436 lines
16 KiB
R
436 lines
16 KiB
R
set.seed(999) ## for same random sequence
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#require(hacks)
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#13/05/2015
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#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
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## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
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## For example
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## stock res yr ct
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## cap-icel Medium 1984 1234.32
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## filename <- "RAM_MSY.csv"
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##filename <- "ICESct2.csv"
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cat("Step 1","\n")
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TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
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filename <- "D20.csv"
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outfile <- "CatchMSY_Output.csv"
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outfile2 <- paste("NonProcessedSpecies.csv",sep="")
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#cdat <- read.csv2(filename, header=T, dec=".")
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cdat1 <- read.csv(filename)
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cat("\n", "File", filename, "read successfully","\n")
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cat("Step 2","\n")
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if(file.exists("cdat.RData"))
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{load("cdat.RData")} else
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{
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dim(cdat1)
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yrs=1950:2013
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# to set NA as 0
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cdat1[is.na(cdat1)] <- 0
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nrow <- length(cdat1[,1])
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ndatColn <- length(cdat1[1,c(-1:-12)])
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rownames(cdat1) <- NULL
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cdat <- NULL
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for(i in 1:nrow)
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#for(i in 1:5)
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{#i=1
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#a <- ctotal3[i,-1]
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tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
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species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
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yr=yrs,ct=unlist(c(cdat1[i,-c(1:12)])),
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res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
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cdat <- rbind(cdat,tmp)
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#edit(cdat)
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}
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save(cdat,file="cdat.RData")
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}
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StockList=unique(as.character(cdat$stock))
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cat("Step 3","\n")
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## FUNCTIONS are going to be used subsequently
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.schaefer <- function(theta)
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{
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with(as.list(theta), { ## for all combinations of ri & ki
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bt=vector()
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ell = 0 ## initialize ell
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J=0 #Ye
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for (j in startbt)
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{
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if(ell == 0)
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{
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bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
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ell = 0
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if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
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ell = 1
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J=j # Ye
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}
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}
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return(list(ell=ell,J=J)) # Ye adding J=J
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})
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}
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sraMSY <-function(theta, N)
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{
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#This function conducts the stock reduction
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#analysis for N trials
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#args:
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# theta - a list object containing:
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# r (lower and upper bounds for r)
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# k (lower and upper bounds for k)
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# lambda (limits for current depletion)
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with(as.list(theta),
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{
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ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
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ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
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itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
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## assign ri, ki, and final biomass range to itheta
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M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
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i=1:N
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## prototype objective function
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get.ell=function(i) M[[i]]$ell
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ell = sapply(i, get.ell)
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get.J=function(i) M[[i]]$J # Ye
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J=sapply(i,get.J) # Ye
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return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
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})
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}
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getBiomass <- function(r, k, j)
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{
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BT <- NULL
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bt=vector()
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for (v in 1:length(r))
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{
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bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
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for(i in 1:nyr) ## for all years in the time series
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{
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xt=rnorm(1,0, sigR)
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bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
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## calculate biomass as function of previous year's biomass plus net production minus catch
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}
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BT=rbind(BT, t(t(bt)))
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}
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return(BT)
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}
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## The End of Functions section
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cat("Step 4","\n")
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stockLoop <- StockList
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# randomly select stocks from randomly selected 5 area codes first
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if(TestRUN)
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{
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set.seed(999)
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AreaCodeList <- unique(cdat1$AREA_Code)
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sampledAC <- sample(AreaCodeList,size=5,replace=F)
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stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
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}
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#setup counters
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counter1 <- 0
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counter2 <- 0
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cat("Step 4","\n")
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## Loop through stocks
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for(stock in stockLoop)
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{
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t0<-Sys.time()
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##stock = "3845" # NB only for test single loop!
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## make graph file names:
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b <- with(cdat1,cdat1[Stock_ID == stock,c(1,3,5,12)]) # Stock_ID,AREA_Names,Country,"Species"
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bb <- do.call(paste,b)
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yr <- cdat$yr[as.character(cdat$stock)==stock]
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ct <- as.numeric(cdat$ct[as.character(cdat$stock)==stock])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes
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res <- unique(as.character(cdat$res[as.character(cdat$stock)==stock])) ## resilience from FishBase, if needed, enable in PARAMETER SECTION
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nyr <- length(yr) ## number of years in the time series
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cat("\n","Stock",stock,"\n")
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flush.console()
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## PARAMETER SECTION
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mvlen=3
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ma=function(x,n=mvlen){filter(x,rep(1/n,n),sides=1)}
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## If resilience is to be used, delete ## in rows 1-4 below and set ## in row 5 below
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start_r <- if(res == "Very low"){c(0.015, 0.1)}else{
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if(res == "Low") {c(0.05,0.5)}else {
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if(res == "High") {c(0.6,1.5)}else {c(0.2,1)}
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}
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}
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## Medium, or default if no res is found
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##start_r <- c(0.5,1.5) ## disable this line if you use resilience
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start_k <- c(max(ct),50*max(ct)) ## default for upper k e.g. 100 * max catch
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## startbio <- c(0.8,1) ## assumed biomass range at start of time series, as fraction of k
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##startbio <- if(ct[1]/max(ct) < 0.5) {c(0.5,0.9)} else {c(0.3,0.6)} ## use for batch processing
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## NB: Yimin's new idea on 20Jan14
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startbio<- if(mean(ct[1:5])/max(ct) < 0.3) {c(0.6,0.95)} else {
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if(mean(ct[1:5])/max(ct)>0.3&mean(ct[1:5])/max(ct)<0.6) {c(0.3,0.7)} else {
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c(0.2,0.6)}}
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interyr <- yr[2] ## interim year within time series for which biomass estimate is available; set to yr[2] if no estimates are available
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interbio <- c(0, 1) ## biomass range for interim year, as fraction of k; set to 0 and 1 if not available
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## finalbio <- c(0.8, 0.9) ## biomass range after last catches, as fraction of k
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## finalbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.3,0.7)} else {c(0.01,0.4)} ## use for batch processing
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## Yimin's new stuff on 10Mar14
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#######> pre-classification
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pre.clas=ct
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pre.clas[pre.clas==0]=0.1
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tx=ma(as.numeric(pre.clas),n=mvlen)
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Myr=which.max(tx)
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Maxc=pre.clas[which.max(tx)]
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if(Myr==1)startbio=c(0.05,0.6)else
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{
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if (ct[1]/Maxc>=0.5) startbio=c(0.4,0.85)
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else startbio=c(0.65,0.95)
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}
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if (Myr==length(yr))finalbio=c(.4,.95) else # ie from fully to overexploited
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{
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if (tx[length(ct)]/Maxc>=0.5) finalbio=c(.4,.85)
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else finalbio=c(.05,.6)
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}
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# if (Myr==length(yr))finalbio=c(.5,.9)
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# #if (Myr<length(yr)){
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# # if ((tx[length(ct)]/Maxc)>=0.8) finalbio=c(.4,.8) else
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# # if (tx[length(ct)]/Maxc>0.5) finalbio=c(.3,.7) else finalbio=c(.05,.6)}
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# # below is the last used (20 Feb)
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# if (Myr<length(yr))
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# {
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# if (tx[length(ct)]/Maxc>0.5) finalbio=c(.2,.8)
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# else finalbio=c(.05,.6)
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# }
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##############<
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n <- 30000 ## number of iterations, e.g. 100000
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sigR <- 0.0 ## process error; 0 if deterministic model; 0.05 reasonable value? 0.2 is too high
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startbt <- seq(startbio[1], startbio[2], by = 0.05) ## apply range of start biomass in steps of 0.05
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parbound <- list(r = start_r, k = start_k, lambda = finalbio, sigR)
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cat("Last year =",max(yr),", last catch =",1000*ct[nyr],"\n")
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cat("Resilience =",res,"\n")
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cat("Process error =", sigR,"\n")
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cat("Assumed initial biomass (B/k) =", startbio[1],"-", startbio[2], " k","\n")
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cat("Assumed intermediate biomass (B/k) in", interyr, " =", interbio[1],"-",interbio[2]," k","\n")
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cat("Assumed final biomass (B/k) =", parbound$lambda[1],"-",parbound$lambda[2]," k","\n")
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cat("Initial bounds for r =", parbound$r[1], "-", parbound$r[2],"\n")
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cat("Initial bounds for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n")
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flush.console()
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## MAIN
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R1 = sraMSY(parbound, n)
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## Get statistics on r, k, MSY and determine new bounds for r and k
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r1 <- R1$r[R1$ell==1]
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k1 <- R1$k[R1$ell==1]
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j1 <- R1$J[R1$ell==1] # Ye
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msy1 <- r1*k1/4
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mean_msy1 <- exp(mean(log(msy1)))
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max_k1a <- min(k1[r1<1.1*parbound$r[1]]) ## smallest k1 near initial lower bound of r
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max_k1b <- max(k1[r1*k1/4<mean_msy1]) ## largest k1 that gives mean MSY
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max_k1 <- if(max_k1a < max_k1b) {max_k1a} else {max_k1b}
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if(length(r1)<10)
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{
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cat("Too few (", length(r1), ") possible r-k combinations,
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check input parameters","\n")
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appendPar <- ifelse(counter1==0,F,T)
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colnamePar <- ifelse(counter1==0,T,F)
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NoModellingSpe <- as.data.frame(cbind(stock,length(r1),b))
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names(NoModellingSpe) <- c("Stock","No_of_r1",names(b))
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write.table(NoModellingSpe,file=outfile2,
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append = appendPar, row.names = FALSE,
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col.names=colnamePar,sep=",")
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flush.console()
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counter1 <- counter1 + 1
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}
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if(length(r1)>=10)
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{
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## set new upper bound of r to 1.2 max r1
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parbound$r[2] <- 1.2*max(r1)
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## set new lower bound for k to 0.9 min k1 and upper bound to max_k1
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parbound$k <- c(0.9 * min(k1), max_k1)
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cat("First MSY =", format(1000*mean_msy1, digits=3),"\n")
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cat("First r =", format(exp(mean(log(r1))), digits=3),"\n")
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cat("New upper bound for r =", format(parbound$r[2],digits=2),"\n")
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cat("New range for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n")
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## Repeat analysis with new r-k bounds
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R1 = sraMSY(parbound, n)
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## Get statistics on r, k and msy
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r = R1$r[R1$ell==1]
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k = R1$k[R1$ell==1]
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j = R1$J[R1$ell==1] # Ye
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msy = r * k / 4
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mean_ln_msy = mean(log(msy))
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##############################################################
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##> Ye
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# BT=0
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##
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R2<-getBiomass(r, k, j)
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#R2<-R2[-1,]
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runs<-rep(1:length(r), each=nyr+1)
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years=rep(yr[1]:(yr[length(yr)]+1),length=length(r)*(length(yr)+1))
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runs=t(runs)
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years=t(years)
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stock_id=rep(stock,length(runs))
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R3<-cbind(as.numeric(runs), as.numeric(years), stock_id, as.numeric(R2) )
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## changed this, as otherwise biomass is the level of the factor below
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R4<-data.frame(R3, stringsAsFactors=FALSE)
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names(R4)<-c("Run", "Year", "Stock","Biomass")
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Bmsy_x<-k*0.5
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Run<-c(1:length(r))
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BMSY<-cbind(Run, Bmsy_x)
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R5<-merge(R4, BMSY, by="Run", all.x=T, all.y=F)
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R5$B_Bmsy<-as.numeric(paste(R5$Biomass))/R5$Bmsy_x
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### B/Bmsy calculated for all feasible combinations of r,K,B0
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R6<-aggregate(log(B_Bmsy)~as.numeric(Year)+Stock, data=R5,
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FUN=function(z){c(mean=mean(z),sd=sd(z),upr=exp(quantile(z, p=0.975)),
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lwr=exp(quantile(z, p=0.025)), lwrQ=exp(quantile(z, p=0.25)),
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uprQ=exp(quantile(z, p=0.75)))}) # from directly calculated from R5 becasue B_Bmsy has a lognormal dist
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R6<-data.frame(cbind(R6[,1:2],R6[,3][,1],R6[,3][,2],R6[,3][,3],R6[,3][,4],R6[,3][,5], R6[,3][,6]))
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names(R6)<-c("Year", "Stock", "BoverBmsy", "BoverBmsySD","BoverBmsyUpper","BoverBmsyLower","BoverBmsylwrQ","BoverBmsyuprQ")
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##remove last entry as it is 1 greater than number of years
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## removed final year here for ease of dataframe output below
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R6<-R6[-length(R6),]
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## geometric mean
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GM_B_Bmsy<-exp(R6$BoverBmsy)
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GM_B_BmsySD=R6$BoverBmsySD #add
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## arithmetic mean
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M_B_Bmsy<-exp(R6$BoverBmsy+R6$BoverBmsySD^2/2)
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### r,k, and MSY
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#del GM_B_Bmsy=c(rep(0,(min(yr)-1940)),GM_B_Bmsy)
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#del GM_B_BmsySD=c(rep(0,(min(yr)-1940)),GM_B_BmsySD) ######
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#del M_B_Bmsy=c(rep(0,(min(yr)-1940)),M_B_Bmsy)
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#del yr1=seq(1940,max(yr))
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yr1=yr #add
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stockInfo <- with(cdat1,cdat1[Stock_ID==stock,1:12])
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temp=c(startbio[1],startbio[2],finalbio[1],finalbio[2],res,
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mean(log(r)),sd(log(r)),mean(log(k)),sd(log(k)),mean(log(msy)),
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sd(log(msy)),sigR,min(yr),max(yr),max(ct),length(r),GM_B_Bmsy,GM_B_BmsySD,M_B_Bmsy)
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#add, adding "GM_B_BmsySD" in the line above
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output=as.data.frame(matrix(temp,nrow=1))
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output <- cbind(stockInfo,output)
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names(output) <- c(names(cdat1)[1:12],"startbio[1]","startbio[2]","finalbio[1]","finalbio[2]",
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"res","mean(log(r))","sd(log(r))","mean(log(k))","sd(log(k))",
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"mean(log(msy))","sd(log(msy))","sigR","min(yr)","max(yr)","max(ct)",
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"length(r)",paste("GM_B_msy",yr1,sep="_"),paste("GM_B_msySD",yr1,sep="_"),paste("M_B_Bmsy",yr1,sep="_"))
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#add, adding "paste("GM_B_msySD",yr1,sep="_")"in the line above
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######< Ye
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########################################################
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## plot MSY over catch data
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pdf(paste(bb,"graph.pdf",sep="_"))
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par(mfcol=c(2,3))
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plot(yr, ct, type="l", ylim = c(0, max(ct)), xlab = "Year",
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ylab = "Catch (1000 t)",main = paste("StockID",stock,sep=":"))
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abline(h=exp(mean(log(msy))),col="red", lwd=2)
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abline(h=exp(mean_ln_msy - 2 * sd(log(msy))),col="red")
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abline(h=exp(mean_ln_msy + 2 * sd(log(msy))),col="red")
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hist(r, freq=F, xlim=c(0, 1.2 * max(r)), main = "")
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abline(v=exp(mean(log(r))),col="red",lwd=2)
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abline(v=exp(mean(log(r))-2*sd(log(r))),col="red")
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abline(v=exp(mean(log(r))+2*sd(log(r))),col="red")
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plot(r1, k1, xlim = start_r, ylim = start_k, xlab="r", ylab="k (1000t)")
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hist(k, freq=F, xlim=c(0, 1.2 * max(k)), xlab="k (1000t)", main = "")
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abline(v=exp(mean(log(k))),col="red", lwd=2)
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abline(v=exp(mean(log(k))-2*sd(log(k))),col="red")
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abline(v=exp(mean(log(k))+2*sd(log(k))),col="red")
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plot(log(r), log(k),xlab="ln(r)",ylab="ln(k)")
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abline(v=mean(log(r)))
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abline(h=mean(log(k)))
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abline(mean(log(msy))+log(4),-1, col="red",lwd=2)
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abline(mean(log(msy))-2*sd(log(msy))+log(4),-1, col="red")
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abline(mean(log(msy))+2*sd(log(msy))+log(4),-1, col="red")
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hist(msy, freq=F, xlim=c(0, 1.2 * max(msy)), xlab="MSY (1000t)",main = "")
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abline(v=exp(mean(log(msy))),col="red", lwd=2)
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abline(v=exp(mean_ln_msy - 2 * sd(log(msy))),col="red")
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abline(v=exp(mean_ln_msy + 2 * sd(log(msy))),col="red")
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graphics.off()
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cat("Possible combinations = ", length(r),"\n")
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cat("geom. mean r =", format(exp(mean(log(r))),digits=3), "\n")
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cat("r +/- 2 SD =", format(exp(mean(log(r))-2*sd(log(r))),digits=3),"-",format(exp(mean(log(r))+2*sd(log(r))),digits=3), "\n")
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cat("geom. mean k =", format(1000*exp(mean(log(k))),digits=3), "\n")
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cat("k +/- 2 SD =", format(1000*exp(mean(log(k))-2*sd(log(k))),digits=3),"-",format(1000*exp(mean(log(k))+2*sd(log(k))),digits=3), "\n")
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cat("geom. mean MSY =", format(1000*exp(mean(log(msy))),digits=3),"\n")
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cat("MSY +/- 2 SD =", format(1000*exp(mean_ln_msy - 2 * sd(log(msy))),digits=3), "-", format(1000*exp(mean_ln_msy + 2 * sd(log(msy))),digits=3), "\n")
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## Write results into outfile, in append mode (no header in file, existing files will be continued)
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## output = data.frame(stock, sigR, startbio[1], startbio[2], interbio[1], interbio[2], finalbio[1], finalbio[2], min(yr), max(yr), res, max(ct), ct[1], ct[nyr], length(r), exp(mean(log(r))), sd(log(r)), min(r), quantile(r,0.05), quantile(r,0.25), median(r), quantile(r,0.75), quantile(r,0.95), max(r), exp(mean(log(k))), sd(log(k)), min(k), quantile(k, 0.05), quantile(k, 0.25), median(k), quantile(k, 0.75), quantile(k, 0.95), max(k), exp(mean(log(msy))), sd(log(msy)), min(msy), quantile(msy, 0.05), quantile(msy, 0.25), median(msy), quantile(msy, 0.75), quantile(msy, 0.95), max(msy))
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#write.table(output, file = outfile, append = TRUE, sep = ";", dec = ".", row.names = FALSE, col.names = FALSE)
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appendPar <- ifelse(counter2==0,F,T)
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colnamePar <- ifelse(counter2==0,T,F)
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write.table(output, file = outfile, append = appendPar, sep = ",", dec = ".",
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row.names = FALSE, col.names = colnamePar)
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counter2 <- counter2 + 1
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}
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cat("Elapsed: ",Sys.time()-t0," \n")
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} ## End of stock loop, get next stock or exit
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