MATSS

Overview

MATSS is a package for conducting Macroecological Analyses of Time Series Structure. We designed it to help researchers quickly get started in analyses of ecological time series, and to reinforce and spread good practices in computational analyses.

We provide functionality to:

• obtain time series data from ecological communities, processed into a common data format
• perform basic processing and summaries of those datasets; see data processing
• build an analysis pipeline for macroecological analyses, using the workflow framework of the drake package
• package the above data analytical work in a reproducible way in a research compendium

Contributing

For more information about contributing code, datasets, or analyses, please check out the Contributing Guide.

Installation

You can install MATSS from github with:

# install.packages("remotes")
remotes::install_github("weecology/MATSS", build_opts = c("--no-resave-data", "--no-manual"))

MATSS also uses the rdataretriever package to download additional datasets. To get this package and its dependencies wokring, we recommend following the online installation instructions.

Datasets

MATSS pulls data from a variety of sources, including:

• 10 individual datasets that we’ve added,
• the North American Breeding Bird Survey database (spanning 3903 separate datasets),
• the Global Population Dynamics Database (spanning 120 separate datasets),
• and the BioTime database (spanning 361 separate datasets).

Combined, there are 320483 individual time series across all of these data sources.

Getting Started

We recommend you take a look at our vignette on Getting Started for more details about how to begin using MATSS.

If you have the package installed, you can also view the vignette from within R:

vignette("MATSS")

Example Use Cases

Here are some examples of analyses built on MATSS:

• MATSS-LDATS applies the LDATS package to investigate changepoints in community dynamics across the datasets in MATSS
• MATSS-Forecasting investigates which properties are associated with the predictability of population time series across the datasets in MATSS

Acknowledgments

We thank Erica Christensen and Joan Meiners for their contributions and input on early prototypes of this project. This project would not be possible without the support of Henry Senyondo and the retriever team. Finally, we thank Will Landau and the drake team for their input and responsiveness to feedback.

Package development is supported through various funding sources: including the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative, Grant GBMF4563 to E. P. White (supporting also the time of J. Simonis and H. Ye), the National Science Foundation, Grant DEB-1622425 to S. K. M. Ernest, and a National Science Foundation Graduate Research Fellowship (No. DGE-1315138 and DGE-1842473) to R. Diaz.