data/AtlanticMenhadenSA/projection/ModF_proj_stochastic-menhaden-constant landings NLTS rec.r
In Bai-Li-NOAA/IFA4EBFM: Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management
###############################################################################
# SEDAR40, Atlantic menhaden, for Menhaden TC
# Amy Schueller 9/26/2014
# Stochastic projections: Constant landings scenario
###############################################################################
"L.calc.wgt" <- function(F.age, Z.age, N.age, wgt) {
# fcn computes landings in wgt
# INPUT
# F.age = vector of fishing mortality rate at age
# Z.age = vector of total mortality rate at age including dead discards
# N.age = vector of abundance at age
# wgt = vector of weight at age
L.age <- rep(NA, times = length(N.age))
L.age <- wgt * F.age * N.age * (1.0 - exp(-Z.age)) / Z.age
L <- sum(L.age)
return(L)
} # end fcn L.calc
"L.calc.num" <- function(F.age, Z.age, N.age) {
# fcn computes landings in numbers
# INPUT
# F.age = vector of fishing mortality rate at age
# Z.age = vector of total mortality rate at age including dead discards
# N.age = vector of abundance at age
# wgt = vector of weight at age
L.age <- rep(NA, times = length(N.age))
L.age <- F.age * N.age * (1.0 - exp(-Z.age)) / Z.age
L <- sum(L.age)
return(L)
} # end fcn L.calc.num
F.fit <- function(x, landings, numbers, wgt, sel.crs, sel.crn, sel.cbs, sel.cbn, MforZ) {
# fcn finds the F that matches landings, called by the optimize fcn
# INPUT
# x = the range of values for F over which to find a minimum
# landings = the amount of total landings in metric tons
# numbers = vector of numbers at age
# sel.crs, sel.crn, sel.cbs, sel.cbn = fishery selectivities
F <- x
L.cRs.F <- rep(NA, nages)
L.cRn.F <- rep(NA, nages)
L.cBs.F <- rep(NA, nages)
L.cBn.F <- rep(NA, nages)
Z.F <- Z.calc(F, MforZ, sel.crs, sel.crn, sel.cbs, sel.cbn)
L.cRs.F <- wgt * F * prop.F.cRs * sel.crs * numbers * (1 - exp(-Z.F)) / Z.F
L.cRn.F <- wgt * F * prop.F.cRn * sel.crn * numbers * (1 - exp(-Z.F)) / Z.F
L.cBs.F <- wgt * F * prop.F.cBs * sel.cbs * numbers * (1 - exp(-Z.F)) / Z.F
L.cBn.F <- wgt * F * prop.F.cBn * sel.cbn * numbers * (1 - exp(-Z.F)) / Z.F
L.F <- sum(L.cRs.F) + sum(L.cRn.F) + sum(L.cBs.F) + sum(L.cBn.F)
L.min <- (abs(L.F - landings))^2
return(L.min)
} # end fcn F.fit
Z.calc <- function(fullF, MforZ, sel.crs, sel.crn, sel.cbs, sel.cbn) {
# fcn calculates age-specific total mortality
# INPUT
# fullF = estimated full F from landings (which were an input)
Zmort <- MforZ
Zmort <- Zmort + prop.F.cRs * fullF * sel.crs
Zmort <- Zmort + prop.F.cRn * fullF * sel.crn
Zmort <- Zmort + prop.F.cBs * fullF * sel.cbs
Zmort <- Zmort + prop.F.cBn * fullF * sel.cbn
return(Zmort)
} # end fcn Z.calc
L.calc <- function(f, N.age, wgt, sel.crs, sel.crn, sel.cbs, sel.cbn, MforZ) {
# fcn computes landings given vector of fishery specific F's
# f = fishing mortality
# N.age = vector of numbers at age
F.crn <- f[1]
F.crs <- f[2]
F.cbs <- f[3]
F.cbn <- f[4]
Z <- MforZ
Z <- Z + F.crs * sel.crs
Z <- Z + F.crn * sel.crn
Z <- Z + F.cbs * sel.cbs
Z <- Z + F.cbn * sel.cbn
L.cRs <- wgt * F.crs * sel.crs * N.age * (1 - exp(-Z)) / Z
L.cRn <- wgt * F.crn * sel.crn * N.age * (1 - exp(-Z)) / Z
L.cBs <- wgt * F.cbs * sel.cbs * N.age * (1 - exp(-Z)) / Z
L.cBn <- wgt * F.cbn * sel.cbn * N.age * (1 - exp(-Z)) / Z
L <- sum(L.cRs) + sum(L.cRn) + sum(L.cBs) + sum(L.cBn)
return(L)
} # end fcn L.calc
"project.stoch" <- function(yr1, nyrs.current, nyrs, sel.L, N.inc, ages,
wgt, wgt.L, reprod, M, R0, nboot, quants, boot.type,
boot.Mage, boot.Nage2009, boot.selLage, boot.R0, boot.mat, boot.fec,
boot.sel.crs, boot.sel.crn, boot.sel.cbs, boot.sel.cbn, fec.age, boot.r.devs, projections) {
# fcn runs stochastic projections
# INPUT
# yr1 = first calendar year of projection
# nyrs.current = number years to maintain F.current
# nyrs = total number years to project
# sel.L = vector of selectivity at age to compute landings
# N.inc = vector of abundance at age in yr1 used to add variability
# ages = vector of ages
# wgt = vector of weight at age of popn
# wgt.L = vector of weight at age of landings
# reprod = vector of reproductive contribution at age to SSB
# M = natural mortality rate
# R0 = virgin recruitment of spawner-recruit fcn
# nboot = number of bootstraps
# quants = quantiles of nboot projections reported in output
# boot.type = defines type of bootstrapping
# boot.Mage, boot.Nage2009, boot.selLage,
# boot.SSB2008, boot.R0, boot.mat: used if boot.type=2
nages <- length(ages)
Z.age <- rep(NA, nages) # total mortality by age
M.age <- rep(NA, nages) # natural mortality by age
W.age <- matrix(rep(NA, nboot * (nyrs * nages)), nrow = nboot) # weight at age
reprod <- rep(NA, nages)
wgt1 <- rep(NA, nages)
F.age <- rep(NA, nages) # fishing mortality by age
F.age.boot <- array(rep(NA, nboot * nyrs * nages), dim = c(nboot, nyrs, nages))
Z.age.boot <- array(rep(NA, nboot * nyrs * nages), dim = c(nboot, nyrs, nages))
N.age <- matrix(rep(NA, (nyrs + 1) * nages), nrow = nyrs + 1) # numbers at age by year
N.age.boot <- array(rep(NA, nboot * (nyrs + 1) * nages), dim = c(nboot, nyrs + 1, nages))
N.ssb.age <- matrix(rep(NA, (nyrs + 1) * nages), nrow = nyrs + 1) # numbers at age by year
logNic1 <- rep(NA, nages - 1) # For initial numbers at age-in order to include some variability
SSB <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # spawning stock biomass (in same units as reprod)
B <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # total stock biomass (mt)
Recruits <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # recruits in numbers
F <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # full F (/yr)
Mdevs <- matrix(rep(NA, nboot * (nyrs * nages)), nrow = nboot) # natural mortality deviations
Landings <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # landings in weight
logR.dev <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # recruitment deviations (log-space)--TYPE 1
######## Begin bootstrap#########################################################
for (iboot in 1:nboot) {
# Initial conditions
if (boot.type == 2) {
M <- as.numeric(boot.m[iboot, ])
mat <- as.numeric(boot.mat[iboot, ])
fec <- as.numeric(boot.fec[iboot, ])
sel.L <- as.numeric(boot.selLage[iboot, ])
N.age[1, ] <- as.numeric(boot.Nage2009[iboot, ])
# R0=as.numeric(boot.est[iboot])
sel.crs <- as.numeric(boot.sel.crs[iboot, ])
sel.crn <- as.numeric(boot.sel.crn[iboot, ])
sel.cbs <- as.numeric(boot.sel.cbs[iboot, ])
sel.cbn <- as.numeric(boot.sel.cbn[iboot, ])
# r.dev=as.numeric(boot.r.devs[iboot,])
proj.r <- as.numeric(projections[iboot, ])
}
for (iyr in 1:(nyrs - 1)) {
if (iyr < (nyrs.current + 1)) { # period to estimate F
numbers <- N.age[iyr, ]
landings <- L.cR.cB[iyr]
FLtemp <- optimize(
f = F.fit, interval = c(0.001, 3.0), tol = 0.0000000000001, landings = landings, numbers = numbers, wgt = wgt.L,
sel.crs = sel.crs, sel.crn = sel.crn, sel.cbs = sel.cbs, sel.cbn = sel.cbn, MforZ = M
)
F[iboot, iyr] <- FLtemp$minimum
F.vec.tmp <- c(prop.F.cRs * F[iboot, iyr], prop.F.cRn * F[iboot, iyr], prop.F.cBs * F[iboot, iyr], prop.F.cBn * F[iboot, iyr])
# Landings[iboot,iyr]=L.calc(F.vec.tmp,number,wgt.L,sel.crs,sel.crn,sel.cbs,sel.cbn,M)
}
F.age <- sel.L * F[iboot, iyr]
Z.age <- M + F.age
F.age.boot[iboot, iyr, ] <- F.age
Z.age.boot[iboot, iyr, ] <- Z.age
if (iyr == 1) {
N.ssb.age[1, ] <- N.age[1, ]
N.age[1, 1] <- proj.r[1]
# N.age[1,1]=exp(as.numeric(sample(r.dev,1,replace=TRUE)))+R0
}
reprod1 <- 0.5 * mat * fec
SSB[iboot, iyr] <- sum(N.ssb.age[iyr, ] * reprod1)
# print(c(iboot,iyr,SSB[iboot,iyr]))
B[iboot, iyr] <- sum(N.age[iyr, ] * wgt)
# logR.dev=as.numeric(sample(r.dev,1,replace=TRUE))
# N.age[iyr+1,1]=exp(logR.dev)+R0
N.age[iyr + 1, 1] <- proj.r[iyr + 1]
N.age[(iyr + 1), 2:nages] <- N.age[iyr, 1:(nages - 1)] * (exp(-1.0 * Z.age[1:(nages - 1)]))
N.age[(iyr + 1), nages] <- N.age[(iyr + 1), nages] +
N.age[iyr, nages] * (exp(-1.0 * Z.age[nages])) # plus group
N.ssb.age[(iyr + 1), ] <- N.age[(iyr + 1), ]
# print(c(iboot,iyr,N.age[iboot,iyr]))
} # end iyr loop
# F[iboot,nyrs]=F.proj
# F.age=sel.L*F[iboot,nyrs]
# for (iage in 1:nages) {
# M.iage=Mdevs[iboot,((nages*nyrs)-nages+iage)]
# M.age[iage]=M.iage
# reprod1[iage]=reprod.random[iboot,((nages*iyr)-nages+iage)]
# wgt1[iage]=W.age[iboot,((nages*iyr)-nages+iage)]
# }
# Z.age=M.age+F.age
N.age.boot[iboot, , ] <- N.age
SSB[iboot, nyrs] <- sum(N.ssb.age[nyrs, ] * reprod1)
B[iboot, nyrs] <- sum(N.age[nyrs, ] * wgt)
Recruits[iboot, ] <- N.age.boot[iboot, -(nyrs + 1), 1]
} # end iboot
# calculate quantiles from bootstrap replicates
SSB.quants <- apply(SSB[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
B.quants <- apply(B[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
R.quants <- apply(Recruits[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
F.quants <- apply(F[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
# L.quants=apply(Landings[,1:nyrs],2,quantile,probs=quants,na.rm=T)
yrs <- yr1:(yr1 + nyrs - 1)
return(list(
yrs = yrs, F.quants = F.quants, # L.quants=L.quants,
SSB.quants = SSB.quants,
B.quants = B.quants, R.quants = R.quants, SSB = SSB, # landings=Landings,
Ftocheck = F,
SSB.boot = SSB, B.boot = B, R.boot = Recruits, # L.boot=Landings,
N.boot = N.age.boot,
Z.boot = Z.age.boot, F.boot = F
))
}
Bai-Li-NOAA/IFA4EBFM documentation built on May 1, 2024, 9:24 p.m.
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###############################################################################
# SEDAR40, Atlantic menhaden, for Menhaden TC
# Amy Schueller 9/26/2014
# Stochastic projections: Constant landings scenario
###############################################################################
"L.calc.wgt" <- function(F.age, Z.age, N.age, wgt) {
# fcn computes landings in wgt
# INPUT
# F.age = vector of fishing mortality rate at age
# Z.age = vector of total mortality rate at age including dead discards
# N.age = vector of abundance at age
# wgt = vector of weight at age
L.age <- rep(NA, times = length(N.age))
L.age <- wgt * F.age * N.age * (1.0 - exp(-Z.age)) / Z.age
L <- sum(L.age)
return(L)
} # end fcn L.calc
"L.calc.num" <- function(F.age, Z.age, N.age) {
# fcn computes landings in numbers
# INPUT
# F.age = vector of fishing mortality rate at age
# Z.age = vector of total mortality rate at age including dead discards
# N.age = vector of abundance at age
# wgt = vector of weight at age
L.age <- rep(NA, times = length(N.age))
L.age <- F.age * N.age * (1.0 - exp(-Z.age)) / Z.age
L <- sum(L.age)
return(L)
} # end fcn L.calc.num
F.fit <- function(x, landings, numbers, wgt, sel.crs, sel.crn, sel.cbs, sel.cbn, MforZ) {
# fcn finds the F that matches landings, called by the optimize fcn
# INPUT
# x = the range of values for F over which to find a minimum
# landings = the amount of total landings in metric tons
# numbers = vector of numbers at age
# sel.crs, sel.crn, sel.cbs, sel.cbn = fishery selectivities
F <- x
L.cRs.F <- rep(NA, nages)
L.cRn.F <- rep(NA, nages)
L.cBs.F <- rep(NA, nages)
L.cBn.F <- rep(NA, nages)
Z.F <- Z.calc(F, MforZ, sel.crs, sel.crn, sel.cbs, sel.cbn)
L.cRs.F <- wgt * F * prop.F.cRs * sel.crs * numbers * (1 - exp(-Z.F)) / Z.F
L.cRn.F <- wgt * F * prop.F.cRn * sel.crn * numbers * (1 - exp(-Z.F)) / Z.F
L.cBs.F <- wgt * F * prop.F.cBs * sel.cbs * numbers * (1 - exp(-Z.F)) / Z.F
L.cBn.F <- wgt * F * prop.F.cBn * sel.cbn * numbers * (1 - exp(-Z.F)) / Z.F
L.F <- sum(L.cRs.F) + sum(L.cRn.F) + sum(L.cBs.F) + sum(L.cBn.F)
L.min <- (abs(L.F - landings))^2
return(L.min)
} # end fcn F.fit
Z.calc <- function(fullF, MforZ, sel.crs, sel.crn, sel.cbs, sel.cbn) {
# fcn calculates age-specific total mortality
# INPUT
# fullF = estimated full F from landings (which were an input)
Zmort <- MforZ
Zmort <- Zmort + prop.F.cRs * fullF * sel.crs
Zmort <- Zmort + prop.F.cRn * fullF * sel.crn
Zmort <- Zmort + prop.F.cBs * fullF * sel.cbs
Zmort <- Zmort + prop.F.cBn * fullF * sel.cbn
return(Zmort)
} # end fcn Z.calc
L.calc <- function(f, N.age, wgt, sel.crs, sel.crn, sel.cbs, sel.cbn, MforZ) {
# fcn computes landings given vector of fishery specific F's
# f = fishing mortality
# N.age = vector of numbers at age
F.crn <- f[1]
F.crs <- f[2]
F.cbs <- f[3]
F.cbn <- f[4]
Z <- MforZ
Z <- Z + F.crs * sel.crs
Z <- Z + F.crn * sel.crn
Z <- Z + F.cbs * sel.cbs
Z <- Z + F.cbn * sel.cbn
L.cRs <- wgt * F.crs * sel.crs * N.age * (1 - exp(-Z)) / Z
L.cRn <- wgt * F.crn * sel.crn * N.age * (1 - exp(-Z)) / Z
L.cBs <- wgt * F.cbs * sel.cbs * N.age * (1 - exp(-Z)) / Z
L.cBn <- wgt * F.cbn * sel.cbn * N.age * (1 - exp(-Z)) / Z
L <- sum(L.cRs) + sum(L.cRn) + sum(L.cBs) + sum(L.cBn)
return(L)
} # end fcn L.calc
"project.stoch" <- function(yr1, nyrs.current, nyrs, sel.L, N.inc, ages,
wgt, wgt.L, reprod, M, R0, nboot, quants, boot.type,
boot.Mage, boot.Nage2009, boot.selLage, boot.R0, boot.mat, boot.fec,
boot.sel.crs, boot.sel.crn, boot.sel.cbs, boot.sel.cbn, fec.age, boot.r.devs, projections) {
# fcn runs stochastic projections
# INPUT
# yr1 = first calendar year of projection
# nyrs.current = number years to maintain F.current
# nyrs = total number years to project
# sel.L = vector of selectivity at age to compute landings
# N.inc = vector of abundance at age in yr1 used to add variability
# ages = vector of ages
# wgt = vector of weight at age of popn
# wgt.L = vector of weight at age of landings
# reprod = vector of reproductive contribution at age to SSB
# M = natural mortality rate
# R0 = virgin recruitment of spawner-recruit fcn
# nboot = number of bootstraps
# quants = quantiles of nboot projections reported in output
# boot.type = defines type of bootstrapping
# boot.Mage, boot.Nage2009, boot.selLage,
# boot.SSB2008, boot.R0, boot.mat: used if boot.type=2
nages <- length(ages)
Z.age <- rep(NA, nages) # total mortality by age
M.age <- rep(NA, nages) # natural mortality by age
W.age <- matrix(rep(NA, nboot * (nyrs * nages)), nrow = nboot) # weight at age
reprod <- rep(NA, nages)
wgt1 <- rep(NA, nages)
F.age <- rep(NA, nages) # fishing mortality by age
F.age.boot <- array(rep(NA, nboot * nyrs * nages), dim = c(nboot, nyrs, nages))
Z.age.boot <- array(rep(NA, nboot * nyrs * nages), dim = c(nboot, nyrs, nages))
N.age <- matrix(rep(NA, (nyrs + 1) * nages), nrow = nyrs + 1) # numbers at age by year
N.age.boot <- array(rep(NA, nboot * (nyrs + 1) * nages), dim = c(nboot, nyrs + 1, nages))
N.ssb.age <- matrix(rep(NA, (nyrs + 1) * nages), nrow = nyrs + 1) # numbers at age by year
logNic1 <- rep(NA, nages - 1) # For initial numbers at age-in order to include some variability
SSB <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # spawning stock biomass (in same units as reprod)
B <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # total stock biomass (mt)
Recruits <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # recruits in numbers
F <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # full F (/yr)
Mdevs <- matrix(rep(NA, nboot * (nyrs * nages)), nrow = nboot) # natural mortality deviations
Landings <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # landings in weight
logR.dev <- matrix(rep(NA, nboot * nyrs), nrow = nboot) # recruitment deviations (log-space)--TYPE 1
######## Begin bootstrap#########################################################
for (iboot in 1:nboot) {
# Initial conditions
if (boot.type == 2) {
M <- as.numeric(boot.m[iboot, ])
mat <- as.numeric(boot.mat[iboot, ])
fec <- as.numeric(boot.fec[iboot, ])
sel.L <- as.numeric(boot.selLage[iboot, ])
N.age[1, ] <- as.numeric(boot.Nage2009[iboot, ])
# R0=as.numeric(boot.est[iboot])
sel.crs <- as.numeric(boot.sel.crs[iboot, ])
sel.crn <- as.numeric(boot.sel.crn[iboot, ])
sel.cbs <- as.numeric(boot.sel.cbs[iboot, ])
sel.cbn <- as.numeric(boot.sel.cbn[iboot, ])
# r.dev=as.numeric(boot.r.devs[iboot,])
proj.r <- as.numeric(projections[iboot, ])
}
for (iyr in 1:(nyrs - 1)) {
if (iyr < (nyrs.current + 1)) { # period to estimate F
numbers <- N.age[iyr, ]
landings <- L.cR.cB[iyr]
FLtemp <- optimize(
f = F.fit, interval = c(0.001, 3.0), tol = 0.0000000000001, landings = landings, numbers = numbers, wgt = wgt.L,
sel.crs = sel.crs, sel.crn = sel.crn, sel.cbs = sel.cbs, sel.cbn = sel.cbn, MforZ = M
)
F[iboot, iyr] <- FLtemp$minimum
F.vec.tmp <- c(prop.F.cRs * F[iboot, iyr], prop.F.cRn * F[iboot, iyr], prop.F.cBs * F[iboot, iyr], prop.F.cBn * F[iboot, iyr])
# Landings[iboot,iyr]=L.calc(F.vec.tmp,number,wgt.L,sel.crs,sel.crn,sel.cbs,sel.cbn,M)
}
F.age <- sel.L * F[iboot, iyr]
Z.age <- M + F.age
F.age.boot[iboot, iyr, ] <- F.age
Z.age.boot[iboot, iyr, ] <- Z.age
if (iyr == 1) {
N.ssb.age[1, ] <- N.age[1, ]
N.age[1, 1] <- proj.r[1]
# N.age[1,1]=exp(as.numeric(sample(r.dev,1,replace=TRUE)))+R0
}
reprod1 <- 0.5 * mat * fec
SSB[iboot, iyr] <- sum(N.ssb.age[iyr, ] * reprod1)
# print(c(iboot,iyr,SSB[iboot,iyr]))
B[iboot, iyr] <- sum(N.age[iyr, ] * wgt)
# logR.dev=as.numeric(sample(r.dev,1,replace=TRUE))
# N.age[iyr+1,1]=exp(logR.dev)+R0
N.age[iyr + 1, 1] <- proj.r[iyr + 1]
N.age[(iyr + 1), 2:nages] <- N.age[iyr, 1:(nages - 1)] * (exp(-1.0 * Z.age[1:(nages - 1)]))
N.age[(iyr + 1), nages] <- N.age[(iyr + 1), nages] +
N.age[iyr, nages] * (exp(-1.0 * Z.age[nages])) # plus group
N.ssb.age[(iyr + 1), ] <- N.age[(iyr + 1), ]
# print(c(iboot,iyr,N.age[iboot,iyr]))
} # end iyr loop
# F[iboot,nyrs]=F.proj
# F.age=sel.L*F[iboot,nyrs]
# for (iage in 1:nages) {
# M.iage=Mdevs[iboot,((nages*nyrs)-nages+iage)]
# M.age[iage]=M.iage
# reprod1[iage]=reprod.random[iboot,((nages*iyr)-nages+iage)]
# wgt1[iage]=W.age[iboot,((nages*iyr)-nages+iage)]
# }
# Z.age=M.age+F.age
N.age.boot[iboot, , ] <- N.age
SSB[iboot, nyrs] <- sum(N.ssb.age[nyrs, ] * reprod1)
B[iboot, nyrs] <- sum(N.age[nyrs, ] * wgt)
Recruits[iboot, ] <- N.age.boot[iboot, -(nyrs + 1), 1]
} # end iboot
# calculate quantiles from bootstrap replicates
SSB.quants <- apply(SSB[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
B.quants <- apply(B[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
R.quants <- apply(Recruits[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
F.quants <- apply(F[, 1:nyrs], 2, quantile, probs = quants, na.rm = T)
# L.quants=apply(Landings[,1:nyrs],2,quantile,probs=quants,na.rm=T)
yrs <- yr1:(yr1 + nyrs - 1)
return(list(
yrs = yrs, F.quants = F.quants, # L.quants=L.quants,
SSB.quants = SSB.quants,
B.quants = B.quants, R.quants = R.quants, SSB = SSB, # landings=Landings,
Ftocheck = F,
SSB.boot = SSB, B.boot = B, R.boot = Recruits, # L.boot=Landings,
N.boot = N.age.boot,
Z.boot = Z.age.boot, F.boot = F
))
}
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