paleoMAT: Paleotemperature Reconstruction from Pollen using the Modern Analog Technique

paleoMAT is an R package implementing functions to perform temporal paleoclimate reconstruction from pollen using the MAT (Modern Analog Technique).

This is the official R package for paleoMAT, which contains all code associated with the analyses described and presented, including figures and tables, in Gillreath-Brown et al. 2024:

Gillreath-Brown, A., R. K. Bocinsky, and T. A. Kohler (2024). A Low-Frequency Summer Temperature Reconstruction for the United States Southwest, 3000 BC – AD 2000. The Holocene 34(4):451-466. https://doi.org/10.1177/09596836231219482

All code for analysis and reconstruction is in UUSS_MAT_Reconstruction.Rmd and all code for figures and tables is in Paleomat_Figures.Rmd.

Installation

You can install paleoMAT from GitHub with these lines of R code (Windows users are recommended to install a separate program, Rtools, before proceeding with this step):

if (!require("devtools")) install.packages("devtools")
devtools::install_github("Archaeo-Programmer/paleomat")

Repository Contents

The 📁 vignettes directory contains:

• 📄 UUSS_MAT_Reconstruction: R Markdown document with all analysis for Gillreath-Brown et al. 2024. It also has a rendered version, UUSS_MAT_Reconstruction.html.
• 📄 Paleomat_Figures: R Markdown document that includes code to reproduce the figures and tables for Gillreath-Brown et al. 2024. It also has a rendered version, Paleomat_Figures.html.
• 📁 figures: Plots, figures, and illustrations in the paper, including supplementary materials.
• 📁 tables: Tables in the paper, including supplementary materials.
• 📁 data: Data (.rds) output files from various points throughout the analysis in the R Markdown, UUSS_MAT_Reconstruction.

How to Run the Code?

To reproduce the analysis, output, figures, and tables, you will need to clone the repository. To clone the repository, you can do the following from your Terminal:

git clone https://github.com/Archaeo-Programmer/paleomat.git
cd paleomat

After installing the paleoMAT package (via install_github as shown above or by using devtools::install()), then you can render the analysis, visualizations, and tables. You can compile the paleoMAT analysis within R by entering the following in the console:

rmarkdown::render(here::here('vignettes/UUSS_MAT_Reconstruction.Rmd'), output_dir = here::here('vignettes'))

You can also compile the figures and tables from the paleoMAT analysis within R by entering the following in the console:

rmarkdown::render(here::here('vignettes/Paleomat_Figures.Rmd'), output_dir = here::here('vignettes'))

If you do not want to compile the R Markdowns, then you can retrieve a readable HTML file by navigating to UUSS_MAT_Reconstruction.html. Then, click "Raw" and save the file as "UUSS_MAT_Reconstruction.html" (i.e., save file with .html extension or as HTML file type). Another option, after installing the paleoMAT package, is to use rstudioapi::viewer in the R console:

rstudioapi::viewer(here::here('vignettes/UUSS_MAT_Reconstruction.html'))

Another option for reproducing the results is to use the package itself and follow along with the vignette, UUSS_MAT_Reconstruction. Data and functions are already loaded into the package. There are also comments that point to output saved in the 📁 data directory and the 📁 vignettes data directory. The purpose of the output files in 📁 vignettes data directory was to save various stages of output throughout the analysis. So, if there is a chunk of code that you do not want to run, then you can run the accompanying commented out line to read in each variable. However, if you want to run the code yourself, then you can just ignore these commented lines of code.

Licenses

Code: GNU GPLv3

Data: CC-0 attribution requested in reuse

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grants SMA-1637171 and SMA-1620462, and by the Office of the Chancellor, Washington State University-Pullman. Data were obtained from the Neotoma Paleoecology Database on October 23, 2021 using the neotoma 1.0 R package and its constituent database(s) (North American Pollen Database). The work of data contributors, data stewards, and the Neotoma community is gratefully acknowledged.