funrar (version 1.5.0)

distinctiveness_stack: Functional Distinctiveness on a stacked data.frame

Description

Compute Functional Distinctiveness for several communities, from a stacked (or tidy) data.frame of communities, with one column for species identity, one for community identity and an optional one for relative abundances. Also needs a species functional distances matrix. Functional distinctiveness relates to the functional "originality" of a species in a community. The closer to 1 the more the species is functionally distinct from the rest of the community. See distinctiveness() function or the functional rarity indices vignette included in the package (type vignette("rarity_indices", package = "funrar")), for more details on the metric. IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1. You can either use _stack() or _tidy() functions as they are aliases of one another.

Usage

distinctiveness_stack(
  com_df,
  sp_col,
  com,
  abund = NULL,
  dist_matrix,
  relative = FALSE
)
distinctiveness_tidy(
  com_df,
  sp_col,
  com,
  abund = NULL,
  dist_matrix,
  relative = FALSE
)

Value

the same data.frame with the additional Di column giving functional distinctiveness values for each species

Arguments

com_df

a stacked (= tidy) data.frame from a single community with each row representing a species in a community

sp_col

a character vector, the name of the species column in com_df

com

a character vector, the column name for communities names

abund

a character vector, the name of the column containing relative abundances values

dist_matrix

a functional distance matrix as given by compute_dist_matrix(), with species name as row and column names

relative

a logical indicating if distinctiveness should be scaled relatively to the community (scaled by max functional distance among the species of the targeted community)

See Also

scarcity_stack(), uniqueness_stack(), restrictedness_stack(); distinctiveness() Details section for detail on the index

Examples

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

# Example of trait table
tra = aravo$traits[, c("Height", "SLA", "N_mass")]
# Distance matrix
dist_mat = compute_dist_matrix(tra)

# Site-species matrix converted into data.frame
mat = as.matrix(aravo$spe)
mat = make_relative(mat)
dat = matrix_to_stack(mat, "value", "site", "species")
dat$site = as.character(dat$site)
dat$species = as.character(dat$species)

di_df = distinctiveness_stack(dat, "species", "site", "value", dist_mat)
head(di_df)