This README file from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the western Pacific Northwest, USA, was generated on 2022-07-06 by Paul B. Reed

GENERAL INFORMATION

1. Title of Dataset: Data from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the western Pacific Northwest, USA
2. Author Information
   A. Principal Investigator Contact Information
   Name: Paul B. Reed
   Institution: Institute for Applied Ecology
   Address: 4950 SW Hout St
   Corvallis, OR 97333
   Email: paulreed@appliedeco.org
3. Date of data collection: 2022-02-01
4. Geographic location of data collection: Willamette Valley, Oregon, USA
5. Information about funding sources that supported the collection of the data:
   USDA-NIFA postdoctoral fellowship award # 2021-67034-35136

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: N/A
2. Links to publications that cite or use the data: https://doi.org/10.1007/s10980-023-01754-3
3. Links to other publicly accessible locations of the data:
   1. https://rangelands.app/products/#data-download
   2. https://gdg.sc.egov.usda.gov/
   3. https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1
   4. https://www.mrlc.gov/data?f%5B0%5D=category%3ALand%20Cover&f%5B1%5D=category%3Aland%20cover&f%5B2%5D=region%3Aconus&f%5B3%5D=year%3A2019
4. Links/relationships to ancillary data sets: N/A
5. Was data derived from another source? yes
   A. If yes, list source(s):
   1. Rangeland Analysis Platform
   2. USDA gSSURGO database
   3. Shuttle Radar Topography Mission (SRTM GL1) Global 30m
   4. National Land Cover Database (NLCD)
6. Recommended citation for this dataset: Reed, P.B., and Hallett, L.M. (2022), Data from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the western Pacific Northwest, USA.

DATA & FILE OVERVIEW

1. File List:

   1. "df_RAP.csv": The Rangeland Analysis Platform (RAP) data yearly vegetation cover estimates
   2. "df_ratechange.csv": Rate of change data in RAP annual forb and grass (AFG) cover estimates
   3. "df_xvars.csv": Soils and topographic variables
   4. "sites16shape": Compressed (zipped) folder containing the shapefile information for 16 study sites.

2. Relationship between files, if important:
   The .csv data provided are needed to conduct all analyses in the associated manuscript. The rate of change data in "df_ratechange.csv" were calculated from linear regressions of AFG cover against year in the "df_RAP.csv" dataset. For relating cover estimates to landscape predictors, the "df_RAP.csv" and "df_xvars.csv" files were merged by pixel ID.

3. Additional related data collected that was not included in the current data package: N/A

4. Are there multiple versions of the dataset? no
   A. If yes, name of file(s) that was updated:
   i. Why was the file updated?
   ii. When was the file updated?

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data:

1. "df_RAP.csv": Annual forb and grass (AFG) and perennial forb and grass (PFG) cover data were collected directly from the Rangeland Analysis Platform public datasets via Google Earth Engine. Total (herbaceous) cover was calculated by adding AFG and PFG cover.
2. "df_ratechange.csv": Rate of change data in "df_ratechange.csv" were calculated from linear regressions of AFG cover against year from the "df_RAP.csv" dataset.
3. "df_xvars.csv": Soil and topographic variables were collected/calculated directly from public platforms.
4. "sites16shape": Shapefiles for the 16 study sites of interest were obtained from colleagues in the Willamette Valley Oak-Prairie Cooperative (https://willamettepartnership.org/wvopc/).

2. Methods for processing the data:

1. df_RAP.csv: Yearly (1986-2020) vegetation cover data of annual and perennial forbs and grasses for the Willamette Valley were downloaded as TIFF files (30-m resolution) from the Rangeland Analysis Platform's Google Earth Engine catalog. To eliminate forested and other non-grassland areas, we masked RAP data to grassland/herbaceous, pasture/hay, and shrub/scrub classifications in the National Land Cover Database 2019 Land Cover dataset using the raster package in R. Prior to this, we had previously resampled the land cover dataset from 10-m to 30-m resolution and reprojected to WGS 1984 using ArcMap 10.5. For each year in the 1986-2020 RAP dataset, we calculated new rasters for total herbaceous cover by summing the annual and perennial layers. We then filtered to cells with ≥20% average total herbaceous cover across the 35 years to avoid areas which may have skewed estimates due to low herbaceous cover in general (e.g., grassland borders near forests). Finally, we filtered to the 16 sites of interest using shapefiles of site polygon boundaries.
2. df_ratechange.csv: We used our processed RAP data in R to conduct pixel-by-pixel linear regressions of annual percent cover against year. We then generated a new raster layer for the rate of change using the coefficients from these models.
3. df_xvars.csv: For soil data (depth, sand, silt, and clay), we obtained 2019 gSSURGO data (10-m resolution) for the state of Oregon through the USDA Natural Resources Conservation Service web portal (https://gdg.sc.egov.usda.gov/). Within this geodatabase, each soil map unit corresponds to one or more soil polygons represented in raster form. Each map unit contains several soil component records, which each have a percent attribute indicating their relative proportional composition within the map unit. A component record may have several associated ‘chorizon’ records, which each indicate their top and bottom depths. For soil depth, we used the ‘brockdepmin’ variable in the ‘muaggatt’ (map unit-aggregated) table when available. For map unit records with no data for ‘brockdepmin’, we calculated a minimum depth by choosing the deepest non-rock horizon of each component (i.e., the max ‘hzdept_r’ from the ‘chorizon’ table), and then averaging these numbers weighted by the component percent. For percent sand, silt, and clay, we used the attribute for the topmost horizon in the dominant components of the mapunit (e.g., ‘sandtotal_r’ from the ‘chorizon’ table where ‘hzdept_r’ = 0). We then converted these layers to WGS 1984 and resampled from 10-m to 30-m resolution using ArcMap 10.5. For topographic variables (elevation, slope, aspect, and heatload), we downloaded a 30-m resolution digital elevation model file from OpenTopography (https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1). To calculate slope and aspect, we used the Slope and Aspect tools from the Spatial Analyst toolkit in ArcMap 10.5. Heat load is a unitless index of the amount of heat a ground surface receives through solar radiation based on its slope, aspect, and latitude. We calculated heat load in R using the McCune and Keon (2002) method with south-facing slopes (180°) at a maximum. Once we had calculated raster layers for all these soils and topographic variables, we masked them to the RAP raster data layers.

1. Instrument- or software-specific information needed to interpret the data:
   Datasets were processed using ArcMap 10.5 and R software version 4.0.2, and data were analysed using R version 4.0.2.
2. Standards and calibration information, if appropriate: N/A
3. Environmental/experimental conditions: N/A
4. Describe any quality-assurance procedures performed on the data: N/A
5. People involved with sample collection, processing, analysis and/or submission: N/A

DATA-SPECIFIC INFORMATION FOR: "df_RAP.csv" (The Rangeland Analysis Platform (RAP) data yearly vegetation cover estimates)

1. Number of variables: 9
2. Number of cases/rows: 2367751
3. Variable List (column name, followed by column description in parentheses):
   • pixel (pixel ID; concatenation of x, y (longitude, latitude) variables)
   • site (site name)
   • x (longitude; units: degrees)
   • y (latitude; units: degrees)
   • year (year of cover estimate)
   • years (years in half-decade intervals, where '86-90 = 1986-1990, etc.)
   • cover_AFG (percent cover estimates for annual forbs and grasses; units: %)
   • cover_PFG (percent cover estimates for perennial forbs and grasses; units: %)
   • cover_total (percent cover estimates for total herbaceous cover (the sum of AFG and PFG); units: %)
4. Missing data codes: N/A
5. Specialized formats or other abbreviations used: N/A

DATA-SPECIFIC INFORMATION FOR: "df_ratechange.csv" (Rate of change data for AFG cover against year)

1. Number of variables: 8
2. Number of cases/rows: 66392
3. Variable List:
   • pixel (pixel ID; concatenation of x, y (longitude, latitude) variables)
   • site (site name)
   • x (longitude; units: degrees)
   • y (latitude; units: degrees)
   • ratechange (coefficients from linear regressions of AFG cover against year; units: % cover per year)
   • Pval (P-values from linear regressions of AFG cover against year)
   • Rsquared (R-squared values from linear regressions of AFG cover against year)
   • Status (indicates whether a pixel exhibited a significant decline in AFG cover, a significant increase in AFG cover, or no significant change [ns])
4. Missing data codes: N/A
5. Specialized formats or other abbreviations used: N/A

DATA-SPECIFIC INFORMATION FOR: "df_xvars.csv" (Soil and topographic variables)

1. Number of variables: 12
2. Number of cases/rows: 66392
3. Variable List:
   • pixel (pixel ID; concatenation of x, y (longitude, latitude) variables)
   • site (site name)
   • x (longitude; units: degrees)
   • y (latitude; units: degrees)
   • depth (soil depth; units: cm)
   • sand (sand content; units: %)
   • silt (silt content; units: %)
   • clay (clay content; units: %)
   • elevation (land surface elevation above sea level; units: m)
   • slope (land surface slope; units: degrees)
   • aspect (land surface aspect; units: degrees)
   • heatload (land surface heat load, derived from slope, aspect, and latitude; unitless)
4. Missing data codes: N/A
5. Specialized formats or other abbreviations used: N/A