fundiversity

fundiversity provides a lightweight package to compute common functional diversity indices. To a get a glimpse of what fundiversity can do refer to the introductory vignette. The package is built using clear, public design principles inspired from our own experience and user feedback.

Installation

You can install the stable version from CRAN with:

install.packages("fundiversity")

Alternatively, you can install the development version with:

install.packages("fundiversity", repos = "https://bisaloo.r-universe.dev")

Examples

fundiversity lets you compute six functional diversity indices: Functional Richness with fd_fric(), intersection with between convex hulls with fd_fric_intersect(), Functional Divergence with fd_fdiv(), Rao’s Quadratic Entropy with fd_raoq(), Functional Dispersion with fd_fdis() and Functional Evenness with fd_feve(). You can have a brief overview of the indices in the introductory vignette.

All indices can be computed either using global trait data or at the site-level:

library(fundiversity)

# Get trait data included in the package
data("traits_birds")

# Compute Functional Richness of all birds included
fd_fric(traits_birds)
#>   site     FRic
#> 1   s1 230967.7

# Compute Functional Divergence
fd_fdiv(traits_birds)
#>   site      FDiv
#> 1   s1 0.7282172

# Compute Rao's Quadratic Entropy
fd_raoq(traits_birds)
#>   site        Q
#> 1   s1 170.0519

# Compute Functional Dispersion
fd_fdis(traits_birds)
#>   site     FDis
#> 1   s1 146.2072

# Compute Functional Evenness
fd_feve(traits_birds)
#>   site      FEve
#> 1   s1 0.3743341

To compute Rao’s Quadratic Entropy, the user can also provide a distance matrix between species directly:

dist_traits_birds = as.matrix(dist(traits_birds))

fd_raoq(traits = NULL, dist_matrix = dist_traits_birds)
#>   site        Q
#> 1   s1 170.0519

Function Summary

Function Name	Index Name	Parallelizable[1]	Memoizable[2]
`fd_fric()`	FRic	✅	✅
`fd_fric_intersect()`	FRic_intersect	✅	✅
`fd_fdiv()`	FDiv	✅	✅
`fd_feve()`	FEve	✅	❌
`fd_fdis()`	FDis	✅	❌
`fd_raoq()`	Rao’s Q	❌	❌

Parallelization

Thanks to the future.apply package, all functions (except fd_raoq()) within fundiversity support parallelization through the future backend. To toggle parallelization follow the future syntax:

future::plan(future::multisession)
fd_fdiv(traits_birds)
#>   site      FDiv
#> 1   s1 0.7282172

For more details please refer to the parallelization vignette or use vignette("parallel", package = "fundiversity") within R.

Available functional diversity indices

According to Pavoine & Bonsall (2011) classification, functional diversity indices can be classified in three “domains” that assess different properties of the functional space: richness, divergence, and regularity. fundiversity provides function to compute indices that assess this three facets at the site scale:

Scale	Richness	Divergence	Evenness
α-diversity (= among sites)	FRic with `fd_fric()`	FDiv with `fd_fdiv()` Rao’s QE with `fd_raoq()` FDis with `fd_fdis()`	FEve with `fd_feve()`
β-diversity (= between sites)	FRic pairwise intersection with `fd_fric_intersect()` alternatives available in `betapart`	available in `entropart`, `betapart` or `hillR`	available in `BAT`

Several other packages exist that compute functional diversity indices. We did a performance comparison between related packages. We here mention some of them (but do not mention the numerous wrappers around these packages):

Package Name	Indices included	Has vignettes	Has tests	On GitHub
`adiv`	Functional Entropy, Functional Redundancy	✅	❌	❌
`BAT`	β-diversity indices, Richness, divergence, and evenness with hypervolumes	❌	❌	✅
`betapart`	Functional β-diversity	❌	❌	❌
`entropart`	Functional Entropy	✅	✅	✅
`FD`	FRic, FDiv, FDis, FEve, Rao’s QE, Functional Group Richness	❌	❌	❌
`hillR`	Functional Diversity Hill Number	❌	✅	✅
`hypervolume`	Hypervolume measure of functional diversity (~FRic)	❌	❌	✅
`mFD`	Functional α- and β-diversity indices, including FRic, FDiv, FDis, FEve, FIde, FMPD, FNND, FOri, FSpe, Hill Numbers	✅	❌	✅
`TPD`	FRic, FDiv, FEve but for probability distributions	✅	❌	❌
`vegan`	Only dendrogram-based FD (`treedive()`)	✅	✅	✅