R/PRE_FATE.selectDominant.R
In leca-dev/RFate: FATE with R
Defines functions
PRE_FATE.selectDominant
Documented in
PRE_FATE.selectDominant
### HEADER #####################################################################
##' @title Selection of dominant species from abundance releves
##'
##' @name PRE_FATE.selectDominant
##'
##' @author Isabelle Boulangeat, Maya Guéguen
##'
##' @description This script is designed to select dominant species from
##' abundance records, and habitat if the information is available.
##'
##' @param mat.observations a \code{data.frame} with at least 3 columns : \cr
##' \code{sites}, \code{species}, \code{abund}
##' \cr (\emph{and optionally, \code{habitat}})
##' \cr (see \href{PRE_FATE.selectDominant#details}{\code{Details}})
##' @param doRuleA default \code{TRUE}. \cr If \code{TRUE}, selection
##' is done including constraints on number of occurrences
##' @param rule.A1 default \code{10}. \cr If \code{doRuleA = TRUE} or
##' \code{doRuleC = TRUE}, minimum number of releves required for each species
##' @param rule.A2_quantile default \code{0.9}. \cr If \code{doRuleA = TRUE}
##' or \code{doRuleC = TRUE}, quantile corresponding to the minimum number of
##' total occurrences required for each species (between \code{0} and \code{1})
##' @param doRuleB default \code{FALSE}. \cr If \code{TRUE}, selection is done
##' including constraints on relative abundances
##' @param rule.B1_percentage default \code{0.25}. \cr If \code{doRuleB = TRUE},
##' minimum relative abundance required for each species in at least
##' \code{rule.B1_number} sites (between \code{0} and \code{1})
##' @param rule.B1_number default \code{5}. \cr If \code{doRuleB = TRUE},
##' minimum number of sites in which each species has relative abundance
##' \code{>= rule.B1_percentage}
##' @param rule.B2 default \code{0.5}. \cr If \code{doRuleB = TRUE}, minimum
##' average relative abundance required for each species (between \code{0} and
##' \code{1})
##' @param doRuleC default \code{FALSE}. \cr If \code{TRUE}, selection is done
##' including constraints on number of occurrences at the habitat level (with
##' the values of \code{rule.A1} and \code{rule.A2_quantile})
##' @param opt.doRobustness (\emph{optional}) default \code{FALSE}. \cr
##' If \code{TRUE}, selection is also done on subsets of
##' \code{mat.observations}, keeping only a percentage of releves or sites, to
##' visualize the robustness of the selection
##' @param opt.robustness_percent (\emph{optional}) default \code{c(0.1, 0.2,
##' 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)}. \cr If \code{opt.doRobustness = TRUE},
##' \code{vector} containing values between \code{0} and \code{1} corresponding
##' to the percentages with which to build subsets to evaluate robustness
##' @param opt.robustness_rep (\emph{optional}) default \code{10}. \cr If
##' \code{opt.doRobustness = TRUE}, number of repetitions for each percentage
##' value defined by \code{opt.robustness_percent} to evaluate robustness
##' @param opt.doSitesSpecies (\emph{optional}) default \code{TRUE}. \cr
##' If \code{TRUE}, building of abundances / occurrences tables for selected
##' species will be processed, saved and returned.
##' @param opt.doPlot (\emph{optional}) default \code{TRUE}. \cr If \code{TRUE},
##' plot(s) will be processed, otherwise only the calculation and reorganization
##' of outputs will occur, be saved and returned.
##'
##'
##' @details
##'
##' This function provides a way to \strong{select dominant species based on
##' presence/abundance sampling information}. \cr \cr
##'
##'
##' Three rules can be applied to make the species selection :
##'
##' \describe{
##' \item{A. Presence releves}{ both conditions must be fullfilled
##' \describe{
##' \item{on number of releves}{the species should be found a minimum
##' number of times (\code{rule.A1})
##' \cr \emph{This should ensure that the species has been given sufficient
##' minimum sampling effort. This criterion MUST ALWAYS be fullfilled.}}
##' \item{on number of sites}{the species should be found in a certain
##' number of sites, which corresponds to the quantile
##' \code{rule.A2_quantile} of the total number of records per species
##' \cr \emph{This should ensure that the species is covering all the
##' studied area (or at least a determining part of it, assuming that
##' the releves are well distributed throughout the area).}}
##' }
##' }
##' \item{B. Abundance releves : }{at least one of the two conditions is
##' required
##' \describe{
##' \item{on dominancy}{the species should be dominant (i.e. represent at
##' least \code{rule.B1_percentage \%} of the coverage of the site) in at
##' least \code{rule.B1_number} sites
##' \cr \emph{This should ensure the selection of species frequently
##' abundant.}}
##' \item{on average abundance}{the species should have a mean relative
##' abundance superior or equal to \code{rule.B2}
##' \cr \emph{This should ensure the selection of species not frequent but
##' representative of the sites in which it is found. \cr \cr}}
##' }
##' }
##' \item{C. Presence releves \cr per habitat : }{If habitat information is
##' available (e.g. type of environment : urban, desert, grassland... ; type
##' of vegetation : shrubs, forest, alpine grasslands... ; etc), the same
##' rules than \strong{A} can be applied but for each habitat.
##' \cr \emph{This should help to keep species that are not dominant at the
##' large scale but could be representative of a specific habitat. \cr \cr}
##' }
##' }
##'
##'
##' A table is created containing for each species whether or not it fullfills
##' the conditions selected, for example : \cr \cr
##'
##' \strong{\code{| ___A1 ___A2 ___B1 ___B2 grass lands |}} \cr
##' \code{_______________________________________} \cr
##' \code{| _TRUE FALSE FALSE _TRUE _TRUE FALSE |} \strong{species a} \cr
##' \code{| _TRUE _TRUE _TRUE FALSE FALSE FALSE |} \strong{species b} \cr
##' \code{| FALSE FALSE FALSE FALSE FALSE _TRUE |} \strong{species c} \cr \cr
##'
##' This table is transformed into Euclidean distance matrix (with
##' \code{\link[FD]{gowdis}} and \code{\link[ade4]{quasieuclid}} functions) \cr
##' to cluster and represent species (see
##' \href{PRE_FATE.selectDominant#value}{\code{.pdf} output files}) :
##' \itemize{
##' \item through phylogenetic tree (with \code{\link[stats]{hclust}} and
##' \code{\link[ape]{as.phylo}} functions)
##' \item through Principal Component Analysis (with
##' \code{\link[ade4]{dudi.pco}})
##' }
##' according to their selection rules :
##' \itemize{
##' \item \strong{A2} : spatial dominancy (\emph{widespread but poorly
##' abundant})
##' \item \strong{B1} : local dominancy (\emph{relatively abundant or
##' dominant in a certain number of sites})
##' \item \strong{B2} : local dominancy (\emph{not widespread but dominant in
##' few sites})
##' \item \strong{C} : habitat dominancy (\emph{not widespread but dominant in
##' a specific habitat})
##' \item \strong{A2 & B1} : (\emph{widespread and relatively abundant})
##' \item \strong{A2 & B2} : (\emph{widespread and dominant in few sites})
##' \item \strong{A2 & B1 & B2} : (\emph{widespread and dominant})
##' \item \strong{B1 & B2} : (\emph{relatively widespread but dominant})
##' \cr \cr
##' }
##'
##' \strong{NB :} \cr
##' Species not meeting any criteria or only A1 are considered as
##' "\emph{Not selected}". \cr Priority is set to A2, B1 and B2 rules, rather
##' than C. Hence, species selected according to A2, B1 and/or B2 can also meet
##' criterion C while species selected according to C do not meet any of the
##' three criteria. \cr Species selected according to one (or more) criterion
##' but not meeting criterion A1 are also considered as "\emph{Not selected}".
##'
##'
##' @return A \code{list} containing one \code{vector}, four or five
##' \code{data.frame} objects with the following columns, and up to five
##' \code{ggplot2} objects :
##'
##' \describe{
##' \item{species.selected}{the names of the selected species}
##' \item{tab.rules}{
##' \describe{
##' \item{\code{A1,A2,B1,B2, hab}}{if the rule has been used, if the
##' species fullfills this condition or not}
##' \item{\code{species}}{the concerned species}
##' \item{\code{SELECTION}}{the summary of rules with which the species
##' was selected, or not}
##' \item{\code{SELECTED}}{\code{TRUE} if the species fullfills \code{A1}
##' and at least one other condition, \code{FALSE} otherwise}
##' }
##' }
##' \item{tab.robustness}{
##' \describe{
##' \item{\code{...}}{same as \code{tab.rules}}
##' \item{\code{type}}{the type of subset (either \code{releves} or
##' \code{sites})}
##' \item{\code{percent}}{the concerned percentage of values extraction}
##' \item{\code{rep}}{the repetition ID}
##' }
##' }
##' \item{tab.dom.AB}{table containing sums of abundances for all selected
##' species (sites in rows, species in columns)}
##' \item{tab.dom.PA}{table containing counts of presences for all selected
##' species (sites in rows, species in columns)}
##' \item{plot.A}{\code{ggplot2} object, representing the selection of
##' species according to rules A1 and A2}
##' \item{plot.B}{\code{ggplot2} object, representing the selection of
##' species according to rules B}
##' \item{plot.C}{\code{ggplot2} object, representing the selection of
##' species according to rules C (A1 and A2 per habitat)}
##' \item{plot.pco}{\code{ggplot2} object, representing selected species with
##' Principal Coordinates Analysis (see \code{\link[ade4]{dudi.pco}})}
##' \item{plot.robustness}{\code{ggplot2} object, representing the robustness
##' of the selection of species for each rule \cr \cr}
##' }
##'
##'
##'
##' The information is written in
##' \file{PRE_FATE_DOMINANT_[...].csv} files :
##' \describe{
##' \item{\file{TABLE_complete}}{the complete table of all species and the
##' selection rules described above (\code{tab.rules})}
##' \item{\file{TABLE_species}}{only the names / ID of the species selected}
##' \item{\file{TABLE_sitesXspecies_AB}}{abundances table of selected species}
##' \item{\file{TABLE_sitesXspecies_PA}}{presence/absence table of selected
##' species}
##' }
##'
##' Up to six \file{PRE_FATE_DOMINANT_[...].pdf} files are also created :
##' \describe{
##' \item{\file{STEP_1_rule_A}}{\strong{\file{STEP_2_selectedSpecies_PHYLO}}}
##' \item{\file{STEP_1_rule_B}}{\strong{\file{STEP_2_selectedSpecies_PCO}}}
##' \item{\file{STEP_1_rule_C}}{\strong{\file{STEP_2_selectedSpecies_robustness}}}
##' }
##'
##' @keywords abundance, dominant species, quantile, habitat class
##'
##' @seealso \code{\link{PRE_FATE.abundBraunBlanquet}},
##' \code{\link[stats]{quantile}},
##' \code{\link[FD]{gowdis}},
##' \code{\link[ade4]{quasieuclid}},
##' \code{\link[stats]{hclust}},
##' \code{\link[ape]{as.phylo}},
##' \code{\link[ade4]{dudi.pco}}
##'
##' @examples
##'
##' ## Load example data
##' Champsaur_PFG = .loadData('Champsaur_PFG', 'RData')
##'
##' ## Species observations
##' tab = Champsaur_PFG$sp.observations
##'
##' ## No habitat, no robustness -------------------------------------------------
##' tab.occ = tab[, c('sites', 'species', 'abund')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.rules)
##' str(sp.SELECT$tab.dom.PA)
##' plot(sp.SELECT$plot.A)
##' plot(sp.SELECT$plot.B$abs)
##' plot(sp.SELECT$plot.B$rel)
##'
##' ## Habitat, change parameters, no robustness (!quite long!) --------------------
##' \dontrun{
##' tab.occ = tab[, c('sites', 'species', 'abund', 'habitat')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ
##' , doRuleA = TRUE
##' , rule.A1 = 10
##' , rule.A2_quantile = 0.9
##' , doRuleB = TRUE
##' , rule.B1_percentage = 0.2
##' , rule.B1_number = 10
##' , rule.B2 = 0.4
##' , doRuleC = TRUE)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.rules)
##' plot(sp.SELECT$plot.C)
##' plot(sp.SELECT$plot.pco$Axis1_Axis2)
##' plot(sp.SELECT$plot.pco$Axis1_Axis3)
##' }
##'
##' ## No habitat, robustness (!quite long!) --------------------
##' \dontrun{
##' tab.occ = tab[, c('sites', 'species', 'abund')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ
##' , opt.doSitesSpecies = FALSE
##' , opt.doRobustness = TRUE
##' , opt.robustness_percent = seq(0.1,0.9,0.1)
##' , opt.robustness_rep = 10)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.robustness)
##' names(sp.SELECT$plot.robustness)
##' plot(sp.SELECT$plot.robustness$`All dataset`)
##' }
##'
##'
##' @export
##'
##' @importFrom stats quantile as.dist hclust cutree
##' @importFrom utils write.csv txtProgressBar setTxtProgressBar
##' @importFrom grDevices pdf dev.off col2rgb rgb
##' @importFrom graphics legend title plot
##'
##' @importFrom foreach foreach %do%
##' @importFrom reshape2 melt
##' @importFrom FD gowdis
##' @importFrom ade4 is.euclid quasieuclid dudi.pco inertia.dudi
##' @importFrom ape as.phylo plot.phylo
##'
##' @importFrom ggplot2 ggplot ggsave aes_string
##' geom_point geom_hline geom_vline geom_histogram geom_segment geom_path
##' geom_boxplot geom_smooth geom_col annotate sec_axis
##' scale_color_manual scale_color_continuous scale_x_discrete
##' scale_x_continuous scale_y_continuous scale_y_log10 scale_alpha
##' scale_linetype_manual scale_size_manual scale_fill_identity
##' facet_wrap as_labeller labs theme element_text
##' @importFrom ggthemes theme_fivethirtyeight
##' @importFrom ggrepel geom_label_repel
##' @importFrom colorspace sequential_hcl
##'
## END OF HEADER ###############################################################
PRE_FATE.selectDominant = function(mat.observations
, doRuleA = TRUE
, rule.A1 = 10
, rule.A2_quantile = 0.9
, doRuleB = TRUE
, rule.B1_percentage = 0.25
, rule.B1_number = 5
, rule.B2 = 0.5
, doRuleC = FALSE
, opt.doRobustness = FALSE
, opt.robustness_percent = seq(0.1, 0.9, 0.1)
, opt.robustness_rep = 10
, opt.doSitesSpecies = TRUE
, opt.doPlot = TRUE
){
#############################################################################
## CHECK parameter mat.observations
if (.testParam_notDf(mat.observations))
{
.stopMessage_beDataframe("mat.observations")
} else
{
mat.observations = as.data.frame(mat.observations)
if (nrow(mat.observations) == 0 || !(ncol(mat.observations) %in% c(3, 4)))
{
.stopMessage_numRowCol("mat.observations", c("sites", "species", "abund", "(habitat)"))
} else
{
notCorrect = switch(as.character(ncol(mat.observations))
, "3" = .testParam_notColnames(mat.observations, c("sites", "species", "abund"))
, "4" = .testParam_notColnames(mat.observations, c("sites", "species", "abund", "habitat"))
, TRUE)
if (notCorrect){
.stopMessage_columnNames("mat.observations", c("sites", "species", "abund", "(habitat)"))
}
}
mat.observations$sites = as.character(mat.observations$sites)
mat.observations$species = as.character(mat.observations$species)
.testParam_notNum.m("mat.observations$abund", mat.observations$abund)
}
## CHECK parameter doRuleA / doRuleC
if (doRuleA || doRuleC)
{
.testParam_notNum.m("rule.A1", rule.A1)
.testParam_notBetween.m("rule.A2_quantile", rule.A2_quantile, 0, 1)
}
## CHECK parameter doRuleB
if (doRuleB)
{
if (.testParam_notInValues(mat.observations$abund, c(NA, 0, 1)))
{
.testParam_notNum.m("rule.B1_number", rule.B1_number)
.testParam_notBetween.m("rule.B1_percentage", rule.B1_percentage, 0, 1)
.testParam_notBetween.m("rule.B2", rule.B2, 0, 1)
} else
{
doRuleB = FALSE
}
}
## CHECK parameter doRuleC
if (doRuleC == FALSE ||
sum(colnames(mat.observations) == "habitat") == 0 ||
length(unique(mat.observations$habitat)) == 1)
{
doRuleC = FALSE
mat.observations$habitat = "all"
}
mat.observations$habitat = as.character(mat.observations$habitat)
categories = sort(unique(mat.observations$habitat))
categories = unique(c("all", categories))
## CHECK parameter opt.doRobustness
if (opt.doRobustness)
{
.testParam_notNum.m("opt.robustness_rep", opt.robustness_rep)
.testParam_notBetween.m("opt.robustness_percent", opt.robustness_percent, 0, 1)
opt.robustness_percent = sort(unique(round(opt.robustness_percent, 1)))
}
## CHECK duplicated rows
if (nrow(mat.observations) != nrow(unique(mat.observations)))
{
warning(paste0("`mat.observations` contains duplicated rows ("
, sum(duplicated(mat.observations))
, "). These rows have been removed. Please check."))
mat.observations = unique(mat.observations)
}
cat("\n\n #------------------------------------------------------------#")
cat("\n # PRE_FATE.selectDominant")
cat("\n #------------------------------------------------------------# \n")
#############################################################################
### PREPARATION OF DATA : informations
#############################################################################
MO = mat.observations
MO.no_releves = nrow(MO)
MO.sites = unique(MO$sites)
MO.no_sites = length(MO.sites)
MO.species = unique(MO$species)
MO.no_species = length(MO.species)
ind.notNA = which(!is.na(MO$abund))
MO.no_releves.notNA = length(ind.notNA)
MO.no_sites.notNA = length(unique(MO$sites[ind.notNA]))
MO.no_species.notNA = length(unique(MO$species[ind.notNA]))
{
cat("\n ---------- INFORMATION : SAMPLING \n")
cat("\n Number of releves : ", MO.no_releves)
cat("\n Number of sites : ", MO.no_sites)
cat("\n Number of species : ", MO.no_species)
cat("\n")
cat("\n ---------- INFORMATION : ABUNDANCE \n")
cat("\n Percentage of releves with abundance information : "
, round(100 * MO.no_releves.notNA / MO.no_releves, 2), "%")
cat("\n Percentage of sites with abundance information : "
, round(100 * MO.no_sites.notNA / MO.no_sites, 2), "%")
cat("\n Percentage of species with abundance information : "
, round(100 * MO.no_species.notNA / MO.no_species, 2), "%")
cat("\n")
if (MO.no_releves.notNA / MO.no_releves == 0)
{
warning(paste0("NO abundance information in your data. "
, "Dominant species selection will only be done with "
, "the criteria based on number of presences."))
}
if (MO.no_species.notNA / MO.no_species < 1)
{
warning(paste0("Species with NO abundance information can only be "
, "selected with the criteria based on number of presences."))
}
cat("\n ---------- STATISTICS COMPUTATION \n")
cat("\n For each species (site level) :")
if (doRuleA) {
cat("\n - total frequency and abundance (rule A1 & A2)")
}
if (doRuleB) {
cat("\n - mean relative abundance (rule B2)")
cat("\n - frequency (absolute and relative) of each relative abundance class (rule B1)")
}
if (doRuleC) {
cat("\n For each species (habitat level) :")
cat("\n - frequency and abundance (absolute and relative) within each habitat (rule C)")
}
if (!doRuleA && !doRuleB && !doRuleC) {
cat("\n Nothing! No rule selected! Please check.")
stop()
}
cat("\n\n")
}
#############################################################################
## Prepare combinations to run statistics calculation
combi = data.frame(rep = 1, percent = 1, type = "releves"
, stringsAsFactors = FALSE)
if (opt.doRobustness)
{
opt.robustness_percent = opt.robustness_percent[which(opt.robustness_percent > 0 &
opt.robustness_percent < 1)]
combi = rbind(combi
, expand.grid(rep = 1:opt.robustness_rep
, percent = opt.robustness_percent
, type = c("releves", "sites")
, stringsAsFactors = FALSE))
PROGRESS = txtProgressBar(min = 0, max = nrow(combi), style = 3)
}
combi$iter = 1:nrow(combi)
## Run statistics calculation
RULES.robustness = foreach(i.type = combi$type
, i.rep = combi$rep
, i.percent = combi$percent
, i.iter = combi$iter
, .combine = "rbind"
) %do%
{
## If opt.doRobustness, select only a fraction of the observations
if (i.type == "releves")
{
sel.vec = 1:MO.no_releves
sel.no = round(i.percent * MO.no_releves)
sel.ind = sample(sel.vec, sel.no)
} else
{
sel.vec = 1:MO.no_sites
sel.no = round(i.percent * MO.no_sites)
sel.ind = sample(sel.vec, sel.no)
sel.ind = which(MO$sites %in% MO.sites[sel.ind])
}
if (length(sel.ind) > 0)
{
mat.observations = MO[sel.ind, , drop = FALSE]
no.species = length( unique(mat.observations$species))
#########################################################################
### PREPARATION OF DATA : relative abundances
#########################################################################
class_breaks = seq(0, 1, 0.05)
class_breaks.levels = foreach(i = 1:(length(class_breaks)-1)
, j = 2:length(class_breaks)
, .combine = "c") %do%
{
paste0("(", class_breaks[i], ",", class_breaks[j], "]")
}
## Calculate relative abundances per site
list.dat.sites = split(mat.observations, mat.observations$sites)
mat.obs_rel = foreach(i.dat = 1:length(list.dat.sites)
, .combine = "rbind"
) %do%
{
dat = list.dat.sites[[i.dat]]
dat$sites.abund = sum(dat$abund, na.rm = TRUE)
dat$abund_rel.sites = dat$abund / sum(dat$abund, na.rm = TRUE)
return(dat)
}
mat.obs_rel$class_rel.sites = cut(mat.obs_rel$abund_rel.sites
, breaks = class_breaks)
## Calculate relative abundances per habitat
list.dat.habitat = split(mat.obs_rel, mat.obs_rel$habitat)
mat.obs_rel = foreach(i.dat = 1:length(list.dat.habitat)
, .combine = "rbind"
) %do%
{
dat = list.dat.habitat[[i.dat]]
dat$habitat.abund = sum(dat$abund, na.rm = TRUE)
dat$abund_rel.habitat = dat$abund / sum(dat$abund, na.rm = TRUE)
return(dat)
}
#########################################################################
### STATISTICS
#########################################################################
## SELECTION RULE A -----------------------------------------------------
if (doRuleA || doRuleB)
{
## Calculate the number of releves per species
mat1 = table(mat.obs_rel$species)
mat1 = data.frame(species = names(mat1)
, freq_tot = as.vector(mat1)
, stringsAsFactors = FALSE)
## Calculate the total abundance per species
mat2 = tapply(X = mat.obs_rel$abund
, INDEX = list(mat.obs_rel$species)
, FUN = sum
, na.rm = TRUE)
mat2 = data.frame(species = names(mat2)
, abund_tot = as.vector(mat2)
, stringsAsFactors = FALSE)
## Gather information
mat.A_B2 = merge(mat1, mat2, by = "species")
rm(list = c("mat1", "mat2"))
}
## SELECTION RULE B -----------------------------------------------------
if (doRuleB)
{
## Calculate mean relative abundance per species
mat3 = tapply(X = mat.obs_rel$abund_rel.sites
, INDEX = list(mat.obs_rel$species)
, FUN = mean
, na.rm = TRUE)
mat3 = data.frame(species = names(mat3)
, abund_rel.sites.mean = as.vector(mat3)
, stringsAsFactors = FALSE)
mat.A_B2 = merge(mat.A_B2, mat3, by = "species", all.x = TRUE)
# mat.A_B2$class_rel.sites.mean = cut(mat.A_B2$abund_rel.sites.mean
# , breaks = class_breaks)
rm(list = c("mat3"))
## Calculate the number of relative abundance class per species
mat.B1 = table(mat.obs_rel$species, mat.obs_rel$class_rel.sites)
mat.B1 = as.data.frame(mat.B1, stringsAsFactors = FALSE)
colnames(mat.B1) = c("species", "class_rel.sites", "freq.class_rel.sites")
mat.B1 = mat.B1[which(mat.B1$freq.class_rel.sites > 0), ]
## Calculate the relative frequency
mat.B1 = merge(mat.B1, mat.A_B2[, c("species", "freq_tot")], by = "species")
mat.B1$freq_rel.class_rel.sites = mat.B1$freq.class_rel.sites / mat.B1$freq_tot
}
## SELECTION RULE C -----------------------------------------------------
if (doRuleC)
{
## Calculate the number of releves per species per habitat
mat.C = table(mat.obs_rel$species, mat.obs_rel$habitat)
mat.C = as.data.frame(mat.C, stringsAsFactors = FALSE)
colnames(mat.C) = c("species", "habitat", "freq.habitat")
mat.C = mat.C[which(mat.C$freq.habitat > 0), ]
## Calculate the relative frequency
mat4 = table(mat.obs_rel$habitat)
mat4 = data.frame(habitat = names(mat4)
, freq_tot.habitat = as.vector(mat4)
, stringsAsFactors = FALSE)
mat.C = merge(mat.C, mat4, by = "habitat")
mat.C$freq_rel.habitat = mat.C$freq.habitat / mat.C$freq_tot.habitat
## Calculate the total abundance per species per habitat
mat5 = tapply(X = mat.obs_rel$abund
, INDEX = list(mat.obs_rel$species
, mat.obs_rel$habitat)
, FUN = sum
, na.rm = TRUE)
mat5 = as.data.frame(mat5, stringsAsFactors = FALSE)
mat5$species = rownames(mat5)
mat5 = melt(mat5, id.vars = "species")
colnames(mat5) = c("species", "habitat", "abund_tot.habitat")
mat5 = mat5[which(mat5$abund_tot.habitat > 0), ]
## Calculate the relative abundance per habitat
mat.C = merge(mat.C, mat5, by = c("species", "habitat"), all = TRUE)
mat.C = merge(mat.C, unique(mat.obs_rel[, c("habitat", "habitat.abund")]), by = "habitat")
mat.C$abund_rel.habitat = mat.C$abund_tot.habitat / mat.C$habitat.abund
rm(list = c("mat4", "mat5"))
}
#########################################################################
### SELECTION OF DOMINANT SPECIES
#########################################################################
## Get information about how each species has been selected
## (over all area, or within specific habitat and which one(s), or both)
sp_ruleA1 = sp_ruleA2 = sp_ruleB1 = sp_ruleB2 = sp_ruleCA2 = vector()
if (doRuleA)
{
## A1 : minimum number of occurrences
sp_ruleA1 = mat.A_B2$species[which(mat.A_B2$freq_tot >= rule.A1)]
## A2 : minimum number of occurrences (quantile)
rule.A2_value = quantile(mat.A_B2$freq_tot, p = rule.A2_quantile)
sp_ruleA2 = mat.A_B2$species[which(mat.A_B2$freq_tot >= rule.A2_value)]
}
if (doRuleB)
{
## B1 : relative abundance or dominancy in a certain number of sites
class_B1 = class_breaks.levels[(class_breaks[-1] >= rule.B1_percentage)]
tmpB1 = mat.B1[which(mat.B1$class_rel.sites %in% class_B1), ]
sp_ruleB1 = foreach(i.sp = unique(as.character(tmpB1$species)), .combine = "c") %do%
{
tab = tmpB1[which(tmpB1$species == i.sp), ]
if (sum(tab$freq.class_rel.sites) >= rule.B1_number) { return(i.sp) }
}
sp_ruleB1 = unique(as.character(sp_ruleB1))
## B2 : minimum mean relative abundance
sp_ruleB2 = mat.A_B2$species[which(mat.A_B2$abund_rel.sites.mean >= rule.B2)]
}
if (doRuleC)
{
## C2 : similar to A2 for each habitat
sp_ruleCA2 = foreach(i.hab = categories[-1]) %do%
{
tab = mat.C[which(mat.C$habitat == i.hab), ]
quanti = quantile(tab$freq.habitat, p = rule.A2_quantile)
return(tab$species[which(tab$freq.habitat >= quanti)])
}
names(sp_ruleCA2) = categories[-1]
## C1 : similar to B1 for each habitat
# class_C1 = levels(mat.C$class_rel.sites)[(class_breaks[-1] >= rule.B1_percentage)]
# class_C1 = class_breaks.levels[(class_breaks[-1] >= rule.B1_percentage)]
# sp_ruleCB1 = mat.B1$species[which(mat.C$class_rel.sites %in% class_C1 &
# mat.C$freq.class_rel.sites >= rule.B1_number)]
# sp_ruleCB1 = unique(as.character(sp_ruleCB1))
}
## COMBINE all rules ----------------------------------------------------
sp_rule_ALL = c(list(A1 = sp_ruleA1
, A2 = sp_ruleA2
, B1 = sp_ruleB1
, B2 = sp_ruleB2)
, sp_ruleCA2)
RULES = foreach(i.rule = 1:length(sp_rule_ALL), .combine = "rbind") %do%
{
mat = matrix(FALSE, nrow = 1, ncol = no.species
, dimnames = list(names(sp_rule_ALL)[i.rule]
, unique(mat.observations$species)))
mat[, which(colnames(mat) %in% sp_rule_ALL[[i.rule]])] = TRUE
return(mat)
}
RULES = as.data.frame(t(RULES))
tab.sp = rowSums(RULES)
tab.rules = colSums(RULES)
## Add information about how each species has been selected or not
RULES$species = rownames(RULES)
RULES$SELECTION = sapply(paste0(RULES$A2, "_", RULES$B1, "_", RULES$B2)
, function(x)
{
switch(x
, "FALSE_FALSE_FALSE" = "Not selected (A2, B1, B2)"
, "TRUE_FALSE_FALSE" = "A2"
, "FALSE_TRUE_FALSE" = "B1"
, "FALSE_FALSE_TRUE" = "B2"
, "TRUE_TRUE_FALSE" = "A2 & B1"
, "FALSE_TRUE_TRUE" = "B1 & B2"
, "TRUE_FALSE_TRUE" = "A2 & B2"
, "TRUE_TRUE_TRUE" = "A2 & B1 & B2")
})
RULES$SELECTION[which(tab.sp == 0)] = "Not selected"
RULES$SELECTION[which(tab.sp == 1 &
RULES$A1 == TRUE)] = "Not selected"
RULES$SELECTION[which(tab.sp > 1 &
RULES$A1 == TRUE &
RULES$SELECTION == "Not selected (A2, B1, B2)")] = "C"
RULES$SELECTION[which(tab.sp > 0 &
RULES$A1 == FALSE &
RULES$SELECTION == "Not selected (A2, B1, B2)")] = "C" ## not A1
RULES$SELECTION = factor(RULES$SELECTION, c("Not selected"
, "A2"
, "A2 & B1"
, "A2 & B2"
, "A2 & B1 & B2"
, "B1"
, "B2"
, "B1 & B2"
, "C"))
## Summary : has each species been selected or not
RULES$SELECTED = FALSE
RULES$SELECTED[which(RULES$SELECTION != "Not selected" & RULES$A1 == TRUE)] = TRUE
## SAVE summary tables --------------------------------------------------
if (i.percent == 1 && i.type == "releves" && i.rep == 1)
{
end_filename = end_filenameA = end_filenameB = end_filenameC = ""
if (doRuleA) {
end_filenameA = paste0("_A_", rule.A1
, "_", rule.A2_quantile)
write.csv(mat.A_B2
, file = "PRE_FATE_DOMINANT_mat.A_B2.csv"
, row.names = FALSE)
}
if (doRuleB) {
end_filenameB = paste0("_B_", rule.B1_number
, "_", rule.B1_percentage
, "_", rule.B2)
write.csv(mat.B1
, file = "PRE_FATE_DOMINANT_mat.B1.csv"
, row.names = FALSE)
}
if (doRuleC) {
end_filenameC = paste0("_C_", rule.A1
, "_", rule.A2_quantile)
write.csv(mat.C
, file = "PRE_FATE_DOMINANT_mat.C.csv"
, row.names = FALSE)
}
end_filename = paste0(end_filenameA, end_filenameB, end_filenameC)
write.csv(RULES
, file = paste0("PRE_FATE_DOMINANT_TABLE_complete"
, end_filename
, ".csv")
, row.names = FALSE)
write.csv(RULES[which(RULES$SELECTED == TRUE), "species"]
, file = paste0("PRE_FATE_DOMINANT_TABLE_species"
, end_filename
, ".csv")
, row.names = FALSE)
message(paste0("\n The output files \n"
, ifelse(doRuleA
, " > PRE_FATE_DOMINANT_mat.A_B2.csv \n"
, "")
, ifelse(doRuleB
, " > PRE_FATE_DOMINANT_mat.B1.csv \n"
, "")
, ifelse(doRuleC
, " > PRE_FATE_DOMINANT_mat.C.csv \n"
, "")
, " > PRE_FATE_DOMINANT_TABLE_complete"
, end_filename
, ".csv \n"
, " > PRE_FATE_DOMINANT_TABLE_species"
, end_filename
, ".csv \n"
, "have been successfully created !\n"))
}
## RETURN table ---------------------------------------------------------
if (opt.doRobustness)
{
setTxtProgressBar(pb = PROGRESS, value = i.iter)
RULES$type = i.type
RULES$percent = i.percent
RULES$rep = i.rep
}
return(RULES)
}
}
cat("\n ---------- SELECTION OF DOMINANT SPECIES \n")
## Keep only part corresponding to full dataset
if (opt.doRobustness)
{
close(PROGRESS)
RULES = RULES.robustness[which(RULES.robustness$percent == 1 &
RULES.robustness$type == "releves" &
RULES.robustness$rep == 1), ]
RULES = RULES[, -which(colnames(RULES) %in% c("type", "percent", "rep"))]
} else
{
RULES = RULES.robustness
}
## Get selected species and corresponding observations
RULES.sel = RULES[which(RULES$SELECTION != "Not selected"), ]
sel.sp = RULES.sel$species[which(RULES.sel$A1 == TRUE)]
sel.obs = MO[which(MO$species %in% sel.sp), ]
{
cat("\n - Number of selected species :", length(sel.sp))
cat("\n - Representativity of species :"
, round(100 * length(sel.sp) / MO.no_species)
, "%")
cat("\n - Representativity of sites :"
, round(100 * length(unique(sel.obs$sites)) / MO.no_sites)
, "%")
cat("\n - Representativity of total abundance :"
, round(100 * sum(sel.obs$abund, na.rm = T) / sum(MO$abund, na.rm = T))
, "%")
cat("\n Complete table of information can be find in output files.")
cat("\n")
}
#############################################################################
## BUILD SITES x SPECIES MATRIX FOR DOMINANT SPECIES ONLY
#############################################################################
## Keep only part corresponding to full dataset
if (opt.doSitesSpecies)
{
cat("\n ---------- PRODUCING ABUNDANCES / OCCURRENCES TABLES \n")
## Transform observations of selected species into sites x species tables
tab.dom.AB = tapply(X = sel.obs$abund
, INDEX = list(sel.obs$sites, sel.obs$species)
, FUN = sum)
## Change sum of abundBB and NA to 1 in tab.dom.PA
tab.dom.PA = tab.dom.AB
tab.dom.PA[which(tab.dom.PA[] > 0)] = 1
write.csv(tab.dom.AB
, file = paste0("PRE_FATE_DOMINANT_TABLE_sitesXspecies_AB"
, end_filename
, ".csv")
, row.names = TRUE)
write.csv(tab.dom.PA
, file = paste0("PRE_FATE_DOMINANT_TABLE_sitesXspecies_PA"
, end_filename
, ".csv")
, row.names = TRUE)
message(paste0("\n The output files \n"
, " > PRE_FATE_DOMINANT_TABLE_sitesXspecies_AB"
, end_filename
, ".csv \n"
, " > PRE_FATE_DOMINANT_TABLE_sitesXspecies_PA"
, end_filename
, ".csv \n"
, "have been successfully created !\n"))
}
#############################################################################
## GRAPHICS TO HELP ADJUST PARAMETERS TO SELECT DOMINANT SPECIES
#############################################################################
if (opt.doPlot)
{
cat("\n ---------- PRODUCING PLOT(S) \n")
###########################################################################
## ILLUSTRATION of rules A1, A2 and C -------------------------------------
if (doRuleA || doRuleC)
{
cat("\n> Illustration of rules A and C")
tab.plot = mat.A_B2[, c("species", "freq_tot", "abund_tot")]
tab.plot$habitat = "all"
names.plot = c("tot")
if (doRuleC)
{
tmp = mat.C[, c("habitat", "species", "freq.habitat", "abund_tot.habitat")]
colnames(tmp) = c("habitat", "species", "freq_tot", "abund_tot")
tab.plot = rbind(tab.plot, tmp)
names.plot = c("tot", "hab")
}
## Correct total abundance value to 1 when no abundance is provided (NA)
tab.plot$abund_tot[which(is.na(tab.plot$abund_tot))] = 1
## Add information about selection rules A1 & A2
tab.quant = foreach(i.hab = categories, .combine = "rbind") %do%
{
tab = tab.plot[which(tab.plot$habitat == i.hab), ]
quanti = quantile(tab$freq_tot, p = rule.A2_quantile)
return(data.frame(habitat = i.hab
, quantile = quanti
, stringsAsFactors = FALSE))
}
tab.plot = merge(tab.plot, tab.quant, by = "habitat")
tab.plot$class_A = factor(ifelse(tab.plot$freq_tot < rule.A1
, 1
, ifelse(tab.plot$freq_tot >= tab.plot$quantile
, 3, 2)))
rm(list = c("tab.quant"))
doLogA = max(tab.plot$abund_tot, na.rm = TRUE)
## ----------------------------------------------------------------------
pp_shared = 'geom_histogram(aes_string("freq_tot")
, fill = "grey60"
, alpha = 0.5
, binwidth = function(x) 2 * IQR(x) / (length(x)^(1/4))
, na.rm = FALSE) +
geom_point(aes_string(x = "freq_tot"
, y = "abund_tot"
, color = "class_A")
, alpha = 0.5) +
geom_vline(xintercept = rule.A1, lty = 2) +
geom_vline(aes_string(xintercept = "quantile"), lty = 2) +
geom_label_repel(data = tab.quanti
, aes_string(x = "quantile"
, y = "y_quanti"
, label = "lab_quanti")
, color = tab.quanti$col_quanti
, fontface = "bold"
, direction = "x"
, label.size = 0
, label.padding = unit(0.3, "lines")) +
scale_color_manual(guide = "none"
, values = c("1" = "brown"
, "2" = "chocolate"
, "3" = "darkgoldenrod")) +
labs(x = "\nNumber of occurrences"
, y = "Total abundance\n") +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))'
if (doLogA >= 100)
{
pp_shared = paste0(pp_shared, '+
scale_y_log10(name = "Total abundance\n")')
}
## ----------------------------------------------------------------------
for(i.plot in names.plot)
{
if (i.plot == "tot") {
tab = tab.plot[which(tab.plot$habitat == "all"), ]
} else {
tab = tab.plot[which(tab.plot$habitat != "all"), ]
}
if(nrow(tab) > 0)
{
## Add quantiles corresponding to rules A1 and A2
tab.quanti1 = data.frame(habitat = unique(tab$habitat)
, quantile = rule.A1
, col_quanti = "chocolate"
, lab_quanti = "A1"
, stringsAsFactors = FALSE)
tab.quanti2 = unique(tab[, c("habitat", "quantile")])
tab.quanti2$col_quanti = "darkgoldenrod"
tab.quanti2$lab_quanti = "A2"
tab.quanti = rbind(tab.quanti1, tab.quanti2)
tab.quanti$y_quanti = ifelse(doLogA >= 100, 0.5, doLogA)
pp = eval(parse(text = paste0('ggplot(tab) +', pp_shared)))
if (i.plot == "tot") {
pp_tot = pp +
annotate(geom = "label"
, x = max(tab.plot$freq_tot) * c(0.05, 0.6, 0.6)
, y = doLogA * c(0.6, 0.6, 0.01)
, label = c("Very abundant species \nwith narrow distributions"
, "Very abundant and\n widespread species"
, "Widespread species \nbut poorly abundant")
, colour = c("grey30", "darkgoldenrod", "darkgoldenrod")
, fontface = c("italic", "bold", "bold")
, hjust = "inward"
, alpha = 0.5) +
labs(title = "STEP 1 : Selection of dominant species - rule A"
, subtitle = paste0("Criteria concerning occurrences within all sites :\n"
, " > A1 = minimum number of releves required : "
, rule.A1, "\n"
, " > A2 = minimum number of occurrences required : quantile("
, rule.A2_quantile * 100, "%) = "
, round(rule.A2_value), "\n"
, "\n"))
## ----------------------------------------------------------------
ggsave(filename = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameA, ".pdf")
, plot = pp_tot, device = "pdf", width = 10, height = 8)
} else {
pp_hab = pp +
facet_wrap(~ habitat, scales = "free_x") +
labs(title = "STEP 1 : Selection of dominant species - rule C"
, subtitle = paste0("Criteria concerning occurrences within all habitats :\n"
, " > A1 = minimum number of releves required : "
, rule.A1, "\n"
, " > A2 = minimum number of occurrences required : quantile("
, rule.A2_quantile * 100, "%) = "
, paste0(paste0(tab.quanti2$habitat, ": "
, round(tab.quanti2$quantile))
, collapse = " / "), "\n"
, "\n"))
## ----------------------------------------------------------------
ggsave(filename = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameC, ".pdf")
, plot = pp_hab, device = "pdf", width = 12, height = 9)
}
}
}
}
###########################################################################
## ILLUSTRATION of rules B1 -----------------------------------------------
if (doRuleB)
{
cat("\n> Illustration of rule B")
tab.plot = mat.B1
## Associate a color to each species : rule B1
pal_sp = sequential_hcl(n = length(sp_ruleB1)
, palette = "Teal", rev = TRUE)
pal_sp = data.frame(species = sp_ruleB1
, id_color = pal_sp
, stringsAsFactors = FALSE)
tab.plot = merge(tab.plot, pal_sp, by = "species", all.x = TRUE)
tab.plot$id_color[which(is.na(tab.plot$id_color))] = "darkgray"
tab.plot$class_B = ifelse(tab.plot$id_color == "darkgray"
, 1, 2)
tab.plot$class_rel.sites = factor(tab.plot$class_rel.sites, class_breaks.levels)
## Associate a color to each species : rule B2
pal_sp = sequential_hcl(n = length(sp_ruleB2)
, palette = "Greens", rev = TRUE)
for(i.sp in 1:length(sp_ruleB2))
{
tab.plot$id_color[which(tab.plot$species == sp_ruleB2[i.sp])] = pal_sp[i.sp]
tab.plot$class_B[which(tab.plot$species == sp_ruleB2[i.sp])] = 3
}
doLogB = max(tab.plot$freq.class_rel.sites, na.rm = TRUE)
x_B1 = (class_breaks < rule.B1_percentage)
x_B1 = max(which(x_B1 == TRUE))
x_B2 = (class_breaks < rule.B2)
x_B2 = max(which(x_B2 == TRUE))
## FREQ -----------------------------------------------------------------
pp_B = ggplot(tab.plot, aes_string(x = "as.numeric(class_rel.sites) + 0.3 * (class_B - 1)"
, y = "freq.class_rel.sites"
, fill = "id_color")) +
geom_col(position = "identity"
, alpha = 0.5
, width = 0.3
, na.rm = TRUE) +
geom_hline(yintercept = rule.B1_number, lty = 2) +
annotate(geom = "label"
, x = 0
, y = rule.B1_number
, colour = "dodgerblue4"
, fontface = "bold"
, label = "B1.no"
, label.size = 0
, label.padding = unit(0.25, "lines")) +
geom_vline(xintercept = c(x_B1, x_B2), lty = 2) +
annotate(geom = "label"
, x = c(x_B1, x_B2)
, y = ifelse(doLogB >= 100, 0.5, doLogB)
, colour = c("dodgerblue4", "darkgreen")
, fontface = "bold"
, label = c("B1.%", "B2")
, label.size = 0
, label.padding = unit(1, "lines")) +
annotate(geom = "label"
, x = 20 * c(0.15, 0.5, 0.9)
, y = doLogB * c(0.8, ifelse(doLogB >= 100, 0.05, 0.5)
, ifelse(doLogB >= 100, 0.003, 0.1))
, label = c("Poorly abundant\n species \nbut widespread"
, "Species relatively abundant\n or dominant \n in a certain number of sites"
, "Dominant species \nbut in few sites")
, colour = c("grey30", "dodgerblue4", "darkgreen")
, fontface = c("italic", "bold", "bold")
, alpha = 0.5) +
scale_x_continuous(name = "\nWithin-site relative abundance (%)"
, breaks = seq(1, length(class_breaks) - 1)
, labels = 100 * class_breaks[-1]) +
scale_fill_identity(guide = "none") +
scale_color_continuous(guide = "none") +
labs(y = "Releves frequency\n"
, title = "STEP 1 : Selection of dominant species - rule B"
, subtitle = paste0("Criteria concerning abundances within all concerned sites :\n"
, " > B1.no = minimum number of sites required : "
, rule.B1_number, "\n"
, " > B1.% = with a minimum relative abundance of : "
, 100 * rule.B1_percentage, "% \n"
, " > B2 = minimum mean relative abundance required : "
, 100 * rule.B2, "% \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
if (doLogB >= 100)
{
pp_B = pp_B +
scale_y_log10(name = "Releves frequency\n")
}
## FREQ.REL -------------------------------------------------------------
pp_B.rel = ggplot(tab.plot, aes_string(x = "as.numeric(class_rel.sites) + 0.3 * (class_B - 1)"
, y = "freq_rel.class_rel.sites"
, fill = "id_color")) +
geom_col(position = "identity"
, alpha = 0.5
, width = 0.3
, na.rm = TRUE) +
geom_vline(xintercept = c(x_B1, x_B2), lty = 2) +
annotate(geom = "label"
, x = c(x_B1, x_B2)
, y = 1.1
, colour = c("dodgerblue4", "darkgreen")
, fontface = "bold"
, label = c("B1.%", "B2")
, label.size = 0
, label.padding = unit(1, "lines")) +
scale_x_continuous(name = "\nWithin-site relative abundance (%)"
, breaks = seq(1, length(class_breaks) - 1)
, labels = 100 * class_breaks[-1]) +
scale_y_continuous(name = "Relative releves frequency (representativity) (%)\n"
, breaks = seq(0, 1, 0.2)
, labels = seq(0, 1, 0.2) * 100) +
scale_fill_identity(guide = "none") +
scale_color_continuous(guide = "none") +
labs(title = "STEP 1 : Selection of dominant species - rule B"
, subtitle = paste0("Criteria concerning abundances within all concerned sites :\n"
, " > B1.no = minimum number of sites required : "
, rule.B1_number, "\n"
, " > B1.% = with a minimum relative abundance of : "
, 100 * rule.B1_percentage, "% \n"
, " > B2 = minimum mean relative abundance required : "
, 100 * rule.B2, "% \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
## ----------------------------------------------------------------------
pdf(file = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameB, ".pdf")
, width = 10, height = 8)
plot(pp_B)
plot(pp_B.rel)
dev.off()
}
###########################################################################
## ILLUSTRATION of SELECTED SPECIES ---------------------------------------
{
cat("\n> Illustration of selected species")
if (length(sel.sp) <= 1)
{
warning("Too few species selected for representation. No phylogeny and PCO graphics.")
} else
{
## Associate a color to each species according to selection rules
pal_col = c("Not selected" = "grey"
, "A2" = "darkgoldenrod"
, "A2 & B1" = "coral2"
, "A2 & B2" = "hotpink3"
, "A2 & B1 & B2" = "mediumorchid3"
, "B1" = "dodgerblue4"
, "B2" = "darkgreen"
, "B1 & B2" = "darkolivegreen3"
, "C" = "lightsalmon4")
tip_col = pal_col[RULES.sel$SELECTION]
pal_col = pal_col[which(names(pal_col) %in% unique(RULES.sel$SELECTION))]
## Transform results of selection rules into distance matrix
mat.dist = RULES.sel[, -which(colnames(RULES.sel) %in% c("species", "SELECTION"))]
mat.dist = as.matrix(mat.dist)
mat.dist[] = as.numeric(mat.dist[])
mat.dist = as.dist(gowdis(mat.dist))
if (is.na(is.euclid(mat.dist)))
{
warning("No euclidean distance. No phylogeny and PCO graphics.")
} else
{
if (!is.euclid(mat.dist)) mat.dist = quasieuclid(mat.dist)
## ----------------------------------------------------------------------
## Transform distance matrix into phylogeny -----------------------------
hcp = as.phylo(hclust(mat.dist))
clust = cutree(hclust(mat.dist), h = 0.75)
clust.table = table(clust)
.getAncestors = function(groups, group.i)
{
## tips corresponding to the class
ind_group.i = which(groups == group.i)
ind_tot = vector()
while(length(ind_tot) < (2 * table(groups)[group.i] - 2))
{
ind_tips = which(hcp$edge[, 2] %in% ind_group.i)
## ancestors corresponding to the tips
ind_ancestors = unique(hcp$edge[ind_tips, 1])
ind_tot = unique(c(ind_tot, ind_tips))
ind_group.i = ind_ancestors
}
return(ind_tot)
}
## GET edges color
edge_col = rep("grey", nrow(hcp$edge))
if (length(clust.table) > 1)
{
for(i.class in 1:length(clust.table))
{
ind = .getAncestors(groups = clust, group.i = i.class)
edge_col[ind] = paste0("grey", round(seq(20, 80, length.out = length(clust.table))))[i.class]
}
}
## GET edges linetype
edge_lty = rep(1, nrow(hcp$edge))
if (doRuleA && length(which(RULES.sel$A1 == FALSE)) > 0)
{
ind = .getAncestors(groups = RULES.sel$A1, group.i = "FALSE")
edge_lty[ind] = 2
}
## GET tips color
tip_col = foreach(i = 1:length(tip_col), .combine = "c") %do%
{
aa = col2rgb(col = tip_col[i])[, 1]
bb = rgb(red = aa["red"]/255
, green = aa["green"]/255
, blue = aa["blue"]/255
, alpha = ifelse(RULES.sel$A1[i] == TRUE, 1, 0.2))
return(bb)
}
## ----------------------------------------------------------------------
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_PHYLO.pdf"
, width = 10, height = 10)
pp_phylo = {
plot(hcp
, type = "fan" ## unrooted, phylogram
, cex = 0.5
, label.offset = 0.01
, lab4ut = "axial"
, edge.color = edge_col
, edge.width = 1.5
, edge.lty = edge_lty
, tip.color = tip_col
)
legend("topleft"
, legend = names(pal_col)
, col = pal_col
, pch = 15
, pt.cex = 2
, bty ="n"
, xpd = TRUE)
title(main = "STEP 2 : Selected dominant species"
, sub = paste0("Colors highlight the rules of selection.\n"
, "Species not meeting any criteria or only A1 have been removed.\n"
, "Priority has been set to A2, B1 and B2 rules, rather than C. \n"
, "Hence, species selected according to A2, B1 and/or B2 can also meet criterion C\n"
, "while species selected according to C do not meet any of the three criteria.\n"
, "Species selected according to one (or more) criterion but not meeting criterion A1 are transparent."
, "\n")
, adj = 0)
}
dev.off()
## ----------------------------------------------------------------------
## Transform distance matrix into PCO -----------------------------------
PCO = dudi.pco(mat.dist, scannf = FALSE, nf = 3) ## PCO
if (ncol(PCO$li) > 1)
{
PCO.li = PCO$li
PCO.li$PFG = factor(RULES.sel$SELECTION)
PCO.li$selected = RULES.sel$A1
ind_sel = which(PCO.li$selected == TRUE)
## GET inertia values
inert = inertia.dudi(PCO)$tot.inertia
inert = c(inert$`cum(%)`[1]
, inert$`cum(%)`[2] - inert$`cum(%)`[1]
, inert$`cum(%)`[3] - inert$`cum(%)`[2])
## --------------------------------------------------------------------
num.axis = 2:length(which(!is.na(inert)))
pp_pco.list = foreach(i.axis = num.axis) %do%
{
## GET ellipses when A1 = TRUE
PCO.ELL = .getELLIPSE(xy = PCO.li[ind_sel, c("A1", paste0("A", i.axis))]
, fac = PCO.li$PFG[ind_sel])
PCO.ELL$selected = TRUE
PCO.pts = merge(PCO.li[ind_sel, ]
, unique(PCO.ELL[, c("xlabel", "ylabel", "PFG")])
, by = "PFG")
PCO.pts$selected = TRUE
labels.ELL = unique(PCO.ELL[, c("xlabel", "ylabel", "PFG", "selected")])
## GET ellipses when A1 = FALSE
if (length(unique(PCO.li$PFG[-ind_sel])) > 1)
{
tmp.ELL = .getELLIPSE(xy = PCO.li[-ind_sel, c("A1", paste0("A", i.axis))]
, fac = PCO.li$PFG[-ind_sel])
tmp.ELL$selected = FALSE
tmp.pts = merge(PCO.li[-ind_sel, ]
, unique(tmp.ELL[, c("xlabel", "ylabel", "PFG")])
, by = "PFG")
tmp.pts$selected = FALSE
PCO.ELL = rbind(PCO.ELL, tmp.ELL)
PCO.pts = rbind(PCO.pts, tmp.pts)
}
pp_pco = ggplot(PCO.li, aes_string(x = "A1"
, y = paste0("A", i.axis)
, color = "PFG"
, alpha = "as.numeric(selected)"
, linetype = "selected")) +
geom_point() +
geom_hline(yintercept = 0, color = "grey30", lwd = 1) +
geom_vline(xintercept = 0, color = "grey30", lwd = 1) +
geom_segment(data = PCO.pts, aes_string(xend = "xlabel"
, yend = "ylabel"
, size = "selected")) +
geom_path(data = PCO.ELL, aes_string(x = "x", y = "y", size = "selected")) +
geom_label_repel(data = labels.ELL, aes_string(x = "xlabel"
, y = "ylabel"
, label = "PFG")) +
scale_y_continuous(position = "right", labels = NULL
, sec.axis = sec_axis(~ . + 0)) +
scale_color_manual(guide = "none", values = pal_col) +
scale_alpha(guide = "none", range = c(0.2, 1)) +
scale_linetype_manual(guide = "none", values = c("TRUE" = 1, "FALSE" = 2)) +
scale_size_manual(guide = "none", values = c("TRUE" = 0.8, "FALSE" = 0.5)) +
labs(x = paste0("\nAXIS 1 = ", round(inert[1], 1), "% of inertia")
, y = paste0("AXIS ", i.axis, " = ", round(inert[i.axis], 1), "% of inertia\n")
, title = "STEP 2 : Selected dominant species"
, subtitle = paste0("Colors highlight the rules of selection.\n"
, "Species not meeting any criteria or only A1 have been removed.\n"
, "Priority has been set to A2, B1 and B2 rules, rather than C. \n"
, "Hence, species selected according to A2, B1 and/or B2 can also meet criterion C\n"
, "while species selected according to C do not meet any of the three criteria.\n"
, "Species selected according to one (or more) criterion but not meeting criterion A1 are transparent."
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
return(pp_pco)
}
names(pp_pco.list) = paste0("Axis1_Axis", num.axis)
pp_pco.list = pp_pco.list[names(pp_pco.list)[which(sapply(pp_pco.list, is.null) == FALSE)]]
## --------------------------------------------------------------------
if (!is.null(pp_pco.list))
{
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_PCO.pdf"
, width = 10, height = 8)
for (pp in pp_pco.list) if (!is.null(pp)) plot(pp)
dev.off()
}
} ## END ncol(PCO$li) > 1
} ## END is.na(is.euclid(mat.dist))
} ## END length(sel.sp) <= 1
}
###########################################################################
## ILLUSTRATION of robustness ---------------------------------------------
if (opt.doRobustness)
{
cat("\n> Illustration of robustness")
names.subset = c("All dataset", levels(RULES.robustness$SELECTION))
pp_rob.list = foreach(i.subset = names.subset) %do%
{
if (i.subset != "Not selected")
{
if (i.subset == "All dataset") {
tab.subset = RULES.robustness
} else {
tab.subset = RULES.robustness[which(RULES.robustness$SELECTION == i.subset), ]
}
if (nrow(tab.subset) > 0)
{
## NO OF SPECIES
tab.noSp = tapply(X = as.numeric(tab.subset$SELECTED)
, INDEX = list(tab.subset$type
, tab.subset$percent
, tab.subset$rep)
, FUN = sum
, na.rm = TRUE)
tab.noSp = melt(tab.noSp)
colnames(tab.noSp) = c("type", "percent", "rep", "value")
tab.noSp$analysis = "no.sp"
tab.noSp$value = tab.noSp$value / length(sel.sp)
## SIMILAR SPECIES
tab.simSp = foreach(i.type = combi$type
, i.rep = combi$rep
, i.percent = combi$percent
, .combine = "rbind") %do%
{
tmp = tab.subset[which(tab.subset$type == i.type &
tab.subset$percent == i.percent &
tab.subset$rep == i.rep), ]
tmp.sp1 = tmp$species[which(tmp$SELECTED == TRUE)]
tmp.sp2 = tmp.sp1[which(tmp.sp1 %in% sel.sp)]
percent1 = length(tmp.sp2) / length(tmp.sp1)
percent2 = length(tmp.sp2) / length(sel.sp)
all.sp = union(sel.sp, tmp.sp1)
tmp.all = matrix(0, nrow = length(all.sp), ncol = 2, dimnames = list(all.sp, c("O", "S")))
tmp.all = as.data.frame(t(tmp.all))
tmp.all["O", sel.sp] = 1
tmp.all["S", tmp.sp1] = 1
beta.val = beta.pair(x = tmp.all)
return(data.frame(type = i.type
, percent = i.percent
, rep = i.rep
, analysis = c("percent", "all", names(beta.val))
, value = c(percent1, percent2, unlist(beta.val))
, stringsAsFactors = FALSE))
}
## PLOT
tab.plot = rbind(tab.noSp, tab.simSp)
tab.plot$analysis = factor(tab.plot$analysis, c("no.sp", "all", "percent"
, "beta.sor", "beta.sne", "beta.sim"))
tab.plot$percent_fac = factor(tab.plot$percent, seq(0, 1, 0.1))
tab.plot$percent_num = as.numeric(tab.plot$percent_fac)
pp_rob = ggplot(tab.plot, aes_string(y = "value", color = "type")) +
geom_boxplot(aes_string(x = "percent_fac"), na.rm = TRUE) +
geom_smooth(aes_string(x = "percent_num"), method = "loess", na.rm = TRUE) +
facet_wrap(~ analysis
, ncol = 3
, labeller = as_labeller(c("no.sp" = "a"
, "all" = "b"
, "percent" = "c"
, "beta.sor" = "d"
, "beta.sne" = "e"
, "beta.sim" = "f"))) +
scale_x_discrete(name = "\nPercentage of observations (%)"
, breaks = seq(0, 1, 0.2)
, labels = seq(0, 1, 0.2) * 100
, drop = FALSE) +
scale_y_continuous(name = "Percentage of dissimilarity (%) Percentage of similarity (%)\n"
, breaks = seq(0, 1, 0.1)
, labels = seq(0, 1, 0.1) * 100) +
scale_color_manual(guide = "none"
, values = c("releves" = "midnightblue"
, "sites" = "brown")) +
labs(title = paste0("STEP 2 : Selected dominant species - robustness(", i.subset, ")")
, subtitle = paste0("Selection is run on a subset S, keeping only a "
, "percentage of releves (blue) "
, "or sites (red) from original data set O :\n\n"
, " > a = number(S species) / number(O species) \n"
, " > b = number(S & O species) / number(O species) \n"
, " > c = number(S & O species) / number(S species) \n\n"
, " > d = (1 - Sorensen dissimilarity) \n"
, " > e = (1 - nestedness-fraction of Sorensen dissimilarity) \n"
, " > f = (1 - turnover-fraction of Sorensen dissimilarity) \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
return(pp_rob)
}
}
}
names(pp_rob.list) = names.subset
pp_rob.list = pp_rob.list[names(pp_rob.list)[which(sapply(pp_rob.list, is.null) == FALSE)]]
## ----------------------------------------------------------------------
if (!is.null(pp_rob.list))
{
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_robustness.pdf"
, width = 10, height = 8)
for (pp in pp_rob.list) if (!is.null(pp)) plot(pp)
dev.off()
}
} ## opt.doRobustness
}
#############################################################################
cat("\n> Done!\n")
results = list(species.selected = sel.sp, tab.rules = RULES)
if (opt.doRobustness) {
results$tab.robustness = RULES.robustness
}
if (opt.doSitesSpecies) {
results$tab.dom.AB = tab.dom.AB
results$tab.dom.PA = tab.dom.PA
}
if (opt.doPlot) {
if (doRuleA && exists("pp_tot")) results$plot.A = pp_tot
if (doRuleB && exists("pp_B")) results$plot.B = list(abs = pp_B, rel = pp_B.rel)
if (doRuleC && exists("pp_hab")) results$plot.C = pp_hab
if (exists("pp_pco.list")) results$plot.pco = pp_pco.list
if (opt.doRobustness && exists("pp_rob.list")) results$plot.robustness = pp_rob.list
}
return(results)
}
leca-dev/RFate documentation built on Sept. 19, 2024, 6:09 a.m.
R Package Documentation
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Defines functions PRE_FATE.selectDominant
Documented in PRE_FATE.selectDominant
### HEADER #####################################################################
##' @title Selection of dominant species from abundance releves
##'
##' @name PRE_FATE.selectDominant
##'
##' @author Isabelle Boulangeat, Maya Guéguen
##'
##' @description This script is designed to select dominant species from
##' abundance records, and habitat if the information is available.
##'
##' @param mat.observations a \code{data.frame} with at least 3 columns : \cr
##' \code{sites}, \code{species}, \code{abund}
##' \cr (\emph{and optionally, \code{habitat}})
##' \cr (see \href{PRE_FATE.selectDominant#details}{\code{Details}})
##' @param doRuleA default \code{TRUE}. \cr If \code{TRUE}, selection
##' is done including constraints on number of occurrences
##' @param rule.A1 default \code{10}. \cr If \code{doRuleA = TRUE} or
##' \code{doRuleC = TRUE}, minimum number of releves required for each species
##' @param rule.A2_quantile default \code{0.9}. \cr If \code{doRuleA = TRUE}
##' or \code{doRuleC = TRUE}, quantile corresponding to the minimum number of
##' total occurrences required for each species (between \code{0} and \code{1})
##' @param doRuleB default \code{FALSE}. \cr If \code{TRUE}, selection is done
##' including constraints on relative abundances
##' @param rule.B1_percentage default \code{0.25}. \cr If \code{doRuleB = TRUE},
##' minimum relative abundance required for each species in at least
##' \code{rule.B1_number} sites (between \code{0} and \code{1})
##' @param rule.B1_number default \code{5}. \cr If \code{doRuleB = TRUE},
##' minimum number of sites in which each species has relative abundance
##' \code{>= rule.B1_percentage}
##' @param rule.B2 default \code{0.5}. \cr If \code{doRuleB = TRUE}, minimum
##' average relative abundance required for each species (between \code{0} and
##' \code{1})
##' @param doRuleC default \code{FALSE}. \cr If \code{TRUE}, selection is done
##' including constraints on number of occurrences at the habitat level (with
##' the values of \code{rule.A1} and \code{rule.A2_quantile})
##' @param opt.doRobustness (\emph{optional}) default \code{FALSE}. \cr
##' If \code{TRUE}, selection is also done on subsets of
##' \code{mat.observations}, keeping only a percentage of releves or sites, to
##' visualize the robustness of the selection
##' @param opt.robustness_percent (\emph{optional}) default \code{c(0.1, 0.2,
##' 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)}. \cr If \code{opt.doRobustness = TRUE},
##' \code{vector} containing values between \code{0} and \code{1} corresponding
##' to the percentages with which to build subsets to evaluate robustness
##' @param opt.robustness_rep (\emph{optional}) default \code{10}. \cr If
##' \code{opt.doRobustness = TRUE}, number of repetitions for each percentage
##' value defined by \code{opt.robustness_percent} to evaluate robustness
##' @param opt.doSitesSpecies (\emph{optional}) default \code{TRUE}. \cr
##' If \code{TRUE}, building of abundances / occurrences tables for selected
##' species will be processed, saved and returned.
##' @param opt.doPlot (\emph{optional}) default \code{TRUE}. \cr If \code{TRUE},
##' plot(s) will be processed, otherwise only the calculation and reorganization
##' of outputs will occur, be saved and returned.
##'
##'
##' @details
##'
##' This function provides a way to \strong{select dominant species based on
##' presence/abundance sampling information}. \cr \cr
##'
##'
##' Three rules can be applied to make the species selection :
##'
##' \describe{
##' \item{A. Presence releves}{ both conditions must be fullfilled
##' \describe{
##' \item{on number of releves}{the species should be found a minimum
##' number of times (\code{rule.A1})
##' \cr \emph{This should ensure that the species has been given sufficient
##' minimum sampling effort. This criterion MUST ALWAYS be fullfilled.}}
##' \item{on number of sites}{the species should be found in a certain
##' number of sites, which corresponds to the quantile
##' \code{rule.A2_quantile} of the total number of records per species
##' \cr \emph{This should ensure that the species is covering all the
##' studied area (or at least a determining part of it, assuming that
##' the releves are well distributed throughout the area).}}
##' }
##' }
##' \item{B. Abundance releves : }{at least one of the two conditions is
##' required
##' \describe{
##' \item{on dominancy}{the species should be dominant (i.e. represent at
##' least \code{rule.B1_percentage \%} of the coverage of the site) in at
##' least \code{rule.B1_number} sites
##' \cr \emph{This should ensure the selection of species frequently
##' abundant.}}
##' \item{on average abundance}{the species should have a mean relative
##' abundance superior or equal to \code{rule.B2}
##' \cr \emph{This should ensure the selection of species not frequent but
##' representative of the sites in which it is found. \cr \cr}}
##' }
##' }
##' \item{C. Presence releves \cr per habitat : }{If habitat information is
##' available (e.g. type of environment : urban, desert, grassland... ; type
##' of vegetation : shrubs, forest, alpine grasslands... ; etc), the same
##' rules than \strong{A} can be applied but for each habitat.
##' \cr \emph{This should help to keep species that are not dominant at the
##' large scale but could be representative of a specific habitat. \cr \cr}
##' }
##' }
##'
##'
##' A table is created containing for each species whether or not it fullfills
##' the conditions selected, for example : \cr \cr
##'
##' \strong{\code{| ___A1 ___A2 ___B1 ___B2 grass lands |}} \cr
##' \code{_______________________________________} \cr
##' \code{| _TRUE FALSE FALSE _TRUE _TRUE FALSE |} \strong{species a} \cr
##' \code{| _TRUE _TRUE _TRUE FALSE FALSE FALSE |} \strong{species b} \cr
##' \code{| FALSE FALSE FALSE FALSE FALSE _TRUE |} \strong{species c} \cr \cr
##'
##' This table is transformed into Euclidean distance matrix (with
##' \code{\link[FD]{gowdis}} and \code{\link[ade4]{quasieuclid}} functions) \cr
##' to cluster and represent species (see
##' \href{PRE_FATE.selectDominant#value}{\code{.pdf} output files}) :
##' \itemize{
##' \item through phylogenetic tree (with \code{\link[stats]{hclust}} and
##' \code{\link[ape]{as.phylo}} functions)
##' \item through Principal Component Analysis (with
##' \code{\link[ade4]{dudi.pco}})
##' }
##' according to their selection rules :
##' \itemize{
##' \item \strong{A2} : spatial dominancy (\emph{widespread but poorly
##' abundant})
##' \item \strong{B1} : local dominancy (\emph{relatively abundant or
##' dominant in a certain number of sites})
##' \item \strong{B2} : local dominancy (\emph{not widespread but dominant in
##' few sites})
##' \item \strong{C} : habitat dominancy (\emph{not widespread but dominant in
##' a specific habitat})
##' \item \strong{A2 & B1} : (\emph{widespread and relatively abundant})
##' \item \strong{A2 & B2} : (\emph{widespread and dominant in few sites})
##' \item \strong{A2 & B1 & B2} : (\emph{widespread and dominant})
##' \item \strong{B1 & B2} : (\emph{relatively widespread but dominant})
##' \cr \cr
##' }
##'
##' \strong{NB :} \cr
##' Species not meeting any criteria or only A1 are considered as
##' "\emph{Not selected}". \cr Priority is set to A2, B1 and B2 rules, rather
##' than C. Hence, species selected according to A2, B1 and/or B2 can also meet
##' criterion C while species selected according to C do not meet any of the
##' three criteria. \cr Species selected according to one (or more) criterion
##' but not meeting criterion A1 are also considered as "\emph{Not selected}".
##'
##'
##' @return A \code{list} containing one \code{vector}, four or five
##' \code{data.frame} objects with the following columns, and up to five
##' \code{ggplot2} objects :
##'
##' \describe{
##' \item{species.selected}{the names of the selected species}
##' \item{tab.rules}{
##' \describe{
##' \item{\code{A1,A2,B1,B2, hab}}{if the rule has been used, if the
##' species fullfills this condition or not}
##' \item{\code{species}}{the concerned species}
##' \item{\code{SELECTION}}{the summary of rules with which the species
##' was selected, or not}
##' \item{\code{SELECTED}}{\code{TRUE} if the species fullfills \code{A1}
##' and at least one other condition, \code{FALSE} otherwise}
##' }
##' }
##' \item{tab.robustness}{
##' \describe{
##' \item{\code{...}}{same as \code{tab.rules}}
##' \item{\code{type}}{the type of subset (either \code{releves} or
##' \code{sites})}
##' \item{\code{percent}}{the concerned percentage of values extraction}
##' \item{\code{rep}}{the repetition ID}
##' }
##' }
##' \item{tab.dom.AB}{table containing sums of abundances for all selected
##' species (sites in rows, species in columns)}
##' \item{tab.dom.PA}{table containing counts of presences for all selected
##' species (sites in rows, species in columns)}
##' \item{plot.A}{\code{ggplot2} object, representing the selection of
##' species according to rules A1 and A2}
##' \item{plot.B}{\code{ggplot2} object, representing the selection of
##' species according to rules B}
##' \item{plot.C}{\code{ggplot2} object, representing the selection of
##' species according to rules C (A1 and A2 per habitat)}
##' \item{plot.pco}{\code{ggplot2} object, representing selected species with
##' Principal Coordinates Analysis (see \code{\link[ade4]{dudi.pco}})}
##' \item{plot.robustness}{\code{ggplot2} object, representing the robustness
##' of the selection of species for each rule \cr \cr}
##' }
##'
##'
##'
##' The information is written in
##' \file{PRE_FATE_DOMINANT_[...].csv} files :
##' \describe{
##' \item{\file{TABLE_complete}}{the complete table of all species and the
##' selection rules described above (\code{tab.rules})}
##' \item{\file{TABLE_species}}{only the names / ID of the species selected}
##' \item{\file{TABLE_sitesXspecies_AB}}{abundances table of selected species}
##' \item{\file{TABLE_sitesXspecies_PA}}{presence/absence table of selected
##' species}
##' }
##'
##' Up to six \file{PRE_FATE_DOMINANT_[...].pdf} files are also created :
##' \describe{
##' \item{\file{STEP_1_rule_A}}{\strong{\file{STEP_2_selectedSpecies_PHYLO}}}
##' \item{\file{STEP_1_rule_B}}{\strong{\file{STEP_2_selectedSpecies_PCO}}}
##' \item{\file{STEP_1_rule_C}}{\strong{\file{STEP_2_selectedSpecies_robustness}}}
##' }
##'
##' @keywords abundance, dominant species, quantile, habitat class
##'
##' @seealso \code{\link{PRE_FATE.abundBraunBlanquet}},
##' \code{\link[stats]{quantile}},
##' \code{\link[FD]{gowdis}},
##' \code{\link[ade4]{quasieuclid}},
##' \code{\link[stats]{hclust}},
##' \code{\link[ape]{as.phylo}},
##' \code{\link[ade4]{dudi.pco}}
##'
##' @examples
##'
##' ## Load example data
##' Champsaur_PFG = .loadData('Champsaur_PFG', 'RData')
##'
##' ## Species observations
##' tab = Champsaur_PFG$sp.observations
##'
##' ## No habitat, no robustness -------------------------------------------------
##' tab.occ = tab[, c('sites', 'species', 'abund')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.rules)
##' str(sp.SELECT$tab.dom.PA)
##' plot(sp.SELECT$plot.A)
##' plot(sp.SELECT$plot.B$abs)
##' plot(sp.SELECT$plot.B$rel)
##'
##' ## Habitat, change parameters, no robustness (!quite long!) --------------------
##' \dontrun{
##' tab.occ = tab[, c('sites', 'species', 'abund', 'habitat')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ
##' , doRuleA = TRUE
##' , rule.A1 = 10
##' , rule.A2_quantile = 0.9
##' , doRuleB = TRUE
##' , rule.B1_percentage = 0.2
##' , rule.B1_number = 10
##' , rule.B2 = 0.4
##' , doRuleC = TRUE)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.rules)
##' plot(sp.SELECT$plot.C)
##' plot(sp.SELECT$plot.pco$Axis1_Axis2)
##' plot(sp.SELECT$plot.pco$Axis1_Axis3)
##' }
##'
##' ## No habitat, robustness (!quite long!) --------------------
##' \dontrun{
##' tab.occ = tab[, c('sites', 'species', 'abund')]
##' sp.SELECT = PRE_FATE.selectDominant(mat.observations = tab.occ
##' , opt.doSitesSpecies = FALSE
##' , opt.doRobustness = TRUE
##' , opt.robustness_percent = seq(0.1,0.9,0.1)
##' , opt.robustness_rep = 10)
##' names(sp.SELECT)
##' str(sp.SELECT$tab.robustness)
##' names(sp.SELECT$plot.robustness)
##' plot(sp.SELECT$plot.robustness$`All dataset`)
##' }
##'
##'
##' @export
##'
##' @importFrom stats quantile as.dist hclust cutree
##' @importFrom utils write.csv txtProgressBar setTxtProgressBar
##' @importFrom grDevices pdf dev.off col2rgb rgb
##' @importFrom graphics legend title plot
##'
##' @importFrom foreach foreach %do%
##' @importFrom reshape2 melt
##' @importFrom FD gowdis
##' @importFrom ade4 is.euclid quasieuclid dudi.pco inertia.dudi
##' @importFrom ape as.phylo plot.phylo
##'
##' @importFrom ggplot2 ggplot ggsave aes_string
##' geom_point geom_hline geom_vline geom_histogram geom_segment geom_path
##' geom_boxplot geom_smooth geom_col annotate sec_axis
##' scale_color_manual scale_color_continuous scale_x_discrete
##' scale_x_continuous scale_y_continuous scale_y_log10 scale_alpha
##' scale_linetype_manual scale_size_manual scale_fill_identity
##' facet_wrap as_labeller labs theme element_text
##' @importFrom ggthemes theme_fivethirtyeight
##' @importFrom ggrepel geom_label_repel
##' @importFrom colorspace sequential_hcl
##'
## END OF HEADER ###############################################################
PRE_FATE.selectDominant = function(mat.observations
, doRuleA = TRUE
, rule.A1 = 10
, rule.A2_quantile = 0.9
, doRuleB = TRUE
, rule.B1_percentage = 0.25
, rule.B1_number = 5
, rule.B2 = 0.5
, doRuleC = FALSE
, opt.doRobustness = FALSE
, opt.robustness_percent = seq(0.1, 0.9, 0.1)
, opt.robustness_rep = 10
, opt.doSitesSpecies = TRUE
, opt.doPlot = TRUE
){
#############################################################################
## CHECK parameter mat.observations
if (.testParam_notDf(mat.observations))
{
.stopMessage_beDataframe("mat.observations")
} else
{
mat.observations = as.data.frame(mat.observations)
if (nrow(mat.observations) == 0 || !(ncol(mat.observations) %in% c(3, 4)))
{
.stopMessage_numRowCol("mat.observations", c("sites", "species", "abund", "(habitat)"))
} else
{
notCorrect = switch(as.character(ncol(mat.observations))
, "3" = .testParam_notColnames(mat.observations, c("sites", "species", "abund"))
, "4" = .testParam_notColnames(mat.observations, c("sites", "species", "abund", "habitat"))
, TRUE)
if (notCorrect){
.stopMessage_columnNames("mat.observations", c("sites", "species", "abund", "(habitat)"))
}
}
mat.observations$sites = as.character(mat.observations$sites)
mat.observations$species = as.character(mat.observations$species)
.testParam_notNum.m("mat.observations$abund", mat.observations$abund)
}
## CHECK parameter doRuleA / doRuleC
if (doRuleA || doRuleC)
{
.testParam_notNum.m("rule.A1", rule.A1)
.testParam_notBetween.m("rule.A2_quantile", rule.A2_quantile, 0, 1)
}
## CHECK parameter doRuleB
if (doRuleB)
{
if (.testParam_notInValues(mat.observations$abund, c(NA, 0, 1)))
{
.testParam_notNum.m("rule.B1_number", rule.B1_number)
.testParam_notBetween.m("rule.B1_percentage", rule.B1_percentage, 0, 1)
.testParam_notBetween.m("rule.B2", rule.B2, 0, 1)
} else
{
doRuleB = FALSE
}
}
## CHECK parameter doRuleC
if (doRuleC == FALSE ||
sum(colnames(mat.observations) == "habitat") == 0 ||
length(unique(mat.observations$habitat)) == 1)
{
doRuleC = FALSE
mat.observations$habitat = "all"
}
mat.observations$habitat = as.character(mat.observations$habitat)
categories = sort(unique(mat.observations$habitat))
categories = unique(c("all", categories))
## CHECK parameter opt.doRobustness
if (opt.doRobustness)
{
.testParam_notNum.m("opt.robustness_rep", opt.robustness_rep)
.testParam_notBetween.m("opt.robustness_percent", opt.robustness_percent, 0, 1)
opt.robustness_percent = sort(unique(round(opt.robustness_percent, 1)))
}
## CHECK duplicated rows
if (nrow(mat.observations) != nrow(unique(mat.observations)))
{
warning(paste0("`mat.observations` contains duplicated rows ("
, sum(duplicated(mat.observations))
, "). These rows have been removed. Please check."))
mat.observations = unique(mat.observations)
}
cat("\n\n #------------------------------------------------------------#")
cat("\n # PRE_FATE.selectDominant")
cat("\n #------------------------------------------------------------# \n")
#############################################################################
### PREPARATION OF DATA : informations
#############################################################################
MO = mat.observations
MO.no_releves = nrow(MO)
MO.sites = unique(MO$sites)
MO.no_sites = length(MO.sites)
MO.species = unique(MO$species)
MO.no_species = length(MO.species)
ind.notNA = which(!is.na(MO$abund))
MO.no_releves.notNA = length(ind.notNA)
MO.no_sites.notNA = length(unique(MO$sites[ind.notNA]))
MO.no_species.notNA = length(unique(MO$species[ind.notNA]))
{
cat("\n ---------- INFORMATION : SAMPLING \n")
cat("\n Number of releves : ", MO.no_releves)
cat("\n Number of sites : ", MO.no_sites)
cat("\n Number of species : ", MO.no_species)
cat("\n")
cat("\n ---------- INFORMATION : ABUNDANCE \n")
cat("\n Percentage of releves with abundance information : "
, round(100 * MO.no_releves.notNA / MO.no_releves, 2), "%")
cat("\n Percentage of sites with abundance information : "
, round(100 * MO.no_sites.notNA / MO.no_sites, 2), "%")
cat("\n Percentage of species with abundance information : "
, round(100 * MO.no_species.notNA / MO.no_species, 2), "%")
cat("\n")
if (MO.no_releves.notNA / MO.no_releves == 0)
{
warning(paste0("NO abundance information in your data. "
, "Dominant species selection will only be done with "
, "the criteria based on number of presences."))
}
if (MO.no_species.notNA / MO.no_species < 1)
{
warning(paste0("Species with NO abundance information can only be "
, "selected with the criteria based on number of presences."))
}
cat("\n ---------- STATISTICS COMPUTATION \n")
cat("\n For each species (site level) :")
if (doRuleA) {
cat("\n - total frequency and abundance (rule A1 & A2)")
}
if (doRuleB) {
cat("\n - mean relative abundance (rule B2)")
cat("\n - frequency (absolute and relative) of each relative abundance class (rule B1)")
}
if (doRuleC) {
cat("\n For each species (habitat level) :")
cat("\n - frequency and abundance (absolute and relative) within each habitat (rule C)")
}
if (!doRuleA && !doRuleB && !doRuleC) {
cat("\n Nothing! No rule selected! Please check.")
stop()
}
cat("\n\n")
}
#############################################################################
## Prepare combinations to run statistics calculation
combi = data.frame(rep = 1, percent = 1, type = "releves"
, stringsAsFactors = FALSE)
if (opt.doRobustness)
{
opt.robustness_percent = opt.robustness_percent[which(opt.robustness_percent > 0 &
opt.robustness_percent < 1)]
combi = rbind(combi
, expand.grid(rep = 1:opt.robustness_rep
, percent = opt.robustness_percent
, type = c("releves", "sites")
, stringsAsFactors = FALSE))
PROGRESS = txtProgressBar(min = 0, max = nrow(combi), style = 3)
}
combi$iter = 1:nrow(combi)
## Run statistics calculation
RULES.robustness = foreach(i.type = combi$type
, i.rep = combi$rep
, i.percent = combi$percent
, i.iter = combi$iter
, .combine = "rbind"
) %do%
{
## If opt.doRobustness, select only a fraction of the observations
if (i.type == "releves")
{
sel.vec = 1:MO.no_releves
sel.no = round(i.percent * MO.no_releves)
sel.ind = sample(sel.vec, sel.no)
} else
{
sel.vec = 1:MO.no_sites
sel.no = round(i.percent * MO.no_sites)
sel.ind = sample(sel.vec, sel.no)
sel.ind = which(MO$sites %in% MO.sites[sel.ind])
}
if (length(sel.ind) > 0)
{
mat.observations = MO[sel.ind, , drop = FALSE]
no.species = length( unique(mat.observations$species))
#########################################################################
### PREPARATION OF DATA : relative abundances
#########################################################################
class_breaks = seq(0, 1, 0.05)
class_breaks.levels = foreach(i = 1:(length(class_breaks)-1)
, j = 2:length(class_breaks)
, .combine = "c") %do%
{
paste0("(", class_breaks[i], ",", class_breaks[j], "]")
}
## Calculate relative abundances per site
list.dat.sites = split(mat.observations, mat.observations$sites)
mat.obs_rel = foreach(i.dat = 1:length(list.dat.sites)
, .combine = "rbind"
) %do%
{
dat = list.dat.sites[[i.dat]]
dat$sites.abund = sum(dat$abund, na.rm = TRUE)
dat$abund_rel.sites = dat$abund / sum(dat$abund, na.rm = TRUE)
return(dat)
}
mat.obs_rel$class_rel.sites = cut(mat.obs_rel$abund_rel.sites
, breaks = class_breaks)
## Calculate relative abundances per habitat
list.dat.habitat = split(mat.obs_rel, mat.obs_rel$habitat)
mat.obs_rel = foreach(i.dat = 1:length(list.dat.habitat)
, .combine = "rbind"
) %do%
{
dat = list.dat.habitat[[i.dat]]
dat$habitat.abund = sum(dat$abund, na.rm = TRUE)
dat$abund_rel.habitat = dat$abund / sum(dat$abund, na.rm = TRUE)
return(dat)
}
#########################################################################
### STATISTICS
#########################################################################
## SELECTION RULE A -----------------------------------------------------
if (doRuleA || doRuleB)
{
## Calculate the number of releves per species
mat1 = table(mat.obs_rel$species)
mat1 = data.frame(species = names(mat1)
, freq_tot = as.vector(mat1)
, stringsAsFactors = FALSE)
## Calculate the total abundance per species
mat2 = tapply(X = mat.obs_rel$abund
, INDEX = list(mat.obs_rel$species)
, FUN = sum
, na.rm = TRUE)
mat2 = data.frame(species = names(mat2)
, abund_tot = as.vector(mat2)
, stringsAsFactors = FALSE)
## Gather information
mat.A_B2 = merge(mat1, mat2, by = "species")
rm(list = c("mat1", "mat2"))
}
## SELECTION RULE B -----------------------------------------------------
if (doRuleB)
{
## Calculate mean relative abundance per species
mat3 = tapply(X = mat.obs_rel$abund_rel.sites
, INDEX = list(mat.obs_rel$species)
, FUN = mean
, na.rm = TRUE)
mat3 = data.frame(species = names(mat3)
, abund_rel.sites.mean = as.vector(mat3)
, stringsAsFactors = FALSE)
mat.A_B2 = merge(mat.A_B2, mat3, by = "species", all.x = TRUE)
# mat.A_B2$class_rel.sites.mean = cut(mat.A_B2$abund_rel.sites.mean
# , breaks = class_breaks)
rm(list = c("mat3"))
## Calculate the number of relative abundance class per species
mat.B1 = table(mat.obs_rel$species, mat.obs_rel$class_rel.sites)
mat.B1 = as.data.frame(mat.B1, stringsAsFactors = FALSE)
colnames(mat.B1) = c("species", "class_rel.sites", "freq.class_rel.sites")
mat.B1 = mat.B1[which(mat.B1$freq.class_rel.sites > 0), ]
## Calculate the relative frequency
mat.B1 = merge(mat.B1, mat.A_B2[, c("species", "freq_tot")], by = "species")
mat.B1$freq_rel.class_rel.sites = mat.B1$freq.class_rel.sites / mat.B1$freq_tot
}
## SELECTION RULE C -----------------------------------------------------
if (doRuleC)
{
## Calculate the number of releves per species per habitat
mat.C = table(mat.obs_rel$species, mat.obs_rel$habitat)
mat.C = as.data.frame(mat.C, stringsAsFactors = FALSE)
colnames(mat.C) = c("species", "habitat", "freq.habitat")
mat.C = mat.C[which(mat.C$freq.habitat > 0), ]
## Calculate the relative frequency
mat4 = table(mat.obs_rel$habitat)
mat4 = data.frame(habitat = names(mat4)
, freq_tot.habitat = as.vector(mat4)
, stringsAsFactors = FALSE)
mat.C = merge(mat.C, mat4, by = "habitat")
mat.C$freq_rel.habitat = mat.C$freq.habitat / mat.C$freq_tot.habitat
## Calculate the total abundance per species per habitat
mat5 = tapply(X = mat.obs_rel$abund
, INDEX = list(mat.obs_rel$species
, mat.obs_rel$habitat)
, FUN = sum
, na.rm = TRUE)
mat5 = as.data.frame(mat5, stringsAsFactors = FALSE)
mat5$species = rownames(mat5)
mat5 = melt(mat5, id.vars = "species")
colnames(mat5) = c("species", "habitat", "abund_tot.habitat")
mat5 = mat5[which(mat5$abund_tot.habitat > 0), ]
## Calculate the relative abundance per habitat
mat.C = merge(mat.C, mat5, by = c("species", "habitat"), all = TRUE)
mat.C = merge(mat.C, unique(mat.obs_rel[, c("habitat", "habitat.abund")]), by = "habitat")
mat.C$abund_rel.habitat = mat.C$abund_tot.habitat / mat.C$habitat.abund
rm(list = c("mat4", "mat5"))
}
#########################################################################
### SELECTION OF DOMINANT SPECIES
#########################################################################
## Get information about how each species has been selected
## (over all area, or within specific habitat and which one(s), or both)
sp_ruleA1 = sp_ruleA2 = sp_ruleB1 = sp_ruleB2 = sp_ruleCA2 = vector()
if (doRuleA)
{
## A1 : minimum number of occurrences
sp_ruleA1 = mat.A_B2$species[which(mat.A_B2$freq_tot >= rule.A1)]
## A2 : minimum number of occurrences (quantile)
rule.A2_value = quantile(mat.A_B2$freq_tot, p = rule.A2_quantile)
sp_ruleA2 = mat.A_B2$species[which(mat.A_B2$freq_tot >= rule.A2_value)]
}
if (doRuleB)
{
## B1 : relative abundance or dominancy in a certain number of sites
class_B1 = class_breaks.levels[(class_breaks[-1] >= rule.B1_percentage)]
tmpB1 = mat.B1[which(mat.B1$class_rel.sites %in% class_B1), ]
sp_ruleB1 = foreach(i.sp = unique(as.character(tmpB1$species)), .combine = "c") %do%
{
tab = tmpB1[which(tmpB1$species == i.sp), ]
if (sum(tab$freq.class_rel.sites) >= rule.B1_number) { return(i.sp) }
}
sp_ruleB1 = unique(as.character(sp_ruleB1))
## B2 : minimum mean relative abundance
sp_ruleB2 = mat.A_B2$species[which(mat.A_B2$abund_rel.sites.mean >= rule.B2)]
}
if (doRuleC)
{
## C2 : similar to A2 for each habitat
sp_ruleCA2 = foreach(i.hab = categories[-1]) %do%
{
tab = mat.C[which(mat.C$habitat == i.hab), ]
quanti = quantile(tab$freq.habitat, p = rule.A2_quantile)
return(tab$species[which(tab$freq.habitat >= quanti)])
}
names(sp_ruleCA2) = categories[-1]
## C1 : similar to B1 for each habitat
# class_C1 = levels(mat.C$class_rel.sites)[(class_breaks[-1] >= rule.B1_percentage)]
# class_C1 = class_breaks.levels[(class_breaks[-1] >= rule.B1_percentage)]
# sp_ruleCB1 = mat.B1$species[which(mat.C$class_rel.sites %in% class_C1 &
# mat.C$freq.class_rel.sites >= rule.B1_number)]
# sp_ruleCB1 = unique(as.character(sp_ruleCB1))
}
## COMBINE all rules ----------------------------------------------------
sp_rule_ALL = c(list(A1 = sp_ruleA1
, A2 = sp_ruleA2
, B1 = sp_ruleB1
, B2 = sp_ruleB2)
, sp_ruleCA2)
RULES = foreach(i.rule = 1:length(sp_rule_ALL), .combine = "rbind") %do%
{
mat = matrix(FALSE, nrow = 1, ncol = no.species
, dimnames = list(names(sp_rule_ALL)[i.rule]
, unique(mat.observations$species)))
mat[, which(colnames(mat) %in% sp_rule_ALL[[i.rule]])] = TRUE
return(mat)
}
RULES = as.data.frame(t(RULES))
tab.sp = rowSums(RULES)
tab.rules = colSums(RULES)
## Add information about how each species has been selected or not
RULES$species = rownames(RULES)
RULES$SELECTION = sapply(paste0(RULES$A2, "_", RULES$B1, "_", RULES$B2)
, function(x)
{
switch(x
, "FALSE_FALSE_FALSE" = "Not selected (A2, B1, B2)"
, "TRUE_FALSE_FALSE" = "A2"
, "FALSE_TRUE_FALSE" = "B1"
, "FALSE_FALSE_TRUE" = "B2"
, "TRUE_TRUE_FALSE" = "A2 & B1"
, "FALSE_TRUE_TRUE" = "B1 & B2"
, "TRUE_FALSE_TRUE" = "A2 & B2"
, "TRUE_TRUE_TRUE" = "A2 & B1 & B2")
})
RULES$SELECTION[which(tab.sp == 0)] = "Not selected"
RULES$SELECTION[which(tab.sp == 1 &
RULES$A1 == TRUE)] = "Not selected"
RULES$SELECTION[which(tab.sp > 1 &
RULES$A1 == TRUE &
RULES$SELECTION == "Not selected (A2, B1, B2)")] = "C"
RULES$SELECTION[which(tab.sp > 0 &
RULES$A1 == FALSE &
RULES$SELECTION == "Not selected (A2, B1, B2)")] = "C" ## not A1
RULES$SELECTION = factor(RULES$SELECTION, c("Not selected"
, "A2"
, "A2 & B1"
, "A2 & B2"
, "A2 & B1 & B2"
, "B1"
, "B2"
, "B1 & B2"
, "C"))
## Summary : has each species been selected or not
RULES$SELECTED = FALSE
RULES$SELECTED[which(RULES$SELECTION != "Not selected" & RULES$A1 == TRUE)] = TRUE
## SAVE summary tables --------------------------------------------------
if (i.percent == 1 && i.type == "releves" && i.rep == 1)
{
end_filename = end_filenameA = end_filenameB = end_filenameC = ""
if (doRuleA) {
end_filenameA = paste0("_A_", rule.A1
, "_", rule.A2_quantile)
write.csv(mat.A_B2
, file = "PRE_FATE_DOMINANT_mat.A_B2.csv"
, row.names = FALSE)
}
if (doRuleB) {
end_filenameB = paste0("_B_", rule.B1_number
, "_", rule.B1_percentage
, "_", rule.B2)
write.csv(mat.B1
, file = "PRE_FATE_DOMINANT_mat.B1.csv"
, row.names = FALSE)
}
if (doRuleC) {
end_filenameC = paste0("_C_", rule.A1
, "_", rule.A2_quantile)
write.csv(mat.C
, file = "PRE_FATE_DOMINANT_mat.C.csv"
, row.names = FALSE)
}
end_filename = paste0(end_filenameA, end_filenameB, end_filenameC)
write.csv(RULES
, file = paste0("PRE_FATE_DOMINANT_TABLE_complete"
, end_filename
, ".csv")
, row.names = FALSE)
write.csv(RULES[which(RULES$SELECTED == TRUE), "species"]
, file = paste0("PRE_FATE_DOMINANT_TABLE_species"
, end_filename
, ".csv")
, row.names = FALSE)
message(paste0("\n The output files \n"
, ifelse(doRuleA
, " > PRE_FATE_DOMINANT_mat.A_B2.csv \n"
, "")
, ifelse(doRuleB
, " > PRE_FATE_DOMINANT_mat.B1.csv \n"
, "")
, ifelse(doRuleC
, " > PRE_FATE_DOMINANT_mat.C.csv \n"
, "")
, " > PRE_FATE_DOMINANT_TABLE_complete"
, end_filename
, ".csv \n"
, " > PRE_FATE_DOMINANT_TABLE_species"
, end_filename
, ".csv \n"
, "have been successfully created !\n"))
}
## RETURN table ---------------------------------------------------------
if (opt.doRobustness)
{
setTxtProgressBar(pb = PROGRESS, value = i.iter)
RULES$type = i.type
RULES$percent = i.percent
RULES$rep = i.rep
}
return(RULES)
}
}
cat("\n ---------- SELECTION OF DOMINANT SPECIES \n")
## Keep only part corresponding to full dataset
if (opt.doRobustness)
{
close(PROGRESS)
RULES = RULES.robustness[which(RULES.robustness$percent == 1 &
RULES.robustness$type == "releves" &
RULES.robustness$rep == 1), ]
RULES = RULES[, -which(colnames(RULES) %in% c("type", "percent", "rep"))]
} else
{
RULES = RULES.robustness
}
## Get selected species and corresponding observations
RULES.sel = RULES[which(RULES$SELECTION != "Not selected"), ]
sel.sp = RULES.sel$species[which(RULES.sel$A1 == TRUE)]
sel.obs = MO[which(MO$species %in% sel.sp), ]
{
cat("\n - Number of selected species :", length(sel.sp))
cat("\n - Representativity of species :"
, round(100 * length(sel.sp) / MO.no_species)
, "%")
cat("\n - Representativity of sites :"
, round(100 * length(unique(sel.obs$sites)) / MO.no_sites)
, "%")
cat("\n - Representativity of total abundance :"
, round(100 * sum(sel.obs$abund, na.rm = T) / sum(MO$abund, na.rm = T))
, "%")
cat("\n Complete table of information can be find in output files.")
cat("\n")
}
#############################################################################
## BUILD SITES x SPECIES MATRIX FOR DOMINANT SPECIES ONLY
#############################################################################
## Keep only part corresponding to full dataset
if (opt.doSitesSpecies)
{
cat("\n ---------- PRODUCING ABUNDANCES / OCCURRENCES TABLES \n")
## Transform observations of selected species into sites x species tables
tab.dom.AB = tapply(X = sel.obs$abund
, INDEX = list(sel.obs$sites, sel.obs$species)
, FUN = sum)
## Change sum of abundBB and NA to 1 in tab.dom.PA
tab.dom.PA = tab.dom.AB
tab.dom.PA[which(tab.dom.PA[] > 0)] = 1
write.csv(tab.dom.AB
, file = paste0("PRE_FATE_DOMINANT_TABLE_sitesXspecies_AB"
, end_filename
, ".csv")
, row.names = TRUE)
write.csv(tab.dom.PA
, file = paste0("PRE_FATE_DOMINANT_TABLE_sitesXspecies_PA"
, end_filename
, ".csv")
, row.names = TRUE)
message(paste0("\n The output files \n"
, " > PRE_FATE_DOMINANT_TABLE_sitesXspecies_AB"
, end_filename
, ".csv \n"
, " > PRE_FATE_DOMINANT_TABLE_sitesXspecies_PA"
, end_filename
, ".csv \n"
, "have been successfully created !\n"))
}
#############################################################################
## GRAPHICS TO HELP ADJUST PARAMETERS TO SELECT DOMINANT SPECIES
#############################################################################
if (opt.doPlot)
{
cat("\n ---------- PRODUCING PLOT(S) \n")
###########################################################################
## ILLUSTRATION of rules A1, A2 and C -------------------------------------
if (doRuleA || doRuleC)
{
cat("\n> Illustration of rules A and C")
tab.plot = mat.A_B2[, c("species", "freq_tot", "abund_tot")]
tab.plot$habitat = "all"
names.plot = c("tot")
if (doRuleC)
{
tmp = mat.C[, c("habitat", "species", "freq.habitat", "abund_tot.habitat")]
colnames(tmp) = c("habitat", "species", "freq_tot", "abund_tot")
tab.plot = rbind(tab.plot, tmp)
names.plot = c("tot", "hab")
}
## Correct total abundance value to 1 when no abundance is provided (NA)
tab.plot$abund_tot[which(is.na(tab.plot$abund_tot))] = 1
## Add information about selection rules A1 & A2
tab.quant = foreach(i.hab = categories, .combine = "rbind") %do%
{
tab = tab.plot[which(tab.plot$habitat == i.hab), ]
quanti = quantile(tab$freq_tot, p = rule.A2_quantile)
return(data.frame(habitat = i.hab
, quantile = quanti
, stringsAsFactors = FALSE))
}
tab.plot = merge(tab.plot, tab.quant, by = "habitat")
tab.plot$class_A = factor(ifelse(tab.plot$freq_tot < rule.A1
, 1
, ifelse(tab.plot$freq_tot >= tab.plot$quantile
, 3, 2)))
rm(list = c("tab.quant"))
doLogA = max(tab.plot$abund_tot, na.rm = TRUE)
## ----------------------------------------------------------------------
pp_shared = 'geom_histogram(aes_string("freq_tot")
, fill = "grey60"
, alpha = 0.5
, binwidth = function(x) 2 * IQR(x) / (length(x)^(1/4))
, na.rm = FALSE) +
geom_point(aes_string(x = "freq_tot"
, y = "abund_tot"
, color = "class_A")
, alpha = 0.5) +
geom_vline(xintercept = rule.A1, lty = 2) +
geom_vline(aes_string(xintercept = "quantile"), lty = 2) +
geom_label_repel(data = tab.quanti
, aes_string(x = "quantile"
, y = "y_quanti"
, label = "lab_quanti")
, color = tab.quanti$col_quanti
, fontface = "bold"
, direction = "x"
, label.size = 0
, label.padding = unit(0.3, "lines")) +
scale_color_manual(guide = "none"
, values = c("1" = "brown"
, "2" = "chocolate"
, "3" = "darkgoldenrod")) +
labs(x = "\nNumber of occurrences"
, y = "Total abundance\n") +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))'
if (doLogA >= 100)
{
pp_shared = paste0(pp_shared, '+
scale_y_log10(name = "Total abundance\n")')
}
## ----------------------------------------------------------------------
for(i.plot in names.plot)
{
if (i.plot == "tot") {
tab = tab.plot[which(tab.plot$habitat == "all"), ]
} else {
tab = tab.plot[which(tab.plot$habitat != "all"), ]
}
if(nrow(tab) > 0)
{
## Add quantiles corresponding to rules A1 and A2
tab.quanti1 = data.frame(habitat = unique(tab$habitat)
, quantile = rule.A1
, col_quanti = "chocolate"
, lab_quanti = "A1"
, stringsAsFactors = FALSE)
tab.quanti2 = unique(tab[, c("habitat", "quantile")])
tab.quanti2$col_quanti = "darkgoldenrod"
tab.quanti2$lab_quanti = "A2"
tab.quanti = rbind(tab.quanti1, tab.quanti2)
tab.quanti$y_quanti = ifelse(doLogA >= 100, 0.5, doLogA)
pp = eval(parse(text = paste0('ggplot(tab) +', pp_shared)))
if (i.plot == "tot") {
pp_tot = pp +
annotate(geom = "label"
, x = max(tab.plot$freq_tot) * c(0.05, 0.6, 0.6)
, y = doLogA * c(0.6, 0.6, 0.01)
, label = c("Very abundant species \nwith narrow distributions"
, "Very abundant and\n widespread species"
, "Widespread species \nbut poorly abundant")
, colour = c("grey30", "darkgoldenrod", "darkgoldenrod")
, fontface = c("italic", "bold", "bold")
, hjust = "inward"
, alpha = 0.5) +
labs(title = "STEP 1 : Selection of dominant species - rule A"
, subtitle = paste0("Criteria concerning occurrences within all sites :\n"
, " > A1 = minimum number of releves required : "
, rule.A1, "\n"
, " > A2 = minimum number of occurrences required : quantile("
, rule.A2_quantile * 100, "%) = "
, round(rule.A2_value), "\n"
, "\n"))
## ----------------------------------------------------------------
ggsave(filename = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameA, ".pdf")
, plot = pp_tot, device = "pdf", width = 10, height = 8)
} else {
pp_hab = pp +
facet_wrap(~ habitat, scales = "free_x") +
labs(title = "STEP 1 : Selection of dominant species - rule C"
, subtitle = paste0("Criteria concerning occurrences within all habitats :\n"
, " > A1 = minimum number of releves required : "
, rule.A1, "\n"
, " > A2 = minimum number of occurrences required : quantile("
, rule.A2_quantile * 100, "%) = "
, paste0(paste0(tab.quanti2$habitat, ": "
, round(tab.quanti2$quantile))
, collapse = " / "), "\n"
, "\n"))
## ----------------------------------------------------------------
ggsave(filename = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameC, ".pdf")
, plot = pp_hab, device = "pdf", width = 12, height = 9)
}
}
}
}
###########################################################################
## ILLUSTRATION of rules B1 -----------------------------------------------
if (doRuleB)
{
cat("\n> Illustration of rule B")
tab.plot = mat.B1
## Associate a color to each species : rule B1
pal_sp = sequential_hcl(n = length(sp_ruleB1)
, palette = "Teal", rev = TRUE)
pal_sp = data.frame(species = sp_ruleB1
, id_color = pal_sp
, stringsAsFactors = FALSE)
tab.plot = merge(tab.plot, pal_sp, by = "species", all.x = TRUE)
tab.plot$id_color[which(is.na(tab.plot$id_color))] = "darkgray"
tab.plot$class_B = ifelse(tab.plot$id_color == "darkgray"
, 1, 2)
tab.plot$class_rel.sites = factor(tab.plot$class_rel.sites, class_breaks.levels)
## Associate a color to each species : rule B2
pal_sp = sequential_hcl(n = length(sp_ruleB2)
, palette = "Greens", rev = TRUE)
for(i.sp in 1:length(sp_ruleB2))
{
tab.plot$id_color[which(tab.plot$species == sp_ruleB2[i.sp])] = pal_sp[i.sp]
tab.plot$class_B[which(tab.plot$species == sp_ruleB2[i.sp])] = 3
}
doLogB = max(tab.plot$freq.class_rel.sites, na.rm = TRUE)
x_B1 = (class_breaks < rule.B1_percentage)
x_B1 = max(which(x_B1 == TRUE))
x_B2 = (class_breaks < rule.B2)
x_B2 = max(which(x_B2 == TRUE))
## FREQ -----------------------------------------------------------------
pp_B = ggplot(tab.plot, aes_string(x = "as.numeric(class_rel.sites) + 0.3 * (class_B - 1)"
, y = "freq.class_rel.sites"
, fill = "id_color")) +
geom_col(position = "identity"
, alpha = 0.5
, width = 0.3
, na.rm = TRUE) +
geom_hline(yintercept = rule.B1_number, lty = 2) +
annotate(geom = "label"
, x = 0
, y = rule.B1_number
, colour = "dodgerblue4"
, fontface = "bold"
, label = "B1.no"
, label.size = 0
, label.padding = unit(0.25, "lines")) +
geom_vline(xintercept = c(x_B1, x_B2), lty = 2) +
annotate(geom = "label"
, x = c(x_B1, x_B2)
, y = ifelse(doLogB >= 100, 0.5, doLogB)
, colour = c("dodgerblue4", "darkgreen")
, fontface = "bold"
, label = c("B1.%", "B2")
, label.size = 0
, label.padding = unit(1, "lines")) +
annotate(geom = "label"
, x = 20 * c(0.15, 0.5, 0.9)
, y = doLogB * c(0.8, ifelse(doLogB >= 100, 0.05, 0.5)
, ifelse(doLogB >= 100, 0.003, 0.1))
, label = c("Poorly abundant\n species \nbut widespread"
, "Species relatively abundant\n or dominant \n in a certain number of sites"
, "Dominant species \nbut in few sites")
, colour = c("grey30", "dodgerblue4", "darkgreen")
, fontface = c("italic", "bold", "bold")
, alpha = 0.5) +
scale_x_continuous(name = "\nWithin-site relative abundance (%)"
, breaks = seq(1, length(class_breaks) - 1)
, labels = 100 * class_breaks[-1]) +
scale_fill_identity(guide = "none") +
scale_color_continuous(guide = "none") +
labs(y = "Releves frequency\n"
, title = "STEP 1 : Selection of dominant species - rule B"
, subtitle = paste0("Criteria concerning abundances within all concerned sites :\n"
, " > B1.no = minimum number of sites required : "
, rule.B1_number, "\n"
, " > B1.% = with a minimum relative abundance of : "
, 100 * rule.B1_percentage, "% \n"
, " > B2 = minimum mean relative abundance required : "
, 100 * rule.B2, "% \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
if (doLogB >= 100)
{
pp_B = pp_B +
scale_y_log10(name = "Releves frequency\n")
}
## FREQ.REL -------------------------------------------------------------
pp_B.rel = ggplot(tab.plot, aes_string(x = "as.numeric(class_rel.sites) + 0.3 * (class_B - 1)"
, y = "freq_rel.class_rel.sites"
, fill = "id_color")) +
geom_col(position = "identity"
, alpha = 0.5
, width = 0.3
, na.rm = TRUE) +
geom_vline(xintercept = c(x_B1, x_B2), lty = 2) +
annotate(geom = "label"
, x = c(x_B1, x_B2)
, y = 1.1
, colour = c("dodgerblue4", "darkgreen")
, fontface = "bold"
, label = c("B1.%", "B2")
, label.size = 0
, label.padding = unit(1, "lines")) +
scale_x_continuous(name = "\nWithin-site relative abundance (%)"
, breaks = seq(1, length(class_breaks) - 1)
, labels = 100 * class_breaks[-1]) +
scale_y_continuous(name = "Relative releves frequency (representativity) (%)\n"
, breaks = seq(0, 1, 0.2)
, labels = seq(0, 1, 0.2) * 100) +
scale_fill_identity(guide = "none") +
scale_color_continuous(guide = "none") +
labs(title = "STEP 1 : Selection of dominant species - rule B"
, subtitle = paste0("Criteria concerning abundances within all concerned sites :\n"
, " > B1.no = minimum number of sites required : "
, rule.B1_number, "\n"
, " > B1.% = with a minimum relative abundance of : "
, 100 * rule.B1_percentage, "% \n"
, " > B2 = minimum mean relative abundance required : "
, 100 * rule.B2, "% \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
## ----------------------------------------------------------------------
pdf(file = paste0("PRE_FATE_DOMINANT_STEP_1_rule"
, end_filenameB, ".pdf")
, width = 10, height = 8)
plot(pp_B)
plot(pp_B.rel)
dev.off()
}
###########################################################################
## ILLUSTRATION of SELECTED SPECIES ---------------------------------------
{
cat("\n> Illustration of selected species")
if (length(sel.sp) <= 1)
{
warning("Too few species selected for representation. No phylogeny and PCO graphics.")
} else
{
## Associate a color to each species according to selection rules
pal_col = c("Not selected" = "grey"
, "A2" = "darkgoldenrod"
, "A2 & B1" = "coral2"
, "A2 & B2" = "hotpink3"
, "A2 & B1 & B2" = "mediumorchid3"
, "B1" = "dodgerblue4"
, "B2" = "darkgreen"
, "B1 & B2" = "darkolivegreen3"
, "C" = "lightsalmon4")
tip_col = pal_col[RULES.sel$SELECTION]
pal_col = pal_col[which(names(pal_col) %in% unique(RULES.sel$SELECTION))]
## Transform results of selection rules into distance matrix
mat.dist = RULES.sel[, -which(colnames(RULES.sel) %in% c("species", "SELECTION"))]
mat.dist = as.matrix(mat.dist)
mat.dist[] = as.numeric(mat.dist[])
mat.dist = as.dist(gowdis(mat.dist))
if (is.na(is.euclid(mat.dist)))
{
warning("No euclidean distance. No phylogeny and PCO graphics.")
} else
{
if (!is.euclid(mat.dist)) mat.dist = quasieuclid(mat.dist)
## ----------------------------------------------------------------------
## Transform distance matrix into phylogeny -----------------------------
hcp = as.phylo(hclust(mat.dist))
clust = cutree(hclust(mat.dist), h = 0.75)
clust.table = table(clust)
.getAncestors = function(groups, group.i)
{
## tips corresponding to the class
ind_group.i = which(groups == group.i)
ind_tot = vector()
while(length(ind_tot) < (2 * table(groups)[group.i] - 2))
{
ind_tips = which(hcp$edge[, 2] %in% ind_group.i)
## ancestors corresponding to the tips
ind_ancestors = unique(hcp$edge[ind_tips, 1])
ind_tot = unique(c(ind_tot, ind_tips))
ind_group.i = ind_ancestors
}
return(ind_tot)
}
## GET edges color
edge_col = rep("grey", nrow(hcp$edge))
if (length(clust.table) > 1)
{
for(i.class in 1:length(clust.table))
{
ind = .getAncestors(groups = clust, group.i = i.class)
edge_col[ind] = paste0("grey", round(seq(20, 80, length.out = length(clust.table))))[i.class]
}
}
## GET edges linetype
edge_lty = rep(1, nrow(hcp$edge))
if (doRuleA && length(which(RULES.sel$A1 == FALSE)) > 0)
{
ind = .getAncestors(groups = RULES.sel$A1, group.i = "FALSE")
edge_lty[ind] = 2
}
## GET tips color
tip_col = foreach(i = 1:length(tip_col), .combine = "c") %do%
{
aa = col2rgb(col = tip_col[i])[, 1]
bb = rgb(red = aa["red"]/255
, green = aa["green"]/255
, blue = aa["blue"]/255
, alpha = ifelse(RULES.sel$A1[i] == TRUE, 1, 0.2))
return(bb)
}
## ----------------------------------------------------------------------
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_PHYLO.pdf"
, width = 10, height = 10)
pp_phylo = {
plot(hcp
, type = "fan" ## unrooted, phylogram
, cex = 0.5
, label.offset = 0.01
, lab4ut = "axial"
, edge.color = edge_col
, edge.width = 1.5
, edge.lty = edge_lty
, tip.color = tip_col
)
legend("topleft"
, legend = names(pal_col)
, col = pal_col
, pch = 15
, pt.cex = 2
, bty ="n"
, xpd = TRUE)
title(main = "STEP 2 : Selected dominant species"
, sub = paste0("Colors highlight the rules of selection.\n"
, "Species not meeting any criteria or only A1 have been removed.\n"
, "Priority has been set to A2, B1 and B2 rules, rather than C. \n"
, "Hence, species selected according to A2, B1 and/or B2 can also meet criterion C\n"
, "while species selected according to C do not meet any of the three criteria.\n"
, "Species selected according to one (or more) criterion but not meeting criterion A1 are transparent."
, "\n")
, adj = 0)
}
dev.off()
## ----------------------------------------------------------------------
## Transform distance matrix into PCO -----------------------------------
PCO = dudi.pco(mat.dist, scannf = FALSE, nf = 3) ## PCO
if (ncol(PCO$li) > 1)
{
PCO.li = PCO$li
PCO.li$PFG = factor(RULES.sel$SELECTION)
PCO.li$selected = RULES.sel$A1
ind_sel = which(PCO.li$selected == TRUE)
## GET inertia values
inert = inertia.dudi(PCO)$tot.inertia
inert = c(inert$`cum(%)`[1]
, inert$`cum(%)`[2] - inert$`cum(%)`[1]
, inert$`cum(%)`[3] - inert$`cum(%)`[2])
## --------------------------------------------------------------------
num.axis = 2:length(which(!is.na(inert)))
pp_pco.list = foreach(i.axis = num.axis) %do%
{
## GET ellipses when A1 = TRUE
PCO.ELL = .getELLIPSE(xy = PCO.li[ind_sel, c("A1", paste0("A", i.axis))]
, fac = PCO.li$PFG[ind_sel])
PCO.ELL$selected = TRUE
PCO.pts = merge(PCO.li[ind_sel, ]
, unique(PCO.ELL[, c("xlabel", "ylabel", "PFG")])
, by = "PFG")
PCO.pts$selected = TRUE
labels.ELL = unique(PCO.ELL[, c("xlabel", "ylabel", "PFG", "selected")])
## GET ellipses when A1 = FALSE
if (length(unique(PCO.li$PFG[-ind_sel])) > 1)
{
tmp.ELL = .getELLIPSE(xy = PCO.li[-ind_sel, c("A1", paste0("A", i.axis))]
, fac = PCO.li$PFG[-ind_sel])
tmp.ELL$selected = FALSE
tmp.pts = merge(PCO.li[-ind_sel, ]
, unique(tmp.ELL[, c("xlabel", "ylabel", "PFG")])
, by = "PFG")
tmp.pts$selected = FALSE
PCO.ELL = rbind(PCO.ELL, tmp.ELL)
PCO.pts = rbind(PCO.pts, tmp.pts)
}
pp_pco = ggplot(PCO.li, aes_string(x = "A1"
, y = paste0("A", i.axis)
, color = "PFG"
, alpha = "as.numeric(selected)"
, linetype = "selected")) +
geom_point() +
geom_hline(yintercept = 0, color = "grey30", lwd = 1) +
geom_vline(xintercept = 0, color = "grey30", lwd = 1) +
geom_segment(data = PCO.pts, aes_string(xend = "xlabel"
, yend = "ylabel"
, size = "selected")) +
geom_path(data = PCO.ELL, aes_string(x = "x", y = "y", size = "selected")) +
geom_label_repel(data = labels.ELL, aes_string(x = "xlabel"
, y = "ylabel"
, label = "PFG")) +
scale_y_continuous(position = "right", labels = NULL
, sec.axis = sec_axis(~ . + 0)) +
scale_color_manual(guide = "none", values = pal_col) +
scale_alpha(guide = "none", range = c(0.2, 1)) +
scale_linetype_manual(guide = "none", values = c("TRUE" = 1, "FALSE" = 2)) +
scale_size_manual(guide = "none", values = c("TRUE" = 0.8, "FALSE" = 0.5)) +
labs(x = paste0("\nAXIS 1 = ", round(inert[1], 1), "% of inertia")
, y = paste0("AXIS ", i.axis, " = ", round(inert[i.axis], 1), "% of inertia\n")
, title = "STEP 2 : Selected dominant species"
, subtitle = paste0("Colors highlight the rules of selection.\n"
, "Species not meeting any criteria or only A1 have been removed.\n"
, "Priority has been set to A2, B1 and B2 rules, rather than C. \n"
, "Hence, species selected according to A2, B1 and/or B2 can also meet criterion C\n"
, "while species selected according to C do not meet any of the three criteria.\n"
, "Species selected according to one (or more) criterion but not meeting criterion A1 are transparent."
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
return(pp_pco)
}
names(pp_pco.list) = paste0("Axis1_Axis", num.axis)
pp_pco.list = pp_pco.list[names(pp_pco.list)[which(sapply(pp_pco.list, is.null) == FALSE)]]
## --------------------------------------------------------------------
if (!is.null(pp_pco.list))
{
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_PCO.pdf"
, width = 10, height = 8)
for (pp in pp_pco.list) if (!is.null(pp)) plot(pp)
dev.off()
}
} ## END ncol(PCO$li) > 1
} ## END is.na(is.euclid(mat.dist))
} ## END length(sel.sp) <= 1
}
###########################################################################
## ILLUSTRATION of robustness ---------------------------------------------
if (opt.doRobustness)
{
cat("\n> Illustration of robustness")
names.subset = c("All dataset", levels(RULES.robustness$SELECTION))
pp_rob.list = foreach(i.subset = names.subset) %do%
{
if (i.subset != "Not selected")
{
if (i.subset == "All dataset") {
tab.subset = RULES.robustness
} else {
tab.subset = RULES.robustness[which(RULES.robustness$SELECTION == i.subset), ]
}
if (nrow(tab.subset) > 0)
{
## NO OF SPECIES
tab.noSp = tapply(X = as.numeric(tab.subset$SELECTED)
, INDEX = list(tab.subset$type
, tab.subset$percent
, tab.subset$rep)
, FUN = sum
, na.rm = TRUE)
tab.noSp = melt(tab.noSp)
colnames(tab.noSp) = c("type", "percent", "rep", "value")
tab.noSp$analysis = "no.sp"
tab.noSp$value = tab.noSp$value / length(sel.sp)
## SIMILAR SPECIES
tab.simSp = foreach(i.type = combi$type
, i.rep = combi$rep
, i.percent = combi$percent
, .combine = "rbind") %do%
{
tmp = tab.subset[which(tab.subset$type == i.type &
tab.subset$percent == i.percent &
tab.subset$rep == i.rep), ]
tmp.sp1 = tmp$species[which(tmp$SELECTED == TRUE)]
tmp.sp2 = tmp.sp1[which(tmp.sp1 %in% sel.sp)]
percent1 = length(tmp.sp2) / length(tmp.sp1)
percent2 = length(tmp.sp2) / length(sel.sp)
all.sp = union(sel.sp, tmp.sp1)
tmp.all = matrix(0, nrow = length(all.sp), ncol = 2, dimnames = list(all.sp, c("O", "S")))
tmp.all = as.data.frame(t(tmp.all))
tmp.all["O", sel.sp] = 1
tmp.all["S", tmp.sp1] = 1
beta.val = beta.pair(x = tmp.all)
return(data.frame(type = i.type
, percent = i.percent
, rep = i.rep
, analysis = c("percent", "all", names(beta.val))
, value = c(percent1, percent2, unlist(beta.val))
, stringsAsFactors = FALSE))
}
## PLOT
tab.plot = rbind(tab.noSp, tab.simSp)
tab.plot$analysis = factor(tab.plot$analysis, c("no.sp", "all", "percent"
, "beta.sor", "beta.sne", "beta.sim"))
tab.plot$percent_fac = factor(tab.plot$percent, seq(0, 1, 0.1))
tab.plot$percent_num = as.numeric(tab.plot$percent_fac)
pp_rob = ggplot(tab.plot, aes_string(y = "value", color = "type")) +
geom_boxplot(aes_string(x = "percent_fac"), na.rm = TRUE) +
geom_smooth(aes_string(x = "percent_num"), method = "loess", na.rm = TRUE) +
facet_wrap(~ analysis
, ncol = 3
, labeller = as_labeller(c("no.sp" = "a"
, "all" = "b"
, "percent" = "c"
, "beta.sor" = "d"
, "beta.sne" = "e"
, "beta.sim" = "f"))) +
scale_x_discrete(name = "\nPercentage of observations (%)"
, breaks = seq(0, 1, 0.2)
, labels = seq(0, 1, 0.2) * 100
, drop = FALSE) +
scale_y_continuous(name = "Percentage of dissimilarity (%) Percentage of similarity (%)\n"
, breaks = seq(0, 1, 0.1)
, labels = seq(0, 1, 0.1) * 100) +
scale_color_manual(guide = "none"
, values = c("releves" = "midnightblue"
, "sites" = "brown")) +
labs(title = paste0("STEP 2 : Selected dominant species - robustness(", i.subset, ")")
, subtitle = paste0("Selection is run on a subset S, keeping only a "
, "percentage of releves (blue) "
, "or sites (red) from original data set O :\n\n"
, " > a = number(S species) / number(O species) \n"
, " > b = number(S & O species) / number(O species) \n"
, " > c = number(S & O species) / number(S species) \n\n"
, " > d = (1 - Sorensen dissimilarity) \n"
, " > e = (1 - nestedness-fraction of Sorensen dissimilarity) \n"
, " > f = (1 - turnover-fraction of Sorensen dissimilarity) \n"
, "\n")) +
.getGraphics_theme() +
theme(axis.title = element_text(size = 12))
return(pp_rob)
}
}
}
names(pp_rob.list) = names.subset
pp_rob.list = pp_rob.list[names(pp_rob.list)[which(sapply(pp_rob.list, is.null) == FALSE)]]
## ----------------------------------------------------------------------
if (!is.null(pp_rob.list))
{
pdf(file = "PRE_FATE_DOMINANT_STEP_2_selectedSpecies_robustness.pdf"
, width = 10, height = 8)
for (pp in pp_rob.list) if (!is.null(pp)) plot(pp)
dev.off()
}
} ## opt.doRobustness
}
#############################################################################
cat("\n> Done!\n")
results = list(species.selected = sel.sp, tab.rules = RULES)
if (opt.doRobustness) {
results$tab.robustness = RULES.robustness
}
if (opt.doSitesSpecies) {
results$tab.dom.AB = tab.dom.AB
results$tab.dom.PA = tab.dom.PA
}
if (opt.doPlot) {
if (doRuleA && exists("pp_tot")) results$plot.A = pp_tot
if (doRuleB && exists("pp_B")) results$plot.B = list(abs = pp_B, rel = pp_B.rel)
if (doRuleC && exists("pp_hab")) results$plot.C = pp_hab
if (exists("pp_pco.list")) results$plot.pco = pp_pco.list
if (opt.doRobustness && exists("pp_rob.list")) results$plot.robustness = pp_rob.list
}
return(results)
}
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