Other functions of interest

library(funrar)

In addition to single index functions funrar proposes several functions to help you compute functional rarity indices. The aim of this vignette is to provide detailed examples of how to use each of them.

In this vignette we will use the dataset aravo (you can learn more about it through help("aravo", package = "ade4")) from the ade4 package as for the “Getting Started” vignette:

data("aravo", package = "ade4")

# Extract the traits of all species of aravo
traits = aravo$traits head(traits) #> Height Spread Angle Area Thick SLA N_mass Seed #> Agro.rupe 6 10 80 60.0 0.12 8.1 218.70 0.08 #> Alop.alpi 5 20 20 190.9 0.20 15.1 203.85 0.21 #> Anth.nipp 15 5 50 280.0 0.08 18.0 219.60 0.54 #> Heli.sede 0 30 80 600.0 0.20 10.6 233.20 1.72 #> Aven.vers 12 30 60 420.0 0.14 12.5 156.25 1.17 #> Care.rosa 30 20 80 180.0 0.40 6.5 208.65 1.68 Compute Distinctiveness from Global Pool - distinctiveness_global() While functional distinctiveness has been envisioned initially at local scale, that’s what the distinctiveness() function computes, it can be useful to compute it at regional scales. This functions provides exactly that based on a provided functional dissimilarity matrix. Using the aravo dataset we can compute the global distinctiveness of all species across the dataset in the following way: # Compute a Euclidean distance matrix (because all traits are quantitative) dist_matrix = compute_dist_matrix(traits, metric = "euclidean") # Compute global distinctiveness global_di = distinctiveness_global(dist_matrix) head(global_di) #> species global_di #> 1 Agro.rupe 248.7796 #> 2 Alop.alpi 231.9763 #> 3 Anth.nipp 248.7366 #> 4 Heli.sede 444.0815 #> 5 Aven.vers 330.0033 #> 6 Care.rosa 233.4236 The global distinctiveness is available in column global_di. You can rename this column using the second argument of distinctiveness_global(): aravo_di = distinctiveness_global(dist_matrix, di_name = "aravo_di") head(aravo_di) #> species aravo_di #> 1 Agro.rupe 248.7796 #> 2 Alop.alpi 231.9763 #> 3 Anth.nipp 248.7366 #> 4 Heli.sede 444.0815 #> 5 Aven.vers 330.0033 #> 6 Care.rosa 233.4236 NB: distinctiveness_global() assumes that all species in the provided trait dataset are present at global/regional scale. It also assumes only a presence-absence matrix. Compute functional rarity indices across combinations of traits Distinctiveness is generally computed through a dissimilarity matrix that itself comes from aggregating differences across multiple traits. Sometimes it can be useful to compare it to distinctiveness computed on each trait separately. This way, it is possible that some species appear distinct on certain traits and not others. The two functions below let you do exactly that for distinctiveness and uniqueness. Compare Distinctiveness computed on all traits and each trait - distinctiveness_dimensions() The function distinctiveness_dimensions() computes species local distinctiveness values from a site-species matrix and a trait data.frame. It will return a data frame with distinctiveness computed from all traits taken together and each trait taken separately. Let’s do this with the traits from the aravo dataset. The first argument of distinctiveness_dimensions() is supposed to be site-species matrix while the second should be a traits table: di_dim = distinctiveness_dimensions(as.matrix(aravo$spe), traits, metric = "euclidean")

str(di_dim, max.level = 1)
#> List of 9
#>  $di_Height: num [1:75, 1:82] NA NA 5.6 NA NA ... #> ..- attr(*, "dimnames")=List of 2 #>$ di_Spread: num [1:75, 1:82] NA NA 9.35 NA NA ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $di_Angle : num [1:75, 1:82] NA NA 31.5 NA NA ... #> ..- attr(*, "dimnames")=List of 2 #>$ di_Area  : num [1:75, 1:82] NA NA 218 NA NA ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $di_Thick : num [1:75, 1:82] NA NA 0.16 NA NA ... #> ..- attr(*, "dimnames")=List of 2 #>$ di_SLA   : num [1:75, 1:82] NA NA 4.44 NA NA ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $di_N_mass: num [1:75, 1:82] NA NA 39.5 NA NA ... #> ..- attr(*, "dimnames")=List of 2 #>$ di_Seed  : num [1:75, 1:82] NA NA 0.755 NA NA ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $di_all : num [1:75, 1:82] NA NA 240 NA NA ... #> ..- attr(*, "dimnames")=List of 2 We see that it returns a list of 9 local distinctiveness matrices: one for each trait and one for all traits taken together (named di_all). Compare Uniqueness computed on all traits and each trait - uniqueness_dimensions() The function uniqueness_dimensions() computes species regional uniqueness values from a site-species matrix and a trait data.frame. It will return a data frame with uniqueness computed from all traits taken together and each trait taken separately. Let’s do this with the traits from the aravo dataset. The first argument of uniqueness_dimensions() is supposed to be site-species matrix while the second should be a traits table: ui_dim = uniqueness_dimensions(as.matrix(aravo$spe), traits, metric = "euclidean")

str(ui_dim, max.level = 1)
#> 'data.frame':    82 obs. of  10 variables:
#>  $species : chr "Agro.rupe" "Alch.glau" "Alch.pent" "Alch.vulg" ... #>$ Ui_Height: num  1 0 0 0 0 0 0 0 0 0 ...
#>  $Ui_Spread: num 0 0 0 0 0 0 0 0 0 0 ... #>$ Ui_Angle : num  0 0 0 0 0 0 0 0 0 0 ...
#>  $Ui_Area : num 0 9.8 2.4 170.7 7.9 ... #>$ Ui_Thick : num  0 0 0 0 0 0 0 0 0 0 ...
#>  $Ui_SLA : num 0 0.1 0.5 0.5 0.1 ... #>$ Ui_N_mass: num  0.3 0.34 2.52 8.41 0.52 ...
#>  $Ui_Seed : num 0 0 0 0 0 ... #>$ Ui_all   : num  14.1 23.1 56.9 180 28.7 ...

We see that it returns a data frame with 10 columns: one for species names, one for each trait separately, and one for all traits taken together (named Ui_all).

Compute Relative Abundances - make_relative()

This function, probably largest source of citations of funrar, transforms a matrix of absolute abundances to relative abundances. It takes an abundance matrix and returns the same matrix with relative abundances.

We can check that with aravo site-species matrix:

aravo_site_sp = as.matrix(aravo\$spe)

# There are clearly abundances and not only presence-absence in this table
aravo_site_sp[1:5, 1:5]
#>      Agro.rupe Alop.alpi Anth.nipp Heli.sede Aven.vers
#> AR07         0         0         0         0         0
#> AR71         0         0         0         0         0
#> AR26         3         0         1         0         1
#> AR54         0         0         0         2         0
#> AR60         0         0         0         0         0

# Compute total abundance per site
site_abundance = rowSums(aravo_site_sp)

#> AR07 AR71 AR26 AR54 AR60 AR70
#>   15   23   37   23   13   32

# Compute a relative abundance matrix
relative_site_sp = make_relative(aravo_site_sp)

relative_site_sp[1:5, 1:5]
#>       Agro.rupe Alop.alpi  Anth.nipp  Heli.sede  Aven.vers
#> AR07 0.00000000         0 0.00000000 0.00000000 0.00000000
#> AR71 0.00000000         0 0.00000000 0.00000000 0.00000000
#> AR26 0.08108108         0 0.02702703 0.00000000 0.02702703
#> AR54 0.00000000         0 0.00000000 0.08695652 0.00000000
#> AR60 0.00000000         0 0.00000000 0.00000000 0.00000000

rel_site_abundance = rowSums(relative_site_sp)

#>    1    1    1    1    1    1
We see, through rel_site_abundance, that make_relative() transformed the abundances in each site so that they all sum to one. It divides the abundance of each species by the total abundance of the site.