This repo contains data and code for the manuscript "Woody plant encroachment intensifies under climate change across tundra and savanna biomes".
Mariana García Criado, Isla H. Myers-Smith, Anne D. Bjorkman, Caroline E.R. Lehmann and Nicola Stevens
Contact: Mariana García Criado, email@example.com
Data use guidelines
Data are publicly available using a Creative Commons Attribution 4.0 International copyright (CC BY 4.0; see license.txt). Data are fully public but should be appropriately referenced by citing the paper. Although not mandatory, we additionally suggest that data users contact and collaborate with data contributors if this dataset will contribute a substantial proportion of observations used in a particular paper or analysis. DOI for this dataset is 10.5281/zenodo.3601454
Data availability & access
The data and code for this manuscript will be mantained at this GitHub repository (https://github.com/marianagarciacriado/WoodyEncroachmentHub).
García Criado, M., Myers-Smith, I.H., Bjorkman, A.D., Lehmann, C.E.R. and Stevens, N. 2020. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13072
Acknowledgments from the manuscript
M.G.C. was supported by the Principal Career’s Development PhD Scholarship from The University of Edinburgh. I.M.S. was supported by the NERC Shrub Tundra grant (NE/M016323/1). We thank all tundra and savanna data collectors, including members of the International Tundra Experiment Network (ITEX), for their efforts in data collection and for making their data accessible. We are grateful to Jakob Assmann for his assistance in extracting and manipulating the CHELSA climatic data. We thank local and indigenous peoples of the tundra and savanna biomes for the opportunity to work with data collected on their lands.
All data for our analyses can be found in the
All the analyses undertaken for this manuscript are split between multiple R scripts within the
They can be followed in a sequential order (i.e., 01 to 12), but I recommend not to run scripts #5 and #6 as they deal with the extraction and manipulation of raster data from CHELSA (http://chelsa-climate.org/). Both the climatologies and time series are large files and are thus stored in a hard drive so it is not possible to run the scripts without access to the climatic raster files.
The figures generated in R are stored in the
Full model outputs for statistical analyses are stored in the
R version 3.4.3 or greater.
tidyverse, proj4, scales, ggalt, ggplot2, dplyr, cowplot, MCMCglmm, ggbpubr, raster, rgdal, rasterVis, sp, tidyr, readr, broom, stargazer