provides new information towards understanding how management practices influence grazing livestock movements in space and time. The common experiment at CPER is called Collaborative Adaptive Rangeland Management (CARM) and is a ten-year ranch-scale (2,600-ha) social-ecological experiment designed to examine how adaptive rotations of a single large cattle herd among paddocks within a heterogeneous landscape during the growing season (collaborative, adaptive rangeland management; CARM) contrasts with continuous, season-long grazing of paddocks by small non-rotational herds (traditional rangeland management; TRM). Differences in movement patterns between the two treatments were examined with data collected from global positioning system tracking collars (Lotek 3300LR GPS) combined with activity sensors. These data were used to determine daily metrics of foraging behavior by steers in both treatments at five-minute intervals and include (1) location, (2) distance moved within 5 minutes, and (3) and grazing activity. These data are from the first half of the CARM experiment to support the publication, "Adaptive, multi-paddock, rotational grazing management alters foraging behavior and spatial grazing distribution of free-ranging cattle.",Resources in this dataset:,,

These data were compiled for an assessment of rangeland ecosystem conditions of the Grand Canyon - Parashant National Monument. The approximately one-million-acre Grand Canyon-Parashant National Monument (PARA) is located in the northwest corner of Arizona and co-managed by the Bureau of Land Management (BLM) and National Park Service (NPS). This report is focused on the ca. 200,000 acres of NPS administered lands—one of the largest NPS units where livestock grazing is a permitted land-use activity. Many ecosystems in PARA are characterized by a low degree of resilience to improper grazing due to low and variable precipitation. PARA is marked by an extremely high degree of environmental heterogeneity, including a large elevation gradient, widely differing precipitation patterns, a diversity of geologic substrates, and unique combinations of plant species. Locations for rangeland assessments were selected using a stratified, spatially balanced random sampling method based on allotment, soil type, slope, distance to cattle water locations, and accessibility. A total of 155 plots were established and sampled between March and November of 2012 and 2013. Data collection at each plot included soil geomorphic setting descriptions, plant and soil cover, and soil aggregate stability.

These data were compiled for an assessment of rangeland ecosystem conditions of the Grand Canyon - Parashant National Monument. The approximately one-million-acre Grand Canyon-Parashant National Monument (PARA) is located in the northwest corner of Arizona and co-managed by the Bureau of Land Management (BLM) and National Park Service (NPS). This report is focused on the ca. 200,000 acres of NPS administered lands—one of the largest NPS units where livestock grazing is a permitted land-use activity. Many ecosystems in PARA are characterized by a low degree of resilience to improper grazing due to low and variable precipitation. PARA is marked by an extremely high degree of environmental heterogeneity, including a large elevation gradient, widely differing precipitation patterns, a diversity of geologic substrates, and unique combinations of plant species. Locations for rangeland assessments were selected using a stratified, spatially balanced random sampling method based on allotment, soil type, slope, distance to cattle water locations, and accessibility. A total of 155 plots were established and sampled between March and November of 2012 and 2013. Data collection at each plot included soil geomorphic setting descriptions, plant and soil cover, and soil aggregate stability.

Data were collected in 2017 by researchers at the USGS, USDA-ARS, and University of Wyoming on the food webs of plants, prairie dogs, arthropods, and birds in the Thunder Basin National Grassland. Data were collected from 87 sites in order to parameterize a structural equation model linking prairie dog impacts to changes in vegetation, arthropods, and birds. Abiotic information such as topographic wetness index, terrain roughness, and soil characteristics were estimated at the same set of plots in order to account for abiotic variation across the landscape.

Data were collected in 2017 by researchers at the USGS, USDA-ARS, and University of Wyoming on the food webs of plants, prairie dogs, arthropods, and birds in the Thunder Basin National Grassland. Data were collected from 87 sites in order to parameterize a structural equation model linking prairie dog impacts to changes in vegetation, arthropods, and birds. Abiotic information such as topographic wetness index, terrain roughness, and soil characteristics were estimated at the same set of plots in order to account for abiotic variation across the landscape.

The file 'ssm_data_3.25.csv' contains data necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level (relative grazing index), timing, and NDVI (Normalized Difference Vegetation Index) in Wyoming, USA. In this case, all covariates were measured within 3.25 km of lek sites.

The file 'ssm_data_3.25.csv' contains data necessary for analyzing state-space models for male greater sage-grouse (Centrocercus urophasianus) populations in response to grazing level (relative grazing index), timing, and NDVI (Normalized Difference Vegetation Index) in Wyoming, USA. In this case, all covariates were measured within 3.25 km of lek sites.