The mFD package provides a “user friendly” interface to compute a global assessment of functional diversity by gathering computation of alpha and beta functional indices. As no package before, it guides users through functional analysis with one function per action to complete, several arguments that can be changed and allows personalized graphical outputs. Various tutorials are available on the mFD website to guide the user through the functional workflow.
You can install the stable version from CRAN with:
Or you can install the development version from GitHub with:
## Install < remotes > package (if not already installed) ----
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
## Install dev version of < mFD > from GitHub ----
remotes::install_github("CmlMagneville/mFD", build_vignettes = TRUE)To compute functional diversity indices, users need:
a data frame summarizing species traits (species in rows, traits in columns). The mFD package works with all kind of traits: quantitative, ordinal, nominal, circular, and fuzzy-coded.
a matrix summarizing species gathering into assemblages (assemblages in rows, species in columns). All assemblages must at least contain one species.
a data frame summarizing traits category (first column with traits name, second column with traits type, third column with fuzzy name of fuzzy traits - if no fuzzy traits: NA).
For a complete understanding of the functional workflow and the package possibilities, please refer to the mFD General Workflow.
Please cite this package as:
Magneville, C., Loiseau, N., Albouy, C., Casajus, N., Claverie, T., Escalas, A., Leprieur, F., Maire, E., Mouillot, D., Villéger, S. (2022). mFD: an R package to compute and illustrate the multiple facets of functional diversity. Ecography https://onlinelibrary.wiley.com/doi/10.1111/ecog.05904
You can also run:
SV, NL, CA, FL and CM coded the functions and their help. SV and CM led tutorial writings. All authors tested the functions and contributed to writing of helps and tutorials. NC optimized the package and made it ready for CRAN submission.