Aligning renewable energy expansion with climate-driven range shifts
Cite this dataset
Ashraf, Uzma; Morelli, Toni Lyn; Smith, Adam B.; Hernandez, Rebecca R. (2024). Aligning renewable energy expansion with climate-driven range shifts [Dataset]. Dryad. https://doi.org/10.5061/dryad.bnzs7h4j0
Abstract
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.
README: Aligning renewable energy expansion with climate-driven range shifts
https://doi.org/10.5061/dryad.bnzs7h4j0
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)
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.
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 (http://www.ccafs-climate.org/), 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.
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)
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.
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.
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.
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Methods
Mentioned in the Article
Funding
Alfred P. Sloan Foundation, Award: G-2022-17177