sim.metapopgen.monoecious <- function(input.type,demographic.data,N1,sigma,phi_F,phi_M,mu,delta,recr.dd,kappa0,T_max,save.res=F,save.res.T=seq(1,T_max),verbose=F) {
##########################################################################
# Initial definitions
##########################################################################
if (input.type=="data.frame") {
print("Input type = data.frame")
a <- metapopgen.input.convert.monoecious(demographic.data)
N1 <- a[[1]]
sigma <- a[[2]]
phi_M <- a[[3]]
phi_F <- a[[4]]
rm(a)
} else {
if (input.type == "array") {
print("Input type = array")
} else {
stop("Unknown value for argument input.type. It must be either data.frame, array or txt")
}
}
# Define basic variables
m <- dim(N1)[1] # Number of genotypes
l <- (sqrt(1+8*m)-1)/2 # Nuber of alleles
n <- dim(N1)[2] # Number of demes
z <- dim(N1)[3] # Number of age-classes
# If only one age-class and recr.dd=="adults", gives an error
if (z == 1 & recr.dd == "adults") {
stop("Detected only one age class (z=1) and recruitment probability dependent on adult density (recr.dd == 'adults'). This combination is not supported. Use recr.dd == 'settlers' instead.")
}
##########################################################################
# Check if input data are time-dependent or not
##########################################################################
# Survival
if (is.na(dim(sigma)[4])) {
sigma <- array(rep(sigma,T_max),c(m,n,z,T_max))
}
# Female fecundity
if (is.na(dim(phi_F)[4])) {
phi_F <- array(rep(phi_F,T_max),c(m,n,z,T_max))
}
# Male fecundity
if (is.na(dim(phi_M)[4])) {
phi_M <- array(rep(phi_M,T_max),c(m,n,z,T_max))
}
# Dispersal
if (is.na(dim(delta)[3])) {
delta <- array(rep(delta,T_max),c(n,n,T_max))
}
# Carrying capacity
if (is.vector(kappa0)) {
kappa0 <- array(rep(kappa0,T_max),c(n,T_max))
}
##########################################################################
# Initialize state variables
##########################################################################
print("Initializing variables...")
if (save.res){
N <- N1
rm(N1)
} else {
N <- array(NA,dim=c(m,n,z,T_max))
N[,,,1] <- N1
rm(N1)
L <- array(NA,dim=c(m,n,T_max))
S <- array(0,dim=c(m,n,T_max))
}
##########################################################################
# Define functions
##########################################################################
# Survival
surv <-
function(sigma,N) {
rbinom(1,N,sigma)
}
# Reproduction
repr <-
function(Nprime,phi_F,phi_M,mu,i,l,m) {
# Adjust dimensions if there is only one age-class and/or one deme
#if (length(dim(phi_F))==2) dim(phi_F)[3]<-1
#if (length(dim(phi_M))==2) dim(phi_M)[3]<-1
dim(phi_F) <- c(m,n,z)
dim(phi_M) <- c(m,n,z)
# n and z are not passed to the function? But it works...
if (verbose) print("Calculates total number of female gametes")
fecx<- array(0,dim=c(m,z)) # Number of female gametes produced by all the individuals of each genotype in each age class
fec<- array(0,dim=m) # Number of female gametes produced by all the individuals of each genotype
for (k in 1 : m) {
for (x in 1 : z) {
fecx[k,x] <- sum(as.numeric(rpois(Nprime[k,i,x],phi_F[k,i,x]))) # This is the contribution of variation in reproductive success among individuals to genetic drift
}
fec[k] <- sum(fecx[k,])
}
if (verbose) print("Calculate number of gametes for each allele")
G_F <- array(0,dim=l)
k <- 1
for (j in 1 : l) {
for (jj in j : l) {
if (j == jj) {
G_F[j] <- G_F[j] + fec[k]
} else {
meiosis_j<- rbinom(1,fec[k],0.5)# This is the contribution of Mendelian segregation to genetic drift
meiosis_jj<- fec[k] - meiosis_j
G_F[j]<- G_F[j] + meiosis_j
G_F[jj]<- G_F[jj] + meiosis_jj
}
k <- k + 1
}
}
if (verbose) print("Calculates total number of male gametes")
fecx <- array(0,dim=c(m,z)) # Number of male gametes produced by all the individuals of each genotype in each age class
fec <- array(0,dim=m) # Number of male gametes produced by all the individuals of each genotype
for (k in 1 : m) {
for (x in 1 : z) {
fecx[k,x] <- sum(as.numeric(rpois(Nprime[k,i,x],phi_M[k,i,x]))) # This is the contribution of variation in reproductive success among individuals to genetic drift
}
fec[k] <- sum(fecx[k,])
}
if (verbose) print("Calculate number of gametes for each allele")
G_M <- array(0,dim=l)
k <- 1
for (j in 1 : l) {
for (jj in j : l) {
if (j == jj) {
G_M[j] <- G_M[j] + fec[k]
} else {
meiosis_j<- rbinom(1,fec[k],0.5)
meiosis_jj<- fec[k] - meiosis_j
G_M[j]<- G_M[j] + meiosis_j
G_M[jj]<- G_M[jj] + meiosis_jj
}
k <- k + 1
}
}
if (verbose) print("Mutation")
Gprime_F <- array(0,dim=l)
Gprime_M <- array(0,dim=l)
for (j in 1 : l){
Gprime_F <- Gprime_F + as.vector(rmultinom(1,G_F[j],mu[,j]))
Gprime_M <- Gprime_M + as.vector(rmultinom(1,G_M[j],mu[,j]))
}
G_F <- Gprime_F
G_M <- Gprime_M
if (verbose) print("Union of gametes to form zygotes")
if (sum(G_F) <= sum(G_M)) {
mat_geno<- array(0,dim=c(l,l))
Gprime_M<- G_M
for (j in 1 : l) {
in_dist<- Gprime_M
odds<- array(1,dim=l)
ndraws<- G_F[j]
err<- try(rMWNCHypergeo(1,in_dist,ndraws,odds),silent=T)
if (class(err)=="try-error") {
extr<- as.numeric(rmultinom(1,ndraws,in_dist))
} else {
extr<- err
}
mat_geno[j,]<- extr
Gprime_M<- Gprime_M - extr
}
mat_geno_l<- mat_geno
mat_geno_l[upper.tri(mat_geno_l)]<- 0
mat_geno_u<- mat_geno
mat_geno_u[lower.tri(mat_geno_u,diag=T)]<- 0
mat_geno_f<- mat_geno_l + t(mat_geno_u)
L<- mat_geno_f[lower.tri(mat_geno_f,diag=T)]
} else {
mat_geno<- array(0,dim=c(l,l))
Gprime_F<- G_F
for (j in 1 : l) {
in_dist<- Gprime_F
odds<- array(1,dim=l)
ndraws<- G_M[j]
err<- try(rMWNCHypergeo(1,in_dist,ndraws,odds),silent=T)
if (class(err)=="try-error") {
extr<- as.numeric(rmultinom(1,ndraws,in_dist))
} else {
extr<- err
}
mat_geno[j,]<- extr
Gprime_F<- Gprime_F - extr
}
mat_geno_l<- mat_geno
mat_geno_l[upper.tri(mat_geno_l)]<- 0
mat_geno_u<- mat_geno
mat_geno_u[lower.tri(mat_geno_u,diag=T)]<- 0
mat_geno_f<- mat_geno_l + t(mat_geno_u)
L<- mat_geno_f[lower.tri(mat_geno_f,diag=T)]
}
return(L)
}
# Dispersal
disp <-
function(L,delta){
delta_lost <- max(0,1 - sum(delta)) # The maximum function is needed to avoid errors due to precision
delta_add <- c(delta,delta_lost)
S <- rmultinom(1,L,delta_add)
return(S)
}
# Recruitment
# S Number of settlers of all genotypes. Dimension: m
# N Number of adults of all genotyps and age-classes. Dimensions: m*z
# m Number of genotypes
# kappa0 Carrying capacity. Scalar.
# recr.dd String
# S[,i],Nprime[,i,],m,kappa0[i,t],recr.dd
#S <- S[,i]
#N <- Nprime[,i,]
#kappa0 <- kappa0[i,t]
recr <-
function(S,N,m,kappa0,recr.dd) {
switch(recr.dd,
# Dependence on settler density
settlers = {
Ntot <- sum(S)
sigma0 <- settler.survival(Ntot,kappa0)
},
# Dependence on adult density
adults = {
if (z==1) Ntot <- 0 else Ntot <- sum(N[,1:(z-1)]) # Does not count z because they will "shift out" with the aging function
Stot <- sum(S)
Recr <- kappa0 - Ntot
if (Recr <= 0){
sigma0 <- 0
} else {
sigma0 <- Recr / Stot
}
if (sigma0 > 1) sigma0 <- 1
},
# No-match: error
stop("Unknown value for argument recr.dd. Valid values: 'settlers', 'adults'")
)
# Use recruitment probability to calculate the number of recruits
R <- array(0,dim=m)
for (k in 1 : m) {
R[k] <- rbinom(1,S[k],sigma0)
}
return(R)
}
##########################################################################
# Simulate metapopulation genetics
##########################################################################
if (save.res){
dir.res.name <- paste(getwd(),format(Sys.time(), "%Y-%b-%d-%H.%M.%S"),sep="/")
dir.create(dir.res.name)
if (1 %in% save.res.T) {
file.name <- "N1.RData"
save(N,file=paste(dir.res.name,file.name,sep="/"))
}
}
print("Running simulation...")
for (t in 1 : (T_max-1)) {
print(t)
# At each time-step, redefine variable Nprime
# If save.res, redefine also larval and settlers numbers
if (save.res) {
Nprime <- array(NA,dim=c(m,n,z))
L <- array(NA,dim=c(m,n))
S <- array(0,dim=c(m,n))
} else {
Nprime <- array(NA,dim=c(m,n,z))
}
### Survival
# If there is only one age-class, we must force the third dimension. What if only one year?
if (length(dim(sigma))==2) dim(sigma)[3] <- 1
if (verbose) print("Apply survival function")
for (i in 1 : n) {
for (x in 1 : z) {
for (k in 1 : m) {
if (save.res){
Nprime[k,i,x] = surv(sigma[k,i,x,t],N[k,i,x])
} else {
Nprime[k,i,x] = surv(sigma[k,i,x,t],N[k,i,x,t])
}
}
}
}
if (verbose) print("Apply reproduction function")
# If there is only one age-class, we must force the third dimension
if (length(dim(phi_F))==2) dim(phi_F)[3] <- 1
if (length(dim(phi_M))==2) dim(phi_M)[3] <- 1
for (i in 1 : n) {
if (save.res) {
if (sum(Nprime[,i,])==0) { # To save computing time
L[,i] = 0
next
} else {
L[,i] <- repr(Nprime,phi_F[,,,t],phi_M[,,,t],mu,i,l,m)
}
} else {
if (sum(Nprime[,i,])==0) { # To save computing time
L[,i,t] = 0
next
} else {
L[,i,t] <- repr(Nprime,phi_F[,,,t],phi_M [,,,t],mu,i,l,m)
}
}
}
if (verbose) print("Apply dispersal function")
for (i in 1 : n) {
for (k in 1 : m) {
if (save.res) {
y = disp(L[k,i],delta[,i,t])
S[k,] <- S[k,] + y[1:n]
} else {
y = disp(L[k,i,t],delta[,i,t])
S[k,,t] <- S[k,,t] + y[1:n]
}
}
}
if (verbose) print("Apply recruitment function")
for (i in 1 : n) {
if (save.res) {
N[,i,1] <- recr(S[,i],Nprime[,i,],m,kappa0[i,t],recr.dd)
} else {
N[,i,1,t+1] <- recr(S[,i,t],Nprime[,i,],m,kappa0[i,t],recr.dd)
}
}
if (verbose) print("Calculates N at t+1")
for (i in 1 : n){
for (x in 1 : z) {
if (x == 1) next
for (k in 1 : m) {
if (save.res) {
N[k,i,x] <- Nprime[k,i,x-1]
} else {
N[k,i,x,t+1] <- Nprime[k,i,x-1]
}
}
}
}
# Save results if save.res=T
if (save.res){
if ((t+1) %in% save.res.T) {
file.name <- paste("N",(t+1),".RData",sep="")
save(N,file=paste(dir.res.name,file.name,sep="/"))
}
}
}
print(T_max)
print("...done")
if (save.res==F) return(N)
}
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