Nothing
#' Function that runs the Monte Carlo simulation for the Mussel population model
#'
#' @param Param a vector containing model parameters
#' @param times integration extremes and integration timestep
#' @param IC initial condition
#' @param Tint the interpolated water temperature time series
#' @param Phyint the interpolated phytoplankton time series
#' @param DTint the interpolated detritus time series
#' @param POCint the interpolated POC time series
#' @param Ccont the C/C content of the POC
#' @param Ncont the N/C content of POC
#' @param Pcont the P/C content of POC
#' @param POMint the interpolated POM time series
#' @param TSSint the interpolated TSS time series
#' @param N time series with number of individuals
#' @param userpath the path where the working folder is located
#'
#' @return a list with RK solver outputs
#'
#' @import matrixStats plotrix rstudioapi
#'
Mussel_pop_loop<-function(Param, times, IC, Tint, Phyint, DTint, POCint, Ccont, Ncont, Pcont, POMint, TSSint,N,userpath) {
cat("Population processing\n")
ti=times[1]
tf=times[2]
t0=times[4]
# Read files with population parameters and management strategies
Pop_matrix=read.csv(paste0(userpath,"/Mussel_population/Inputs/Parameters//Population.csv"),sep=",") # Reading the matrix containing population parameters and their description
Management=read.csv(paste0(userpath,"/Mussel_population/Inputs/Population_management//Management.csv"),sep=",") # Reading the matrix containing seeding and harvesting management
# Extract population parameters
meanWd=as.double(as.matrix(Pop_matrix[1,3])) # [g] Dry weight average
deltaWd=as.double(as.matrix(Pop_matrix[2,3])) # [g] Dry weight standard deviation
Wdlb=as.double(as.matrix(Pop_matrix[3,3])) # [g] Dry weight lower bound
meanCRmax=as.double(as.matrix(Pop_matrix[4,3])) # [l/d gDW] Clearence rate average
deltaCRmax=as.double(as.matrix(Pop_matrix[5,3])) # [l/d gDW] Clearance rate standard deviation
Nseed=as.double(as.matrix(Pop_matrix[6,3])) # [-] number of seeded individuals
mort=as.double(as.matrix(Pop_matrix[7,3])) # [1/d] natural mortality rate
nruns=as.double(as.matrix(Pop_matrix[8,3])) # [-] number of runs for population simulation
# Prepare management values
manag=as.matrix(matrix(0,nrow=length(Management[,1]),ncol=2))
for (i in 1:length(Management[,1])) {
manag[i,1]=as.numeric(as.Date(Management[i,1], "%d/%m/%Y"))-t0
if ((Management[i,2])=="h") {
manag[i,2]=-as.numeric(Management[i,3])
} else {
manag[i,2]=as.numeric(Management[i,3])
}
}
# Vectors initialization
saveIC=as.vector(matrix(0,nrow=nruns))
saveCRmax=as.vector(matrix(0,nrow=nruns))
Wb=as.matrix(matrix(0,nrow=nruns,ncol=tf))
R=as.matrix(matrix(0,nrow=nruns,ncol=tf))
Wd=as.matrix(matrix(0,nrow=nruns,ncol=tf))
W=as.matrix(matrix(0,nrow=nruns,ncol=tf))
L=as.matrix(matrix(0,nrow=nruns,ncol=tf))
fecC=as.matrix(matrix(0,nrow=nruns,ncol=tf))
fecN=as.matrix(matrix(0,nrow=nruns,ncol=tf))
fecP=as.matrix(matrix(0,nrow=nruns,ncol=tf))
psC=as.matrix(matrix(0,nrow=nruns,ncol=tf))
psN=as.matrix(matrix(0,nrow=nruns,ncol=tf))
psP=as.matrix(matrix(0,nrow=nruns,ncol=tf))
Cmyt=as.matrix(matrix(0,nrow=nruns,ncol=tf))
Nmyt=as.matrix(matrix(0,nrow=nruns,ncol=tf))
Pmyt=as.matrix(matrix(0,nrow=nruns,ncol=tf))
A=as.matrix(matrix(0,nrow=nruns,ncol=tf))
C=as.matrix(matrix(0,nrow=nruns,ncol=tf))
O2=as.matrix(matrix(0,nrow=nruns,ncol=tf))
NH4=as.matrix(matrix(0,nrow=nruns,ncol=tf))
# Loop for ODE solution
pb <- txtProgressBar(min = 0, max = nruns, style = 3)
for (ii in 1:nruns){
# Weight initialization
IC=rnorm(1,meanWd,deltaWd) # [g] initial weight extracted from a normal distribution
IC=max(IC, Wdlb) # Lower bound for weight distribution
saveIC[ii]=IC # Saves initial condition values on Wd for each run
# Maximum clearance rate initialization
CRmax=rnorm(1,meanCRmax,deltaCRmax) # [l/d gDW] Maximum clearance rate extracted from a normal distribution
CRmax=max(CRmax,0) # Forces maximum clearance rate to be positive
saveCRmax[ii]=CRmax # Saves initial condition values on CRmax for each run
# Perturbe the parameters vector
Param[7]=CRmax
# Solves ODE with perturbed parameters
output<-Mussel_pop_RKsolver(Param, times, IC, Tint, Phyint, DTint, POCint, Ccont, Ncont, Pcont, POMint, TSSint, N)
# Extract outputs
weight=t(output[[1]])
pfec=output[[2]]
fec=output[[3]]
comp=output[[4]]
Tfun=output[[5]]
metab=output[[6]]
cons=output[[7]]
amm=output[[8]]
# Saves results of each run to compute statistics
Wb[ii,1:length(weight[,1])]=weight[,1] # Somatic tissue dry weight [g]
R[ii,1:length(weight[,2])]=weight[,2] # Gonadic tissue dry weight [g]
Wd[ii,1:length(weight[,3])]=weight[,3] # Total dry weight [g]
W[ii,1:length(weight[,4])]=weight[,4] # Mussel weight with shell [g]
L[ii,1:length(weight[,5])]=weight[,5] # Mussel length [g]
psC[ii,1:length(fec[,1])]=pfec[,1] # pseudofecies C production
psN[ii,1:length(fec[,2])]=pfec[,2] # pseudofecies N production
psP[ii,1:length(fec[,3])]=pfec[,3] # pseudofecies P production
fecC[ii,1:length(fec[,1])]=fec[,1] # fecies C production
fecN[ii,1:length(fec[,1])]=fec[,2] # fecies N production
fecP[ii,1:length(fec[,1])]=fec[,3] # fecies P production
Cmyt[ii,1:length(comp[,1])]=comp[,1] # Mussel C content
Nmyt[ii,1:length(comp[,2])]=comp[,2] # Mussel N content
Pmyt[ii,1:length(comp[,3])]=comp[,3] # Mussel P content
A[ii,1:length(metab[,1])]=metab[,1] # Net anabolism [J/d]
C[ii,1:length(metab[,2])]=metab[,2] # Fasting catabolism [J/d]
O2[ii,1:length(cons)]=cons # Oxygen consumtion rate
NH4[ii,1:length(amm)]=amm # ammonium release
setTxtProgressBar(pb, ii)
} # Close population loop
close(pb)
# Temperaure limitation functions
fgT=Tfun[,1] # Optimum dependance from temperature for ingestion
frT=Tfun[,2] # Exponential dependance from temperature for catabolism
# Statistics computation
Wb_stat=rbind(colMeans(Wb), colSds(Wb))
R_stat=rbind(colMeans(R), colSds(R))
Wd_stat=rbind(colMeans(Wd), colSds(Wd))
W_stat=rbind(colMeans(W), colSds(W))
L_stat=rbind(colMeans(L), colSds(L))
fecC_stat=rbind(colMeans(fecC), colSds(fecC))
fecN_stat=rbind(colMeans(fecN), colSds(fecN))
fecP_stat=rbind(colMeans(fecP), colSds(fecP))
psC_stat=rbind(colMeans(psC), colSds(psC))
psN_stat=rbind(colMeans(psN), colSds(psN))
psP_stat=rbind(colMeans(psP), colSds(psP))
Cmyt_stat=rbind(colMeans(Cmyt), colSds(Cmyt))
Nmyt_stat=rbind(colMeans(Nmyt), colSds(Nmyt))
Pmyt_stat=rbind(colMeans(Pmyt), colSds(Pmyt))
A_stat=rbind(colMeans(A), colSds(A))
C_stat=rbind(colMeans(C), colSds(C))
O2_stat=rbind(colMeans(O2), colSds(O2))
NH4_stat=rbind(colMeans(NH4), colSds(NH4))
output=list(Wb_stat,R_stat,Wd_stat,W_stat,L_stat,fecC_stat,fecN_stat,fecP_stat,psC_stat,psN_stat,psP_stat,Cmyt_stat,Nmyt_stat,Pmyt_stat,A_stat,C_stat,O2_stat,NH4_stat,fgT,frT)
return(output)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.