Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
Data files
Mar 06, 2024 version files 232.75 MB
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README.md
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Solar_Development_Driven_Land_Alteration.zip
Abstract
The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km2 of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.
README: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
This dataset contains results from an analysis of the extent to which solar energy development in the continental United States may drive land-cover change and alteration of landscapes with high value for animal movement. It contains JavaScript code from a spatial analysis within Google Earth Engine to derive initial results, and R code to parse, manipulate, and synthesize inital results into more accessible dataframes.
Packages Used
This workflow uses the packages 'tidyverse' (version 2.0.0) and 'ggpubr' (version 0.6.0).
Description of the Data and file structure
The file structure is contained in the R Project folder entitled '[Revised] Solar Development Driven LCC'. The folder contains the R project file as well as seven folders, eight R Markdown files, and a text file containing JavaScript code for Google Earth Engine. The folders hold all raw .csv data derived from Google Earth Engine Analyses. These data represent the area of solar energy development intersecting with categories of interest for this manuscript.
Those folders that begin with NLCD contain data on land cover; those that begin with HVC contain data on high-value corridors; those that begin with RCL contain data on resilient and connected landscapes. Two folders are present for each of those data types: one for data aggregated at the national level, and another for data parsed at the state level. One folder, entitled 'Solar on Tribal Lands', contains data regarding the area of solar development projected to occur on tribal lands.
The eight R Markdown files follow the same naming convention, and are identifiable by their level of spatial aggregation (either 'By State' or 'National') and their data type ('LC' for land cover, 'HVC' for high-value corridor, and 'RCL' for resilient and connected landscape). The two R Markdown files that do not follow this convention are 'Revised Code for Figures' and 'Area of Solar on Tribal Land'. The former contains code for the generation of figures 2, 3, and 4 in the manuscript, while the latter contains code to parse and calculate the total area of solar development on tribal lands.
The text file featuring Javascript code can be copied and pasted into Google Earth Engine and successfully run. All assets used in the process are publicly available and callable through Google Earth Engine.
If the R Project is located on the desktop, all R Markdown files should run successfully as is as they pull .csv data directly from their corresponding folders. Other than those abbreviations described above, no additional abbreviations are present in the code.
Recommended Workflow
We recommend beginning with the R Markdown files related to calculations at the national scale, beginning with landcover (National LC Analyses.rmd), high value corridors (National HVC Analyses.rmd), and resilient and connected landscapes (National RCL Analyses.rmd). Calculations at the state scale should follow, beginning with with landcover (By State LC Analyses.rmd), high value corridors (By State HVC Analyses.rmd), and resilient and connected landscapes (By State RCL Analyses.rmd). Those calculations describing the extent of solar development on tribal land that was excluded from these analyses should be run next (Area of Solar on Tribal Land.rmd), followed by the generation of the figures published in this manuscript (Revised Code for Figures.rmd).
Sharing/access Information
Data sources include the following:
- The Net-Zero America Project's spatial projects of renewable energy development, accessible at https://maps.princeton.edu/?bbox=-138.955078+-3.425692+-27.861328+62.995158&q=netzeroamerica&search_field=all_fields&utf8=%E2%9C%93
- High-value corridor data was collected from 'Identifying Corridors among Large Protected Areas in the United States' by Belote et al. 2016, and is accessible at http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/terrestrial/resilience/Pages/Downloads.aspx
- Resilient and Connected Landscapes data was collected from The Nature Conservancy, and is accessible at http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/terrestrial/resilience/Pages/Downloads.aspx
- National Land Cover Database data was collected within Google Earth Engine, which is where all data were initially processed and analyses took place.
Methods
Data was collected from the Net-Zero America Project's spatial projects of renewable energy development, accessible at https://maps.princeton.edu/?bbox=-138.955078+-3.425692+-27.861328+62.995158&q=netzeroamerica&search_field=all_fields&utf8=%E2%9C%93
Data describing corridors between large protected areas was collected from 'Identifying Corridors among Large Protected Areas in the United States' by Belote et al. 2016, and is accessible at http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/terrestrial/resilience/Pages/Downloads.aspx
Resilisnt and Connected Landscapes was collected from The Nature Conservancy, and is accessible at http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/terrestrial/resilience/Pages/Downloads.aspx
National Land Cover Database data was collected within Google Earth Engine, which is where all data were processed and analyses took place.
Usage notes
All spatial analyses were conducted in Google Earth Engine, and spatial visualization was conducted in QGIS. All data manipulation and visualization was conducted in R Studio.