Nothing
R/species_table.R
In ausplotsR: TERN AusPlots Australian Ecosystem Monitoring Data
Defines functions
species_table
Documented in
species_table
species_table <- function(veg.PI, m_kind=c("PA", "percent_cover", "freq", "IVI"), cover_type=c("PFC", "OCC"), species_name=c("SN","HD","GS"), strip_bryophytes=FALSE) {
#input checks
if(!inherits(veg.PI, "data.frame")) {stop("veg.PI must be a data.frame")}
if(missing(m_kind)){stop("Please specify the desired species scoring method with 'm_kind'")}
if(!is.character(m_kind)) {stop("m_kind must be a character vector")}
if(!is.character(cover_type)) {stop("cover_type must be a character vector")}
if(!is.logical(strip_bryophytes)) {stop("strip_bryophytes must be TRUE or FALSE")}
#setting defaults
if(missing(cover_type)) {
if(m_kind %in% c("percent_cover", "IVI")) {
cover_type <- "PFC"
warning("No cover_type supplied, defaulting to 'PFC'")
}
}
if(missing(species_name)) {
species_name = "SN"
warning("No species_name supplied, defaulting to species_name='SN'")
}
#####
hits <- veg.PI
if(species_name == "SN") {
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "standardised_name"),])), c("site_unique", "standardised_name")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$standardised_name), ] #remove hots not determined as a species
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "standardised_name")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$standardised_name), ] #remove NA standardised_name
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "standardised_name", "transect")) #count PI records for each uniqe plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "standardised_name")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #
} #end SN
###################using herbarium determination################
if(species_name == "HD"){
# warning("herbarium determinations are provided by state herbaria and may differ between states and international databases.
# See details for more information. Consider using SN or GS for consistency between plots")
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "herbarium_determination"),])), c("site_unique", "herbarium_determination")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$herbarium_determination), ] #remove hots not determined as a speciesemove hots not determined as a species
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "herbarium_determination")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$herbarium_determination), ] ##remove hits not determined as a species
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "herbarium_determination", "transect")) #count PI records for each unique plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "herbarium_determination")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #end IVI section
} # end species_name=="HD"
#########genus_species##########################
if(species_name == "GS"){ #using genus species
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "genus_species"),])), c("site_unique", "genus_species")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$genus_species), ] ##remove hits not determined as a species
#alternatively, we might assign it a no data term, in which case
#hits <- hits[!(hits$genus_species == "No ID",]
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "genus_species")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$genus_species), ] ##remove hits not determined as a species
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "genus_species", "transect")) #count PI records for each uniqe plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "genus_species")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
##################
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #
} # end genus_species
return(round(species_matrix, digits=1))
} #end function
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Defines functions species_table
Documented in species_table
species_table <- function(veg.PI, m_kind=c("PA", "percent_cover", "freq", "IVI"), cover_type=c("PFC", "OCC"), species_name=c("SN","HD","GS"), strip_bryophytes=FALSE) {
#input checks
if(!inherits(veg.PI, "data.frame")) {stop("veg.PI must be a data.frame")}
if(missing(m_kind)){stop("Please specify the desired species scoring method with 'm_kind'")}
if(!is.character(m_kind)) {stop("m_kind must be a character vector")}
if(!is.character(cover_type)) {stop("cover_type must be a character vector")}
if(!is.logical(strip_bryophytes)) {stop("strip_bryophytes must be TRUE or FALSE")}
#setting defaults
if(missing(cover_type)) {
if(m_kind %in% c("percent_cover", "IVI")) {
cover_type <- "PFC"
warning("No cover_type supplied, defaulting to 'PFC'")
}
}
if(missing(species_name)) {
species_name = "SN"
warning("No species_name supplied, defaulting to species_name='SN'")
}
#####
hits <- veg.PI
if(species_name == "SN") {
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "standardised_name"),])), c("site_unique", "standardised_name")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$standardised_name), ] #remove hots not determined as a species
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "standardised_name")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$standardised_name), ] #remove NA standardised_name
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "standardised_name", "transect")) #count PI records for each uniqe plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "standardised_name")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #
} #end SN
###################using herbarium determination################
if(species_name == "HD"){
# warning("herbarium determinations are provided by state herbaria and may differ between states and international databases.
# See details for more information. Consider using SN or GS for consistency between plots")
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "herbarium_determination"),])), c("site_unique", "herbarium_determination")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$herbarium_determination), ] #remove hots not determined as a speciesemove hots not determined as a species
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "herbarium_determination")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$herbarium_determination), ] ##remove hits not determined as a species
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "herbarium_determination", "transect")) #count PI records for each unique plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "herbarium_determination")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #end IVI section
} # end species_name=="HD"
#########genus_species##########################
if(species_name == "GS"){ #using genus species
if(m_kind == "PA") {
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
hits <- hits[which(!duplicated(hits[,c("site_unique", "genus_species"),])), c("site_unique", "genus_species")] #remove duplicated hits (i.e. same species in a given plot - we just want binary presence/absence here)
hits <- hits[!is.na(hits$genus_species), ] ##remove hits not determined as a species
#alternatively, we might assign it a no data term, in which case
#hits <- hits[!(hits$genus_species == "No ID",]
hits$presence <- rep(1, nrow(hits)) #add a column of '1's for presence (for mama function)
cover_matrix_binary <- ma_ausplot_ma(hits) #generate an occurrence matrix (binary)
species_matrix <- cover_matrix_binary
} # end PA section
if(m_kind == "percent_cover" | m_kind == "IVI") {
#count combined (PI) cover scores for each species in each plot:
total.points.fun <- function(x) {return(length(unique(hits[which(hits$site_unique == x),]$hits_unique)))} #function to go through a list of plot names and count how many unique hits there were (for a standard plot, this will equal 1010)
total.points <- data.frame(site_unique = unique(hits$site_unique), total.points = unlist(lapply(unique(hits$site_unique), total.points.fun))) #site/visit and associated number of unique PI hits taken, by applying the above function
#now that we have plot total.points, we remove unwanted points
#1. remove in canopy sky hits if projected foliage cover required:
if(cover_type == "PFC") {
hits <- hits[which(as.character(hits$in_canopy_sky) == "FALSE"),]
} #close if PFC
#2. strip bryophytes if requested
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
covers <- plyr::count(hits, c("site_unique", "genus_species")) #counts number of rows with same site and species name
covers <- merge(covers, total.points, by="site_unique") #merge to get the hit counts for species in a df along with the total hits taken for each plot
covers$percent <- covers$freq/covers$total.points*100 #add a new column to convert from number of hits to percent. Some plots have less or more than the standard 1010 PI hits for various reasons, so this ensures dividing by the actual number of unique hits, which is calculated from the raw data and unique transect/number combos
covers <- stats::na.omit(covers) #to remove percents for records with no herbarium determination
#generate sites by species matrix:
cover_matrix <- ma_ausplot_ma(covers[,-c(3, 4)]) #third/fourth columns (count of individual hits and total hits) excluded as we want to the mama function to read our fourth column 'percent' [percent cover] as the abundance value (assumed to be the 3rd column): this generates a data.frame with sites as rows and species as columns, with percent covers from PI hits as values
species_matrix <- cover_matrix
} # close cover matrix section
if(m_kind == "freq" | m_kind == "IVI") {
hits <- hits[!is.na(hits$genus_species), ] ##remove hits not determined as a species
#if(strip_bryophytes) {hits <- subset(hits, taxa_group != "bryophytes")}
if(strip_bryophytes) {warning("Argument 'strip_bryophytes' is deprecated. species_name = 'HD' returns all determinations, whereas 'SN' returns matches with the Australian Plant Census, which excludes bryophytes.")}
transects <- plyr::count(hits, c("site_unique", "genus_species", "transect")) #count PI records for each uniqe plot/species/transect combo
transects$freq <- 1 #revert to 1 rather than a count (presence on a transect within a plot)
freqs <- plyr::count(transects, c("site_unique", "genus_species")) #count transect presences per species per plot, e.g. 5 means that species was recorded on 5 of the transects
number.transects <- plyr::count(transects[!duplicated(transects[,c("site_unique", "transect")]),], "site_unique") #some plots have more or less than 10 transects so this is a data frame of site_unique IDs and the number of transects
freqs <- merge(freqs, number.transects, by="site_unique") #this maps the number of recorded transects for each plot to the species records and number of transect hits for each. Columns are now freq.x and freq.y
freqs$freq.z <- freqs$freq.x/freqs$freq.y #divide by the actual number of transects (should range from 0.1 to 1)
freq_matrix <- ma_ausplot_ma(freqs[,c(1,2,5),]) #create a species ~ sites matrix with the frequencies as values, and zeros if species occur on no transect for a plot; selecting columns 1,2 and 5 to exclude the freq.x and freq.y cols for mam
species_matrix <- freq_matrix
} #end M-kind == freq
##################
if(m_kind == "IVI") {
IVI <- (cover_matrix/rowSums(cover_matrix))*100 + (freq_matrix/rowSums(freq_matrix))*100 #combine relative freq and cover i.e. as proportion of total across all species, to get a measure of IVI
species_matrix <- IVI #note that the frequencies appear to dominate the output as they are overall much higher than the covers
} #
} # end genus_species
return(round(species_matrix, digits=1))
} #end function
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