Package 'funrar'

Description

Internal function to compute combinations of distance matrices from a data.frame of traits, using compute_dist_matrix().

Usage

combination_trait_dist(traits_table, ...)

Arguments

Value

A list of functional distance matrices, one for each provided trait plus an additional matrix for all traits taken altogether

Description

Wrapper for cluster::daisy() function in cluster package, to compute distance matrix of trait between each pair of species present in given traits_table, each row represents a species and each column a trait. To be able to compute other metrics traits_table must have species name as row names.

Usage

compute_dist_matrix(
  traits_table,
  metric = "gower",
  center = FALSE,
  scale = FALSE
)

Arguments

Details

The functional distance matrix can be computed using any type of distance metric. When traits are both quantitative and qualitative Gower's (Gower, 1971; Podani, 1999) distance can be used. Otherwise, any other distance metric (Euclidean, Manhattan, Minkowski) can be used - as long as the rows and the columns are named following the species. When using mixed data consider also Gower's distance extension by Pavoine et al. (2009). IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1.

Value

A functional distance matrix, column and row names follow species name from traits_table row names.

References

Gower, J.C. (1971) A general coefficient of similarity and some of its
properties. Biometrics, 857–871.

Podani, J. (1999) Extending Gower’s general coefficient of similarity
to ordinal characters. Taxon, 331–340.

Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S., & Daniel, H. (2009)
On the challenge of treating various types of variables: application for
improving the measurement of functional diversity. Oikos, 118, 391–402.

See Also

cluster::daisy() which this function wraps, base stats::dist() or ade4::dist.ktab() for Pavoine et al. (2009) extension of Gower's distance.

Examples

set.seed(1)  # For reproducibility
trait = data.frame(
   sp = paste("sp", 1:5),
   trait_1 = runif(5),
   trait_2 = as.factor(c("A", "A", "A", "B", "B")))

rownames(trait) = trait$sp

dist_mat = compute_dist_matrix(trait[, -1])

Description

Computes functional distinctiveness from a site-species matrix (containing presence-absence or relative abundances) of species with provided functional distance matrix. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

distinctiveness(pres_matrix, dist_matrix, relative = FALSE)

Arguments

Details

The Functional Distinctiveness of a species is the average functional distance from a species to all the other in the given community. It is computed as such:

$D_i = \frac{\sum_{j = 0, i \neq j}^N d_{ij}}{N-1},$

with $D_i$ the functional distinctiveness of species $i$, $N$ the total number of species in the community and $d_{ij}$ the functional distance between species $i$ and species $j$. IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1.

Value

a similar matrix from provided pres_matrix with Distinctiveness values in lieu of presences or relative abundances, species absent from communities will have an NA value (see Note section)

Note

Absent species should be coded by 0 or NA in input matrices.

When a species is alone in its community the functional distinctiveness cannot be computed (denominator = 0 in formula), and its value is assigned as NaN.

For speed and memory efficiency sparse matrices can be used as input of the function using as(pres_matrix, "dgCMatrix") from the Matrix package. (see vignette("sparse_matrices", package = "funrar"))

Examples

data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)

# Compute relative abundances
mat = make_relative(mat)

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

di = distinctiveness(pres_matrix = mat, dist_matrix = dist_mat)
di[1:5, 1:5]

# Compute distinctiveness for all species in the regional pool
# i.e., with all the species in all the communities
# Here considering each species present evenly in the regional pool
reg_pool = matrix(1, ncol = ncol(mat))
colnames(reg_pool) = colnames(mat)
row.names(reg_pool) = c("Regional_pool")

reg_di = distinctiveness(reg_pool, dist_mat)

Description

Computes functional distinctiveness from a site-species matrix (containing presence-absence or relative abundances) of species with provided functional distance matrix considering only species within a given range in the functional space. Basically species are cutoff when their dissimilarity is above the input threshold. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

distinctiveness_alt(pres_matrix, dist_matrix, given_range)

Arguments

Details

The Functional Distinctiveness of a species is the average functional distance from a species to all the other in the given community. It is computed as such:

$D_i(T) = \frac{ \sum\limits_{j = 1 ~,j \neq i ~}^S \left[ \frac{d_{ij}}{T} + \theta(d_{ij} - T) \left(1 - \frac{d_{ij}}{T} \right) \right] }{ S - 1 }$

with $D_i$ the functional distinctiveness of species $i$, $N$ the total number of species in the community and $d_{ij}$ the functional distance between species $i$ and species $j$. $T$ is the chosen maximal range considered. The function $\theta(d_ij - T)$ is an indicator function that returns 1 when $d_{ij} \geq T$ and 0 when $d_{ij} < T$. IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1.

Value

a similar matrix from provided pres_matrix with Distinctiveness values in lieu of presences or relative abundances, species absent from communities will have an NA value (see Note section)

Note

Absent species should be coded by 0 or NA in input matrices.

When a species is alone in its community the functional distinctiveness cannot be computed (denominator = 0 in formula), and its value is assigned as NaN.

For speed and memory efficiency sparse matrices can be used as input of the function using as(pres_matrix, "dgCMatrix") from the Matrix package. (see vignette("sparse_matrices", package = "funrar"))

Description

Given a stacked data.frame and a distance matrix compute the functional distinctiveness for a single community. 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.

Usage

distinctiveness_com(
  com_df,
  sp_col,
  abund = NULL,
  dist_matrix,
  relative = FALSE
)

Arguments

Value

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

Caution

This function is meant for internal uses mostly, thus it does not include any tests on inputs and may fail unexpectedly. Please use distinctiveness_stack() to avoid input errors.

See Also

scarcity_com(), vignette("rarity_indices", package = "funrar") and distinctiveness() Details section for detail on the index

Description

From a trait data.frame and a site-species matrix compute Distinctiveness (average pairwise functional distance) for each species in each community on each provided trait and on all traits taken altogether.

Usage

distinctiveness_dimensions(pres_matrix, traits_table, ...)

Arguments

Value

a list of site-species matrix with functional distinctiveness values per species per site, with elements Di_X for distinctiveness computed on trait X and Di_all for distinctiveness computed on all traits.

See Also

uniqueness_dimensions(), distinctiveness(), distinctiveness_stack() and compute_dist_matrix() for additional arguments

Examples

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

# Site-species matrix
mat = as.matrix(aravo$spe)
rel_mat = make_relative(mat)

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

di_dim = distinctiveness_dimensions(rel_mat, tra)

Description

Given a distance (or dissimilarity) matrix or dist() objects compute regional/global level distinctiveness as if all species were present in the same community.

Usage

distinctiveness_global(dist_obj, di_name = "global_di")

Arguments

Value

a data.frame with two columns: by default species that contains the species names and global_di that contains the distinctiveness values. The first column that contains species names can renamed based on dist_obj dimnames, while the second column is renamed through the di_name argument.'

See Also

vignette("rarity_indices", package = "funrar") and distinctiveness() Details section for detail on the index

Description

Computes functional distinctiveness from a site-species matrix (containing presence-absence or relative abundances) of species with provided functional distance matrix considering only species within a given range in the functional space. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

distinctiveness_range(pres_matrix, dist_matrix, given_range, relative = FALSE)

Arguments

Details

The Functional Distinctiveness of a species is the average functional distance from a species to all the other in the given community. It is computed as such:

$D_i(T) = 1 ~~ if ~~ T < min(d_{ij}), \\ D_i(T) = \left( \frac{ \sum\limits_{j = 1 ~, j \neq i ~, d_{ij} \leq T}^S d_{ij} \times Ab_j }{ \sum\limits_{ j = 1 ~, j \neq i ~, d_{ij} \leq T}^S Ab_j } \right) \times \left(1 - \frac{ \sum\limits_{ j = 1 ~, j \neq i ~, d_{ij} \leq T}^S Ab_j }{ N } \right) ~~ if ~~ T \geq min(d_{ij}),$

with $D_i$ the functional distinctiveness of species $i$, $N$ the total number of species in the community and $d_{ij}$ the functional distance between species $i$ and species $j$. $T$ is the chosen maximal range considered. When presence-absence are used $Ab_j = 1/N$ and the term $\left(1 - \frac{ \sum\limits_{ j = 1 ~, j \neq i ~, d_{ij} \leq T}^S Ab_j }{ N } \right)$ is replaced by 1. IMPORTANT NOTE: in order to get functional rarity indices between 0 and 1, the distance metric has to be scaled between 0 and 1.

Value

a similar matrix from provided pres_matrix with Distinctiveness values in lieu of presences or relative abundances, species absent from communities will have an NA value (see Note section)

Note

Absent species should be coded by 0 or NA in input matrices.

When a species is alone in its community the functional distinctiveness cannot be computed (denominator = 0 in formula), and its value is assigned as NaN.

For speed and memory efficiency sparse matrices can be used as input of the function using as(pres_matrix, "dgCMatrix") from the Matrix package. (see vignette("sparse_matrices", package = "funrar"))

Examples

data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)

# Compute relative abundances
mat = make_relative(mat)

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

di = distinctiveness_range(pres_matrix = mat, dist_matrix = dist_mat, 0.2)
di[1:5, 1:5]

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
)

Arguments

Value

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

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)

Description

From a site-species matrix and functional distance matrix compute all indices included in the package: functional uniqueness (regional, functional), functional distinctiveness (local, functional), geographical restrictedness (regional, extent), scarcity (local, abundance). Note: scarcity can only be computed if relative abundances are provided in the site-species matrix.

Usage

funrar(pres_matrix, dist_matrix, rel_abund = FALSE)

Arguments

Value

A list of 3 objects (or 4 if rel_abund = TRUE):

Ui

a vector containing uniqueness values per species,

Di

a site-species matrix with functional distinctiveness values per species per site,

Ri

a vector containing geographical restrictedness values per species,

and if rel_abund = TRUE,

Si

a site-species matrix with scarcity values per species per site.

See Also

uniqueness(), distinctiveness(), restrictedness(), scarcity()

Description

From a stacked (= tidy) data.frame and functional distance matrix compute all indices included in the package: functional uniqueness (regional, functional), functional distinctiveness (local, functional), geographical restrictedness (regional, extent), scarcity (local, abundance). Note: scarcity can only be computed if relative abundances are provided in the data.frame.

Usage

funrar_stack(com_df, sp_col, com, abund = NULL, dist_matrix)

Arguments

Value

A list of 3 objects (or 4 if abund is not NULL):

Ui

a vector containing uniqueness values per species,

Di

a site-species matrix with functional distinctiveness values per species per site,

Ri

a vector containing geographical restrictedness values per species,

and if abund is not NULL,

Si

a site-species matrix with scarcity values per species per site.

See Also

uniqueness_stack(), distinctiveness_stack(), restrictedness_stack(), scarcity_stack()

Description

From an abundance/presence-absence matrix or data.frame tells if it contains relative abundances or absolute abundances. Checks if all abundances are between 1 and 0 but never checks sum of abundances per community.

Usage

is_relative(given_obj, abund = NULL)

Arguments

Value

TRUE if the input has relative abundances FALSE otherwise

See Also

make_relative() to transform matrix into a relative abundance matrix.

Examples

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

# Site-species matrix
mat = as.matrix(aravo$spe)
head(mat)[, 1:5]  # Has absolute abundances
rel_mat = make_relative(mat)
head(rel_mat)  # Relative abundances

# Forced to use ':::' becasue function is not exported
funrar:::is_relative(mat)      # FALSE
funrar:::is_relative(rel_mat)  # TRUE

Description

From an abundance matrix (numbers of individuals of a given species at a site) returns a relative abundance matrix (proportion of individuals of a given species at a given site). This function works also with sparse matrices.

Usage

make_relative(abund_matrix)

Arguments

Value

Similar shaped matrix as the input but with relative abundances instead

Examples

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

# Site-species matrix
mat = as.matrix(aravo$spe)
head(mat)[, 1:5]  # Has absolute abundances
rel_mat = make_relative(mat)
head(rel_mat)  # Relative abundances

Description

From a matrix with values to a stacked (= tidy) data.frame, exclude NA from given data.frame. If supplied object is not a matrix, try to coerce object to matrix first. matrix_to_tidy() is an alias of this function.

Usage

matrix_to_stack(
  my_mat,
  value_col = "value",
  row_to_col = names(dimnames(my_mat))[1],
  col_to_col = names(dimnames(my_mat))[2]
)

Arguments

Value

a stacked (= tidy) data.frame with, a column for row names, one for column names and a third one for the values.

See Also

stack_to_matrix() for the reverse operation

Examples

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

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

Description

Computes geographical restrictedness from a site-species matrix. Geographical restrictedness is an index related to the extent of a species in a given dataset, it is close to 1 when the species is present in only a single site of the dataset (restricted) and close to 0 when the species is present at all sites. It estimates the geographical extent of a species in a dataset. See Details section to have details on the formula used for the computation. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

restrictedness(pres_matrix, relative = FALSE)

Arguments

Details

Geographical Restrictedness aims to measure the regional extent of a species in funrar it is computed the simplest way possible: a ratio of the number of sites where a species is present over the total number of sites in the dataset. We take this ratio off 1 to have a index between 0 and 1 that represents how restricted a species is:

$R_i = 1 - \frac{N_i}{N_tot},$

where $R_i$ is the geographical restrictedness value, $N_i$ the total number of sites where species $i$ occur and $N_tot$ the total number of sites in the dataset. When relative = TRUE, restrictedness is computed relatively to the restrictedness of a species present in a single site:

$R_i = \frac{R_i}{R_one}$

$R_i = \frac{1 - \frac{K_i}{K_tot}}{1 - \frac{1}{K_tot}}$

$R_i = \frac{K_tot - K_i}{K_tot - 1}$

Other approaches can be used to measure the geographical extent (convex hulls, occupancy models, etc.) but for the sake of simplicity only the counting method is implemented in funrar.

Value

A stacked data.frame containing species' names and their restrictedness value in the Ri column, similar to what uniqueness() returns.

Examples

data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)
ri = restrictedness(mat)
head(ri)

Description

Compute the geographical restrictedness for each species present in the stacked data.frame. Geographical restrictedness is an index related to the extent of a species in a given dataset, it is close to 1 when the species is present in only a single site of the dataset (restricted) and close to 0 when the species is present at all sites. It estimates the geographical extent of a species in a dataset. See restrictedness() for details on restrictedness computation. You can either use ⁠_stack()⁠ or ⁠_tidy()⁠ functions as they are aliases of one another.

Usage

restrictedness_stack(com_df, sp_col, com, relative = FALSE)

restrictedness_tidy(com_df, sp_col, com, relative = FALSE)

Arguments

Value

A stacked data.frame containing species' names and their restrictedness value in the Ri column, similar to what uniqueness_stack() returns.

See Also

restrictedness(), uniqueness_stack()

Examples

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

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

Description

Computes scarcity from a relative abundance matrix of species. Scarcity is close to 1 when a species is rare in a community and close to 0 when it is abundant. It requires a site-species matrix with relative abundances. See Details section for the formula. The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

scarcity(pres_matrix)

Arguments

Details

The scarcity of species is computed as follow:

$S_i = \exp{-N \log{2} A_i},$

with $S_i$ the scarcity of species $i$, $N$ the total number of species in the community and $A_i$ the relative abundance of species $i$ in the community. Scarcity is thus a measure of the local rarity in terms of abundances. If $S_i$ is close to 1 the species has a very low abundances while if it's close to 0, it is quite abundant in the community.

Value

a similar matrix to pres_matrix with scarcity values in lieu of relative abundances.

See Also

vignette("rarity_indices", package = "funrar") for details on the scarcity metric; distinctiveness(), restrictedness(), uniqueness()

Examples

data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)
mat = make_relative(mat)

si = scarcity(pres_matrix = mat)
si[1:5, 1:5]

Description

Given a stacked data.frame compute species scarcity. Scarcity measures how abundant is a species locally. Scarcity is close to 1 when a species is rare in a community and close to 0 when it is abundant. See scarcity() function or the functional rarity indices vignette included in the package (type vignette("rarity_indices", package = "funrar")) for details about the index.

Usage

scarcity_com(com_df, sp_col, abund)

Arguments

Value

the same data.frame with the additional Si column giving scarcity values for each species

Caution

This function is meant for internal uses mostly, thus it does not include any tests on inputs and may fail unexpectedly. Please use scarcity_stack() to avoid input errors.

See Also

scarcity() and vignette("rarity_indices", package = "funrar") for details on the scarcity metric; distinctiveness_com() to compute distinctiveness on a single community

Examples

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

# 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)

si_df = scarcity_com(subset(dat, site == "AR07"), "species", "value")
head(si_df)

Description

Compute scarcity values for several communities. Scarcity computation requires relative abundances. Scarcity is close to 1 when a species is rare in a community and close to 0 when it is abundant. See scarcity() function or the functional rarity indices vignette included in the package (type vignette("rarity_indices", package = "funrar")) for details about the index. You can either use ⁠_stack()⁠ or ⁠_tidy()⁠ functions as they are aliases of one another.

Usage

scarcity_stack(com_df, sp_col, com, abund)

scarcity_tidy(com_df, sp_col, com, abund)

Arguments

Value

The same table as com_df with an added $S_i$ column for Scarcity values.

See Also

scarcity() and vignette("rarity_indices", package = "funrar") for details on the scarcity metric; distinctiveness_stack(), restrictedness_stack(), uniqueness_stack()

Examples

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

# 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)

si_df = scarcity_stack(dat, "species", "site", "value")
head(si_df)

Description

Passes from a stacked (= tidy) data.frame to a matrix. tidy_to_matrix() is an alias of this function.

Usage

stack_to_matrix(
  my_df,
  col_to_row,
  col_to_col,
  col_value = NULL,
  sparse = FALSE
)

Arguments

Value

a matrix with given col_to_row column in rows and col_to_col column in columns. If some cells are not present in the data.frame (e.g. some species not present at some sites), the matrix will have a NA value.

See Also

matrix_to_stack() for the reverse operation

Examples

example = data.frame("sites" = c(rep("1", 3), rep("2", 2)),
 "species" = c("A", "B", "C", "B", "D"),
  "abundance" = c(0.33, 0.33, 0.33, 0.4, 0.6))

mat = stack_to_matrix(example, "sites", "species", "abundance")
mat

Description

Computes the functional uniqueness from a site-species matrix with the provided functional distance matrix. Functional Uniqueness represents how "isolated" is a species in the global species pool, it is the functional distance to the nearest neighbor of the species of interest (see Details section for the formula). The sites-species matrix should have sites in rows and species in columns, similar to vegan package defaults.

Usage

uniqueness(pres_matrix, dist_matrix)

Arguments

Details

Functional Uniqueness $U_i$ is computed as follow:

$U_i = \min(d_{ij}) \forall j, j \neq i,$

with $U_i$ the functional uniqueness of species $i$, and $d_ij$ the functional distance between species $i$ and species $j$

Value

A data.frame with functional uniqueness values per species, with one column with provided species column name and the Ui column with functional uniqueness values.

See Also

distinctiveness(), restrictedness(), scarcity()

Examples

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

# Site-species matrix
mat = as.matrix(aravo$spe)
colnames(mat) = as.character(colnames(mat))

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

ui = uniqueness(mat, dist_mat)
head(ui)

# Computing uniqueness for each community
com_ui = apply(mat, 1,
                function(x, dist_m) {
                    smaller_com = x[x > 0 & !is.na(x)]
                    uniqueness(t(as.matrix(smaller_com)), dist_m)
                }, dist_m = dist_mat)

Description

From a trait table and a site-species matrix compute Uniqueness (nearest functional distance) for each species and each trait, plus computes it for all the traits.

Usage

uniqueness_dimensions(pres_matrix, traits_table, ...)

Arguments

Value

a data.frame containing species' names and their uniqueness values for each traits (Ui_X column for trait X), as well as a column for the uniqueness value for all traits (Ui_all column)

See Also

distinctiveness_dimensions(), uniqueness(), uniqueness_stack() and compute_dist_matrix() for additional arguments

Examples

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

# Site-species matrix
mat = as.matrix(aravo$spe)
rel_mat = make_relative(mat)

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

ui_dim = uniqueness_dimensions(rel_mat, tra)

Description

Computes functional uniqueness values over a given regional pool. Functional uniqueness gives the functional distance to the nearest-neighbor of a given species in the provided distance matrix. See uniqueness() function for details on computation. You can either use ⁠_stack()⁠ or ⁠_tidy()⁠ functions as they are aliases of one another.

Usage

uniqueness_stack(com_df, sp_col, dist_matrix)

uniqueness_tidy(com_df, sp_col, dist_matrix)

Arguments

Value

A data.frame with uniqueness value per species, with one column with provided species column name and the Ui column with the uniqueness values.

See Also

uniqueness() and vignette("rarity_indices", package = "funrar") for details on the uniqueness metric; distinctiveness_stack(), restrictedness_stack(), scarcity_stack()

Examples

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

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

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

ui_df = uniqueness_stack(dat, "species", dist_mat)
head(ui_df)