DOI: 10.5061/dryad.rv15dv4dq
title: Data from: Increases in understory plant cover and richness persist following restoration treatments in Pinus ponderosa forests

description: A combination of forest thinning followed by prescribed burning is widely
 applied in the western US to increase ecosystem resistance and resilience
 to disturbances. Understory plant community responses may be driven both
 by management treatments and climatic factors. Thus, responses to
 treatments during a 20-year megadrought have implications for the role of
 management in fostering adaptive capacity to climate change. We used a
 network of five sites (600 plots) spanning an environmental gradient in
 ponderosa pine (Pinus ponderosa) forests of the American Southwest, an
 ecosystem that is broadly distributed and actively managed throughout the
 western US.  We used repeated long-term monitoring data to
 quantify plant community responses to treatment 1-5, 6-10, and &gt;10
 years post-implementation. Specifically, we focused on the effects of
 treatment and abiotic conditions on native and nonnative plant cover and
 species richness, and on the proportion of native species with northern
 (cool-mesic) biogeographic affinities. Overall, thinning and prescribed
 burning nearly doubled native cover and increased native species richness
 by about 50% relative to untreated controls. These effects persisted for
 over a decade after treatment, even under the influence of significant and
 persistent drought. Cover and richness were also greater on intermediate
 to wet sites. Finally, native species with northern biogeographic
 affinities were reduced for up to five years after treatment relative to
 those with southern (warm-xeric) affinities, and in dry years, indicating
 that both management and interannual climate variability may foster shifts
 in plant communities that are more resilient to a warming climate.
 Synthesis and applications: In ponderosa pine forests of the American
 Southwest, tree thinning followed by prescribed burning will generally
 promote restoration goals of increasing resilience to climate change by
 enhancing the diversity and abundance of native understory plant species,
 even during a persistent 20-year megadrought.
description: This archive includes long-term plant community data and forest
 monitoring data from 5 experimental sites (~ 600 plots) in Arizona, USA.
 These sites were established in the late 1990s to early 2000s to describe
 the effects of thinning and burning treatments on ecological processes in
 southwestern US ponderosa pine forests. Understory plant community data
 describe the presence and abundance of herbaceous and short-statured woody
 plants, with at least one survey before treatment, and multiple surveys
 after treatment at each site, as well as data from paired untreated
 control sites. Plants were identified in the field by trained botanists,
 or pressed and identified in the laboratory. Most individuals were
 identified to the species level, but occasioanlly could only be identified
 to genus. For descriptions of different fiels in the data arrchive, as
 well as methods of collection, see the "README.md"
 file
description: # Increases in understory plant cover and richness persist following
 restoration treatments in *Pinus ponderosa* forests ## Authors Judith D.
 Springer, Michael T. Stoddard, Kyle C. Rodman, David W. Huffman, Paula J.
 Fornwalt, Rory J. Pedersen, Daniel C. Laughlin, Christopher M. McGlone,
 Mark L. Daniels, Peter Z. Fulé, Margaret M. Moore, Becky K. Kerns, Jens T.
 Stevens, Julie E. Korb, Sara Souther ## Overview This repository includes
 field data, R code, and model outputs from "Increases in understory
 plant cover and richness persist following restoration treatments in
 *Pinus ponderosa* forests" by Springer et al. (In Review), J Appl
 Ecol. Data were archived in .7z format using 7Zip v 23.01 on Windows 11 v
 22H2 ## File Structure This repository is split into the following
 subfolders which contain different portions of the project. Descriptions
 of each file contained in these folders are given within each subsection.
 ### AnalysisOutputs This subfolder contains generalized linear mixed model
 (GLMM) objects created in the "Step3\_RichnessCoverGLMMs.R"
 script. Models were fit using the spaMM and glmmTMB packages in R and can
 be read into R using the "readRDS" function. spaMM and glmmTMB
 packages should be loaded for easier loading and viewing of these model
 objects. * coverGLMM.rds: the fitted model object for the binomial GLMM of
 understory plant cover * nAffGLMM.rds: the fitted model object for the
 binomial GLMM of proportion northern affinity species * richnessGLMM.rds:
 the fitted model object for the Poisson GLMM of understory species
 richness ### Code This subfolder contains R scripts used to process,
 analyze, or visualize data for the project. Raw data were filtered and/or
 queried using SQL queries in Microsoft Access, which are not provided
 here. * Step1a\_HLI\_Calcs.R: Calculates a 30-m raster version of heat
 load index (HLI) using Eq. 3 of McCune and Keon (2002).
 "Data/SpatialData/SoilsAndTerrain/hliUTM12N.tif", described
 below, is the output file. HLI was used in water balance model
 calculations * Step1b\_TPI\_Calcs.R: Calculates a 30-m raster version of
 Topographic Position Index (TPI) following Weiss (2001).
 "Data/SpatialData/SoilsAndTerrain/tpi15x15.tif", described
 below, is the output file * Step1c\_GIDS-Downscaling.R: Downscales gridded
 climate data from PRISM to a 30-m resolution using gradient inverse and
 distance-squared weighted interpolation of Nalder and Wein (1998). Digital
 elevation models at 800, 270, 90, and 30m were used s ancilary data *
 Step1d\_CWD-Calcs.R: Uses downscaled climate data from
 "Step1c..." and heat load index from "Step1a...",
 along with soil data, to run monthly water balance models following Lutz
 et al. (2010). Output datasets are annual climatic water deficit (CWD) and
 actual evapotranspiration (AET) for each site in
 "Data/SpatialData/WaterBalance" * Step2\_ExtractSpatial.R:
 Organizes summarized field data into an analysis-ready format. TPI and CWD
 are extracted at the location of each field plot and added as additional
 columns in "Data/plantDataWithSpatial.csv", which is produced in
 this script. * Step3\_RichnessCoverGLMMs.R: This script pulls in the
 "Data/plantDataWithSpatial.csv" file and fits GLMMs for
 understory cover, species richness, and proportion of northern affinity
 species. Also makes plots to visualize the results of these models *
 Step4\_ClimateSeries.R: This script pulls in monthly climate data at each
 site, and formats it to plot annual precipitation values at each site over
 time, as part of the study area figure in the paper ### Data This
 subfolder contains raw and summarized datasets used in this study. Data
 include field surveys of plant understory communities at each of five
 experimental sites in Arizona, as well as topographic data, soils data,
 downscaled climate data, and water balance model outputs at each site. The
 summarized dataset "plantDataWithSpatial.csv" was used to
 develop all GLMMs, and is located within the main "Data" folder,
 rather than subfolders. Each file is described below *
 plantDataWithSpatial.csv: This file contains all summarized plot-level
 data and model covariates included in GLMMs of understory cover, species
 richness, and proportional dominance of northern affinity species. Columns
 have a range of data types. It was created using the
 "Step2\_ExtractSpatial.R" script described above. Fields differ
 in data type and the range of values. Rows represent unique combinations
 of plots and survey years. * Site: Two-letter code for each study site. CF
 = Centennial Forest, FV = Fort Valley, GV = Grandview, MC = Mineral Creek,
 MT = Mt. Trumbull * Year: Calendar year for a given survey * Block:
 Experimental block ID. Replicated block within a site. Integer from 1 to 4
 * Trt: Treatment unit for a given plot. Values of 1 or 10 are
 "control units" which never received a treatment. Values of 2
 are treated units which received thinning and burning treatments at some
 point during the study. Thus, initial surveys at treated units were still
 "untreated" * Plot: Unique plot number. When combined with Site,
 Block, and Trt, this identifies a unique plot ID in the dataset. * Long:
 Longitude (in decimal degrees) of the center of a plot/understory
 transect. Coordinates are in geographic coordinate system EPSG:4326 (WGS
 84; Lat/Long) * Lat: Latitude (in decimal degrees) of the center of a
 plot/understory transect. Coordinates are in geographic coordinate system
 EPSG:4326 (WGS 84; Lat/Long) * UTMx: Easting (in meters) of the center of
 a plot/understory transect. Coordinates are in projected coordinate system
 EPSG:26912 (NAD83 UTM zone 12N). * UTMy: Northing (in meters) of the
 center of a plot/understory transect. Coordinates are in projected
 coordinate system EPSG:26912 (NAD83 UTM zone 12N). *
 Year\_from\_Initial\_treatment: For plots that received treatment, the
 number of years since initial thinning was implemented at a site.
 Measurements that were prior to initial treatment, or in untreated units
 have "NA" values. Integer values from 4 to 20 * freq\_Tot: Total
 understory plant cover in a given plot and survey year, as the number of
 plant "hits" across the 166 line transect points/166 \* 100.
 Because a small number of points had multiple plant "hits",
 where separate plants intersected the transect at a given point, the total
 value of this variable could occasionally exceed 100 for a plot (n = 4
 plot measurements). In these cases, these values were truncated to [0,100]
 for subsequent analyses to allow use of binomial GLMMs, though untruncated
 values are provided in this archive. Used as a response variable in the
 GLMM of total plant cover. Floating point values from 0.0 to 131.9277 *
 freq\_I: Understory plant cover of introduced species in a given plot and
 survey year, as a percentage of the 166 line transect points that had
 introduced vegetation. See "Data/Raw/speciesList.csv" for
 descriptions of nativity status for individual species. Floating point
 values from 0.0 to 65.66265 * freq\_N: Understory plant cover of native
 species in a given plot and survey year, as a percentage of the 166 line
 transect points that had native vegetation. See
 "Data/Raw/speciesList.csv" for descriptions of nativity status
 for individual species. Floating point values from 0.0 to 66.26506 *
 rich\_Tot: Total understory plant richness in a given plot and survey
 year, as the number of unique plant species on belt transects. Used as a
 response variable in the GLMM of total plant richness. Integer values from
 1 to 74 * rich\_I: Richness of introduced species in a given plot and
 survey year, as the number of unique introduced plant species on belt
 transects. See "Data/Raw/speciesList.csv" for descriptions of
 nativity status for individual species. Integer values from 1 to 10 *
 rich\_N: Richness of native species in a given plot and survey year, as
 the number of unique native plant species on belt transects. See
 "Data/Raw/speciesList.csv" for descriptions of nativity status
 for individual species. Integer values from 1 to 68 * propNorthern:
 Proportion of points with vegetation, where a norther affinity species
 represented the top plant strata. Used as a response variable in the GLMM
 of northern affinity proportional dominance. Measurements had no
 vegetation cover or had no species that could be classified with a
 biogeographic affinity have "NA" values. Floating point values
 from 0.0 to 1.0 * vegCount: The number of points with vegetation in line
 transects. Used as a "n trials" in binomial model of
 proportional northern affinity species dominance. Integer values from 0 to
 147 * tpi15x15: Topographic position index (TPI) of Weiss (2001)\*10.
 Extracted at the center of each plot from "tpi15x15.tif", which
 is further described below. Integer values from -11 to 21 *
 avgCWD\_1991\_2020: Average (1991-2020) annual climatic water deficit
 (CWD) at a given plot in mm, calculated using the monthly water balance
 model of Lutz et al. (2010). Extracted at the center of each plot from
 "[Site]\_CWD\_1981-2021.tif" files, which are further described
 below. Annual values from 1991 to 2020 were extracted separately, and then
 averaged using the arithmetic mean. Floating point values from 74.53267 to
 348.3973 * annCWD\_z: Z-score transformed devations of a sampling year
 from the longer-term a (1991-2020) annual climatic water deficit (CWD) at
 a given plot in mm, calculated using the monthly water balance model of
 Lutz et al. (2010). Extracted at the center of each plot from
 "[Site]\_CWD\_1981-2021.tif" files, which are further described
 below. Annual values from 1991 to 2020 were extracted separately, and then
 averaged using the arithmetic mean. * treatmentCode: A simplified
 categorical code representing both treatment status and time since initial
 thinning treatments for a given plot and year. Character/factor variables
 with four unique values. Untreated, Treated (1-5 yr), Treated (6-10 yr),
 Treated (&gt;10 yr). Used as a model covariate in GLMMs #### FieldData
 This folder includes a single .xlsx file
 ("LEARN\_TransQuadSummaries.xlsx") with field data obtained from
 MS Access queries and summaries of raw field data (shown in
 "Data/Raw"). Descriptions of individual fields and rows are
 identical to those included in "Data/plantDataWithSpatial.csv"
 below. This .xlsx file is only included in the archive for reproducibility
 of the "Step2\_ExtractSpatial.R" script described above #### Raw
 This folder includes four raw data files, which describe understory plant
 community data collected at each of the five research sites in Arizona,
 USA. These are likely to be the files that are of the greatest interest to
 other users. Three of the files are specific to plant communities, while
 one file gives the years in which treatments were implemented at each site
 and experimental block * speciesList.csv: This file summarizes information
 about each plant species found at our research sites including their
 taxonomy (as of fall 2023), nativity status in Arizona, biogeographic
 affinity, common name, and six-letter species code (which helps to link
 this table to other raw data files). Each row represents a single plant
 species * SpeciesCode: Shortened species code (i.e., first three letters
 of genus and first three level of species). Used to uniquely identify each
 species in other datasets * Family: Family-level taxonomy for a given
 species * Genus: Genus-level taxonomy for a given species * Species:
 Species-level taxonomy for a given species * NativityStatus: N (native) or
 I (introduced). Whether a species is native to the study sites in Arizona,
 USA * BiogeoAffinity: Biogeographic affinity of each species (as described
 in supplemental information for the paper). Values are: * CFP: California
 Floristic Province * MAm: Meso-American (warm/xeric affinity) * MaT:
 Madro-Tertiary (warm/xeric affinity) * NA: Non-native species unable to be
 assigned (species removed from analysis of biogeographic affinities) *
 NTm: North-Temperate (cool/mesic affinity) * SAm: South American
 (warm/xeric affinity) * Unk: Unknown affinity, no information available
 (species removed from analysis of biogeographic affinities) * WTD:
 Warm-Temperate/Desert (warm/xeric affinity) * CommonName: English common
 name for a given species * beltTransects.csv: Data used to describe
 species richness at each plot and survey year. Rows represent each
 combination of plot, survey year, and plant species. Only species that
 were "present" in a given plot and survey year are shown in this
 file. Absences are omitted to reduce file sizes, but species-level
 absences can be obtained by joining this file with the
 "speciesList.csv" file. Any species included in
 "speciesList.csv" but without a row in a given plot/year
 combination was not observed in that location and survey. * Site:
 Two-letter code for each study site. CF = Centennial Forest, FV = Fort
 Valley, GV = Grandview, MC = Mineral Creek, MT = Mt. Trumbull * Year:
 Calendar year for a given survey * Block: Experimental block ID.
 Replicated block within a site. Integer from 1 to 4 * Trt: Treatment unit
 for a given plot. Values of 1 or 10 are "control units" which
 never received a treatment. Values of 2 are treated units which received
 thinning and burning treatments at some point during the study. Thus,
 initial surveys at treated units were still "untreated" * Plot:
 Unique plot number. When combined with Site, Block, and Trt, this
 identifies a unique plot ID in the dataset. * SpeciesCode: Six-letter
 species code for which presence is being assessed in a given plot and
 year. Codes correspond to those in "speciesList.csv" *
 lineTransects.csv: Point-line-intercept transects used to describe plant
 cover and the dominance of northern affinity species. Rows represent
 individual points (166 per plot and year) along a transect, in a given
 plot and year. Only points with vegetation are included in this file (to
 reduce file sizes). Therefore, "missing" points within a plot
 and year had no cover of any understory plant species. Replicating these
 missing values (such that each combination of site, block, Trt, Plot, and
 Year have points ranging from 1-166 and a SpeciesCode of "NONE")
 may be needed for some intended uses. * Site: Two-letter code for each
 study site. CF = Centennial Forest, FV = Fort Valley, GV = Grandview, MC =
 Mineral Creek, MT = Mt. Trumbull * Year: Calendar year for a given survey
 * Block: Experimental block ID. Replicated block within a site. Integer
 from 1 to 4 * Trt: Treatment unit for a given plot. Values of 1 or 10 are
 "control units" which never received a treatment. Values of 2
 are treated units which received thinning and burning treatments at some
 point during the study. Thus, initial surveys at treated units were still
 "untreated" * Plot: Unique plot number. When combined with Site,
 Block, and Trt, this identifies a unique plot ID in the dataset. *
 SpeciesCode: Six-letter species code for the top plant strata at a given
 point "speciesList.csv". Multiple species could be identified at
 each point, however, only the top strata is given here. Belt transects
 provide better information about the individual species present at a site.
 * treatmentYears.csv: File describing the years in which different
 treatments where implemented at each site and treatment-control unit pair
 (i.e., block). NOTE: Years in this file correspond to the first growing
 season in which a block might be considered post-treatment. For treatments
 in the spring, this was the same year as treatment implementation. for
 treatments in the summer/fall, this was the year after treatment
 implementation. * Site: Two-letter code for each study site. CF =
 Centennial Forest, FV = Fort Valley, GV = Grandview, MC = Mineral Creek,
 MT = Mt. Trumbull * Block: Experimental block ID. Replicated block within
 a site. Integer from 1 to 4 * ThinningYear: The first growing season in
 which the treated unit of a block was considered "thinned".
 Integer from 1999 to 2007. * BurningYear1: The first growing season in
 which the treated unit of a block was considered "burned".
 Integer from 2000 to 2012. * BurningYear2: The first growing season in
 which the treated unit of a block was considered "burned" more
 than once. Integer from 2007 to 2008. "NA" indicates that a plot
 was only burned once * BurningYear3: The first growing season in which the
 treated unit of a block was considered "burned" more than twice.
 Integer from 2015 to 2015. "NA" indicates that a plot was burned
 one or two times only * BurningYear4: The first growing season in which
 the treated unit of a block was considered "burned" more than
 three times. Integer from 2020 to 2020. "NA" indicates that a
 plot was burned one, two, or three times #### SpatialData This subfolder
 includes all spatial datasets used to predict understory plant cover,
 richness, and dominance of northern affinity species in GLMMs. Files are
 further divided into subfolders below, which give specific kinds of
 spatial data used in this study ##### SpatialData/PRISM\_coarse Monthly
 climate data (4-km spatial resolution) from the gridded PRISM dataset
 (PRISM Climate Group, 2022), cropped to the extent of our study sites.
 Each file is a multi-band raster file (GeoTIFF format) with 489 individual
 bands. Bands correspond to months in the period of January 1981 (Band 1)
 to September 2021 (Band 489), and are arranged chronologically (e.g.,
 February 1981 is Band 2). Files are in 32-bit floating point format, to
 preserve decimals in data, in projected coordinate system EPSG:26912 (NAD
 83, UTM zone 12 N). * PRISM\_ppt.tif: Total monthly precipitation (in mm)
 for the corresponding month and year. * PRISM\_tmax.tif: Average maximum
 temperature (in C) for the corresponding month and year. *
 PRISM\_tmin.tif: Average minimum temperature (in C) for the corresponding
 month and year. ##### SpatialData/PRISM\_downscaled Files in this
 subfolder are not included in this archive due to large file sizes.
 However, these files can be re-created using the
 "Step1c\_GIDS-Downscaling.R" script, which downscales files in
 the "SpatialData/PRISM\_coarse" folder using digital elevation
 models in "SpatialData/SoilsAndTerrain". Subfolder is included
 here for reproducibility of this script. Output of this downscaling script
 gives statistically downscaled (i.e., 30-m spatial resolution; monthly
 temporal resoluion) climate data, cropped to the extent of each individual
 site. ##### SpatialData/SiteLocations Files in this subfolder give the
 locations of all plots included in our analyses. There are four files, all
 of which are different components of a vector-based GIS point dataset in
 shapefile (.shp) format. Points give the center location of each
 understory vegetation transect. This file is in projected coordinate
 system EPSG:26912 (NAD 83, UTM zone 12 N), with the following fields in
 the attribute table (i.e., .dbf file). * Site: Two-letter code for each
 study site. CF = Centennial Forest, FV = Fort Valley, GV = Grandview, MC =
 Mineral Creek, MT = Mt. Trumbull * Plot\_ID: A concatenated version of the
 block, treatment, and plot ID of a corresponding point. The format follows
 the convention [Block]-[Trt]-[Plot]. This field and the site field can be
 used to join this dataset with raw plant data in
 "beltTransects.csv", "lineTransects.csv", etc. * Long:
 Longitude (in decimal degrees) of the center of a plot/understory
 transect. Coordinates are in geographic coordinate system EPSG:4326 (WGS
 84; Lat/Long) * Lat: Latitude (in decimal degrees) of the center of a
 plot/understory transect. Coordinates are in geographic coordinate system
 EPSG:4326 (WGS 84; Lat/Long) ##### SpatialData/SoilsAndTerrain This
 subfolder includes various biophysical variables included as covariates in
 GLMMs (e.g., tpi15x15.tif), used to downscale gridded climate data (e.g.,
 demUTM12N.tif), or used to run water balance models (e.g., hliUTM12N.tif,
 awcUTM12N.tif). All files are raster datasets (GeoTIFF format) in
 projected coordinate system EPSG:26912 (NAD83 UTM zone 12N). Most files
 have a 30-m spatial resolution and are aligned to the same reference grid,
 unless otherwise mentioned below. * aspectUTM12N.tif: Aspect of a
 hillslope, with units giving the azimuth at which a slope faces. 0/360 =
 due North and 180 = due South. Units are in 32-bit floating point format
 to preserve decimal values. Derived from a 30-m digital elevation model
 (i.e., demUTM12N.tif) * awcUTM12N.tif: Water holding capacity (in mm) of
 the top 200 cm of the soil profile, based on data from POLARIS (Cheney et
 al. 2016). A combination of soil texture and depth to bedrock
 characteristics. Units are in 32-bit floating point format to preserve
 decimal values. Used for water balance modeling * demUTM12N.tif: Digital
 elevation model from the USGS 3DEP dataset (USGS, 2021). Originally
 acquired at 10-m spatial resolution and aggregated to 30-m resolution
 using "average" resampling in GDAL. Used for statistical
 downscaling of climate data and to develop aspect and slope grids. 16-bit
 integer format with pixel values in meters. * demUTM12N\_[resolution].tif:
 Digital elevation models from the USGS 3DEP dataset (USGS, 2021).
 Originally acquired at 10-m spatial resolution and aggregated to
 [resolution] (i.e., 90m, 270m, 900m, or 4000m) using "average"
 resampling in GDAL. Used for statistical downscaling of climate data.
 32-bit floating point format with pixel values in meters. * hliUTM12N.tif:
 Heat load index following Eq. 3 of McCune and Keon (2002). Higher values
 are common in steep, southwest-facing slopes, and low values are common on
 steep, northeast-facing slopes. Calculated from slope, aspect, and
 latitude grids in the same subfolder. 32-bit floating point format *
 latitudeUTM12N.tif: Latitude (in decimal degrees) of the center of a 30-m
 pixel. Used to calculate "hliUTM12N.tif" file. 32-bit floating
 point format * slopeUTM12N.tif: Slope angle (in degrees) of each 30-m
 pixel. Used to calculate "hliUTM12N.tif" file. 32-bit floating
 point format * tpi15x15.tif: Topographic position index (TPI) of Weiss
 (2001), which gives the difference between the elevation of a 30-m pixel
 and the mean elevation of a 450-m square window centered on the 30-m
 pixel. Units are in meters\*10 to convert to integer values and save a
 decimal of precision. Low, negative values are indicative of valley
 bottoms, whereas high, positive values are indicative of ridgetops. Used
 as a covariate in GLMMs. 16-bit signed integer data format #####
 SpatialData/WaterBalance This subfolder includes outputs of monthly water
 balance models, calculated in the "Step1d\_CWD-Calcs.R" script.
 Outputs are multi-band raster files (GeoTIFF format), with 41 bands each,
 a 30-m spatial resolution, and in projected coordinate system EPSG:26912
 (NAD83 UTM zone 12N). Bands give the annual total (summed by water year;
 preceding October 1st to September 30th in focal year) of each metric
 described below, cropped to the extent of a given site. In each file, band
 1 gives the water year total for 1981 (i.e., Oct 1980-Sept 1981), band 2
 gives the water year total for 1982 and band 41 gives the water year total
 for 2021. Most files have a 30-m spatial resolution and are aligned to the
 same reference grid * [Site]\_AET\_1981-2021: Within a [Site] (site codes
 are identical to those described in datasets above), this dataset gives
 actual evapotranspiration (AET) in mm/yr, for each water year from 1981 to
 2021. Calculated using monthly water balance models of Lutz et al. (2010)
 and a modified Thornthwaite approach. Not included as a covariate in GLMMs
 because of high correlation with CWD, and weaker explanatory power. 32-bit
 floating point format * [Site]\_CWD\_1981-2021: Within a [Site] (site
 codes are identical to those described in datasets above), this dataset
 gives climatic water deficit (CWD) in mm/yr, for each water year from 1981
 to 2021. Calculated using the monthly water balance models of Lutz et al.
 (2010) and a modified Thornthwaite approach. Used as a covariate in GLMMs.
 32-bit floating point format ##### SpatialData/WorkingDirectory This
 folder is intentionally empty, but is the location in which temporary
 output files are written during processing of some of the R scripts in the
 "Code" subfolder. Included in this archive for reproducibility
 of these scripts. ## References * Chaney, N. W., Wood, E. F., McBratney,
 A. B., Hempel, J. W., Nauman, T. W., Brungard, C. W., & Odgers, N.
 P. (2016). POLARIS: A 30-meter probabilistic soil series map of the
 contiguous United States. Geoderma, 274, 54-67. * Lutz, J. A., Van
 Wagtendonk, J. W., & Franklin, J. F. (2010). Climatic water
 deficit, tree species ranges, and climate change in Yosemite National
 Park. Journal of Biogeography, 37(5), 936-950. * McCune, B., &
 Keon, D. (2002). Equations for potential annual direct incident radiation
 and heat load. Journal of vegetation science, 13(4), 603-606. * Nalder, I.
 A., & Wein, R. W. (1998). Spatial interpolation of climatic
 normals: test of a new method in the Canadian boreal forest. Agricultural
 and forest meteorology, 92(4), 211-225. * PRISM Climate Group, Oregon
 State University. (2022). http://prism.oregornstate.edu. * United States
 Geological Survey. (2021). USGS 3D Elevation Program Digital Elevation
 Model. Accessed from:
 https://developers.google.com/earth-engine/datasets/catalog/USGS\_3DEP\_10m * Weiss, A. (2001). Topographic position and landforms analysis. ESRI user conference, San Diego, CA.