R/Building.R
In pzylstra/frame_r: Fire Research and Modelling Environment
Defines functions
updateTraits
ausTraitTable
buildParams
buildSpeciesValues
buildStrataMeta
buildSiteMeta
paramBuilder
unitBuilder
speciesBuilder
strataBuilder
siteBuilder
Documented in
ausTraitTable
buildParams
buildSiteMeta
buildSpeciesValues
buildStrataMeta
paramBuilder
siteBuilder
speciesBuilder
strataBuilder
unitBuilder
updateTraits
#' Builds the dataframe site.meta from input tables
#'
#' @param site A dataframe with the six fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' slope - slope in degrees
#' wind - velocity in km/h
#' temp - ambient temperature deg. C
#' dfmc - moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' litter - weight in t/ha of fine dead organic material forming the O horizon
#' diameter - mean diameter of surface litter in m
#' fline - the fireline length in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right. Acceptable values
#' are t, f, or blank, where the outcome will be decided by the relative stratum heights.
#' @param a The number of the record for the site
siteBuilder <- function(site, Structure, a)
{
# CREATE site.meta
paramSite <- c('overlapping','overlapping','overlapping','overlapping','overlapping', 'fuelLoad',
'meanFuelDiameter','meanFinenessLeaves', 'fireLineLength', 'slope', 'temperature',
'deadFuelMoistureProp', 'windSpeed')
unitsSite <- c(NA, NA, NA, NA, NA, 't/ha', 'm', 'm', 'm', 'deg', 'degC', NA, 'km/h')
site.meta <- data.frame(matrix(NA, nrow=13, ncol = 5))
names(site.meta) <- c("stratum", "species", "param", "value", "units")
site.meta$param <- paramSite
site.meta$units <- unitsSite
# ENTER VARIABLES
site.meta$value[1] <- ifelse(Structure$ns_e[Structure$record==a]=='t',"near surface, elevated, overlapped",
ifelse(Structure$ns_e[Structure$record==a]=='f',"near surface, elevated, not overlapped",
"near surface, elevated, automatic"))
site.meta$value[2] <- ifelse(Structure$ns_m[Structure$record==a]=='t',"near surface, midstorey, overlapped",
ifelse(Structure$ns_m[Structure$record==a]=='f',"near surface, midstorey, not overlapped",
"near surface, midstorey, automatic"))
site.meta$value[3] <- ifelse(Structure$e_m[Structure$record==a]=='t',"elevated, midstorey, overlapped",
ifelse(Structure$e_m[Structure$record==a]=='f',"elevated, midstorey, not overlapped",
"elevated, midstorey, automatic"))
site.meta$value[4] <- ifelse(Structure$e_c[Structure$record==a]=='t',"elevated, canopy, overlapped",
ifelse(Structure$e_c[Structure$record==a]=='f',"elevated, canopy, not overlapped",
"elevated, canopy, automatic"))
site.meta$value[5] <- ifelse(Structure$m_c[Structure$record==a]=='t',"midstorey, canopy, overlapped",
ifelse(Structure$m_c[Structure$record==a]=='f',"midstorey, canopy, not overlapped",
"midstorey, canopy, automatic"))
site.meta$value[6] <- site$litter[site$record==a]
site.meta$value[7] <- if (is.null(site$diameter[site$record==a])) {
0.005
} else {
site$diameter[site$record==a]
}
site.meta$value[8] <- 0.00025
site.meta$value[9] <- site$fLine[site$record==a]
site.meta$value[10] <- site$slope[site$record==a]
site.meta$value[11] <- site$temp[site$record==a]
site.meta$value[12] <- site$dfmc[site$record==a]
site.meta$value[13] <- site$wind[site$record==a]
return(site.meta)
}
#' Builds the dataframe strata.meta
#'
#' @param Structure
#' @param Flora
#' @param a
#'
#' @return dataframe
#'
strataBuilder <- function(Structure, Flora, a)
{
# Collect subsets for record
st <- Structure[Structure$record==a,]
fl <- Flora[Flora$record==a,]
# CREATE strata.meta
strata.meta <- data.frame(matrix(NA, nrow=8, ncol = 5))
names(strata.meta) <- c("stratum", "species", "param", "value", "units")
# Fill levelNames
strata.meta$param[1] <- "levelName"
strata.meta$value[1] <- "near surface"
strata.meta$param[2] <- "plantSeparation"
strata.meta$value[2] <- st$NS[1]
strata.meta$units[2] <- "m"
strata.meta$param[3] <- "levelName"
strata.meta$value[3] <- "elevated"
strata.meta$param[4] <- "plantSeparation"
strata.meta$value[4] <- st$El[1]
strata.meta$units[4] <- "m"
strata.meta$param[5] <- "levelName"
strata.meta$value[5] <- "midstorey"
strata.meta$param[6] <- "plantSeparation"
strata.meta$value[6] <- st$Mid[1]
strata.meta$units[6] <- "m"
strata.meta$param[7] <- "levelName"
strata.meta$value[7] <- "canopy"
strata.meta$param[8] <- "plantSeparation"
strata.meta$value[8] <- st$Can[1]
strata.meta$units[8] <- "m"
# Find empty strata and remove them
deleteVector <- vector()
for (ro in 2:8) {
if(is.na(strata.meta$value[ro])){
deleteVector<- c(deleteVector, ro-1, ro)
}
}
if(length(deleteVector)>0){
strata.meta <- strata.meta[ -deleteVector, ]}
# Number strata
rows <- as.numeric(nrow(strata.meta))
num <- 0.5
for (ro in 1:rows) {
numb <- ceiling(num)
strata.meta$stratum[ro] <- numb
num = num + 0.5
}
return(strata.meta)
}
#' Builds the dataframe species.values
#'
#' @param Flora
#' @param site
#' @param a
#'
#' @return dataframe
speciesBuilder <- function(Flora, site, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a,]
si <- site[site$record==a,]
# CREATE species.values
ro <- as.numeric(nrow(fl))
species.values <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.values) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.values$stratum <- as.numeric(fl$stratum)
species.values$name <- as.character(fl$species)
species.values$liveLeafMoisture <- fl$moisture
species.values$hc <- fl$base
species.values$hp <- fl$top
species.values$he <- fl$he
species.values$ht <- fl$ht
species.values$w <- fl$w
species.values$clumpDiameter <- pmin((fl$top-fl$base),fl$w)*fl$clump
species.values$clumpSeparation <- fl$openness*species.values$clumpDiameter
species.values$composition <- as.numeric(fl$comp)
species.values$deadLeafMoisture <- si$dfmc
species.values <- species.values[order(species.values$stratum),]
species.values$species <- as.numeric(c(1:ro))
# Calculate gaps
for(a in 1:ro) {
if(is.na(species.values$he[a])){
species.values$he[a] <- species.values$hc[a]
}
if(is.na(species.values$ht[a])){
species.values$ht[a] <- species.values$hp[a]
}
if(is.na(species.values$liveLeafMoisture[a])){
species.values$liveLeafMoisture[a] <- 1
}
}
# Ensure strata are numbered consecutively
stratCurr <- as.data.frame(unique(species.values$stratum))
names(stratCurr) <- c("orig")
stratCurr$cor <- 1:as.numeric(nrow(stratCurr))
#Check values
for(b in 1:as.numeric(nrow(stratCurr))) {
species.values$stratum[species.values$stratum == stratCurr$orig[b]] <- stratCurr$cor[b]
}
return(species.values)
}
#' Formats units for parameter files
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' species - the name of the species, which will call trait data from 'default.species.params'
#' moisture - the moisture content of the species in whole numbers (eg 1 for 100 percent ODW)
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum. If absent, all species will be considered equally
#' hc, he, ht, hp & w - canopy dimensions for that species (m)
#' clump - mean ratio of clump diameter to crown diameter
#' openness - proportion of plant canopy occupied by gaps between clumps
#' @param a The record number for which to build the table
unitBuilder <- function(Flora, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a,]
fl <- fl[fl$species != "Litter",] %>%
arrange(stratum)
# CREATE species.units
ro <- as.numeric(nrow(fl))
species.units <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.units) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.units$stratum <- fl$stratum
species.units$species <- c(1:ro)
species.units$clumpDiameter <- "m"
species.units$clumpSeparation <- "m"
species.units$hc <- "m"
species.units$he <- "m"
species.units$hp <- "m"
species.units$ht <- "m"
species.units$w <- "m"
return(species.units)
}
#' Constructs parameter files from formatted datasets
#'
#' @param site A dataframe with the six fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' slope - slope in degrees
#' wind - velocity in km/h
#' temp - ambient temperature deg. C
#' dfmc - moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' litter - weight in t/ha of fine dead organic material forming the O horizon
#' diameter - mean diameter of surface litter in m
#' fline - the fireline length in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right. Acceptable values
#' are t, f, or blank, where the outcome will be decided by the relative stratum heights.
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' species - the name of the species, which will call trait data from 'default.species.params'
#' moisture - the moisture content of the species in whole numbers (eg 1 for 100 percent ODW)
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum. If absent, all species will be considered equally
#' hc, he, ht, hp & w - canopy dimensions for that species (m)
#' clump - mean ratio of clump diameter to crown diameter
#' openness - proportion of plant canopy occupied by gaps between clumps
#' @param default.species.params Leaf traits database
#' @param a The record number for which to build the table
#' @export
#' @examples
#' record <- 1
#' data(site)
#' data(structure)
#' data(flora)
#' data(traits)
#' base.params <- paramBuilder(site, structure, flora, traits, record)
paramBuilder <- function(site, Structure, Flora, default.species.params, a)
{
# Construct component tables
site.meta <- siteBuilder(site, Structure, a)
strata.meta <- strataBuilder(Structure, Flora, a)
species.values <- speciesBuilder(Flora, site, a)
species.units <- unitBuilder(Flora, a)
# Build parameter file
components <- list(site.meta, strata.meta, species.values, species.units)
names(components) <- c("site.meta", "strata.meta", "species.values", "species.units")
param <- ffm_assemble_table(components)
# Find weighted mean of crown widths per stratum
species.values$weightedW <- species.values$composition * species.values$w
ww <- species.values%>%
select(stratum, composition, weightedW)%>%
group_by(stratum) %>%
summarize_all(sum) %>%
mutate(mw = weightedW/composition)
# Ensure separation is greater than weighted mean of crown widths
for (stNum in 1:max(ww$stratum)) {
sep <- filter(strata.meta, stratum == stNum)
param <- ffm_set_stratum_param(param, stNum, "plantSeparation",
pmax(as.numeric(sep$value[2]),ww$mw[stNum]))
}
# Change species param leafForm to characters
default.species.params$leafForm <- as.character(default.species.params$leafForm)
# Fill empty traits
param <- ffm_complete_params(param,default.species.params)
return(param)
}
#' Builds the dataframe site.meta from input tables
#' Building from MEE inputs requires function siteBuilder
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param a The number of the record for the site
#' @param fLine The fireline length in m
#' @param slope slope in degrees
#' @param temp Ambient temperature deg. C
#' @param dfmc Moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' @param wind Velocity in km/h
buildSiteMeta <- function(Structure, Flora, a, fLine = 100, slope = 0,
temp = 30, dfmc = 0.1, wind = 10)
{
# CREATE site.meta
paramSite <- c('overlapping','overlapping','overlapping','overlapping','overlapping', 'fuelLoad',
'meanFuelDiameter','meanFinenessLeaves', 'fireLineLength', 'slope', 'temperature',
'deadFuelMoistureProp', 'windSpeed')
unitsSite <- c(NA, NA, NA, NA, NA, 't/ha', 'm', 'm', 'm', 'deg', 'degC', NA, 'km/h')
site.meta <- data.frame(matrix(NA, nrow=13, ncol = 5))
names(site.meta) <- c("stratum", "species", "param", "value", "units")
site.meta$param <- paramSite
site.meta$units <- unitsSite
# ENTER VARIABLES
site.meta$value[1] <- ifelse(Structure$ns_e[Structure$record==a]==TRUE,"near surface, elevated, overlapped",
ifelse(Structure$ns_e[Structure$record==a]==FALSE,"near surface, elevated, not overlapped",
"near surface, elevated, automatic"))
site.meta$value[2] <- ifelse(Structure$ns_m[Structure$record==a]==TRUE,"near surface, midstorey, overlapped",
ifelse(Structure$ns_m[Structure$record==a]==FALSE,"near surface, midstorey, not overlapped",
"near surface, midstorey, automatic"))
site.meta$value[3] <- ifelse(Structure$e_m[Structure$record==a]==TRUE,"elevated, midstorey, overlapped",
ifelse(Structure$e_m[Structure$record==a]==FALSE,"elevated, midstorey, not overlapped",
"elevated, midstorey, automatic"))
site.meta$value[4] <- ifelse(Structure$e_c[Structure$record==a]==TRUE,"elevated, canopy, overlapped",
ifelse(Structure$e_c[Structure$record==a]==FALSE,"elevated, canopy, not overlapped",
"elevated, canopy, automatic"))
site.meta$value[5] <- ifelse(Structure$m_c[Structure$record==a]==TRUE,"midstorey, canopy, overlapped",
ifelse(Structure$m_c[Structure$record==a]==FALSE,"midstorey, canopy, not overlapped",
"midstorey, canopy, automatic"))
site.meta$value[6] <- as.numeric(Flora$weight[Flora$record==a & Flora$species=="Litter"])
site.meta$value[7] <- if (is.null(as.numeric(Flora$diameter[Flora$record==a & Flora$species=="Litter"]))) {
0.005
} else {
as.numeric(Flora$diameter[Flora$record==a & Flora$species=="Litter"])
}
site.meta$value[8] <- 0.00025
site.meta$value[9] <- fLine
site.meta$value[10] <- slope
site.meta$value[11] <- temp
site.meta$value[12] <- dfmc
site.meta$value[13] <- wind
return(site.meta)
}
#' Builds the dataframe strata.meta from input tables
#' Compatible with MEE inputs
#'
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param a The number of the record for the site
buildStrataMeta <- function(Structure, a)
{
# Collect subsets for record
st <- Structure[Structure$record==a,]
# CREATE strata.meta
strata.meta <- data.frame(matrix(NA, nrow=8, ncol = 5))
names(strata.meta) <- c("stratum", "species", "param", "value", "units")
# Fill levelNames
strata.meta$param[1] <- "levelName"
strata.meta$value[1] <- "near surface"
strata.meta$param[2] <- "plantSeparation"
strata.meta$value[2] <- st$NS[1]
strata.meta$units[2] <- "m"
strata.meta$param[3] <- "levelName"
strata.meta$value[3] <- "elevated"
strata.meta$param[4] <- "plantSeparation"
strata.meta$value[4] <- st$El[1]
strata.meta$units[4] <- "m"
strata.meta$param[5] <- "levelName"
strata.meta$value[5] <- "midstorey"
strata.meta$param[6] <- "plantSeparation"
strata.meta$value[6] <- st$Mid[1]
strata.meta$units[6] <- "m"
strata.meta$param[7] <- "levelName"
strata.meta$value[7] <- "canopy"
strata.meta$param[8] <- "plantSeparation"
strata.meta$value[8] <- st$Can[1]
strata.meta$units[8] <- "m"
# Find empty strata and remove them
deleteVector <- vector()
for (ro in 2:8) {
if(is.na(strata.meta$value[ro])){
deleteVector<- c(deleteVector, ro-1, ro)
}
}
if(length(deleteVector)>0){
strata.meta <- strata.meta[ -deleteVector, ]}
# Number strata
rows <- as.numeric(nrow(strata.meta))
num <- 0.5
for (ro in 1:rows) {
numb <- ceiling(num)
strata.meta$stratum[ro] <- numb
num = num + 0.5
}
return(strata.meta)
}
#' Builds the dataframe species.values from input tables
#' Building from MEE inputs requires function speciesBuilder
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param default.species.params Plant traits database
buildSpeciesValues <- function(Flora, default.species.params, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a & Flora$species != "Litter",] %>%
mutate("name" = species) %>%
left_join(default.species.params, by = "name")
if (exists("maxW", where = as.environment(fl))) {
fl <- fl %>%
select(species, stratum, comp, base, top, he, ht, w, moisture, C.C_rat, G.C_rat, maxW)
} else {
fl <- fl %>%
select(species, stratum, comp, base, top, he, ht, w, moisture, C.C_rat, G.C_rat)
}
# CREATE species.values
ro <- as.numeric(nrow(fl))
species.values <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.values) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.values$stratum <- as.numeric(fl$stratum)
species.values$name <- as.character(fl$species)
species.values$liveLeafMoisture <- fl$moisture
species.values$hc <- fl$base
species.values$hp <- fl$top
species.values$he <- fl$he
species.values$ht <- fl$ht
species.values$w <- fl$w
if (exists("maxW", where = as.environment(fl))) {
species.values$clumpDiameter <- pmin(pmin((as.numeric(fl$top)-as.numeric(fl$base)), as.numeric(fl$w))*fl$C.C_rat, as.numeric(fl$maxW))
} else {
species.values$clumpDiameter <- pmin((as.numeric(fl$top)-as.numeric(fl$base)), as.numeric(fl$w))*fl$C.C_rat
}
species.values$clumpSeparation <- fl$G.C_rat*species.values$clumpDiameter
species.values$composition <- as.numeric(fl$comp)
species.values <- species.values[order(species.values$stratum),]
species.values$species <- as.numeric(c(1:ro))
# Calculate gaps
for(a in 1:ro) {
if(is.na(species.values$he[a])){
species.values$he[a] <- species.values$hc[a]
}
if(is.na(species.values$ht[a])){
species.values$ht[a] <- species.values$hp[a]
}
if(is.na(species.values$liveLeafMoisture[a])){
species.values$liveLeafMoisture[a] <- 1
}
}
# Ensure strata are numbered consecutively
stratCurr <- as.data.frame(unique(species.values$stratum))
names(stratCurr) <- c("orig")
stratCurr$cor <- 1:as.numeric(nrow(stratCurr))
#Check values
for(b in 1:as.numeric(nrow(stratCurr))) {
species.values$stratum[species.values$stratum == stratCurr$orig[b]] <- stratCurr$cor[b]
}
return(species.values)
}
#' Constructs parameter files from imported tables
#'
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param a The number of the record for the site
#' @param fLine The fireline length in m
#' @param slope slope in degrees
#' @param temp Ambient temperature deg. C
#' @param dfmc Moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' @param wind Velocity in km/h
#' @param default.species.params Plant traits database
#' @param a The record number for which to build the table
#' @export
buildParams <- function(Structure, Flora, default.species.params, a,
fLine = 100, slope = 0, temp = 30, dfmc = 0.1, wind = 10)
{
# Remove empty species, leaving litter
Flora <- Flora[which(Flora$species == "Litter" | Flora$comp != 0), ]
# Construct component tables
site.meta <- buildSiteMeta(Structure = Structure, Flora = Flora, a = a, fLine = fLine, slope = slope,
temp = temp, dfmc = dfmc, wind = wind)
strata.meta <- buildStrataMeta(Structure, a)
species.values <- buildSpeciesValues(Flora, default.species.params, a)
species.values$deadLeafMoisture <- dfmc
species.units <- unitBuilder(Flora, a)
# Build parameter file
components <- list(site.meta, strata.meta, species.values, species.units)
names(components) <- c("site.meta", "strata.meta", "species.values", "species.units")
param <- ffm_assemble_table(components)
# Find weighted mean of crown widths per stratum
species.values$weightedW <- species.values$composition * as.numeric(species.values$w)
ww <- species.values%>%
select(stratum, composition, weightedW)%>%
group_by(stratum) %>%
summarize_all(sum) %>%
mutate(mw = weightedW/composition)
# Ensure separation is greater than weighted mean of crown widths
for (stNum in 1:max(ww$stratum)) {
sep <- filter(strata.meta, stratum == stNum)
param <- ffm_set_stratum_param(param, stNum, "plantSeparation",
pmax(as.numeric(sep$value[2]),ww$mw[stNum]))
}
# Change species param leafForm to characters
default.species.params$leafForm <- as.character(default.species.params$leafForm)
# Fill empty traits
param <- ffm_complete_params(param,default.species.params)
return(param)
}
#' Constructs a default.species.params table using traits available in the austraits database
#' Collects traits from ausTraits for use in shade and fire effects modelling
#'
#' @param version Version of austraits
#' @param path
#' @param shadeTolerance The Whole Plant Light Compensation Point below which
#' plants are considered shade tolerant
#'
#' @return list of tables
#' @export
#' @examples
#' Traits <- ausTraitTable(version = "3.0.2", path = "data/austraits")
#'
ausTraitTable <- function(version = "3.0.2", path = "data/austraits", shadeTolerance = 3) {
Austraits <- austraits::load_austraits(version = version, path = path)
# Create tables of each trait in FRaME units
leaf_length <- (austraits::extract_trait(Austraits, "leaf_length"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafLength = value/1000) %>%
select(taxon_name, leafLength)
leaf_width <- (austraits::extract_trait(Austraits, "leaf_width"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafWidth = value/1000) %>%
select(taxon_name, leafWidth)
leaf_shape <- (austraits::extract_trait(Austraits, "leaf_shape"))$traits
leaf_shape$leafForm <- case_when(leaf_shape$value == "needle terete" ~ "Round",
leaf_shape$value == "terete" ~ "Round",
leaf_shape$value == "subulate terete" ~ "Round",
leaf_shape$value == "needle" ~ "Round",
leaf_shape$value == "linear terete" ~ "Round",
leaf_shape$value == "clavate linear terete" ~ "Round",
leaf_shape$value == "terete_compressed" ~ "Round",
leaf_shape$value == "scale terete" ~ "Round",
leaf_shape$value == "trigonous_terete" ~ "Round",
leaf_shape$value == "half_terete" ~ "Round",
leaf_shape$value == "terete_compressed" ~ "Round",
TRUE ~ "Flat")
leaf_shape <- leaf_shape %>% select(taxon_name, leafForm)
leaf_thickness <- (austraits::extract_trait(Austraits, "leaf_thickness"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafThickness = value/1000) %>%
select(taxon_name, leafThickness)
# List species
tNames <- unique(Austraits[[1]]$taxon_name)
T_names <- tNames[!grepl("\\d+", tNames)]
traits <- data.frame(taxon_name = T_names)
list_of_dfs <- list(traits, leaf_length, leaf_width, leaf_shape, leaf_thickness)
result <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name") %>%
mutate(name = taxon_name,
propDead = 0) %>%
select(name, propDead, leafForm, leafThickness, leafWidth, leafLength) %>%
mutate(leafSeparation = NA,
stemOrder = NA,
ignitionTemp = NA,
moisture = 1,
G.C_rat = NA,
C.C_rat = NA)
result <- arrange(result, name)
# Secondary traits
traitNames <- data.frame(taxon_name = T_names)
#___________________________________
# Shade tolerance
Dark_respiration <- (austraits::extract_trait(Austraits, "leaf_dark_respiration_per_dry_mass"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(Dark_respiration = value,
WPLCP = (390*value)+0.78,
Shade = WPLCP < shadeTolerance) %>%
select(taxon_name, WPLCP, Shade)
# Approximate from area measurement
respArea <- (austraits::extract_trait(Austraits, "leaf_dark_respiration_per_area"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median)
list_of_dfs <- list(traitNames, Dark_respiration, respArea)
sh <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name")
sh <- sh[complete.cases(sh),]
LM<-lm(sh$value ~ sh$WPLCP)
LMSum <- base::summary(LM)
eqThres <- LMSum$coefficients[1] + LMSum$coefficients[2] * shadeTolerance
respArea <- respArea %>%
mutate(Shade = value < eqThres)
ShadeTolerance <- respArea[!(respArea$taxon_name %in% Dark_respiration$taxon_name), ]%>%
right_join(Dark_respiration, by = c("taxon_name", "Shade")) %>%
select(taxon_name, Shade)
list_of_dfs <- list(traitNames, ShadeTolerance)
resultII <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name") %>%
mutate(Genus = word(taxon_name, 1),
name = taxon_name)
# Generalise genera
genera <- resultII[complete.cases(resultII), ] %>%
group_by(Genus)%>%
summarise(Shade = as.logical(round(mean(Shade),0))) %>%
mutate(name = Genus) %>%
select(name, Shade)
resultII <- resultII %>%
select(name, Shade)%>%
full_join(genera, by = c("name", "Shade"))
resultII <- arrange(resultII, name)
remList <- vector()
for (r in 2:nrow(resultII)) {
if (tolower(resultII$name[r]) == tolower(resultII$name[r-1])) {
remList[length(remList)+1] <- r-1
}
}
if (length(remList)>0) {
resultII <- resultII[-remList,]
}
return(list(result, resultII))
}
#' Checks traits table for column names and adds data from a new table
#'
#' @param traits Existing trait table
#' @param traitsNew Table containing new trait data
#' @param deleteReplicates If set to TRUE, retains the first mention of a species, then removes all following mentions
#' @param fill If set to TRUE, fills empty numeric values with mean values of the genus
#'
#' @return table
#' @export
#' @examples
#' Traits <- ausTraitTable(version = "3.0.2", path = "data/austraits")
#' TraitsNew <- read.csv("Traits.csv")
#' Tr <- updateTraits(traits = Traits, traitsNew = TraitsNew, deleteReplicates = TRUE, fill = TRUE)
#'
updateTraits <- function(traits, traitsNew, deleteReplicates = TRUE, fill = TRUE) {
# Ensure traits table has all necessary columns
cols_to_check <- c("name", "propDead", "leafForm", "leafThickness", "leafWidth",
"leafLength", "leafSeparation", "stemOrder", "ignitionTemp", "moisture", "G.C_rat", "C.C_rat")
for (colName in cols_to_check) {
if(!colName %in% names(traits)) {
traits$colName == ""
}
}
# Add any missing species to existing list
missing_species <- unique(traitsNew$name)[!(unique(traitsNew$name) %in% unique(traits$name))]
newSp <- traitsNew[traitsNew$name %in% missing_species, ]
traits <- rbind(traits,newSp)
traits <- arrange(traits, name)
newCols <- names(traitsNew)
for (sp in unique(traitsNew$name)) {
for (colName in newCols) {
if(!is.na(traitsNew[traitsNew$name == sp,colName])){traits[traits$name == sp,colName] <- traitsNew[traitsNew$name == sp,colName]}
}
}
# Generalise genera
traits <- traits %>%
mutate(Genus = word(name, 1),
leafThickness = as.numeric(leafThickness),
leafWidth = as.numeric(leafWidth),
leafLength = as.numeric(leafLength),
leafSeparation = as.numeric(leafSeparation),
stemOrder = as.numeric(stemOrder),
ignitionTemp = as.numeric(ignitionTemp),
moisture = as.numeric(moisture),
G.C_rat = as.numeric(G.C_rat),
C.C_rat = as.numeric(C.C_rat))
genera <- traits %>%
group_by(Genus)%>%
summarise_if(is.numeric, ~mean(., na.rm=TRUE)) %>%
mutate(leafForm = "Flat")
# Find most common leaf form
for (g in 1:nrow(genera)) {
genSub <- traits[traits$Genus == genera$Genus[g],]
Round <- nrow(genSub[genSub$leafForm == "Round",])
Flat <- nrow(genSub[genSub$leafForm == "Flat",])
if (Round > Flat) {
genera$leafForm[g] <- "Round"
}
}
# Fill empty traits with means if chosen
if (fill) {
traits <- left_join(traits, genera, by = "Genus")%>%
mutate(propDead = ifelse(!is.na(propDead.x), propDead.x, propDead.y),
leafThickness = ifelse(!is.na(leafThickness.x), leafThickness.x, leafThickness.y),
leafForm = ifelse(!is.na(leafForm.x), leafForm.x, leafForm.y),
leafWidth = ifelse(!is.na(leafWidth.x), leafWidth.x,leafWidth.y),
leafLength = ifelse(!is.na(leafLength.x), leafLength.x, leafLength.y),
leafSeparation= ifelse(!is.na(leafSeparation.x), leafSeparation.x,leafSeparation.y),
stemOrder = ifelse(!is.na(stemOrder.x), stemOrder.x, stemOrder.y),
ignitionTemp = ifelse(!is.na(ignitionTemp.x), ignitionTemp.x, ignitionTemp.y),
moisture = ifelse(!is.na(moisture.x), moisture.x, moisture.y),
G.C_rat = ifelse(!is.na(G.C_rat.x), G.C_rat.x, G.C_rat.y),
C.C_rat = ifelse(!is.na(C.C_rat.x), C.C_rat.x, C.C_rat.y))
}
traits <- traits %>%
dplyr::select(name, propDead, leafForm, leafThickness, leafWidth, leafLength, leafSeparation, stemOrder, ignitionTemp, moisture, G.C_rat, C.C_rat)
genera <- genera %>%
mutate(name = Genus) %>%
dplyr::select(name, propDead, leafForm, leafThickness, leafWidth, leafLength, leafSeparation, stemOrder, ignitionTemp, moisture, G.C_rat, C.C_rat)
traits <- genera %>%
rbind(traits)
traits$propDead[which(is.nan(traits$propDead))] <- NA
traits$leafThickness[which(is.nan(traits$leafThickness))] <- NA
traits$leafWidth[which(is.nan(traits$leafWidth))] <- NA
traits$leafLength[which(is.nan(traits$leafLength))] <- NA
traits$leafSeparation[which(is.nan(traits$leafSeparation))] <- NA
traits$stemOrder[which(is.nan(traits$stemOrder))] <- NA
traits$ignitionTemp[which(is.nan(traits$ignitionTemp))] <- NA
traits$moisture[which(is.nan(traits$moisture))] <- NA
traits$G.C_rat[which(is.nan(traits$G.C_rat))] <- NA
traits$C.C_rat[which(is.nan(traits$C.C_rat))] <- NA
traits <- arrange(traits,name)
if (deleteReplicates) {
# Check for repeated species
traits <- arrange(traits, name)
remList <- vector()
for (r in 2:nrow(traits)) {
if (tolower(traits$name[r]) == tolower(traits$name[r-1])) {
remList[length(remList)+1] <- r
}
}
if (length(remList)>0) {
traits <- traits[-remList,]
cat(length(remList), "duplicate species were removed")
}
}
return(traits)
}
pzylstra/frame_r documentation built on Nov. 12, 2023, 1:55 a.m.
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Defines functions updateTraits ausTraitTable buildParams buildSpeciesValues buildStrataMeta buildSiteMeta paramBuilder unitBuilder speciesBuilder strataBuilder siteBuilder
Documented in ausTraitTable buildParams buildSiteMeta buildSpeciesValues buildStrataMeta paramBuilder siteBuilder speciesBuilder strataBuilder unitBuilder updateTraits
#' Builds the dataframe site.meta from input tables
#'
#' @param site A dataframe with the six fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' slope - slope in degrees
#' wind - velocity in km/h
#' temp - ambient temperature deg. C
#' dfmc - moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' litter - weight in t/ha of fine dead organic material forming the O horizon
#' diameter - mean diameter of surface litter in m
#' fline - the fireline length in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right. Acceptable values
#' are t, f, or blank, where the outcome will be decided by the relative stratum heights.
#' @param a The number of the record for the site
siteBuilder <- function(site, Structure, a)
{
# CREATE site.meta
paramSite <- c('overlapping','overlapping','overlapping','overlapping','overlapping', 'fuelLoad',
'meanFuelDiameter','meanFinenessLeaves', 'fireLineLength', 'slope', 'temperature',
'deadFuelMoistureProp', 'windSpeed')
unitsSite <- c(NA, NA, NA, NA, NA, 't/ha', 'm', 'm', 'm', 'deg', 'degC', NA, 'km/h')
site.meta <- data.frame(matrix(NA, nrow=13, ncol = 5))
names(site.meta) <- c("stratum", "species", "param", "value", "units")
site.meta$param <- paramSite
site.meta$units <- unitsSite
# ENTER VARIABLES
site.meta$value[1] <- ifelse(Structure$ns_e[Structure$record==a]=='t',"near surface, elevated, overlapped",
ifelse(Structure$ns_e[Structure$record==a]=='f',"near surface, elevated, not overlapped",
"near surface, elevated, automatic"))
site.meta$value[2] <- ifelse(Structure$ns_m[Structure$record==a]=='t',"near surface, midstorey, overlapped",
ifelse(Structure$ns_m[Structure$record==a]=='f',"near surface, midstorey, not overlapped",
"near surface, midstorey, automatic"))
site.meta$value[3] <- ifelse(Structure$e_m[Structure$record==a]=='t',"elevated, midstorey, overlapped",
ifelse(Structure$e_m[Structure$record==a]=='f',"elevated, midstorey, not overlapped",
"elevated, midstorey, automatic"))
site.meta$value[4] <- ifelse(Structure$e_c[Structure$record==a]=='t',"elevated, canopy, overlapped",
ifelse(Structure$e_c[Structure$record==a]=='f',"elevated, canopy, not overlapped",
"elevated, canopy, automatic"))
site.meta$value[5] <- ifelse(Structure$m_c[Structure$record==a]=='t',"midstorey, canopy, overlapped",
ifelse(Structure$m_c[Structure$record==a]=='f',"midstorey, canopy, not overlapped",
"midstorey, canopy, automatic"))
site.meta$value[6] <- site$litter[site$record==a]
site.meta$value[7] <- if (is.null(site$diameter[site$record==a])) {
0.005
} else {
site$diameter[site$record==a]
}
site.meta$value[8] <- 0.00025
site.meta$value[9] <- site$fLine[site$record==a]
site.meta$value[10] <- site$slope[site$record==a]
site.meta$value[11] <- site$temp[site$record==a]
site.meta$value[12] <- site$dfmc[site$record==a]
site.meta$value[13] <- site$wind[site$record==a]
return(site.meta)
}
#' Builds the dataframe strata.meta
#'
#' @param Structure
#' @param Flora
#' @param a
#'
#' @return dataframe
#'
strataBuilder <- function(Structure, Flora, a)
{
# Collect subsets for record
st <- Structure[Structure$record==a,]
fl <- Flora[Flora$record==a,]
# CREATE strata.meta
strata.meta <- data.frame(matrix(NA, nrow=8, ncol = 5))
names(strata.meta) <- c("stratum", "species", "param", "value", "units")
# Fill levelNames
strata.meta$param[1] <- "levelName"
strata.meta$value[1] <- "near surface"
strata.meta$param[2] <- "plantSeparation"
strata.meta$value[2] <- st$NS[1]
strata.meta$units[2] <- "m"
strata.meta$param[3] <- "levelName"
strata.meta$value[3] <- "elevated"
strata.meta$param[4] <- "plantSeparation"
strata.meta$value[4] <- st$El[1]
strata.meta$units[4] <- "m"
strata.meta$param[5] <- "levelName"
strata.meta$value[5] <- "midstorey"
strata.meta$param[6] <- "plantSeparation"
strata.meta$value[6] <- st$Mid[1]
strata.meta$units[6] <- "m"
strata.meta$param[7] <- "levelName"
strata.meta$value[7] <- "canopy"
strata.meta$param[8] <- "plantSeparation"
strata.meta$value[8] <- st$Can[1]
strata.meta$units[8] <- "m"
# Find empty strata and remove them
deleteVector <- vector()
for (ro in 2:8) {
if(is.na(strata.meta$value[ro])){
deleteVector<- c(deleteVector, ro-1, ro)
}
}
if(length(deleteVector)>0){
strata.meta <- strata.meta[ -deleteVector, ]}
# Number strata
rows <- as.numeric(nrow(strata.meta))
num <- 0.5
for (ro in 1:rows) {
numb <- ceiling(num)
strata.meta$stratum[ro] <- numb
num = num + 0.5
}
return(strata.meta)
}
#' Builds the dataframe species.values
#'
#' @param Flora
#' @param site
#' @param a
#'
#' @return dataframe
speciesBuilder <- function(Flora, site, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a,]
si <- site[site$record==a,]
# CREATE species.values
ro <- as.numeric(nrow(fl))
species.values <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.values) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.values$stratum <- as.numeric(fl$stratum)
species.values$name <- as.character(fl$species)
species.values$liveLeafMoisture <- fl$moisture
species.values$hc <- fl$base
species.values$hp <- fl$top
species.values$he <- fl$he
species.values$ht <- fl$ht
species.values$w <- fl$w
species.values$clumpDiameter <- pmin((fl$top-fl$base),fl$w)*fl$clump
species.values$clumpSeparation <- fl$openness*species.values$clumpDiameter
species.values$composition <- as.numeric(fl$comp)
species.values$deadLeafMoisture <- si$dfmc
species.values <- species.values[order(species.values$stratum),]
species.values$species <- as.numeric(c(1:ro))
# Calculate gaps
for(a in 1:ro) {
if(is.na(species.values$he[a])){
species.values$he[a] <- species.values$hc[a]
}
if(is.na(species.values$ht[a])){
species.values$ht[a] <- species.values$hp[a]
}
if(is.na(species.values$liveLeafMoisture[a])){
species.values$liveLeafMoisture[a] <- 1
}
}
# Ensure strata are numbered consecutively
stratCurr <- as.data.frame(unique(species.values$stratum))
names(stratCurr) <- c("orig")
stratCurr$cor <- 1:as.numeric(nrow(stratCurr))
#Check values
for(b in 1:as.numeric(nrow(stratCurr))) {
species.values$stratum[species.values$stratum == stratCurr$orig[b]] <- stratCurr$cor[b]
}
return(species.values)
}
#' Formats units for parameter files
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' species - the name of the species, which will call trait data from 'default.species.params'
#' moisture - the moisture content of the species in whole numbers (eg 1 for 100 percent ODW)
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum. If absent, all species will be considered equally
#' hc, he, ht, hp & w - canopy dimensions for that species (m)
#' clump - mean ratio of clump diameter to crown diameter
#' openness - proportion of plant canopy occupied by gaps between clumps
#' @param a The record number for which to build the table
unitBuilder <- function(Flora, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a,]
fl <- fl[fl$species != "Litter",] %>%
arrange(stratum)
# CREATE species.units
ro <- as.numeric(nrow(fl))
species.units <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.units) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.units$stratum <- fl$stratum
species.units$species <- c(1:ro)
species.units$clumpDiameter <- "m"
species.units$clumpSeparation <- "m"
species.units$hc <- "m"
species.units$he <- "m"
species.units$hp <- "m"
species.units$ht <- "m"
species.units$w <- "m"
return(species.units)
}
#' Constructs parameter files from formatted datasets
#'
#' @param site A dataframe with the six fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' slope - slope in degrees
#' wind - velocity in km/h
#' temp - ambient temperature deg. C
#' dfmc - moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' litter - weight in t/ha of fine dead organic material forming the O horizon
#' diameter - mean diameter of surface litter in m
#' fline - the fireline length in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right. Acceptable values
#' are t, f, or blank, where the outcome will be decided by the relative stratum heights.
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' species - the name of the species, which will call trait data from 'default.species.params'
#' moisture - the moisture content of the species in whole numbers (eg 1 for 100 percent ODW)
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum. If absent, all species will be considered equally
#' hc, he, ht, hp & w - canopy dimensions for that species (m)
#' clump - mean ratio of clump diameter to crown diameter
#' openness - proportion of plant canopy occupied by gaps between clumps
#' @param default.species.params Leaf traits database
#' @param a The record number for which to build the table
#' @export
#' @examples
#' record <- 1
#' data(site)
#' data(structure)
#' data(flora)
#' data(traits)
#' base.params <- paramBuilder(site, structure, flora, traits, record)
paramBuilder <- function(site, Structure, Flora, default.species.params, a)
{
# Construct component tables
site.meta <- siteBuilder(site, Structure, a)
strata.meta <- strataBuilder(Structure, Flora, a)
species.values <- speciesBuilder(Flora, site, a)
species.units <- unitBuilder(Flora, a)
# Build parameter file
components <- list(site.meta, strata.meta, species.values, species.units)
names(components) <- c("site.meta", "strata.meta", "species.values", "species.units")
param <- ffm_assemble_table(components)
# Find weighted mean of crown widths per stratum
species.values$weightedW <- species.values$composition * species.values$w
ww <- species.values%>%
select(stratum, composition, weightedW)%>%
group_by(stratum) %>%
summarize_all(sum) %>%
mutate(mw = weightedW/composition)
# Ensure separation is greater than weighted mean of crown widths
for (stNum in 1:max(ww$stratum)) {
sep <- filter(strata.meta, stratum == stNum)
param <- ffm_set_stratum_param(param, stNum, "plantSeparation",
pmax(as.numeric(sep$value[2]),ww$mw[stNum]))
}
# Change species param leafForm to characters
default.species.params$leafForm <- as.character(default.species.params$leafForm)
# Fill empty traits
param <- ffm_complete_params(param,default.species.params)
return(param)
}
#' Builds the dataframe site.meta from input tables
#' Building from MEE inputs requires function siteBuilder
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param a The number of the record for the site
#' @param fLine The fireline length in m
#' @param slope slope in degrees
#' @param temp Ambient temperature deg. C
#' @param dfmc Moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' @param wind Velocity in km/h
buildSiteMeta <- function(Structure, Flora, a, fLine = 100, slope = 0,
temp = 30, dfmc = 0.1, wind = 10)
{
# CREATE site.meta
paramSite <- c('overlapping','overlapping','overlapping','overlapping','overlapping', 'fuelLoad',
'meanFuelDiameter','meanFinenessLeaves', 'fireLineLength', 'slope', 'temperature',
'deadFuelMoistureProp', 'windSpeed')
unitsSite <- c(NA, NA, NA, NA, NA, 't/ha', 'm', 'm', 'm', 'deg', 'degC', NA, 'km/h')
site.meta <- data.frame(matrix(NA, nrow=13, ncol = 5))
names(site.meta) <- c("stratum", "species", "param", "value", "units")
site.meta$param <- paramSite
site.meta$units <- unitsSite
# ENTER VARIABLES
site.meta$value[1] <- ifelse(Structure$ns_e[Structure$record==a]==TRUE,"near surface, elevated, overlapped",
ifelse(Structure$ns_e[Structure$record==a]==FALSE,"near surface, elevated, not overlapped",
"near surface, elevated, automatic"))
site.meta$value[2] <- ifelse(Structure$ns_m[Structure$record==a]==TRUE,"near surface, midstorey, overlapped",
ifelse(Structure$ns_m[Structure$record==a]==FALSE,"near surface, midstorey, not overlapped",
"near surface, midstorey, automatic"))
site.meta$value[3] <- ifelse(Structure$e_m[Structure$record==a]==TRUE,"elevated, midstorey, overlapped",
ifelse(Structure$e_m[Structure$record==a]==FALSE,"elevated, midstorey, not overlapped",
"elevated, midstorey, automatic"))
site.meta$value[4] <- ifelse(Structure$e_c[Structure$record==a]==TRUE,"elevated, canopy, overlapped",
ifelse(Structure$e_c[Structure$record==a]==FALSE,"elevated, canopy, not overlapped",
"elevated, canopy, automatic"))
site.meta$value[5] <- ifelse(Structure$m_c[Structure$record==a]==TRUE,"midstorey, canopy, overlapped",
ifelse(Structure$m_c[Structure$record==a]==FALSE,"midstorey, canopy, not overlapped",
"midstorey, canopy, automatic"))
site.meta$value[6] <- as.numeric(Flora$weight[Flora$record==a & Flora$species=="Litter"])
site.meta$value[7] <- if (is.null(as.numeric(Flora$diameter[Flora$record==a & Flora$species=="Litter"]))) {
0.005
} else {
as.numeric(Flora$diameter[Flora$record==a & Flora$species=="Litter"])
}
site.meta$value[8] <- 0.00025
site.meta$value[9] <- fLine
site.meta$value[10] <- slope
site.meta$value[11] <- temp
site.meta$value[12] <- dfmc
site.meta$value[13] <- wind
return(site.meta)
}
#' Builds the dataframe strata.meta from input tables
#' Compatible with MEE inputs
#'
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param a The number of the record for the site
buildStrataMeta <- function(Structure, a)
{
# Collect subsets for record
st <- Structure[Structure$record==a,]
# CREATE strata.meta
strata.meta <- data.frame(matrix(NA, nrow=8, ncol = 5))
names(strata.meta) <- c("stratum", "species", "param", "value", "units")
# Fill levelNames
strata.meta$param[1] <- "levelName"
strata.meta$value[1] <- "near surface"
strata.meta$param[2] <- "plantSeparation"
strata.meta$value[2] <- st$NS[1]
strata.meta$units[2] <- "m"
strata.meta$param[3] <- "levelName"
strata.meta$value[3] <- "elevated"
strata.meta$param[4] <- "plantSeparation"
strata.meta$value[4] <- st$El[1]
strata.meta$units[4] <- "m"
strata.meta$param[5] <- "levelName"
strata.meta$value[5] <- "midstorey"
strata.meta$param[6] <- "plantSeparation"
strata.meta$value[6] <- st$Mid[1]
strata.meta$units[6] <- "m"
strata.meta$param[7] <- "levelName"
strata.meta$value[7] <- "canopy"
strata.meta$param[8] <- "plantSeparation"
strata.meta$value[8] <- st$Can[1]
strata.meta$units[8] <- "m"
# Find empty strata and remove them
deleteVector <- vector()
for (ro in 2:8) {
if(is.na(strata.meta$value[ro])){
deleteVector<- c(deleteVector, ro-1, ro)
}
}
if(length(deleteVector)>0){
strata.meta <- strata.meta[ -deleteVector, ]}
# Number strata
rows <- as.numeric(nrow(strata.meta))
num <- 0.5
for (ro in 1:rows) {
numb <- ceiling(num)
strata.meta$stratum[ro] <- numb
num = num + 0.5
}
return(strata.meta)
}
#' Builds the dataframe species.values from input tables
#' Building from MEE inputs requires function speciesBuilder
#'
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param default.species.params Plant traits database
buildSpeciesValues <- function(Flora, default.species.params, a)
{
# Collect subsets for site
fl <- Flora[Flora$record==a & Flora$species != "Litter",] %>%
mutate("name" = species) %>%
left_join(default.species.params, by = "name")
if (exists("maxW", where = as.environment(fl))) {
fl <- fl %>%
select(species, stratum, comp, base, top, he, ht, w, moisture, C.C_rat, G.C_rat, maxW)
} else {
fl <- fl %>%
select(species, stratum, comp, base, top, he, ht, w, moisture, C.C_rat, G.C_rat)
}
# CREATE species.values
ro <- as.numeric(nrow(fl))
species.values <- data.frame(matrix(NA, nrow=ro, ncol = 13))
names(species.values) <- c("stratum", "species", "name", "clumpDiameter", "clumpSeparation", "composition",
"deadLeafMoisture", "hc", "he", "hp", "ht", "w", "liveLeafMoisture")
# Enter values
species.values$stratum <- as.numeric(fl$stratum)
species.values$name <- as.character(fl$species)
species.values$liveLeafMoisture <- fl$moisture
species.values$hc <- fl$base
species.values$hp <- fl$top
species.values$he <- fl$he
species.values$ht <- fl$ht
species.values$w <- fl$w
if (exists("maxW", where = as.environment(fl))) {
species.values$clumpDiameter <- pmin(pmin((as.numeric(fl$top)-as.numeric(fl$base)), as.numeric(fl$w))*fl$C.C_rat, as.numeric(fl$maxW))
} else {
species.values$clumpDiameter <- pmin((as.numeric(fl$top)-as.numeric(fl$base)), as.numeric(fl$w))*fl$C.C_rat
}
species.values$clumpSeparation <- fl$G.C_rat*species.values$clumpDiameter
species.values$composition <- as.numeric(fl$comp)
species.values <- species.values[order(species.values$stratum),]
species.values$species <- as.numeric(c(1:ro))
# Calculate gaps
for(a in 1:ro) {
if(is.na(species.values$he[a])){
species.values$he[a] <- species.values$hc[a]
}
if(is.na(species.values$ht[a])){
species.values$ht[a] <- species.values$hp[a]
}
if(is.na(species.values$liveLeafMoisture[a])){
species.values$liveLeafMoisture[a] <- 1
}
}
# Ensure strata are numbered consecutively
stratCurr <- as.data.frame(unique(species.values$stratum))
names(stratCurr) <- c("orig")
stratCurr$cor <- 1:as.numeric(nrow(stratCurr))
#Check values
for(b in 1:as.numeric(nrow(stratCurr))) {
species.values$stratum[species.values$stratum == stratCurr$orig[b]] <- stratCurr$cor[b]
}
return(species.values)
}
#' Constructs parameter files from imported tables
#'
#' @param Structure A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - a unique identifier per site
#' NS, El, Mid & Can - the mean separation between plants (m) per stratum
#' ns_e, ns_m, e_m, e_c, m_c - Logical field indicating whether plants in the stratum
#' on the left grow directly beneath those in the stratum on the right.
#' Acceptable values are TRUE, FALSE, or blank, where the outcome
#' will be decided by the relative stratum heights.
#' nsR, eR, mR, cR. Species richness (number of species) in each stratum
#' @param Flora A dataframe with the fields:
#' record - a unique, consecutively numbered identifier per site
#' site - A name for the site
#' species - the name of the species, which will call trait data from
#' 'default.species.params'
#' stratum - numeric value from 1 to 4, counting from lowest stratum
#' comp - Percent composition of that species in the stratum.
#' If absent, all species will be considered equally.
#' base - base height of plant crowns (m)
#' he - he height of plant crowns (m)
#' ht - ht height of plant crowns (m)
#' top - top height of plant crowns (m)
#' w - width of plant crowns (m)
#' Hs - standard deviation of the top height of plant crowns (m)
#' Hr - range of the top height of plant crowns (m)
#' weight - weight in t/ha of fine dead organic material forming
#' the surface and suspNS layers
#' diameter - mean diameter of surface and suspNS litter in m
#' @param a The number of the record for the site
#' @param fLine The fireline length in m
#' @param slope slope in degrees
#' @param temp Ambient temperature deg. C
#' @param dfmc Moisture content of fine dead fuels in whole numbers (eg 0.1 for 10 percent)
#' @param wind Velocity in km/h
#' @param default.species.params Plant traits database
#' @param a The record number for which to build the table
#' @export
buildParams <- function(Structure, Flora, default.species.params, a,
fLine = 100, slope = 0, temp = 30, dfmc = 0.1, wind = 10)
{
# Remove empty species, leaving litter
Flora <- Flora[which(Flora$species == "Litter" | Flora$comp != 0), ]
# Construct component tables
site.meta <- buildSiteMeta(Structure = Structure, Flora = Flora, a = a, fLine = fLine, slope = slope,
temp = temp, dfmc = dfmc, wind = wind)
strata.meta <- buildStrataMeta(Structure, a)
species.values <- buildSpeciesValues(Flora, default.species.params, a)
species.values$deadLeafMoisture <- dfmc
species.units <- unitBuilder(Flora, a)
# Build parameter file
components <- list(site.meta, strata.meta, species.values, species.units)
names(components) <- c("site.meta", "strata.meta", "species.values", "species.units")
param <- ffm_assemble_table(components)
# Find weighted mean of crown widths per stratum
species.values$weightedW <- species.values$composition * as.numeric(species.values$w)
ww <- species.values%>%
select(stratum, composition, weightedW)%>%
group_by(stratum) %>%
summarize_all(sum) %>%
mutate(mw = weightedW/composition)
# Ensure separation is greater than weighted mean of crown widths
for (stNum in 1:max(ww$stratum)) {
sep <- filter(strata.meta, stratum == stNum)
param <- ffm_set_stratum_param(param, stNum, "plantSeparation",
pmax(as.numeric(sep$value[2]),ww$mw[stNum]))
}
# Change species param leafForm to characters
default.species.params$leafForm <- as.character(default.species.params$leafForm)
# Fill empty traits
param <- ffm_complete_params(param,default.species.params)
return(param)
}
#' Constructs a default.species.params table using traits available in the austraits database
#' Collects traits from ausTraits for use in shade and fire effects modelling
#'
#' @param version Version of austraits
#' @param path
#' @param shadeTolerance The Whole Plant Light Compensation Point below which
#' plants are considered shade tolerant
#'
#' @return list of tables
#' @export
#' @examples
#' Traits <- ausTraitTable(version = "3.0.2", path = "data/austraits")
#'
ausTraitTable <- function(version = "3.0.2", path = "data/austraits", shadeTolerance = 3) {
Austraits <- austraits::load_austraits(version = version, path = path)
# Create tables of each trait in FRaME units
leaf_length <- (austraits::extract_trait(Austraits, "leaf_length"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafLength = value/1000) %>%
select(taxon_name, leafLength)
leaf_width <- (austraits::extract_trait(Austraits, "leaf_width"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafWidth = value/1000) %>%
select(taxon_name, leafWidth)
leaf_shape <- (austraits::extract_trait(Austraits, "leaf_shape"))$traits
leaf_shape$leafForm <- case_when(leaf_shape$value == "needle terete" ~ "Round",
leaf_shape$value == "terete" ~ "Round",
leaf_shape$value == "subulate terete" ~ "Round",
leaf_shape$value == "needle" ~ "Round",
leaf_shape$value == "linear terete" ~ "Round",
leaf_shape$value == "clavate linear terete" ~ "Round",
leaf_shape$value == "terete_compressed" ~ "Round",
leaf_shape$value == "scale terete" ~ "Round",
leaf_shape$value == "trigonous_terete" ~ "Round",
leaf_shape$value == "half_terete" ~ "Round",
leaf_shape$value == "terete_compressed" ~ "Round",
TRUE ~ "Flat")
leaf_shape <- leaf_shape %>% select(taxon_name, leafForm)
leaf_thickness <- (austraits::extract_trait(Austraits, "leaf_thickness"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(leafThickness = value/1000) %>%
select(taxon_name, leafThickness)
# List species
tNames <- unique(Austraits[[1]]$taxon_name)
T_names <- tNames[!grepl("\\d+", tNames)]
traits <- data.frame(taxon_name = T_names)
list_of_dfs <- list(traits, leaf_length, leaf_width, leaf_shape, leaf_thickness)
result <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name") %>%
mutate(name = taxon_name,
propDead = 0) %>%
select(name, propDead, leafForm, leafThickness, leafWidth, leafLength) %>%
mutate(leafSeparation = NA,
stemOrder = NA,
ignitionTemp = NA,
moisture = 1,
G.C_rat = NA,
C.C_rat = NA)
result <- arrange(result, name)
# Secondary traits
traitNames <- data.frame(taxon_name = T_names)
#___________________________________
# Shade tolerance
Dark_respiration <- (austraits::extract_trait(Austraits, "leaf_dark_respiration_per_dry_mass"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median) %>%
mutate(Dark_respiration = value,
WPLCP = (390*value)+0.78,
Shade = WPLCP < shadeTolerance) %>%
select(taxon_name, WPLCP, Shade)
# Approximate from area measurement
respArea <- (austraits::extract_trait(Austraits, "leaf_dark_respiration_per_area"))$traits %>%
group_by(taxon_name)%>%
summarise_if(is.numeric, median)
list_of_dfs <- list(traitNames, Dark_respiration, respArea)
sh <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name")
sh <- sh[complete.cases(sh),]
LM<-lm(sh$value ~ sh$WPLCP)
LMSum <- base::summary(LM)
eqThres <- LMSum$coefficients[1] + LMSum$coefficients[2] * shadeTolerance
respArea <- respArea %>%
mutate(Shade = value < eqThres)
ShadeTolerance <- respArea[!(respArea$taxon_name %in% Dark_respiration$taxon_name), ]%>%
right_join(Dark_respiration, by = c("taxon_name", "Shade")) %>%
select(taxon_name, Shade)
list_of_dfs <- list(traitNames, ShadeTolerance)
resultII <- list_of_dfs %>%
purrr::reduce(left_join, by = "taxon_name") %>%
mutate(Genus = word(taxon_name, 1),
name = taxon_name)
# Generalise genera
genera <- resultII[complete.cases(resultII), ] %>%
group_by(Genus)%>%
summarise(Shade = as.logical(round(mean(Shade),0))) %>%
mutate(name = Genus) %>%
select(name, Shade)
resultII <- resultII %>%
select(name, Shade)%>%
full_join(genera, by = c("name", "Shade"))
resultII <- arrange(resultII, name)
remList <- vector()
for (r in 2:nrow(resultII)) {
if (tolower(resultII$name[r]) == tolower(resultII$name[r-1])) {
remList[length(remList)+1] <- r-1
}
}
if (length(remList)>0) {
resultII <- resultII[-remList,]
}
return(list(result, resultII))
}
#' Checks traits table for column names and adds data from a new table
#'
#' @param traits Existing trait table
#' @param traitsNew Table containing new trait data
#' @param deleteReplicates If set to TRUE, retains the first mention of a species, then removes all following mentions
#' @param fill If set to TRUE, fills empty numeric values with mean values of the genus
#'
#' @return table
#' @export
#' @examples
#' Traits <- ausTraitTable(version = "3.0.2", path = "data/austraits")
#' TraitsNew <- read.csv("Traits.csv")
#' Tr <- updateTraits(traits = Traits, traitsNew = TraitsNew, deleteReplicates = TRUE, fill = TRUE)
#'
updateTraits <- function(traits, traitsNew, deleteReplicates = TRUE, fill = TRUE) {
# Ensure traits table has all necessary columns
cols_to_check <- c("name", "propDead", "leafForm", "leafThickness", "leafWidth",
"leafLength", "leafSeparation", "stemOrder", "ignitionTemp", "moisture", "G.C_rat", "C.C_rat")
for (colName in cols_to_check) {
if(!colName %in% names(traits)) {
traits$colName == ""
}
}
# Add any missing species to existing list
missing_species <- unique(traitsNew$name)[!(unique(traitsNew$name) %in% unique(traits$name))]
newSp <- traitsNew[traitsNew$name %in% missing_species, ]
traits <- rbind(traits,newSp)
traits <- arrange(traits, name)
newCols <- names(traitsNew)
for (sp in unique(traitsNew$name)) {
for (colName in newCols) {
if(!is.na(traitsNew[traitsNew$name == sp,colName])){traits[traits$name == sp,colName] <- traitsNew[traitsNew$name == sp,colName]}
}
}
# Generalise genera
traits <- traits %>%
mutate(Genus = word(name, 1),
leafThickness = as.numeric(leafThickness),
leafWidth = as.numeric(leafWidth),
leafLength = as.numeric(leafLength),
leafSeparation = as.numeric(leafSeparation),
stemOrder = as.numeric(stemOrder),
ignitionTemp = as.numeric(ignitionTemp),
moisture = as.numeric(moisture),
G.C_rat = as.numeric(G.C_rat),
C.C_rat = as.numeric(C.C_rat))
genera <- traits %>%
group_by(Genus)%>%
summarise_if(is.numeric, ~mean(., na.rm=TRUE)) %>%
mutate(leafForm = "Flat")
# Find most common leaf form
for (g in 1:nrow(genera)) {
genSub <- traits[traits$Genus == genera$Genus[g],]
Round <- nrow(genSub[genSub$leafForm == "Round",])
Flat <- nrow(genSub[genSub$leafForm == "Flat",])
if (Round > Flat) {
genera$leafForm[g] <- "Round"
}
}
# Fill empty traits with means if chosen
if (fill) {
traits <- left_join(traits, genera, by = "Genus")%>%
mutate(propDead = ifelse(!is.na(propDead.x), propDead.x, propDead.y),
leafThickness = ifelse(!is.na(leafThickness.x), leafThickness.x, leafThickness.y),
leafForm = ifelse(!is.na(leafForm.x), leafForm.x, leafForm.y),
leafWidth = ifelse(!is.na(leafWidth.x), leafWidth.x,leafWidth.y),
leafLength = ifelse(!is.na(leafLength.x), leafLength.x, leafLength.y),
leafSeparation= ifelse(!is.na(leafSeparation.x), leafSeparation.x,leafSeparation.y),
stemOrder = ifelse(!is.na(stemOrder.x), stemOrder.x, stemOrder.y),
ignitionTemp = ifelse(!is.na(ignitionTemp.x), ignitionTemp.x, ignitionTemp.y),
moisture = ifelse(!is.na(moisture.x), moisture.x, moisture.y),
G.C_rat = ifelse(!is.na(G.C_rat.x), G.C_rat.x, G.C_rat.y),
C.C_rat = ifelse(!is.na(C.C_rat.x), C.C_rat.x, C.C_rat.y))
}
traits <- traits %>%
dplyr::select(name, propDead, leafForm, leafThickness, leafWidth, leafLength, leafSeparation, stemOrder, ignitionTemp, moisture, G.C_rat, C.C_rat)
genera <- genera %>%
mutate(name = Genus) %>%
dplyr::select(name, propDead, leafForm, leafThickness, leafWidth, leafLength, leafSeparation, stemOrder, ignitionTemp, moisture, G.C_rat, C.C_rat)
traits <- genera %>%
rbind(traits)
traits$propDead[which(is.nan(traits$propDead))] <- NA
traits$leafThickness[which(is.nan(traits$leafThickness))] <- NA
traits$leafWidth[which(is.nan(traits$leafWidth))] <- NA
traits$leafLength[which(is.nan(traits$leafLength))] <- NA
traits$leafSeparation[which(is.nan(traits$leafSeparation))] <- NA
traits$stemOrder[which(is.nan(traits$stemOrder))] <- NA
traits$ignitionTemp[which(is.nan(traits$ignitionTemp))] <- NA
traits$moisture[which(is.nan(traits$moisture))] <- NA
traits$G.C_rat[which(is.nan(traits$G.C_rat))] <- NA
traits$C.C_rat[which(is.nan(traits$C.C_rat))] <- NA
traits <- arrange(traits,name)
if (deleteReplicates) {
# Check for repeated species
traits <- arrange(traits, name)
remList <- vector()
for (r in 2:nrow(traits)) {
if (tolower(traits$name[r]) == tolower(traits$name[r-1])) {
remList[length(remList)+1] <- r
}
}
if (length(remList)>0) {
traits <- traits[-remList,]
cat(length(remList), "duplicate species were removed")
}
}
return(traits)
}
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