# Measuring Functional Diversity from Multiple Traits, and Other Tools for Functional Ecology

### Description

FD is a package to compute different multidimensional functional diversity (FD) indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other tools for functional ecologists (e.g. `maxent`

).

### Details

Package: | FD |

Type: | Package |

Version: | 1.0-12 |

Date: | 2014-08-19 |

License: | GPL-2 |

LazyLoad: | yes |

LazyData: | yes |

FD computes different multidimensional FD indices. To compute FD indices, a species-by-trait(s) matrix is required (or at least a species-by-species distance matrix). `gowdis`

computes the Gower dissimilarity from different trait types (continuous, ordinal, nominal, or binary), and tolerates `NA`

s. It can treat ordinal variables as described by Podani (1999), and can handle asymetric binary variables and variable weights. `gowdis`

is called by `dbFD`

, the main function of FD.

`dbFD`

uses principal coordinates analysis (PCoA) to return PCoA axes, which are then used as ‘traits’ to compute FD. `dbFD`

computes several multidimensional FD indices, including the three indices of Villéger et al. (2008): functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv). It also computes functional dispersion (FDis) (Laliberté and Legendre 2010), Rao's quadratic entropy (Q) (Botta-Dukát 2005), a posteriori functional group richness (FGR), and the community-level weighted means of trait values (CWM), an index of functional composition. Some of these indices can be weighted by species abundances. `dbFD`

includes several options for flexibility.

### Author(s)

Etienne Laliberté, Pierre Legendre and Bill Shipley

Maintainer: Etienne Laliberté <etiennelaliberte@gmail.com> http://www.elaliberte.info

### References

Botta-Dukát, Z. (2005) Rao's quadratic entropy as a measure of functional diversity based on multiple traits. *Journal of Vegetation Science* **16**:533-540.

Laliberté, E. and P. Legendre (2010) A distance-based framework for measuring functional diversity from multiple traits. *Ecology* **91**:299-305.

Podani, J. (1999) Extending Gower's general coefficient of similarity to ordinal characters. *Taxon* **48**:331-340.

Villéger, S., N. W. H. Mason and D. Mouillot (2008) New multidimensional functional diversity indices for a multifaceted framework in functional ecology. *Ecology* **89**:2290-2301.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
# examples with a dummy dataset
ex1 <- gowdis(dummy$trait)
ex1
ex2 <- functcomp(dummy$trait, dummy$abun)
ex2
ex3 <- dbFD(dummy$trait, dummy$abun)
ex3
# examples with real data from New Zealand short-tussock grasslands
# these examples may take a few seconds to a few minutes each to run
ex4 <- gowdis(tussock$trait)
ex5 <- functcomp(tussock$trait, tussock$abun)
# 'lingoes' correction used because 'sqrt' does not work in that case
ex6 <- dbFD(tussock$trait, tussock$abun, corr = "lingoes")
## Not run:
# ward clustering to compute FGR, cailliez correction
ex7 <- dbFD(tussock$trait, tussock$abun, corr = "cailliez",
calc.FGR = TRUE, clust.type = "ward")
# choose 'g' for number of groups
# 6 groups seems to make good ecological sense
ex7
# however, calinksi criterion in 'kmeans' suggests
# that 6 groups may not be optimal
ex8 <- dbFD(tussock$trait, tussock$abun, corr = "cailliez",
calc.FGR = TRUE, clust.type = "kmeans", km.sup.gr = 10)
## End(Not run)
``` |