R/FDindexes.R
In ibartomeus/fundiv: tools to calculate several functional trait diversity indexes
#' Wrapper to Space and Dendrogram Based Functional Diversity Indices
#'
#' Calculate functional trait diversity for a set of communities using FD_dendro
#' and dbFD and returns a single dataframe
#'
#' @param S matrix or data frame of functional traits. Traits can be numeric, ordered,
#' or factor. NAs are tolerated.\code{}
#' @param A matrix containing the abundances of the species in S (or presence-absence,
#' i.e. 0 or 1). Rows are sites and species are columns. NA not tolerated. The number of
#' species (columns) in A must match the number of species (rows) in S. In addition,
#' the species labels in A and S must be identical and in the same order.\code{}
#' @param w vector listing the weights for the traits in x. Can be missing,
#' in which case all traits have equal weights.\code{}
#' @param Distance.method metric to calculate the species distance matrix. Only Gower is
#' fully implemented. \code{}
#' @param ord character string specifying the method to be used for ordinal traits
#' (i.e. ordered). "podani" refers to Eqs. 2a-b of Podani (1999), while "metric"
#' refers to his Eq. 3. See gowdis for more details.\code{}
#' @param Cluster.method Distance method used to produce the tree. UPGMA="average" is
#' usually giving th ebest results (Podani et al. 2011)\code{}
#' @param stand.x ogical; if all traits are numeric, should they be standardized
#' to mean 0 and unit variance? If not all traits are numeric, Gower's (1971)
#' standardization by the range is automatically used; see gowdis for more details.\code{}
#' @param stand.FD logical; should FD be standardized by the max FD, so that FD
#' is constrained between 0 and 1?\code{}
#' @param character string specifying the correction method to use when the
#' species-by-species distance matrix cannot be represented in a Euclidean space.
#' Options are "sqrt", "cailliez", "lingoes", or "none". Can be abbreviated.
#' Default is "sqrt". See ‘details’ section.
#' @param stand.FRic logical; should FRic be standardized by the ‘global’ FRic
#' that include all species, so that FRic is constrained between 0 and 1?
#' @param m the number of PCoA axes to keep as ‘traits’ for calculating FRic
#' (when FRic is measured as the convex hull volume) and FDiv. Options are:
#' any integer >1, "min" (maximum number of traits that allows the s >= 2^t
#' condition to be met, where s is the number of species and t the number of
#' traits), or "max" (maximum number of axes that allows the s > t condition
#' to be met). See ‘dbFD’ details section.
#' @param scale.RaoQ logical; should Rao's Q be scaled by its maximal value over
#' all frequency distributions? See divc.
#' @param Weigthedby character string indicating weighted by biomass should be done
#' on `biomassValue` or corrected first for Carabids or bees. \code{}
#' @param biomassValue numerical vector with body weigh (or length) values for each species
#' in the same order as species are provided. It can also be a matrix or data
#' frame with one mass value for each community and species (both communities and species
#' arranged like in A). Default is 1, implying no weightening \code{}
#' @param op_qhull access the options for calculating the volume of the convex hull.
#' If a long scary warning appears mentioning qhull read convhulln{geometry} help page.
#' default in FD is= "FA", but sometimes is needed to add stuff.
#' @return comm vector with the name of the community
#' @return n_sp vector listing the number of species for each community
#' @return n_tr vector listing the number of traits used
#' @return FDpg vector listing FDpg (petchey and gaston) for each community
#' @return FDw vector listing FD weighthed by species relative abundances
#' in each community
#' @return FDwcomm vector listing FD weighthed by species abundances
#' across all communities
#' @return qual.FD vector repeating the quality of the dendogram representation.
#' clustering performance is assessed by the correlation with the cophenetic distance
#' @return FDw_bm FDw vector listing FD weighthed by species relative biomass
#' in each community
#' @return FDwcomm_bm vector listing FD weighthed by species biomass
#' across all communities
#' @return sing.sp vector listing the number of functionally singular species
#' in each community. If all species are functionally different, sing.sp will
#' be identical to nbsp.
#' @return qual.FRic quality of the reduced-space representation required to
#' compute FRic and FDiv.
#' @return Frich vector listing the FRic of each community
#' @return Fdis vector listing the Fdis of each community
#' @return Fdis_bm vector listing the Fdis weighted by biomass of each community
#' @return Feve vector listing the Feve of each community
#' @return Feve_bm vector listing the Feve weighted by biomass of each community
#' @return Fdiv vector listing the Fdiv of each community
#' @return Fdiv_bm vector listing the Fdiv weighted by biomass of each community
#' @return RaoQ vector listing the RaoQ of each community
#' @return RaoQ_bm vector listing the RaoQ weighted by biomass of each community
#' @return Seve vector listing the species eveness (pielous J') of each community
#' @return Shannon vector listing the diversitit Shannon index of each community
#' @return Abund vector listing the Total abundance of each community
#' @return TotalBiomass vector listing the total biomass of each community
#' @return EvenessBiomass vector listing the eveness of biomass distribution
#' of each community
#' @return ShannonBiomass vector listing the Shannon of the diversity weighted by biomass
#' of each community. I am not sure this one makes much sense.
#'
#' @export
#'
#' @examples
#' ex1 <- FDindexes(dummy$trait, A = dummy$abun, Distance.method= "gower", ord= "podani",
#' Cluster.method= "average", corr= "cailliez", Weigthedby = "biomassValue",
#' biomassValue = c(1.2, 2.3, 0.6, 1.0, 3.4, 0.2, 1.6, 2.2))
#' ex1
FDindexes <- function(S, A, w = NA, Distance.method = "gower", ord= c("podani", "metric"),
Cluster.method = c(ward="ward",single="single",complete="complete",
UPGMA="average",UPGMC="centroid",WPGMC="median",
WPGMA="mcquitty"),
calc.FRic = TRUE, stand.x = TRUE, corr = c("sqrt", "cailliez", "lingoes", "none"),
stand.FRic = FALSE, m = "max", stand.FD = FALSE, scale.RaoQ = FALSE,
Weigthedby = c("biomasCarabids", "biomasBees", "biomassValue"),
biomassValue = rep(1,nrow(S)),
op_qhull = "FA"){
require(FD)
require(cluster)
require(vegan)
Out <- data.frame(comm = rep(NA,nrow(A)),
n_sp = rep(NA,nrow(A)),
n_tr = rep(NA,nrow(A)),
FDpg = rep(NA,nrow(A)),
FDw = rep(NA,nrow(A)),
FDwcomm = rep(NA,nrow(A)),
qual.FD = rep(NA,nrow(A)),
FDw_bm = rep(NA,nrow(A)),
FDwcomm_bm = rep(NA,nrow(A)),
sing.sp = rep(NA,nrow(A)),
qual.FRic = rep(NA,nrow(A)),
Frich = rep(NA,nrow(A)),
Fdis = rep(NA,nrow(A)),
Fdis_bm = rep(NA,nrow(A)),
Feve = rep(NA,nrow(A)),
Feve_bm = rep(NA,nrow(A)),
Fdiv = rep(NA,nrow(A)),
Fdiv_bm = rep(NA,nrow(A)),
RaoQ = rep(NA,nrow(A)),
RaoQ_bm = rep(NA,nrow(A)),
Seve = rep(NA,nrow(A)),
Shannon = rep(NA,nrow(A)),
Abund = rep(NA,nrow(A)),
TotalBiomass = rep(NA,nrow(A)),
EvenessBiomass = rep(NA,nrow(A)),
ShannonBiomass = rep(NA,nrow(A)))
#calculate FD for metrics using dendrograms
Outdendro <- FD_dendro(S, A, w = w, Distance.method = Distance.method, ord= ord,
Cluster.method = Cluster.method, stand.x = stand.x, stand.FD = stand.FD,
Weigthedby = "abundance", biomassValue = NA)
Out[,"comm"] <- Outdendro$comm
Out[,"n_sp"] <- Outdendro$n_sp
Out[,"n_tr"] <- Outdendro$n_tr
Out[,"FDpg"] <- Outdendro$FDpg
Out[,"FDw"] <- Outdendro$FDw
Out[,"FDwcomm"] <- Outdendro$FDwcomm
Out[,"qual.FD"] <- Outdendro$qual.FD
Outdendro_bm <- FD_dendro(S, A, w = w, Distance.method = Distance.method, ord= ord,
Cluster.method = Cluster.method, stand.x = stand.x, stand.FD = stand.FD,
Weigthedby = Weigthedby, biomassValue = biomassValue)
Out[,"FDw_bm"] <- Outdendro_bm$FDw
Out[,"FDwcomm_bm"] <- Outdendro_bm$FDwcomm
#calculate FRichness according to Manson, villager, laliberte et al. using a tweaked version of FD package
FD <- dbFD_w(S, A, calc.CWM = FALSE, corr = corr, stand.FRic = stand.FRic,
calc.FRic = calc.FRic, ord = ord, m = m, stand.x = stand.x,
scale.RaoQ = scale.RaoQ, Weigthedby = "abundance", biomassValue = NA,
op_qhull = op_qhull)
Out[,"sing.sp"] <- FD$sing.sp
if(calc.FRic == TRUE){
Out[,"qual.FRic"] <- rep(FD$qual.FRic, nrow(A))
Out[,"Frich"] <- FD$FRic
}
Out[,"Fdis"] <- FD$FDis
FD_bm <- dbFD_w(S, A, calc.CWM = FALSE, corr = corr, stand.FRic = stand.FRic,
ord = ord, m = m, stand.x = stand.x, scale.RaoQ = scale.RaoQ,
Weigthedby = Weigthedby, calc.FRic = calc.FRic,
biomassValue = biomassValue,
op_qhull = op_qhull)
Out[,"Fdis_bm"] <- FD_bm$FDis
Out[,"Feve"] <- FD$FEve
Out[,"Feve_bm"] <- FD_bm$FEve
Out[,"Fdiv"] <- FD$FDiv
Out[,"Fdiv_bm"] <- FD_bm$FDiv
Out[,"RaoQ"] <- FD$RaoQ
Out[,"RaoQ_bm"] <- FD_bm$RaoQ
#calculate species eveness Pielous
eve <- rep(NA,nrow(A))
for (k in 1:nrow(A)){
if(specnumber(A[k,]) == 1){
eve[k] <- NA
}else{
eve[k] <- diversity(A[k,])/log(specnumber(A[k,]))
}
}
Out[,"Seve"] <- eve
#calculate abundance and shannon
Out[,"Shannon"] <- diversity(A)
Out[,"Abund"] <- rowSums(A)
if(Weigthedby == "biomasCarabids"){
biomassValue2 <- Jelaska(biomassValue)
}
if(Weigthedby == "biomasBees"){
biomassValue2 <- Cane(biomassValue)
}else{
biomassValue2 <- biomassValue
}
AA <- A
#if biomassValue2 is a marix (with a different value for each community /species)
if(is.vector(biomassValue2)){
for(j in 1:ncol(A)) AA[,j] <- A[,j]*biomassValue2[j]
}else{
AA <- AA * biomassValue2
}
#eveness and Shannon of total biomass
#Total biomass
Out[,"TotalBiomass"] <- rowSums(AA)
#Eveness_bm
eve_bm <- rep(NA,nrow(AA))
for (k in 1:nrow(AA)){
if(specnumber(AA[k,]) == 1){
eve[k] <- NA
}else{
eve_bm[k] <- diversity(AA[k,])/log(specnumber(AA[k,]))
}
}
Out[,"EvenessBiomass"] <- eve_bm
#Shannon_bm
Out[,"ShannonBiomass"] <- diversity(AA)
Out
}
ibartomeus/fundiv documentation built on May 18, 2019, 1:29 a.m.
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#' Wrapper to Space and Dendrogram Based Functional Diversity Indices
#'
#' Calculate functional trait diversity for a set of communities using FD_dendro
#' and dbFD and returns a single dataframe
#'
#' @param S matrix or data frame of functional traits. Traits can be numeric, ordered,
#' or factor. NAs are tolerated.\code{}
#' @param A matrix containing the abundances of the species in S (or presence-absence,
#' i.e. 0 or 1). Rows are sites and species are columns. NA not tolerated. The number of
#' species (columns) in A must match the number of species (rows) in S. In addition,
#' the species labels in A and S must be identical and in the same order.\code{}
#' @param w vector listing the weights for the traits in x. Can be missing,
#' in which case all traits have equal weights.\code{}
#' @param Distance.method metric to calculate the species distance matrix. Only Gower is
#' fully implemented. \code{}
#' @param ord character string specifying the method to be used for ordinal traits
#' (i.e. ordered). "podani" refers to Eqs. 2a-b of Podani (1999), while "metric"
#' refers to his Eq. 3. See gowdis for more details.\code{}
#' @param Cluster.method Distance method used to produce the tree. UPGMA="average" is
#' usually giving th ebest results (Podani et al. 2011)\code{}
#' @param stand.x ogical; if all traits are numeric, should they be standardized
#' to mean 0 and unit variance? If not all traits are numeric, Gower's (1971)
#' standardization by the range is automatically used; see gowdis for more details.\code{}
#' @param stand.FD logical; should FD be standardized by the max FD, so that FD
#' is constrained between 0 and 1?\code{}
#' @param character string specifying the correction method to use when the
#' species-by-species distance matrix cannot be represented in a Euclidean space.
#' Options are "sqrt", "cailliez", "lingoes", or "none". Can be abbreviated.
#' Default is "sqrt". See ‘details’ section.
#' @param stand.FRic logical; should FRic be standardized by the ‘global’ FRic
#' that include all species, so that FRic is constrained between 0 and 1?
#' @param m the number of PCoA axes to keep as ‘traits’ for calculating FRic
#' (when FRic is measured as the convex hull volume) and FDiv. Options are:
#' any integer >1, "min" (maximum number of traits that allows the s >= 2^t
#' condition to be met, where s is the number of species and t the number of
#' traits), or "max" (maximum number of axes that allows the s > t condition
#' to be met). See ‘dbFD’ details section.
#' @param scale.RaoQ logical; should Rao's Q be scaled by its maximal value over
#' all frequency distributions? See divc.
#' @param Weigthedby character string indicating weighted by biomass should be done
#' on `biomassValue` or corrected first for Carabids or bees. \code{}
#' @param biomassValue numerical vector with body weigh (or length) values for each species
#' in the same order as species are provided. It can also be a matrix or data
#' frame with one mass value for each community and species (both communities and species
#' arranged like in A). Default is 1, implying no weightening \code{}
#' @param op_qhull access the options for calculating the volume of the convex hull.
#' If a long scary warning appears mentioning qhull read convhulln{geometry} help page.
#' default in FD is= "FA", but sometimes is needed to add stuff.
#' @return comm vector with the name of the community
#' @return n_sp vector listing the number of species for each community
#' @return n_tr vector listing the number of traits used
#' @return FDpg vector listing FDpg (petchey and gaston) for each community
#' @return FDw vector listing FD weighthed by species relative abundances
#' in each community
#' @return FDwcomm vector listing FD weighthed by species abundances
#' across all communities
#' @return qual.FD vector repeating the quality of the dendogram representation.
#' clustering performance is assessed by the correlation with the cophenetic distance
#' @return FDw_bm FDw vector listing FD weighthed by species relative biomass
#' in each community
#' @return FDwcomm_bm vector listing FD weighthed by species biomass
#' across all communities
#' @return sing.sp vector listing the number of functionally singular species
#' in each community. If all species are functionally different, sing.sp will
#' be identical to nbsp.
#' @return qual.FRic quality of the reduced-space representation required to
#' compute FRic and FDiv.
#' @return Frich vector listing the FRic of each community
#' @return Fdis vector listing the Fdis of each community
#' @return Fdis_bm vector listing the Fdis weighted by biomass of each community
#' @return Feve vector listing the Feve of each community
#' @return Feve_bm vector listing the Feve weighted by biomass of each community
#' @return Fdiv vector listing the Fdiv of each community
#' @return Fdiv_bm vector listing the Fdiv weighted by biomass of each community
#' @return RaoQ vector listing the RaoQ of each community
#' @return RaoQ_bm vector listing the RaoQ weighted by biomass of each community
#' @return Seve vector listing the species eveness (pielous J') of each community
#' @return Shannon vector listing the diversitit Shannon index of each community
#' @return Abund vector listing the Total abundance of each community
#' @return TotalBiomass vector listing the total biomass of each community
#' @return EvenessBiomass vector listing the eveness of biomass distribution
#' of each community
#' @return ShannonBiomass vector listing the Shannon of the diversity weighted by biomass
#' of each community. I am not sure this one makes much sense.
#'
#' @export
#'
#' @examples
#' ex1 <- FDindexes(dummy$trait, A = dummy$abun, Distance.method= "gower", ord= "podani",
#' Cluster.method= "average", corr= "cailliez", Weigthedby = "biomassValue",
#' biomassValue = c(1.2, 2.3, 0.6, 1.0, 3.4, 0.2, 1.6, 2.2))
#' ex1
FDindexes <- function(S, A, w = NA, Distance.method = "gower", ord= c("podani", "metric"),
Cluster.method = c(ward="ward",single="single",complete="complete",
UPGMA="average",UPGMC="centroid",WPGMC="median",
WPGMA="mcquitty"),
calc.FRic = TRUE, stand.x = TRUE, corr = c("sqrt", "cailliez", "lingoes", "none"),
stand.FRic = FALSE, m = "max", stand.FD = FALSE, scale.RaoQ = FALSE,
Weigthedby = c("biomasCarabids", "biomasBees", "biomassValue"),
biomassValue = rep(1,nrow(S)),
op_qhull = "FA"){
require(FD)
require(cluster)
require(vegan)
Out <- data.frame(comm = rep(NA,nrow(A)),
n_sp = rep(NA,nrow(A)),
n_tr = rep(NA,nrow(A)),
FDpg = rep(NA,nrow(A)),
FDw = rep(NA,nrow(A)),
FDwcomm = rep(NA,nrow(A)),
qual.FD = rep(NA,nrow(A)),
FDw_bm = rep(NA,nrow(A)),
FDwcomm_bm = rep(NA,nrow(A)),
sing.sp = rep(NA,nrow(A)),
qual.FRic = rep(NA,nrow(A)),
Frich = rep(NA,nrow(A)),
Fdis = rep(NA,nrow(A)),
Fdis_bm = rep(NA,nrow(A)),
Feve = rep(NA,nrow(A)),
Feve_bm = rep(NA,nrow(A)),
Fdiv = rep(NA,nrow(A)),
Fdiv_bm = rep(NA,nrow(A)),
RaoQ = rep(NA,nrow(A)),
RaoQ_bm = rep(NA,nrow(A)),
Seve = rep(NA,nrow(A)),
Shannon = rep(NA,nrow(A)),
Abund = rep(NA,nrow(A)),
TotalBiomass = rep(NA,nrow(A)),
EvenessBiomass = rep(NA,nrow(A)),
ShannonBiomass = rep(NA,nrow(A)))
#calculate FD for metrics using dendrograms
Outdendro <- FD_dendro(S, A, w = w, Distance.method = Distance.method, ord= ord,
Cluster.method = Cluster.method, stand.x = stand.x, stand.FD = stand.FD,
Weigthedby = "abundance", biomassValue = NA)
Out[,"comm"] <- Outdendro$comm
Out[,"n_sp"] <- Outdendro$n_sp
Out[,"n_tr"] <- Outdendro$n_tr
Out[,"FDpg"] <- Outdendro$FDpg
Out[,"FDw"] <- Outdendro$FDw
Out[,"FDwcomm"] <- Outdendro$FDwcomm
Out[,"qual.FD"] <- Outdendro$qual.FD
Outdendro_bm <- FD_dendro(S, A, w = w, Distance.method = Distance.method, ord= ord,
Cluster.method = Cluster.method, stand.x = stand.x, stand.FD = stand.FD,
Weigthedby = Weigthedby, biomassValue = biomassValue)
Out[,"FDw_bm"] <- Outdendro_bm$FDw
Out[,"FDwcomm_bm"] <- Outdendro_bm$FDwcomm
#calculate FRichness according to Manson, villager, laliberte et al. using a tweaked version of FD package
FD <- dbFD_w(S, A, calc.CWM = FALSE, corr = corr, stand.FRic = stand.FRic,
calc.FRic = calc.FRic, ord = ord, m = m, stand.x = stand.x,
scale.RaoQ = scale.RaoQ, Weigthedby = "abundance", biomassValue = NA,
op_qhull = op_qhull)
Out[,"sing.sp"] <- FD$sing.sp
if(calc.FRic == TRUE){
Out[,"qual.FRic"] <- rep(FD$qual.FRic, nrow(A))
Out[,"Frich"] <- FD$FRic
}
Out[,"Fdis"] <- FD$FDis
FD_bm <- dbFD_w(S, A, calc.CWM = FALSE, corr = corr, stand.FRic = stand.FRic,
ord = ord, m = m, stand.x = stand.x, scale.RaoQ = scale.RaoQ,
Weigthedby = Weigthedby, calc.FRic = calc.FRic,
biomassValue = biomassValue,
op_qhull = op_qhull)
Out[,"Fdis_bm"] <- FD_bm$FDis
Out[,"Feve"] <- FD$FEve
Out[,"Feve_bm"] <- FD_bm$FEve
Out[,"Fdiv"] <- FD$FDiv
Out[,"Fdiv_bm"] <- FD_bm$FDiv
Out[,"RaoQ"] <- FD$RaoQ
Out[,"RaoQ_bm"] <- FD_bm$RaoQ
#calculate species eveness Pielous
eve <- rep(NA,nrow(A))
for (k in 1:nrow(A)){
if(specnumber(A[k,]) == 1){
eve[k] <- NA
}else{
eve[k] <- diversity(A[k,])/log(specnumber(A[k,]))
}
}
Out[,"Seve"] <- eve
#calculate abundance and shannon
Out[,"Shannon"] <- diversity(A)
Out[,"Abund"] <- rowSums(A)
if(Weigthedby == "biomasCarabids"){
biomassValue2 <- Jelaska(biomassValue)
}
if(Weigthedby == "biomasBees"){
biomassValue2 <- Cane(biomassValue)
}else{
biomassValue2 <- biomassValue
}
AA <- A
#if biomassValue2 is a marix (with a different value for each community /species)
if(is.vector(biomassValue2)){
for(j in 1:ncol(A)) AA[,j] <- A[,j]*biomassValue2[j]
}else{
AA <- AA * biomassValue2
}
#eveness and Shannon of total biomass
#Total biomass
Out[,"TotalBiomass"] <- rowSums(AA)
#Eveness_bm
eve_bm <- rep(NA,nrow(AA))
for (k in 1:nrow(AA)){
if(specnumber(AA[k,]) == 1){
eve[k] <- NA
}else{
eve_bm[k] <- diversity(AA[k,])/log(specnumber(AA[k,]))
}
}
Out[,"EvenessBiomass"] <- eve_bm
#Shannon_bm
Out[,"ShannonBiomass"] <- diversity(AA)
Out
}
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