DOI: 10.5061/dryad.ksn02v7bx
title: Sagebrush plant community response to livestock grazing in the context of abiotic variability

description: Drylands, which cover more than 40% of the Earth's terrestrial
 surface, are confronted with rising agricultural demand and the influence
 of climate change. Understanding how livestock grazing pressure and local
 climate affect these environments is pivotal for informed land management.
 We studied big sagebrush plant communities in southwestern Wyoming over
 grazing gradients created by artificial livestock watering points. To
 explore the role of abiotic factors in shaping plant community response to
 grazing, we assessed the response of plant functional groups to grazing
 while accounting for soil texture variability across a precipitation
 gradient.
description: # Sagebrush plant community response to livestock grazing in the context
 of abiotic variability
 [https://doi.org/10.5061/dryad.ksn02v7bx](https://doi.org/10.5061/dryad.ksn02v7bx) These data were collected to assess the response of big sagebrush plant communities to variable grazing pressure in southwestern Wyoming. We found that increasing livestock grazing intensity was associated with increases in big sagebrush and bare ground as well as decreases in perennial bunchgrass cover. However, after scaling grazing intensity and abiotic variables, we found that precipitation and sand content had a stronger effect on all plant functional types. ## General Information Year and location of data collection: 6/2020 - 8/2020 on public land managed by the BLM in Southwest Wyoming Funding sources: Yale School of the Environment ## Data and File Overview File List: 1. Grazing_Gradient_Analysis_4Dryad.R 2. herb_long.csv 3. Scat_Binomial.csv 4. Scat_summary.csv 5. shrub_survey.csv 6. Well_info.csv 7. 2017 Soils.csv 8. PRISM_ppt_summer_2020.csv 9. PRISM_ppt_tmean_30yr_normals.csv Information for: [herb_long.csv] 1. Number of variables: 24 2. Number of rows: 103,851 3. Variable List: 1. \_: artifact of data cleaning 2. Date: date of sample collection 3. Transect: the transect of at each livestock well. Either 1 or 2 4. Site: Well number from Jordan et al. (2022) study 5. Direction: The compass direction from each livestock well where each transect began 6. Distance: The distance from each livestock well for each sampling plot (m) 7. Quadrat: The individual 20x50 quadrat identifiers 8. bare: bare ground cover to the nearest 5% 9. litter: vegetative litter cover to the nearest 5% 10. Scat_Cover: scat cover to the nearest 5% (NA values indicate where there was no scat observed within a quadrat) 11. Scat_Species: the animal associated with the scat in a given quadrat (NA values indicate where there was no scat observed within a quadrat) 12. Forb_Cover: the total visually estimated cover of all forbs in a given quadrat 13. Forb_Density: the number of forbs in a given quadrat 14. Forb_Species: the numebr of forb species that make of the forb component in a given quadrat 15. Species_richness: the total number of species occurring in a given quadrat 16. Species: In the long data form each row is associated with a singe species that occurred in a given quadrat. Starting in this column the remaining variables are associated with the cover/density of a species species. This column is a four letter code to identify each species 17. Cover: the visually estimated cover to the nearest 5% of a given species 18. Interaction: an interaction variable of the Site, Transect, Quadrat and Species 19. Density: the number of individuals of the noted species 20. Pft: the plant functional type of the noted species 21. Native: the status of the noted species - Native species (Y) and non-native (N) 22. Sci.Name: the scientific name of the observed plant species Information for: [Scat_Binomial.csv] 1. Number of variables: 4 2. Number of rows: 85 3. Variable List: 1. Distance: the distance from the livestock well (m) 2. Species: species of dung - Cattle or Prong 3. Occurance: the number of quadrats where the dung of a given species was present this was extracted from the scat_summary.csv file in excel 4. Miss: the number of quadrats without dung - calculated in excel by subtracting the number of occurrences from the total number of quadrats (130) Information for: [Scat_summary.csv] 1. Number of variables: 4 2. Number of rows: 85 3. Variable List: 1. Distance: the distance from the livestock well (m) 2. Species: species of dung - None, Cattle, Grouse, Prong, Elk, Hare or Horse 3. Count: the number of quadrats where the dung of a given species was present 4. Frequency: the percent of quadrats at each distance where dung of a given species was present (total = 130) Information for: [shrub_survey.csv] (NA values indicate a column is not applicable to a given plant functional type (e.g., all shrub measurements when the functional type is a grass)) 1. Number of variables: 15 2. Number of rows: 865 3. Variable List: 1. Site: Well number from Jordan et al. (2022) study 2. distance: the distance in meters from the livestock well 3. Transect: the transect of at each livestock well. Either 1 or 2 4. height: the average heigh of grasses in the 4m^2 plot. NA for other plant functional types 5. cover: visually estimated cover in the 4m^2 plot to the nearest 5% 6. pft: either grass or shrub 7. count_rabbit: the number of rabbitbrush individuals in the given 4m^2 plot 8. max.height: the tallest individual in a plot (cm) 9. min.height: the shortest individual in a plot (cm) 10. mean.height: the average height of shrubs in the 4m^2 plot (cm) 11. max.canopy.area: the largest shrub canopy area in cm^2 12. min.canopy.area: the smallest shrub canopy area in cm^2 13. mean.canopy.area: the average shrub canopy area in cm^2 14. log.canopy.area: the log of the average canopy area in each 4m^2 plot 15. count_sage: the number of sage in a given 4m^2 sampling unit Information for: [Well_info.csv] 1. Number of variables: 13 2. Number of rows: 13 3. Variable List: 1. ID: Numerical site identifier 2. Latitude: Latitude of livestock well center taken with Garmin GPS 3. Longitude: Longitude of livestock well center taken with Garmin GPS 4. Elevation: elevation in meters taken with Garmin GPS 5. Well: Well number from Jordan et al. (2022) study to match soils data 6. Allotment: Name of the associated grazing allotment with each well 7. Precip: mean annual precipitation in mm extracted from the PRISM data 8. Temp: mean annual temperature in °C extracted from the Prism data 9. Age: number of years since the installation of a given livestock well, NA represents unknowns 10. Sacrifice_Zone: classification of the type of vegetation around the livestock well using GIS image. These are not incorporated in the analysis 11. Notes: notes from the GIS images prior to sampling Information for: [2017_Soils.csv] 1. Number of variables: 17 2. Number of rows: 201 3. Variable List: 1. ID: Site identifier (categorical). 2. Site: Numerical site identifier corresponding to the well sites from which samples were collected. 3. Plot: The specific plot within each site where samples were collected (categorical). 4. Depth (cm): Soil sampling depth in centimeters. 5. Soil Weight (g): Weight of the soil sample in grams. 6. Hydrometer reading (g) after 40 sec: Hydrometer reading in grams taken 40 seconds after sediment suspension. 7. Hydrometer reading (g) after 2 hrs: Hydrometer reading in grams taken after 2 hours of sediment suspension. 8. Temperature (°C): Temperature of the solution during the hydrometer test in Celsius. 9. Corrected hydrometer reading (g): Hydrometer reading adjusted based on temperature correction. 10. K (g): Calculated mass of suspended particles in grams, based on the 40-second hydrometer reading. 11. L (g): Calculated mass of suspended particles in grams, based on the 2-hour hydrometer reading. 12. D (g): Total mass of the sample in grams. 13. Percent sand: Percentage of sand in the sample, calculated as (K ÷ D) × 100. 14. Percent clay: Percentage of clay in the sample, calculated as (L ÷ D) × 100. 15. Percent silt: Percentage of silt in the sample, calculated as 100 - (Percent sand + Percent clay). 16. Notes: Any notes relevant to the hydrometer calibration, such as temperature adjustments. Information for: [PRISM_ppt_summer_2020.csv] 1. Dataset description can be found at [https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/) Information for: [PRISM_ppt_tmean_30yr_normals.csv] 1. Dataset description can be found at [https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/) ## Sharing/Access information Data was collected in the field from June to August of 2020, with the exception of soils.csv and the climate data. The former was derived from soil texture sampling conducted by Jordan et al (2022) and can be accessed here: [https://zenodo.org/records/5705665](https://zenodo.org/records/5705665) The climate data was downloaded from the PRISM Climate Group and can be accessed here: [https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/) ## Code/Software Methods for processing the data: Raw data were processed in Excel and in R. Software-specific information needed to interpret the data: Analyses were conducted in R v.4.0.5 (R Core Team. 2021 R: a language and environment for statistical computing. Vienna, Austria. See [https://www.R-project.org/.)](https://www.R-project.org/.\))