Climate-limited vegetation change in the conterminous United States of America

Parra, Adriana1 ; Greenberg, Jonathan1

Published Mar 05, 2024 on Dryad. https://doi.org/10.5061/dryad.j0zpc86nm

Abstract

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.

README

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

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

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

Methods

Input data

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).

Preprocessing of vegetation fractional cover data

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).

Preprocessing of climate variables data

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.

Estimation of climate potential

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.

Identifying climate-limited areas

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.