This tutorial describes the basic workflow showing how to compute step by step
functional diversity (FD) indices in a multidimensional space using mFD
package. Other functions are available and their uses are illustrated in others
tutorials.
DATA The dataset used to illustrate this tutorial is a fruits dataset based on 25 types of fruits (i.e. species) distributed in 10 fruits baskets (i.e. assemblages). Each fruit is characterized by five traits values summarized in the following table:
| Trait name | Trait measurement | Trait type | Number of classes | Classes code | Unit | |:------------:|:------------------:|:-------------:|:-------------------:|:----------------------------------:|:------:| | Size | Maximal diameter | Ordinal | 5 | 0-1 ; 1-3 ; 3-5 ; 5-10 ; 10-20 | cm | | Plant | Growth form | Categorical | 4 | tree; shrub; vine; forb | NA | | Climate | Climatic niche | Ordinal | 3 | temperate ; subtropical ; tropical | NA | | Seed | Seed type | Ordinal | 3 | none ; pip ; pit | NA | | Sugar | Sugar | Continuous | NA | NA | g/kg |
The use of the mFD
package is based on two datasets:
fruits_traits
in this tutorial# Load data: data("fruits_traits", package = "mFD") # Remove fuzzy traits in this tutorial: fruits_traits <- fruits_traits[ , -c(6:8)] # Display the table: knitr::kable(head(fruits_traits), caption = "Species x traits data frame")
baskets_fruits_weights
in this tutorial. Weights in this matrix can be occurrence data, abundance,
biomass, coverage, etc. This matrix must have assemblages names in rownames.
The studied example works with biomass (i.e. grams of
a fruit in a basket) and this matrix looks as follows:# Load data: data("baskets_fruits_weights", package = "mFD") # Display the table: knitr::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]), centering = TRUE, caption = "Species x assemblages matrix based on the **fruits** dataset")
This tutorial will guide you through the main framework, illustrated in the flowchart below, step by step.
knitr::include_graphics("basic_framework.PNG")
The first thing to do before starting analyses is to know your data. To do so,
you must be able to characterize the traits you are using (i.e. tell the package what type of traits you are using). That is why mFD
package needs a data frame summarizing the type of each trait (i.e. each column of the fruits_traits
data frame).
NB You need to set up a data frame with the same columns names as the below example and traits names need to be in the same order as in
the fruits_traits
data frame:
# Load data: data("fruits_traits_cat", package = "mFD") # Remove fuzzy traits in this tutorial: fruits_traits_cat <- fruits_traits_cat[-c(6:8), ] # Thus remove the "fuzzy_name" column: fruits_traits_cat <- fruits_traits_cat[ , -3] # Display the table: knitr::kable(head(fruits_traits_cat), caption = "Traits types based on **fruits & baskets** dataset")
The first column contains traits name. The second column contains traits type following this code:
mFD
function used to compute functional distance but ok for summary function
and function to group species into Functional Entities)fruits_traits
data frame)You can add a third column if your dataset use fuzzy traits (then the third column summarizes to which fuzzy trait belongs each column that refers to a fuzzy trait) or if you want to give weight to each traits (then the third column summarizes traits weights).
NOTE The traits types dataframe thus has: two columns if no fuzzy traits and
no weight given to traits (columns names: trait_name
and trait_type
) ;
three columns if fuzzy traits (columns names: trait_name
,trait_type
and fuzzy_name
) or if no fuzzy traits and weight given to traits
(columns names: trait_name
,trait_type
and trait_weight
). See the first
part of the Compute Functional Diversity Hill Indices to see how the fruits_traits
and fruits_traits_cat
look like with
fuzzy traits.
The mFD
package helps you to summarize your data using two distinct
functions: mFD::sp.tr.summary()
and mFD::asb.sp.summary()
.
The function mFD::sp.tr.summary()
summarizes the fruits_traits
dataframe and
returns a list gathering several tables and lists:
tables with summaries for non-fuzzy & fuzzy traits. For non-fuzzy traits, the table sums up the number of species having each category for ordinal, nominal and circular traits or minimum/first quartile/median/mean/third quartile/maximum for continuous traits. For fuzzy traits, the table sums up minimum/first quartile/median/mean/third quartile/maximum for each category of each fuzzy trait.
a list gathering traits types for non-fuzzy traits
a list gathering modalities of non-continuous and non-fuzzy traits
USAGE
# Species traits summary: fruits_traits_summ <- mFD::sp.tr.summary( tr_cat = fruits_traits_cat, sp_tr = fruits_traits, stop_if_NA = TRUE)
fruits_traits_summ$"tr_types" # Traits types
fruits_traits_summ$"mod_list" # Traits types for non-continuous and non-fuzzy traits
The second function helping you to summarize your data in the mFD
package is
mFD::asb.sp.summary()
. It summarizes the baskets_fruits_weights
matrix and
returns a list gathering a matrix, a list and several vectors:
a matrix of species occurrences
a vector gathering species total biomass in all assemblages
a vector gathering the total abundance/biomass per assemblage
a vector gathering species richness per assemblage
a list gathering species names present in each assemblage
USAGE
# Summary of the assemblages * species dataframe: asb_sp_fruits_summ <- mFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights)
head(asb_sp_fruits_summ$"asb_sp_occ", 3) # Species occurrences for the first 3 assemblages asb_sp_fruits_occ <- asb_sp_fruits_summ$"asb_sp_occ"
asb_sp_fruits_summ$"sp_tot_w" # Species total biomass in all assemblages
asb_sp_fruits_summ$"asb_tot_w" # Total biomass per assemblage
asb_sp_fruits_summ$"asb_sp_richn" # Species richness per assemblage
asb_sp_fruits_summ$"asb_sp_nm"[[1]] # Names of species present in the first assemblage
asb_sp_fruits_occ <- asb_sp_fruits_summ$"asb_sp_occ"
If you have many species described by few categorical and ordinal traits only, then you might want to group them into Functional Entities (FE), i.e groups of species with same trait values when species are described with categorical and/or ordinal traits. It is particularly useful when using large datasets with "functionally similar" species.
In this tutorial, this function is not illustrated (FE for the fruits dataset have a single species) and thus functional diversity indices based on FE are not computed. You can have a look to the Compute Functional Diversity Indices based on Functional Entities tutorial for further analysis using FE.
mFD
also allows the user to compute FD indices based on Functional Entities
(FEs). Computed indices are Functional Redundancy (FRed),
Functional OverRedundancy (FORed) and Functional Vulnerability (FVuln)
(Mouillot et al. 2014).
The fruits & baskets example does not allow to compute FEs, thus FD indices
based on FEs can not be compute. Check the Compute functional diversity indices
based on Functional
Entities
tutorial to see how to compute them.
The next step toward the computation of functional diversity indices is to estimate functional traits-based distances between species in order to build the functional space in which indices will be computed.
To compute trait-based distances, we will use the mFD::funct.dist()
function
which includes the following arguments:
USAGE
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)
sp_tr
is the species x trait data frame
tr_cat
is the data frame summarizing trait type for each trait
metric
is a character string referring to the metric used to compute
distances. Two metrics are available and the choice depends on your traits
data:
if all traits are continuous use the Euclidean distance (metric =
"euclidean"
) and check the Compute Functional Diversity Indices based on
Only Continuous
Traits
tutorial which explains how to build a multidimensional space from traits
through PCA analysis or considering directly each trait as a dimension.
if you have non-continuous traits use the Gower distance (metric =
"gower"
) as this method allows traits weighting. This method can also deal
with fuzzy traits.
scale_euclid
is a character string referring to the way the user wants to
scale euclidean traits. You can either chose to scale by range range
, use
the center transformation center
, use the scale transformation scale
, use
the scale-center transformation scale_center
or you can chose not to scale
noscale
.
ordinal_var
is a character string specifying the method to be used for
ordinal variables (i.e. ordered). You can either chose to treat ordinal
variables as continuous variables (with "classic"
option) or to treat ordinal
variables as ranks (with metric
or podani
options, see mFD::funct.dist()
help file for details).
weight_type
is a character string referring to the type of method to weight
traits. You can either chose to define weights using the tr_cat
dataframe (cf
step 1.1) (user
option) or you can chose to give the same weight to all
traits (equal
option). (NB Using mFD
, you can not define weights for fuzzy
traits, use
gawdis
package
instead) (NB If you only have continuous traits (see the
Continuous traits Framework: using up to the 1.0.6.9 version of the mFD
package does not allow
weighting, it will be done in a next version of the package. You can use the
col.w
argument of the PCA
function of the FactomineR package.)
stop_if_NA
is a logical value to stop or not the process if the sp_tr
data frame contains NA. If the sp_tr
data frame contains NA
you can either
chose to compute anyway functional distances (but keep in mind that Functional
measures are sensitive to missing traits!) or you can delete species with
missing or extrapolate missing traits (see Johnson et al.
(2020)).
NB If your data gather a high number of species and/or traits, this function might take time to run (and you might have memory issues).
This function returns a dist
object with traits-based distances between all
pairs of species:
round(sp_dist_fruits, 3) # Output of the function mFD::funct.dist()
In order to generate a multidimensional space in which functional diversity
indices
are computed (Mouillot et al. 2013,
we will perform a PCoA using the trait-based distances (and if
required a functional dendrogram).
mFD
evaluates the quality of PCoA-based multidimensional spaces according
to the deviation between trait-based distances and distances in the functional
space (extension of Maire et al. (2015) framework).
For that, we will use the mFD::quality.fspaces()
function:
USAGE
fspaces_quality_fruits <- mFD::quality.fspaces( sp_dist = sp_dist_fruits, maxdim_pcoa = 10, deviation_weighting = "absolute", fdist_scaling = FALSE, fdendro = "average")
sp_dist
is the dist
object with pairwise trait-based distance between
species as computed in step 3
maxdim_pcoa
is the maximum number of PCoA axes to consider to build
multidimensional spaces. Actually, the maximum number of dimensions considered
depends on the number of PCoA axes with positive eigenvalues.
deviation_weighting
refers to the method(s) used to weight the difference
between species pairwise distances in the functional space and trait-based
distances.
You can chose between:
absolute
: absolute differences are used to compute the mean absolute
deviation (mad) . It reflects the actual magnitude of errors that will
affect FD metrics.squared
: squared differences are used to compute the root of mean square
deviation (rmsd). This weighting puts more weight to the large deviations
between trait-based distances and distances in the functional space.
misplaced in the functional space. Both quality metrics can be used with deviation_weighting = c("absolute",
"squared")
.
fdist_scaling
specifies whether distances in the functional space should be
scaled before computing differences with trait-based distances. Scaling ensures
that trait-based distances and distances in the functional space have the same
maximum. Scaling distances implies that the quality of the functional space
accounts for congruence in distances rather than their equality.
NOTE The combination of deviation_weighting
and fdist_scaling
arguments leads
to four possible quality metrics: mad
, rmsd
, mad_scaled
and
rmsd_scaled
fdendro
specifies the clustering algorithm to compute a functional
dendrogram. NULL
means no dendrogram computed. The chosen algorithm must be one
of the method recognized by the stats::hclust()
function from the
stats
package.This function returns a list various objects:
round(fspaces_quality_fruits$"quality_fspaces", 3) # Quality metrics of spaces
NOTE The space with the best quality has the lowest quality metric. Here, thanks to mad values, we can see that the 4D space is the best one. That is why the following of this tutorial will use this multidimensional space.
With the mFD
package, it is possible to illustrate the quality of PCoA-based
multidimensional spaces according to deviation between trait-based distances and
distances in the functional space. For that, we use the
mFD::quality.fspace.plot()
function with the following arguments:
USAGE
mFD::quality.fspaces.plot( fspaces_quality = fspaces_quality_fruits, quality_metric = "mad", fspaces_plot = c("tree_average", "pcoa_2d", "pcoa_3d", "pcoa_4d", "pcoa_5d", "pcoa_6d"), name_file = NULL, range_dist = NULL, range_dev = NULL, range_qdev = NULL, gradient_deviation = c(neg = "darkblue", nul = "grey80", pos = "darkred"), gradient_deviation_quality = c(low = "yellow", high = "red"), x_lab = "Trait-based distance")
fspaces_quality
is the output of the mFD::quality.fspaces()
function
(step 4.1).
quality_metric
refers to the quality metric used. It should be one of the
column name(s) of the table gathering quality metric values (output of
mFD::quality.fspaces()
called quality_fspaces
) (here:
fspaces_quality_fruits$quality_fspaces
) Thus it can be: mad
, rmsd
,
mad_scaled
or rmsd_scaled
(see step 4.1)
fspaces_plot
refers to the names of spaces for which quality has to be
illustrated (up to 10). Names are those used in the output of
mFD::quality.fspaces()
function showing the values of the quality metric.
name_file
refers to the name of file to save (without extension) if the user
wants to save the figure. If the user only wants the plot to be displayed,
then name_file = NULL
.
range_dist
, range_dev
, range_qdev
are arguments to set ranges of panel
axes (check function help for further information).
gradient_deviation
and gradient_deviation_quality
are arguments to set
points colors (check function help for further information).
xlab
is a parameter to set x-axis label.
This function generates a figure with three panels (in rows) for each selected functional space (in columns). Each column represents a functional space, the value of the quality metric is written on the top of each column. The x-axis of all panels represents trait-based distances. The y-axis is different for each row:
mFD::quality.fspaces.plot( fspaces_quality = fspaces_quality_fruits, quality_metric = "mad", fspaces_plot = c("tree_average", "pcoa_2d", "pcoa_3d", "pcoa_4d", "pcoa_5d", "pcoa_6d"), name_file = NULL, range_dist = NULL, range_dev = NULL, range_qdev = NULL, gradient_deviation = c(neg = "darkblue", nul = "grey80", pos = "darkred"), gradient_deviation_quality = c(low = "yellow", high = "red"), x_lab = "Trait-based distance")
For the 2D space, on the top row there are a lot of points below the 1:1 lines, meaning that distances are overestimated in this multidimensional space. Looking at panels, we can see that the 4D space is the one in which points are the closest to the 1:1 line on the top row,and the closest to the x-axis for the two bottom rows, which reflects a better quality compared to other functional spaces / dendrogram. For the dendrogram, we can see on the top row that species pairs arrange in horizontal lines, meaning that different trait-based distances have then the same cophenetic distance on the dendrogram.
NOTE To know more and better understand how to interpret quality of functional spaces, you should read the Compute and Interpret Quality of Functional Space tutorial.
mFD
allows to test for correlations between traits and functional axes and
then illustrate possible correlations. For continuous traits, a linear model is
computed and r2 and associated p-value are returned. For non-continuous traits,
a Kruskal-Wallis test is computed and eta2 statistic is returned. The function
mFD::traits.faxes.cor()
allows to test and plot correlation and needs the
following arguments:
sp_tr
is the species x traits data framesp_faxes_coord
is a matrix of species coordinates taken from the outputs of
the mFD::quality.fspaces()
function with columns representing axes on which
functional space must be computed. For instance, in this tutorial, we will
plot the functional space for 4 and 10 dimensions (cf. the two examples
below). The whole sp_faxes_coord
can be retrieved through the output of the
mFD::quality.fspaces()
function:
sp_faxes_coord_fruits <- fspaces_quality_fruits$"details_fspaces"$"sp_pc_coord"
plot
is a logical value indicating whether correlations should be
illustrated or not. If this option is set to TRUE
, traits-axis relationships
are plotted through scatterplot for continuous traits and boxplot for
non-continuous traits.mFD::traits.faxes.cor
works as follows:
USAGE
fruits_tr_faxes <- mFD::traits.faxes.cor( sp_tr = fruits_traits, sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")], plot = TRUE)
We can print only traits with significant effect on position along one of the
axis and look at the plots:
# Print traits with significant effect: fruits_tr_faxes$"tr_faxes_stat"[which(fruits_tr_faxes$"tr_faxes_stat"$"p.value" < 0.05), ] # Return plots: fruits_tr_faxes$"tr_faxes_plot"
We can thus see that PC1 is mostly driven by Climate (temperate on the left and tropical on the right) and Plant Type (forb & shrub on the left vs tree & vine on the right) and Size (large fruits on the right) with weaker influence of Seed (eta2 < 0.25). Then, PC2 is mostly driven by Seed (no seed on the left and pit seed on the right) with weaker influence of Plant Type. PC3 is driven by only one trait, Size. And finally PC4 is mostly driven by Sugar (high sugar content on the right and low sugar content on the left) with a weaker influence of Plant Type.
Once the user has selected the dimensionality of the functional space, mFD
allows you to plot the given multidimensional functional space and the position
of species in all 2-dimensions spaces made by pairs of axes.
The mFD::funct.space.plot()
function allows to illustrate the position of all
species along pairs of space axes.
This function allows to plot with many possibilities to change colors/shapes of each plotted element. Here are listed the main arguments:
sp_faxes_coord
is a matrix of species coordinates taken from the outputs of
the mFD::quality.fspaces()
function with columns representing axes on which
functional space must be computed. For instance, in this tutorial, we will
plot the functional space for 4 and 10 dimensions (cf. the two examples
below). The whole sp_faxes_coord
can be retrieved through the output of the
mFD::quality.fspaces()
function:sp_faxes_coord_fruits <- fspaces_quality_fruits$"details_fspaces"$"sp_pc_coord"
faxes
is a vector containing names of axes to plot. If set to NULL
, the
first four functional axes will be plotted.
faxes_nm
is a vector containing labels of faxes
(following faxes vector
rank). If NULL
, labels follow faxes
vector names.
range_faxes
is a vector to complete if the user wants to set specific
limits for functional axes. If range_faxes = c(NA, NA)
, the range is
computed according to the range of values among all axes.
plot_ch
is a logical value used to draw or not the 2D convex-hull filled
by the global pool of species. Color, fill and opacity of the convex hull
can be chosen through other inputs , please refer to the function's help.
plot_sp_nm
is a vector containing species names to plot. If NULL
, no
species names plotted. Name size, color and font can be chosen through other
inputs, please refer to the function's help.
plot_vertices
is a logical value used to plot or not vertices with a
different shape than other species. Be careful: these representations are
2D representations, thus vertices of the convex-hull in the n-multidimensional
space can be close to the center of the hull projected in 2D. Color, fill,
shape and size of vertices can be chosen through other inputs, please refer to
the function's help.
color_bg
is a R color or an hexadecimal color code referring to the
color of the background of the plot.
other inputs are used to chose color, fill, size, and shape of species from the global pool, please refer to the function's help.
check_input
is a recurrent argument in the mFD
package. It defines
whether inputs should be checked before computation or not. Possible error
messages will thus be more understandable for the user than R error messages
(Recommendation: set it as TRUE
).
Here are the plots for the fruits & baskets dataset for the first four PCoA axis:
USAGE
big_plot <- mFD::funct.space.plot( sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")], faxes = c("PC1", "PC2", "PC3", "PC4"), name_file = NULL, faxes_nm = NULL, range_faxes = c(NA, NA), color_bg = "grey95", color_pool = "darkgreen", fill_pool = "white", shape_pool = 21, size_pool = 1, plot_ch = TRUE, color_ch = "black", fill_ch = "white", alpha_ch = 0.5, plot_vertices = TRUE, color_vert = "blueviolet", fill_vert = "blueviolet", shape_vert = 23, size_vert = 1, plot_sp_nm = NULL, nm_size = 3, nm_color = "black", nm_fontface = "plain", check_input = TRUE)
Here, the convex-hull of the species pool is plotted in white and axis have the same range to get rid of bias based on different axis scales. Species beign vertices of the 4D convex hull are in purple.
Here are the plots for the fruits & baskets dataset for the first ten PCoA axis:
big_plot <- mFD::funct.space.plot( sp_faxes_coord = sp_faxes_coord_fruits, faxes = NULL, name_file = NULL, faxes_nm = NULL, range_faxes = c(NA, NA), color_bg = "grey95", color_pool = "darkgreen", fill_pool = "white", shape_pool = 21, size_pool = 1, plot_ch = TRUE, color_ch = "black", fill_ch = "white", alpha_ch = 0.5, plot_vertices = TRUE, color_vert = "blueviolet", fill_vert = "blueviolet", shape_vert = 23, size_vert = 1, plot_sp_nm = NULL, nm_size = 3, nm_color = "black", nm_fontface = "plain", check_input = TRUE) # Plot the graph with all pairs of axes: big_plot$patchwork
Here, all the species are vertices compared with the last example with only four dimensions.
The mFD::alpha.fd.multidim()
function allows computing many alpha FD indices:
USAGE
alpha_fd_indices_fruits <- mFD::alpha.fd.multidim( sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")], asb_sp_w = baskets_fruits_weights, ind_vect = c("fdis", "fmpd", "fnnd", "feve", "fric", "fdiv", "fori", "fspe", "fide"), scaling = TRUE, check_input = TRUE, details_returned = TRUE)
The arguments and their use are listed below:
sp_faxes_coord
is the species coordinates matrix. This dataframe gathers
only axis of the functional space you have chosen based on step 4.
asb_sp_w
is the matrix linking species and assemblages they belong to
(summarized in step 1).
ind_vect
is a vector with names of diversity functional indices to compute.
FD indices computed in the mFD
package can be (explanations based on
(Mouillot et al.
2013):
FDis
Functional Dispersion: the biomass weighted deviation of species
traits values from the center of the functional space filled by the assemblage
i.e. the biomass-weighted mean distance to the biomass-weighted mean trait
values of the assemblage.
FRic
Functional Richness: the proportion of functional space filled by
species of the studied assemblage, i.e. the volume inside the convex-hull
shaping species. To compute FRic
the number of species must be at least
higher than the number of functional axis + 1.
FDiv
Functional Divergence: the proportion of the biomass supported by
the species with the most extreme functional traits i.e. the ones located
close to the edge of the convex-hull filled by the assemblage.
FEve
Functional Evenness: the regularity of biomass distribution in
the functional space using the Minimum Spanning Tree linking all species
present in the assemblage.
FSpe
Functional Specialization: the biomass weighted mean distance to
the mean position of species from the global pool (present in all
assemblages).
FMPD
Functional Mean Pairwise Distance: the mean weighted distance
between all species pairs.
FNND
Functional Mean Nearest Neighbour Distance: the weighted distance
to the nearest neighbor within the assemblage.
FIde
Functional Identity: the mean traits values for the assemblage.
FIde
is always computed when FDis
is computed.
FOri
Functional Originality: the weighted mean distance to the nearest
species from the global species pool.
scaling
is a logical value indicating whether indices should be scaled
between 0 and 1. If scaling is to be done, this argument must be set to TRUE
.
check_input
is a recurrent argument in the mFD
package. It defines whether
inputs should be checked before computation or not. Possible error messages will
thus be more understandable for the user than R error messages
(Recommendation: set it as TRUE
).
details_returned
is used if the user wants to store information that are
used in graphical functions. If the user wants to plot FD indices, then
details_returned
must be set to TRUE
.
NB Use lowercase letters to enter FD indices names
The function has two main outputs:
FIde
values, there are as many columns as there are axes to the studied functional
space).fd_ind_values_fruits <- alpha_fd_indices_fruits$"functional_diversity_indices" fd_ind_values_fruits
details_list_fruits <- alpha_fd_indices_fruits$"details"
Then, you can plot functional indices using the mFD::alpha.multidim.plot()
function as follows:
USAGE
plots_alpha <- mFD::alpha.multidim.plot( output_alpha_fd_multidim = alpha_fd_indices_fruits, plot_asb_nm = c("basket_1", "basket_5"), ind_nm = c("fdis", "fide", "fnnd", "feve", "fric", "fdiv", "fori", "fspe"), faxes = NULL, faxes_nm = NULL, range_faxes = c(NA, NA), color_bg = "grey95", shape_sp = c(pool = 3, asb1 = 21, asb2 = 21), size_sp = c(pool = 0.7, asb1 = 1, asb2 = 1), color_sp = c(pool = "grey50", asb1 = "#1F968BFF", asb2 = "#DCE319FF"), color_vert = c(pool = "grey50", asb1 = "#1F968BFF", asb2 = "#DCE319FF"), fill_sp = c(pool = NA, asb1 = "#1F968BFF", asb2 = "#DCE319FF"), fill_vert = c(pool = NA, asb1 = "#1F968BFF", asb2 = "#DCE319FF"), color_ch = c(pool = NA, asb1 = "#1F968BFF", asb2 = "#DCE319FF"), fill_ch = c(pool = "white", asb1 = "#1F968BFF", asb2 = "#DCE319FF"), alpha_ch = c(pool = 1, asb1 = 0.3, asb2 = 0.3), shape_centroid_fdis = c(asb1 = 22, asb2 = 24), shape_centroid_fdiv = c(asb1 = 22, asb2 = 24), shape_centroid_fspe = 23, color_centroid_fspe = "black", size_sp_nm = 3, color_sp_nm = "black", plot_sp_nm = NULL, fontface_sp_nm = "plain", save_file = FALSE, check_input = TRUE)
As you can see, this function has a lot of arguments: most of them are graphical arguments allowing the user to chose colors, shapes, sizes, scales, etc. This tutorial only presents main arguments. To learn about the use of graphical arguments, check the function help file. The main arguments of this function are listed below:
output_alpha_fd_multidim
is the output of the `mFD::alpha.fd.multidim()
function.
plot_asb_nm
is a vector gathering name(s) of assemblage(s) to plot.
ind_vect
is a vector gathering FD indices to plot. Plots are available for
FDis
, FIde
, FEve
, FRic
, FDiv
, FOri
, FSpe
, and FNND.
faxes
is a vector containing names of axes to plot. You can only plot from
two to four axes labels for graphical reasons.
faxes_nm
is a vector with axes labels if the user ants different axes labels
than faxes
ones.
range_faxes
is a vector with minimum and maximum values for axes.
If range_faxes = c(NA, NA)
, the range is computed according to the range
of values among all axes, all axes having thus the same range. To have a fair
representation of species positions in all plots, all axes must have the same
range.
plot_sp_nm
is a vector containing species names to plot. If NULL
, then no
name is plotted.
size, color, fill, and shape arguments for each component of the graphs i.e.
species of the global pool, species of the studied assemblage(s), vertices,
centroids and segments. If you have to plot two assemblages, then inputs
should be formatted as follow: c(pool = ..., asb1 = ..., asb2 = ...)
for
inputs used for global pool and studied assemblages and
c(asb1 = ..., asb2 = ...)
for inputs used for studied assemblages only.
check_input
is a recurrent argument in mFD
. It defines whether inputs
should be checked before computation or not. Possible error messages will thus
be more understandable for the user than R error messages (Recommendation:
set it as TRUE
.
Then, using these arguments, here are the output plots for the fruits & baskets dataset:
FRic
representation: the colored shapes reflect the convex-hull of the
studied assemblages and the white shape reflects the convex-hull of the global
pool of species:plots_alpha$"fric"$"patchwork"
FDiv
representation: the gravity centers of vertices (i.e. species with the
most extreme functional traits) of each assemblages are plotted as a square and
a triangle. Species of each assemblage have
different size given their relative weight into the assemblage.plots_alpha$"fdiv"$"patchwork"
FSpe
representation: colored traits represent distances of each species from
a given assemblage to the center of gravity of the global pool (i.e center of
the functional space). the center of gravity is plotted with a purple diamond.
Species of each assemblage have different size given their relative weight into
the assemblage.plots_alpha$"fspe"$"patchwork"
FDis
representation: colored traits represent distances of each species from
a given assemblage to the center of gravity of species of the assemblage
(defined by FIde values). The center of gravity of each assemblage is plotted
using a square and a triangle. Species of each assemblage havedifferent size
given their relative weight into the assemblage.plots_alpha$"fdis"$"patchwork"
FIde
representation:colored lines refer to the weighted average position of
species of each assemblage along each axis. Species of each assemblage have
different size given their relative weight into the assemblage.plots_alpha$"fide"$"patchwork"
FEve
representation: colored traits represent the Minimum Spanning Tree
linking species of each assemblage. Species of each assemblage have different
size given their relative weight into the assemblage.plots_alpha$"feve"$"patchwork"
FOri
representation: colored arrows represent the distances of each species
from each assemblage to the nearest species in the global species pool. Species
of each assemblage have different size given their relative weight into the
assemblage.plots_alpha$"fori"$"patchwork"
FNND
representation: colored arrows represent the distances of each species
from each assemblage to the nearest species in the studied assemblage. Species
of each assemblage have different size given their relative weight into the
assemblage. plots_alpha$"fnnd"$"patchwork"
NOTE: Some Mac OS X 10.15 may encounter some issues with the beta_*() functions.
mFD
package allows you to compute beta diversity indices for each assemblage
pairs following Villeger et al.
2013. For that we
will use the mFD::beta.fd.multidim()
function.
NOTE This function can compute two families of functional beta diversity indices, either Jaccard or Sorensen.
In this example, we will use Jaccard index. For each assemblages pair, the dissimilarity index is decomposed into two additive components: turnover and nestedness-resultant. NB The turnover component is the highest if there is no shared traits combination between the two assemblages. The nestedness component is the highest if one assemblage hosts a small subset of the functional strategies present in the other.
The mFD::beta.fd.multidim()
function has the main following arguments:
USAGE
beta_fd_indices_fruits <- mFD::beta.fd.multidim( sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")], asb_sp_occ = asb_sp_fruits_occ, check_input = TRUE, beta_family = c("Jaccard"), details_returned = TRUE)
sp_faxes_coord
is the species coordinates matrix. This dataframe gathers
only axis of the functional space you have chosen based on step 4.
asb_sp_occ
is the matrix of occurrence (coded as 0/1) of species assemblages
(summarized in step 1).
check_input
is a recurrent argument in the mFD
package. It defines whether
inputs should be checked before computation or not. Possible error messages will
thus be more understandable for the user than R error messages (Recommendation:
set it as TRUE
.
beta_family
a character string for the type of beta-diversity index to
compute, it can either be Jaccard
or Sorensen
.
details_returned
is a logical value indicating whether details of outputs
must be stored. It should be stored if you plan to use the graphical function to
illustrate beta diversity indices thereafter.
There are also other arguments for parallelisation options. Check the function help file for more explanation.
The function returns a list containing:
a vector containing the FRic
value for each assemblage retrieved through
the details_beta
list
a list of vectors containing names of species being vertices of the convex
hull for each assemblage retrieved through the details_beta
list
Then, the package allows the user to illustrate functional beta-diversity
indices for a pair of assemblages in a multidimensional space using the
mFD::beta.multidim.plot()
function. The output of this function is a figure
showing the overlap between convex hulls shaping each of the two species
assemblages.
The plotting function has a large number of arguments, allowing the user to chose graphical options. Arguments are listed below:
USAGE
beta_plot_fruits <- mFD::beta.multidim.plot( output_beta_fd_multidim = beta_fd_indices_fruits, plot_asb_nm = c("basket_1", "basket_4"), beta_family = c("Jaccard"), plot_sp_nm = c("apple", "lemon", "pear"), faxes = paste0("PC", 1:4), name_file = NULL, faxes_nm = NULL, range_faxes = c(NA, NA), color_bg = "grey95", shape_sp = c("pool" = 3.0, asb1 = 22, asb2 = 21), size_sp = c("pool" = 0.8, asb1 = 1, asb2 = 1), color_sp = c("pool" = "grey50", asb1 = "blue", asb2 = "red"), fill_sp = c("pool" = NA, asb1 = "white", asb2 = "white"), fill_vert = c("pool" = NA, asb1 = "blue", asb2 = "red"), color_ch = c("pool" = NA, asb1 = "blue", asb2 = "red"), fill_ch = c("pool" = "white", asb1 = "blue", asb2 = "red"), alpha_ch = c("pool" = 1, asb1 = 0.3, asb2 = 0.3), nm_size = 3, nm_color = "black", nm_fontface = "plain", check_input = TRUE)
output_beta_fd_multidim
is the output of the mFD::beta.fd.multidim()
function retrieved before as beta_fd_indices
.
plot_asb_nm
is a vector containing the name of the two assemblages to plot.
Here plots of indices will be shown for basket_1 and basket_4.
beta_family
refers to the family of the plotted index. It must be the same
as the family chosen to compute beta functional indices values with the
mFD::beta.fd.multidim()
function.
plot_sp_nm
is a vector containing the names of species the user want to
plot, if any. If no the user does not want to plot any species name, then this
argument must be set up to NULL
. Here, apple, cherry and lemon will be
plotted on the graph.
faxes
is a vector containing the names of the functional axes of the plotted
functional space. Here, the figure will be plotted for PC1, PC2 and PC3.
This function allows you to plot between two and four axes for graphical
reasons.
name_file
is a character string with the name of the file to save the figure
(without extension). If the user does not want to save the file and only display
it, this argument must be set up to NULL
.
faxes_nm
is a vector containing the axes labels for the figure if the user
wants to set up different labels than those contained in faxes
.
range_faxes
is a vector with minimum and maximum values of functional axes.
To have a fair representation of the position of species in all plots, axes
should have the same range. If the user wants the range to be computed according
to the range of values among all axes, this argument must be set up to
c(NA, NA)
.
check_input
is a recurrent argument in the mFD
package. It defines whether
inputs should be checked before computation or not. Possible error messages will
thus be more understandable for the user than R error messages
(Recommendation: set it as TRUE
)
Others arguments to set up colors, shapes, sizes and, text fonts are also available. For more information about them, read the function help file.
Then, the function returns each graph for each functional axes combination and also a multipanel plot with all combinations of axes and the graph caption.
For each assemblage, the associated convex hull is plotted in a different colour and indices values are printed on the right corner of the plot. Vertices of the convex hull of a given assemblage can be plotted with a different symbol such as in this example. Species of all assemblages are plotted with gray cross and the associated convex hull is plotted in white.
Johnson et al. (2020) Handling missing values in trait data. Global Ecology and Biogeography, 30, 51-62.
Maire et al. (2015) How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Global Ecology and Biogeography, 24, 728-740.
Mouillot et al. (2013) A functional approach reveals community responses to disturbances. Trends in Ecology and Evolution, 28, 167-177.
Mouillot et al. (2014) Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS, 38, 13757-13762.
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