Package index • 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. They also help to work with FEs.

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

fe.sp.df.computation(): Get a data frame linking Functional Entities names and species names

from.fecoord.to.spcoord(): Convert the data frame of FEs coordinates to a species coordinates one

from.spfe.to.feasb(): Build the assemblage-FEs dataframe from the assemblages-species one

search.sp.nm(): Get the names of species belonging to a specific Functional Entity (FE)

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