set.seed(999) ## for same random sequence #require(hacks) #13/05/2015 #setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/") ## 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 ## For example ## stock res yr ct ## cap-icel Medium 1984 1234.32 ## filename <- "RAM_MSY.csv" ##filename <- "ICESct2.csv" cat("Step 1","\n") TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run! filename <- "D20.csv" outfile <- "CatchMSY_Output.csv" outfile2 <- paste("NonProcessedSpecies.csv",sep="") #cdat <- read.csv2(filename, header=T, dec=".") cdat1 <- read.csv(filename) cat("\n", "File", filename, "read successfully","\n") cat("Step 2","\n") if(file.exists("cdat.RData")) {load("cdat.RData")} else { dim(cdat1) yrs=1950:2013 # to set NA as 0 cdat1[is.na(cdat1)] <- 0 nrow <- length(cdat1[,1]) ndatColn <- length(cdat1[1,c(-1:-12)]) rownames(cdat1) <- NULL cdat <- NULL for(i in 1:nrow) #for(i in 1:5) {#i=1 #a <- ctotal3[i,-1] tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn), species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn), yr=yrs,ct=unlist(c(cdat1[i,-c(1:12)])), res=rep(cdat1[i,"ResilienceIndex"],ndatColn)) cdat <- rbind(cdat,tmp) #edit(cdat) } save(cdat,file="cdat.RData") } StockList=unique(as.character(cdat$stock)) cat("Step 3","\n") ## FUNCTIONS are going to be used subsequently .schaefer <- function(theta) { with(as.list(theta), { ## for all combinations of ri & ki bt=vector() ell = 0 ## initialize ell J=0 #Ye for (j in startbt) { if(ell == 0) { bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year for(i in 1:nyr) ## for all years in the time series { xt=rnorm(1,0, sigR) bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt) ## calculate biomass as function of previous year's biomass plus net production minus catch } #Bernoulli likelihood, assign 0 or 1 to each combination of r and k ell = 0 if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2]) ell = 1 J=j # Ye } } return(list(ell=ell,J=J)) # Ye adding J=J }) } sraMSY <-function(theta, N) { #This function conducts the stock reduction #analysis for N trials #args: # theta - a list object containing: # r (lower and upper bounds for r) # k (lower and upper bounds for k) # lambda (limits for current depletion) with(as.list(theta), { ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR) ## assign ri, ki, and final biomass range to itheta M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta i=1:N ## prototype objective function get.ell=function(i) M[[i]]$ell ell = sapply(i, get.ell) get.J=function(i) M[[i]]$J # Ye J=sapply(i,get.J) # Ye return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J }) } getBiomass <- function(r, k, j) { BT <- NULL bt=vector() for (v in 1:length(r)) { bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year for(i in 1:nyr) ## for all years in the time series { xt=rnorm(1,0, sigR) bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt) ## calculate biomass as function of previous year's biomass plus net production minus catch } BT=rbind(BT, t(t(bt))) } return(BT) } ## The End of Functions section cat("Step 4","\n") stockLoop <- StockList # randomly select stocks from randomly selected 5 area codes first if(TestRUN) { set.seed(999) AreaCodeList <- unique(cdat1$AREA_Code) sampledAC <- sample(AreaCodeList,size=5,replace=F) stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")] } #setup counters counter1 <- 0 counter2 <- 0 cat("Step 4","\n") ## Loop through stocks for(stock in stockLoop) { t0<-Sys.time() ##stock = "3845" # NB only for test single loop! ## make graph file names: b <- with(cdat1,cdat1[Stock_ID == stock,c(1,3,5,12)]) # Stock_ID,AREA_Names,Country,"Species" bb <- do.call(paste,b) yr <- cdat$yr[as.character(cdat$stock)==stock] ct <- as.numeric(cdat$ct[as.character(cdat$stock)==stock])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes res <- unique(as.character(cdat$res[as.character(cdat$stock)==stock])) ## resilience from FishBase, if needed, enable in PARAMETER SECTION nyr <- length(yr) ## number of years in the time series cat("\n","Stock",stock,"\n") flush.console() ## PARAMETER SECTION mvlen=3 ma=function(x,n=mvlen){filter(x,rep(1/n,n),sides=1)} ## If resilience is to be used, delete ## in rows 1-4 below and set ## in row 5 below start_r <- if(res == "Very low"){c(0.015, 0.1)}else{ if(res == "Low") {c(0.05,0.5)}else { if(res == "High") {c(0.6,1.5)}else {c(0.2,1)} } } ## Medium, or default if no res is found ##start_r <- c(0.5,1.5) ## disable this line if you use resilience start_k <- c(max(ct),50*max(ct)) ## default for upper k e.g. 100 * max catch ## startbio <- c(0.8,1) ## assumed biomass range at start of time series, as fraction of k ##startbio <- if(ct[1]/max(ct) < 0.5) {c(0.5,0.9)} else {c(0.3,0.6)} ## use for batch processing ## NB: Yimin's new idea on 20Jan14 startbio<- if(mean(ct[1:5])/max(ct) < 0.3) {c(0.6,0.95)} else { if(mean(ct[1:5])/max(ct)>0.3&mean(ct[1:5])/max(ct)<0.6) {c(0.3,0.7)} else { c(0.2,0.6)}} interyr <- yr[2] ## interim year within time series for which biomass estimate is available; set to yr[2] if no estimates are available interbio <- c(0, 1) ## biomass range for interim year, as fraction of k; set to 0 and 1 if not available ## finalbio <- c(0.8, 0.9) ## biomass range after last catches, as fraction of k ## finalbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.3,0.7)} else {c(0.01,0.4)} ## use for batch processing ## Yimin's new stuff on 10Mar14 #######> pre-classification pre.clas=ct pre.clas[pre.clas==0]=0.1 tx=ma(as.numeric(pre.clas),n=mvlen) Myr=which.max(tx) Maxc=pre.clas[which.max(tx)] if(Myr==1)startbio=c(0.05,0.6)else { if (ct[1]/Maxc>=0.5) startbio=c(0.4,0.85) else startbio=c(0.65,0.95) } if (Myr==length(yr))finalbio=c(.4,.95) else # ie from fully to overexploited { if (tx[length(ct)]/Maxc>=0.5) finalbio=c(.4,.85) else finalbio=c(.05,.6) } # if (Myr==length(yr))finalbio=c(.5,.9) # #if (Myr=0.8) finalbio=c(.4,.8) else # # if (tx[length(ct)]/Maxc>0.5) finalbio=c(.3,.7) else finalbio=c(.05,.6)} # # below is the last used (20 Feb) # if (Myr0.5) finalbio=c(.2,.8) # else finalbio=c(.05,.6) # } ##############< n <- 30000 ## number of iterations, e.g. 100000 sigR <- 0.0 ## process error; 0 if deterministic model; 0.05 reasonable value? 0.2 is too high startbt <- seq(startbio[1], startbio[2], by = 0.05) ## apply range of start biomass in steps of 0.05 parbound <- list(r = start_r, k = start_k, lambda = finalbio, sigR) cat("Last year =",max(yr),", last catch =",1000*ct[nyr],"\n") cat("Resilience =",res,"\n") cat("Process error =", sigR,"\n") cat("Assumed initial biomass (B/k) =", startbio[1],"-", startbio[2], " k","\n") cat("Assumed intermediate biomass (B/k) in", interyr, " =", interbio[1],"-",interbio[2]," k","\n") cat("Assumed final biomass (B/k) =", parbound$lambda[1],"-",parbound$lambda[2]," k","\n") cat("Initial bounds for r =", parbound$r[1], "-", parbound$r[2],"\n") cat("Initial bounds for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n") flush.console() ## MAIN R1 = sraMSY(parbound, n) ## Get statistics on r, k, MSY and determine new bounds for r and k r1 <- R1$r[R1$ell==1] k1 <- R1$k[R1$ell==1] j1 <- R1$J[R1$ell==1] # Ye msy1 <- r1*k1/4 mean_msy1 <- exp(mean(log(msy1))) max_k1a <- min(k1[r1<1.1*parbound$r[1]]) ## smallest k1 near initial lower bound of r max_k1b <- max(k1[r1*k1/4=10) { ## set new upper bound of r to 1.2 max r1 parbound$r[2] <- 1.2*max(r1) ## set new lower bound for k to 0.9 min k1 and upper bound to max_k1 parbound$k <- c(0.9 * min(k1), max_k1) cat("First MSY =", format(1000*mean_msy1, digits=3),"\n") cat("First r =", format(exp(mean(log(r1))), digits=3),"\n") cat("New upper bound for r =", format(parbound$r[2],digits=2),"\n") cat("New range for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n") ## Repeat analysis with new r-k bounds R1 = sraMSY(parbound, n) ## Get statistics on r, k and msy r = R1$r[R1$ell==1] k = R1$k[R1$ell==1] j = R1$J[R1$ell==1] # Ye msy = r * k / 4 mean_ln_msy = mean(log(msy)) ############################################################## ##> Ye # BT=0 ## R2<-getBiomass(r, k, j) #R2<-R2[-1,] runs<-rep(1:length(r), each=nyr+1) years=rep(yr[1]:(yr[length(yr)]+1),length=length(r)*(length(yr)+1)) runs=t(runs) years=t(years) stock_id=rep(stock,length(runs)) R3<-cbind(as.numeric(runs), as.numeric(years), stock_id, as.numeric(R2) ) ## changed this, as otherwise biomass is the level of the factor below R4<-data.frame(R3, stringsAsFactors=FALSE) names(R4)<-c("Run", "Year", "Stock","Biomass") Bmsy_x<-k*0.5 Run<-c(1:length(r)) BMSY<-cbind(Run, Bmsy_x) R5<-merge(R4, BMSY, by="Run", all.x=T, all.y=F) R5$B_Bmsy<-as.numeric(paste(R5$Biomass))/R5$Bmsy_x ### B/Bmsy calculated for all feasible combinations of r,K,B0 R6<-aggregate(log(B_Bmsy)~as.numeric(Year)+Stock, data=R5, FUN=function(z){c(mean=mean(z),sd=sd(z),upr=exp(quantile(z, p=0.975)), lwr=exp(quantile(z, p=0.025)), lwrQ=exp(quantile(z, p=0.25)), uprQ=exp(quantile(z, p=0.75)))}) # from directly calculated from R5 becasue B_Bmsy has a lognormal dist 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])) names(R6)<-c("Year", "Stock", "BoverBmsy", "BoverBmsySD","BoverBmsyUpper","BoverBmsyLower","BoverBmsylwrQ","BoverBmsyuprQ") ##remove last entry as it is 1 greater than number of years ## removed final year here for ease of dataframe output below R6<-R6[-length(R6),] ## geometric mean GM_B_Bmsy<-exp(R6$BoverBmsy) GM_B_BmsySD=R6$BoverBmsySD #add ## arithmetic mean M_B_Bmsy<-exp(R6$BoverBmsy+R6$BoverBmsySD^2/2) ### r,k, and MSY #del GM_B_Bmsy=c(rep(0,(min(yr)-1940)),GM_B_Bmsy) #del GM_B_BmsySD=c(rep(0,(min(yr)-1940)),GM_B_BmsySD) ###### #del M_B_Bmsy=c(rep(0,(min(yr)-1940)),M_B_Bmsy) #del yr1=seq(1940,max(yr)) yr1=yr #add stockInfo <- with(cdat1,cdat1[Stock_ID==stock,1:12]) temp=c(startbio[1],startbio[2],finalbio[1],finalbio[2],res, mean(log(r)),sd(log(r)),mean(log(k)),sd(log(k)),mean(log(msy)), sd(log(msy)),sigR,min(yr),max(yr),max(ct),length(r),GM_B_Bmsy,GM_B_BmsySD,M_B_Bmsy) #add, adding "GM_B_BmsySD" in the line above output=as.data.frame(matrix(temp,nrow=1)) output <- cbind(stockInfo,output) names(output) <- c(names(cdat1)[1:12],"startbio[1]","startbio[2]","finalbio[1]","finalbio[2]", "res","mean(log(r))","sd(log(r))","mean(log(k))","sd(log(k))", "mean(log(msy))","sd(log(msy))","sigR","min(yr)","max(yr)","max(ct)", "length(r)",paste("GM_B_msy",yr1,sep="_"),paste("GM_B_msySD",yr1,sep="_"),paste("M_B_Bmsy",yr1,sep="_")) #add, adding "paste("GM_B_msySD",yr1,sep="_")"in the line above ######< Ye ######################################################## ## plot MSY over catch data pdf(paste(bb,"graph.pdf",sep="_")) par(mfcol=c(2,3)) plot(yr, ct, type="l", ylim = c(0, max(ct)), xlab = "Year", ylab = "Catch (1000 t)",main = paste("StockID",stock,sep=":")) abline(h=exp(mean(log(msy))),col="red", lwd=2) abline(h=exp(mean_ln_msy - 2 * sd(log(msy))),col="red") abline(h=exp(mean_ln_msy + 2 * sd(log(msy))),col="red") hist(r, freq=F, xlim=c(0, 1.2 * max(r)), main = "") abline(v=exp(mean(log(r))),col="red",lwd=2) abline(v=exp(mean(log(r))-2*sd(log(r))),col="red") abline(v=exp(mean(log(r))+2*sd(log(r))),col="red") plot(r1, k1, xlim = start_r, ylim = start_k, xlab="r", ylab="k (1000t)") hist(k, freq=F, xlim=c(0, 1.2 * max(k)), xlab="k (1000t)", main = "") abline(v=exp(mean(log(k))),col="red", lwd=2) abline(v=exp(mean(log(k))-2*sd(log(k))),col="red") abline(v=exp(mean(log(k))+2*sd(log(k))),col="red") plot(log(r), log(k),xlab="ln(r)",ylab="ln(k)") abline(v=mean(log(r))) abline(h=mean(log(k))) abline(mean(log(msy))+log(4),-1, col="red",lwd=2) abline(mean(log(msy))-2*sd(log(msy))+log(4),-1, col="red") abline(mean(log(msy))+2*sd(log(msy))+log(4),-1, col="red") hist(msy, freq=F, xlim=c(0, 1.2 * max(msy)), xlab="MSY (1000t)",main = "") abline(v=exp(mean(log(msy))),col="red", lwd=2) abline(v=exp(mean_ln_msy - 2 * sd(log(msy))),col="red") abline(v=exp(mean_ln_msy + 2 * sd(log(msy))),col="red") graphics.off() cat("Possible combinations = ", length(r),"\n") cat("geom. mean r =", format(exp(mean(log(r))),digits=3), "\n") 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") cat("geom. mean k =", format(1000*exp(mean(log(k))),digits=3), "\n") 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") cat("geom. mean MSY =", format(1000*exp(mean(log(msy))),digits=3),"\n") 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") ## Write results into outfile, in append mode (no header in file, existing files will be continued) ## 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)) #write.table(output, file = outfile, append = TRUE, sep = ";", dec = ".", row.names = FALSE, col.names = FALSE) appendPar <- ifelse(counter2==0,F,T) colnamePar <- ifelse(counter2==0,T,F) write.table(output, file = outfile, append = appendPar, sep = ",", dec = ".", row.names = FALSE, col.names = colnamePar) counter2 <- counter2 + 1 } cat("Elapsed: ",Sys.time()-t0," \n") } ## End of stock loop, get next stock or exit