R/runMSE.R
In Blue-Matter/MSEtool: Management Strategy Evaluation Toolkit
Defines functions
runMSE_sac
runMSE
Simulate
Documented in
runMSE
Simulate
#' @describeIn runMSE Run the Historical Simulations from an object of class `OM`
#' @export
#
Simulate <- function(OM=MSEtool::testOM, parallel=FALSE, silent=FALSE) {
# ---- Initial Checks and Setup ---
OM <- CheckOM(OM, !silent)
if (OM@nsim <=1)
stop("OM@nsim must be > 1", call.=FALSE)
# Set up parallel processing
ncpus <- set_parallel(any(unlist(parallel)))
# Set pbapply functions
if (requireNamespace("pbapply", quietly = TRUE) && !silent) {
.lapply <- pbapply::pblapply
.sapply <- pbapply::pbsapply
# Argument to pass parallel cluster (if running)
formals(.lapply)$cl <- formals(.sapply)$cl <- substitute(if (snowfall::sfIsRunning()) snowfall::sfGetCluster() else NULL)
} else if (snowfall::sfIsRunning()) {
.lapply <- snowfall::sfLapply
.sapply <- snowfall::sfSapply
} else {
.lapply <- base::lapply
.sapply <- base::sapply
}
set.seed(OM@seed) # set seed for reproducibility
nsim <- OM@nsim # number of simulations
nyears <- OM@nyears # number of historical years
proyears <- OM@proyears # number of projection years
interval <- OM@interval # management interval (annual)
maxF <- OM@maxF # maximum apical F
pstar <- OM@pstar # Percentile of the sample of the TAC for each MP
reps <- OM@reps # Number of samples of the management recommendation for each MP
# plusgroup
plusgroup <- 1 # default now is to use plusgroup
if(!is.null(OM@cpars$plusgroup) && !OM@cpars$plusgroup) {
plusgroup <- 0; OM@cpars$plusgroup <- NULL
}
# ---- Control Parameters Options ----
control <- OM@cpars$control; OM@cpars$control <- NULL
# Shiny progress bar
inc.progress <- FALSE
if("progress" %in% names(control)) {
if(control$progress) {
if (requireNamespace("shiny", quietly = TRUE)) {
inc.progress <- TRUE
} else {
warning('package `shiny` needs to be installed for progress bar')
}
}
}
if (inc.progress)
shiny::incProgress(0.2, detail = 'Sampling OM Parameters')
# Option to optimize depletion for vulnerable biomass instead of spawning biomass
control$optVB <- FALSE
if (!is.null(control$D) && control$D == "VB") control$optVB <- TRUE
# Option to optimize depletion for sp biomass MSY instead of spawning biomass
control$optSBMSY <- FALSE
if (!is.null(control$D) && control$D == "SBMSY") control$optSBMSY <- TRUE
# --- Sample OM parameters ----
if(!silent) message("Loading operating model")
# custom parameters exist - sample and write to list
SampCpars <- list()
if (length(OM@cpars)>0) {
SampCpars <- SampleCpars(cpars=OM@cpars, nsim, silent=silent)
}
set.seed(OM@seed) # set seed again after cpars has been sampled
# Stock Parameters
StockPars <- SampleStockPars(Stock=OM,
nsim,
nyears,
proyears,
cpars=SampCpars,
msg=!silent)
# Check for custom stock-recruit function
StockPars <- Check_custom_SRR(StockPars, SampCpars, nsim)
# Checks
if (any(range(StockPars$M_ageArray) <=tiny))
stop("range of StockPars$M_ageArray is: ", paste0(range(StockPars$M_ageArray), collapse="-"))
# Fleet Parameters
FleetPars <- SampleFleetPars(Fleet=SubOM(OM, "Fleet"),
Stock=StockPars,
nsim,
nyears,
proyears,
cpars=SampCpars)
# Obs Parameters
ObsPars <- SampleObsPars(OM, nsim, cpars=SampCpars, Stock=StockPars,
nyears, proyears)
# Imp Parameters
ImpPars <- SampleImpPars(OM, nsim, cpars=SampCpars, nyears, proyears)
# Bio-Economic Parameters
BioEcoPars <- c("RevCurr", "CostCurr", "Response", "CostInc", "RevInc", "LatentEff")
if (all(lapply(SampCpars[BioEcoPars], length) == 0)) {
# no bio-economic model
# if (!silent) message("No bio-economic model parameters found. \nTAC and TAE assumed to be caught in full")
RevCurr <- CostCurr <- Response <- CostInc <- RevInc <- LatentEff <- rep(NA, nsim)
} else {
if (!silent) message("Bio-economic model parameters found.")
# Checks
if (length(SampCpars$CostCurr) != nsim) stop("OM@cpars$CostCurr is not length OM@nsim", call.=FALSE)
if (length(SampCpars$RevCurr) != nsim) stop("OM@cpars$RevCurr is not length OM@nsim", call.=FALSE)
if (length(SampCpars$Response) != nsim) stop("OM@cpars$Response is not length OM@nsim", call.=FALSE)
if (length(SampCpars$RevInc) != nsim) SampCpars$RevInc <- rep(0, nsim)
if (length(SampCpars$CostInc) != nsim) SampCpars$CostInc <- rep(0, nsim)
if (length(SampCpars$LatentEff) != nsim) SampCpars$LatentEff <- rep(NA, nsim)
RevCurr <- SampCpars$RevCurr
CostCurr <- SampCpars$CostCurr
Response <- SampCpars$Response
CostInc <- SampCpars$CostInc
RevInc <- SampCpars$RevInc
LatentEff <- SampCpars$LatentEff
}
# ---- Initialize Arrays ----
n_age <- StockPars$maxage + 1 # number of age classes (starting at age-0)
StockPars$n_age <- n_age
nareas <- StockPars$nareas
N <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # stock numbers array
Biomass <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # stock biomass array
VBiomass <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # vulnerable biomass array
SSN <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # spawning stock numbers array
SSB <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # spawning stock biomass array
FM <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # fishing mortality rate array
FMret <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # fishing mortality rate array for retained fish
Z <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # total mortality rate array
Agearray <- array(rep(0:StockPars$maxage, each = nsim), dim = c(nsim, n_age)) # Age array
# ---- Pre Equilibrium calcs ----
# Set up array indexes sim (S) age (A) year (Y) region/area (R)
SAYR <- as.matrix(expand.grid(1:nareas, 1, 1:n_age, 1:nsim)[4:1])
SAY <- SAYR[, 1:3]
SAR <- SAYR[, c(1,2,4)]
SA <- Sa <- SAYR[, 1:2]
SR <- SAYR[, c(1, 4)]
S <- SAYR[, 1]
SY <- SAYR[, c(1, 3)]
Sa[,2]<- n_age-Sa[,2] + 1 # This is the process error index for initial year
# ---- Calculate initial distribution if mov provided in cpars ----
if (is.null(StockPars$initdist)) {
# mov has been passed in cpars - initdist hasn't been defined
StockPars$initdist <- CalcDistribution(StockPars, FleetPars, SampCpars, nyears,
maxF,
plusgroup, checks=FALSE)
}
# Unfished recruitment by area - INITDIST OF AGE 1.
StockPars$R0a <- matrix(StockPars$R0, nrow=nsim, ncol=nareas, byrow=FALSE) * StockPars$initdist[,1,] #
# ---- Unfished Equilibrium calcs ----
# unfished survival
surv <- lapply(1:nsim, calc_survival, StockPars=StockPars,
plusgroup=plusgroup, inc_spawn_time=FALSE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
# unfished survival (spawning)
SBsurv <- lapply(1:nsim, calc_survival, StockPars=StockPars,
plusgroup=plusgroup, inc_spawn_time=TRUE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
Nfrac <- SBsurv * StockPars$Mat_age # predicted numbers of mature ages in all years (accounting for `spawn_time_frac`)
# indices for all years
SAYR_a <- as.matrix(expand.grid(1:nareas, 1:(nyears+proyears), 1:n_age, 1:nsim)[4:1])
SAY_a <- SAYR_a[, 1:3]
SAR_a <- SAYR_a[, c(1,2,4)]
SA_a <- SAYR_a[, 1:2]
SR_a <- SAYR_a[, c(1, 4)]
S_a <- SAYR_a[, 1]
SY_a <- SAYR_a[, c(1, 3)]
# arrays for unfished biomass for all years
SSN_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
N_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
Biomass_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
SSB_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
# Calculate initial spawning stock numbers for all years
SSN_a[SAYR_a] <- Nfrac[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a]
N_a[SAYR_a] <- StockPars$R0[S_a] * surv[SAY_a] * StockPars$initdist[SAR_a]
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars$Wt_age[SAY_a]
SSB_a[SAYR_a] <- SBsurv[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a] * StockPars$Fec_Age[SAY_a]
SSN0_a <- apply(SSN_a, c(1,3), sum) # unfished spawning numbers for each year
N0_a <- apply(N_a, c(1,3), sum) # unfished numbers for each year)
SSB0_a <- apply(SSB_a, c(1,3), sum) # unfished spawning biomass for each year
SSB0a_a <- apply(SSB_a, c(1, 3,4), sum) # Calculate unfished spawning stock biomass by area for each year
B0_a <- apply(Biomass_a, c(1,3), sum) # unfished biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * FleetPars$V_real, c(1,3), sum) # unfished vulnerable biomass for each year
# ---- Unfished Reference Points ----
SSBpRa <- array(SSB0_a/matrix(StockPars$R0, nrow=nsim, ncol=nyears+proyears),
dim = c(nsim, nyears+proyears))
UnfishedRefs <- sapply(1:nsim, CalcUnfishedRefs, ageM=StockPars$ageM, N0_a=N0_a, SSN0_a=SSN0_a,
SSB0_a=SSB0_a, B0_a=B0_a, VB0_a=VB0_a, SSBpRa=SSBpRa, SSB0a_a=SSB0a_a)
N0 <- UnfishedRefs[1,] %>% unlist() # average unfished numbers
SSN0 <- UnfishedRefs[2,] %>% unlist() # average unfished spawning numbers
SSB0 <- UnfishedRefs[3,] %>% unlist() # average unfished spawning biomass
B0 <- UnfishedRefs[4,] %>% unlist() # average unfished biomass
VB0 <- UnfishedRefs[5,] %>% unlist() # average unfished vulnerable biomass
Unfished_Equilibrium <- list(
N_at_age=N_a,
B_at_age=Biomass_a,
SSB_at_age=SSB_a,
SSN_at_age=SSN_a,
VB_at_age=Biomass_a * replicate(nareas, FleetPars$V_real)
)
# average spawning stock biomass per recruit
SSBpR <- matrix(UnfishedRefs[6,] %>% unlist(), nrow=nsim, ncol=nareas)
# average unfished biomass
SSB0a <- UnfishedRefs[7,] %>% unlist() %>% matrix(nrow=nsim, ncol=nareas, byrow = TRUE)
bR <- matrix(log(5 * StockPars$hs)/(0.8 * SSB0a), nrow=nsim) # Ricker SR params
aR <- matrix(exp(bR * SSB0a)/SSBpR, nrow=nsim) # Ricker SR params
# --- Optimize for Initial Depletion ----
initD <- SampCpars$initD #
if (!is.null(initD)) { # initial depletion is not unfished
if (!silent) message("Optimizing for user-specified depletion in first historical year")
Perrmulti <- sapply(1:nsim, optDfunwrap, initD=initD, R0=StockPars$R0,
StockPars$initdist,
Perr_y=StockPars$Perr_y, surv=surv[,,1], Fec_age=StockPars$Fec_Age,
SSB0=SSB0,
StockPars$n_age)
StockPars$Perr_y[,1:StockPars$maxage] <- StockPars$Perr_y[, 1:StockPars$maxage] * Perrmulti
}
# --- Non-equilibrium Initial Year ----
SSN[SAYR] <- Nfrac[SAY] * StockPars$R0[S] *
StockPars$initdist[SAR]*StockPars$Perr_y[Sa] # Calculate initial spawning stock numbers
N[SAYR] <- StockPars$R0[S] * surv[SAY] * StockPars$initdist[SAR]*StockPars$Perr_y[Sa] # Calculate initial stock numbers
Biomass[SAYR] <- N[SAYR] * StockPars$Wt_age[SAY] # Calculate initial stock biomass
SSB[SAYR] <- SBsurv[SAY] * StockPars$R0[S] * StockPars$initdist[SAR]*StockPars$Perr_y[Sa] * StockPars$Fec_Age[SAY] # Calculate spawning stock biomass
VBiomass[SAYR] <- N[SAYR] * FleetPars$Wt_age_C[SAY] * FleetPars$V_real[SAY] # Calculate vulnerable biomass
StockPars$aR <- aR
StockPars$bR <- bR
StockPars$SSB0 <- SSB0
StockPars$VB0 <- VB0
StockPars$N <- N
StockPars$plusgroup <- plusgroup[1]
StockPars$maxF <- OM@maxF
StockPars$SSBpR <- SSBpR
if (!is.null(control$checks) && !is.null(initD)) { # check initial depletion
plot(apply(SSB[,,1,], 1, sum)/SSB0, initD)
if (!any(round(apply(SSB[,,1,], 1, sum)/SSB0, 2) == round(initD,2)))
warning('problem with initial depletion')
}
# --- Historical Spatial closures ----
if (!is.null(SampCpars$MPA)) {
# MPA by year passed in cpars
MPA <- SampCpars$MPA
if (any(dim(MPA) != c(nyears+proyears, nareas))) {
stop('cpars$MPA must be a matrix with `nyears+proyears` rows and `nareas` columns', .call=FALSE)
}
if (any(MPA !=1 & MPA!=0))
stop('values in cpars$MPA must be either 0 (closed) or open (1)', .call=FALSE)
if (any(MPA!=1)) {
for (a in 1:nareas) {
yrs <- which(MPA[,a] == 0)
if (length(yrs)>0) {
if (!silent)
message('Spatial closure detected in area ', a, ' in years ',
paste(findIntRuns(yrs), collapse=", "))
}
}
}
} else {
MPA <- matrix(1, nrow=nyears+proyears, ncol=nareas)
if (FleetPars$MPA) {
if (!silent) message('Historical MPA in Area 1 for all years')
MPA[,1] <- 0
}
}
FleetPars$MPA <- MPA
# --- Calculate MSY statistics for each year ----
if (inc.progress)
shiny::incProgress(0.2, detail = 'Calculating Reference Points')
# ignores spatial closures
# assumes all vulnerable fish are caught - ie no discarding
MSY_y <- array(0, dim=c(nsim, nyears+proyears)) # store MSY for each sim and year
FMSY_y <- MSY_y # store FMSY for each sim, and year
SSBMSY_y <- MSY_y # store SSBMSY for each sim, and year
BMSY_y <- MSY_y # store BMSY for each sim, and year
VBMSY_y <- MSY_y # store VBMSY for each sim, and year
R0_y <- MSY_y # store R0 for each sim, and year
h_y <- MSY_y # store h for each sim, and year
N0_y <- MSY_y
SN0_y <- MSY_y
B0_y <- MSY_y
SSB0_y <- MSY_y
VB0_y <- MSY_y
F01_YPR_y <- MSY_y # store F01 for each sim, and year
Fmax_YPR_y <- MSY_y # store Fmax for each sim, and year
Fcrash_y <- MSY_y # store Fcrash for each sim, and year
SPRcrash_y <- MSY_y # store SPRcrash for each sim, and year
Fmed_y <- MSY_y # store Fmed (F that generates the median historical SSB/R) for each sim, and year
SPR_target <- seq(0.2, 0.6, 0.05)
F_SPR_y <- array(0, dim = c(nsim, length(SPR_target), nyears + proyears)) %>%
structure(dimnames = list(NULL, paste0("F_", 100*SPR_target, "%"), NULL)) #array of F-SPR% by sim, SPR%, year
if(!silent) message("Calculating MSY reference points for each year")
# average life-history parameters over ageM years
# Assuming all vulnerable fish are kept; ie MSY is total removals
MSYrefsYr <- .lapply(1:nsim, function(x) {
sapply(1:(nyears+proyears), function(y) {
optMSY_eq(x,
yr.ind=y,
StockPars,
FleetPars$V_real_2)
})
})
MSY_y[] <- sapply(MSYrefsYr, function(x) x["Yield", ]) %>% t()
FMSY_y[] <- sapply(MSYrefsYr, function(x) x["F", ]) %>% t()
SSBMSY_y[] <- sapply(MSYrefsYr, function(x) x["SB", ]) %>% t()
BMSY_y[] <- sapply(MSYrefsYr, function(x) x["B", ]) %>% t()
VBMSY_y[] <- sapply(MSYrefsYr, function(x) x["VB", ]) %>% t()
R0_y[] <- sapply(MSYrefsYr, function(x) x["R0", ]) %>% t()
h_y[] <- sapply(MSYrefsYr, function(x) x["h", ]) %>% t()
N0_y[] <- sapply(MSYrefsYr, function(x) x["N0", ]) %>% t()
SN0_y[] <- sapply(MSYrefsYr, function(x) x["SN0", ]) %>% t()
B0_y[] <- sapply(MSYrefsYr, function(x) x["B0", ]) %>% t()
SSB0_y[] <- sapply(MSYrefsYr, function(x) x["SB0", ]) %>% t()
VB0_y[] <- sapply(MSYrefsYr, function(x) x["VB", ]/x["VB_VB0", ]) %>% t()
if (any(FMSY_y == 0)) {
message_warn("FMSY = 0 for some simulations and years.")
}
# --- MSY reference points ----
MSYRefPoints <- sapply(1:nsim, CalcMSYRefs, MSY_y=MSY_y, FMSY_y=FMSY_y,
SSBMSY_y=SSBMSY_y, BMSY_y=BMSY_y, VBMSY_y=VBMSY_y,
ageM=StockPars$ageM, nyears=nyears)
MSY <- MSYRefPoints[1,] %>% unlist() # record the MSY results (Vulnerable)
FMSY <- MSYRefPoints[2,] %>% unlist() # instantaneous FMSY (Vulnerable)
SSBMSY <- MSYRefPoints[3,] %>% unlist() # Spawning Stock Biomass at MSY
BMSY <- MSYRefPoints[4,] %>% unlist() # total biomass at MSY
VBMSY <- MSYRefPoints[5,] %>% unlist() # Biomass at MSY (Vulnerable)
UMSY <- MSY/VBMSY # exploitation rate
FMSY_M <- FMSY/StockPars$M # ratio of true FMSY to natural mortality rate M
SSBMSY_SSB0 <- SSBMSY/SSB0 # SSBMSY relative to unfished (SSB)
BMSY_B0 <- BMSY/B0 # Biomass relative to unfished (B0)
VBMSY_VB0 <- VBMSY/VB0 # VBiomass relative to unfished (VB0)
# --- Dynamic Unfished Reference Points ----
Unfished <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,1,],
nyears+proyears,
M_age=StockPars$M_ageArray[x,,],
Asize_c=StockPars$Asize[x,],
MatAge=StockPars$Mat_age[x,,],
FecAge=StockPars$Fec_Age[x,,],
WtAge=StockPars$Wt_age[x,,],
Vuln=FleetPars$V_real[x,,],
Retc=FleetPars$retA_real[x,,],
Prec=StockPars$Perr_y[x,],
movc=split_along_dim(StockPars$mov[x,,,,],4),
SRrelc=StockPars$SRrel[x],
Effind=rep(0, nyears+proyears),
Spat_targc=FleetPars$Spat_targ[x],
hc=StockPars$hs[x],
R0c=StockPars$R0a[x,],
SSBpRc=StockPars$SSBpR[x,],
aRc=StockPars$aR[x,],
bRc=StockPars$bR[x,],
Qc=0,
Fapic=0,
MPA=FleetPars$MPA,
maxF=StockPars$maxF,
control=1,
SSB0c=StockPars$SSB0[x],
SRRfun=StockPars$SRRfun,
SRRpars = StockPars$SRRpars[[x]],
plusgroup=StockPars$plusgroup,
spawn_time_frac=StockPars$spawn_time_frac[x]))
N_unfished <- aperm(array(as.numeric(unlist(Unfished[1,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
Biomass_unfished <- aperm(array(as.numeric(unlist(Unfished[2,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSN_unfished <- aperm(array(as.numeric(unlist(Unfished[3,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSB_unfished <- aperm(array(as.numeric(unlist(Unfished[4,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
VBiomass_unfished <- aperm(array(as.numeric(unlist(Unfished[5,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
StockPars$MSY <- MSY
StockPars$FMSY <- FMSY
StockPars$SSBMSY <- SSBMSY
StockPars$BMSY <- BMSY
StockPars$VBMSY <- VBMSY
StockPars$UMSY <- UMSY
StockPars$FMSY_M <- FMSY_M
StockPars$SSBMSY_SSB0 <- SSBMSY_SSB0
StockPars$BMSY_B0 <- BMSY_B0
StockPars$VBMSY_VB0 <- VBMSY_VB0
# --- Optimize catchability (q) to fit depletion ----
if (inc.progress)
shiny::incProgress(0.2, detail = 'Optimizing for Depletion')
bounds <- c(0.0001, 15) # q bounds for optimizer
# find the q that gives current stock depletion - optVB = depletion for vulnerable biomass else SB
if (!is.null(SampCpars$qs)) {
if(!silent)
message_info("Skipping optimization for depletion - using catchability (q) from OM@cpars.")
qs <- SampCpars$qs
} else {
if(!silent)
message("Optimizing for user-specified depletion in last historical year")
qs <- .sapply(1:nsim, CalculateQ, StockPars, FleetPars, pyears=nyears, bounds, control=control)
}
# --- Check that q optimizer has converged ----
LimBound <- c(1.1, 0.9)*range(bounds)
# bounds for q (catchability). Flag if bounded optimizer hits the bounds
if (!is.null(SampCpars$qs)) {
probQ <- numeric(0)
} else{
probQ <- which(qs > max(LimBound) | qs < min(LimBound))
Nprob <- length(probQ)
}
fracD <- 0.05; ntrials <- 50
if (!is.null(control$ntrials)) ntrials <- control$ntrials
if (!is.null(control$fracD)) fracD <- control$fracD
# If q has hit bound, re-sample depletion and try again. Tries 'ntrials' times and then alerts user
if (length(probQ) > 0) {
Err <- TRUE
if(!silent) message_info(Nprob,' simulations have final biomass that is not close to sampled depletion')
if(!silent) message_info('Re-sampling depletion, recruitment error, and fishing effort')
count <- 0
OM2 <- OM
while (Err & count < ntrials) {
# Re-sample Stock Parameters
Nprob <- length(probQ)
OM2@nsim <- Nprob
SampCpars2 <- list()
if (length(OM2@cpars)>0) SampCpars2 <- SampleCpars(OM2@cpars, OM2@nsim, silent=TRUE)
ResampStockPars <- SampleStockPars(OM2, cpars=SampCpars2, msg=FALSE)
ResampStockPars$CAL_bins <- StockPars$CAL_bins
ResampStockPars$CAL_binsmid <- StockPars$CAL_binsmid
ResampStockPars$nCALbins <- length(StockPars$CAL_binsmid )
StockPars$D[probQ] <- ResampStockPars$D # Re-sampled depletion
StockPars$procsd[probQ] <- ResampStockPars$procsd # Re-sampled recruitment deviations
StockPars$AC[probQ] <- ResampStockPars$AC
StockPars$Perr_y[probQ,] <- ResampStockPars$Perr_y
StockPars$hs[probQ] <- ResampStockPars$hs # Re-sampled steepness
# Re-sample historical fishing effort
ResampFleetPars <- SampleFleetPars(SubOM(OM2, "Fleet"), Stock=ResampStockPars,
OM2@nsim, nyears, proyears, cpars=SampCpars2)
FleetPars$Esd[probQ] <- ResampFleetPars$Esd
FleetPars$Find[probQ, ] <- ResampFleetPars$Find
FleetPars$dFfinal[probQ] <- ResampFleetPars$dFfinal
# Optimize for q
if (!snowfall::sfIsRunning()) {
qs[probQ] <- sapply(probQ, CalculateQ, StockPars, FleetPars,
pyears=nyears, bounds, control)
} else {
qs[probQ] <- snowfall::sfSapply(probQ, CalculateQ, StockPars, FleetPars,
pyears=nyears, bounds, control=control)
}
probQ <- which(qs > max(LimBound) | qs < min(LimBound))
count <- count + 1
if (length(probQ) == 0) Err <- FALSE
}
if (Err) { # still a problem
tooLow <- length(which(qs > max(LimBound)))
tooHigh <- length(which(qs < min(LimBound)))
prErr <- length(probQ)/nsim
if (prErr > fracD & length(probQ) > 1) {
if (length(tooLow) > 0) message_info(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0) message_info(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent) message_info("More than ", fracD*100, "% of simulations can't get to the specified level of depletion with these Operating Model parameters")
stop("Change OM@seed and try again for a complete new sample, modify the input parameters, or increase OM@cpars$control$ntrials (default 50)")
} else {
if (length(tooLow) > 0) message_info(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0) message_info(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent) message_info("More than ", 100-fracD*100, "% simulations can get to the sampled depletion.\nContinuing")
}
}
}
# --- Simulate historical years ----
if(!silent) message("Calculating historical stock and fishing dynamics") # Print a progress update
if (inc.progress)
shiny::incProgress(0.2, detail = 'Simulating Historical Dynamics')
if(!is.null(control$unfished)) { # generate unfished historical simulations
if(!silent) message("Simulating unfished historical period")
Hist <- TRUE
qs <- rep(0, nsim) # no fishing
}
FleetPars$qs <- qs
histYrs <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,1,],
nyears,
M_age=StockPars$M_ageArray[x,,],
Asize_c=StockPars$Asize[x,],
MatAge=StockPars$Mat_age[x,,],
WtAge=StockPars$Wt_age[x,,],
FecAge=StockPars$Fec_Age[x,,],
Vuln=FleetPars$V_real[x,,],
Retc=FleetPars$retA_real[x,,],
Prec=StockPars$Perr_y[x,],
movc=split_along_dim(StockPars$mov[x,,,,],4),
SRrelc=StockPars$SRrel[x],
Effind=FleetPars$Find[x,],
Spat_targc=FleetPars$Spat_targ[x],
hc=StockPars$hs[x],
R0c=StockPars$R0a[x,],
SSBpRc=StockPars$SSBpR[x,],
aRc=StockPars$aR[x,],
bRc=StockPars$bR[x,],
Qc=FleetPars$qs[x],
Fapic=0,
maxF=StockPars$maxF,
MPA=FleetPars$MPA,
control=1,
SSB0c=StockPars$SSB0[x],
SRRfun=StockPars$SRRfun,
SRRpars = StockPars$SRRpars[[x]],
plusgroup=StockPars$plusgroup,
spawn_time_frac = StockPars$spawn_time_frac[x]))
# Number at the beginning of each year
N <- aperm(array(as.numeric(unlist(histYrs[1,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Biomass at the beginning of each year
Biomass <- aperm(array(as.numeric(unlist(histYrs[2,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Spawning numbers at the beginning of each year
SSN <- aperm(array(as.numeric(unlist(histYrs[3,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Spawning biomass at the beginning of each year
SSB <- aperm(array(as.numeric(unlist(histYrs[4,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Vulnerable biomass at the beginning of each year
VBiomass <- aperm(array(as.numeric(unlist(histYrs[5,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Fishing mortality during each year
FM <- aperm(array(as.numeric(unlist(histYrs[6,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Retained fishing mortality during each year
FMret <- aperm(array(as.numeric(unlist(histYrs[7,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Total mortality during each year
Z <- aperm(array(as.numeric(unlist(histYrs[8,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
if (control$optVB) {
Depletion <- apply(VBiomass[,,nyears,],1,sum)/VB0
} else {
Depletion <- apply(SSB[,,nyears,],1,sum)/SSB0
}
# Historical Fishing Effort
# Effort <- matrix(NA, nrow=nsim, ncol=nyears)
# for (x in 1:nsim) {
# for (y in 1:nyears) {
# Effort[x,y] <- max(FM[x,,y,] / FleetPars$V_real[x,,y] / FleetPars$qs[x])
# }
# }
StockPars$N <- N
StockPars$Biomass <- Biomass
StockPars$SSN <- SSN
StockPars$SSB <- SSB
StockPars$VBiomass <- VBiomass
StockPars$FM <- FM
StockPars$FMret <- FMret
StockPars$Z <- Z
StockPars$Depletion <- Depletion
# update OM@D
if (!is.null(OM@cpars$qs)) StockPars$D <- StockPars$Depletion
# Check that depletion is correct
if (!is.null(control$checks)) {
if (prod(round(StockPars$D, 2)/ round(StockPars$Depletion,2)) != 1) {
print(cbind(round(StockPars$D,2), round(StockPars$Depletion,2)))
warning("Possible problem in depletion calculations")
}
}
if (!is.null(control$checks)) {
Btemp <- apply(StockPars$SSB, c(1,3), sum)
x <- Btemp[,nyears]/StockPars$SSBMSY
y <- StockPars$D/StockPars$SSBMSY_SSB0
plot(x,y, xlim=c(0,max(x)), ylim=c(0,max(y)), xlab="SSB/SSBMSY", ylab="D/SSBMSY_SSB0")
lines(c(-10,10),c(-10,10))
}
# ---- Calculate per-recruit reference points ----
if (!silent) message("Calculating per-recruit reference points")
per_recruit_F <- .lapply(1:nsim, function(x) {
lapply(1:(nyears+proyears), function(y) {
per_recruit_F_calc(x, yr.ind=y,
StockPars=StockPars,
V=FleetPars$V_real_2,
SPR_target=SPR_target)
})
})
F_SPR_y[] <- lapply(per_recruit_F, function(x) sapply(x, getElement, 1)) %>%
simplify2array() %>% aperm(c(3, 1, 2))
F01_YPR_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["YPR_F01"])) %>% t()
Fmax_YPR_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["YPR_Fmax"])) %>% t()
SPRcrash_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["SPRcrash"])) %>% t()
Fcrash_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["Fcrash"])) %>% t()
Fmed_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["Fmed"])) %>% t()
# ---- Calculate annual SPR ----
SPR_hist <- list()
SPR_hist$Equilibrium <- CalcSPReq(StockPars$FM, StockPars, n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
SPR_hist$Dynamic <- CalcSPRdyn(StockPars$FM, StockPars, n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
# ---- Calculate Mean Generation Time ----
MarrayArea <- replicate(nareas, StockPars$M_ageArray[,,1:nyears])
Mnow<-apply(MarrayArea[,,nyears,]*N[,,nyears,],1:2,sum)/apply(N[,,nyears,],1:2,sum)
MGTsurv<-t(exp(-apply(Mnow,1,cumsum)))
MGT<-apply(Agearray*(StockPars$Mat_age[,,nyears]*MGTsurv),1,sum)/apply(StockPars$Mat_age[,,nyears]*MGTsurv,1,sum)
if(all(is.na(MGT))) MGT <- StockPars$ageMarray[, nyears]
# --- Calculate B-low ----
Blow <- rep(NA,nsim)
HZN=2; Bfrac=0.5
if (!is.null(control$HZN)) HZN <- control$HZN
if (!is.null(control$Bfrac)) Bfrac <- control$Bfrac
if(!silent) message("Calculating B-low reference points")
MGThorizon<-floor(HZN*MGT)
if (!snowfall::sfIsRunning()) {
Blow <- sapply(1:nsim,getBlow,
StockPars$N,
StockPars$Asize,
StockPars$SSBMSY,
StockPars$SSBpR,
FleetPars$MPA,
StockPars$SSB0,
StockPars$nareas,
FleetPars$retA_real,
MGThorizon,
FleetPars$Find,
StockPars$Perr_y,
StockPars$M_ageArray,
StockPars$hs,
StockPars$Mat_age,
StockPars$Wt_age,
StockPars$Fec_Age,
StockPars$R0a,
FleetPars$V_real,
nyears,
StockPars$maxage,
StockPars$mov,
FleetPars$Spat_targ,
StockPars$SRrel,
StockPars$aR,
StockPars$bR,
Bfrac,
maxF,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars,
spawn_time_frac=StockPars$spawn_time_frac)
} else {
Blow <- sfSapply(1:nsim,getBlow,
StockPars$N,
StockPars$Asize,
StockPars$SSBMSY,
StockPars$SSBpR,
FleetPars$MPA,
StockPars$SSB0,
StockPars$nareas,
FleetPars$retA_real,
MGThorizon,
FleetPars$Find,
StockPars$Perr_y,
StockPars$M_ageArray,
StockPars$hs,
StockPars$Mat_age,
StockPars$Wt_age,
StockPars$Fec_Age,
StockPars$R0a,
FleetPars$V_real,
nyears,
StockPars$maxage,
StockPars$mov,
FleetPars$Spat_targ,
StockPars$SRrel,
StockPars$aR,
StockPars$bR,
Bfrac,
maxF,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars,
spawn_time_frac=StockPars$spawn_time_frac)
}
StockPars$Blow <- Blow
# --- Calculate Reference Yield ----
if(!silent) message("Calculating reference yield - best fixed F strategy")
if (!snowfall::sfIsRunning()) {
RefY <- sapply(1:nsim, calcRefYield, StockPars, FleetPars, proyears,
Ncurr=StockPars$N[,,nyears,], nyears, proyears)
} else {
RefY <- sfSapply(1:nsim, calcRefYield, StockPars, FleetPars, proyears,
Ncurr=StockPars$N[,,nyears,], nyears, proyears)
}
# ---- Store Reference Points ----
# arrays for unfished biomass for all years
SSN_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
N_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
Biomass_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
SSB_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
# Calculate initial spawning stock numbers for all years
SSN_a[SAYR_a] <- Nfrac[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a]
N_a[SAYR_a] <- StockPars$R0[S_a] * surv[SAY_a] * StockPars$initdist[SAR_a] # Calculate initial stock numbers for all years
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars$Wt_age[SAY_a] # Calculate initial stock biomass
SSB_a[SAYR_a] <- SSN_a[SAYR_a] * StockPars$Wt_age[SAY_a] # Calculate spawning stock biomass
SSN0_a <- apply(SSN_a, c(1,3), sum) # unfished spawning numbers for each year
N0_a <- apply(N_a, c(1,3), sum) # unfished numbers for each year)
SSB0_a <- apply(SSB_a, c(1,3), sum) # unfished spawning biomass for each year
SSB0a_a <- apply(SSB_a, c(1, 3,4), sum) # Calculate unfished spawning stock biomass by area for each year
B0_a <- apply(Biomass_a, c(1,3), sum) # unfished biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * FleetPars$V, c(1,3), sum) # unfished vulnerable biomass for each year
ReferencePoints <- list(
ByYear=list(
N0=N0_y,
SN0=SN0_y,
B0=B0_y,
SSB0=SSB0_y,
VB0=VB0_y,
R0=R0_y,
h=h_y,
MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
BMSY=BMSY_y,
VBMSY=VBMSY_y,
F01_YPR=F01_YPR_y,
Fmax_YPR=Fmax_YPR_y,
F_SPR=F_SPR_y,
Fcrash=Fcrash_y,
Fmed=Fmed_y,
SPRcrash=SPRcrash_y
),
Dynamic_Unfished=list(
N0=apply(N_unfished, c(1,3), sum),
B0=apply(Biomass_unfished, c(1,3), sum),
SN0=apply(SSN_unfished, c(1,3), sum),
SSB0=apply(SSB_unfished, c(1,3), sum),
VB0=apply(VBiomass_unfished, c(1,3), sum),
Rec=apply(N_unfished[,1,,], c(1,2), sum)
),
ReferencePoints=data.frame(
N0=N0,
B0=B0,
SSB0=SSB0,
SSN0=SSN0,
VB0=VB0,
MSY=MSY,
FMSY=FMSY,
SSBMSY=SSBMSY,
BMSY=BMSY,
VBMSY=VBMSY,
UMSY=UMSY,
FMSY_M=FMSY_M,
SSBMSY_SSB0=SSBMSY_SSB0,
BMSY_B0=BMSY_B0,
VBMSY_VB0=VBMSY_VB0,
RefY=RefY,
MGT=MGT,
Blow=Blow
)
)
# --- Calculate Historical Catch ----
# Calculate catch-at-age
if (!is.null( FleetPars$Wt_age_C)) {
w_at_age <- replicate(nareas, FleetPars$Wt_age_C[,,1:OM@nyears])
B2 <- N * w_at_age
CB <- B2 * (1 - exp(-Z)) * (FM/Z)
CB[is.na(CB)] <- 0
} else {
CB <- Biomass * (1 - exp(-Z)) * (FM/Z) # Catch in biomass (removed from population)
CB[is.na(CB)] <- 0
}
# Calculate retained-at-age
Cret <- N * (1 - exp(-Z)) * (FMret/Z) # Retained catch in numbers
Cret[is.na(Cret)] <- 0
if (!is.null(FleetPars$Wt_age_C)) {
CBret <- B2 * (1 - exp(-Z)) * (FMret/Z) # Retained catch in biomass
CBret[is.na(CBret)] <- 0
} else {
CBret <- Biomass * (1 - exp(-Z)) * (FMret/Z) # Retained catch in biomass
CBret[is.na(CBret)] <- 0
}
# --- Sampling by area ----
valNames <- c("Catch", 'BInd', 'SBInd', 'VInd', 'RecInd',
'CAA', 'CAL')
Sample_Area_array <- array(1, dim=c(nsim, nyears+proyears, StockPars$nareas))
Sample_Area <- rep(list(Sample_Area_array), length(valNames))
names(Sample_Area) <- valNames
if (!is.null(OM@cpars$Sample_Area)) {
Sample_Area_in <- OM@cpars$Sample_Area
inval <- names(Sample_Area_in)[!names(Sample_Area_in) %in% valNames]
if (length(inval)>0)
stop("Invalid names in OM@cpars$Sample_Area.\nValid names are:\n", paste(valNames, collapse="\n"))
for (nm in names(Sample_Area_in)) {
dd <- dim(Sample_Area_in[[nm]])
if (length(dd)!=4) { # Sample_area from Hist
Sample_Area[[nm]] <- Sample_Area_in[[nm]]
if (any(dim(Sample_Area_in[[nm]]) != c(nsim, nyears+proyears, nareas)))
stop("OM@cpars$Sample_Area$", nm, " must be dimensions: nsim, nareas, nyears+proyears", call. = FALSE)
}
}
}
nms <- c("Catch", "BInd", "SBInd", "VInd", "CAA", "CAL")
for (nm in nms) {
dd <- dim(Sample_Area[[nm]])
if (length(dd)!=4) { # Sample_area from Hist
temp <- replicate(n_age, Sample_Area[[nm]])
Sample_Area[[nm]] <- aperm(temp, c(1,4,2,3))
}
}
# -- TODO --
# add to ObsPars
ObsPars$Sample_Area <- Sample_Area
if (inc.progress)
shiny::incProgress(0.2, detail = 'Simulating Data')
# --- Populate Data object with Historical Data ----
Data <- makeData(Biomass,
CBret,
Cret,
N,
SSB,
VBiomass,
StockPars,
FleetPars,
ObsPars,
ImpPars,
RefPoints=ReferencePoints$ReferencePoints,
SampCpars,
StockPars$initD,
Sample_Area,
Name=OM@Name,
nyears,
proyears,
nsim,
nareas=StockPars$nareas,
reps,
CurrentYr=OM@CurrentYr,
silent=silent,
control)
# --- Condition Simulated Data on input Data object (if it exists) & calculate error stats ----
StockPars$CBret <- CBret
StockPars$Biomass <- Biomass
StockPars$SSB <- SSB
StockPars$VBiomass <- VBiomass
StockPars$N <- N
if (methods::is(SampCpars$Data, "Data")) {
# real data has been passed in cpars
updatedData <- AddRealData(SimData=Data,
RealData=SampCpars$Data,
ObsPars,
StockPars,
FleetPars,
nsim,
nyears,
proyears,
SampCpars,
msg=!silent,
control,
Sample_Area)
Data <- updatedData$Data
ObsPars <- updatedData$ObsPars
}
OMPars <- Data@OM
# ---- Add Stock & Fleet Dynamics to Data ----
Data@Misc$StockPars <- StockPars
Data@Misc$FleetPars <- FleetPars
Data@Misc$ReferencePoints <- ReferencePoints
# --- Return Historical Simulations and Data from last historical year ----
Hist <- new("Hist")
# Data@Misc <- list()
Hist@Data <- Data
ind <- vapply(ObsPars, function(x) is.atomic(x) && length(x) == nsim, logical(1))
obs <- data.frame(ObsPars[ind])
OMPars <- data.frame(OMPars, obs)
Hist@OMPars <- OMPars
Hist@AtAge <- list(Length=StockPars$Len_age,
Weight=StockPars$Wt_age,
Select=FleetPars$V_real_2,
Retention=FleetPars$retA_real,
Maturity=StockPars$Mat_age,
N.Mortality=StockPars$M_ageArray,
Z.Mortality=StockPars$Z,
F.Mortality=FM,
Fret.Mortality=FMret,
Number=StockPars$N,
Biomass=StockPars$Biomass,
VBiomass=StockPars$VBiomass,
SBiomass=StockPars$SSB,
Removals=CB,
Landings=CBret,
Discards=CB-CBret
)
Hist@TSdata <- list(
Number=apply(StockPars$N,c(1,3,4), sum),
Biomass=apply(StockPars$Biomass,c(1,3,4), sum),
VBiomass=apply(StockPars$VBiomass,c(1,3,4), sum),
SBiomass=apply(StockPars$SSB,c(1,3,4), sum),
Removals=apply(CB, c(1,3,4), sum),
Landings=apply(CBret,c(1,3,4), sum),
Discards=apply(CB-CBret,c(1,3,4), sum),
Find=FleetPars$Find,
RecDev=StockPars$Perr_y,
SPR=SPR_hist,
Unfished_Equilibrium=Unfished_Equilibrium
)
Hist@Ref <- ReferencePoints
Hist@SampPars <- list()
# cpars_Stock <- StockPars[which(lapply(StockPars, length) != nsim)]
# cpars_Fleet <- FleetPars[which(lapply(FleetPars, length) != nsim)]
# cpars_Obs <- ObsPars[which(lapply(ObsPars, length) != nsim)]
# cpars_Imp <- ImpPars[which(lapply(ImpPars, length) != nsim)]
StockPars$N <- StockPars$Biomass <- StockPars$SSN <- StockPars$SSB <-
StockPars$VBiomass <- StockPars$FM <- StockPars$FMret <- StockPars$Z <- NULL
Hist@SampPars <- list(
Stock=StockPars,
Fleet=FleetPars,
Obs=ObsPars,
Imp=ImpPars
)
temp <- OM@cpars$Data
OM@cpars <- list()
OM@cpars$control <- control
OM@cpars$Data <- temp
Hist@OM <- OM
Hist@Misc <- list()
Hist@Misc$BioEco <- data.frame(RevCurr, CostCurr, Response, CostInc, RevInc, LatentEff)
attr(Hist, "version") <- packageVersion("MSEtool")
attr(Hist, "date") <- date()
attr(Hist, "R.version") <- R.version
Hist
}
#' @describeIn runMSE Run the Forward Projections
#'
#' @param Hist An Historical Simulation object (class `Hist`)
#'
#' @export
Project <- function (Hist=NULL, MPs=NA, parallel=FALSE, silent=FALSE,
extended=FALSE, checkMPs=TRUE) {
# ---- Setup ----
if (!methods::is(Hist,'Hist'))
stop('Must provide an object of class `Hist`')
OM <- Hist@OM
set.seed(OM@seed) # set seed for reproducibility
nsim <- OM@nsim # number of simulations
nyears <- OM@nyears # number of historical years
proyears <- OM@proyears # number of projection years
interval <- OM@interval # management interval (annual)
maxF <- OM@maxF # maximum apical F
pstar <- OM@pstar # Percentile of the sample of the TAC for each MP
reps <- OM@reps # Number of samples of the management recommendation for each MP
control <- OM@cpars$control; OM@cpars$control <- NULL
# ---- Check MPs ----
if (checkMPs)
MPs <- CheckMPs(MPs=MPs, silent=silent)
# ---- Set up parallel processing of MPs ---
runparallel <- FALSE
if (any(parallel==TRUE)) runparallel <- TRUE
if (methods::is(parallel, 'list')) runparallel <- TRUE
nMP <- length(MPs) # the total number of methods used
if (nMP < 1) stop("No valid MPs found", call.=FALSE)
isrunning <- snowfall::sfIsRunning()
if (!runparallel & isrunning) snowfall::sfStop()
# Don't run MPs in parallel unless specified
parallel_MPs <- rep(FALSE, nMP)
if (methods::is(parallel, 'list')) {
parallel <- parallel[parallel==TRUE]
ind <- match(names(parallel), MPs)
ind <- ind[!is.na(ind)]
parallel_MPs[ind] <- TRUE
}
if (runparallel & any(parallel_MPs)) {
if (!isrunning) setup()
Export_customMPs(MPs)
}
# ---- Set Management Interval for each MP ----
if (length(interval) != nMP) interval <- rep(interval, nMP)[1:nMP]
if (!all(interval == interval[1])) {
if (!silent) message("Variable management intervals:")
df <- data.frame(MP=MPs,interval=interval)
for (i in 1:nrow(df)) {
message(df$MP[i], 'has management interval:', df$interval[i])
}
}
# --- Add nMP dimension to MSY stats ----
# extract from Hist object
MSY_y <- Hist@Ref$ByYear$MSY
FMSY_y <- Hist@Ref$ByYear$FMSY
SSBMSY_y <- Hist@Ref$ByYear$SSBMSY
BMSY_y <- Hist@Ref$ByYear$BMSY
VBMSY_y <- Hist@Ref$ByYear$VBMSY
F01_YPR_y <- Hist@Ref$ByYear$F01_YPR
Fmax_YPR_y <- Hist@Ref$ByYear$Fmax_YPR
F_SPR_y <- Hist@Ref$ByYear$F_SPR
SPR_target <- F_SPR_y %>% dimnames() %>% getElement(2) %>% substr(3,4) %>% as.numeric()
SPR_target <- SPR_target/100
# store MSY for each sim, MP and year
MSY_y <- array(MSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
FMSY_y <- array(FMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
SSBMSY_y <- array(SSBMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
BMSY_y <- array(BMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
VBMSY_y <- array(VBMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
F01_YPR_y <- array(F01_YPR_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
Fmax_YPR_y <- array(Fmax_YPR_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
F_SPR_y <- array(F_SPR_y, dim=c(nsim, length(SPR_target), nyears+proyears, nMP)) %>% aperm(c(1,4,2,3))
# ---- Set-up arrays and objects for projections ----
# create a data object for each method
# (they have identical historical data and branch in projected years)
Data <- Hist@Data
# no need to replicate Data@Misc across MPs
Data_Misc <- Data@Misc
Data@Misc <- list()
MSElist <- list(Data)[rep(1, nMP)]
SB_SBMSY_a <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected SB_SBMSY
F_FMSYa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected F_FMSY
Ba <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected Biomass
SSBa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected SSB
VBa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected vulnerable biomass
FMa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected fishing mortality rate
Ca <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected removed catch
CaRet <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected retained catch
TACa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected TAC recommendation
Effort <- array(NA, dim = c(nsim, nMP, proyears)) # store the Effort
# PAAout <- array(NA, dim = c(nsim, nMP, maxage)) # store the population-at-age in last projection year
# CAAout <- array(NA, dim = c(nsim, nMP, maxage)) # store the catch-at-age in last projection year
# CALout <- array(NA, dim = c(nsim, nMP, nCALbins)) # store the population-at-length in last projection year
SPReqa <- array(NA,dim=c(nsim,nMP,proyears)) # store the equilibrium Spawning Potential Ratio
SPRdyna <- array(NA,dim=c(nsim,nMP,proyears)) # store the dynamic Spawning Potential Ratio
Cost_out <- array(NA, dim = c(nsim, nMP, proyears)) # store Total Cost
Rev_out <- array(NA, dim = c(nsim, nMP, proyears)) # store Total Revenue
LatEffort_out<- array(NA, dim = c(nsim, nMP, proyears)) # store the Latent Effort
TAE_out <- array(NA, dim = c(nsim, nMP, proyears)) # store the TAE
# ---- Grab Stock, Fleet, Obs, and Imp Pars from Hist ----
StockPars <- Hist@SampPars$Stock
FleetPars <- Hist@SampPars$Fleet
ObsPars <- Hist@SampPars$Obs
ImpPars <- Hist@SampPars$Imp
StockPars$N <- Hist@AtAge$Number
StockPars$Z <- Hist@AtAge$Z.Mortality
StockPars$FM <- Hist@AtAge$F.Mortality
StockPars$FMret <- Hist@AtAge$Fret.Mortality
StockPars$CB <- Hist@AtAge$Removals
StockPars$CBret <- Hist@AtAge$Landings
StockPars$Biomass <- Hist@AtAge$Biomass
StockPars$SSB <- Hist@AtAge$SBiomass
StockPars$VBiomass <- Hist@AtAge$VBiomass
n_age <- StockPars$n_age
nareas <- StockPars$nareas
RealData <- Hist@OM@cpars$Data
ReferencePoints <- Hist@Ref$ReferencePoints
LatentEff <- Hist@Misc$BioEco$LatentEff
RevCurr <- Hist@Misc$BioEco$RevCurr
CostCurr <- Hist@Misc$BioEco$CostCurr
Response <- Hist@Misc$BioEco$Response
CostInc <- Hist@Misc$BioEco$CostInc
RevInc <- Hist@Misc$BioEco$RevInc
# projection arrays for storing all info (by simulation, age, MP, years, areas)
N_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
B_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
SB_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
VB_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
Catch_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
Removals_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
FM_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
FMret_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
spawn_time_frac <- StockPars$spawn_time_frac
spawn_time_frac <- replicate(StockPars$maxage+1, spawn_time_frac)
spawn_time_frac <- replicate(proyears, spawn_time_frac)
spawn_time_frac <- replicate(nareas, spawn_time_frac)
# ---- Begin loop over MPs ----
mm <- 1 # for debugging
for (mm in 1:nMP) { # MSE Loop over methods
Data_MP <- MSElist[[mm]]
Data_MP@Misc <- Data_Misc # add StockPars etc back to Data object
if(!silent) message(mm, "/", nMP, " Running MSE for", MPs[mm], ifelse(parallel_MPs[mm], "in parallel", ""))
checkNA <- rep(0, OM@proyears) # save number of NAs
# years management is updated
upyrs <- seq(from=1, to=proyears, by=interval[mm])
# reset selectivity & retention parameters for projections
L5_P <- FleetPars$L5_y
LFS_P <- FleetPars$LFS_y
Vmaxlen_P <- FleetPars$Vmaxlen_y
# updated to use the realized selectivity/retention curves
SLarray_P <- FleetPars$SLarray_real # selectivity at length array - projections
V_P <- FleetPars$V # selectivity at age array - selectivity of gear
V_P_real <- FleetPars$V_real # selectivity at age array - realized selectivity (max may be less than 1)
V_P_real_2 <- FleetPars$V_real_2 # selectivity at age array - realized selectivity (max = 1)
if (is.null(V_P_real_2)) # hack for backward compatability
V_P_real_2 <- V_P_real
LR5_P <- FleetPars$LR5_y
LFR_P <- FleetPars$LFR_y
Rmaxlen_P <- FleetPars$Rmaxlen_y
retA_P <- FleetPars$retA # retention at age array - projections
retA_P_real <- FleetPars$retA_real # retention at age array - realized selectivity (max may be less than 1)
retA_P_real_2 <- FleetPars$retA_real_2
if (is.null(retA_P_real_2)) # hack for backward compatability
retA_P_real_2 <- retA_P_real
retL_P <- FleetPars$retL_real # retention at length array - projections
Fdisc_P <- FleetPars$Fdisc_array1 # Discard mortality for projections - by age and year
DR_P <- FleetPars$DR_y # Discard ratio for projections by year
LatentEff_MP <- LatentEff # Historical latent effort
# projection arrays
N_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
Biomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
VBiomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
SSN_P <-array(NA, dim = c(nsim, n_age, proyears, nareas))
SSB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
FM_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
FM_Pret <- array(NA, dim = c(nsim, n_age, proyears, nareas)) # retained F
Z_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
CB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
CB_Pret <- array(NA, dim = c(nsim, n_age, proyears, nareas)) # retained catch
# indexes
SAYRL <- as.matrix(expand.grid(1:nsim, 1:n_age, nyears, 1:nareas)) # Final historical year
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, 1 + nyears, 1:nareas)) # Trajectory year
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, 1, 1:nareas))
SYt <- SAYRt[, c(1, 3)]
SAYt <- SAYRt[, 1:3]
SR <- SAYR[, c(1, 4)]
SA1 <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SY1 <- SAYR[, c(1, 3)]
SAY1 <- SAYRt[, 1:3]
SYA <- as.matrix(expand.grid(1:nsim, 1, 1:n_age)) # Projection year
SY <- SYA[, 1:2]
SA <- SYA[, c(1, 3)]
SAY <- SYA[, c(1, 3, 2)]
S <- SYA[, 1]
# -- First projection year ----
y <- 1
if (requireNamespace("pbapply", quietly = TRUE)) {
pb <- pbapply::timerProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
} else {
pb <- txtProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
}
# Mortality in first year
NextYrN <- lapply(1:nsim, function(x)
popdynOneTScpp(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,nyears,],
Zcurr=StockPars$Z[x,,nyears,],
plusgroup = StockPars$plusgroup))
# The stock at the beginning of projection period
N_P[,,1,] <- aperm(array(unlist(NextYrN), dim=c(n_age, nareas, nsim, 1)), c(3,1,4,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P_real[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAY1] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAY1]
# Movement of stock at beginning of first projection year
Ntemp <- lapply(1:nsim, function(x)
movestockCPP(nareas, StockPars$maxage,
StockPars$mov[x,,,,y+nyears],
N_P[x,,y,])
)
N_P[,,y,] <- array(unlist(Ntemp), dim=c(n_age, nareas, nsim)) %>% aperm(c(3,1,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P_real[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAY1] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAY1]
StockPars$N_P <- N_P
# -- Apply MP in initial projection year ----
Data_MP@Misc$StockPars <- StockPars
Data_MP@Misc$StockPars$CB_Pret <- CB_Pret
Data_MP@Misc$StockPars$Biomass_P <- Biomass_P
Data_MP@Misc$StockPars$SSB_P <- SSB_P
Data_MP@Misc$StockPars$VBiomass_P <- VBiomass_P
Data_MP@Misc$StockPars$N_P <- N_P
runMP <- applyMP(Data=Data_MP, MPs = MPs[mm], reps = reps, silent=TRUE,
parallel = parallel_MPs[mm]) # Apply MP
MPRecs <- runMP[[1]][[1]] # MP recommendations
Data_p <- runMP[[2]] # Data object object with saved info from MP
Data_p@TAC <- MPRecs$TAC
LastSpatial <- array(FleetPars$MPA[nyears,], dim=c(nareas, nsim)) #
LastAllocat <- rep(1, nsim) # default assumption of reallocation of effort to open areas
LastTAC <- LastCatch <- apply(StockPars$CBret[,,nyears,], 1, sum)
# calculate pstar quantile of TAC recommendation dist
TACused <- apply(Data_p@TAC, 2, quantile, p = pstar, na.rm = T)
if (length(MPRecs$TAC) >0) {
# a TAC has been recommended
checkNA[y] <- sum(is.na(TACused))
TACused[is.na(TACused)] <- LastTAC[is.na(TACused)] # set to last yr TAC if NA
TACused[TACused<tiny] <- tiny
TACa[, mm, y] <- TACused # recommended TAC
}
# -- Bio-Economics ----
# Calculate Profit from last historical year
RevPC <- RevCurr/LastCatch # cost-per unit catch in last historical year
PMargin <- 1 - CostCurr/(RevPC * LastCatch) # profit margin in last historical year
Profit <- (RevPC * LastCatch) - CostCurr # profit in last historical year
HistEffort <- rep(1, nsim) # future effort is relative to today's effort
Effort_pot <- HistEffort + Response*Profit # potential effort in first projection year
Effort_pot[Effort_pot<0] <- tiny #
# Latent Effort - Maximum Effort Limit
if (!all(is.na(LatentEff_MP))) {
LastTAE <- histTAE <- HistEffort / (1 - LatentEff_MP) # current TAE limit exists
} else {
LastTAE <- histTAE <- rep(NA, nsim) # no current TAE exists
}
# -- Calc stock dynamics ----
MPCalcs <- CalcMPDynamics(MPRecs, y, nyears, proyears, nsim, Biomass_P,
VBiomass_P, LastTAE, histTAE, LastSpatial, LastAllocat,
LastTAC, TACused, maxF,LR5_P, LFR_P, Rmaxlen_P,
retL_P, retA_P, retA_P_real, retA_P_real_2,
L5_P, LFS_P, Vmaxlen_P,
SLarray_P,
V_P, V_P_real, V_P_real_2,
Fdisc_P, DR_P, FM_P,
FM_Pret, Z_P, CB_P, CB_Pret, Effort_pot,
StockPars, FleetPars, ImpPars, control=control)
TACa[, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial <- MPCalcs$Si
LastAllocat <- MPCalcs$Ai
LastTAE <- MPCalcs$TAE # TAE set by MP
LastTAC <- MPCalcs$TACrec # TAC et by MP
Effort[, mm, y] <- MPCalcs$Effort
CB_P <- MPCalcs$CB_P # removals
CB_Pret <- MPCalcs$CB_Pret # retained catch
FM_P <- MPCalcs$FM_P # fishing mortality
FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
Z_P <- MPCalcs$Z_P # total mortality
retL_P <- MPCalcs$retL_P # retained-at-length
SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,mm,y] <- MPCalcs$Ftot
retA_P <- MPCalcs$retA_P # retained-at-age
retA_P_real <- MPCalcs$retA_P_real
retA_P_real_2 <- MPCalcs$retA_P_real_2
retL_P <- MPCalcs$retL_P # retained-at-length
V_P <- MPCalcs$V_P # vulnerable-at-age
V_P_real <- MPCalcs$V_P_real # vulnerable-at-age
V_P_real_2 <- MPCalcs$V_P_real_2 # vulnerable-at-age
LR5_P <- MPCalcs$LR5_P
LFR_P <- MPCalcs$LFR_P
Rmaxlen_P <- MPCalcs$Rmaxlen_P
L5_P <- MPCalcs$L5_P
LFS_P <- MPCalcs$LFS_P
Vmaxlen_P <- MPCalcs$Vmaxlen_P
Fdisc_P <- MPCalcs$Fdisc_P
DR_P <- MPCalcs$DR_P
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
N_Psp <- N_P
for (a in 1:n_age) {
N_Psp[,a,1,] <- N_Psp[,a,1,] * exp(-(Z_P[,a,1,]*spawn_time_frac[,a,1,]))
}
SSN_P[SAYR] <- N_Psp[SAYR] * StockPars$Mat_age[SAY1] # update spawning stock numbers
SSB_P[SAYR] <- N_Psp[SAYR] * StockPars$Fec_Age[SAY1] # update spawning biomass
# recruitment in first projection year
SSBcurr <- apply(SSB_P[,,1,],c(1,3), sum)
recdev <- StockPars$Perr_y[, nyears+n_age]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars$hs,
aR= StockPars$aR, bR=StockPars$bR, R0a=StockPars$R0a,
SSBpR=StockPars$SSBpR,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars)
N_P[,1,y,] <- t(rec_area)
# update to include recruitment
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P[SAYt] # Calculate vulnerable biomass
StockPars$N_P <- N_P
# ---- Bio-economics ----
RetainCatch <- apply(CB_Pret[,,y,], 1, sum) # retained catch this year
RetainCatch[RetainCatch<=0] <- tiny
Cost_out[,mm,y] <- Effort[, mm, y] * CostCurr*(1+CostInc/100)^y # cost of effort this year
Rev_out[,mm,y] <- (RevPC*(1+RevInc/100)^y * RetainCatch)
PMargin <- 1 - Cost_out[,mm,y]/Rev_out[,mm,y] # profit margin this year
Profit <- Rev_out[,mm,y] - Cost_out[,mm,y] # profit this year
Effort_pot <- Effort_pot + Response*Profit # bio-economic effort next year
Effort_pot[Effort_pot<0] <- tiny #
LatEffort_out[,mm,y] <- LastTAE - Effort[, mm, y] # store the Latent Effort
TAE_out[,mm,y] <- LastTAE # store the TAE
# ---- Begin projection years ----
for (y in 2:proyears) {
if (!silent) setTxtProgressBar(pb, y) # Works with pbapply
SelectChanged <- FALSE
if (any(range(retA_P[,,nyears+y] - FleetPars$retA_real[,,nyears+y]) !=0)) SelectChanged <- TRUE
if (any(range(V_P[,,nyears+y] - FleetPars$V_real[,,nyears+y]) !=0)) SelectChanged <- TRUE
# -- Calculate MSY stats for this year ----
if (SelectChanged) { #
y1 <- nyears + y
MSYrefsYr <- sapply(1:nsim, optMSY_eq,
yr.ind=y1, StockPars,
V_P)
MSY_y[,mm,y1] <- MSYrefsYr[1, ]
FMSY_y[,mm,y1] <- MSYrefsYr[2,]
SSBMSY_y[,mm,y1] <- MSYrefsYr[3,]
per_recruit_F <- lapply(1:nsim, per_recruit_F_calc,
yr.ind=y1,
StockPars=StockPars,
V=V_P,
SPR_target=SPR_target)
F_SPR_y[,mm,,y1] <- sapply(per_recruit_F, getElement, 1) %>% t()
F01_YPR_y[,mm,y1] <- sapply(per_recruit_F, function(x) x[[2]][1])
Fmax_YPR_y[,mm,y1] <- sapply(per_recruit_F, function(x) x[[2]][2])
}
# TACa[, mm, y] <- TACa[, mm, y-1] # TAC same as last year unless changed
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, y + nyears, 1:nareas)) # Trajectory year
SAYt <- SAYRt[, 1:3]
SAYtMP <- cbind(SAYt, mm)
SYt <- SAYRt[, c(1, 3)]
SAY1R <- as.matrix(expand.grid(1:nsim, 1:n_age, y - 1, 1:nareas))
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, y, 1:nareas))
SY <- SAYR[, c(1, 3)]
SA <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SAY <- SAYR[, 1:3]
S <- SAYR[, 1]
SR <- SAYR[, c(1, 4)]
SA2YR <- as.matrix(expand.grid(1:nsim, 2:n_age, y, 1:nareas))
SA1YR <- as.matrix(expand.grid(1:nsim, 1:(n_age - 1), y -1, 1:nareas))
# --- Age & Growth ----
NextYrN <- lapply(1:nsim, function(x)
popdynOneTScpp(nareas, StockPars$maxage,
Ncurr=N_P[x,,y-1,],
Zcurr=Z_P[x,,y-1,],
plusgroup=StockPars$plusgroup))
N_P[,,y,] <- aperm(array(unlist(NextYrN), dim=c(n_age, nareas, nsim, 1)), c(3,1,4,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAYt] # Calculate spawning stock numbers (beginning of year)
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAYt] # Calculate spawning stock biomass (beginning of year)
# movement this year
Ntemp <- lapply(1:nsim, function(x)
movestockCPP(nareas, StockPars$maxage,
StockPars$mov[x,,,,y+nyears],
N_P[x,,y,])
)
N_P[,,y,] <- array(unlist(Ntemp), dim=c(n_age, nareas, nsim)) %>% aperm(c(3,1,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAYt] * V_P[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAYt] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAYt] # Calculate spawning stock biomass
StockPars$N_P <- N_P
# --- An update year - update data and run MP ----
if (y %in% upyrs) {
# --- Update Data object ----
Data_MP <- updateData(Data=Data_MP, OM, MPCalcs, Effort,
Biomass=StockPars$Biomass, StockPars$N,
Biomass_P, CB_Pret, N_P, SSB=StockPars$SSB,
SSB_P, VBiomass=StockPars$VBiomass, VBiomass_P,
RefPoints=ReferencePoints,
retA_P, retL_P, StockPars,
FleetPars, ObsPars, ImpPars, V_P,
upyrs, interval, y, mm,
Misc=Data_p@Misc, RealData,
Sample_Area=ObsPars$Sample_Area)
Data_MP@Misc$StockPars$CB_Pret <- CB_Pret
Data_MP@Misc$StockPars$Biomass_P <- Biomass_P
Data_MP@Misc$StockPars$SSB_P <- SSB_P
Data_MP@Misc$StockPars$VBiomass_P <- VBiomass_P
Data_MP@Misc$StockPars$N_P <- N_P
# --- apply MP ----
runMP <- applyMP(Data=Data_MP, MPs = MPs[mm], reps = reps, silent=TRUE,
parallel = parallel_MPs[mm]) # Apply MP
MPRecs <- runMP[[1]][[1]] # MP recommendations
Data_p <- runMP[[2]] # Data object object with saved info from MP
Data_p@TAC <- MPRecs$TAC
}
# calculate pstar quantile of TAC recommendation dist
TACused <- apply(Data_p@TAC, 2, quantile, p = pstar, na.rm = T)
if (length(MPRecs$TAC) >0) {
# a TAC has been recommended
checkNA[y] <- sum(is.na(TACused))
TACused[is.na(TACused)] <- LastTAC[is.na(TACused)] # set to last yr TAC if NA
TACused[TACused<tiny] <- tiny
TACa[, mm, y] <- TACused # recommended TAC
}
# ----- Calc stock dynamics ----
MPCalcs <- CalcMPDynamics(MPRecs, y, nyears, proyears, nsim, Biomass_P,
VBiomass_P, LastTAE, histTAE, LastSpatial, LastAllocat,
LastTAC, TACused, maxF,LR5_P, LFR_P, Rmaxlen_P,
retL_P, retA_P, retA_P_real, retA_P_real_2,
L5_P, LFS_P, Vmaxlen_P,
SLarray_P,
V_P, V_P_real, V_P_real_2,
Fdisc_P, DR_P, FM_P,
FM_Pret, Z_P, CB_P, CB_Pret, Effort_pot,
StockPars, FleetPars, ImpPars, control=control)
LastSpatial <- MPCalcs$Si
LastAllocat <- MPCalcs$Ai
LastTAE <- MPCalcs$TAE # adjustment to TAE
Effort[, mm, y] <- MPCalcs$Effort
FMa[,mm,y] <- MPCalcs$Ftot
CB_P <- MPCalcs$CB_P # removals
CB_Pret <- MPCalcs$CB_Pret # retained catch
LastTAC <- TACa[, mm, y] # apply(CB_Pret[,,y,], 1, sum, na.rm=TRUE)
FM_P <- MPCalcs$FM_P # fishing mortality
FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
Z_P <- MPCalcs$Z_P # total mortality
retA_P <- MPCalcs$retA_P # retained-at-age
retA_P_real <- MPCalcs$retA_P_real
retA_P_real_2 <- MPCalcs$retA_P_real_2
retL_P <- MPCalcs$retL_P # retained-at-length
V_P <- MPCalcs$V_P # vulnerable-at-age
V_P_real <- MPCalcs$V_P_real # vulnerable-at-age
V_P_real_2 <- MPCalcs$V_P_real_2 # vulnerable-at-age
SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
LR5_P <- MPCalcs$LR5_P
LFR_P <- MPCalcs$LFR_P
Rmaxlen_P <- MPCalcs$Rmaxlen_P
L5_P <- MPCalcs$L5_P
LFS_P <- MPCalcs$LFS_P
Vmaxlen_P <- MPCalcs$Vmaxlen_P
Fdisc_P <- MPCalcs$Fdisc_P
DR_P <- MPCalcs$DR_P
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
N_Psp <- N_P
for (a in 1:n_age) {
N_Psp[,a,y,] <- N_Psp[,a,y,] * exp(-(Z_P[,a,y,]*spawn_time_frac[,a,y,]))
}
SSN_P[SAYR] <- N_Psp[SAYR] * StockPars$Mat_age[SAYt] # update spawning stock numbers
SSB_P[SAYR] <- N_Psp[SAYR] * StockPars$Fec_Age[SAYt] # update spawning biomass
# recruitment in this year
SSBcurr <- apply(SSB_P[,,y,],c(1,3), sum)
recdev <- StockPars$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars$hs, aR=StockPars$aR,
bR=StockPars$bR, R0a=StockPars$R0a,
SSBpR=StockPars$SSBpR,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars)
N_P[,1,y,] <- t(rec_area)
StockPars$N_P <- N_P
# update to include recruitment
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAYt] * V_P[SAYt] # Calculate vulnerable biomass
# ---- Bio-economics ----
RetainCatch <- apply(CB_Pret[,,y,], 1, sum) # retained catch this year
RetainCatch[RetainCatch<=0] <- tiny
Cost_out[,mm,y] <- Effort[, mm, y] * CostCurr*(1+CostInc/100)^y # cost of effort this year
Rev_out[,mm,y] <- (RevPC*(1+RevInc/100)^y * RetainCatch)
Profit <- Rev_out[,mm,y] - Cost_out[,mm,y] # profit this year
Effort_pot <- Effort_pot + Response*Profit # bio-economic effort next year
Effort_pot[Effort_pot<0] <- tiny #
LatEffort_out[,mm,y] <- LastTAE - Effort[, mm, y] # store the Latent Effort
TAE_out[,mm,y] <- LastTAE # store the TAE
if ("progress" %in% names(control)) {
if (control$progress) {
if (requireNamespace("shiny", quietly = TRUE)) {
shiny::setProgress((mm - 1 + y/proyears)/nMP,
detail = paste0(round((mm - 1 + y/proyears)/nMP * 100),
"% \n Management procedure ", mm, "/", nMP, " (", MPs[mm], ")"))
} else {
warning('package `shiny` needs to be installed for progress bar')
}
}
}
} # end of year loop
if (!silent) close(pb) # use pbapply::closepb(pb) for shiny related stuff
if (max(upyrs) < proyears) { # One more call to complete Data object
Data_MP <- updateData(Data=Data_MP, OM, MPCalcs, Effort,
StockPars$Biomass, StockPars$N,
Biomass_P, CB_Pret, N_P, StockPars$SSB, SSB_P,
StockPars$VBiomass, VBiomass_P,
RefPoints=ReferencePoints,
retA_P, retL_P, StockPars,
FleetPars, ObsPars, ImpPars, V_P,
upyrs=c(upyrs, proyears),
interval=rep(proyears-max(upyrs), length(interval)), y, mm,
Misc=Data_p@Misc, RealData, ObsPars$Sample_Area
)
}
SB_SBMSY_a[, mm, ] <- apply(SSB_P, c(1, 3), sum, na.rm=TRUE)/SSBMSY_y[,mm,(OM@nyears+1):(OM@nyears+OM@proyears)] # SSB relative to SSBMSY
F_FMSYa[, mm, ] <- FMa[, mm, ]/FMSY_y[,mm,(OM@nyears+1):(OM@nyears+OM@proyears)]
Ba[, mm, ] <- apply(Biomass_P, c(1, 3), sum, na.rm=TRUE) # biomass
SSBa[, mm, ] <- apply(SSB_P, c(1, 3), sum, na.rm=TRUE) # spawning stock biomass
VBa[, mm, ] <- apply(VBiomass_P, c(1, 3), sum, na.rm=TRUE) # vulnerable biomass
Ca[, mm, ] <- apply(CB_P, c(1, 3), sum, na.rm=TRUE) # removed
CaRet[, mm, ] <- apply(CB_Pret, c(1, 3), sum, na.rm=TRUE) # retained catch
SPReqa[, mm, ] <- CalcSPReq(FM_P, StockPars, n_age, nareas, nyears, proyears, nsim)
SPRdyna[, mm, ] <- CalcSPRdyn(abind::abind(StockPars$FM, FM_P, along = 3),
StockPars, n_age, nareas, nyears, proyears, nsim)
if (!silent) {
cat("\n")
if (all(checkNA[upyrs] != nsim) & !all(checkNA == 0)) {
ntot <- sum(checkNA[upyrs])
totyrs <- sum(checkNA[upyrs] >0)
nfrac <- round(ntot/(length(upyrs)*nsim),2)*100
message(totyrs, ' years had TAC = NA for some simulations (', nfrac, "% of total simulations)")
message('Used TAC_y = TAC_y-1')
}
}
# Store all info (return if argument `extended=TRUE`)
N_P_mp[,,mm,,] <- N_P
B_P_mp[,,mm,,] <- Biomass_P
SB_P_mp[,,mm,,] <- SSB_P
VB_P_mp[,,mm,,] <- VBiomass_P
Catch_P_mp[,,mm,,] <- CB_Pret
Removals_P_mp[,,mm,,] <- CB_P
FM_P_mp[,,mm,,] <- FM_P
FMret_P_mp[,,mm,,] <- FM_Pret
# drop StockPars etc from Data_MP - already reported in Hist object
Data_MP@Misc$StockPars <- Data_MP@Misc$FleetPars <- Data_MP@Misc$ReferencePoints <- NULL
MSElist[[mm]] <- Data_MP # update MSElist with PPD for this MP
} # end of MP loop
# ---- Create MSE Object ----
CB_hist <- apply(Hist@TSdata$Landings, 1:2, sum)
FM_hist <- Hist@TSdata$Find * Hist@SampPars$Fleet$qs
SSB_hist <- apply(Hist@TSdata$SBiomass, 1:2, sum)
Misc <- list()
Misc$extended <- list()
if (extended) {
histN <- replicate(nMP, StockPars$N) %>% aperm(c(1,2,5,3,4))
N_all <- abind::abind(histN, N_P_mp, along=4)
histB <- replicate(nMP, StockPars$Biomass) %>% aperm(c(1,2,5,3,4))
B_all <- abind::abind(histB, B_P_mp, along=4)
histSSB <- replicate(nMP, StockPars$SSB) %>% aperm(c(1,2,5,3,4))
SB_all <- abind::abind(histSSB, SB_P_mp, along=4)
histVB <- replicate(nMP, StockPars$VBiomass) %>% aperm(c(1,2,5,3,4))
VB_all <- abind::abind(histVB, VB_P_mp, along=4)
histCatch <- replicate(nMP, StockPars$CBret) %>% aperm(c(1,2,5,3,4))
Catch_all <- abind::abind(histCatch, Catch_P_mp, along=4)
histRemovals <- replicate(nMP, StockPars$CB) %>% aperm(c(1,2,5,3,4))
Removals_all <- abind::abind(histRemovals, Removals_P_mp, along=4)
histF <- replicate(nMP, StockPars$FM) %>% aperm(c(1,2,5,3,4))
FM_all <- abind::abind(histF, FM_P_mp, along=4)
histFret <- replicate(nMP, StockPars$FMret) %>% aperm(c(1,2,5,3,4))
FMret_all <- abind::abind(histFret, FMret_P_mp, along=4)
Misc$extended <- list(
N = N_all,
B = B_all,
SSB = SB_all,
VB = VB_all,
Catch = Catch_all,
Removals = Removals_all,
FM=FM_all,
FMret=FMret_all
)
Hist_out <- Hist
} else {
Hist_out <- new("Hist")
Hist_out@AtAge <- Hist@AtAge
Hist_out@TSdata <- Hist@TSdata
# Hist@Data <- new('Data')
# Hist@OMPars <- data.frame()
# Hist@AtAge <- list()
# Hist@Ref <- list()
# Hist@SampPars <- list()
}
MSEout <- new("MSE",
Name = OM@Name,
nyears=nyears, proyears=proyears, nMPs=nMP,
MPs=MPs, nsim=nsim,
OM=Data@OM,
Obs=Data@Obs,
SB_SBMSY=SB_SBMSY_a,
F_FMSY=F_FMSYa,
N=N_P_mp, # apply(N_P_mp, c(1,3,4), sum),
B=Ba,
SSB=SSBa,
VB=VBa,
FM=FMa,
SPR=list(Equilibrium = SPReqa, Dynamic = SPRdyna),
Catch=CaRet,
Removals=Ca,
Effort=Effort,
TAC=TACa,
TAE=TAE_out,
BioEco=list(LatEffort=LatEffort_out,
Revenue=Rev_out,
Cost=Cost_out
),
RefPoint=list(MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
F_SPR=F_SPR_y,
Dynamic_Unfished=Hist@Ref$Dynamic_Unfished,
ByYear=Hist@Ref$ByYear
),
CB_hist=CB_hist,
FM_hist=FM_hist,
SSB_hist=SSB_hist,
Hist=Hist_out,
PPD=MSElist,
Misc=Misc
)
# Store MSE info
attr(MSEout, "version") <- packageVersion("MSEtool")
attr(MSEout, "date") <- date()
attr(MSEout, "R.version") <- R.version
MSEout
}
#' Run a Management Strategy Evaluation
#'
#' Functions to run the Management Strategy Evaluation (closed-loop
#' simulation) for a specified operating model
#'
#' @param OM An operating model object (class `OM` or class `Hist`)
#' @param MPs A vector of methods (character string) of class MP
#' @param Hist Should model stop after historical simulations? Returns an object of
#' class 'Hist' containing all historical data
#' @param silent Should messages be printed out to the console?
#' @param parallel Logical or a named list. Should MPs be run using parallel processing?
#' For \code{runMSE}, can also be \code{"sac"} to run the entire MSE in parallel
#' using the split-apply-combine technique. See Details for more information.
#' @param extended Logical. Return extended projection results?
#' if TRUE, `MSE@Misc$extended` is a named list with extended data
#' (including historical and projection by area), and extended version of `MSE@Hist`
#' is returned.
#' @param checkMPs Logical. Check if the specified MPs exist and can be run on `SimulatedData`?
#'
#' @describeIn runMSE Run the Historical Simulations and Forward Projections
#' from an object of class `OM
#' @details
#' ## Running MPs in parallel
#'
#' For most MPs, running in parallel can actually lead to an increase in computation time, due to the overhead in sending the
#' information over to the cores. Consequently, by default the MPs will not be run in parallel if `parallel=TRUE`
#' (although other internal code will be run in parallel mode).
#'
#' To run MPs in parallel, specify a named list with the name of the MP(s) assigned as TRUE. For example,`parallel=list(AvC=TRUE`)
#' will run the `AvC` MP in parallel mode.
#'
#' ## Split-apply-combine MSE in parallel
#'
#' Additional savings in computation time can be achieved by running the entire simulation in batches. Individual simulations of the operating model
#' are divided into separate cores using \link{SubCpars}, `Simulate` and `Project` are applied independently for each core via `snowfall::sfClusterApplyLB`, and the
#' output (a list of MSE objects) is stitched back together into a single MSE object using \link{joinMSE}.
#'
#' The ideal number of cores will be determined based on the number of simulations and available cores.
#'
#' There are several issues to look out for when using this split-apply-combine technique:
#'
#' \itemize{
#' \item Numerical optimization for depletion may fail in individual cores when \code{OM@cpars$qs} is not specified.
#' \item Length bins should be specified in the operating model in \code{OM@cpars$CAL_bins}. Otherwise, length bins can vary by core and
#' create problems when combining into a single object.
#' \item Compared to non-parallel runs, sampled parameters in the operating model will vary despite the same value in \code{OM@seed}.
#' \item If there is an error in individual cores or while combining the parallel output into a single Hist or MSE object, the list of output (from the cores) will be returned.
#' }
#'
#' @return Functions return objects of class \linkS4class{Hist} or \linkS4class{MSE}
#' \itemize{
#' \item Simulate - An object of class \linkS4class{Hist}
#' \item Project - An object of class \linkS4class{MSE}
#' \item runMSE - An object of class \linkS4class{MSE} if \code{Hist = TRUE} otherwise a class \linkS4class{Hist} object
#' }
#' @export
runMSE <- function(OM=MSEtool::testOM, MPs = NA, Hist=FALSE, silent=FALSE,
parallel=FALSE, extended=FALSE, checkMPs=TRUE) {
# ---- Initial Checks and Setup ----
if (methods::is(OM,'OM')) {
if (OM@nsim <=1) stop("OM@nsim must be > 1", call.=FALSE)
} else if (methods::is(OM,'Hist')) {
if (!silent) message("Using `Hist` object to reproduce historical dynamics")
# # --- Extract cpars from Hist object ----
# cpars <- list()
# cpars <- c(OM@SampPars$Stock, OM@SampPars$Fleet, OM@SampPars$Obs,
# OM@SampPars$Imp, OM@OMPars, OM@OM@cpars)
#
# # --- Populate a new OM object ----
# newOM <- OM@OM
# newOM@cpars <- cpars
# OM <- newOM
} else {
stop("You must specify an operating model")
}
# check MPs
if (checkMPs & !Hist)
MPs <- CheckMPs(MPs=MPs, silent=silent)
if (is.character(parallel) && parallel == "sac") {
MSEout <- runMSE_sac(OM, MPs, Hist = Hist, silent = silent, extended = extended)
return(MSEout)
}
if (methods::is(OM,'OM')) {
HistSims <- Simulate(OM, parallel, silent)
} else {
HistSims <- OM
}
if (Hist) {
if(!silent) message("Returning historical simulations")
return(HistSims)
}
if(!silent) message("Running forward projections")
MSEout <- try(Project(Hist=HistSims, MPs, parallel, silent, extended = extended, checkMPs=FALSE), silent=TRUE)
if (methods::is(MSEout, 'try-error')) {
message('The following error occured when running the forward projections: ',
crayon::red(attributes(MSEout)$condition))
message('Returning the historical simulations (class `Hist`). To avoid re-running spool up, ',
'the forward projections can be run with ',
'`runMSE(Hist, MPs, ...)`')
return(HistSims)
}
MSEout
}
runMSE_sac <- function(OM, MPs, Hist = FALSE, silent = FALSE, extended = FALSE) {
if (OM@nsim <= 48) stop("OM@nsim should be greater than 48 to effectively use split-apply-combine.")
if (is.null(OM@cpars$CAL_bins)) warning("OM@cpars$CAL_bins was not provided which can create issues with parallel = \"sac\"")
ncpus <- set_parallel(TRUE)
nsim <- OM@nsim
nits <- ceiling(nsim/48)
itsim <- rep(48,nits)
if (nits < ncpus) {
if (nits < 4) {
nits <- 4
itsim <- rep(ceiling(nsim/4), 4)
} else{
nits <- ncpus
itsim <- rep(ceiling(nsim/ncpus), ncpus)
}
}
cnt <- 1
while (sum(itsim) != nsim | any(itsim<2)) {
diff <- nsim - sum(itsim)
if (diff >0) {
itsim[cnt] <- itsim[cnt]+1
}
if(diff < 0) {
itsim[cnt] <- itsim[cnt]-1
}
cnt <- cnt+1
if (cnt > length(itsim)) cnt <- 1
}
#### Split
if (!silent) {
message("Running MSE using split-apply-combine on ", length(itsim), " cores with these number of simulations:\n",
paste0(itsim, collapse = ", "))
}
sims <- lapply(1:length(itsim), function(i) {
if (i > 1) {
(sum(itsim[1:(i-1)]) + 1): sum(itsim[1:i])
} else {
1:itsim[i]
}
})
#### Apply Simulate() in parallel
if (!silent) message("Running Simulate() in parallel..")
HistList <- snowfall::sfClusterApplyLB(1:nits, function(i, OM, iter) {
OM@seed <- OM@seed + i
tryCatch(Simulate(SubCpars(OM, sims = iter[[i]]), silent = TRUE), error = function(e) as.character(e))
}, OM = OM, iter = sims)
# Error check Hist objects
HistErr <- sapply(HistList, function(x) !inherits(x, "Hist"))
if (any(HistErr)) {
warning("Returning list of Hist objects. There was an error when running Simulate() for core(s): ", paste0(c(1:length(HistErr))[HistErr], collapse = ", "))
return(HistList)
}
# Combine Hist objects and exit
if (Hist) {
Histout <- try(joinHist(HistList), silent = TRUE)
if (methods::is(Histout, "try-error")) {
warning("Error in joinHist() for combining Hist objects. Returning list of Hist objects.")
Histout <- HistList
}
return(Histout)
}
#### Apply Project() in parallel
Export_customMPs(MPs)
if (!silent) message("Running Project() in parallel with ", length(MPs), " MPs..")
MSElist <- snowfall::sfClusterApplyLB(HistList, function(i, MPs, extended) {
tryCatch(Project(i, MPs = MPs, parallel = FALSE, silent = TRUE, extended = extended, checkMPs = FALSE),
error = function(e) as.character(e))
}, MPs = MPs, extended = extended)
#### Combine MSE objects
MSEout <- try(joinMSE(MSElist), silent = TRUE)
if (methods::is(MSEout, "try-error")) {
warning("Error in joinMSE() for combining MSE objects. Returning list of MSE objects.")
MSEout <- MSElist
}
return(MSEout)
}
Blue-Matter/MSEtool documentation built on April 25, 2024, 12:30 p.m.
R Package Documentation
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Defines functions runMSE_sac runMSE Simulate
Documented in runMSE Simulate
#' @describeIn runMSE Run the Historical Simulations from an object of class `OM`
#' @export
#
Simulate <- function(OM=MSEtool::testOM, parallel=FALSE, silent=FALSE) {
# ---- Initial Checks and Setup ---
OM <- CheckOM(OM, !silent)
if (OM@nsim <=1)
stop("OM@nsim must be > 1", call.=FALSE)
# Set up parallel processing
ncpus <- set_parallel(any(unlist(parallel)))
# Set pbapply functions
if (requireNamespace("pbapply", quietly = TRUE) && !silent) {
.lapply <- pbapply::pblapply
.sapply <- pbapply::pbsapply
# Argument to pass parallel cluster (if running)
formals(.lapply)$cl <- formals(.sapply)$cl <- substitute(if (snowfall::sfIsRunning()) snowfall::sfGetCluster() else NULL)
} else if (snowfall::sfIsRunning()) {
.lapply <- snowfall::sfLapply
.sapply <- snowfall::sfSapply
} else {
.lapply <- base::lapply
.sapply <- base::sapply
}
set.seed(OM@seed) # set seed for reproducibility
nsim <- OM@nsim # number of simulations
nyears <- OM@nyears # number of historical years
proyears <- OM@proyears # number of projection years
interval <- OM@interval # management interval (annual)
maxF <- OM@maxF # maximum apical F
pstar <- OM@pstar # Percentile of the sample of the TAC for each MP
reps <- OM@reps # Number of samples of the management recommendation for each MP
# plusgroup
plusgroup <- 1 # default now is to use plusgroup
if(!is.null(OM@cpars$plusgroup) && !OM@cpars$plusgroup) {
plusgroup <- 0; OM@cpars$plusgroup <- NULL
}
# ---- Control Parameters Options ----
control <- OM@cpars$control; OM@cpars$control <- NULL
# Shiny progress bar
inc.progress <- FALSE
if("progress" %in% names(control)) {
if(control$progress) {
if (requireNamespace("shiny", quietly = TRUE)) {
inc.progress <- TRUE
} else {
warning('package `shiny` needs to be installed for progress bar')
}
}
}
if (inc.progress)
shiny::incProgress(0.2, detail = 'Sampling OM Parameters')
# Option to optimize depletion for vulnerable biomass instead of spawning biomass
control$optVB <- FALSE
if (!is.null(control$D) && control$D == "VB") control$optVB <- TRUE
# Option to optimize depletion for sp biomass MSY instead of spawning biomass
control$optSBMSY <- FALSE
if (!is.null(control$D) && control$D == "SBMSY") control$optSBMSY <- TRUE
# --- Sample OM parameters ----
if(!silent) message("Loading operating model")
# custom parameters exist - sample and write to list
SampCpars <- list()
if (length(OM@cpars)>0) {
SampCpars <- SampleCpars(cpars=OM@cpars, nsim, silent=silent)
}
set.seed(OM@seed) # set seed again after cpars has been sampled
# Stock Parameters
StockPars <- SampleStockPars(Stock=OM,
nsim,
nyears,
proyears,
cpars=SampCpars,
msg=!silent)
# Check for custom stock-recruit function
StockPars <- Check_custom_SRR(StockPars, SampCpars, nsim)
# Checks
if (any(range(StockPars$M_ageArray) <=tiny))
stop("range of StockPars$M_ageArray is: ", paste0(range(StockPars$M_ageArray), collapse="-"))
# Fleet Parameters
FleetPars <- SampleFleetPars(Fleet=SubOM(OM, "Fleet"),
Stock=StockPars,
nsim,
nyears,
proyears,
cpars=SampCpars)
# Obs Parameters
ObsPars <- SampleObsPars(OM, nsim, cpars=SampCpars, Stock=StockPars,
nyears, proyears)
# Imp Parameters
ImpPars <- SampleImpPars(OM, nsim, cpars=SampCpars, nyears, proyears)
# Bio-Economic Parameters
BioEcoPars <- c("RevCurr", "CostCurr", "Response", "CostInc", "RevInc", "LatentEff")
if (all(lapply(SampCpars[BioEcoPars], length) == 0)) {
# no bio-economic model
# if (!silent) message("No bio-economic model parameters found. \nTAC and TAE assumed to be caught in full")
RevCurr <- CostCurr <- Response <- CostInc <- RevInc <- LatentEff <- rep(NA, nsim)
} else {
if (!silent) message("Bio-economic model parameters found.")
# Checks
if (length(SampCpars$CostCurr) != nsim) stop("OM@cpars$CostCurr is not length OM@nsim", call.=FALSE)
if (length(SampCpars$RevCurr) != nsim) stop("OM@cpars$RevCurr is not length OM@nsim", call.=FALSE)
if (length(SampCpars$Response) != nsim) stop("OM@cpars$Response is not length OM@nsim", call.=FALSE)
if (length(SampCpars$RevInc) != nsim) SampCpars$RevInc <- rep(0, nsim)
if (length(SampCpars$CostInc) != nsim) SampCpars$CostInc <- rep(0, nsim)
if (length(SampCpars$LatentEff) != nsim) SampCpars$LatentEff <- rep(NA, nsim)
RevCurr <- SampCpars$RevCurr
CostCurr <- SampCpars$CostCurr
Response <- SampCpars$Response
CostInc <- SampCpars$CostInc
RevInc <- SampCpars$RevInc
LatentEff <- SampCpars$LatentEff
}
# ---- Initialize Arrays ----
n_age <- StockPars$maxage + 1 # number of age classes (starting at age-0)
StockPars$n_age <- n_age
nareas <- StockPars$nareas
N <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # stock numbers array
Biomass <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # stock biomass array
VBiomass <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # vulnerable biomass array
SSN <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # spawning stock numbers array
SSB <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # spawning stock biomass array
FM <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # fishing mortality rate array
FMret <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # fishing mortality rate array for retained fish
Z <- array(NA, dim = c(nsim, n_age, nyears, nareas)) # total mortality rate array
Agearray <- array(rep(0:StockPars$maxage, each = nsim), dim = c(nsim, n_age)) # Age array
# ---- Pre Equilibrium calcs ----
# Set up array indexes sim (S) age (A) year (Y) region/area (R)
SAYR <- as.matrix(expand.grid(1:nareas, 1, 1:n_age, 1:nsim)[4:1])
SAY <- SAYR[, 1:3]
SAR <- SAYR[, c(1,2,4)]
SA <- Sa <- SAYR[, 1:2]
SR <- SAYR[, c(1, 4)]
S <- SAYR[, 1]
SY <- SAYR[, c(1, 3)]
Sa[,2]<- n_age-Sa[,2] + 1 # This is the process error index for initial year
# ---- Calculate initial distribution if mov provided in cpars ----
if (is.null(StockPars$initdist)) {
# mov has been passed in cpars - initdist hasn't been defined
StockPars$initdist <- CalcDistribution(StockPars, FleetPars, SampCpars, nyears,
maxF,
plusgroup, checks=FALSE)
}
# Unfished recruitment by area - INITDIST OF AGE 1.
StockPars$R0a <- matrix(StockPars$R0, nrow=nsim, ncol=nareas, byrow=FALSE) * StockPars$initdist[,1,] #
# ---- Unfished Equilibrium calcs ----
# unfished survival
surv <- lapply(1:nsim, calc_survival, StockPars=StockPars,
plusgroup=plusgroup, inc_spawn_time=FALSE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
# unfished survival (spawning)
SBsurv <- lapply(1:nsim, calc_survival, StockPars=StockPars,
plusgroup=plusgroup, inc_spawn_time=TRUE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
Nfrac <- SBsurv * StockPars$Mat_age # predicted numbers of mature ages in all years (accounting for `spawn_time_frac`)
# indices for all years
SAYR_a <- as.matrix(expand.grid(1:nareas, 1:(nyears+proyears), 1:n_age, 1:nsim)[4:1])
SAY_a <- SAYR_a[, 1:3]
SAR_a <- SAYR_a[, c(1,2,4)]
SA_a <- SAYR_a[, 1:2]
SR_a <- SAYR_a[, c(1, 4)]
S_a <- SAYR_a[, 1]
SY_a <- SAYR_a[, c(1, 3)]
# arrays for unfished biomass for all years
SSN_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
N_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
Biomass_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
SSB_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
# Calculate initial spawning stock numbers for all years
SSN_a[SAYR_a] <- Nfrac[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a]
N_a[SAYR_a] <- StockPars$R0[S_a] * surv[SAY_a] * StockPars$initdist[SAR_a]
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars$Wt_age[SAY_a]
SSB_a[SAYR_a] <- SBsurv[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a] * StockPars$Fec_Age[SAY_a]
SSN0_a <- apply(SSN_a, c(1,3), sum) # unfished spawning numbers for each year
N0_a <- apply(N_a, c(1,3), sum) # unfished numbers for each year)
SSB0_a <- apply(SSB_a, c(1,3), sum) # unfished spawning biomass for each year
SSB0a_a <- apply(SSB_a, c(1, 3,4), sum) # Calculate unfished spawning stock biomass by area for each year
B0_a <- apply(Biomass_a, c(1,3), sum) # unfished biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * FleetPars$V_real, c(1,3), sum) # unfished vulnerable biomass for each year
# ---- Unfished Reference Points ----
SSBpRa <- array(SSB0_a/matrix(StockPars$R0, nrow=nsim, ncol=nyears+proyears),
dim = c(nsim, nyears+proyears))
UnfishedRefs <- sapply(1:nsim, CalcUnfishedRefs, ageM=StockPars$ageM, N0_a=N0_a, SSN0_a=SSN0_a,
SSB0_a=SSB0_a, B0_a=B0_a, VB0_a=VB0_a, SSBpRa=SSBpRa, SSB0a_a=SSB0a_a)
N0 <- UnfishedRefs[1,] %>% unlist() # average unfished numbers
SSN0 <- UnfishedRefs[2,] %>% unlist() # average unfished spawning numbers
SSB0 <- UnfishedRefs[3,] %>% unlist() # average unfished spawning biomass
B0 <- UnfishedRefs[4,] %>% unlist() # average unfished biomass
VB0 <- UnfishedRefs[5,] %>% unlist() # average unfished vulnerable biomass
Unfished_Equilibrium <- list(
N_at_age=N_a,
B_at_age=Biomass_a,
SSB_at_age=SSB_a,
SSN_at_age=SSN_a,
VB_at_age=Biomass_a * replicate(nareas, FleetPars$V_real)
)
# average spawning stock biomass per recruit
SSBpR <- matrix(UnfishedRefs[6,] %>% unlist(), nrow=nsim, ncol=nareas)
# average unfished biomass
SSB0a <- UnfishedRefs[7,] %>% unlist() %>% matrix(nrow=nsim, ncol=nareas, byrow = TRUE)
bR <- matrix(log(5 * StockPars$hs)/(0.8 * SSB0a), nrow=nsim) # Ricker SR params
aR <- matrix(exp(bR * SSB0a)/SSBpR, nrow=nsim) # Ricker SR params
# --- Optimize for Initial Depletion ----
initD <- SampCpars$initD #
if (!is.null(initD)) { # initial depletion is not unfished
if (!silent) message("Optimizing for user-specified depletion in first historical year")
Perrmulti <- sapply(1:nsim, optDfunwrap, initD=initD, R0=StockPars$R0,
StockPars$initdist,
Perr_y=StockPars$Perr_y, surv=surv[,,1], Fec_age=StockPars$Fec_Age,
SSB0=SSB0,
StockPars$n_age)
StockPars$Perr_y[,1:StockPars$maxage] <- StockPars$Perr_y[, 1:StockPars$maxage] * Perrmulti
}
# --- Non-equilibrium Initial Year ----
SSN[SAYR] <- Nfrac[SAY] * StockPars$R0[S] *
StockPars$initdist[SAR]*StockPars$Perr_y[Sa] # Calculate initial spawning stock numbers
N[SAYR] <- StockPars$R0[S] * surv[SAY] * StockPars$initdist[SAR]*StockPars$Perr_y[Sa] # Calculate initial stock numbers
Biomass[SAYR] <- N[SAYR] * StockPars$Wt_age[SAY] # Calculate initial stock biomass
SSB[SAYR] <- SBsurv[SAY] * StockPars$R0[S] * StockPars$initdist[SAR]*StockPars$Perr_y[Sa] * StockPars$Fec_Age[SAY] # Calculate spawning stock biomass
VBiomass[SAYR] <- N[SAYR] * FleetPars$Wt_age_C[SAY] * FleetPars$V_real[SAY] # Calculate vulnerable biomass
StockPars$aR <- aR
StockPars$bR <- bR
StockPars$SSB0 <- SSB0
StockPars$VB0 <- VB0
StockPars$N <- N
StockPars$plusgroup <- plusgroup[1]
StockPars$maxF <- OM@maxF
StockPars$SSBpR <- SSBpR
if (!is.null(control$checks) && !is.null(initD)) { # check initial depletion
plot(apply(SSB[,,1,], 1, sum)/SSB0, initD)
if (!any(round(apply(SSB[,,1,], 1, sum)/SSB0, 2) == round(initD,2)))
warning('problem with initial depletion')
}
# --- Historical Spatial closures ----
if (!is.null(SampCpars$MPA)) {
# MPA by year passed in cpars
MPA <- SampCpars$MPA
if (any(dim(MPA) != c(nyears+proyears, nareas))) {
stop('cpars$MPA must be a matrix with `nyears+proyears` rows and `nareas` columns', .call=FALSE)
}
if (any(MPA !=1 & MPA!=0))
stop('values in cpars$MPA must be either 0 (closed) or open (1)', .call=FALSE)
if (any(MPA!=1)) {
for (a in 1:nareas) {
yrs <- which(MPA[,a] == 0)
if (length(yrs)>0) {
if (!silent)
message('Spatial closure detected in area ', a, ' in years ',
paste(findIntRuns(yrs), collapse=", "))
}
}
}
} else {
MPA <- matrix(1, nrow=nyears+proyears, ncol=nareas)
if (FleetPars$MPA) {
if (!silent) message('Historical MPA in Area 1 for all years')
MPA[,1] <- 0
}
}
FleetPars$MPA <- MPA
# --- Calculate MSY statistics for each year ----
if (inc.progress)
shiny::incProgress(0.2, detail = 'Calculating Reference Points')
# ignores spatial closures
# assumes all vulnerable fish are caught - ie no discarding
MSY_y <- array(0, dim=c(nsim, nyears+proyears)) # store MSY for each sim and year
FMSY_y <- MSY_y # store FMSY for each sim, and year
SSBMSY_y <- MSY_y # store SSBMSY for each sim, and year
BMSY_y <- MSY_y # store BMSY for each sim, and year
VBMSY_y <- MSY_y # store VBMSY for each sim, and year
R0_y <- MSY_y # store R0 for each sim, and year
h_y <- MSY_y # store h for each sim, and year
N0_y <- MSY_y
SN0_y <- MSY_y
B0_y <- MSY_y
SSB0_y <- MSY_y
VB0_y <- MSY_y
F01_YPR_y <- MSY_y # store F01 for each sim, and year
Fmax_YPR_y <- MSY_y # store Fmax for each sim, and year
Fcrash_y <- MSY_y # store Fcrash for each sim, and year
SPRcrash_y <- MSY_y # store SPRcrash for each sim, and year
Fmed_y <- MSY_y # store Fmed (F that generates the median historical SSB/R) for each sim, and year
SPR_target <- seq(0.2, 0.6, 0.05)
F_SPR_y <- array(0, dim = c(nsim, length(SPR_target), nyears + proyears)) %>%
structure(dimnames = list(NULL, paste0("F_", 100*SPR_target, "%"), NULL)) #array of F-SPR% by sim, SPR%, year
if(!silent) message("Calculating MSY reference points for each year")
# average life-history parameters over ageM years
# Assuming all vulnerable fish are kept; ie MSY is total removals
MSYrefsYr <- .lapply(1:nsim, function(x) {
sapply(1:(nyears+proyears), function(y) {
optMSY_eq(x,
yr.ind=y,
StockPars,
FleetPars$V_real_2)
})
})
MSY_y[] <- sapply(MSYrefsYr, function(x) x["Yield", ]) %>% t()
FMSY_y[] <- sapply(MSYrefsYr, function(x) x["F", ]) %>% t()
SSBMSY_y[] <- sapply(MSYrefsYr, function(x) x["SB", ]) %>% t()
BMSY_y[] <- sapply(MSYrefsYr, function(x) x["B", ]) %>% t()
VBMSY_y[] <- sapply(MSYrefsYr, function(x) x["VB", ]) %>% t()
R0_y[] <- sapply(MSYrefsYr, function(x) x["R0", ]) %>% t()
h_y[] <- sapply(MSYrefsYr, function(x) x["h", ]) %>% t()
N0_y[] <- sapply(MSYrefsYr, function(x) x["N0", ]) %>% t()
SN0_y[] <- sapply(MSYrefsYr, function(x) x["SN0", ]) %>% t()
B0_y[] <- sapply(MSYrefsYr, function(x) x["B0", ]) %>% t()
SSB0_y[] <- sapply(MSYrefsYr, function(x) x["SB0", ]) %>% t()
VB0_y[] <- sapply(MSYrefsYr, function(x) x["VB", ]/x["VB_VB0", ]) %>% t()
if (any(FMSY_y == 0)) {
message_warn("FMSY = 0 for some simulations and years.")
}
# --- MSY reference points ----
MSYRefPoints <- sapply(1:nsim, CalcMSYRefs, MSY_y=MSY_y, FMSY_y=FMSY_y,
SSBMSY_y=SSBMSY_y, BMSY_y=BMSY_y, VBMSY_y=VBMSY_y,
ageM=StockPars$ageM, nyears=nyears)
MSY <- MSYRefPoints[1,] %>% unlist() # record the MSY results (Vulnerable)
FMSY <- MSYRefPoints[2,] %>% unlist() # instantaneous FMSY (Vulnerable)
SSBMSY <- MSYRefPoints[3,] %>% unlist() # Spawning Stock Biomass at MSY
BMSY <- MSYRefPoints[4,] %>% unlist() # total biomass at MSY
VBMSY <- MSYRefPoints[5,] %>% unlist() # Biomass at MSY (Vulnerable)
UMSY <- MSY/VBMSY # exploitation rate
FMSY_M <- FMSY/StockPars$M # ratio of true FMSY to natural mortality rate M
SSBMSY_SSB0 <- SSBMSY/SSB0 # SSBMSY relative to unfished (SSB)
BMSY_B0 <- BMSY/B0 # Biomass relative to unfished (B0)
VBMSY_VB0 <- VBMSY/VB0 # VBiomass relative to unfished (VB0)
# --- Dynamic Unfished Reference Points ----
Unfished <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,1,],
nyears+proyears,
M_age=StockPars$M_ageArray[x,,],
Asize_c=StockPars$Asize[x,],
MatAge=StockPars$Mat_age[x,,],
FecAge=StockPars$Fec_Age[x,,],
WtAge=StockPars$Wt_age[x,,],
Vuln=FleetPars$V_real[x,,],
Retc=FleetPars$retA_real[x,,],
Prec=StockPars$Perr_y[x,],
movc=split_along_dim(StockPars$mov[x,,,,],4),
SRrelc=StockPars$SRrel[x],
Effind=rep(0, nyears+proyears),
Spat_targc=FleetPars$Spat_targ[x],
hc=StockPars$hs[x],
R0c=StockPars$R0a[x,],
SSBpRc=StockPars$SSBpR[x,],
aRc=StockPars$aR[x,],
bRc=StockPars$bR[x,],
Qc=0,
Fapic=0,
MPA=FleetPars$MPA,
maxF=StockPars$maxF,
control=1,
SSB0c=StockPars$SSB0[x],
SRRfun=StockPars$SRRfun,
SRRpars = StockPars$SRRpars[[x]],
plusgroup=StockPars$plusgroup,
spawn_time_frac=StockPars$spawn_time_frac[x]))
N_unfished <- aperm(array(as.numeric(unlist(Unfished[1,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
Biomass_unfished <- aperm(array(as.numeric(unlist(Unfished[2,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSN_unfished <- aperm(array(as.numeric(unlist(Unfished[3,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSB_unfished <- aperm(array(as.numeric(unlist(Unfished[4,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
VBiomass_unfished <- aperm(array(as.numeric(unlist(Unfished[5,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
StockPars$MSY <- MSY
StockPars$FMSY <- FMSY
StockPars$SSBMSY <- SSBMSY
StockPars$BMSY <- BMSY
StockPars$VBMSY <- VBMSY
StockPars$UMSY <- UMSY
StockPars$FMSY_M <- FMSY_M
StockPars$SSBMSY_SSB0 <- SSBMSY_SSB0
StockPars$BMSY_B0 <- BMSY_B0
StockPars$VBMSY_VB0 <- VBMSY_VB0
# --- Optimize catchability (q) to fit depletion ----
if (inc.progress)
shiny::incProgress(0.2, detail = 'Optimizing for Depletion')
bounds <- c(0.0001, 15) # q bounds for optimizer
# find the q that gives current stock depletion - optVB = depletion for vulnerable biomass else SB
if (!is.null(SampCpars$qs)) {
if(!silent)
message_info("Skipping optimization for depletion - using catchability (q) from OM@cpars.")
qs <- SampCpars$qs
} else {
if(!silent)
message("Optimizing for user-specified depletion in last historical year")
qs <- .sapply(1:nsim, CalculateQ, StockPars, FleetPars, pyears=nyears, bounds, control=control)
}
# --- Check that q optimizer has converged ----
LimBound <- c(1.1, 0.9)*range(bounds)
# bounds for q (catchability). Flag if bounded optimizer hits the bounds
if (!is.null(SampCpars$qs)) {
probQ <- numeric(0)
} else{
probQ <- which(qs > max(LimBound) | qs < min(LimBound))
Nprob <- length(probQ)
}
fracD <- 0.05; ntrials <- 50
if (!is.null(control$ntrials)) ntrials <- control$ntrials
if (!is.null(control$fracD)) fracD <- control$fracD
# If q has hit bound, re-sample depletion and try again. Tries 'ntrials' times and then alerts user
if (length(probQ) > 0) {
Err <- TRUE
if(!silent) message_info(Nprob,' simulations have final biomass that is not close to sampled depletion')
if(!silent) message_info('Re-sampling depletion, recruitment error, and fishing effort')
count <- 0
OM2 <- OM
while (Err & count < ntrials) {
# Re-sample Stock Parameters
Nprob <- length(probQ)
OM2@nsim <- Nprob
SampCpars2 <- list()
if (length(OM2@cpars)>0) SampCpars2 <- SampleCpars(OM2@cpars, OM2@nsim, silent=TRUE)
ResampStockPars <- SampleStockPars(OM2, cpars=SampCpars2, msg=FALSE)
ResampStockPars$CAL_bins <- StockPars$CAL_bins
ResampStockPars$CAL_binsmid <- StockPars$CAL_binsmid
ResampStockPars$nCALbins <- length(StockPars$CAL_binsmid )
StockPars$D[probQ] <- ResampStockPars$D # Re-sampled depletion
StockPars$procsd[probQ] <- ResampStockPars$procsd # Re-sampled recruitment deviations
StockPars$AC[probQ] <- ResampStockPars$AC
StockPars$Perr_y[probQ,] <- ResampStockPars$Perr_y
StockPars$hs[probQ] <- ResampStockPars$hs # Re-sampled steepness
# Re-sample historical fishing effort
ResampFleetPars <- SampleFleetPars(SubOM(OM2, "Fleet"), Stock=ResampStockPars,
OM2@nsim, nyears, proyears, cpars=SampCpars2)
FleetPars$Esd[probQ] <- ResampFleetPars$Esd
FleetPars$Find[probQ, ] <- ResampFleetPars$Find
FleetPars$dFfinal[probQ] <- ResampFleetPars$dFfinal
# Optimize for q
if (!snowfall::sfIsRunning()) {
qs[probQ] <- sapply(probQ, CalculateQ, StockPars, FleetPars,
pyears=nyears, bounds, control)
} else {
qs[probQ] <- snowfall::sfSapply(probQ, CalculateQ, StockPars, FleetPars,
pyears=nyears, bounds, control=control)
}
probQ <- which(qs > max(LimBound) | qs < min(LimBound))
count <- count + 1
if (length(probQ) == 0) Err <- FALSE
}
if (Err) { # still a problem
tooLow <- length(which(qs > max(LimBound)))
tooHigh <- length(which(qs < min(LimBound)))
prErr <- length(probQ)/nsim
if (prErr > fracD & length(probQ) > 1) {
if (length(tooLow) > 0) message_info(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0) message_info(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent) message_info("More than ", fracD*100, "% of simulations can't get to the specified level of depletion with these Operating Model parameters")
stop("Change OM@seed and try again for a complete new sample, modify the input parameters, or increase OM@cpars$control$ntrials (default 50)")
} else {
if (length(tooLow) > 0) message_info(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0) message_info(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent) message_info("More than ", 100-fracD*100, "% simulations can get to the sampled depletion.\nContinuing")
}
}
}
# --- Simulate historical years ----
if(!silent) message("Calculating historical stock and fishing dynamics") # Print a progress update
if (inc.progress)
shiny::incProgress(0.2, detail = 'Simulating Historical Dynamics')
if(!is.null(control$unfished)) { # generate unfished historical simulations
if(!silent) message("Simulating unfished historical period")
Hist <- TRUE
qs <- rep(0, nsim) # no fishing
}
FleetPars$qs <- qs
histYrs <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,1,],
nyears,
M_age=StockPars$M_ageArray[x,,],
Asize_c=StockPars$Asize[x,],
MatAge=StockPars$Mat_age[x,,],
WtAge=StockPars$Wt_age[x,,],
FecAge=StockPars$Fec_Age[x,,],
Vuln=FleetPars$V_real[x,,],
Retc=FleetPars$retA_real[x,,],
Prec=StockPars$Perr_y[x,],
movc=split_along_dim(StockPars$mov[x,,,,],4),
SRrelc=StockPars$SRrel[x],
Effind=FleetPars$Find[x,],
Spat_targc=FleetPars$Spat_targ[x],
hc=StockPars$hs[x],
R0c=StockPars$R0a[x,],
SSBpRc=StockPars$SSBpR[x,],
aRc=StockPars$aR[x,],
bRc=StockPars$bR[x,],
Qc=FleetPars$qs[x],
Fapic=0,
maxF=StockPars$maxF,
MPA=FleetPars$MPA,
control=1,
SSB0c=StockPars$SSB0[x],
SRRfun=StockPars$SRRfun,
SRRpars = StockPars$SRRpars[[x]],
plusgroup=StockPars$plusgroup,
spawn_time_frac = StockPars$spawn_time_frac[x]))
# Number at the beginning of each year
N <- aperm(array(as.numeric(unlist(histYrs[1,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Biomass at the beginning of each year
Biomass <- aperm(array(as.numeric(unlist(histYrs[2,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Spawning numbers at the beginning of each year
SSN <- aperm(array(as.numeric(unlist(histYrs[3,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Spawning biomass at the beginning of each year
SSB <- aperm(array(as.numeric(unlist(histYrs[4,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Vulnerable biomass at the beginning of each year
VBiomass <- aperm(array(as.numeric(unlist(histYrs[5,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Fishing mortality during each year
FM <- aperm(array(as.numeric(unlist(histYrs[6,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Retained fishing mortality during each year
FMret <- aperm(array(as.numeric(unlist(histYrs[7,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
# Total mortality during each year
Z <- aperm(array(as.numeric(unlist(histYrs[8,], use.names=FALSE)),
dim=c(n_age, nyears, nareas, nsim)), c(4,1,2,3))
if (control$optVB) {
Depletion <- apply(VBiomass[,,nyears,],1,sum)/VB0
} else {
Depletion <- apply(SSB[,,nyears,],1,sum)/SSB0
}
# Historical Fishing Effort
# Effort <- matrix(NA, nrow=nsim, ncol=nyears)
# for (x in 1:nsim) {
# for (y in 1:nyears) {
# Effort[x,y] <- max(FM[x,,y,] / FleetPars$V_real[x,,y] / FleetPars$qs[x])
# }
# }
StockPars$N <- N
StockPars$Biomass <- Biomass
StockPars$SSN <- SSN
StockPars$SSB <- SSB
StockPars$VBiomass <- VBiomass
StockPars$FM <- FM
StockPars$FMret <- FMret
StockPars$Z <- Z
StockPars$Depletion <- Depletion
# update OM@D
if (!is.null(OM@cpars$qs)) StockPars$D <- StockPars$Depletion
# Check that depletion is correct
if (!is.null(control$checks)) {
if (prod(round(StockPars$D, 2)/ round(StockPars$Depletion,2)) != 1) {
print(cbind(round(StockPars$D,2), round(StockPars$Depletion,2)))
warning("Possible problem in depletion calculations")
}
}
if (!is.null(control$checks)) {
Btemp <- apply(StockPars$SSB, c(1,3), sum)
x <- Btemp[,nyears]/StockPars$SSBMSY
y <- StockPars$D/StockPars$SSBMSY_SSB0
plot(x,y, xlim=c(0,max(x)), ylim=c(0,max(y)), xlab="SSB/SSBMSY", ylab="D/SSBMSY_SSB0")
lines(c(-10,10),c(-10,10))
}
# ---- Calculate per-recruit reference points ----
if (!silent) message("Calculating per-recruit reference points")
per_recruit_F <- .lapply(1:nsim, function(x) {
lapply(1:(nyears+proyears), function(y) {
per_recruit_F_calc(x, yr.ind=y,
StockPars=StockPars,
V=FleetPars$V_real_2,
SPR_target=SPR_target)
})
})
F_SPR_y[] <- lapply(per_recruit_F, function(x) sapply(x, getElement, 1)) %>%
simplify2array() %>% aperm(c(3, 1, 2))
F01_YPR_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["YPR_F01"])) %>% t()
Fmax_YPR_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["YPR_Fmax"])) %>% t()
SPRcrash_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["SPRcrash"])) %>% t()
Fcrash_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["Fcrash"])) %>% t()
Fmed_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$FYPR["Fmed"])) %>% t()
# ---- Calculate annual SPR ----
SPR_hist <- list()
SPR_hist$Equilibrium <- CalcSPReq(StockPars$FM, StockPars, n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
SPR_hist$Dynamic <- CalcSPRdyn(StockPars$FM, StockPars, n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
# ---- Calculate Mean Generation Time ----
MarrayArea <- replicate(nareas, StockPars$M_ageArray[,,1:nyears])
Mnow<-apply(MarrayArea[,,nyears,]*N[,,nyears,],1:2,sum)/apply(N[,,nyears,],1:2,sum)
MGTsurv<-t(exp(-apply(Mnow,1,cumsum)))
MGT<-apply(Agearray*(StockPars$Mat_age[,,nyears]*MGTsurv),1,sum)/apply(StockPars$Mat_age[,,nyears]*MGTsurv,1,sum)
if(all(is.na(MGT))) MGT <- StockPars$ageMarray[, nyears]
# --- Calculate B-low ----
Blow <- rep(NA,nsim)
HZN=2; Bfrac=0.5
if (!is.null(control$HZN)) HZN <- control$HZN
if (!is.null(control$Bfrac)) Bfrac <- control$Bfrac
if(!silent) message("Calculating B-low reference points")
MGThorizon<-floor(HZN*MGT)
if (!snowfall::sfIsRunning()) {
Blow <- sapply(1:nsim,getBlow,
StockPars$N,
StockPars$Asize,
StockPars$SSBMSY,
StockPars$SSBpR,
FleetPars$MPA,
StockPars$SSB0,
StockPars$nareas,
FleetPars$retA_real,
MGThorizon,
FleetPars$Find,
StockPars$Perr_y,
StockPars$M_ageArray,
StockPars$hs,
StockPars$Mat_age,
StockPars$Wt_age,
StockPars$Fec_Age,
StockPars$R0a,
FleetPars$V_real,
nyears,
StockPars$maxage,
StockPars$mov,
FleetPars$Spat_targ,
StockPars$SRrel,
StockPars$aR,
StockPars$bR,
Bfrac,
maxF,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars,
spawn_time_frac=StockPars$spawn_time_frac)
} else {
Blow <- sfSapply(1:nsim,getBlow,
StockPars$N,
StockPars$Asize,
StockPars$SSBMSY,
StockPars$SSBpR,
FleetPars$MPA,
StockPars$SSB0,
StockPars$nareas,
FleetPars$retA_real,
MGThorizon,
FleetPars$Find,
StockPars$Perr_y,
StockPars$M_ageArray,
StockPars$hs,
StockPars$Mat_age,
StockPars$Wt_age,
StockPars$Fec_Age,
StockPars$R0a,
FleetPars$V_real,
nyears,
StockPars$maxage,
StockPars$mov,
FleetPars$Spat_targ,
StockPars$SRrel,
StockPars$aR,
StockPars$bR,
Bfrac,
maxF,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars,
spawn_time_frac=StockPars$spawn_time_frac)
}
StockPars$Blow <- Blow
# --- Calculate Reference Yield ----
if(!silent) message("Calculating reference yield - best fixed F strategy")
if (!snowfall::sfIsRunning()) {
RefY <- sapply(1:nsim, calcRefYield, StockPars, FleetPars, proyears,
Ncurr=StockPars$N[,,nyears,], nyears, proyears)
} else {
RefY <- sfSapply(1:nsim, calcRefYield, StockPars, FleetPars, proyears,
Ncurr=StockPars$N[,,nyears,], nyears, proyears)
}
# ---- Store Reference Points ----
# arrays for unfished biomass for all years
SSN_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
N_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
Biomass_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
SSB_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
# Calculate initial spawning stock numbers for all years
SSN_a[SAYR_a] <- Nfrac[SAY_a] * StockPars$R0[S_a] * StockPars$initdist[SAR_a]
N_a[SAYR_a] <- StockPars$R0[S_a] * surv[SAY_a] * StockPars$initdist[SAR_a] # Calculate initial stock numbers for all years
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars$Wt_age[SAY_a] # Calculate initial stock biomass
SSB_a[SAYR_a] <- SSN_a[SAYR_a] * StockPars$Wt_age[SAY_a] # Calculate spawning stock biomass
SSN0_a <- apply(SSN_a, c(1,3), sum) # unfished spawning numbers for each year
N0_a <- apply(N_a, c(1,3), sum) # unfished numbers for each year)
SSB0_a <- apply(SSB_a, c(1,3), sum) # unfished spawning biomass for each year
SSB0a_a <- apply(SSB_a, c(1, 3,4), sum) # Calculate unfished spawning stock biomass by area for each year
B0_a <- apply(Biomass_a, c(1,3), sum) # unfished biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * FleetPars$V, c(1,3), sum) # unfished vulnerable biomass for each year
ReferencePoints <- list(
ByYear=list(
N0=N0_y,
SN0=SN0_y,
B0=B0_y,
SSB0=SSB0_y,
VB0=VB0_y,
R0=R0_y,
h=h_y,
MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
BMSY=BMSY_y,
VBMSY=VBMSY_y,
F01_YPR=F01_YPR_y,
Fmax_YPR=Fmax_YPR_y,
F_SPR=F_SPR_y,
Fcrash=Fcrash_y,
Fmed=Fmed_y,
SPRcrash=SPRcrash_y
),
Dynamic_Unfished=list(
N0=apply(N_unfished, c(1,3), sum),
B0=apply(Biomass_unfished, c(1,3), sum),
SN0=apply(SSN_unfished, c(1,3), sum),
SSB0=apply(SSB_unfished, c(1,3), sum),
VB0=apply(VBiomass_unfished, c(1,3), sum),
Rec=apply(N_unfished[,1,,], c(1,2), sum)
),
ReferencePoints=data.frame(
N0=N0,
B0=B0,
SSB0=SSB0,
SSN0=SSN0,
VB0=VB0,
MSY=MSY,
FMSY=FMSY,
SSBMSY=SSBMSY,
BMSY=BMSY,
VBMSY=VBMSY,
UMSY=UMSY,
FMSY_M=FMSY_M,
SSBMSY_SSB0=SSBMSY_SSB0,
BMSY_B0=BMSY_B0,
VBMSY_VB0=VBMSY_VB0,
RefY=RefY,
MGT=MGT,
Blow=Blow
)
)
# --- Calculate Historical Catch ----
# Calculate catch-at-age
if (!is.null( FleetPars$Wt_age_C)) {
w_at_age <- replicate(nareas, FleetPars$Wt_age_C[,,1:OM@nyears])
B2 <- N * w_at_age
CB <- B2 * (1 - exp(-Z)) * (FM/Z)
CB[is.na(CB)] <- 0
} else {
CB <- Biomass * (1 - exp(-Z)) * (FM/Z) # Catch in biomass (removed from population)
CB[is.na(CB)] <- 0
}
# Calculate retained-at-age
Cret <- N * (1 - exp(-Z)) * (FMret/Z) # Retained catch in numbers
Cret[is.na(Cret)] <- 0
if (!is.null(FleetPars$Wt_age_C)) {
CBret <- B2 * (1 - exp(-Z)) * (FMret/Z) # Retained catch in biomass
CBret[is.na(CBret)] <- 0
} else {
CBret <- Biomass * (1 - exp(-Z)) * (FMret/Z) # Retained catch in biomass
CBret[is.na(CBret)] <- 0
}
# --- Sampling by area ----
valNames <- c("Catch", 'BInd', 'SBInd', 'VInd', 'RecInd',
'CAA', 'CAL')
Sample_Area_array <- array(1, dim=c(nsim, nyears+proyears, StockPars$nareas))
Sample_Area <- rep(list(Sample_Area_array), length(valNames))
names(Sample_Area) <- valNames
if (!is.null(OM@cpars$Sample_Area)) {
Sample_Area_in <- OM@cpars$Sample_Area
inval <- names(Sample_Area_in)[!names(Sample_Area_in) %in% valNames]
if (length(inval)>0)
stop("Invalid names in OM@cpars$Sample_Area.\nValid names are:\n", paste(valNames, collapse="\n"))
for (nm in names(Sample_Area_in)) {
dd <- dim(Sample_Area_in[[nm]])
if (length(dd)!=4) { # Sample_area from Hist
Sample_Area[[nm]] <- Sample_Area_in[[nm]]
if (any(dim(Sample_Area_in[[nm]]) != c(nsim, nyears+proyears, nareas)))
stop("OM@cpars$Sample_Area$", nm, " must be dimensions: nsim, nareas, nyears+proyears", call. = FALSE)
}
}
}
nms <- c("Catch", "BInd", "SBInd", "VInd", "CAA", "CAL")
for (nm in nms) {
dd <- dim(Sample_Area[[nm]])
if (length(dd)!=4) { # Sample_area from Hist
temp <- replicate(n_age, Sample_Area[[nm]])
Sample_Area[[nm]] <- aperm(temp, c(1,4,2,3))
}
}
# -- TODO --
# add to ObsPars
ObsPars$Sample_Area <- Sample_Area
if (inc.progress)
shiny::incProgress(0.2, detail = 'Simulating Data')
# --- Populate Data object with Historical Data ----
Data <- makeData(Biomass,
CBret,
Cret,
N,
SSB,
VBiomass,
StockPars,
FleetPars,
ObsPars,
ImpPars,
RefPoints=ReferencePoints$ReferencePoints,
SampCpars,
StockPars$initD,
Sample_Area,
Name=OM@Name,
nyears,
proyears,
nsim,
nareas=StockPars$nareas,
reps,
CurrentYr=OM@CurrentYr,
silent=silent,
control)
# --- Condition Simulated Data on input Data object (if it exists) & calculate error stats ----
StockPars$CBret <- CBret
StockPars$Biomass <- Biomass
StockPars$SSB <- SSB
StockPars$VBiomass <- VBiomass
StockPars$N <- N
if (methods::is(SampCpars$Data, "Data")) {
# real data has been passed in cpars
updatedData <- AddRealData(SimData=Data,
RealData=SampCpars$Data,
ObsPars,
StockPars,
FleetPars,
nsim,
nyears,
proyears,
SampCpars,
msg=!silent,
control,
Sample_Area)
Data <- updatedData$Data
ObsPars <- updatedData$ObsPars
}
OMPars <- Data@OM
# ---- Add Stock & Fleet Dynamics to Data ----
Data@Misc$StockPars <- StockPars
Data@Misc$FleetPars <- FleetPars
Data@Misc$ReferencePoints <- ReferencePoints
# --- Return Historical Simulations and Data from last historical year ----
Hist <- new("Hist")
# Data@Misc <- list()
Hist@Data <- Data
ind <- vapply(ObsPars, function(x) is.atomic(x) && length(x) == nsim, logical(1))
obs <- data.frame(ObsPars[ind])
OMPars <- data.frame(OMPars, obs)
Hist@OMPars <- OMPars
Hist@AtAge <- list(Length=StockPars$Len_age,
Weight=StockPars$Wt_age,
Select=FleetPars$V_real_2,
Retention=FleetPars$retA_real,
Maturity=StockPars$Mat_age,
N.Mortality=StockPars$M_ageArray,
Z.Mortality=StockPars$Z,
F.Mortality=FM,
Fret.Mortality=FMret,
Number=StockPars$N,
Biomass=StockPars$Biomass,
VBiomass=StockPars$VBiomass,
SBiomass=StockPars$SSB,
Removals=CB,
Landings=CBret,
Discards=CB-CBret
)
Hist@TSdata <- list(
Number=apply(StockPars$N,c(1,3,4), sum),
Biomass=apply(StockPars$Biomass,c(1,3,4), sum),
VBiomass=apply(StockPars$VBiomass,c(1,3,4), sum),
SBiomass=apply(StockPars$SSB,c(1,3,4), sum),
Removals=apply(CB, c(1,3,4), sum),
Landings=apply(CBret,c(1,3,4), sum),
Discards=apply(CB-CBret,c(1,3,4), sum),
Find=FleetPars$Find,
RecDev=StockPars$Perr_y,
SPR=SPR_hist,
Unfished_Equilibrium=Unfished_Equilibrium
)
Hist@Ref <- ReferencePoints
Hist@SampPars <- list()
# cpars_Stock <- StockPars[which(lapply(StockPars, length) != nsim)]
# cpars_Fleet <- FleetPars[which(lapply(FleetPars, length) != nsim)]
# cpars_Obs <- ObsPars[which(lapply(ObsPars, length) != nsim)]
# cpars_Imp <- ImpPars[which(lapply(ImpPars, length) != nsim)]
StockPars$N <- StockPars$Biomass <- StockPars$SSN <- StockPars$SSB <-
StockPars$VBiomass <- StockPars$FM <- StockPars$FMret <- StockPars$Z <- NULL
Hist@SampPars <- list(
Stock=StockPars,
Fleet=FleetPars,
Obs=ObsPars,
Imp=ImpPars
)
temp <- OM@cpars$Data
OM@cpars <- list()
OM@cpars$control <- control
OM@cpars$Data <- temp
Hist@OM <- OM
Hist@Misc <- list()
Hist@Misc$BioEco <- data.frame(RevCurr, CostCurr, Response, CostInc, RevInc, LatentEff)
attr(Hist, "version") <- packageVersion("MSEtool")
attr(Hist, "date") <- date()
attr(Hist, "R.version") <- R.version
Hist
}
#' @describeIn runMSE Run the Forward Projections
#'
#' @param Hist An Historical Simulation object (class `Hist`)
#'
#' @export
Project <- function (Hist=NULL, MPs=NA, parallel=FALSE, silent=FALSE,
extended=FALSE, checkMPs=TRUE) {
# ---- Setup ----
if (!methods::is(Hist,'Hist'))
stop('Must provide an object of class `Hist`')
OM <- Hist@OM
set.seed(OM@seed) # set seed for reproducibility
nsim <- OM@nsim # number of simulations
nyears <- OM@nyears # number of historical years
proyears <- OM@proyears # number of projection years
interval <- OM@interval # management interval (annual)
maxF <- OM@maxF # maximum apical F
pstar <- OM@pstar # Percentile of the sample of the TAC for each MP
reps <- OM@reps # Number of samples of the management recommendation for each MP
control <- OM@cpars$control; OM@cpars$control <- NULL
# ---- Check MPs ----
if (checkMPs)
MPs <- CheckMPs(MPs=MPs, silent=silent)
# ---- Set up parallel processing of MPs ---
runparallel <- FALSE
if (any(parallel==TRUE)) runparallel <- TRUE
if (methods::is(parallel, 'list')) runparallel <- TRUE
nMP <- length(MPs) # the total number of methods used
if (nMP < 1) stop("No valid MPs found", call.=FALSE)
isrunning <- snowfall::sfIsRunning()
if (!runparallel & isrunning) snowfall::sfStop()
# Don't run MPs in parallel unless specified
parallel_MPs <- rep(FALSE, nMP)
if (methods::is(parallel, 'list')) {
parallel <- parallel[parallel==TRUE]
ind <- match(names(parallel), MPs)
ind <- ind[!is.na(ind)]
parallel_MPs[ind] <- TRUE
}
if (runparallel & any(parallel_MPs)) {
if (!isrunning) setup()
Export_customMPs(MPs)
}
# ---- Set Management Interval for each MP ----
if (length(interval) != nMP) interval <- rep(interval, nMP)[1:nMP]
if (!all(interval == interval[1])) {
if (!silent) message("Variable management intervals:")
df <- data.frame(MP=MPs,interval=interval)
for (i in 1:nrow(df)) {
message(df$MP[i], 'has management interval:', df$interval[i])
}
}
# --- Add nMP dimension to MSY stats ----
# extract from Hist object
MSY_y <- Hist@Ref$ByYear$MSY
FMSY_y <- Hist@Ref$ByYear$FMSY
SSBMSY_y <- Hist@Ref$ByYear$SSBMSY
BMSY_y <- Hist@Ref$ByYear$BMSY
VBMSY_y <- Hist@Ref$ByYear$VBMSY
F01_YPR_y <- Hist@Ref$ByYear$F01_YPR
Fmax_YPR_y <- Hist@Ref$ByYear$Fmax_YPR
F_SPR_y <- Hist@Ref$ByYear$F_SPR
SPR_target <- F_SPR_y %>% dimnames() %>% getElement(2) %>% substr(3,4) %>% as.numeric()
SPR_target <- SPR_target/100
# store MSY for each sim, MP and year
MSY_y <- array(MSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
FMSY_y <- array(FMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
SSBMSY_y <- array(SSBMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
BMSY_y <- array(BMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
VBMSY_y <- array(VBMSY_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
F01_YPR_y <- array(F01_YPR_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
Fmax_YPR_y <- array(Fmax_YPR_y, dim=c(nsim, nyears+proyears, nMP)) %>% aperm(c(1,3,2))
F_SPR_y <- array(F_SPR_y, dim=c(nsim, length(SPR_target), nyears+proyears, nMP)) %>% aperm(c(1,4,2,3))
# ---- Set-up arrays and objects for projections ----
# create a data object for each method
# (they have identical historical data and branch in projected years)
Data <- Hist@Data
# no need to replicate Data@Misc across MPs
Data_Misc <- Data@Misc
Data@Misc <- list()
MSElist <- list(Data)[rep(1, nMP)]
SB_SBMSY_a <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected SB_SBMSY
F_FMSYa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected F_FMSY
Ba <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected Biomass
SSBa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected SSB
VBa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected vulnerable biomass
FMa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected fishing mortality rate
Ca <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected removed catch
CaRet <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected retained catch
TACa <- array(NA, dim = c(nsim, nMP, proyears)) # store the projected TAC recommendation
Effort <- array(NA, dim = c(nsim, nMP, proyears)) # store the Effort
# PAAout <- array(NA, dim = c(nsim, nMP, maxage)) # store the population-at-age in last projection year
# CAAout <- array(NA, dim = c(nsim, nMP, maxage)) # store the catch-at-age in last projection year
# CALout <- array(NA, dim = c(nsim, nMP, nCALbins)) # store the population-at-length in last projection year
SPReqa <- array(NA,dim=c(nsim,nMP,proyears)) # store the equilibrium Spawning Potential Ratio
SPRdyna <- array(NA,dim=c(nsim,nMP,proyears)) # store the dynamic Spawning Potential Ratio
Cost_out <- array(NA, dim = c(nsim, nMP, proyears)) # store Total Cost
Rev_out <- array(NA, dim = c(nsim, nMP, proyears)) # store Total Revenue
LatEffort_out<- array(NA, dim = c(nsim, nMP, proyears)) # store the Latent Effort
TAE_out <- array(NA, dim = c(nsim, nMP, proyears)) # store the TAE
# ---- Grab Stock, Fleet, Obs, and Imp Pars from Hist ----
StockPars <- Hist@SampPars$Stock
FleetPars <- Hist@SampPars$Fleet
ObsPars <- Hist@SampPars$Obs
ImpPars <- Hist@SampPars$Imp
StockPars$N <- Hist@AtAge$Number
StockPars$Z <- Hist@AtAge$Z.Mortality
StockPars$FM <- Hist@AtAge$F.Mortality
StockPars$FMret <- Hist@AtAge$Fret.Mortality
StockPars$CB <- Hist@AtAge$Removals
StockPars$CBret <- Hist@AtAge$Landings
StockPars$Biomass <- Hist@AtAge$Biomass
StockPars$SSB <- Hist@AtAge$SBiomass
StockPars$VBiomass <- Hist@AtAge$VBiomass
n_age <- StockPars$n_age
nareas <- StockPars$nareas
RealData <- Hist@OM@cpars$Data
ReferencePoints <- Hist@Ref$ReferencePoints
LatentEff <- Hist@Misc$BioEco$LatentEff
RevCurr <- Hist@Misc$BioEco$RevCurr
CostCurr <- Hist@Misc$BioEco$CostCurr
Response <- Hist@Misc$BioEco$Response
CostInc <- Hist@Misc$BioEco$CostInc
RevInc <- Hist@Misc$BioEco$RevInc
# projection arrays for storing all info (by simulation, age, MP, years, areas)
N_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
B_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
SB_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
VB_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
Catch_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
Removals_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
FM_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
FMret_P_mp <- array(NA, dim = c(nsim, n_age, nMP, proyears, nareas))
spawn_time_frac <- StockPars$spawn_time_frac
spawn_time_frac <- replicate(StockPars$maxage+1, spawn_time_frac)
spawn_time_frac <- replicate(proyears, spawn_time_frac)
spawn_time_frac <- replicate(nareas, spawn_time_frac)
# ---- Begin loop over MPs ----
mm <- 1 # for debugging
for (mm in 1:nMP) { # MSE Loop over methods
Data_MP <- MSElist[[mm]]
Data_MP@Misc <- Data_Misc # add StockPars etc back to Data object
if(!silent) message(mm, "/", nMP, " Running MSE for", MPs[mm], ifelse(parallel_MPs[mm], "in parallel", ""))
checkNA <- rep(0, OM@proyears) # save number of NAs
# years management is updated
upyrs <- seq(from=1, to=proyears, by=interval[mm])
# reset selectivity & retention parameters for projections
L5_P <- FleetPars$L5_y
LFS_P <- FleetPars$LFS_y
Vmaxlen_P <- FleetPars$Vmaxlen_y
# updated to use the realized selectivity/retention curves
SLarray_P <- FleetPars$SLarray_real # selectivity at length array - projections
V_P <- FleetPars$V # selectivity at age array - selectivity of gear
V_P_real <- FleetPars$V_real # selectivity at age array - realized selectivity (max may be less than 1)
V_P_real_2 <- FleetPars$V_real_2 # selectivity at age array - realized selectivity (max = 1)
if (is.null(V_P_real_2)) # hack for backward compatability
V_P_real_2 <- V_P_real
LR5_P <- FleetPars$LR5_y
LFR_P <- FleetPars$LFR_y
Rmaxlen_P <- FleetPars$Rmaxlen_y
retA_P <- FleetPars$retA # retention at age array - projections
retA_P_real <- FleetPars$retA_real # retention at age array - realized selectivity (max may be less than 1)
retA_P_real_2 <- FleetPars$retA_real_2
if (is.null(retA_P_real_2)) # hack for backward compatability
retA_P_real_2 <- retA_P_real
retL_P <- FleetPars$retL_real # retention at length array - projections
Fdisc_P <- FleetPars$Fdisc_array1 # Discard mortality for projections - by age and year
DR_P <- FleetPars$DR_y # Discard ratio for projections by year
LatentEff_MP <- LatentEff # Historical latent effort
# projection arrays
N_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
Biomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
VBiomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
SSN_P <-array(NA, dim = c(nsim, n_age, proyears, nareas))
SSB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
FM_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
FM_Pret <- array(NA, dim = c(nsim, n_age, proyears, nareas)) # retained F
Z_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
CB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
CB_Pret <- array(NA, dim = c(nsim, n_age, proyears, nareas)) # retained catch
# indexes
SAYRL <- as.matrix(expand.grid(1:nsim, 1:n_age, nyears, 1:nareas)) # Final historical year
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, 1 + nyears, 1:nareas)) # Trajectory year
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, 1, 1:nareas))
SYt <- SAYRt[, c(1, 3)]
SAYt <- SAYRt[, 1:3]
SR <- SAYR[, c(1, 4)]
SA1 <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SY1 <- SAYR[, c(1, 3)]
SAY1 <- SAYRt[, 1:3]
SYA <- as.matrix(expand.grid(1:nsim, 1, 1:n_age)) # Projection year
SY <- SYA[, 1:2]
SA <- SYA[, c(1, 3)]
SAY <- SYA[, c(1, 3, 2)]
S <- SYA[, 1]
# -- First projection year ----
y <- 1
if (requireNamespace("pbapply", quietly = TRUE)) {
pb <- pbapply::timerProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
} else {
pb <- txtProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
}
# Mortality in first year
NextYrN <- lapply(1:nsim, function(x)
popdynOneTScpp(nareas, StockPars$maxage,
Ncurr=StockPars$N[x,,nyears,],
Zcurr=StockPars$Z[x,,nyears,],
plusgroup = StockPars$plusgroup))
# The stock at the beginning of projection period
N_P[,,1,] <- aperm(array(unlist(NextYrN), dim=c(n_age, nareas, nsim, 1)), c(3,1,4,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P_real[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAY1] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAY1]
# Movement of stock at beginning of first projection year
Ntemp <- lapply(1:nsim, function(x)
movestockCPP(nareas, StockPars$maxage,
StockPars$mov[x,,,,y+nyears],
N_P[x,,y,])
)
N_P[,,y,] <- array(unlist(Ntemp), dim=c(n_age, nareas, nsim)) %>% aperm(c(3,1,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P_real[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAY1] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAY1]
StockPars$N_P <- N_P
# -- Apply MP in initial projection year ----
Data_MP@Misc$StockPars <- StockPars
Data_MP@Misc$StockPars$CB_Pret <- CB_Pret
Data_MP@Misc$StockPars$Biomass_P <- Biomass_P
Data_MP@Misc$StockPars$SSB_P <- SSB_P
Data_MP@Misc$StockPars$VBiomass_P <- VBiomass_P
Data_MP@Misc$StockPars$N_P <- N_P
runMP <- applyMP(Data=Data_MP, MPs = MPs[mm], reps = reps, silent=TRUE,
parallel = parallel_MPs[mm]) # Apply MP
MPRecs <- runMP[[1]][[1]] # MP recommendations
Data_p <- runMP[[2]] # Data object object with saved info from MP
Data_p@TAC <- MPRecs$TAC
LastSpatial <- array(FleetPars$MPA[nyears,], dim=c(nareas, nsim)) #
LastAllocat <- rep(1, nsim) # default assumption of reallocation of effort to open areas
LastTAC <- LastCatch <- apply(StockPars$CBret[,,nyears,], 1, sum)
# calculate pstar quantile of TAC recommendation dist
TACused <- apply(Data_p@TAC, 2, quantile, p = pstar, na.rm = T)
if (length(MPRecs$TAC) >0) {
# a TAC has been recommended
checkNA[y] <- sum(is.na(TACused))
TACused[is.na(TACused)] <- LastTAC[is.na(TACused)] # set to last yr TAC if NA
TACused[TACused<tiny] <- tiny
TACa[, mm, y] <- TACused # recommended TAC
}
# -- Bio-Economics ----
# Calculate Profit from last historical year
RevPC <- RevCurr/LastCatch # cost-per unit catch in last historical year
PMargin <- 1 - CostCurr/(RevPC * LastCatch) # profit margin in last historical year
Profit <- (RevPC * LastCatch) - CostCurr # profit in last historical year
HistEffort <- rep(1, nsim) # future effort is relative to today's effort
Effort_pot <- HistEffort + Response*Profit # potential effort in first projection year
Effort_pot[Effort_pot<0] <- tiny #
# Latent Effort - Maximum Effort Limit
if (!all(is.na(LatentEff_MP))) {
LastTAE <- histTAE <- HistEffort / (1 - LatentEff_MP) # current TAE limit exists
} else {
LastTAE <- histTAE <- rep(NA, nsim) # no current TAE exists
}
# -- Calc stock dynamics ----
MPCalcs <- CalcMPDynamics(MPRecs, y, nyears, proyears, nsim, Biomass_P,
VBiomass_P, LastTAE, histTAE, LastSpatial, LastAllocat,
LastTAC, TACused, maxF,LR5_P, LFR_P, Rmaxlen_P,
retL_P, retA_P, retA_P_real, retA_P_real_2,
L5_P, LFS_P, Vmaxlen_P,
SLarray_P,
V_P, V_P_real, V_P_real_2,
Fdisc_P, DR_P, FM_P,
FM_Pret, Z_P, CB_P, CB_Pret, Effort_pot,
StockPars, FleetPars, ImpPars, control=control)
TACa[, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial <- MPCalcs$Si
LastAllocat <- MPCalcs$Ai
LastTAE <- MPCalcs$TAE # TAE set by MP
LastTAC <- MPCalcs$TACrec # TAC et by MP
Effort[, mm, y] <- MPCalcs$Effort
CB_P <- MPCalcs$CB_P # removals
CB_Pret <- MPCalcs$CB_Pret # retained catch
FM_P <- MPCalcs$FM_P # fishing mortality
FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
Z_P <- MPCalcs$Z_P # total mortality
retL_P <- MPCalcs$retL_P # retained-at-length
SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,mm,y] <- MPCalcs$Ftot
retA_P <- MPCalcs$retA_P # retained-at-age
retA_P_real <- MPCalcs$retA_P_real
retA_P_real_2 <- MPCalcs$retA_P_real_2
retL_P <- MPCalcs$retL_P # retained-at-length
V_P <- MPCalcs$V_P # vulnerable-at-age
V_P_real <- MPCalcs$V_P_real # vulnerable-at-age
V_P_real_2 <- MPCalcs$V_P_real_2 # vulnerable-at-age
LR5_P <- MPCalcs$LR5_P
LFR_P <- MPCalcs$LFR_P
Rmaxlen_P <- MPCalcs$Rmaxlen_P
L5_P <- MPCalcs$L5_P
LFS_P <- MPCalcs$LFS_P
Vmaxlen_P <- MPCalcs$Vmaxlen_P
Fdisc_P <- MPCalcs$Fdisc_P
DR_P <- MPCalcs$DR_P
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
N_Psp <- N_P
for (a in 1:n_age) {
N_Psp[,a,1,] <- N_Psp[,a,1,] * exp(-(Z_P[,a,1,]*spawn_time_frac[,a,1,]))
}
SSN_P[SAYR] <- N_Psp[SAYR] * StockPars$Mat_age[SAY1] # update spawning stock numbers
SSB_P[SAYR] <- N_Psp[SAYR] * StockPars$Fec_Age[SAY1] # update spawning biomass
# recruitment in first projection year
SSBcurr <- apply(SSB_P[,,1,],c(1,3), sum)
recdev <- StockPars$Perr_y[, nyears+n_age]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars$hs,
aR= StockPars$aR, bR=StockPars$bR, R0a=StockPars$R0a,
SSBpR=StockPars$SSBpR,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars)
N_P[,1,y,] <- t(rec_area)
# update to include recruitment
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAY1] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAY1] * V_P[SAYt] # Calculate vulnerable biomass
StockPars$N_P <- N_P
# ---- Bio-economics ----
RetainCatch <- apply(CB_Pret[,,y,], 1, sum) # retained catch this year
RetainCatch[RetainCatch<=0] <- tiny
Cost_out[,mm,y] <- Effort[, mm, y] * CostCurr*(1+CostInc/100)^y # cost of effort this year
Rev_out[,mm,y] <- (RevPC*(1+RevInc/100)^y * RetainCatch)
PMargin <- 1 - Cost_out[,mm,y]/Rev_out[,mm,y] # profit margin this year
Profit <- Rev_out[,mm,y] - Cost_out[,mm,y] # profit this year
Effort_pot <- Effort_pot + Response*Profit # bio-economic effort next year
Effort_pot[Effort_pot<0] <- tiny #
LatEffort_out[,mm,y] <- LastTAE - Effort[, mm, y] # store the Latent Effort
TAE_out[,mm,y] <- LastTAE # store the TAE
# ---- Begin projection years ----
for (y in 2:proyears) {
if (!silent) setTxtProgressBar(pb, y) # Works with pbapply
SelectChanged <- FALSE
if (any(range(retA_P[,,nyears+y] - FleetPars$retA_real[,,nyears+y]) !=0)) SelectChanged <- TRUE
if (any(range(V_P[,,nyears+y] - FleetPars$V_real[,,nyears+y]) !=0)) SelectChanged <- TRUE
# -- Calculate MSY stats for this year ----
if (SelectChanged) { #
y1 <- nyears + y
MSYrefsYr <- sapply(1:nsim, optMSY_eq,
yr.ind=y1, StockPars,
V_P)
MSY_y[,mm,y1] <- MSYrefsYr[1, ]
FMSY_y[,mm,y1] <- MSYrefsYr[2,]
SSBMSY_y[,mm,y1] <- MSYrefsYr[3,]
per_recruit_F <- lapply(1:nsim, per_recruit_F_calc,
yr.ind=y1,
StockPars=StockPars,
V=V_P,
SPR_target=SPR_target)
F_SPR_y[,mm,,y1] <- sapply(per_recruit_F, getElement, 1) %>% t()
F01_YPR_y[,mm,y1] <- sapply(per_recruit_F, function(x) x[[2]][1])
Fmax_YPR_y[,mm,y1] <- sapply(per_recruit_F, function(x) x[[2]][2])
}
# TACa[, mm, y] <- TACa[, mm, y-1] # TAC same as last year unless changed
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, y + nyears, 1:nareas)) # Trajectory year
SAYt <- SAYRt[, 1:3]
SAYtMP <- cbind(SAYt, mm)
SYt <- SAYRt[, c(1, 3)]
SAY1R <- as.matrix(expand.grid(1:nsim, 1:n_age, y - 1, 1:nareas))
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, y, 1:nareas))
SY <- SAYR[, c(1, 3)]
SA <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SAY <- SAYR[, 1:3]
S <- SAYR[, 1]
SR <- SAYR[, c(1, 4)]
SA2YR <- as.matrix(expand.grid(1:nsim, 2:n_age, y, 1:nareas))
SA1YR <- as.matrix(expand.grid(1:nsim, 1:(n_age - 1), y -1, 1:nareas))
# --- Age & Growth ----
NextYrN <- lapply(1:nsim, function(x)
popdynOneTScpp(nareas, StockPars$maxage,
Ncurr=N_P[x,,y-1,],
Zcurr=Z_P[x,,y-1,],
plusgroup=StockPars$plusgroup))
N_P[,,y,] <- aperm(array(unlist(NextYrN), dim=c(n_age, nareas, nsim, 1)), c(3,1,4,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAYt] # Calculate spawning stock numbers (beginning of year)
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAYt] # Calculate spawning stock biomass (beginning of year)
# movement this year
Ntemp <- lapply(1:nsim, function(x)
movestockCPP(nareas, StockPars$maxage,
StockPars$mov[x,,,,y+nyears],
N_P[x,,y,])
)
N_P[,,y,] <- array(unlist(Ntemp), dim=c(n_age, nareas, nsim)) %>% aperm(c(3,1,2))
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAYt] * V_P[SAYt] # Calculate vulnerable biomass
SSN_P[SAYR] <- N_P[SAYR] * StockPars$Mat_age[SAYt] # Calculate spawning stock numbers
SSB_P[SAYR] <- N_P[SAYR] * StockPars$Fec_Age[SAYt] # Calculate spawning stock biomass
StockPars$N_P <- N_P
# --- An update year - update data and run MP ----
if (y %in% upyrs) {
# --- Update Data object ----
Data_MP <- updateData(Data=Data_MP, OM, MPCalcs, Effort,
Biomass=StockPars$Biomass, StockPars$N,
Biomass_P, CB_Pret, N_P, SSB=StockPars$SSB,
SSB_P, VBiomass=StockPars$VBiomass, VBiomass_P,
RefPoints=ReferencePoints,
retA_P, retL_P, StockPars,
FleetPars, ObsPars, ImpPars, V_P,
upyrs, interval, y, mm,
Misc=Data_p@Misc, RealData,
Sample_Area=ObsPars$Sample_Area)
Data_MP@Misc$StockPars$CB_Pret <- CB_Pret
Data_MP@Misc$StockPars$Biomass_P <- Biomass_P
Data_MP@Misc$StockPars$SSB_P <- SSB_P
Data_MP@Misc$StockPars$VBiomass_P <- VBiomass_P
Data_MP@Misc$StockPars$N_P <- N_P
# --- apply MP ----
runMP <- applyMP(Data=Data_MP, MPs = MPs[mm], reps = reps, silent=TRUE,
parallel = parallel_MPs[mm]) # Apply MP
MPRecs <- runMP[[1]][[1]] # MP recommendations
Data_p <- runMP[[2]] # Data object object with saved info from MP
Data_p@TAC <- MPRecs$TAC
}
# calculate pstar quantile of TAC recommendation dist
TACused <- apply(Data_p@TAC, 2, quantile, p = pstar, na.rm = T)
if (length(MPRecs$TAC) >0) {
# a TAC has been recommended
checkNA[y] <- sum(is.na(TACused))
TACused[is.na(TACused)] <- LastTAC[is.na(TACused)] # set to last yr TAC if NA
TACused[TACused<tiny] <- tiny
TACa[, mm, y] <- TACused # recommended TAC
}
# ----- Calc stock dynamics ----
MPCalcs <- CalcMPDynamics(MPRecs, y, nyears, proyears, nsim, Biomass_P,
VBiomass_P, LastTAE, histTAE, LastSpatial, LastAllocat,
LastTAC, TACused, maxF,LR5_P, LFR_P, Rmaxlen_P,
retL_P, retA_P, retA_P_real, retA_P_real_2,
L5_P, LFS_P, Vmaxlen_P,
SLarray_P,
V_P, V_P_real, V_P_real_2,
Fdisc_P, DR_P, FM_P,
FM_Pret, Z_P, CB_P, CB_Pret, Effort_pot,
StockPars, FleetPars, ImpPars, control=control)
LastSpatial <- MPCalcs$Si
LastAllocat <- MPCalcs$Ai
LastTAE <- MPCalcs$TAE # adjustment to TAE
Effort[, mm, y] <- MPCalcs$Effort
FMa[,mm,y] <- MPCalcs$Ftot
CB_P <- MPCalcs$CB_P # removals
CB_Pret <- MPCalcs$CB_Pret # retained catch
LastTAC <- TACa[, mm, y] # apply(CB_Pret[,,y,], 1, sum, na.rm=TRUE)
FM_P <- MPCalcs$FM_P # fishing mortality
FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
Z_P <- MPCalcs$Z_P # total mortality
retA_P <- MPCalcs$retA_P # retained-at-age
retA_P_real <- MPCalcs$retA_P_real
retA_P_real_2 <- MPCalcs$retA_P_real_2
retL_P <- MPCalcs$retL_P # retained-at-length
V_P <- MPCalcs$V_P # vulnerable-at-age
V_P_real <- MPCalcs$V_P_real # vulnerable-at-age
V_P_real_2 <- MPCalcs$V_P_real_2 # vulnerable-at-age
SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
LR5_P <- MPCalcs$LR5_P
LFR_P <- MPCalcs$LFR_P
Rmaxlen_P <- MPCalcs$Rmaxlen_P
L5_P <- MPCalcs$L5_P
LFS_P <- MPCalcs$LFS_P
Vmaxlen_P <- MPCalcs$Vmaxlen_P
Fdisc_P <- MPCalcs$Fdisc_P
DR_P <- MPCalcs$DR_P
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
N_Psp <- N_P
for (a in 1:n_age) {
N_Psp[,a,y,] <- N_Psp[,a,y,] * exp(-(Z_P[,a,y,]*spawn_time_frac[,a,y,]))
}
SSN_P[SAYR] <- N_Psp[SAYR] * StockPars$Mat_age[SAYt] # update spawning stock numbers
SSB_P[SAYR] <- N_Psp[SAYR] * StockPars$Fec_Age[SAYt] # update spawning biomass
# recruitment in this year
SSBcurr <- apply(SSB_P[,,y,],c(1,3), sum)
recdev <- StockPars$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars$hs, aR=StockPars$aR,
bR=StockPars$bR, R0a=StockPars$R0a,
SSBpR=StockPars$SSBpR,
SRRfun=StockPars$SRRfun,
SRRpars=StockPars$SRRpars)
N_P[,1,y,] <- t(rec_area)
StockPars$N_P <- N_P
# update to include recruitment
Biomass_P[SAYR] <- N_P[SAYR] * StockPars$Wt_age[SAYt] # Calculate biomass
VBiomass_P[SAYR] <- N_P[SAYR] * FleetPars$Wt_age_C[SAYt] * V_P[SAYt] # Calculate vulnerable biomass
# ---- Bio-economics ----
RetainCatch <- apply(CB_Pret[,,y,], 1, sum) # retained catch this year
RetainCatch[RetainCatch<=0] <- tiny
Cost_out[,mm,y] <- Effort[, mm, y] * CostCurr*(1+CostInc/100)^y # cost of effort this year
Rev_out[,mm,y] <- (RevPC*(1+RevInc/100)^y * RetainCatch)
Profit <- Rev_out[,mm,y] - Cost_out[,mm,y] # profit this year
Effort_pot <- Effort_pot + Response*Profit # bio-economic effort next year
Effort_pot[Effort_pot<0] <- tiny #
LatEffort_out[,mm,y] <- LastTAE - Effort[, mm, y] # store the Latent Effort
TAE_out[,mm,y] <- LastTAE # store the TAE
if ("progress" %in% names(control)) {
if (control$progress) {
if (requireNamespace("shiny", quietly = TRUE)) {
shiny::setProgress((mm - 1 + y/proyears)/nMP,
detail = paste0(round((mm - 1 + y/proyears)/nMP * 100),
"% \n Management procedure ", mm, "/", nMP, " (", MPs[mm], ")"))
} else {
warning('package `shiny` needs to be installed for progress bar')
}
}
}
} # end of year loop
if (!silent) close(pb) # use pbapply::closepb(pb) for shiny related stuff
if (max(upyrs) < proyears) { # One more call to complete Data object
Data_MP <- updateData(Data=Data_MP, OM, MPCalcs, Effort,
StockPars$Biomass, StockPars$N,
Biomass_P, CB_Pret, N_P, StockPars$SSB, SSB_P,
StockPars$VBiomass, VBiomass_P,
RefPoints=ReferencePoints,
retA_P, retL_P, StockPars,
FleetPars, ObsPars, ImpPars, V_P,
upyrs=c(upyrs, proyears),
interval=rep(proyears-max(upyrs), length(interval)), y, mm,
Misc=Data_p@Misc, RealData, ObsPars$Sample_Area
)
}
SB_SBMSY_a[, mm, ] <- apply(SSB_P, c(1, 3), sum, na.rm=TRUE)/SSBMSY_y[,mm,(OM@nyears+1):(OM@nyears+OM@proyears)] # SSB relative to SSBMSY
F_FMSYa[, mm, ] <- FMa[, mm, ]/FMSY_y[,mm,(OM@nyears+1):(OM@nyears+OM@proyears)]
Ba[, mm, ] <- apply(Biomass_P, c(1, 3), sum, na.rm=TRUE) # biomass
SSBa[, mm, ] <- apply(SSB_P, c(1, 3), sum, na.rm=TRUE) # spawning stock biomass
VBa[, mm, ] <- apply(VBiomass_P, c(1, 3), sum, na.rm=TRUE) # vulnerable biomass
Ca[, mm, ] <- apply(CB_P, c(1, 3), sum, na.rm=TRUE) # removed
CaRet[, mm, ] <- apply(CB_Pret, c(1, 3), sum, na.rm=TRUE) # retained catch
SPReqa[, mm, ] <- CalcSPReq(FM_P, StockPars, n_age, nareas, nyears, proyears, nsim)
SPRdyna[, mm, ] <- CalcSPRdyn(abind::abind(StockPars$FM, FM_P, along = 3),
StockPars, n_age, nareas, nyears, proyears, nsim)
if (!silent) {
cat("\n")
if (all(checkNA[upyrs] != nsim) & !all(checkNA == 0)) {
ntot <- sum(checkNA[upyrs])
totyrs <- sum(checkNA[upyrs] >0)
nfrac <- round(ntot/(length(upyrs)*nsim),2)*100
message(totyrs, ' years had TAC = NA for some simulations (', nfrac, "% of total simulations)")
message('Used TAC_y = TAC_y-1')
}
}
# Store all info (return if argument `extended=TRUE`)
N_P_mp[,,mm,,] <- N_P
B_P_mp[,,mm,,] <- Biomass_P
SB_P_mp[,,mm,,] <- SSB_P
VB_P_mp[,,mm,,] <- VBiomass_P
Catch_P_mp[,,mm,,] <- CB_Pret
Removals_P_mp[,,mm,,] <- CB_P
FM_P_mp[,,mm,,] <- FM_P
FMret_P_mp[,,mm,,] <- FM_Pret
# drop StockPars etc from Data_MP - already reported in Hist object
Data_MP@Misc$StockPars <- Data_MP@Misc$FleetPars <- Data_MP@Misc$ReferencePoints <- NULL
MSElist[[mm]] <- Data_MP # update MSElist with PPD for this MP
} # end of MP loop
# ---- Create MSE Object ----
CB_hist <- apply(Hist@TSdata$Landings, 1:2, sum)
FM_hist <- Hist@TSdata$Find * Hist@SampPars$Fleet$qs
SSB_hist <- apply(Hist@TSdata$SBiomass, 1:2, sum)
Misc <- list()
Misc$extended <- list()
if (extended) {
histN <- replicate(nMP, StockPars$N) %>% aperm(c(1,2,5,3,4))
N_all <- abind::abind(histN, N_P_mp, along=4)
histB <- replicate(nMP, StockPars$Biomass) %>% aperm(c(1,2,5,3,4))
B_all <- abind::abind(histB, B_P_mp, along=4)
histSSB <- replicate(nMP, StockPars$SSB) %>% aperm(c(1,2,5,3,4))
SB_all <- abind::abind(histSSB, SB_P_mp, along=4)
histVB <- replicate(nMP, StockPars$VBiomass) %>% aperm(c(1,2,5,3,4))
VB_all <- abind::abind(histVB, VB_P_mp, along=4)
histCatch <- replicate(nMP, StockPars$CBret) %>% aperm(c(1,2,5,3,4))
Catch_all <- abind::abind(histCatch, Catch_P_mp, along=4)
histRemovals <- replicate(nMP, StockPars$CB) %>% aperm(c(1,2,5,3,4))
Removals_all <- abind::abind(histRemovals, Removals_P_mp, along=4)
histF <- replicate(nMP, StockPars$FM) %>% aperm(c(1,2,5,3,4))
FM_all <- abind::abind(histF, FM_P_mp, along=4)
histFret <- replicate(nMP, StockPars$FMret) %>% aperm(c(1,2,5,3,4))
FMret_all <- abind::abind(histFret, FMret_P_mp, along=4)
Misc$extended <- list(
N = N_all,
B = B_all,
SSB = SB_all,
VB = VB_all,
Catch = Catch_all,
Removals = Removals_all,
FM=FM_all,
FMret=FMret_all
)
Hist_out <- Hist
} else {
Hist_out <- new("Hist")
Hist_out@AtAge <- Hist@AtAge
Hist_out@TSdata <- Hist@TSdata
# Hist@Data <- new('Data')
# Hist@OMPars <- data.frame()
# Hist@AtAge <- list()
# Hist@Ref <- list()
# Hist@SampPars <- list()
}
MSEout <- new("MSE",
Name = OM@Name,
nyears=nyears, proyears=proyears, nMPs=nMP,
MPs=MPs, nsim=nsim,
OM=Data@OM,
Obs=Data@Obs,
SB_SBMSY=SB_SBMSY_a,
F_FMSY=F_FMSYa,
N=N_P_mp, # apply(N_P_mp, c(1,3,4), sum),
B=Ba,
SSB=SSBa,
VB=VBa,
FM=FMa,
SPR=list(Equilibrium = SPReqa, Dynamic = SPRdyna),
Catch=CaRet,
Removals=Ca,
Effort=Effort,
TAC=TACa,
TAE=TAE_out,
BioEco=list(LatEffort=LatEffort_out,
Revenue=Rev_out,
Cost=Cost_out
),
RefPoint=list(MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
F_SPR=F_SPR_y,
Dynamic_Unfished=Hist@Ref$Dynamic_Unfished,
ByYear=Hist@Ref$ByYear
),
CB_hist=CB_hist,
FM_hist=FM_hist,
SSB_hist=SSB_hist,
Hist=Hist_out,
PPD=MSElist,
Misc=Misc
)
# Store MSE info
attr(MSEout, "version") <- packageVersion("MSEtool")
attr(MSEout, "date") <- date()
attr(MSEout, "R.version") <- R.version
MSEout
}
#' Run a Management Strategy Evaluation
#'
#' Functions to run the Management Strategy Evaluation (closed-loop
#' simulation) for a specified operating model
#'
#' @param OM An operating model object (class `OM` or class `Hist`)
#' @param MPs A vector of methods (character string) of class MP
#' @param Hist Should model stop after historical simulations? Returns an object of
#' class 'Hist' containing all historical data
#' @param silent Should messages be printed out to the console?
#' @param parallel Logical or a named list. Should MPs be run using parallel processing?
#' For \code{runMSE}, can also be \code{"sac"} to run the entire MSE in parallel
#' using the split-apply-combine technique. See Details for more information.
#' @param extended Logical. Return extended projection results?
#' if TRUE, `MSE@Misc$extended` is a named list with extended data
#' (including historical and projection by area), and extended version of `MSE@Hist`
#' is returned.
#' @param checkMPs Logical. Check if the specified MPs exist and can be run on `SimulatedData`?
#'
#' @describeIn runMSE Run the Historical Simulations and Forward Projections
#' from an object of class `OM
#' @details
#' ## Running MPs in parallel
#'
#' For most MPs, running in parallel can actually lead to an increase in computation time, due to the overhead in sending the
#' information over to the cores. Consequently, by default the MPs will not be run in parallel if `parallel=TRUE`
#' (although other internal code will be run in parallel mode).
#'
#' To run MPs in parallel, specify a named list with the name of the MP(s) assigned as TRUE. For example,`parallel=list(AvC=TRUE`)
#' will run the `AvC` MP in parallel mode.
#'
#' ## Split-apply-combine MSE in parallel
#'
#' Additional savings in computation time can be achieved by running the entire simulation in batches. Individual simulations of the operating model
#' are divided into separate cores using \link{SubCpars}, `Simulate` and `Project` are applied independently for each core via `snowfall::sfClusterApplyLB`, and the
#' output (a list of MSE objects) is stitched back together into a single MSE object using \link{joinMSE}.
#'
#' The ideal number of cores will be determined based on the number of simulations and available cores.
#'
#' There are several issues to look out for when using this split-apply-combine technique:
#'
#' \itemize{
#' \item Numerical optimization for depletion may fail in individual cores when \code{OM@cpars$qs} is not specified.
#' \item Length bins should be specified in the operating model in \code{OM@cpars$CAL_bins}. Otherwise, length bins can vary by core and
#' create problems when combining into a single object.
#' \item Compared to non-parallel runs, sampled parameters in the operating model will vary despite the same value in \code{OM@seed}.
#' \item If there is an error in individual cores or while combining the parallel output into a single Hist or MSE object, the list of output (from the cores) will be returned.
#' }
#'
#' @return Functions return objects of class \linkS4class{Hist} or \linkS4class{MSE}
#' \itemize{
#' \item Simulate - An object of class \linkS4class{Hist}
#' \item Project - An object of class \linkS4class{MSE}
#' \item runMSE - An object of class \linkS4class{MSE} if \code{Hist = TRUE} otherwise a class \linkS4class{Hist} object
#' }
#' @export
runMSE <- function(OM=MSEtool::testOM, MPs = NA, Hist=FALSE, silent=FALSE,
parallel=FALSE, extended=FALSE, checkMPs=TRUE) {
# ---- Initial Checks and Setup ----
if (methods::is(OM,'OM')) {
if (OM@nsim <=1) stop("OM@nsim must be > 1", call.=FALSE)
} else if (methods::is(OM,'Hist')) {
if (!silent) message("Using `Hist` object to reproduce historical dynamics")
# # --- Extract cpars from Hist object ----
# cpars <- list()
# cpars <- c(OM@SampPars$Stock, OM@SampPars$Fleet, OM@SampPars$Obs,
# OM@SampPars$Imp, OM@OMPars, OM@OM@cpars)
#
# # --- Populate a new OM object ----
# newOM <- OM@OM
# newOM@cpars <- cpars
# OM <- newOM
} else {
stop("You must specify an operating model")
}
# check MPs
if (checkMPs & !Hist)
MPs <- CheckMPs(MPs=MPs, silent=silent)
if (is.character(parallel) && parallel == "sac") {
MSEout <- runMSE_sac(OM, MPs, Hist = Hist, silent = silent, extended = extended)
return(MSEout)
}
if (methods::is(OM,'OM')) {
HistSims <- Simulate(OM, parallel, silent)
} else {
HistSims <- OM
}
if (Hist) {
if(!silent) message("Returning historical simulations")
return(HistSims)
}
if(!silent) message("Running forward projections")
MSEout <- try(Project(Hist=HistSims, MPs, parallel, silent, extended = extended, checkMPs=FALSE), silent=TRUE)
if (methods::is(MSEout, 'try-error')) {
message('The following error occured when running the forward projections: ',
crayon::red(attributes(MSEout)$condition))
message('Returning the historical simulations (class `Hist`). To avoid re-running spool up, ',
'the forward projections can be run with ',
'`runMSE(Hist, MPs, ...)`')
return(HistSims)
}
MSEout
}
runMSE_sac <- function(OM, MPs, Hist = FALSE, silent = FALSE, extended = FALSE) {
if (OM@nsim <= 48) stop("OM@nsim should be greater than 48 to effectively use split-apply-combine.")
if (is.null(OM@cpars$CAL_bins)) warning("OM@cpars$CAL_bins was not provided which can create issues with parallel = \"sac\"")
ncpus <- set_parallel(TRUE)
nsim <- OM@nsim
nits <- ceiling(nsim/48)
itsim <- rep(48,nits)
if (nits < ncpus) {
if (nits < 4) {
nits <- 4
itsim <- rep(ceiling(nsim/4), 4)
} else{
nits <- ncpus
itsim <- rep(ceiling(nsim/ncpus), ncpus)
}
}
cnt <- 1
while (sum(itsim) != nsim | any(itsim<2)) {
diff <- nsim - sum(itsim)
if (diff >0) {
itsim[cnt] <- itsim[cnt]+1
}
if(diff < 0) {
itsim[cnt] <- itsim[cnt]-1
}
cnt <- cnt+1
if (cnt > length(itsim)) cnt <- 1
}
#### Split
if (!silent) {
message("Running MSE using split-apply-combine on ", length(itsim), " cores with these number of simulations:\n",
paste0(itsim, collapse = ", "))
}
sims <- lapply(1:length(itsim), function(i) {
if (i > 1) {
(sum(itsim[1:(i-1)]) + 1): sum(itsim[1:i])
} else {
1:itsim[i]
}
})
#### Apply Simulate() in parallel
if (!silent) message("Running Simulate() in parallel..")
HistList <- snowfall::sfClusterApplyLB(1:nits, function(i, OM, iter) {
OM@seed <- OM@seed + i
tryCatch(Simulate(SubCpars(OM, sims = iter[[i]]), silent = TRUE), error = function(e) as.character(e))
}, OM = OM, iter = sims)
# Error check Hist objects
HistErr <- sapply(HistList, function(x) !inherits(x, "Hist"))
if (any(HistErr)) {
warning("Returning list of Hist objects. There was an error when running Simulate() for core(s): ", paste0(c(1:length(HistErr))[HistErr], collapse = ", "))
return(HistList)
}
# Combine Hist objects and exit
if (Hist) {
Histout <- try(joinHist(HistList), silent = TRUE)
if (methods::is(Histout, "try-error")) {
warning("Error in joinHist() for combining Hist objects. Returning list of Hist objects.")
Histout <- HistList
}
return(Histout)
}
#### Apply Project() in parallel
Export_customMPs(MPs)
if (!silent) message("Running Project() in parallel with ", length(MPs), " MPs..")
MSElist <- snowfall::sfClusterApplyLB(HistList, function(i, MPs, extended) {
tryCatch(Project(i, MPs = MPs, parallel = FALSE, silent = TRUE, extended = extended, checkMPs = FALSE),
error = function(e) as.character(e))
}, MPs = MPs, extended = extended)
#### Combine MSE objects
MSEout <- try(joinMSE(MSElist), silent = TRUE)
if (methods::is(MSEout, "try-error")) {
warning("Error in joinMSE() for combining MSE objects. Returning list of MSE objects.")
MSEout <- MSElist
}
return(MSEout)
}
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