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#' Simulate a community assembled according to habitat filtering
#'
#' Given a simulations.input object, will create an arena settled according to habitat
#' filtering rules (and parameters defined by prepSimulations).
#'
#' @param simulations.input A prepared simulations.input object from prepSimulations
#'
#' @details This is the habitat filtering simulation that was used in our paper
#' (reference below). In short, species have phylogenetically conserved spatial
#' preferences, and individuals of those species are settled near that preferred location
#' with a controllable degree of variation. Species' spatial preferences are smoothed to a
#' uniform distribution. Thus, individuals are fairly evenly distributed throughout the
#' simulated arena.
#'
#' @return A list of 3 elements: the original input regional
#' abundance vector, the new spatial arena, and the dimensions of that arena.
#'
#' @export
#'
#' @references Miller, E. T., D. R. Farine, and C. H. Trisos. 2016. Phylogenetic community
#' structure metrics and null models: a review with new methods and software.
#' Ecography DOI: 10.1111/ecog.02070
#'
#' @examples
#' tree <- geiger::sim.bdtree(b=0.1, d=0, stop="taxa", n=50)
#'
#' prepped <- prepSimulations(tree, arena.length=300, mean.log.individuals=2,
#' length.parameter=5000, sd.parameter=50, max.distance=20, proportion.killed=0.2,
#' competition.iterations=2)
#'
#' test <- filteringArena(prepped)
filteringArena <- function(simulations.input)
{
#evolve two traits independently up phylogeny. returns a list where first element is
#input tree and second element is a matrix of traits (x,y spatial preferences)
evolveTraits.results <- evolveTraits(simulations.input$tree)
#IN THE ORIGINAL SIMULATIONS, WE DID NOT RUN EITHER OF THESE NEXT TWO LINES OF DATA
#TO TRANSFORM TO UNIFORM DISTRIBUTION
#determine what quantile first trait falls in and reclassify the trait to that
#this essentially turns the normal distribution uniform
evolveTraits.results[[2]][,1] <- pnorm(evolveTraits.results[[2]][,1],
mean(evolveTraits.results[[2]][,1]), sd(evolveTraits.results[[2]][,1]))
#do same for second trait
evolveTraits.results[[2]][,2] <- pnorm(evolveTraits.results[[2]][,2],
mean(evolveTraits.results[[2]][,2]), sd(evolveTraits.results[[2]][,2]))
#scale results to size of arena
scaled.results <- scaler(evolveTraits.results[[2]], min.arena=0,
max.arena=simulations.input$arena.length)
#simulate the number of individuals per species to follow a log-normal abundance dist
indivs.per.species <- rlnorm(n=length(evolveTraits.results[[1]]$tip.label),
simulations.input$mean.log.individuals, sdlog=1)
indivs.per.species[indivs.per.species < 0] <- 0
indivs.per.species <- round(indivs.per.species)
individuals <- c()
#expand out the abundance distributions to actual identities
for(i in 1:length(indivs.per.species))
{
individuals <- append(individuals, rep(evolveTraits.results[[1]]$tip.label[i],
times=indivs.per.species[i]))
}
arena <- data.frame(individuals)
X <- c()
Y <- c()
#per species simulate a normal distribution with the length and sd parameters provided
#around each species spatial preference.
for(i in 1:length(individuals))
{
X.options <- rnorm(n=simulations.input$length.parameter,
mean=scaled.results[row.names(scaled.results)==individuals[i], 1],
sd=simulations.input$sd.parameter)
X[i] <- sample(X.options, size=1)
Y.options <- rnorm(n=simulations.input$length.parameter,
mean=scaled.results[row.names(scaled.results)==individuals[i], 2],
sd=simulations.input$sd.parameter)
Y[i] <- sample(Y.options, size=1)
}
arena$X <- X
arena$Y <- Y
#this ugly piece of code for compatibility with older pieces of code I should revise
x.min=0
x.max=simulations.input$arena.length
y.min=0
y.max=simulations.input$arena.length
dims=c(x.min, x.max, y.min, y.max)
results <- list("regional.abundance"=as.character(arena$individuals), "arena"=arena,
"dims"=dims)
return(results)
}
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