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Title:	Compute and Illustrate the Multiple Facets of FunctionalDiversity
Version:	1.0.7
Description:	Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>, Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al. (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009) <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples.
URL:	https://cmlmagneville.github.io/mFD/,https://github.com/CmlMagneville/mFD
BugReports:	https://github.com/CmlMagneville/mFD/issues
License:	GPL-2
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5)
Imports:	ade4, ape, betapart (≥ 1.5.4), cluster, dendextend,FactoMineR, gawdis, geometry, ggplot2, ggrepel, grid, Hmisc,patchwork, reshape2, rstatix, stats, utils, vegan
Suggests:	dplyr (≥ 1.0.6), knitr, magrittr, rmarkdown (≥ 2.6),stringr, tibble, tidyr
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-02-26 10:43:42 UTC; au749321
Author:	Camille Magneville [aut, cre, cph], Nicolas Loiseau [aut], Camille Albouy [aut], Nicolas Casajus [aut], Thomas Claverie [aut], Arthur Escalas [aut], Fabien Leprieur [aut], Eva Maire [aut], David Mouillot [aut], Sebastien Villeger [aut]
Maintainer:	Camille Magneville <camille.magneville@gmail.com>
Repository:	CRAN
Date/Publication:	2024-02-26 14:00:06 UTC

mFD: Compute and Illustrate the Multiple Facets of Functional Diversity

Description

Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018)doi:10.1002/ecm.1343, Maire et al. (2015)doi:10.1111/geb.12299, Mouillot et al. (2013)doi:10.1016/j.tree.2012.10.004, Mouillot et al. (2014)doi:10.1073/pnas.1317625111, Ricotta and Szeidl (2009)doi:10.1016/j.tpb.2009.10.001. Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples.

Author(s)

Maintainer: Camille Magnevillecamille.magneville@gmail.com (ORCID) [copyright holder]

Authors:

• Nicolas Loiseaunicolas.loiseau1@gmail.com (ORCID)

• Camille Albouyalbouycamille@gmail.com (ORCID)

• Nicolas Casajusnicolas.casajus@fondationbiodiversite.fr (ORCID)

• Thomas Claverietclaverie@gmail.com (ORCID)

• Arthur Escalasarthur.escalas@gmail.com (ORCID)

• Fabien Leprieurfabien.leprieur@umontpellier.fr (ORCID)

• Eva Maireemg.maire@gmail.com (ORCID)

• David Mouillotdavid.mouillot@umontpellier.fr (ORCID)

• Sebastien Villegersebastien.villeger@cnrs.fr (ORCID)

See Also

Useful links:

• https://cmlmagneville.github.io/mFD/

• https://github.com/CmlMagneville/mFD

• Report bugs athttps://github.com/CmlMagneville/mFD/issues

Compute the set of indices based on number of species in Functional Entities

Description

This function computes the set of indices based on number of species inFunctional Entities (FEs) following Mouillotet al. (2014).

Usage

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

Arguments

Value

A list with:

• asb_fdfe a matrix containing for each assemblage (rows),values of functional diversity indices (same names than in 'ind_nm')as well as the number of species ('nb_sp') and the number of FE(nb_fe);

• ifdetails_returned isTRUE,

• details_fdfe a list withasb_fe_nbsp a matrix withnumber of species per FE in each assemblage.

Author(s)

Camille Magneville

References

Mouillotet al. (2014) Functional over-redundancy and high functionalvulnerability in global fish faunas on tropical reefs.PNAS,38,13757-13762.

Examples

# Load Species*Traits dataframe:data('fruits_traits', package = 'mFD')# Load Traits categories dataframe:data('fruits_traits_cat', package = 'mFD')# Load Assemblages*Species matrix:data('baskets_fruits_weights', package = 'mFD')# Remove continuous trait:fruits_traits <- fruits_traits[, -5]fruits_traits_cat <- fruits_traits_cat[-5, ]# Compute gathering species into FEs: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) # Get the occurrence dataframe:asb_sp_fruits_summ <- mFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights) asb_sp_fruits_occ <- asb_sp_fruits_summ$'asb_sp_occ'# Compute alpha fd indices: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)

Illustrate Functional Diversity indices based on Functional Entities

Description

Graphical representation of distribution of species in FunctionalEntities (FE) and of indices from Mouillotet al. (2014).To plotfunctional indices, functional indices values must have been computed firstthrough the use of thealpha.fd.fe function.

Usage

alpha.fd.fe.plot(  alpha_fd_fe,  plot_asb_nm,  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)

Arguments

Value

Apatchwork object with a barplot of number of species perFE. Indices names provided in 'plot_ind_nm' are illustrated. FunctionalRedundancy (average number of species per FE) is illustrated with ahorizontal line. Functional Over-redundancy (proportion of species inexcess in FE richer than average) is illustrated with top part of thesebars filled with 'color_fill_fored'. Functional Vulnerability (proportionof FE with a single species) is illustrated with bars of these vulnerableFE filled with 'color_fill_fvuln' and the double-head arrow highlightingtheir number. FE-based indices values on top of the plot. ifname_file is provided, plot saved as a 300dpi png file in theworking directory.

Author(s)

Camille Magneville and Sebastien Villeger

References

Mouillotet al. (2014) Functional over-redundancy and high functionalvulnerability in global fish faunas on tropical reefs.PNAS,111,13757-13762.

Examples

# Load Species*Traits dataframedata("fruits_traits", package = "mFD")# Load Traits categories dataframedata("fruits_traits_cat", package = "mFD")# Load Assemblages*Species matrixdata("baskets_fruits_weights", package = "mFD")# Remove continuous traitfruits_traits  <- fruits_traits[ , -5]fruits_traits_cat <- fruits_traits_cat[-5, ]# Compute gathering species into FEssp_to_fe_fruits <- mFD::sp.to.fe(  sp_tr       = fruits_traits,   tr_cat      = fruits_traits_cat,   fe_nm_type  = "fe_rank",   check_input = TRUE) # Get the occurrence matrixasb_sp_fruits_summ <- mFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights) asb_sp_fruits_occ <- asb_sp_fruits_summ$"asb_sp_occ"# Compute alpha fd indicesalpha_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)  # Plot fd fe indicesmFD::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)

Compute Functional alpha-Diversity indices based on Hill Numbers

Description

Compute functional alpha diversity applied to distance between speciesfollowing the framework from Chaoet al.(2019).

Usage

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

Arguments

Value

A list with:

• asb_FD_Hill a matrix containing indices values for each levelof q (columns, named as 'FD_qx') for each assemblage (rows, named as inasb_sp_w)

• tau_dist the threshold value applied to distance betweenspecies to compute diversity according to function provided intau

• ifdetails_returned turned to TRUE a listdetailswith

  • asb_totw a vector with total weight of each assemblage

  • asb_sp_relw a matrix with relative weight of species inassemblages

Note

FD is computed applying the special case where function 'f' inequation 3c is linear:f(dij(tau)) = dij(tau)/tau, hence f(0) = 0and f(tau) = 1. FD computed with q=2 and tau = 'max' is equivalent tothe Rao's quadratic entropy from Ricotta & Szeidl (2009, J Theor Biol).FD computed with tau = 'min' is equivalent to Hill number taxonomicdiversity, thus with q=0 it is species richness (S), with q = 1 it isexponential of Shannon entropy (H) and with q = 2 it is 1/(1-D) where D isSimpson diversity. Note that even when q=0, weights of species areaccounted for in FD. Hence to compute FD based only on distance betweenspecies present in an assemblage (i.e. a richness-like index) , asb_sp_whas to contain only species presence/absence coded as 0/1 with q=0 andtau = 'mean'. If asb_sp_w contains only 0/1 and q>0, it means that allspecies have the same contribution to FD.

Author(s)

Sebastien Villeger and Camille Magneville

References

Chaoet al. (2019) An attribute diversity approach to functionaldiversity, functional beta diversity, and related (dis)similaritymeasures.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)

Compute a set of alpha functional indices for a set of assemblages

Description

This function computes a set of multidimensional space based indices ofalpha functional diversity. The user can choose which functional indices tocompute.

Usage

alpha.fd.multidim(  sp_faxes_coord,  asb_sp_w,  ind_vect = c("fide", "fdis", "fmpd", "fnnd", "feve", "fric", "fdiv", "fori", "fspe"),  scaling = TRUE,  check_input = TRUE,  details_returned = TRUE,  verbose = TRUE)

Arguments

Value

The following list is returned:

• functional_diversity_indices matrix containing indices values(columns) for each assemblage (rows)

• details list: aasb_sp_occ data.frame of speciesoccurrences in each assemblage ; aasb_sp_relatw matrix ofrelative weight of species in each assemblage ; asp_coord_all_asblist of matrices of species coordinates along functional axes for speciespresent in each assemblage ; avert_nm_all_asb list of vectors ofspecies names being vertices of the convex hull for each assemblage ; amst_all_asb list of data.frames summarizing link between speciesin the minimum spanning tree of each assemblage ; agrav_center_vert_coord_all_asb list of vectors of coordinates ofthe vertices gravity center for each assemblage ; amean_dtogravcenter_all_asb list of vectors containing meandistance to the species gravity center for each assemblage ; adist_gravcenter_global_pool vector containing the distance of eachspecies to the gravity center of all species from the global pool ; adist_nn_global_pool data.frame showing the distances of eachspecies from the global pool to its nearest neighbor ; anm_nn_all_asb data.frame containing the name of each nearestneighbor of each species present in a given assemblage ; adist_nn_all_asb data.frame containing distance of each speciespresent in a given assemblage to its nearest neighbor.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data('fruits_traits', package = 'mFD')# Load Assemblages*Species dataframe:      data('baskets_fruits_weights', package = 'mFD')# Load Traits categories dataframe:data('fruits_traits_cat', 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(  sp_dist             = sp_dist_fruits,   maxdim_pcoa         = 10,  deviation_weighting = 'absolute',  fdist_scaling       = FALSE,  fdendro             = 'average')  # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute alpha diversity indicesalpha_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'),  scaling          = TRUE,   check_input      = TRUE,   details_returned = TRUE)  # Retrieve alpha diversity indices tablefd_ind_values_fruits <- alpha_fd_indices_fruits$functional_diversity_indicesfd_ind_values_fruits

Plot functional space and chosen functional indices

Description

Compute a graphical representation of functional indices.To plotfunctional indices, functional indices values must have been retrievethrough the use of thealpha.fd.multidimfunction.

Usage

alpha.multidim.plot(  output_alpha_fd_multidim,  plot_asb_nm,  ind_nm = c("fide", "fric", "fdiv", "fdis", "feve", "fori", "fspe", "fnnd"),  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 = "#0072B2", asb2 = "#D55E00"),  color_vert = c(pool = "grey50", asb1 = "#0072B2", asb2 = "#D55E00"),  fill_sp = c(pool = NA, asb1 = "#FFFFFF30", asb2 = "#FFFFFF30"),  fill_vert = c(pool = NA, asb1 = "#0072B2", asb2 = "#D55E00"),  color_ch = c(pool = NA, asb1 = "#0072B2", asb2 = "#D55E00"),  fill_ch = c(pool = "white", asb1 = "#0072B2", asb2 = "#D55E00"),  alpha_ch = c(pool = 1, asb1 = 0.3, asb2 = 0.3),  shape_centroid_fdis = c(asb1 = 22, asb2 = 22),  shape_centroid_fdiv = c(asb1 = 24, asb2 = 25),  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)

Arguments

Value

Ifname_file isNULL, it returns a list of oneggplot2 plots per functional index containing plots for combinationsof up to four axes, apatchwork figure gathering all combinations ofaxes and aggplot2 figure showing the plot caption. Ifname_file is notNULL, then those plots are saved locally.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:data("baskets_fruits_weights", package = "mFD")# Load Traits categories dataframe:data("fruits_traits_cat", package = "mFD")# Compute functional distancesp_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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits, maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average")# Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute alpha diversity indices: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"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)# Plot all fd alpha indices: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) # Check FRic plot:plots_alpha$fric$patchwork

Summarize Assemblage x Species data frame

Description

This function computes a summary helping you to picture assemblages. Forthis function to work, there must be no NA in yourasb_sp_w data frame.

Usage

asb.sp.summary(asb_sp_w)

Arguments

Value

A list with:

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Assemblages x Species Matrixdata('baskets_fruits_weights', package = 'mFD')# Summarize Assemblages DatamFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights)

Plot background of multidimensional plots

Description

This function creates a ggplot object with customized axes range(same for both), names and background

Usage

background.plot(range_faxes, faxes_nm, color_bg)

Arguments

Value

A ggplot object plotting background of multidimensional graphs.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

 background <- background.plot(range_faxes = c(-1, 2),                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90") background

Dataset: Baskets Composition in Fruits Species

Description

This dataset represents the abundance of 25 fruits species in 10 baskets.

Usage

baskets_fruits_weights

Format

A matrix of integers with 10 rows (baskets) and 25 columns(species).

See Also

fruits_traits,fruits_traits_cat

Examples

## Not run: # Load Assemblages x Species Matrixdata("baskets_fruits_weights", package = "mFD")baskets_fruits_weights[1:5, 1:5]# Summarize Assemblages DatamFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights)## End(Not run)

Compute Functional beta-Diversity indices based on Hill Numbers

Description

Compute functional beta-diversity indices based on Hill numbers applied todistance between species following the framework from Chaoet al. (2019).

Usage

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

Arguments

Value

A list with:

• asb_FDbeta a list with for each value of q adistobject with beta functional diversity indices for all pairs of assemblagesitem ifstore.details turned to TRUE a listdetails with

  • malpha_fd_q a list with for each value of q adistobject with mean alpha functional diversity indices for all pairs ofassemblages

  • gamma_fd_q a list with for each value of q adistobject with gamma functional diversity indices for all pairs of assemblages

Note

When q=1 Jaccard-like and Sorensen-like beta-diversity are identical.FD computed with tau='min' is equivalent to Hill number taxonomic betadiversity. If tau='min' and there are species with null distance, tau isset to the minimum non-null value and a warning message is displayed.Indices values are stored asdist objects to optimize memory.See below example of how merging distance values in adataframe withdist.to.df

Author(s)

Sebastien Villeger and Camille Magneville

References

Chaoet al. (2019) An attribute diversity approach to functionaldiversity, functional beta diversity, and related (dis)similaritymeasures.Ecological Monographs,89, e01343.

Examples

# Load Species*Traits dataframe:data('fruits_traits', package = 'mFD')# Load Traits types dataframe:      data('fruits_traits_cat', 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 beta functional hill indices:baskets_beta <- beta.fd.hill(      asb_sp_w         = baskets_fruits_weights,       sp_dist          = sp_dist_fruits,       q                = c(0,1,2),       tau              = 'mean',      beta_type        = 'Jaccard',       check_input      = TRUE,       details_returned = TRUE) # Then use the mFD::dist.to.df function to ease visualizing result:## for q = 0:mFD::dist.to.df(list_dist = list(FDq2 = baskets_beta$beta_fd_q$q0))## for q = 1:mFD::dist.to.df(list_dist = list(FDq2 = baskets_beta$beta_fd_q$q1))## for q = 2:mFD::dist.to.df(list_dist = list(FDq2 = baskets_beta$beta_fd_q$q2))

Compute Functional beta-Diversity indices for pairs of assemblages in amultidimensional space

Description

Computes a set of indices of pairwise functional beta-diversity(dissimilarity and its turnover and nestedness-resultant components) basedon overlap between convex hulls in a multidimensional space. For detailsabout indices formulas see Villegeret al. (2013). This functions standsonfunctional.betapart.core.pairwise.

Usage

beta.fd.multidim(  sp_faxes_coord,  asb_sp_occ,  check_input = TRUE,  beta_family = "Jaccard",  details_returned = TRUE,  betapart_step = TRUE,  betapart_chullopt = list(conv1 = "Qt", conv2 = "QJ"),  betapart_para = FALSE,  betapart_para_opt = betapart::beta.para.control())

Arguments

Value

A list with:

• pairasb_fbd_indices a list withfor eachindex adist object with values for all pairs of assemblages.Indices names start with the abbreviation of the type of dissimilarityindex ('jac' for Jaccard-like and 'sor' for Sorensen-like dissimilarity)and end with abbreviation of component ('diss': dissimilarity, 'turn' itsturnover component, and 'nest' its nestedness-resultant component).

• ifstore_details is TRUE,

• details_beta list:inputs a list withsp_faxes_coord andasb_sp_occon which indices were computed (required for drawing graphics),pool_vertices a list of vectors (1 per assemblage) with names ofspecies being vertices of the convex hull shaping all species;asb_FRic a vector with volume of the convex hull shaping eachassemblage (relative to volume filled by all species) ;asb_vertices a list of vectors (1 per assemblage) with names ofspecies being vertices of the convex hull

Note

All assemblages should have a number of species strictly higher thanthe number of functional axes.Computing intersection of convex hulls in space of >5 dimensions is yetimpossible with most computers.This function uses R libraries 'betapart' (> =1.5.4) for indicescomputation.Indices values are stored asdist objects to optimize memory.See below example of how merging distance values in adataframe withdist.to.df.

Author(s)

Sebastien Villeger and Camille Magneville

References

Villegeret al. (2013) Decomposing functional beta-diversity reveals thatlow functional beta-diversity is driven by low functional turnover inEuropean fish assemblages.Global Ecology and Biogeography,22,671-681.

Examples

## Not run:  # Load Species*Traits dataframe:data('fruits_traits', package = 'mFD')# Load Assemblages*Species dataframe:      data('baskets_fruits_weights', package = 'mFD') # Load Traits categories dataframe:data('fruits_traits_cat', 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces( sp_dist             = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = 'absolute', fdist_scaling       = FALSE, fdendro             = 'average') # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Get the occurrence dataframe:asb_sp_fruits_summ <- mFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights) asb_sp_fruits_occ <- asb_sp_fruits_summ$'asb_sp_occ'# Compute beta diversity indices:beta_fd_fruits <- mFD::beta.fd.multidim(  sp_faxes_coord   = sp_faxes_coord_fruits[, c('PC1', 'PC2', 'PC3', 'PC4')],   asb_sp_occ       = asb_sp_fruits_occ,  check_input      = TRUE,  beta_family      = c('Jaccard'),  details_returned = TRUE)# merging pairwise beta-diversity indices in a data.framedist.to.df(beta_fd_fruits$pairasb_fbd_indices)## End(Not run)

Illustrate Functional beta-Diversity indices for pairs of assemblages in amultidimensional space

Description

Illustrate overlap between convex hulls shaping species assemblages in amultidimensional functional space.Before plotting beta functionaldiversity indices should have been computed using thebeta.fd.multidimfunction.

Usage

beta.multidim.plot(  output_beta_fd_multidim,  plot_asb_nm,  beta_family,  plot_sp_nm = NULL,  faxes = NULL,  name_file = NULL,  faxes_nm = NULL,  range_faxes = c(NA, NA),  color_bg = "grey95",  shape_sp = c(pool = 3, asb1 = 22, asb2 = 21),  size_sp = c(pool = 0.7, asb1 = 1.2, 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)

Arguments

Value

Ifname_file isNULL, it returns apatchworkfigure with overlap between convex hulls projected in 2-dimensional spacesfor the given pair of assemblages. Values of functional beta-diversityindices are shown on top-right corner of the figure. Ifname_file isnotNULL, the plot is saved locally.

Author(s)

Sebastien Villeger and Camille Magneville

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe: data("baskets_fruits_weights", package = "mFD")# Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(                                  sp_dist             = sp_dist_fruits,                                  maxdim_pcoa         = 10,                                  deviation_weighting = "absolute",                                  fdist_scaling       = FALSE,                                  fdendro             = "average")# Retrieve species coordinates matrix: sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Get the occurrence dataframe: asb_sp_fruits_summ <- mFD::asb.sp.summary(asb_sp_w = baskets_fruits_weights) asb_sp_fruits_occ <- asb_sp_fruits_summ$"asb_sp_occ"# Compute beta diversity indices: beta_fd_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)# Compute beta fd plots: beta.multidim.plot(   output_beta_fd_multidim = beta_fd_fruits,   plot_asb_nm             = c("basket_1", "basket_6"),   beta_family             = c("Jaccard"),   plot_sp_nm              = c("apple", "cherry", "lemon"),   faxes                   = paste0("PC", 1:4),   name_file               = NULL,   faxes_nm                = NULL,   range_faxes             = c(NA, NA),   color_bg                = "grey95",   shape_sp                = c(pool = 3, 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)

Compute distance of a given point to its nearest neighbor in the functionalspace and the identity of the nearest neighbor

Description

This function is used in functional indices computation.

Usage

dist.nearneighb(sp_faxes_coord, ref_sp)

Arguments

Value

A list containing the nearest neighbor identitynn_id and alist of the distance of the reference point to its nearest neighbornn_ref_sp_dist.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:       data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(                                  sp_dist             = sp_dist_fruits,                                   maxdim_pcoa         = 10,                                  deviation_weighting = "absolute",                                  fdist_scaling       = FALSE,                                  fdendro             = "average") # Retrieve species coordinates matrix: sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute the distance of "pear" to its nearest neighbor(s): dist_nn_pear <- dist.nearneighb(sp_faxes_coord_fruits, ref_sp = "pear") dist_nn_pear

Compute distances of all points to a given point in the functional space

Description

This function computes the distances of all species to a reference species.It is used in FSpe, FOri and FNND computation.

Usage

dist.point(sp_faxes_coord, ref_sp)

Arguments

Value

A vector of species distances to the reference species.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:       data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(                                  sp_dist             = sp_dist_fruits,                                   maxdim_pcoa         = 10,                                  deviation_weighting = "absolute",                                  fdist_scaling       = FALSE,                                  fdendro             = "average") # Retrieve species coordinates matrix: sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Retrieve the distances of all species to "pear": dist_pear <- dist.point(sp_faxes_coord_fruits, ref_sp = "pear") dist_pear

Merge distance object(s) into a single data frame

Description

This function merges distance object(s) into a single data frame which rowsare pairs of elements and column(s) distance metric(s). It stands on thedist_long function.

Usage

dist.to.df(list_dist)

Arguments

Value

A data frame which first and second columns (namesx1 andx2)contain names of the 2 sets involved in each pair, and with one column foreach dist object (named after its name inlist_dist.

Author(s)

Sebastien Villeger

Examples

# Create dist objects: dist_A <- round(dist(matrix(runif(10, 0, 1), 5, 2,                       dimnames = list(letters[1:5], NULL))), 2)dist_B <- round(dist(matrix(runif(10, 0, 1), 5, 2,                       dimnames = list(letters[1:5], NULL))), 2)dist_C <- round(dist(matrix(runif(10, 0, 1), 5, 2,                       dimnames = list(letters[1:5], NULL))), 2)# First example with only 1 distance:dist.to.df(list(dA = dist_A))# Second example with 3 distances:dist.to.df(list(d1 = dist_A, d2 = dist_B, d3 = dist_C))

Plot FDis index

Description

This function plots FDis index for a given pair of functional axes and forone or several assemblages. It adds segments between species relativeweights and assemblage cenntroid on the background plot.

Usage

fdis.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_fide_coord2D,  plot_sp = TRUE,  shape_sp,  color_sp,  fill_sp,  shape_fide,  size_fide,  color_fide,  fill_fide,  color_segment,  width_segment,  linetype_segment)

Arguments

Value

A ggplot object showing FDis index for one or several assemblage(s)and a given pair of functional axes.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm         = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim( sp_faxes_coord    = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("fdis"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve fide values through alpha.fd.multidim outputs: fruits_asb_fide_coord2D <-   alpha_fd_indices_fruits$functional_diversity_indices[c("fide_PC1",                                                          "fide_PC2")] fruits_asb_fide_coord2D_b1 <- fruits_asb_fide_coord2D[c("basket_1"), ] fruits_asb_sp_relatw_b1 <-          alpha_fd_indices_fruits$details$asb_sp_relatw["basket_1", ] # Retrieve FDis plot: fdis_plot <- fdis.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_fide_coord2D = list(basket_1 = fruits_asb_fide_coord2D_b1),           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           shape_fide = list(basket_1 = 18),           size_fide = list(basket_1 = 5),           color_fide = list(basket_1 = "blue"),           fill_fide = list(basket_1 = "blue"),           color_segment = list(basket_1 = "red"),           width_segment = list(basket_1 = 1),           linetype_segment = list(basket_1 = "dashed"))            fdis_plot

Plot FDiv indice

Description

This plot fDiv indice for a given pair of functional axes and one or severalassemblages. This function adds mean distance to center of gravity ofvertices, points and vertices of 1:N assemblages on the background plot

Usage

fdiv.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_vertices_nD,  asb_vertG_coord2D,  plot_sp = TRUE,  shape_sp,  color_sp,  fill_sp,  shape_vert,  color_vert,  fill_vert,  shape_vertG,  size_vertG,  color_vertG,  fill_vertG)

Arguments

Value

A ggplot object plotting background of multidimensional graphs andFDiv indice.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FDiv: alpha_fd_ind <- mFD::alpha.fd.multidim(sp_faxes_coord   = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")],asb_sp_w         = baskets_fruits_weights,ind_vect         = c("fdiv"),scaling          = TRUE,check_input      = TRUE,details_returned = TRUE)   # Retrieve inputs of the fdiv.plot() function for "basket_1" and PC1, PC2 # ... through alpha.fd.multidim outputs: fruits_asb_sp_relatw_b1 <-          alpha_fd_ind$details$asb_sp_relatw["basket_1", ] fruits_asb_vertices_nD_b1_2D <-                      alpha_fd_ind$details$asb_vert_nm["basket_1"] fruits_asb_vertG_coord_b1 <-                      alpha_fd_ind$details$asb_G_coord["basket_1"] fruits_asb_vertG_coord_b1_2D <-              fruits_asb_vertG_coord_b1[[1]][c("PC1", "PC2")]  # Retrieve FDiv plot: fdiv_plot <- fdiv.plot(           ggplot_bg         = ggplot_bg_fruits,           asb_sp_coord2D    = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw     = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_vertices_nD   = fruits_asb_vertices_nD_b1_2D,           asb_vertG_coord2D = list(basket_1 = fruits_asb_vertG_coord_b1_2D),           plot_sp           = TRUE,           shape_sp          = 16,           color_sp          = c(basket_1 = "red"),           fill_sp           = "red",           color_vert        = "red",           fill_vert         = "red",           shape_vert        = 16,           shape_vertG       = list(basket_1 = 18),           size_vertG        = list(basket_1 = 2),           color_vertG       = list(basket_1 = "blue"),           fill_vertG        = list(basket_1 = "blue")) fdiv_plot

Plot FEve index

Description

This function plots FEve index for a given pair of functional axes and oneor several assemblages. It adds Minimum Spanning Tree (MST) of a givenassemblage on the background plot.

Usage

feve.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_mst,  plot_sp = TRUE,  shape_sp,  color_sp,  fill_sp,  color_mst,  width_mst,  linetype_mst)

Arguments

Value

A ggplot object showing FEve index on the background plot.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(  sp_faxes_coord   = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("feve"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve fide values through alpha.fd.multidim outputs: fruits_asb_mst_b1 <-          alpha_fd_indices_fruits$details$asb_mst["basket_1"] fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")] fruits_asb_sp_relatw_b1 <-          alpha_fd_indices_fruits$details$asb_sp_relatw["basket_1", ]          # Retrieve FEve plot: feve_plot <- feve.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_mst = fruits_asb_mst_b1,           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           color_mst = list(basket_1 = "blue"),           width_mst = list(basket_1 = 1),           linetype_mst = list(basket_1 = "solid"))            feve_plot

Plot FIde index

Description

Plot FIde index given a pair of functional axes for one or severalassemblages. It adds the centroid of species for each assemblage andsegments showing centroid coordinates on functional axes on the backgroundplot.

Usage

fide.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_fide_coord2D,  plot_sp = TRUE,  shape_sp,  color_sp,  fill_sp,  shape_fide,  size_fide,  color_fide,  fill_fide,  color_segment,  width_segment,  linetype_segment)

Arguments

Value

A ggplot object with FIde index, species and background.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim( sp_faxes_coord    = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("fide"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve fide values through alpha.fd.multidim outputs: fruits_asb_fide_coord2D <-   alpha_fd_indices_fruits$functional_diversity_indices[c("fide_PC1",                                                          "fide_PC2")] fruits_asb_fide_coord2D_b1 <- fruits_asb_fide_coord2D[c("basket_1"), ] fruits_asb_sp_relatw_b1 <-          alpha_fd_indices_fruits$details$asb_sp_relatw["basket_1", ]  # Retrieve FIde plot: fide_plot <- fide.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_fide_coord2D = list(basket_1 = fruits_asb_fide_coord2D_b1),           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           shape_fide = list(basket_1 = 18),           size_fide = list(basket_1 = 5),           color_fide = list(basket_1 = "blue"),           fill_fide = list(basket_1 = "blue"),           color_segment = list(basket_1 = "red"),           width_segment = list(basket_1 = 1),           linetype_segment = list(basket_1 = "dashed"))            fide_plot

Plot FNND index

Description

This function plots FNND index for a given pair of functional axes and forone or several assemblages

Usage

fnnd.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_nn_asb,  plot_sp = TRUE,  shape_sp,  color_sp,  fill_sp,  color_segment,  width_segment,  linetype_segment)

Arguments

Value

A ggplot object with FNND index.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(  sp_faxes_coord   = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("fnnd"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve nearest neighbor(s) names and relative weights ... # ... through alpha.fd.multidim outputs: fruits_asb_nn_asb_b1 <-          alpha_fd_indices_fruits$details$asb_nm_nn_asb["basket_1"] fruits_asb_sp_relatw_b1 <-          alpha_fd_indices_fruits$details$asb_sp_relatw["basket_1", ] # Retrieve FNND plot: fnnd_plot <- fnnd.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_nn_asb = fruits_asb_nn_asb_b1 ,           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           color_segment = list(basket_1 = "blue"),           width_segment = list(basket_1 = 1),           linetype_segment = list(basket_1 = "solid"))            fnnd_plot

Plot FOri

Description

This function plots FOri index for a given pair of functional axes and forone or several assemblages. It adds the distance of species from the studiedto their nearest neighbour(s) from the global pool.

Usage

fori.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  asb_nn_pool,  pool_coord2D,  plot_pool = TRUE,  plot_sp = TRUE,  shape_pool,  size_pool,  color_pool,  fill_pool,  shape_sp,  color_sp,  fill_sp,  color_segment,  width_segment,  linetype_segment)

Arguments

Value

A ggplot object with FOri index.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

## Not run: # Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(  sp_faxes_coord   = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("fori"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve nearest neighbor(s) names through alpha.fd.multidim outputs: fruits_asb_nn_pool_b1 <-          alpha_fd_indices_fruits$details$asb_nm_nn_pool["basket_1"] # Retrieve FNND plot: fori_plot <- fori.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           asb_nn_pool = fruits_asb_nn_pool_b1,           pool_coord2D = sp_faxes_coord_fruits[, c("PC1", "PC2")],           plot_pool = TRUE,           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           shape_pool = 4,           size_pool = 2,           color_pool = "grey",           fill_pool = "grey",           color_segment = list(basket_1 = "red"),           width_segment = list(basket_1 = 1),           linetype_segment = list(basket_1 = "solid"))            fori_plot## End(Not run)

Plot FRic index

Description

This function plots FRic index for a given pair of functional axes and oneor several assemblages. It adds convex hull(s), points and vertices of1:N assemblages on the background plot

Usage

fric.plot(  ggplot_bg,  asb_sp_coord2D,  asb_vertices_nD,  plot_sp = TRUE,  color_ch,  fill_ch,  alpha_ch,  shape_sp,  size_sp,  color_sp,  fill_sp,  shape_vert,  size_vert,  color_vert,  fill_vert)

Arguments

Value

A ggplot object plotting background of multidimensional graphs andFRic convex hulls.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:  sp_faxes_coord_fruits <-     fspaces_quality_fruits$details_fspaces$sp_pc_coord     # Retrieve species coordinates matrix for the assemblage "basket_1":  sp_filter <- mFD::sp.filter(asb_nm          = c("basket_1"),                               sp_faxes_coord = sp_faxes_coord_fruits,                               asb_sp_w       = baskets_fruits_weights)  sp_faxes_coord_fruits_b1 <- sp_filter$`species coordinates`  # Reduce it to the two studid axes: PC1 and PC2: sp_faxes_coord_fruits_b1_2D <- sp_faxes_coord_fruits_b1[, c("PC1", "PC2")]# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits + c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve vertices names: vert_nm_fruits <- vertices(sp_faxes_coord_fruits_b1_2D,   order_2D = TRUE, check_input = TRUE)                                # Plot in white the convex hull of all fruits species: ggplot_fric <- mFD::fric.plot(           ggplot_bg       = ggplot_bg_fruits,           asb_sp_coord2D  = list(basket_1 = sp_faxes_coord_fruits_b1_2D),           asb_vertices_nD = list(basket_1 = vert_nm_fruits),           plot_sp         = TRUE,           color_ch        = c("basket_1" = "black"),            fill_ch         = c("basket_1" = "white"),            alpha_ch        = c("basket_1" = 0.3),           size_sp = c("basket_1" = 1),           shape_sp = c("basket_1" = 16),           color_sp = c("basket_1" = "red"),           fill_sp = c("basket_1" = "red"),           size_vert = c("basket_1" = 1),           color_vert = c("basket_1" = "red"),           fill_vert = c("basket_1" = "red"),           shape_vert = c("basket_1" = 16)) ggplot_fric

Dataset: Traits Values of Fruits Species

Description

This dataset represents the value of 6 traits for 15 fruits species.Important: Species names must be specified as the data frame row names(not in an additional column).

Usage

fruits_traits

Format

A data frame with 25 rows (species) and the following columns(traits):

Size

an ordered factor describing the size of fruits species

Plant

an unordered factor describing the type of plant

Climate

an ordered factor describing the climate regions

Seed

an ordered factor describing the type of seed

Sugar

a numeric describing the quantity of sugar

Use.raw

an integer (percentage) describing the proportion of a rawuse (fuzzy trait) of the fruit

Use.pastry

an integer (percentage) describing the proportion of apastry use (fuzzy trait) of the fruit

Use.jam

an integer (percentage) describing the proportion of ajam use (fuzzy trait) of the fruit

See Also

fruits_traits_cat,baskets_fruits_weights

Examples

## Not run: # Load Species x Traits Data Framedata("fruits_traits", package = "mFD")fruits_traits# Load Traits Informationdata("fruits_traits_cat", package = "mFD")fruits_traits_cat# Summarize Species x Traits DatamFD::sp.tr.summary(tr_cat = fruits_traits_cat, sp_tr = fruits_traits)## End(Not run)

Dataset: Fruits Traits Informations

Description

This dataset summarizes information about the 6 traits used in thefruits_traits dataset.

Usage

fruits_traits_cat

Format

A data frame with 8 rows (traits) and the following three columns:

trait_name

a character giving the trait name

trait_type

a character giving the trait type (O for Ordinaltrait,N for Nominal trait,Q for Quantitative trait, andFfor Fuzzy-coded trait)

fuzzy_name

a character giving the name of fuzzy-coded traits(i.e.Use) to which 'sub-traits' (i.e.raw,pastry, andjam)belongs

Note

If your dataset does not contain fuzzy trait, the columnfuzzy_namecan be ignored but the first two columns are mandatory.

Traits in this dataset correspond to columns (traits) of thefruits_traitsdataset.

See Also

fruits_traits,baskets_fruits_weights

Examples

## Not run: # Load Traits Informationdata("fruits_traits_cat", package = "mFD")fruits_traits_cat# Load Species x Traits Data Framedata("fruits_traits", package = "mFD")# Summarize Species x Traits DatamFD::sp.tr.summary(tr_cat = fruits_traits_cat, sp_tr = fruits_traits)## End(Not run)

Plot FSpe

Description

This function plots FSpe index for a given pair of functional axes and forone or several assemblages. It adds the mean position of species from theglobal pool and the distance of each species from the studied assemblage(s)on the background plot

Usage

fspe.plot(  ggplot_bg,  asb_sp_coord2D,  asb_sp_relatw,  center_coord2D,  pool_coord2D,  plot_pool = TRUE,  plot_sp = TRUE,  shape_pool,  size_pool,  color_pool,  fill_pool,  shape_sp,  color_sp,  fill_sp,  color_center,  fill_center,  shape_center,  size_center,  color_segment,  width_segment,  linetype_segment)

Arguments

Value

A ggplot object with FSpe index on the background plot.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

## Not run: # Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits +  c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve the matrix of species coordinates for "basket_1" and PC1 and PC2 sp_filter <- mFD::sp.filter(asb_nm          = "basket_1",                              sp_faxes_coord = sp_faxes_coord_fruits,                              asb_sp_w       = baskets_fruits_weights) fruits_asb_sp_coord_b1 <- sp_filter$`species coordinates` fruits_asb_sp_coord2D_b1 <- fruits_asb_sp_coord_b1[, c("PC1", "PC2")]                                # Use alpha.fd.multidim() function to get inputs to plot FIde: alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(  sp_faxes_coord   = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  asb_sp_w         = baskets_fruits_weights,  ind_vect         = c("fspe"),  scaling          = TRUE,  check_input      = TRUE,  details_returned = TRUE)   # Retrieve nearest neighbor(s) names through alpha.fd.multidim outputs: fruits_center_coord2D <-          alpha_fd_indices_fruits$details$pool_O_coord[c("PC1", "PC2")]  # Retrieve FNND plot: fspe_plot <- fspe.plot(ggplot_bg = ggplot_bg_fruits,           asb_sp_coord2D = list(basket_1 = fruits_asb_sp_coord2D_b1),           asb_sp_relatw = list(basket_1 = fruits_asb_sp_relatw_b1),           center_coord2D = fruits_center_coord2D,           pool_coord2D = sp_faxes_coord_fruits[, c("PC1", "PC2")],           plot_pool = TRUE,           plot_sp = TRUE,           shape_sp = 16,           color_sp = "red",           fill_sp = "red",           shape_pool = 4,           size_pool = 2,           color_pool = "grey",           fill_pool = "grey",           color_center = "blue",            fill_center = "blue",           shape_center = 18,            size_center = 3,           color_segment = list(basket_1 = "red"),           width_segment = list(basket_1 = 1),           linetype_segment = list(basket_1 = "solid"))            fspe_plot## End(Not run)

Compute functional distance between species

Description

For a given combination of traits, this function returns the functionaldistance matrix between species.

Usage

funct.dist(  sp_tr,  tr_cat,  metric,  scale_euclid = "scale_center",  ordinal_var = "classic",  weight_type = "equal",  stop_if_NA = TRUE)

Arguments

Value

adist object containing distance between each pair of species.

Note

If thesp_tr data frame containsNA you can eitherchose to compute anyway functional distances (but keep in mind thatFunctional measures are sensitive to missing traits!) or you candelete species with missing or extrapolate missing traits (seeJohnsonet al. (2020)).

Author(s)

Nicolas Loiseau and Sebastien Villeger

References

de Belloet al. (2021) Towards a more balanced combination of multipletraits when computing functional differences between species.Method in Ecology and Evolution,12, 443-448.
Gower (1971 ) A general coefficient of similarity and some of itsproperties.Biometrics,27, 857-871.
Johnsonet al. (2020) Handling missing values in trait data.Global Ecology and Biogeography,30, 51-62.
Podani (1999) Extending Gower's general coefficient of similarity to ordinalcharacters,Taxon,48, 331-340.

Examples

# Load Species x Traits datadata("fruits_traits", package = "mFD")# Load Traits x Categories datadata("fruits_traits_cat", package = "mFD")# Remove fuzzy traits for this example and thus remove lat column:fruits_traits     <- fruits_traits[ , -c(6:8)]fruits_traits_cat <- fruits_traits_cat[-c(6:8), ]fruits_traits_cat <- fruits_traits_cat[ , -3]# Compute Functional Distancesp_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_dist_fruits

Plot species position in a functional space

Description

This function illustrates the position of species along pairs of axes of afunctional space

Usage

funct.space.plot(  sp_faxes_coord,  faxes = NULL,  name_file = NULL,  faxes_nm = NULL,  range_faxes = c(NA, NA),  color_bg = "grey95",  color_pool = "darkturquoise",  fill_pool = "white",  shape_pool = 21,  size_pool = 1,  plot_ch = TRUE,  color_ch = "darkblue",  fill_ch = "white",  alpha_ch = 1,  plot_vertices = TRUE,  color_vert = "darkturquoise",  fill_vert = "darkturquoise",  shape_vert = 22,  size_vert = 1,  plot_sp_nm = NULL,  nm_size = 3,  nm_color = "black",  nm_fontface = "plain",  check_input = TRUE)

Arguments

Value

Ifname_file isNULL, a list containingggplot2objects is returned: plots of functional space along all pairs of axes(named according to axes names, e.g. "PC1_PC2"), figure 'caption', and thefull figure 'patchwork' built using the librarypatchwork.Ifname_file is notNULL a 300dpi png fileis saved in the working directory. Ranges of axes are the same for allpanels and if required projection of the convex hull computed in themultidimensional space provided as inputsp_faxes_coord isillustrated with a polygon. Species being vertices of this convex hull areshown with aesthetics provided as inputs..._vert. Labels forspecies listed inplot_sp_nm are added with if required arrowsusingggrepel. Summary about species and dimensionality are printedon top-right corner of the figure.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe: data("baskets_fruits_weights", package = "mFD")# Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(  sp_dist             = sp_dist_fruits,  maxdim_pcoa         = 10,  deviation_weighting = "absolute",  fdist_scaling       = FALSE,  fdendro             = "average")# Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Plot functional spaces:mFD::funct.space.plot( sp_faxes_coord    = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],  faxes             = NULL,  name_file         = NULL,  faxes_nm          = NULL,  range_faxes       = c(NA, NA),  color_bg          = "grey95",  color_pool          = "darkturquoise",  fill_pool           = "white",  shape_pool          = 21,  size_pool           = 1,  plot_ch           = TRUE,  color_ch          = "darkblue",  fill_ch           = "white",  alpha_ch          = 1,  plot_vertices     = TRUE,  color_vert        = "darkturquoise",  fill_vert         = "darkturquoise",  shape_vert        = 22,  size_vert         = 1, plot_sp_nm         = NULL, nm_size            = 3, nm_color           = "black", nm_fontface        = "plain", check_input        = TRUE)

Compute FUSE (Functionally Unique, Specialized and Endangered)

Description

This index takes into account species functional uniqueness (also calledFunctional Originality), species specialisation and species IUCN status.

Usage

fuse(sp_dist, sp_faxes_coord, nb_NN = 5, GE, standGE = FALSE)

Arguments

Value

A data frame with species in rows and the following metrics incolumns:

• FUSE: functionally unique, specialized and endangered (seePimientoet al. (2020);

• FUn_std: functional uniqueness standardized between 0 and 1 (seeMouillotet al. (2013);

• FSp_std: functional specialization standardized between 0 and 1(see Mouillotet al. (2013);

Author(s)

Fabien Leprieur and Camille Albouy

References

Mouillotet al. (2013) Rare species support vulnerable functions inhigh-diversity ecosystems.PLoS Biology,11, e1001569.
Pimientoet al. (2020) Functional diversity of marine megafauna in theAnthropocene.Science Advances,6, eaay7650.
Violleet al. (2007) Let the concept of trait be functional!Oikos,116, 882-892.

Examples

# Load species traits data:sp_tr <- read.csv(system.file('extdata', 'data_traits_MMA_ursus.csv',   package = 'mFD'), dec = ',', sep = ';', header = TRUE, row.names = 1,  na.strings='NA')# Trait compilation and ordination:dimorphism      <- ordered(sp_tr$dimorphism)breeding_site   <- ordered(sp_tr$breeding_site)social_behavior <- ordered(sp_tr$social_behavior)weight_max      <- log(sp_tr$adult_weight_max)social_group    <- log(sp_tr$social_group_mean) # Trait Matrix construction:sp_tr_end <- data.frame(  main_diet = sp_tr$main_diet,   foraging_water_depth = sp_tr$foraging_water_depth,  foraging_location = sp_tr$foraging_location,   fasting_strategy = sp_tr$fasting_strategy,  female_sexual_maturity = sp_tr$female_sexual_maturity,   weaning = sp_tr$weaning,  gestation = sp_tr$gestation, inter_litter = sp_tr$inter_litter,  breeding_site = sp_tr$breeding_site,   social_group = sp_tr$social_group_mean,  social_behavior = sp_tr$social_behavior,   weight_max = sp_tr$adult_weight_max,  dimorphism = sp_tr$dimorphism)  rownames(sp_tr_end) <- rownames(sp_tr) # Function weigthing vector:v <- c(0.25, 0.25, 0.25, 0.25, 0.20, 0.20, 0.20, 0.20, 0.20, 0.5, 0.5, 0.5,   0.5)   # Gower distance calculation:sp_tr_end$main_diet <- as.factor(sp_tr_end$main_diet)sp_tr_end$foraging_water_depth <- as.factor(sp_tr_end$foraging_water_depth)sp_tr_end$foraging_location <- as.factor(sp_tr_end$foraging_location)sp_tr_end$breeding_site <- as.factor(sp_tr_end$breeding_site)sp_tr_end$social_behavior <- as.factor(sp_tr_end$social_behavior)sp_dist_tr <- cluster::daisy(sp_tr_end, metric = c('gower'),   type = list(symm = c(4)), weights = v)  # Principal coordinate analysesPcoa <- ade4::dudi.pco(ade4::quasieuclid(sp_dist_tr), scann = FALSE,                        nf = 40)sp_faxes_coord <- Pcoa$li[1:40] # FUSE calculation: FUSE_res <- mFD::fuse(    sp_dist        = sp_dist_tr,     sp_faxes_coord = as.matrix(sp_faxes_coord),     nb_NN          = 5,      GE             = sp_tr$IUCN_num,    standGE        = TRUE) FUSE_res     FUSE_res2 <- mFD::fuse(    sp_dist        = sp_dist_tr,     sp_faxes_coord = as.matrix(sp_faxes_coord),     nb_NN          = 5,    GE             = sp_tr$IUCN_50,    standGE        = TRUE) FUSE_res2     FUSE_res3 <- mFD::fuse(    sp_dist        = sp_dist_tr,     sp_faxes_coord = as.matrix(sp_faxes_coord),     nb_NN          = 5,     GE             = sp_tr$IUCN_100,    standGE        = TRUE) FUSE_res3

Compute the Minimum Spanning Tree (MST) linking species of a givenassemblage

Description

This function computes the MST linking species of a given assemblage and isused to compute FEve index.

Usage

mst.computation(sp_faxes_coord_k)

Arguments

Value

A dist object summarizing the MST for all species of a givenassemblagemst_asb_k.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:       data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(                                  sp_dist             = sp_dist_fruits,                                   maxdim_pcoa         = 10,                                  deviation_weighting = "absolute",                                  fdist_scaling       = FALSE,                                  fdendro             = "average") # Retrieve species coordinates matrix: sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute the distance of "pear" to its nearest neighbor(s): mst_fruits <- mst.computation(sp_faxes_coord_fruits) mst_fruits

Plot individual plots along a pair of functional axes into a unique graph

Description

This function gathers panels into a uniquepatchwork graphwith caption.

Usage

panels.to.patchwork(panels, plot_caption)

Arguments

Value

A unique ggplot object gathering functional panels and caption.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

## Retrieve FRic plot:# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:  sp_faxes_coord_fruits <-     fspaces_quality_fruits$details_fspaces$sp_pc_coord     # Retrieve species coordinates matrix for the assemblage "basket_1":  sp_filter <- mFD::sp.filter(asb_nm          = c("basket_1"),                               sp_faxes_coord = sp_faxes_coord_fruits,                               asb_sp_w       = baskets_fruits_weights)  sp_faxes_coord_fruits_b1 <- sp_filter$`species coordinates`  # Reduce it to the two studed axes: PC1 and PC2: sp_faxes_coord_fruits_b1_2D <- sp_faxes_coord_fruits_b1[, c("PC1", "PC2")]# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits) range_faxes_lim <- range_sp_coord_fruits + c(-1, 1)*(range_sp_coord_fruits[2] -  range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve vertices names: vert_nm_fruits <- vertices(sp_faxes_coord_fruits_b1_2D,   order_2D = TRUE, check_input = TRUE)                                # Plot in white the convex hull of all fruits species: ggplot_fric <- mFD::fric.plot(           ggplot_bg       = ggplot_bg_fruits,           asb_sp_coord2D  = list(basket_1 = sp_faxes_coord_fruits_b1_2D),           asb_vertices_nD = list(basket_1 = vert_nm_fruits),           plot_sp         = TRUE,           color_ch        = c("basket_1" = "black"),            fill_ch         = c("basket_1" = "white"),            alpha_ch        = c("basket_1" = 0.3),           size_sp = c("basket_1" = 1),           shape_sp = c("basket_1" = 16),           color_sp = c("basket_1" = "red"),           fill_sp = c("basket_1" = "red"),           size_vert = c("basket_1" = 1),           color_vert = c("basket_1" = "red"),           fill_vert = c("basket_1" = "red"),           shape_vert = c("basket_1" = 16)) ggplot_fric ## Create a caption summing up FRic values # retrieve values to plot:top_fric <- c("Functional richness", "basket_1", "")asb_fd_ind <- 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("fric"),scaling          = TRUE,check_input      = TRUE,details_returned = TRUE)values_fric <- c(round(asb_fd_ind$functional_diversity_indices["basket_1", "fric"], 3), "")# customize position of texts in the plot:spread_faxes <- (range_sp_coord_fruits[2] - range_sp_coord_fruits[1])hh <- c(1, 2.5, 4, 5.5)vv <- 0.3# plot window:x <- NULLy <- NULLplot_caption <- ggplot2::ggplot(data.frame(x = range_sp_coord_fruits,                                           y = range_sp_coord_fruits),                               ggplot2::aes(x = x, y = y)) + ggplot2::scale_x_continuous(limits = range_sp_coord_fruits,  expand = c(0, 0)) + ggplot2::scale_y_continuous(limits = range_sp_coord_fruits,  expand = c(0, 0)) + ggplot2::theme_void() + ggplot2::theme(legend.position = "none") + ggplot2::geom_rect(xmin = range_sp_coord_fruits[1],                     xmax = range_sp_coord_fruits[2],                    ymin = range_sp_coord_fruits[1],                     ymax = range_sp_coord_fruits[2],                    fill = "white", colour ="black")# plot names of index and of assemblages:h   <- NULLv   <- NULLtop <- NULLx <- NULLy <- NULLplot_caption <- plot_caption + ggplot2::geom_text(data = data.frame(   h = range_sp_coord_fruits[1] + spread_faxes * 0.15 * hh[c(1,3:4)],   v = range_sp_coord_fruits[2] - spread_faxes * rep(0.2, 3),   top = top_fric),   ggplot2::aes(x = h, y = v, label = top),   size = 3, hjust = 0.5, fontface = "bold")# plot FRic values:values_lab <- NULLdata_caption <- data.frame(  h = range_sp_coord_fruits[1] + spread_faxes * 0.15 * hh[2:4],  v = range_sp_coord_fruits[2] - spread_faxes*rep(vv, 3),  values_lab = c("FRic", values_fric))plot_caption <- plot_caption +  ggplot2::geom_text(data = data_caption,                    ggplot2::aes(x = h, y = v, label = values_lab),                    size = 3, hjust = 0.5, fontface = "plain")## Create patchwork:patchwork_fric <- mFD::panels.to.patchwork(list(ggplot_fric), plot_caption)patchwork_fric

Plot species from the pool

Description

Plot all species from the study case and associated convex hull

Usage

pool.plot(  ggplot_bg,  sp_coord2D,  vertices_nD,  plot_pool = TRUE,  shape_pool = 3,  size_pool = 0.8,  color_pool = "grey95",  fill_pool = NA,  color_ch = NA,  fill_ch = "white",  alpha_ch = 1,  shape_vert = 3,  size_vert = 1,  color_vert = "black",  fill_vert = NA)

Arguments

Value

A ggplot object plotting background of multidimensional graphs andspecies from the global pool (associated convex hull if asked).

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces(sp_dist = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits_2D <-   fspaces_quality_fruits$details_fspaces$sp_pc_coord[ , c("PC1", "PC2")]# Set faxes limits:# set range of axes if c(NA, NA): range_sp_coord_fruits  <- range(sp_faxes_coord_fruits_2D) range_faxes_lim <- range_sp_coord_fruits +    c(-1, 1)*(range_sp_coord_fruits[2] -    range_sp_coord_fruits[1]) * 0.05  # Retrieve the background plot: ggplot_bg_fruits <- mFD::background.plot(                               range_faxes = range_faxes_lim,                                faxes_nm    = c("PC 1", "PC 2"),                                color_bg    = "grey90")                                 # Retrieve vertices names: vert_nm_fruits <- vertices(sp_faxes_coord_fruits_2D,   order_2D = TRUE, check_input = TRUE)   # Plot the pool: plot_pool_fruits <- pool.plot(ggplot_bg   = ggplot_bg_fruits,           sp_coord2D    = sp_faxes_coord_fruits_2D,           vertices_nD   = vert_nm_fruits,           plot_pool     = TRUE,           shape_pool    = 3,            size_pool     = 0.8,            color_pool    = "grey95",            fill_pool     = NA,            color_ch      = NA,            fill_ch       = "white",            alpha_ch      = 1,            shape_vert    = 3,            size_vert     = 1,            color_vert    = "black",            fill_vert     = NA)  plot_pool_fruits

Compute functional spaces and their quality

Description

Compute a Principal Coordinates Analysis (PCoA) using functional distancebetween species. Then the function evaluates the quality of spaces builtusing an increasing number of principal components. Quality is evaluated asthe (absolute or squared) deviation between trait-based distance (input) anddistance in the PCoA-based space (raw Euclidean distance or scaled distanceaccording to its maximum value and maximum of trait-based distance). Optionto compute a functional dendrogram and its quality. This function is basedon the framework presented in Maireet al. (2015).

Usage

quality.fspaces(  sp_dist,  fdendro = NULL,  maxdim_pcoa = 10,  deviation_weighting = "absolute",  fdist_scaling = FALSE)

Arguments

Value

A list with:

• $quality_fspaces: a data frame with quality metric(s) foreach functional space. Functional spaces are named as 'pcoa_.d' and ifrequired 'tree_clustering method'. Quality metrics are named afterdeviation_weighting ('mad' for 'absolute' and and 'rmsd' for 'squared')and if fdist_scaling is TRUE with suffix '_scaled'.

• $details_trdist a list with 2 elements:$trdist_summarya vector with minimum (min), maximum (max), mean (mean) and standarddeviation (sd) ofsp_dist;$trdist_euclidean a logicalvalue indicating whethersp_dist checks Euclidean properties.

• $details_fspaces a list with 4 elements:$sp_pc_coorda matrix with coordinates of species (rows) along Principal Components(columns) with positive eigenvalues ;$pc_eigenvalues a matrixwith eigenvalues of axes from PCoA ;$dendro a hclustobject with the dendrogram details (null if no dendrogram computed) ;$pairsp_fspaces_dist a dataframe containing for each pair ofspecies (rows), their names in the 2 first columns ('sp.x' and 'sp.y'),their distance based on trait-values ('tr'), and their Euclidean(for PCoA) or cophenetic (for dendrogram if computed) distance in eachof the functional space computed ('pcoa_1d', 'pcoa_2d', ... ,'tree_clust');iffdist_scaling = TRUE,$pairsp_fspaces_dist_scaled a dataframe with scaled values of distances in functional spaces.

• $details_deviation a list of data frames:$dev_distsp a dataframe containing for each space (columns) thedifference for all species pairs (rows) of the distance in thefunctional space and trait-based distance (i.e. positive deviationindicates overestimation of actual distance) ;$abs_dev_distspand/or$sqr_dev_distsp, dataframes with for each space (columns) and allspecies pairs (rows) the absolute or squared deviation of distance ; iffdist_scaling = TRUE$dev_distsp_scaled, and$abs_dev_distsp_scaled and/or$sqr_dev_distsp_scaled, dataframes with deviation computed on scaled distance in functional spaces.

Note

The maximum number of dimensions considered for assessing quality offunctional spaces depends on number of PC axes with positive eigenvalues(i.e. axes with negative eigenvalues are not considered); so it could belower than$maxdim_pcoa.The quality metric obtained with deviation_weighting = 'squared' andfdist_scaling = TRUE is equivalent to the square-root of the 'mSD'originally suggested in Maireet al. (2015).

Author(s)

Sebastien Villeger, Eva Maire, and Camille Magneville

References

Maireet al. (2015) How many dimensions are needed to accurately assessfunctional diversity? A pragmatic approach for assessing the quality offunctional spacesGlobal Ecology and Biogeography,24, 728-740.

Examples

# Load Species x Traits Datadata("fruits_traits", package = "mFD")# Load Traits x Categories Datadata("fruits_traits_cat", package = "mFD")# Compute Functional Distancesp_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 Functional Spaces Quality (to retrieve species coordinates)fspaces_quality_fruits <- mFD::quality.fspaces(  sp_dist             = sp_dist_fruits,  maxdim_pcoa         = 10,  deviation_weighting = "absolute",  fdist_scaling       = FALSE,  fdendro             = "average") fspaces_quality_fruits  # Retrieve Species Coordinatessp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coordsp_faxes_coord_fruits

Plot functional space quality with a chosen quality metric

Description

Plot functional space quality with a chosen quality metric

Usage

quality.fspaces.plot(  fspaces_quality,  quality_metric,  fspaces_plot,  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")

Arguments

Value

A png file (resolution 300dpi) saved in the current workingdirectory. Quality of each functional space is illustrated with threepanels : - top row shows trait-based distance between species vs.space-based distance. - middle row shows trait-based distance vs.deviation between space-based and trait-based distances - bottom row showstrait-based distance between species vs. transformed deviation used tocompute the quality metric All plots have the same X axis. All plots on agiven row have the same Y axis and color palette. Type of distance infunctional space (Euclidean in PCoA, Cophenetic on tree) are abbreviated,as well as type of transformation of distance (scaling) and of deviation(Absolute or Squared)

Author(s)

Sebastien Villeger and Camille Magneville

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:data("baskets_fruits_weights", package = "mFD")# Load Traits categories dataframe:data("fruits_traits_cat", package = "mFD")# Compute functional distancesp_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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces( sp_dist             = sp_dist_fruits, maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Illustrate the quality of functional spaces: 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"),  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")

Retrieve information about species in a given assemblage

Description

This function computes names of species present in an given assemblage,their coordinates in the functional space and their weights. It is used inthealpha_FD_multidim function to filter species and compute eachfunctional indices for each community.

Usage

sp.filter(asb_nm, sp_faxes_coord, asb_sp_w)

Arguments

Value

A vector containing names of species present in a given assemblagesp_name_asb_k, a matrix containing coordinates of species presentin a given assemblagesp_faxes_coord_k, a matrix containing weightof species present in a given assemblageasb_sp_w_k, a matrixcontaining relative weight of species present in a given assemblageasb_sp_relatw_k.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe:data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:      data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe:data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix:fspaces_quality_fruits <- mFD::quality.fspaces( sp_dist             = sp_dist_fruits,  maxdim_pcoa         = 10, deviation_weighting = "absolute", fdist_scaling       = FALSE, fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Filter species of basket_1 assemblage:sp.filter(asb_nm         = "basket_1",           sp_faxes_coord = sp_faxes_coord_fruits,           asb_sp_w       = baskets_fruits_weights)

Compute Functional Entities composition based on a Species x Traits matrix

Description

Compute Functional Entities composition based on a Species x Traits matrix

Usage

sp.to.fe(sp_tr, tr_cat, fe_nm_type = "fe_rank", check_input = TRUE)

Arguments

Details

fe_nm_type = "tr_val" is allowedonly if:

• there are less than 7 traits;

• none of them is fuzzy-coded (so that names are not too long)

• all trait names and all trait values have different 2 first letters

If these 3 conditions are met, names of Functional Entities are made as acharacter string of up to 2 letters for trait name in upper case font thenup to 2 letters for trait value in lower case font, separated by "_" betweentraits. Trait names are abbreviated to a single letter whenever possible.Examples: ("TAc2_TBxx_TCyy", "TAc3_TBff_TCyy") or("A2_Bx_Cy", "A3_Bf_Cy")

Value

A list of objects containing:

• fe_nm: a vector with names of all FE (followingfe_nm_type). FE are ordered according to the decreasing number ofspecies they gather.

• sp_fe: a vector containing for each species the name of theFE it belongs to. FE order is done according to decreasing number ofspecies.

• fe_tr: a data frame containing traits values (variables incolumns) for each FE (rows). FE order is done according to decreasingnumber of species.

• fe_nb_sp: a vector with species number per FE. If all FEhave only one species, a warning message is returned. FE are orderedaccording to the decreasing number of species they gather.

• details_fe: a list containing:fe_codes a vectorcontaining character referring to traits values (like a barcode) withnames as infe_nm_type and sorted according tofe_nb_sp;tr_uval a list containing for each trait a vector of its uniquevalues or a data frame for fuzzy-coded traits;fuzzy_E a listwith for each fuzzy-coded trait a data frame with names of entities (E)and names of species (sp);tr_nb_uval a vector with number ofunique values per trait (or combinations for fuzzy-coded traits);max_nb_fe the maximum number of FE possible given number ofunique values per trait.

Author(s)

Sebastien Villeger, Nicolas Loiseau, and Camille Magneville

Examples

# Load species traits data: data("fruits_traits", package = "mFD")# Transform species traits data:# Only keep the first 4 traits to illustrate FEs: fruits_traits <- fruits_traits[ , c(1:4)]   # Load trait types data: data("fruits_traits_cat", package = "mFD")# Transform the trait types data to only keep traits 1 - 4: fruits_traits_cat <- fruits_traits_cat[c(1:4), ]# Gather species into FEs:## gathering species into FEs (FEs named according to the decreasing...## ...  number of species they gather): sp_FEs <- mFD::sp.to.fe(      sp_tr      = fruits_traits,       tr_cat     = fruits_traits_cat,       fe_nm_type = "fe_rank")## display FEs names:sp_FEs$fe_nm## display for each species the name of the FE it belongs to:sp_FEs$sp_fe## display trait values for each FE:sp_FEs$fe_tr ## display the number of species per FEs:sp_FEs$fe_nb_sp

Summarize Species x Traits data frame

Description

This function computes a summary data helping to choose the type of analysisyou can do with your data. For this function to work, there must be no NA inyoursp_tr data frame.

Usage

sp.tr.summary(tr_cat, sp_tr, stop_if_NA = TRUE)

Arguments

Value

If there is no fuzzy-coded trait, a three-elements list with:

If there is fuzzy-coded trait, a four-elements list with:

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species x Traits datadata('fruits_traits', package = 'mFD')# Load Traits x Categories datadata('fruits_traits_cat', package = 'mFD')# Summarize Species x Traits datamFD::sp.tr.summary(tr_cat = fruits_traits_cat, sp_tr = fruits_traits)

Build a functional space based on continuous traits only

Description

This function computes a functional space based on continuous standardizedtraits or continuous raw traits matrix. User can either choose to computefunctional space based on PCA analysis or using one trait for one functionalaxis. For PCA analysis, center and scale arguments are consideredFALSE:if you want to center, scale or standardize by any mean your data, pleaseusetr.cont.scale function. Option makes it possible tocompute correlation between traits.

Usage

tr.cont.fspace(  sp_tr,  pca = TRUE,  nb_dim = 7,  scaling = "scale_center",  compute_corr = "pearson")

Arguments

Value

A list containing a matrix withmAD andmSD values for eachfunctional space to assess the quality of functional spaces), a matrixcontaining eigenvalues for each axis, the percentage of variance explainedby each axis and the cumulative percentage of variance, a data framecontaining species coordinates on each functional axis, list of distancematrices in the functional space (Euclidean distances based on trait valuesand coordinates in the functional spaces), a dist object containing initialeuclidean distances based on traits and a matrix of correlation coefficientsbetween traits (if required).

Author(s)

Camille Magneville and Sebastien Villeger

Examples

load(system.file('extdata', 'sp_tr_cestes_df', package = 'mFD'))mFD::tr.cont.fspace(    sp_tr        = sp_tr,     pca          = TRUE,     nb_dim       = 7,     scaling      = 'scale_center',    compute_corr = 'pearson')

Scale continuous traits

Description

This function standardizes continuous traits. It can be useful beforecomputing functional space. You will have to choose which standardizedmethod to use based on your data. For this function to work, there must beno NA in yoursp_tr data frame.

Usage

tr.cont.scale(sp_tr, std_method = "scale_center")

Arguments

Value

A data frame of standardized trait values (columns) for each species(rows).

Author(s)

Camille Magneville and Sebastien Villeger

Examples

load(system.file('extdata', 'sp_tr_cestes_df', package = 'mFD'))mFD::tr.cont.scale(sp_tr = sp_tr, std_method = 'scale_center')

Correlation between Traits and Axes

Description

Compute relationship between all traits and all axes of the functionalspace. For continuous trait a linear model is computed and r2 and p-valueare returned. For other types of traits, a Kruskal-Wallis test is computedand eta2 statistics is returned.Option allows to plot trait-axis relationships with scatterplot and boxplotfor continuous and non-continuous traits, respectively.

Usage

traits.faxes.cor(  sp_tr,  sp_faxes_coord,  tr_nm = NULL,  faxes_nm = NULL,  plot = FALSE,  name_file = NULL,  color_signif = "darkblue",  color_non_signif = "gray80",  stop_if_NA = TRUE)

Arguments

Value

1 data frame with for each combination of trait and axis (rows), thename of the test performed, and the corresponding statistics and p-value.Ifplot = TRUE a multi-panel figure with traits as columns and axes asrows is also plotted. When relationships between trait and axis issignificant the points are colored, else they remain grayish.

Author(s)

Nicolas Loiseau and Sebastien Villeger

Examples

# Load Species x Traits Datadata("fruits_traits", package = "mFD")# Load Traits categories dataframedata("fruits_traits_cat", package = "mFD")# Compute Functional Distancesp_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 Functional Spaces Quality (to retrieve species coordinates)fspaces_quality_fruits <- mFD::quality.fspaces(  sp_dist             = sp_dist_fruits,   maxdim_pcoa         = 10,  deviation_weighting = "absolute",  fdist_scaling       = FALSE,  fdendro             = "average")  # Retrieve Species Coordinatessp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute Correlation between Traits and Functional AxesmFD::traits.faxes.cor(  sp_tr          = fruits_traits,   sp_faxes_coord = sp_faxes_coord_fruits,   tr_nm          = NULL,   faxes_nm       = NULL,  name_file      = NULL,   color_signif   = "darkblue",  color_non_signif = "gray80")

Compute vertices of the Minimal Convex Hull shaping species from a singleassemblage in a multidimensional functional space

Description

This function identifies species that are vertices of the minimal convexhull enclosing a community in a multidimensional functional space. Thisfunction is using theconvhulln function.

Usage

vertices(sp_faxes_coord, order_2D = FALSE, check_input = FALSE)

Arguments

Value

A vector containing names of species being verticesvert_nm.

Author(s)

Camille Magneville and Sebastien Villeger

Examples

# Load Species*Traits dataframe: data("fruits_traits", package = "mFD")# Load Assemblages*Species dataframe:       data("baskets_fruits_weights", package = "mFD") # Load Traits categories dataframe: data("fruits_traits_cat", 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 functional spaces quality to retrieve species coordinates matrix: fspaces_quality_fruits <- mFD::quality.fspaces(                                  sp_dist             = sp_dist_fruits,                                   maxdim_pcoa         = 10,                                  deviation_weighting = "absolute",                                  fdist_scaling       = FALSE,                                  fdendro             = "average") # Retrieve species coordinates matrix:sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord# Compute vertices and order them clockwise: vert_nm <- vertices(sp_faxes_coord_fruits[ , c("PC1", "PC2")],   order_2D = TRUE, check_input = TRUE) vert_nm