DOI: 10.5061/dryad.bnzs7h4j0
title: Aligning renewable energy expansion with climate-driven range shifts

description: Fossil fuel dependence can be reduced, in part, by renewable energy (RE)
 expansion. Increasingly, RE siting seeks to avoid significant impacts on
 biodiversity but rarely considers how species ranges will shift under
 climate change. Here, we undertake a systematic literature review on the
 topic and overlay future RE siting maps with the ranges of two threatened
 species under future climate scenarios to highlight this potential
 conflict.
description: Mentioned in the Article 
description: # Aligning renewable energy expansion with climate-driven range shifts
 We conducted a systematic
 literature review to develop a corpus of articles on RE and biodiversity
 and species distribution modeling of Joshua tree and kit fox species. ##
 Description of the data and file structure #### 1. Systematic Literature
 Review ##### a. Code to Count Keywords in the Corpus count_keywords.R:
 This script contains the code used to analyze the systematic literature
 review corpus, counting the occurrence of keywords. ##### b. Articles
 Included in the Systematic Literature Review
 literature_review_articles.csv: This CSV file lists the articles included
 in the systematic literature review, providing details such as title,
 authors, and publication year. #### 2. Species Distribution Modeling (SDM)
 Results ##### a. Vulpes\_macrotis (Kit Fox) ###### i. Grinnell\_Vulpes:
 Grinnell Package Results accessible_area_M.shp: Polygon shapefile
 containing the accessible area to the species using ellipsoids.
 occ_simulation.csv: Occurrence data used for assessing the species
 accessible area. Report.txt: Parameters used for the accessible area M
 simulation. ###### ii. kuenm\_2050: Kuenm Results for 2050 Climate Change
 Scenarios Final_Models: Contains the final model results of Maxent for the
 best models selected during calibration result evaluation with three
 results (E=extrapolation, EC=extrapolation and clamping, NE=no
 extrapolation). These results contain the Maxent direct output files.
 G_variable: Within this section, you'll find a directory named Set 1,
 comprising the first six principal components (PC). Additionally, it
 includes 10 folders containing the PC projecting the future climatic
 variables based on the Representative Concentration Pathways (RCPs) 4.5
 and 8.5 for 2050. These data layers were sourced from the Climate Change,
 Agriculture and Food Security (CCAFS) downscaled General Circulation Model
 (GCM) data portal (), encompassing
 data for two emission scenarios and five GCMs (GISS-E2-R, MIROC-MIROC 5,
 MOHC-HadGEM2-CC, MPI-ESM-LR, NCAR-CCSM 4). The initials of these scenarios
 are used to represent the folder names, where '45' indicates RCP
 4.5 and '85' signifies RCP 8.5. M_variables: Contains a folder
 named Set 1 having the first six principal components of the current
 environmental variables for model input. joint.csv: All occurrence data.
 test.csv: Occurrence data selected by the randomization used to test
 models. train.csv: Occurrence data selected by the randomization used to
 train models. vulpes.rmd: R Markdown file for the script used for Species
 Distribution Modeling (SDM) to run Maxent and select the best model.  
 ###### iii. kuenm\_2070: Kuenm Results for 2070 Climate Change Scenarios
  Final_Models: Contains the final model results of Maxent for the best
 models selected during calibration result evaluation with three results
 (E=extrapolation, EC=extrapolation and clamping, NE=no extrapolation).
 These results contain the Maxent direct output files. G_variable: Contains
 a folder named Set 1 having the first six principal components. It further
 contains 10 folders containing the principal components of the future
 projections of the climatic variables for the 4.5 and 8.5 scenarios.
 Naming of the folder is same is in kuenm_2050. M_variables: Contains a
 folder named Set 1 having the first six principal components of the
 current environmental variables for model input. joint.csv: All occurrence
 data. test.csv: Occurrence data selected by the randomization used to test
 models. train.csv: Occurrence data selected by the randomization used to
 train models. vulpes.rmd: R Markdown file for the script used for Species
 Distribution Modeling (SDM) to run Maxent and select the best model.  
 ##### b. Yucca\_brevifoila (Joshua Tree) ###### i. Grinnell\_Yucca:
 Grinnell Package Results accessible_area_M.shp: Polygon shapefile
 containing the accessible area to the species using ellipsoids.
 occ_simulation.csv: Occurrence data used for assessing the species
 accessible area. Report.txt: Parameters used for the accessible area M
 simulation. ###### ii. kuenm\_2050: Kuenm Results for 2050 Climate Change
 Scenarios Final_Models: Contains the final model results of Maxent for the
 best models selected during calibration result evaluation with three
 results (E=extrapolation, EC=extrapolation and clamping, NE=no
 extrapolation). These results contain the Maxent direct output files.
 G_variable: Contains a folder named Set 1 having the first six principal
 components. It further contains 10 folders containing the principal
 components of the future projections of the climatic variables for the 4.5
 and 8.5 scenarios. Naming of the folder is same is in kuenm_2050 in the
 Vulpes_macrotis folder. M_variables: Contains a folder named Set 1 having
 the first six principal components of the current environmental variables
 for model input. joint.csv: All occurrence data. test.csv: Occurrence data
 selected by the randomization used to test models. train.csv: Occurrence
 data selected by the randomization used to train models. yucca.rmd: R
 Markdown file for the script used for Species Distribution Modeling (SDM)
 to run Maxent and select the best model.   ###### iii. kuenm\_2070: Kuenm
 Results for 2070 Climate Change Scenarios  Final_Models: Contains the
 final model results of Maxent for the best models selected during
 calibration result evaluation with three results (E=extrapolation,
 EC=extrapolation and clamping, NE=no extrapolation). These results contain
 the Maxent direct output files. G_variable: Contains a folder named Set 1
 having the first six principal components. It further contains 10 folders
 containing the principal components of the future projections of the
 climatic variables for the 4.5 and 8.5 scenarios. Naming of the folder is
 same is in kuenm_2050 in the Vulpes_macrotis folder. M_variables: Contains
 a folder named Set 1 having the first six principal components of the
 current environmental variables for model input. joint.csv: All occurrence
 data. test.csv: Occurrence data selected by the randomization used to test
 models. train.csv: Occurrence data selected by the randomization used to
 train models. yucca.rmd: R Markdown file for the script used for Species
 Distribution Modeling (SDM) to run Maxent and select the best model. Feel
 free to explore the datasets and code to reproduce the analyses and
 results presented in the research. If you have any questions or need
 further clarification, please contact the authors. ##