Package index

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

sp.to.fe()
 Compute Functional Entities composition based on a Species x Traits matrix
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 alphaDiversity indices based on Hill Numbers

beta.fd.hill()
 Compute Functional betaDiversity 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 betaDiversity indices for pairs of assemblages in a multidimensional space

alpha.multidim.plot()
 Plot functional space and chosen functional indices

beta.multidim.plot()
 Illustrate Functional betaDiversity 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

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

baskets_fruits_weights
 Dataset: Baskets Composition in Fruits Species

fruits_traits
 Dataset: Traits Values of Fruits Species

fruits_traits_cat
 Dataset: Fruits Traits Informations