Function reference • mFD

Summarize data These two functions are used to summarize up the species-traits and assemblage data.
`sp.tr.summary()`	Summarize Species x Traits data frame
`asb.sp.summary()`	Summarize Assemblage x Species data frame
Functional distances and Functional spaces These functions compute the functional distances between species, build functional spaces (if the data gather only continuous traits or not), assess/plot the quality of functional spaces (and dendrogram if asked), plot the chosen functional space and caracterize its functional axes based on traits correlation with each functional axes.
`funct.dist()`	Compute functional distance between species
`tr.cont.scale()`	Scale continuous traits
`tr.cont.fspace()`	Build a functional space based on continuous traits only
`quality.fspaces()`	Compute functional spaces and their quality
`quality.fspaces.plot()`	Plot functional space quality with a chosen quality metric
`funct.space.plot()`	Plot species position in a functional space
`traits.faxes.cor()`	Correlation between Traits and Axes
Compute Functional Entities This function gathers species into Functional Entities (FEs)
`sp.to.fe()`	Compute Functional Entities composition based on a Species x Traits matrix
Compute and Plot functional indices
Based on FEs These functions compute and plot functional indices based on FEs as in `Mouillot et al. (2014)`. They compute/plot Functional Redundancy, Functional Overredundancy and Functional Vulnerability.
`alpha.fd.fe()`	Compute the set of indices based on number of species in Functional Entities
`alpha.fd.fe.plot()`	Illustrate Functional Diversity indices based on Functional Entities
Based on Hill numbers These functions compute alpha and beta indices based on Hill numbers according to the Chao et al. (2019) framework.
`alpha.fd.hill()`	Compute Functional alpha-Diversity indices based on Hill Numbers
`beta.fd.hill()`	Compute Functional beta-Diversity indices based on Hill Numbers
FUSE index This function computes FUSE (Functionally Unique, Specialized, and Endangered) index that combines functional uniqueness, specialisation and global endangerment to identify threatened species of particular importance for functional diversity based on `Pimiento et al. (2020)`.
`fuse()`	Compute FUSE (Functionally Unique, Specialized and Endangered)
Based on multidimensional space These functions compute/plot alpha and beta indices based on a given multidimensional functional space.
`alpha.fd.multidim()`	Compute a set of alpha functional indices for a set of assemblages
`beta.fd.multidim()`	Compute Functional beta-Diversity indices for pairs of assemblages in a multidimensional space
`alpha.multidim.plot()`	Plot functional space and chosen functional indices
`beta.multidim.plot()`	Illustrate Functional beta-Diversity indices for pairs of assemblages in a multidimensional space
Based on multidimensinal space for more complex graphs These functions return `ggplot` layers for each index allowing users to draw more complex graphs.
`background.plot()`	Plot background of multidimensional plots
`fdiv.plot()`	Plot FDiv indice
`fdis.plot()`	Plot FDis index
`feve.plot()`	Plot FEve index
`fide.plot()`	Plot FIde index
`fnnd.plot()`	Plot FNND index
`fori.plot()`	Plot FOri
`fric.plot()`	Plot FRic index
`fspe.plot()`	Plot FSpe
`panels.to.patchwork()`	Plot individual plots along a pair of functional axes into a unique graph
`pool.plot()`	Plot species from the pool
Other functions Various functions that can be used by the user for diverse usage
`dist.nearneighb()`	Compute distance of a given point to its nearest neighbor in the functional space and the identity of the nearest neighbor
`dist.point()`	Compute distances of all points to a given point in the functional space
`dist.to.df()`	Merge distance object(s) into a single data frame
`mst.computation()`	Compute the Minimum Spanning Tree (MST) linking species of a given assemblage
`sp.filter()`	Retrieve information about species in a given assemblage
`vertices()`	Compute vertices of the Minimal Convex Hull shaping species from a single assemblage in a multidimensional functional space
Data sets The three data sets used for examples and tutorials in the `mFD` package.
`baskets_fruits_weights`	Dataset: Baskets Composition in Fruits Species
`fruits_traits`	Dataset: Traits Values of Fruits Species
`fruits_traits_cat`	Dataset: Fruits Traits Informations

Reference

Summarize data

Functional distances and Functional spaces

Compute Functional Entities

Compute and Plot functional indices

Based on FEs

Based on Hill numbers

FUSE index

Based on multidimensional space

Based on multidimensinal space for more complex graphs

Other functions

Data sets