DOI: 10.5061/dryad.mw6m90628
title: Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States

description: 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.
description: 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. 
description: 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.
description: # 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](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](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](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.