DOI: 10.5061/dryad.j0zpc86nm
title: Climate-limited vegetation change in the conterminous United States of America

description: In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED
 STATES OF AMERICA”, published in the Global Change Biology journal, we
 evaluated the effects of climate conditions on vegetation composition and
 distribution in the conterminous United States (CONUS). To disentangle the
 direct effects of climate change from different non-climate factors, we
 applied "Liebig's law of the minimum" in a geospatial
 context, and determined the climate-limited potential for tree, shrub,
 herbaceous, and non-vegetation fractional cover change. We then compared
 these potential rates against observed change rates for the period 1986 to
 2018 to identify areas of the CONUS where vegetation change is likely
 being limited by climatic conditions. This dataset contains the input and
 the resulting rasters for the study which include a) the observed rates of
 vegetation change, b) the climate derived potential vegetation rates of
 change, c) the difference between potential and observed values and d) the
 identified climatic limiting factor.
description: <strong>Input data</strong>
   We use the available data from the
 “Vegetative Lifeform Cover from Landsat SR for CONUS” product
 (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the
 changes in vegetation fractional cover.
   The information for the climate
 factors was derived from the TerraClimate data catalog
 (https://www.climatologylab.org/terraclimate.html). We downloaded data
 from this catalog for the period 1971 to 2018 for the following
 variables:  minimum temperature (TMIN), precipitation (PPT), actual
 evapotranspiration (AET), potential evapotranspiration (PET), and climatic
 water deficit (DEF).  
 <strong>Preprocessing of vegetation fractional cover
 data</strong>   We
 resampled and aligned the maps of fractional cover using pixel averaging
 to the extent and resolution of the TerraClimate dataset (~ 4 km). Then,
 we calculated rates of lifeform cover change per pixel using the Theil-Sen
 slope analysis (Sen, 1968; Theil, 1992).
   <strong>Preprocessing of climate
 variables data</strong>  
 To process the climate data, we defined a year time step as the
 months from July of one year to July of the next. Following this
 definition, we constructed annual maps of each climate variable for the
 years 1971 to 2018.   The
 annual maps of each climate variable were further summarized per pixel,
 into mean and slope (calculated as the Theil-Sen slope) across one, two,
 three, four, five, ten-, and 15-year lags.
   <strong>Estimation of climate
 potential</strong>  
 We constructed a final multilayer dataset of response and
 predictor variables for the CONUS including the resulting maps of
 fractional cover rate of change (four response variables), the mean and
 slope maps for the climate variables for all the time-lags (70 predictor
 variables), and the initial percent cover for each lifeform in the year
 1986 (four predictor variables).  
 We evaluated for each pixel in the CONUS which of the predictor
 variables produced the minimum potential rate of change in fractional
 cover for each lifeform class. To do that, we first calculated the 100%
 quantile hull of the distribution of each predictor variable against each
 response variable.   To
 calculate the 100% quantile of the predictor variables’ distribution we
 divided the total range of each predictor variable into equal-sized bins.
 The size and number of bins were set specifically per variable due to
 differences in their data distribution. For each of the bins, we
 calculated the maximum value of the vegetation rate of change, which
 resulted in a lookup table with the lower and upper boundaries of each
 bin, and the associated maximum rate of change. We constructed a total of
 296 lookup tables, one per lifeform class and predictor variable
 combination. The resulting lookup tables were used to construct spatially
 explicit maps of maximum vegetation rate of change from each of the
 predictor variable input rasters, and the final climate potential maps
 were constructed by stacking all the resulting maps per lifeform class and
 selecting for each pixel the minimum predicted rate of change and the
 predictor variable that produced that rate.
   <strong>Identifying
 climate-limited areas</strong>  
 We defined climate-limited areas as the parts of the CONUS with
 little or no differences between the estimated climate potential and the
 observed rates of change in fractional cover. To identify these areas, we
 subtracted the raster of observed rates of change from the raster of
 climate potential for each lifeform class.
description: This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL
 INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in
 the conterminous United States of America** 2\. Author Information A.
 First Author Contact Information Name: Adriana Parra Institution:
 University of Nevada, Reno Address: Reno, NV USA Email:
 adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan
 Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA
 Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018
 4\. Geographic location of dataset: Conterminous United States 5\.
 Description: This dataset contains the input and the resulting rasters for
 the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED
 STATES OF AMERICA”, published in the Global Change Biology journal. The
 dataset includes a) the observed rates of vegetation change, b) the
 climate derived potential vegetation rates of change, c) the difference
 between potential and observed values and d) the identified climatic
 limiting factor. Additionally, the dataset includes a legend file for the
 identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION
 1\. Links to publications that cite or use the data: **Parra, A., &
 Greenberg, J. (2024). Climate-limited vegetation change in the
 conterminous United States of America. Global Change Biology, 30, e17204.
 [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)**
 2\. Links to other publicly accessible locations of the data: None 3\.
 Links/relationships to ancillary data sets: None 4\. Was data derived from
 another source? Yes A. If yes, list source(s): "Vegetative Lifeform
 Cover from Landsat SR for CONUS" product publicly available in the
 ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809)
 TerraClimate data catalog publicly available at the website
 https://www.climatologylab.org/terraclimate.html 5\. Recommended citation
 for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited
 vegetation change in the conterminous United States of America. Global
 Change Biology, 30, e17204.
 [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ##
 DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one
 csv file. There are 4 geotiff files per each of the lifeform classes
 evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The
 files corresponding to each lifeform class are indicated by the first two
 letters in the file name, HC indicates herbaceous cover, TC indicates tree
 cover, SC indicates shrub cover, and NC indicates non-vegetation cover.  
 1\. File List: a) Observed change: Trends of vegetation change between
 1986 and 2018. b) Potential predict: Predicted rates of vegetation change
 form the climate limiting factor analysis. c) Potential observed
 difference: Difference between the potential and the observed vegetation
 rates of change. d) Limiting variable: Climate variable identified as the
 limiting factor for each pixel the conterminous United States. e) Legend
 of the Limiting variable raster All the geotiff files are stored as Float
 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The
 csv file included in the dataset is the legend for the limiting variable
 geotiff files. This file includes the name of the climate variable
 corresponding to each number in the limiting variable files, as well as
 information on the variable type and the corresponding time lag. 2\.
 Relationship between files, if important: None 3\. Additional related data
 collected that was not included in the current data package: None 4\. Are
 there multiple versions of the dataset? No A. If yes, name of file(s) that
 was updated: NA i. Why was the file updated? NA ii. When was the file
 updated? NA