How to Deal With Functional Entities"
In mFD: Compute and Illustrate the Multiple Facets of Functional Diversity

1. Why Functional Entities (FEs)?

mFD allows gathering species into functional entities (FEs) i.e. groups of species with same trait values when many species are described with a few categorical or ordinal traits. It is particularly useful when using large datasets with "functionally similar" species. FEs also allow to understand the links between functional diversity and ecological processes as redundant species that are supposed to have similar ecological roles are clustered in this method.

2. Tutorial's data

DATA The dataset used to illustrate this tutorial is a fruits dataset based on 25 types of fruits (i.e. species) distributed in 10 fruit baskets (i.e. assemblages). Each fruit is characterized by six trait values summarized in the following table:

NOTE We reduced the dataset used in mFD General Workflow to only keep ordinal and categorical traits. Categorical traits are restrained to 2 or 3 modalities per traits to limit the number of unique combinations.

The following data frame and matrix are needed:

the data frame summarizing traits values for each species called fruits_traits data frame in this tutorial:

data("fruits_traits", package = "mFD")

fruits_traits <- fruits_traits[ , 1:4]      # only keep the first 4 traits to illustrate FEs

# Decrease the number of modalities per trait for convenience ...
# ... (to have less unique combinations of trait values):

# Size grouped into only 3 categories:
fruits_traits[ , "Size"] <- as.character(fruits_traits[ , "Size"])

fruits_traits[which(fruits_traits[ , "Size"] %in% c("0-1cm", "1-3cm", "3-5cm")), "Size"] <- "small"
fruits_traits[which(fruits_traits[ , "Size"] == "5-10cm"), "Size"]  <- "medium"
fruits_traits[which(fruits_traits[ , "Size"] == "10-20cm"), "Size"] <- "large"

fruits_traits[ , "Size"] <- factor(fruits_traits[, "Size"], levels = c("small", "medium", "large"), ordered = TRUE)

# Plant type grouped into only 2 categories:
fruits_traits[ , "Plant"] <- as.character(fruits_traits[, "Plant"])

fruits_traits[which(fruits_traits[ , "Plant"] != "tree"), "Plant"] <- "Not_tree"
fruits_traits[ , "Plant"] <- factor(fruits_traits[ , "Plant"], levels = c("Not_tree", "tree"), ordered = TRUE)

# Plant Origin grouped into only 2 categories:
fruits_traits[ , "Climate"] <- as.character(fruits_traits[ , "Climate"])

fruits_traits[which(fruits_traits[ , "Climate"] != "temperate"), "Climate"] <- "tropical"
fruits_traits[ , "Climate"] <- factor(fruits_traits[, "Climate"], levels = c("temperate", "tropical"), ordered = TRUE)

# Display the table:
knitr::kable(head(fruits_traits), caption = "Species x traits dataframe based on *fruits* dataset")

the matrix summarizing assemblages called basket_fruits_weights in this tutorial:

data("baskets_fruits_weights", package = "mFD")

knitr::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]), 
             caption = "Species x assemblages dataframe based on *fruits* dataset")

the data frame summarizing traits categories called fruits_traits_cat in this tutorial: (for details: mFD General Workflow)

data("fruits_traits_cat", package = "mFD")

# only keep traits 1 - 4:
fruits_traits_cat <- fruits_traits_cat[1:4, ]

knitr::kable(head(fruits_traits_cat), 
             caption = "Traits types based on *fruits & baskets* dataset")

Using the mFD::asb.sp.summary() function, we can sum up the assemblages data and retrieve species occurrence data:

# summarize species assemblages: 
asb_sp_fruits_summ <- mFD::asb.sp.summary(baskets_fruits_weights)

# retrieve species occurrences for the first 3 assemblages (fruits baskets):
head(asb_sp_fruits_summ$asb_sp_occ, 3)

asb_sp_fruits_occ <- asb_sp_fruits_summ$"asb_sp_occ"

3. Gather species into FEs

mFD allows you to gather species into FEs using the mFD::sp.to.fe() function. It uses the following arguments:

USAGE

mFD::sp.to.fe(
  sp_tr       = fruits_traits, 
  tr_cat      = fruits_traits_cat, 
  fe_nm_type  = "fe_rank", 
  check_input = TRUE)

sp_tr the data frame of species traits
tr_cat the data frame summarizing traits categories
fe_nm_type is a character string referring to the way FEs should be named: they can be named after their decreasing rank in term of number of species (i.e. fe_1 is the one gathering most species) (fe_rank) or they can be named after names of traits
check_input is a logical value reflecting whether inputs should be checked or not. Possible error messages will thus be more understandable for the user than R error messages NOTE Recommendation: set it as TRUE

Let's use this function with the fruits dataset:

sp_to_fe_fruits <- mFD::sp.to.fe(
  sp_tr       = fruits_traits, 
  tr_cat      = fruits_traits_cat, 
  fe_nm_type  = "fe_rank", 
  check_input = TRUE)

mFD::sp.to.fe() returns:

a vector containing FEs names:

sp_to_fe_fruits$"fe_nm"

a vector containing for each species, the FE it belongs to:

sp_fe <- sp_to_fe_fruits$"sp_fe"
sp_fe

a data frame containing for FEs, the values of traits for each FE:

fe_tr <- sp_to_fe_fruits$"fe_tr"
fe_tr

a vector containing the number of species per FE:

fe_nb_sp <- sp_to_fe_fruits$"fe_nb_sp"
fe_nb_sp

a detailed list containing vectors or list with supplementary information about FEs:

sp_to_fe_fruits$"details_fe"

4. Compute alpha and beta functional indices

Then based on the data frame containing the value of traits for each FE, the workflow is the same as the one listed in mFD General Workflow to compute functional trait based distance, multidimensional functional space and associated plots and compute alpha and beta functional indices (step 3 till the end). It will thus not be summed up in this tutorial.

mFD also allows to compute functional indices based on FEs following the framework proposed in Mouillot et al. 2014) using the mFD::alpha.fd.fe() function. It computes:

Functional Redundancy that reflects the average number of species per FE
Functional Overredundancy that reflects the proportion of species in excess in species-rich FE ie it represents the percentage of species that fill functional entities above the mean level of functional redundancy
Functional Vulnerability that reflects the proportion of FE with only one species

mFD::alpha.fd.fe() function is used as follows:

USAGE

mFD::alpha.fd.fe(
  asb_sp_occ       = asb_sp_fruits_occ, 
  sp_to_fe         = sp_to_fe_fruits,
  ind_nm           = c("fred", "fored", "fvuln"),
  check_input      = TRUE,
  details_returned = TRUE)

It takes as inputs:

asb_sp_occ the assemblages-species occurrence dataframe retrieved on step 2 with mFD::sp.tr.summary() function
sp_to_fe a list with details of species clustering into FE from mFD::sp.to.fe()
ind_nm a vector referring to the indices to compute: fred for Functional Redundancy, fored for Functional Overredundancy and fvuln for Functional Vulnerability.
check_input is a logical value reflecting whether inputs should be checked or not. Possible error messages will thus be more understandable for the user than R error messages NOTE Recommendation: set it as TRUE.
details_returned is a logical value indicating whether the user wants to details_returned. Details are used in graphical functions and thus must be kept if the user want to have graphical outputs for the computed indices.

Let's apply this function with the fruits dataset:

alpha_fd_fe_fruits <- mFD::alpha.fd.fe(
  asb_sp_occ       = asb_sp_fruits_occ, 
  sp_to_fe         = sp_to_fe_fruits,
  ind_nm           = c("fred", "fored", "fvuln"),
  check_input      = TRUE,
  details_returned = TRUE)

This function returns a dataframe of indices values for each assemblage and a detailed list containing a matrix gathering the number of species per FE in each assemblage:

# dataframe with indices values for each assemblage:
alpha_fd_fe_fruits$"asb_fdfe"

# a matrix gathering the number of species per FE in each assemblage
alpha_fd_fe_fruits$"details_fdfe"

5. Plot functional indices based on FEs

Then, it is possible to have a graphical representation of FE-based indices for a given assemblage using the mFD::alpha.fe.fd.plot() function:

USAGE

mFD::alpha.fd.fe.plot(
  alpha_fd_fe       = alpha_fd_fe_fruits,
  plot_asb_nm       = c("basket_1"),
  plot_ind_nm       = c("fred", "fored", "fvuln"),
  name_file         = NULL,
  color_fill_fored  = "darkolivegreen2",
  color_line_fred   = "darkolivegreen4",
  color_fill_bar    = "grey80",
  color_fill_fvuln  = "lightcoral",
  color_arrow_fvuln = "indianred4",
  size_line_fred    = 1.5,
  size_arrow_fvuln  = 1,
  check_input       = TRUE)

This function takes as inputs:

alpha_fe_fe_fruits the output from the function mFD::alpha.fd.fe() applied on assemblage of interest with details_returned = TRUE
plot_asb_nm a vector containing the name of the assemblage to plot
plot_ind_nm a vector containing the names of the indices to plot. It fred to plot functional redundancy (FRed), fored to plot functional over-redundancy (FOred) and/or fvuln to plot functional vulnerability (FVuln)
name_file a character string with name of file to save the figure. If set to NULL the plot is only displayed
inputs to personalize the plot (for details: see help file of the mFD::alpha.fd.fe.plot)
check_input is a logical value reflecting whether inputs should be checked or not. Possible error messages will thus be more understandable for the user than R error messages NOTE Recommendation: set it as TRUE

For the studied example, the plot looks as follows:

{r, fig.height = 7, fig.width = 12, fig.align = "center"} mFD::alpha.fd.fe.plot( alpha_fd_fe = alpha_fd_fe_fruits, plot_asb_nm = c("basket_1"), plot_ind_nm = c("fred", "fored", "fvuln"), name_file = NULL, color_fill_fored = "darkolivegreen2", color_line_fred = "darkolivegreen4", color_fill_bar = "grey80", color_fill_fvuln = "lightcoral", color_arrow_fvuln = "indianred4", size_line_fred = 1.5, size_arrow_fvuln = 1, check_input = TRUE)

All FE except "fe_3" contain only one species thus FRed and FVuln are close to 1. Only "fe_3" has more species than the average number of species thus the proportion of species in excess in FE richer than average is quite low (FORed = 0.107).

References

Mouillot et al. (2014) Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS, 38, 13757-13762.