`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.

You can install the stable version from CRAN with:

Alternatively, you can install the development version with:

`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 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 | ❌ | ❌ |

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:

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

within R.

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 | On CRAN (last updated) |
---|---|---|---|---|---|

`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()` ) |
✅ | ✅ | ✅ |

parallelization through the

`future`

backend please refer to the parallelization vignette for details.memoization means that the results of the functions calls are cached and not recomputed when recalled, to toggle it off see the

`fundiversity::fd_fric()`

Details section.