#' @title Rank Abundance Curve Changes
#'
#' @description Calculates change of the five aspects of rank abundance curves
#' (richness, evenness, rank, species gains, and species losses) for a
#' replicate between two time points.
#'
#' @param df A data frame containing time, species, and abundance columns and an
#' optional columns of replicates.
#' @param time.var The name of the time column.
#' @param species.var The name of the species column.
#' @param abundance.var The name of the abundance column.
#' @param replicate.var The name of the optional replicate column. If specified,
#' replicate must be unique within the dataset and cannot be nested within
#' treatments or blocks.
#' @param reference.time The name of the optional time point that all other time
#' points should be compared to (e.g. the first year of data). If not
#' specified, each comparison is between consecutive time points (the first
#' and second year, second and third year, etc.)
#'
#' @return The RAC_change function returns a data frame with a subset of the
#' following columns:
#' \itemize{
#' \item{replicate.var: }{A column that has same name and type as the
#' replicate.var column, if replicate.var is specified.}
#' \item{time.var: }{A column with the specified time.var and a second column,
#' with '2' appended to the name. Time is subtracted from time2.}
#' \item{richness_change: }{A numeric column that is the change in richness
#' between the two time periods for a replicate divided by the total number of
#' unique species in both time periods. A positive value occurs when a there is
#' an increase in species richness over time, and a negative value when there
#' is a decreases in species richness over time.}
#' \item{evenness_change: }{A numeric column that is the change in
#' evenness(measured with Evar) between the two time periods for a replicate. A
#' positive value occurs when evenness increases over time, and a negative
#' value when evenness decreases in over time.}
#' \item{rank_change: }{A numeric column that is the absolute value of the
#' average change in species ranks between the two time periods for a replicate
#' divided by the total number of unique species in both time periods. Species
#' that are not present in both time periods are given the S+1 rank in the
#' sample it is absent in, where S is the number of species in that sample.}
#' \item{gains: }{A numeric column of the number of species that are present at
#' time period 2 that were not present at time period 1 for a replicate divided
#' by the total number of unique species in both time periods. This is
#' equivalent to the turnover function with metric = "appearances".}
#' \item{losses: }{A numeric column of the number of species that are not
#' present at time period 2 but were present at time period 1 for a replicate
#' divided by the total number of unique species in both time periods. This is
#' equivalent to the turnover function with metric = "disappearance".}
#' }
#' @references Avolio et al. Submitted
#' @examples
#' data(pplots)
#' # Without replicates
#' df <- subset(pplots, plot == 25)
#' RAC_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' time.var = "year")
#'
#' # With replicates
#' df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32))
#' RAC_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' replicate.var = "plot",
#' time.var = "year")
#'
#' # With reference year
#' df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32))
#' RAC_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' replicate.var = "plot",
#' time.var = "year",
#' reference.time = 2002)
#'
#' @export
RAC_change <- function(df,
time.var,
species.var,
abundance.var,
replicate.var = NULL,
reference.time = NULL) {
# validate function call and purge extraneous columns
args <- as.list(match.call()[-1])
df <- do.call(check_args, args, envir = parent.frame())
# add zeros for species absent from a time period within a replicate
by <- c(replicate.var)
allsp <- split_apply_combine(df, by, FUN = fill_species, species.var, abundance.var)
# rank species in each time and optionally replicate
by <- c(time.var, replicate.var)
rankdf <- split_apply_combine(allsp, by, FUN = add_ranks, abundance.var)
# merge subsets on time difference of one time step
cross.var <- time.var
cross.var2 <- paste(cross.var, 2, sep = '')
split_by <- c(replicate.var)
merge_to <- !(names(rankdf) %in% split_by)
if (is.null(reference.time)) {
ranktog <- split_apply_combine(rankdf, split_by, FUN = function(x) {
y <- x[merge_to]
cross <- merge(x, y, by = species.var, suffixes = c('', '2'))
f <- factor(cross[[cross.var]])
f2 <- factor(cross[[cross.var2]], levels = levels(f))
idx <- (as.integer(f2) - as.integer(f)) == 1
cross[idx, ]
})
} else {
ranktog <- split_apply_combine(rankdf, split_by, FUN = function(x) {
y <- x[x[[time.var]] != reference.time, merge_to]
x <- x[x[[time.var]] == reference.time, ]
merge(x, y, by = species.var, suffixes = c('', '2'))
})
}
# remove rows with NA for both abundances (preferably only when introduced
# by fill_species)
idx <- is.na(ranktog[[abundance.var]])
abundance.var2 <- paste(abundance.var, 2, sep = '')
idx2 <- is.na(ranktog[[abundance.var2]])
ranktog[idx, abundance.var] <- 0
ranktog[idx2, abundance.var2] <- 0
idx <- ranktog[[abundance.var]] != 0 | ranktog[[abundance.var2]] != 0
ranktog <- ranktog[idx, ]
# apply turnover calculation to all replicates for each time point
by <- c(replicate.var, cross.var, cross.var2)
output <- split_apply_combine(ranktog, by, FUN = SERGL,
species.var, abundance.var, abundance.var2)
if(any(is.na(output$evenness_change)))
warning(paste0("evenness_change values contain NAs because there are plots",
" with only one species"))
output_order <- c(
time.var, paste(time.var, 2, sep = ''),
replicate.var,
'richness_change', 'evenness_change', 'rank_change', 'gains', 'losses')
return(output[intersect(output_order, names(output))])
}
############################################################################
#
# Private functions: these are internal functions not intended for reuse.
# Future package releases may change these without notice. External callers
# should not use them.
#
############################################################################
# A function to calculate RAC changes for a replicate between two consecutive time points
# @param df a dataframe
# @param time.var the name of the time column
# @param rank.var1 the name of the speices rank column for the first time peroid
# @param rank.var2 the name of the species rank column for the second time period
# @param abundance.var1 the name of the abundance column for the first time peroid
# @param abundance.var2 the name of the abundance column for the second time period
SERGL <- function(df, species.var, abundance.var, abundance.var2) {
out <- c(species.var, 'rank', 'rank2', abundance.var, abundance.var2)
out <- unique(df[!(names(df) %in% out)])
# ricness and evenness differences
s_t1 <- S(df[[abundance.var]])
e_t1 <- Evar(as.numeric(df[[abundance.var]]))
s_t2 <- S(df[[abundance.var2]])
e_t2 <- Evar(as.numeric(df[[abundance.var2]]))
delta_s <- (s_t2-s_t1) / nrow(df)
delta_e <- (e_t2-e_t1)
# gains and lqosses
df$gain <- ifelse(df[[abundance.var]] == 0, 1, 0)
df$loss <- ifelse(df[[abundance.var2]] == 0, 1, 0)
gain <- sum(df$gain) / nrow(df)
loss <- sum(df$loss) / nrow(df)
delta_r <- mean(abs(df[['rank']] - df[['rank2']])) / nrow(df)
measures <- data.frame(
richness_change = delta_s, evenness_change = delta_e,
rank_change = delta_r, gains = gain, losses = loss)
return(cbind(out, measures))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.