mFD (version 1.0.7)

alpha.fd.hill: Compute Functional alpha-Diversity indices based on Hill Numbers

Description

Compute functional alpha diversity applied to distance between species following the framework from Chao et al.(2019).

Usage

alpha.fd.hill(
  asb_sp_w,
  sp_dist,
  q = c(0, 1, 2),
  tau = "mean",
  check_input = TRUE,
  details_returned = TRUE
)

Value

A list with:

• asb_FD_Hill a matrix containing indices values for each level of q (columns, named as 'FD_qx') for each assemblage (rows, named as in asb_sp_w)

• tau_dist the threshold value applied to distance between species to compute diversity according to function provided in tau

• if details_returned turned to TRUE a list details with

  • asb_totw a vector with total weight of each assemblage

  • asb_sp_relw a matrix with relative weight of species in assemblages

Arguments

asb_sp_w

a matrix with weight of species (columns) in a set of assemblages (rows). Rows and columns should have names. NA are not allowed.

sp_dist

a matrix or dist object with distance between species. Species names should be provided and match those in 'asb_sp_w'. NA are not allowed.

q

a vector containing values referring to the order of diversity to consider, could be 0, 1 and/or 2.

tau

a character string with name of function to apply to distance matrix (i.e. among all pairs of species) to get the threshold used to define 'functionally indistinct set of species'. Could be 'mean' (default), 'min' or 'max'. If tau = 'min' and there are null values in sp_dist, the threshold is the lowest strictly positive value and a warning message is displayed.

check_input

a logical value indicating whether key features the inputs are checked (e.g. class and/or mode of objects, names of rows and/or columns, missing values). If an error is detected, a detailed message is returned. Default: check.input = TRUE.

details_returned

a logical value indicating whether the user want to store values used for computing indices (see list below)

Author

Sebastien Villeger and Camille Magneville

References

Chao et al. (2019) An attribute diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecological Monographs, 89, e01343.

Examples

# Load Species*Traits dataframe:
data('fruits_traits', package = 'mFD')

# Load Assemblages*Species dataframe:      
data('baskets_fruits_weights', package = 'mFD') 
  
# Compute functional distance 
sp_dist_fruits <- mFD::funct.dist(sp_tr         = fruits_traits,
                                  tr_cat        = fruits_traits_cat,
                                  metric        = "gower",
                                  scale_euclid  = "scale_center",
                                  ordinal_var   = "classic",
                                  weight_type   = "equal",
                                  stop_if_NA    = TRUE)

# Compute alpha fd hill indices:
alpha.fd.hill(
   asb_sp_w         = baskets_fruits_weights, 
   sp_dist          = sp_dist_fruits, 
   q                = c(0, 1, 2),
   tau              = 'mean', 
   check_input      = TRUE, 
   details_returned = TRUE)