DOI: 10.5061/dryad.0zpc86717
title: Data from: Spatiotemporal patterns of rising annual plant abundance in grasslands of the Willamette Valley, Oregon (USA)

description: Context: Plant communities are undergoing compositional changes
 that affect ecosystem function. These changes are not always uniform
 across the landscape due to heterogenous topographic and edaphic
 conditions. To predict areas most at risk of change, it is necessary to
 identify the landscape drivers affecting plant abundance.
 Objectives: Annual plants are increasing across the Wwestern USA,
 largely driven by non-native annual invasions. Here, we quantified change
 in annual plant abundance and identified landscape factors contributing to
 that change over the past 35 years. Methods: We focused on
 Willamette Valley (Oregon) grasslands because they represent a new example
 in this phenomenon. To understand the spatiotemporal patterns of annual
 plant abundances between 1986 and 2020, we combined a remote-sensing
 vegetation cover dataset from the rangeland analysis platform with gridded
 soils data and topographic variables. We determined the rate of change in
 percent cover for each 30 × 30 m pixel and regressed cover against heat
 load, soil depth and sand content for &gt; 5975 hectares to determine
 areas most sensitive to rising annual cover. Results: We found a
 tendency toward increasing annual cover, with a median gain of + 15% cover
 among significantly increasing pixels. However, change was uneven across
 the landscape, with annual cover increasing markedly in areas with high
 heat load and shallower soils. Conclusions: We identified steep,
 south-facing slopes as being particularly sensitive to rising annual
 cover. Annual plant invasions may be lagging in this region compared to
 elsewhere in the Wwestern USA, but trends here suggest it may just be a
 matter of time. To perform this study, this dataset provides all necessary
 variables (as .csv tables), as well as a shapefile for the study area:
 "df_RAP.csv": Annual forb and grass (AFG) and perennial forb and
 grass (PFG) cover data collected directly from the Rangeland Analysis
 Platform public datasets via Google Earth Engine. Total (herbaceous) cover
 was calculated by adding AFG and PFG cover. "df_ratechange.csv":
 Rate of change data calculated from linear regressions of AFG cover
 against year from the "df_RAP.csv" dataset.
 "df_xvars.csv": Soil and topographic variables
 collected/calculated directly from public platforms.
 "sites16shape": Zipped folder containing shapefiles for the 16
 study sites of interest. For a detailed description of the datasets,
 please refer to the README file.
description: 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. 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. 
 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.
description: # Data from: Spatiotemporal patterns of rising annual plant abundance in
 grasslands of the Willamette Valley, Oregon (USA) 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](mailto: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](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](https://rangelands.app/products/#data-download) 2) [https://gdg.sc.egov.usda.gov/](https://gdg.sc.egov.usda.gov/) 3) [https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1](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](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/](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/](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](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