R/multiMSE.R
In Blue-Matter/MSEtool: Management Strategy Evaluation Toolkit
Defines functions
multiMSE
SimulateMOM
Documented in
multiMSE
SimulateMOM
#' @describeIn multiMSE Simulate historical dynamics for multi-OM
#'
#' @export
#'
SimulateMOM <- function(MOM=MSEtool::Albacore_TwoFleet, parallel=TRUE, silent=FALSE) {
# ---- Initial Checks and Setup ----
if (methods::is(MOM,'MOM')) {
if (MOM@nsim <=1) stop("MOM@nsim must be > 1", call.=FALSE)
} else {
stop("You must specify an operating model of class `MOM`")
}
# ---- Set up parallel processing ----
ncpus <- set_parallel(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(MOM@seed) # set seed for reproducibility
nsim <- MOM@nsim
nyears <- MOM@Fleets[[1]][[1]]@nyears # number of historical years
proyears <- MOM@proyears
allyears <- nyears+proyears
Stocks <- MOM@Stocks
Fleets <- MOM@Fleets
Obs <- MOM@Obs
Imps <- MOM@Imps
Rel <- MOM@Rel
SexPars <- MOM@SexPars
Complexes <- MOM@Complexes
CatchFrac <- MOM@CatchFrac
np <- length(Stocks)
nf <- length(Fleets[[1]])
if (MOM@CatchFrac[[1]] %>% nrow() != nsim) { # re-calculate CatchFrac
CatchFrac <- lapply(MOM@CatchFrac, function(x) x[1:nsim, , drop = FALSE])
MOM@CatchFrac <- CatchFrac
}
if (!length(Rel) && np == 1 && nf == 1) {
if (!silent) message_info("You have specified only a single stock and fleet with no MICE relationships. ",
"You should really be using the function MSEtool::runMSE()")
} else if(np > 1 && !length(MOM@Rel) && !length(MOM@SexPars)) {
if (!silent) message_info("You have specified more than one stock but no MICE relationships ",
"(slot MOM@Rel) or sex-specific relationships (slot MOM@SexPars) among these. ",
"As they are independent, consider doing MSE for one stock at a time for ",
"computational efficiency.")
}
maxF <- MOM@maxF
Snames <- SIL(Stocks, "Name")
Fnames <- SIL(Fleets, "Name") %>% make.unique() %>% matrix(nrow = nf)
cpars <- MOM@cpars
# ---- Custom Parameters (cpars) Options ----
control <- cpars$control; cpars$control <- NULL
# Ignore MICE in historical period
if (!is.null(control$HistRel) && !control$HistRel) {
HistRel <- list()
} else {
HistRel <- Rel
}
# Option to optimize depletion for vulnerable biomass instead of spawning biomass
optVB <- FALSE
if (!is.null(control$D) && control$D == "VB") optVB <- TRUE
# Allocation
if(!length(MOM@Allocation)) {
MOM@Allocation <- CatchFrac
if (!silent) message_info("Slot @Allocation of MOM object not specified. Setting slot ",
"@Allocation equal to slot @CatchFrac - current catch fractions")
}
if(!length(MOM@Efactor)) {
MOM@Efactor <- lapply(1:np, function(x) matrix(1, nsim, nf))
if (!silent) message_info("Slot @Efactor of MOM object not specified. Setting slot @Efactor ",
"to current effort for all fleets")
}
# All stocks and sampled parameters must have compatible array sizes (maxage)
maxage_s <- unique(SIL(Stocks, "maxage"))
maxage <- max(maxage_s)
if (length(maxage_s) > 1) {
if (!silent) message_info(paste("Stocks of varying maximum ages have been specified,",
"all simulations will run to",max(maxage_s),"ages"))
Stocks <- lapply(Stocks, function(x) {
x@maxage <- max(maxage)
return(x)
})
}
if (!silent) message("Loading operating model")
StockPars <- FleetPars <- ObsPars <- ImpPars <- SampCpars <- new('list')
plusgroup <- rep(1, np)
for (p in 1:np) {
# Check for plusgroup now, then remove from cpars before SampleCpars call (see Simulate)
if (!is.null(cpars[[p]][[1]]$plusgroup) && all(!cpars[[p]][[1]]$plusgroup)) {
plusgroup[p] <- 0
for (f in 1:nf) cpars[[p]][[f]]$plusgroup <- NULL
}
SampCpars[[p]] <- lapply(1:nf, function(f) {
if (length(cpars) && length(cpars[[p]][[f]])) {
if (!silent) message("Sampling custom parameters for ", Snames[p], Fnames[f, p])
SampleCpars(cpars[[p]][[f]], nsim, silent=silent)
} else {
list()
}
})
set.seed(MOM@seed) # set seed again after cpars has been sampled
# --- Sample Stock Parameters ----
StockPars[[p]] <- SampleStockPars(Stock = Stocks[[p]], nsim, nyears,
proyears, cpars = SampCpars[[p]][[1]],
msg = !silent)
StockPars[[p]]$plusgroup <- plusgroup[p]
StockPars[[p]]$maxF <- MOM@maxF
StockPars[[p]]$n_age <- StockPars[[p]]$maxage+1
# --- custom SRR function ---
# not implemented
StockPars[[p]] <- Check_custom_SRR(StockPars[[p]], SampCpars[[p]][[1]], nsim)
#if (any(StockPars[[p]]$SRrel>2))
# stop('Custom stock recruit function not supported in `multiMSE`')
# --- Sample Fleet Parameters ----
FleetPars[[p]] <- lapply(1:nf, function(f) {
SampleFleetPars(Fleet = Fleets[[p]][[f]],
Stock = StockPars[[p]],
nsim, nyears, proyears,
cpars = SampCpars[[p]][[f]],
msg=!silent)
})
# --- Sample Obs Parameters ----
ObsPars[[p]] <- lapply(1:nf, function(f) {
SampleObsPars(MOM@Obs[[p]][[f]], nsim,
cpars = SampCpars[[p]][[f]],
Stock = StockPars[[p]],
nyears, proyears)
})
# --- Sample Imp Parameters ----
ImpPars[[p]] <- lapply(1:nf, function(f) {
SampleImpPars(MOM@Imps[[p]][[f]], nsim,
cpars = SampCpars[[p]][[f]],
nyears, proyears)
})
}
# --- Update Parameters for two-sex stocks ----
# Depletion, stock-recruit parameters, recdevs, Fleet, Obs, and Imp copied
# from females to males
if (length(SexPars)) {
if (length(SexPars$Herm)) {
SexPars$Herm <- checkHerm(SexPars$Herm, maxage, nsim, nyears, proyears)
}
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) {
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
StockPars_t <- StockPars
FleetPars_t <- FleetPars
slot_s <- c("D", "hs", "AC", "R0", "R0a", "Perr_y")
# slot_f <- c("Esd", "Find", "dFFinal", "Spat_targ", "qinc", "qcv", "qvar", "FinF")
slot_f <- c("Esd", "Spat_targ", "qinc", "qcv", "qvar")
if (!silent) message_info("You have specified sex-specific dynamics,",
"these parameters will be mirrored across sex types according to SexPars$SSBfrom:\n",
paste(c(slot_s, slot_f), collapse = ", "),
", all observation and implementation parameters")
for (p in 1:np) {
StockPars[[p]][slot_s] <- StockPars_t[[parcopy[p]]][slot_s]
for (f in 1:nf) {
FleetPars[[p]][[f]][slot_f] <- FleetPars_t[[parcopy[p]]][[f]][slot_f]
ObsPars[[p]][[f]] <- ObsPars[[parcopy[p]]][[f]]
ImpPars[[p]][[f]] <- ImpPars[[parcopy[p]]][[f]]
}
}
}
} # end of sexpars
nareas_s <- NIL(StockPars, "nareas", lev1 = TRUE)
nareas <- unique(nareas_s)
if(length(unique(nareas_s)) != 1) {
stop("Stocks must have the same specified number of areas - check cpars$mov",
" for each stock object")
}
# ---- Bio-Economic Parameters ----
# TODO
# ---- Initialize arrays ----
n_age <- maxage + 1 # number of age classes (starting at age-0)
N <- Biomass <- Z<- VBiomass<- SSN <- SSB <- array(NA,
dim = c(nsim, np, n_age,
nyears, nareas))
VF <- FretA <- array(NA, dim = c(nsim, np, nf, n_age, allyears))
VBF <- FM <- FMret <- array(NA, dim = c(nsim, np, nf, n_age, nyears, nareas))
SPR <- array(NA, dim = c(nsim, np, n_age, nyears)) # store the Spawning Potential Ratio
MPA <- array(1,c(np,nf, nyears+proyears,nareas))
Agearray <- array(rep(1:n_age, each = nsim), dim = c(nsim, n_age)) # Age array
# ---- Hermaphroditism -----
# (this is the fraction to be kept (after sex change))
# E.g. protygynous (Female to male) is H_1_2 where 1 is female 2 is male
# [sim, stock, maxage] Defaults to all 1s if length(SexPars$Herm)==0
if (!is.null(control$HermEq) && !control$HermEq) {
HermFrac <- expandHerm(list(), maxage = maxage, np = np, nsim = nsim)
} else {
HermFrac <- expandHerm(SexPars$Herm, maxage = maxage, np = np, nsim = nsim)
}
Unfished_Equilibrium <- list()
for(p in 1:np){
# --- Pre Equilibrium calcs ----
# Set up some array indexes sim (S) age (A) year (Y) region/area (R)
SPAYR <- as.matrix(expand.grid(1:nareas, 1, 1:n_age, p, 1:nsim)[5:1])
SPA <- SPAYR[,1:3]
SAY <- SPAYR[, c(1,3,4)]
SAR <- SPAYR[, c(1,3,5)]
SA <- Sa <- SPAYR[, c(1,3)]
SR <- SPAYR[, c(1,5)]
S <- SPAYR[, 1]
SY <- SPAYR[, c(1, 4)]
Sa[,2] <- n_age-Sa[,2]+1 # This is the process error index for initial year
# Calculate initial distribution if mov provided in cpars
# ---- Calculate initial distribution if mov provided in cpars ----
if (is.null(StockPars[[p]]$initdist)) {
# mov has been passed in cpars - initdist hasn't been defined
StockPars[[p]]$initdist <- CalcDistribution(StockPars=StockPars[[p]],
FleetPars=FleetPars[[p]][[1]],
SampCpars=SampCpars[[p]][[1]],
nyears, maxF,
plusgroup[p], checks=FALSE)
}
#*HermFrac[,p,1] # !!!! INITDIST OF AGE 1. Unfished recruitment by area
R0a <- matrix(StockPars[[p]]$R0, nrow=nsim, ncol=nareas, byrow=FALSE) *
StockPars[[p]]$initdist[,1,]
# ---- Unfished Equilibrium calcs ----
# unfished survival for every year
surv <- lapply(1:nsim, calc_survival, StockPars=StockPars[[p]],
plusgroup=plusgroup[p], inc_spawn_time=FALSE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
# unfished survival (spawning)
SBsurv <- lapply(1:nsim, calc_survival, StockPars=StockPars[[p]],
plusgroup=plusgroup[p], inc_spawn_time=TRUE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
Nfrac <- surv * StockPars[[p]]$Mat_age # predicted numbers of mature ages in all years
# 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[[p]]$R0[S_a] * StockPars[[p]]$initdist[SAR_a]
N_a[SAYR_a] <- StockPars[[p]]$R0[S_a] * surv[SAY_a] * StockPars[[p]]$initdist[SAR_a]
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars[[p]]$Wt_age[SAY_a] # Calculate initial stock biomass
# SSB_a[SAYR_a] <- SSN_a[SAYR_a] * StockPars[[p]]$Wt_age[SAY_a] # Calculate spawning stock biomass
SSB_a[SAYR_a] <- SBsurv[SAY_a] * StockPars[[p]]$R0[S_a] * StockPars[[p]]$initdist[SAR_a] * StockPars[[p]]$Fec_Age[SAY_a] # Calculate spawning stock biomass
SSN0_a <- apply(SSN_a, c(1,3), sum) # unfished spawning numbers for each year
N0_a <- apply(N_a, c(1,3), sum) # unfished numbers for each year)
SSB0_a <- apply(SSB_a, c(1,3), sum) # unfished spawning biomass for each year
SSB0a_a <- apply(SSB_a, c(1, 3,4), sum) # Calculate unfished spawning stock biomass by area for each year
B0_a <- apply(Biomass_a, c(1,3), sum) # unfished biomass for each year
Vraw <- array(NIL(listy=FleetPars[[p]],namey="V_real"),c(nsim,n_age,allyears,nf))
Vind <- as.matrix(expand.grid(1:nsim,p,1:nf,1:n_age,1:allyears))
VF[Vind] <- Vraw[Vind[,c(1,4,5,3)]]
Fretraw <- array(NIL(listy=FleetPars[[p]],namey="retA_real"),c(nsim,n_age,allyears,nf))
FretA[Vind] <- Fretraw[Vind[,c(1,4,5,3)]]
if(nf==1){
V <- VF[,p,1,,] #<-SOL(FleetPars[[p]],"V")
}else{
#Weight by catch fraction
V <- array(0,c(nsim,n_age,allyears))
for(f in 1:nf){
V <- V+VF[,p,f,,]*CatchFrac[[p]][,f]
}
#V<-nlz(V,c(1,3),"max") # currently assume unfished vulnerability is equally weighted among fleets
# V includes discards
}
# unfished vulnerable biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * V, c(1,3), sum)
# ---- Unfished Reference Points ----
SSBpRa <- array(SSB0_a/matrix(StockPars[[p]]$R0, nrow=nsim, ncol=nyears+proyears),
dim = c(nsim, nyears+proyears))
UnfishedRefs <- sapply(1:nsim, CalcUnfishedRefs, ageM=StockPars[[p]]$ageM, N0_a=N0_a, SSN0_a=SSN0_a,
SSB0_a=SSB0_a, B0_a=B0_a, VB0_a=VB0_a, SSBpRa=SSBpRa, SSB0a_a=SSB0a_a)
N0 <- UnfishedRefs[1,] %>% unlist() # average unfished numbers
SSN0 <- UnfishedRefs[2,] %>% unlist() # average unfished spawning numbers
SSB0 <- UnfishedRefs[3,] %>% unlist() # average unfished spawning biomass
B0 <- UnfishedRefs[4,] %>% unlist() # average unfished biomass
VB0 <- UnfishedRefs[5,] %>% unlist() # average unfished vulnerable biomass
Unfished_Equilibrium[[p]] <- list(
N_at_age=N_a,
B_at_age=Biomass_a,
SSB_at_age=SSB_a,
SSN_at_age=SSN_a,
VB_at_age=Biomass_a * replicate(nareas,V)
)
# average spawning stock biomass per recruit
SSBpR <- matrix(UnfishedRefs[6,] %>% unlist(), nrow=nsim, ncol=nareas)
# average unfished biomass
SSB0a <- UnfishedRefs[7,] %>% unlist() %>% matrix(nrow=nsim, ncol=nareas, byrow = TRUE)
bR <- matrix(log(5 * StockPars[[p]]$hs)/(0.8 * SSB0a), nrow=nsim) # Ricker SR params
aR <- matrix(exp(bR * SSB0a)/SSBpR, nrow=nsim) # Ricker SR params
# --- Optimize for Initial Depletion ----
# currently done in SS2MOM
# --- Non-equilibrium Initial Year ----
SSN[SPAYR] <- Nfrac[SAY] * StockPars[[p]]$R0[S] * StockPars[[p]]$initdist[SAR] *
StockPars[[p]]$Perr_y[Sa]
# Calculate initial stock numbers
N[SPAYR] <- StockPars[[p]]$R0[S] * surv[SAY] * HermFrac[SPA] *
StockPars[[p]]$initdist[SAR] * StockPars[[p]]$Perr_y[Sa]
# Calculate initial stock biomass
Biomass[SPAYR] <- N[SPAYR] * StockPars[[p]]$Wt_age[SAY]
# Calculate spawning stock biomass
SSB[SPAYR] <- SBsurv[SAY] * StockPars[[p]]$R0[S] * StockPars[[p]]$initdist[SAR] * StockPars[[p]]$Fec_Age[SAY]
# Calculate vunerable biomass
VBiomass[SPAYR] <- Biomass[SPAYR] * V[SAY]
# Assign stock parameters to StockPars object
StockPars[[p]]$SSBpR <- SSBpR
StockPars[[p]]$aR <- aR
StockPars[[p]]$bR <- bR
StockPars[[p]]$SSB0 <- SSB0
StockPars[[p]]$SSN0 <- SSN0
StockPars[[p]]$VB0 <- VB0
StockPars[[p]]$R0a <- R0a
StockPars[[p]]$surv <- surv
StockPars[[p]]$B0 <- B0
StockPars[[p]]$N0 <- N0
# loop over fleets
for(f in 1:nf) {
FleetPars[[p]][[f]]$V_real<-VF[,p,f,,] # update fleet vulnerability for this stock
# --- Historical Spatial closures ----
if (!is.null(SampCpars[[p]][[f]]$MPA)) {
thisMPA <- SampCpars[[p]][[f]]$MPA
if (any(dim(thisMPA) != c(nyears+proyears, nareas))) {
stop('cpars$MPA must be a matrix with dimensions `nyears+proyears, nareas`', .call = FALSE)
}
if (any(thisMPA != 1 & thisMPA != 0)) {
stop('values in cpars$MPA must be either 0 (closed) or open (1)', .call = FALSE)
}
if (any(thisMPA != 1)) {
for (a in 1:nareas) {
yrs <- which(thisMPA[, a] == 0)
if (length(yrs)) {
if (!silent) {
message_info('Spatial closure detected for Stock ' , p, ' and Fleet ', f,
' in area ', a, ' in years ',
paste(findIntRuns(yrs), collapse=", "))
}
}
}
}
MPA[p, f,, ] <- thisMPA
} else {
if (!is.na(FleetPars[[p]][[f]]$MPA) && all(FleetPars[[p]][[f]]$MPA==TRUE)) {
MPA[p, f,, 1] <- 0
if (!silent) message_info('Historical MPA in Area 1 for all years')
}
}
FleetPars[[p]][[f]]$MPA <- MPA[p,f,,]
} # end of loop over fleets
} # end of loop over stocks
# ---- SexPars - Update SSB0 and Ricker SRR parameters for male stock ----
# Other parameters have been updated (R0, h, rec devs) earlier
if (length(SexPars)) {
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) {
if (!silent) message_info("You have specified sex-specific dynamics, unfished spawning biomass",
"and specified stock depletion will be mirrored across sex types according ",
"to SexPars$SSBfrom")
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
StockPars_t <- StockPars # need to store a temporary object for copying to/from
SSB0s <- matrix(NIL(StockPars_t, "SSB0"), nsim, np) # sim, p
for (p in 1:np) {
StockPars[[p]]$SSB0 <- apply(matrix(SexPars$SSBfrom[p, ], nsim, np, byrow = TRUE) * SSB0s, 1, sum)
StockPars[[p]]$SSBpR <- array(StockPars[[p]]$SSB0/StockPars_t[[p]]$R0,c(nsim,nareas)) # SSBpR hardwired to be the same among areas !!!!
# Ricker SR params
SSB0a <- StockPars[[p]]$SSB0 * StockPars_t[[p]]$R0a/apply(StockPars_t[[p]]$R0a, 1, sum)
StockPars[[p]]$bR <- matrix(log(5 * StockPars_t[[p]]$hs)/(0.8 * SSB0a), nrow=nsim)
StockPars[[p]]$aR <- matrix(exp(StockPars[[p]]$bR * SSB0a)/StockPars[[p]]$SSBpR, nrow=nsim)
}
}
if (length(SexPars$Herm)) {
if (!silent) message_info("You have specified sequential hermaphroditism (SexPars$Herm).",
"Unfished stock numbers will be calculated from this vector of fractions ",
"at age. Population dynamics will move individuals from one sex to another.")
}
} # end of SexPars loop
# --- Optimize catchability (q) to fit depletion ----
optD <- TRUE
# skip optimization if qs are provided in cpars
qs <- matrix(NA, nrow = nsim, ncol = np)
qfrac <- array(1, dim = c(nsim, np, nf))
for (p in 1:np) {
for (f in 1:nf) {
if (!is.null(SampCpars[[p]][[f]]$qs)) {
optD <- FALSE
qs[,p] <- SampCpars[[p]][[f]]$qs
if (all(SampCpars[[p]][[f]]$qs==0)) qfrac[, p, f] <- 0
FleetPars[[p]][[f]]$qs <- qs[, p] * qfrac[, p, f]
}
}
}
qs[qs==0] <- 1
bounds <- c(0.0001, 15) # q bounds for optimizer
if (optD) {
exp.time <- (np * nf)/(9*ncpus) * nsim
exp.time <- round(exp.time,2)
if(!silent)
message("Optimizing for user-specified depletion ",
'for ', nsim, 'simulations,', np, ' stocks, and ', nf, 'fleets',
"(could take a while!)")
out <- .lapply(1:nsim, getq_multi_MICE, StockPars, FleetPars, np, nf, nareas,
maxage, nyears, N, VF, FretA, maxF=MOM@maxF,
MPA,CatchFrac, bounds=bounds,tol=1E-6,HistRel,SexPars,
plusgroup=plusgroup, optVB=optVB, silent=silent)
qs <- NIL(out,"qtot") %>% matrix(nsim, np, byrow = TRUE)
qfrac <- NIL(out,"qfrac") %>% array(c(np, nf, nsim)) %>% aperm(c(3, 1, 2))
for (p in 1:np) {
for (f in 1:nf) {
FleetPars[[p]][[f]]$qs <- qs[, p] * qfrac[, p, f]
}
}
}
# --- Check that q optimizer has converged ----
# bounds for q (catchability). Flag if bounded optimizer hits the bounds
LimBound <- c(1.1, 0.9)*range(bounds)
probQ <- which(apply(qs > max(LimBound) | qs < min(LimBound),1,sum)>0)
Nprob <- length(probQ)
# If q has hit bound, re-sample depletion and try again.
# Tries 'ntrials' times and then alerts user
fracD <- 0.05; ntrials <- 50
if (!is.null(control$ntrials)) ntrials <- control$ntrials
if (!is.null(control$fracD)) fracD <- control$fracD
if (length(probQ) > 0 & optD) {
Err <- TRUE
if(!silent) message(Nprob,
' simulations have final biomass that is not ',
'close to sampled depletion')
if(!silent) message('Re-sampling depletion, recruitment error and fishing effort')
count <- 0
MOM2 <- MOM
while (Err & count < ntrials) {
Nprob <- length(probQ)
SampCpars2 <- vector("list", nf)
for(p in 1:np){
for(f in 1:nf){
if(length(cpars)>0 && length(cpars[[p]][[f]])>0){
# check each list object has the same length and if not stop and error report
ncparsim <- cparscheck(cpars[[p]][[f]])
SampCpars2[[f]] <- SampleCpars(cpars[[p]][[f]], Nprob, silent=silent)
}
}
ResampStockPars <- SampleStockPars(MOM2@Stocks[[p]],
nsim=Nprob,nyears=nyears,
proyears=proyears,
cpars=SampCpars2[[1]],
msg=FALSE)
ResampStockPars$CAL_bins <- StockPars[[p]]$CAL_bins
ResampStockPars$CAL_binsmid <- StockPars[[p]]$CAL_binsmid
ResampStockPars$nCALbins <- length(StockPars[[p]]$CAL_binsmid )
# Re-sample depletion
StockPars[[p]]$D[probQ] <- ResampStockPars$D
# Re-sample recruitment deviations
StockPars[[p]]$procsd[probQ] <- ResampStockPars$procsd
StockPars[[p]]$AC[probQ] <- ResampStockPars$AC
StockPars[[p]]$Perr_y[probQ,] <- ResampStockPars$Perr_y
StockPars[[p]]$hs[probQ] <- ResampStockPars$hs
} # end of P
# Re-sample historical fishing effort
ResampFleetPars<- vector("list", nf)
for(p in 1:np){
for(f in 1:nf){
ResampFleetPars <- SampleFleetPars(MOM2@Fleets[[p]][[f]],
Stock=ResampStockPars,
nsim=Nprob,
nyears=nyears,
proyears=proyears,
cpars=SampCpars2[[f]],
msg=FALSE)
FleetPars[[p]][[f]]$Esd[probQ] <- ResampFleetPars$Esd
FleetPars[[p]][[f]]$Find[probQ, ] <- ResampFleetPars$Find
FleetPars[[p]][[f]]$dFfinal[probQ] <- ResampFleetPars$dFfinal
}
}
out2 <- .lapply(probQ,getq_multi_MICE,StockPars, FleetPars, np,nf, nareas,
maxage, nyears, N, VF, FretA, maxF=MOM@maxF,
MPA,CatchFrac, bounds= bounds,tol=1E-6,HistRel,SexPars,
plusgroup=plusgroup, optVB=optVB, silent=silent)
qs2<-t(matrix(NIL(out2,"qtot"),nrow=np))
qout2<-array(NIL(out2,"qfrac"),c(np,nf,nsim))
qfrac2<-array(NA,c(Nprob,np,nf))
qind2<-TEG(dim(qfrac2))
qfrac2[qind2]<-qout2[qind2[,c(2,3,1)]]
qfrac[probQ,,]<-qfrac2
qs[probQ,]<-qs2
probQ <- which(apply(qs > max(LimBound) | qs < min(LimBound),1,sum)>0)
count <- count + 1
if (length(probQ) == 0) Err <- FALSE
} # end of while loop
if (Err) { # still a problem
tooLow <- length(which(qs > max(LimBound)))
tooHigh <- length(which(qs < min(LimBound)))
prErr <- length(probQ)/nsim
if (prErr > fracD & length(probQ) >= 1) {
if (length(tooLow) > 0)
if (!silent) message(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0)
if (!silent) message(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent)
message("More than ", fracD*100, "% of simulations can't get to the ",
"specified level of depletion with these Operating Model parameters")
stop("Change OM@seed and try again for a complete new sample, modify the ",
"input parameters, or increase ntrials")
} else {
if (length(tooLow) > 0)
if (!silent) message(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0)
if (!silent) message(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent)
message("More than ", 100-fracD*100, "% simulations can get to the ",
"sampled depletion.\nContinuing")
}
}
for(p in 1:np)for(f in 1:nf) FleetPars[[p]][[f]]$qs<-qs[,p]*qfrac[,p,f]
} # end of re-optimization conditional
if(!silent)
message("Calculating historical stock and fishing dynamics")
# ---- Run Historical Simulations ----
histYrs <- .sapply(1:nsim, HistMICE,
StockPars=StockPars,
FleetPars=FleetPars,
np=np,
nf=nf,
nareas=nareas,
maxage=maxage,
nyears=nyears,
N=N,
VF=VF,
FretA=FretA,
maxF=MOM@maxF,
MPA=MPA,
Rel=HistRel,
SexPars=SexPars,
qs=qs,
qfrac=qfrac,
plusgroup=plusgroup
)
N <- aperm(array(as.numeric(unlist(histYrs[1,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
Biomass <- aperm(array(as.numeric(unlist(histYrs[2,], use.names=FALSE)),
dim=c(np ,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
SSN <- aperm(array(as.numeric(unlist(histYrs[3,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
SSB <- aperm(array(as.numeric(unlist(histYrs[4,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
VBiomass <- aperm(array(as.numeric(unlist(histYrs[5,], use.names=FALSE)),
dim=c(np, n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
FM <- aperm(array(as.numeric(unlist(histYrs[6,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
FMret <- aperm(array(as.numeric(unlist(histYrs[7,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
Linfarray <- aperm(array(as.numeric(unlist(histYrs[8,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
Karray <- aperm(array(as.numeric(unlist(histYrs[9,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
t0array <- aperm(array(as.numeric(unlist(histYrs[10,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
Len_age <- aperm(array(as.numeric(unlist(histYrs[11,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Wt_age <- aperm(array(as.numeric(unlist(histYrs[12,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Fec_Age <- aperm(array(as.numeric(unlist(histYrs[21,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
VBF <- aperm(array(as.numeric(unlist(histYrs[17,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
Z <- aperm(array(as.numeric(unlist(histYrs[18,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
FMt<-aperm(array(as.numeric(unlist(histYrs[19,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
M_ageArray <- aperm(array(as.numeric(unlist(histYrs[20,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Marray <- aperm(array(as.numeric(unlist(histYrs[13, ], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
spat_targ_out <- array(as.numeric(unlist(histYrs[22,], use.names=FALSE)),
dim=c(np, nf, nsim))
# update StockPars (MICE)
for (p in 1:np) {
StockPars[[p]]$Linfarray[, 0:nyears + 1] <- Linfarray[, p, ]
StockPars[[p]]$Karray[, 0:nyears + 1] <- Karray[, p, ]
StockPars[[p]]$t0array[, 0:nyears + 1] <- t0array[, p, ]
StockPars[[p]]$Len_age[, , 0:nyears + 1] <- Len_age[, p, , ]
StockPars[[p]]$Wt_age[, , 0:nyears + 1] <- Wt_age[, p, , ]
StockPars[[p]]$Fec_Age[, , 0:nyears + 1] <- Fec_Age[, p, , ]
StockPars[[p]]$M_ageArray[, , 1:nyears] <- M_ageArray[, p, , 1:nyears]
StockPars[[p]]$Marray[, 1:nyears] <- Marray[, p, 1:nyears]
# update Spat_targ
for (fl in 1:nf) {
FleetPars[[p]][[fl]]$Spat_targ <- spat_targ_out[p,fl,]
}
}
# TODO - selectivity-at-age should update if growth changes
# Depletion check
SSB0_specified <- array(NIL(StockPars,'SSB0'),c(nsim, np))
D_specified <- array(NIL(StockPars,'D'), c(nsim, np))
if (optVB) {
VB0_specified <- array(NIL(StockPars,'VB0'),c(nsim,np))
Depletion <- apply(VBiomass[,,,nyears,,drop = FALSE], 1:2, sum)/ VB0_specified
} else {
Depletion <- apply(SSB[,,,nyears,,drop = FALSE], 1:2, sum)/ SSB0_specified
}
if (length(SexPars)) {
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) { # need to copy over depletion for a sex-specific model
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
Depletion[, 1:np] <- Depletion[, parcopy]
}
}
for(p in 1:np) StockPars[[p]]$Depletion <- Depletion[, p] # add actual Depletion to StockPars
if (!is.null(control$checks)) {
if (prod(round(Depletion,2)/ round(D_specified,2)) != 1) {
print(cbind(round(Depletion, 4), round(D_specified, 4)) %>%
structure(dimnames = list(Sim = 1:nsim, Depletion = c("Specified", "Estimated"))))
warning("Possible problem in depletion calculations")
}
}
# --- Calculate MSY statistics for each year ----
# ignores spatial closures
# assumes all vulnerable fish are caught - ie no discarding
if(!silent) message("Calculating MSY and per-recruit reference points for each year")
# average life-history parameters over ageM years
SPR_hist <- list()
for (p in 1:np) {
V <- local({
FMt_future <- aperm(replicate(proyears, FMt[,,,nyears,, drop = FALSE]), c(1,2,3,4,6,5)) # Terminal year sel
FMt_all <- abind::abind(FMt[,p,,,], FMt_future[,p,,1,,], along = 3)
V <- apply(FMt_all, 1:3, sum) # sum across years and areas, works if areas are evenly distributed
V[V<=0] <- tiny
nlz(V, c(1, 3), "max")
})
MSYrefsYr <- lapply(1:nsim, function(x) {
sapply(1:(nyears+proyears), function(y) {
optMSY_eq(x,
yr.ind=y,
StockPars=StockPars[[p]],
V=V)
})
})
# --- Annual reference points ----
StockPars[[p]]$MSY_y <- sapply(MSYrefsYr, function(x) x["Yield", ]) %>% t()
StockPars[[p]]$FMSY_y <- sapply(MSYrefsYr, function(x) x["F", ]) %>% t()
StockPars[[p]]$SSBMSY_y <- sapply(MSYrefsYr, function(x) x["SB", ]) %>% t()
StockPars[[p]]$BMSY_y <- sapply(MSYrefsYr, function(x) x["B", ]) %>% t()
StockPars[[p]]$VBMSY_y <- sapply(MSYrefsYr, function(x) x["VB", ]) %>% t()
StockPars[[p]]$R0_y <- sapply(MSYrefsYr, function(x) x["R0", ]) %>% t()
StockPars[[p]]$h_y <- sapply(MSYrefsYr, function(x) x["h", ]) %>% t()
StockPars[[p]]$N0_y <- sapply(MSYrefsYr, function(x) x["N0", ]) %>% t()
StockPars[[p]]$SN0_y <- sapply(MSYrefsYr, function(x) x["SN0", ]) %>% t()
StockPars[[p]]$B0_y <- sapply(MSYrefsYr, function(x) x["B0", ]) %>% t()
StockPars[[p]]$SSB0_y <- sapply(MSYrefsYr, function(x) x["SB0", ]) %>% t()
StockPars[[p]]$VB0_y <- sapply(MSYrefsYr, function(x) x["VB", ]/x["VB_VB0", ]) %>% t()
# --- MSY reference points ----
MSYRefPoints <- sapply(1:nsim, CalcMSYRefs,
MSY_y=StockPars[[p]]$MSY_y,
FMSY_y= StockPars[[p]]$FMSY_y,
SSBMSY_y=StockPars[[p]]$SSBMSY_y,
BMSY_y=StockPars[[p]]$BMSY_y,
VBMSY_y=StockPars[[p]]$VBMSY_y,
ageM=StockPars[[p]]$ageM,
nyears=nyears)
MSY <- MSYRefPoints[1,] %>% unlist() # record the MSY results (Vulnerable)
FMSY <- MSYRefPoints[2,] %>% unlist() # instantaneous FMSY (Vulnerable)
SSBMSY <- MSYRefPoints[3,] %>% unlist() # Spawning Stock Biomass at MSY
BMSY <- MSYRefPoints[4,] %>% unlist() # total biomass at MSY
VBMSY <- MSYRefPoints[5,] %>% unlist() # Biomass at MSY (Vulnerable)
UMSY <- MSY/VBMSY # exploitation rate
FMSY_M <- FMSY/StockPars$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/StockPars[[p]]$VB0 # VBiomass relative to unfished (VB0)
StockPars[[p]]$MSY <- MSY
StockPars[[p]]$FMSY <- FMSY
StockPars[[p]]$SSBMSY <- SSBMSY
StockPars[[p]]$BMSY <- BMSY
StockPars[[p]]$VBMSY <- VBMSY
StockPars[[p]]$UMSY <- UMSY
StockPars[[p]]$FMSY_M <- FMSY_M
StockPars[[p]]$SSBMSY_SSB0 <- SSBMSY_SSB0
StockPars[[p]]$FMSY_M <- StockPars[[p]]$FMSY/StockPars[[p]]$M
StockPars[[p]]$BMSY_B0 <- BMSY_B0
StockPars[[p]]$VBMSY_VB0 <- VBMSY_VB0
# --- Dynamic Unfished Reference Points ---- assumes no MICE rel
Unfished <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars[[p]]$maxage,
Ncurr=N[x,p,,1,],
nyears+proyears,
M_age=StockPars[[p]]$M_ageArray[x,,],
Asize_c=StockPars[[p]]$Asize[x,],
MatAge=StockPars[[p]]$Mat_age[x,,],
WtAge=StockPars[[p]]$Wt_age[x,,],
FecAge=StockPars[[p]]$Fec_Age[x,,],
Vuln=FleetPars[[p]][[1]]$V_real[x,,],
Retc=FleetPars[[p]][[1]]$retA_real[x,,],
Prec=StockPars[[p]]$Perr_y[x,],
movc=split_along_dim(StockPars[[p]]$mov[x,,,,],4),
SRrelc=StockPars[[p]]$SRrel[x],
Effind=rep(0, nyears+proyears),
Spat_targc=FleetPars[[p]][[1]]$Spat_targ[x],
hc=StockPars[[p]]$hs[x],
R0c=StockPars[[p]]$R0a[x,],
SSBpRc=StockPars[[p]]$SSBpR[x,],
aRc=StockPars[[p]]$aR[x,],
bRc=StockPars[[p]]$bR[x,],
Qc=0,
Fapic=0,
MPA=MPA[p,f,,],
maxF=maxF,
control=1,
SSB0c=StockPars[[p]]$SSB0[x],
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars[[x]],
plusgroup=StockPars[[p]]$plusgroup,
spawn_time_frac =StockPars[[p]]$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))
# ---- Calculate per-recruit reference points ----
StockPars[[p]]$SSB <- SSB[,p,,,]
StockPars[[p]]$N <- N[,p,,,]
F01_YPR_y <- array(0, dim=c(nsim, nyears+proyears)) # store F01 for each sim, and year
Fmax_YPR_y <- F01_YPR_y # store Fmax for each sim, and year
Fcrash_y <- F01_YPR_y # store Fcrash for each sim, and year
SPRcrash_y <- F01_YPR_y # store SPRcrash for each sim, and year
Fmed_y <- F01_YPR_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
per_recruit_F <- lapply(1:nsim, function(x) {
lapply(1:(nyears+proyears), function(y) {
per_recruit_F_calc(x,
yr.ind=y,
StockPars=StockPars[[p]],
V=V,
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$FYPR["Fmed"])) %>% t()
# ---- Calculate annual SPR ----
SPR_hist[[p]] <- list()
SPR_hist[[p]]$Equilibrium <- CalcSPReq(FMt[,p,,,], StockPars[[p]],
n_age, nareas, nyears, proyears, nsim,
Hist = TRUE)
SPR_hist[[p]]$Dynamic <- CalcSPRdyn(FMt[,p,,,], StockPars[[p]],
n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
# ---- Calculate Mean Generation Time ----
MarrayArea <- replicate(nareas, StockPars[[p]]$M_ageArray[,,1:nyears])
Mnow<-apply(MarrayArea[,,nyears,]*N[,p,,nyears,],1:2,sum)/apply(N[,p,,nyears,],1:2,sum)
MGTsurv<-t(exp(-apply(Mnow,1,cumsum)))
StockPars[[p]]$MGT<-apply(Agearray*(StockPars[[p]]$Mat_age[,,nyears]*MGTsurv),1,sum)/apply(StockPars[[p]]$Mat_age[,,nyears]*MGTsurv,1,sum)
# ---- Calculate Reference Yield ----
# if(!silent) message("Calculating reference yield - best fixed F strategy")
## TODO - add RefY calcs
StockPars[[p]]$RefY <- StockPars[[p]]$MSY
# ---- Store Reference Points ----
StockPars[[p]]$ReferencePoints <- list(
ByYear=list(
N0=StockPars[[p]]$N0_y,
SN0=StockPars[[p]]$SN0_y,
B0=StockPars[[p]]$B0_y,
SSB0=StockPars[[p]]$SSB0_y,
VB0=StockPars[[p]]$VB0_y,
R0=StockPars[[p]]$R0_y,
h=StockPars[[p]]$h_y,
MSY=StockPars[[p]]$MSY_y,
FMSY=StockPars[[p]]$FMSY_y,
SSBMSY=StockPars[[p]]$SSBMSY_y,
BMSY=StockPars[[p]]$BMSY_y,
VBMSY=StockPars[[p]]$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=StockPars[[p]]$N0,
B0=StockPars[[p]]$B0,
SSB0=StockPars[[p]]$SSB0,
SSN0=StockPars[[p]]$SSN0,
VB0=StockPars[[p]]$VB0,
MSY=StockPars[[p]]$MSY,
FMSY=StockPars[[p]]$FMSY,
SSBMSY=StockPars[[p]]$SSBMSY,
BMSY=StockPars[[p]]$BMSY,
VBMSY=StockPars[[p]]$VBMSY,
UMSY=StockPars[[p]]$UMSY,
FMSY_M=StockPars[[p]]$FMSY_M,
SSBMSY_SSB0=StockPars[[p]]$SSBMSY_SSB0,
BMSY_B0=StockPars[[p]]$BMSY_B0,
VBMSY_VB0=StockPars[[p]]$VBMSY_VB0,
RefY=StockPars[[p]]$RefY,
MGT=StockPars[[p]]$MGT
)
)
}
# --- Calculate Historical Catch ----
# Calculate catch-at-age
# empirical weight-at-age for the catch
Wt_age_C <- array(NA,c(nsim,np,nf,n_age,nyears,nareas))
for (p in 1:np) {
for (f in 1:nf){
Wt_age_C[,p,f,,,] <- replicate(nareas, FleetPars[[p]][[f]]$Wt_age_C[,,1:nyears])
}
}
CB <- CBret <- Cret <- array(NA,c(nsim,np,nf,n_age,nyears,nareas))
CNind <- TEG(dim(CB))
Nind<-CNind[,c(1,2,4,5,6)] # sim, stock, n_age, nyears, nareas
Biomass_C <- array(0, dim=dim(FMret))
Biomass_C[CNind] <- N[Nind] * Wt_age_C[CNind]
CB[CNind] <- Biomass_C[CNind]*(1-exp(-Z[Nind]))*(FM[CNind]/Z[Nind])
CB[!is.finite(CB)] <- 0 # fix for Z=0
if(!is.null(control$checks)) {
for(p in 1:np){
Cp <- local({
num <- apply(CB[, p, , , nyears, , drop = FALSE], c(1, 3), sum) # nsim x nf
num/rowSums(num)
})
if(prod(round(CatchFrac[[p]], 4)/round(Cp, 4)) != 1) {
warning("Possible problem in catch fraction calculations")
print("Possible problem in catch fraction calculations")
print(Snames[p])
print(cbind(CatchFrac[[p]], round(Cp, 4)) %>%
structure(dimnames = list(Sim = 1:nsim, CatchFrac = c("Specified", "Estimated"))))
}
}
}
# Calculate retained-at-age
Cret[CNind] <- N[Nind] * (1-exp(-Z[Nind])) * (FMret[CNind]/Z[Nind]) #apply(Cret,1:5,sum)
Cret[!is.finite(Cret)] <- 0
CBret[CNind] <- Biomass_C[CNind] * (1-exp(-Z[Nind])) * (FMret[CNind]/Z[Nind])
CBret[!is.finite(CBret)] <- 0
# Add to FleetPars
for (p in 1:np) {
for (f in 1:nf){
FleetPars[[p]][[f]]$CBret <- CBret[,p,f,,,]
FleetPars[[p]][[f]]$CB <- CB[,p,f,,,]
FleetPars[[p]][[f]]$Fishing_Mortality <- apply(FM[,p,f,,,], c(1,3), max)
}
}
# --- Sampling by area ----
valNames <- c("Catch", 'BInd', 'SBInd', 'VInd', 'RecInd',
'CAA', 'CAL')
Sample_Area_array <- array(1, dim=c(nsim, nyears+proyears, nareas))
Sample_Area <- rep(list(Sample_Area_array), length(valNames))
names(Sample_Area) <- valNames
# Not currently working
# 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$", nm, " must be dimensions: nsim, nareas, nyears+proyears", call. = FALSE)
# }
# }
# }
nms <- c("Catch", "BInd", "SBInd", "VInd", "CAA", "CAL")
for (nm in nms) {
dd <- dim(Sample_Area[[nm]])
if (length(dd)!=4) { # Sample_area from Hist
temp <- replicate(n_age, Sample_Area[[nm]])
Sample_Area[[nm]] <- aperm(temp, c(1,4,2,3))
}
}
# --- Populate Data object with Historical Data ----
CurrentYr <- MOM@Fleets[[1]][[1]]@CurrentYr
DataList <- new('list')
for (p in 1:np) {
DataList[[p]] <- vector('list', nf)
if (!is.null(control$skipdata)) {
for (f in 1:nf) {
Data <- new('Data')
# ---- Add Stock & Fleet Dynamics to Data ----
StockPars[[p]]$N <- N
StockPars[[p]]$SSB <- SSB
StockPars[[p]]$Biomass <- Biomass
Data@Misc$StockPars <- StockPars[[p]]
Data@Misc$FleetPars <- FleetPars[[p]][[f]]
Data@Misc$ReferencePoints <- StockPars[[p]]$ReferencePoints
DataList[[p]][[f]] <- Data
}
} else {
if (!silent) message('Generating historical data for', Snames[p])
for (f in 1:nf) {
if (!silent) message('Generating historical data for', Fnames[f])
ObsPars[[p]][[f]]$Sample_Area <- Sample_Area # add to Obs Pars
Data <- makeData(Biomass=Biomass[,p,,,],
CBret=CBret[,p,f,,,],
Cret=Cret[,p,f,,,],
N=N[,p,,,],
SSB=SSB[,p,,,],
VBiomass=VBF[,p,f,,,],
StockPars=StockPars[[p]],
FleetPars=FleetPars[[p]][[f]],
ObsPars=ObsPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]],
RefPoints=StockPars[[p]]$ReferencePoints$ReferencePoints,
SampCpars=SampCpars[[p]][[f]],
StockPars[[p]]$initD,
Sample_Area,
Name=paste(Snames[p], Fnames[f, p]),
nyears,
proyears,
nsim,
nareas,
MOM@reps,
CurrentYr,
silent=TRUE,
control)
# ---- Add Stock & Fleet Dynamics to Data ----
StockPars[[p]]$N <- N
StockPars[[p]]$SSB <- SSB
StockPars[[p]]$Biomass <- Biomass
Data@Misc$StockPars <- StockPars[[p]]
Data@Misc$FleetPars <- FleetPars[[p]][[f]]
Data@Misc$ReferencePoints <- StockPars[[p]]$ReferencePoints
DataList[[p]][[f]] <- Data
}
}
}
# ---- Condition Simulated Data on input Data object (if it exists) & calculate error stats ----
Real.Data.Map <- matrix(rep(1:np), nrow=nf, ncol=np, byrow=TRUE)
if (!is.null(SampCpars[[1]][[1]]$Real.Data.Map)) {
Real.Data.Map.t <- SampCpars[[1]][[1]]$Real.Data.Map
# check dimensions
if (any(dim(Real.Data.Map.t) != c(nf, np))) {
message_warn('cpars$Real.Data.Map does not have dimensions n.fleet x n.stock. Not being used')
Real.Data.Map.t <- Real.Data.Map
}
Real.Data.Map <- Real.Data.Map.t
}
# Aggregate comp effective and sample size
# (only used if fleet/stock data are mapped to each other)
CAA_ESS_array <- array(0, dim=c(nsim, np, nf))
CAL_ESS_array <- CAA_nsamp_array <- CAL_nsamp_array <- CAA_ESS_array
for (p in 1:np) {
for (f in 1:nf) {
CAA_ESS_array[,p,f] <- ObsPars[[p]][[f]]$CAA_ESS
CAL_ESS_array[,p,f] <- ObsPars[[p]][[f]]$CAL_ESS
CAA_nsamp_array[,p,f] <- ObsPars[[p]][[f]]$CAA_nsamp
CAL_nsamp_array[,p,f] <- ObsPars[[p]][[f]]$CAL_nsamp
}
}
for (p in 1:np) {
for (f in 1:nf) {
if (methods::is(SampCpars[[p]][[f]]$Data,"Data")) {
# real data has been passed in cpars
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- DataList[[p]][[f]]@OM
DataList[[p]][[f]] <- new('Data')
DataList[[p]][[f]]@OM <- om
ObsPars[[p]][[f]] <- ObsPars[[mapped.to]][[f]]
} else {
ObsPars[[p]][[f]]$CAA_ESS <- apply(CAA_ESS_array[,map.stocks,, drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAL_ESS <- apply(CAL_ESS_array[,map.stocks, ,drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAA_nsamp <- apply(CAA_nsamp_array[,map.stocks,,drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAL_nsamp <- apply(CAL_nsamp_array[,map.stocks,,drop=FALSE], 1, sum)
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ DataList[[i]][[f]]@MPrec
}
# update Data and calculate observation error
updatedData <- AddRealData_MS(SimData=DataList[[p]][[f]],
RealData=SampCpars[[p]][[f]]$Data,
StockPars=StockPars,
FleetPars=FleetPars,
ObsPars=ObsPars,
SampCpars=SampCpars,
map.stocks,
CBret,
VBF,
p,
f,
nsim,
nyears,
proyears,
msg=!silent,
control)
updatedData$ObsPars[[1]][[1]]$VIerr_y[1,1:69]
if (!is.null(MPrec)) {
if (is.na(SampCpars[[p]][[f]]$Data@MPrec)) {
# don't update if MPrec provided in real data
updatedData$Data@MPrec <- MPrec
}
}
DataList[[p]][[f]] <- updatedData$Data
ObsPars <- updatedData$ObsPar
}
}
}
}
multiHist <- vector('list', np)
stock.names <- names(MOM@Stocks)
fleet.names <- names(MOM@Fleets[[1]])
if (is.null(stock.names)) stock.names <- paste('Stock', rep(1:length(MOM@Stocks)))
if (is.null(fleet.names)) fleet.names <- paste('Fleet', rep(1:length(MOM@Fleets[[1]])))
for (p in 1:np) {
multiHist[[p]] <- vector('list', nf)
names(multiHist) <- stock.names
for (f in 1:nf) {
Hist <- new("Hist")
# Data@Misc <- list()
Hist@Data <- DataList[[p]][[f]]
Hist@Data@Obs <- data.frame() # remove
ind <- which(lapply(ObsPars[[p]][[f]], length) == nsim)
obs <- data.frame(ObsPars[[p]][[f]][ind])
ind <- which(lapply(ImpPars[[p]][[f]], length) == nsim)
imp <- data.frame(ImpPars[[p]][[f]][ind])
OMPars <- DataList[[p]][[f]]@OM
if (nrow(OMPars)<1) {
OMPars <- data.frame(obs, imp)
} else {
OMPars <- data.frame(OMPars, obs, imp)
}
Hist@OMPars <- OMPars
if (f==1) {
Hist@AtAge <- list(Length=StockPars[[p]]$Len_age,
Weight=StockPars[[p]]$Wt_age,
Select=FleetPars[[p]][[f]]$V_real_2,
Retention=FleetPars[[p]][[f]]$retA_real,
Maturity=StockPars[[p]]$Mat_age,
N.Mortality=StockPars[[p]]$M_ageArray,
Z.Mortality=Z[,p,,,],
F.Mortality=FM[,p,f,,,],
Fret.Mortality=FMret[,p,f,,,],
Number=N[,p,,,],
Biomass=Biomass[,p,,,],
VBiomass=VBF[,p,f,,,],
SBiomass=SSB[,p,,,],
Removals=CB[,p,f,,,],
Landings=CBret[,p,f,,,],
Discards=CB[,p,f,,,]-CBret[,p,f,,,]
)
} else {
Hist@AtAge <- list(Select=FleetPars[[p]][[f]]$V_real_2,
Retention=FleetPars[[p]][[f]]$retA_real,
Z.Mortality=Z[,p,,,],
F.Mortality=FM[,p,f,,,],
Fret.Mortality=FMret[,p,f,,,],
VBiomass=VBF[,p,f,,,],
Removals=CB[,p,f,,,],
Landings=CBret[,p,f,,,],
Discards=CB[,p,f,,,]-CBret[,p,f,,,]
)
}
if (f==1) {
Hist@TSdata <- list(
Number=apply(N[,p,,,],c(1,3,4), sum),
Biomass=apply(Biomass[,p,,,],c(1,3,4), sum),
VBiomass=apply(VBF[,p,f,,,],c(1,3,4), sum),
SBiomass=apply(SSB[,p,,,],c(1,3,4), sum),
Removals=apply(CB[,p,f,,,], c(1,3,4), sum),
Landings=apply(CBret[,p,f,,,],c(1,3,4), sum),
Discards=apply(CB[,p,f,,,]-CBret[,p,f,,,],c(1,3,4), sum),
Find=FleetPars[[p]][[f]]$Find,
RecDev=StockPars[[p]]$Perr_y,
SPR=SPR_hist[[p]],
Unfished_Equilibrium=Unfished_Equilibrium[[p]]
)
} else {
Hist@TSdata <- list(
VBiomass=apply(VBF[,p,f,,,],c(1,3,4), sum),
Removals=apply(CB[,p,f,,,], c(1,3,4), sum),
Landings=apply(CBret[,p,f,,,],c(1,3,4), sum),
Discards=apply(CB[,p,f,,,]-CBret[,p,f,,,],c(1,3,4), sum),
Find=FleetPars[[p]][[f]]$Find
)
}
if (f==1) {
Hist@Ref <- StockPars[[p]]$ReferencePoints
}
if (f==1) {
Hist@SampPars <- list(
Stock=StockPars[[p]],
Fleet=FleetPars[[p]][[f]],
Obs=ObsPars[[p]][[f]],
Imp=ImpPars[[p]][[f]]
)
} else {
Hist@SampPars <- list(
Fleet=FleetPars[[p]][[f]],
Obs=ObsPars[[p]][[f]],
Imp=ImpPars[[p]][[f]]
)
}
if (f==1) {
temp <- MOM@cpars$Data
MOM@cpars <- list()
MOM@cpars$control <- control
MOM@cpars$Data <- temp
} else {
MOM@cpars <- list()
}
Hist@Misc <- list(
MOM=MOM,
Stock=stock.names[p],
Fleet=fleet.names[f]
)
multiHist[[p]][[f]] <- Hist
}
names(multiHist[[p]]) <- fleet.names
}
class(multiHist) <- c('list', 'multiHist')
attr(multiHist, "version") <- packageVersion("MSEtool")
attr(multiHist, "date") <- date()
attr(multiHist, "R.version") <- R.version
attr(multiHist, "Stocks") <- stock.names
attr(multiHist, "Fleets") <- fleet.names
multiHist[[1]][[1]]@Misc$Real.Data.Map <- Real.Data.Map
multiHist
}
#' @describeIn multiMSE Run Forward Projections for a `MOM` object
#' @param multiHist An Historical Simulation object (class `multiHist`)
#' @export
ProjectMOM <- function (multiHist=NULL, MPs=NA, parallel=FALSE, silent=FALSE,
checkMPs=FALSE, dropHist=FALSE, extended=FALSE) {
# ---- Setup ----
if (! 'multiHist' %in% class(multiHist))
stop('Must provide an object of class `multiHist`')
# Unpack historical simulation data
MOM <- multiHist[[1]][[1]]@Misc$MOM
set.seed(MOM@seed) # set seed for reproducibility
nsim <- MOM@nsim # number of simulations
nyears <- MOM@Fleets[[1]][[1]]@nyears # number of historical years
proyears <- MOM@proyears # number of projection years
interval <- MOM@interval # management interval (annual)
maxF <- MOM@maxF # maximum apical F
pstar <- MOM@pstar # Percentile of the sample of the TAC for each MP
reps <- MOM@reps # Number of samples of the management recommendation for each MP
allyears <- nyears+proyears
Stocks <- MOM@Stocks
Fleets <- MOM@Fleets
Obs <- MOM@Obs
Imps <- MOM@Imps
Rel <- MOM@Rel
SexPars <- MOM@SexPars
Complexes <- MOM@Complexes
CatchFrac <- MOM@CatchFrac
control <- MOM@cpars$control; MOM@cpars$control <- NULL
np <- length(Stocks)
nf <- length(Fleets[[1]])
Real.Data.Map <- multiHist[[1]][[1]]@Misc$Real.Data.Map
if (is.null(Real.Data.Map))
Real.Data.Map <- matrix(rep(1:np), nrow=nf, ncol=np, byrow=TRUE)
# ---- Detect MP Specification ----
MPcond <- "unknown"
if (!length(Rel) && np == 1 && nf == 1) {
if (!silent)
message_info("runMSE checking: you have specified a single stock and fleet with no MICE relationships.",
"For analysis you should be using runMSE(). Use this only for debugging",
"against runMSE.")
if (!methods::is(MPs,"character")) {
stop('`MPs` must be specified as a vector of character names if there is only',
' 1 stock and 1 fleet. `MPs` is currently a ', class(MPs))
}
MPcond <- "complex"
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- unlist(MPs)
# check class of MPs
tt <- try(sapply(MPs, getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
if (!all(MP_classes == MP_classes[1]))
stop('All MPs must be same class (`MP`)')
}
if (methods::is(MPs,'character') && MPcond == 'unknown') {
# check class of MPs
tt <- try(sapply(MPs, getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
if (any(!MP_classes %in% c('MP', 'MMP'))) {
inval <- unique(MP_classes[!MP_classes %in% c('MP', 'MMP')])
stop('Cannot mix MPs of class ', paste0(inval, collapse=","), ' with MPs of class `MP` and `MMP`')
}
MP_class <- unique(MP_classes)
MPcond <- rep(NA, length(MPs))
if ('MMP' %in% MP_class) {
if (!silent) message_info("MMP mode:\nYou have specified multi-fleet, multi-stock MPs of",
"class MMP. This class of MP accepts all data objects (stocks x fleets)",
"to simultaneously make a recommendation specific to each stock and fleet")
MPcond[which(MP_classes == 'MMP')] <- "MMP"
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- MPs
}
if ('MP' %in% MP_class) {
if (!silent) message_info("Complex mode:\nYou have specified a vector of MPs rather than a",
"list of MPs, one list position for MP type. The same MP will",
"be applied to the aggregate data for all stocks and fleets.",
"The MP will, for example, be used to set a single TAC for all",
"stocks and fleets combined. This will be allocated among fleets",
"according to recent catches and among stocks according to",
"available, vulnerable biomass")
MPcond[which(MP_classes == 'MP')] <- "complex"
MPtemp <- MPs
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- unlist(MPs)
}
}
if (methods::is(MPs,'list') && 'unknown' %in% MPcond) {
if(identical(ldim(MPs), ldim(Fleets))){
if (!silent) message_info("Byfleet mode: you have specified an MP for each stock and fleet.",
"Only fleet-specific data (e.g. catches and indices) will be used to set",
"advice for each fleet for each stock")
MPcond <- "byfleet"
nMP <- length(MPs[[1]][[1]])
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[]<-unlist(MPs)
} else if (ldim(MPs)==ldim(Fleets)[1]) { # not a two-tier list
if (!silent) message_info("Bystock mode: you have specified a vector of MPs for each stock,",
"but not a vector of MPs for each stock and fleet. The catch data for these",
" fleets will be combined, a single MP will be used to set a single TAC",
"for all fleets combined that will be allocated between the fleets",
"according to recent catches")
MPcond<-"bystock"
checkN <- unlist(lapply(MPs, length))
if (!all(checkN == checkN[1]))
stop('Must have the same number of MPs for each stock')
nMP<-length(MPs[[1]])
MPrefs<-array(NA,c(nMP,nf,np))
for(p in 1:np)MPrefs[,,p]<-MPs[[p]]
}
tt <- try(sapply(unlist(MPs), getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
}
if ('unknown' %in% MPcond)
stop('`MPs` is not a vector or list with correct dimensions. See `?multiMSE`')
if (length(MPcond) != nMP) MPcond <- rep(MPcond, nMP)
if (methods::is(MPs,"list")) {
allMPs<-unlist(MPs)
}else{
allMPs<-MPs
}
if (nMP < 1) stop("No valid MPs found", call.=FALSE)
# ---- Set up parallel processing of MPs ---
runparallel <- FALSE
if (any(parallel==TRUE)) runparallel <- TRUE
if (methods::is(parallel, 'list')) runparallel <- TRUE
isrunning <- snowfall::sfIsRunning()
if (!runparallel & isrunning) snowfall::sfStop()
# Don't run MPs in parallel unless specified
parallel_MPs <- rep(FALSE, length(allMPs))
names(parallel_MPs) <- allMPs
if (methods::is(parallel, 'list')) {
parallel <- parallel[parallel==TRUE]
ind <- match(names(parallel), allMPs)
ind <- ind[!is.na(ind)]
parallel_MPs[ind] <- TRUE
names(parallel_MPs) <- allMPs
}
if (runparallel & any(parallel_MPs)) {
if (!isrunning) setup()
Export_customMPs(allMPs)
if (!silent) message("MPs running in parallel: ", paste0(names(parallel), collapse = ", "))
}
ncpus <- set_parallel(runparallel, msg=!silent)
# ---- Check MPs ----
if (checkMPs && all(MP_classes=='MP')) {
CheckMPs(MPs=allMPs, silent=silent)
}
# ---- Set Management Interval for each MP ----
# TODO - make same structure as MPs argument
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)) {
if (!silent) message(df$MP[i], 'has management interval:', df$interval[i], ifelse(i == nrow(df), "\n", ""))
}
}
# --- Store MSY statistics for each projection year ----
MSY_y <- FMSY_y <- SSBMSY_y <- BMSY_y <- VBMSY_y <- array(NA, dim=c(nsim,np, nMP, nyears+proyears))
# MSY stats from historical simulations
for (p in 1:np) {
MSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$MSY), c(1,3,2))
FMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$FMSY), c(1,3,2))
SSBMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$SSBMSY), c(1,3,2))
BMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$BMSY), c(1,3,2))
VBMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$VBMSY), c(1,3,2))
}
# ---- Set-up arrays and objects for projections ----
# create a data object for each method
# (they have identical historical data and branch in projected years)
MSElist <- list('list')
for (p in 1:np) {
MSElist[[p]] <- lapply(1:nf, function(f) list(multiHist[[p]][[f]]@Data)[rep(1, nMP)]) # Historical data for this stock and fleet
}
# TODO - update names of stored values
SB_SBMSYa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected SB_SBMSY
Ba <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected Biomass
SSBa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected SSB
VBa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected vulnerable biomass
FMa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected fishing mortality rate
F_FMSYa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected F_FMSY
Ca <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected removed catch
CaRet <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected retained catch
TACa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected TAC recommendation
TAE_out <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected TAE recommendation
Effort <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the Effort
SPReqa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the equilibrium Spawning Potential Ratio
SPRdyna <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the dynamic Spawning Potential Ratio
# ---- Grab Stock, Fleet, Obs and Imp values from Hist ----
StockPars <- FleetPars <- ObsPars <- ImpPars <- list()
for(p in 1:np) {
StockPars[[p]] <- multiHist[[p]][[1]]@SampPars$Stock
FleetPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Fleet)
ObsPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Obs)
ImpPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Imp)
}
nareas <- StockPars[[1]]$nareas
maxage <- StockPars[[1]]$maxage
n_age <- maxage + 1
plusgroup <- sapply(1:np, function(p) StockPars[[p]]$plusgroup) # a vector
# projection arrays for storing all info (by simulation, stock, age, MP, years, areas)
N_P_mp <- array(NA, dim = c(nsim, np, n_age, nMP, proyears, nareas))
# store M, growth, length at age, rec dev by MP due to MICE rel.
if (length(Rel)) {
DV_MICE <- sapply(1:length(Rel), function(r) get_Dnam(Rel[[r]][["terms"]][1]))
DVarray <- c("M" = "M_ageArray", "a" = "Wt_age", "b" = "Wt_age", #"R0", "hs",
"K" = "Len_age", "Linf" = "Len_age", "t0" = "Len_age", "Perr_y" = "Perr_y")
StockPars_MICE <- lapply(unique(DV_MICE), function(r) {
if (r == "Perr_y") {
array(NA_real_, dim = c(nsim, np, nMP, proyears))
} else {
array(NA_real_, dim = c(nsim, np, n_age, nMP, proyears))
}
}) %>%
structure(names = DVarray[unique(DV_MICE)])
}
# ---- Grab Historical N-at-age etc ----
N <- array(NA, dim=c(nsim, np, n_age, nyears, nareas))
Biomass <- SSB <- VBiomass <- N
FM <- FMret <- array(NA, dim=c(nsim, np, nf, n_age, nyears, nareas))
VBF <- array(NA, dim=c(nsim, np, nf, n_age, nyears, nareas))
VF<- array(NA, dim=c(nsim, np, nf, n_age, nyears+proyears))
CB <- CB_ret <- FM
MPA <- array(NA, dim=c(np, nf, nyears+proyears, nareas))
for (p in 1:np) {
N[,p,,,] <- multiHist[[p]][[1]]@AtAge$Number
Biomass[,p,,,] <- multiHist[[p]][[1]]@AtAge$Biomass
SSB[,p,,,] <- multiHist[[p]][[1]]@AtAge$SBiomass
VBiomass[,p,,,] <- multiHist[[p]][[1]]@AtAge$VBiomass
for (f in 1:nf) {
FM[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$F.Mortality
FMret[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Fret.Mortality
VF[,p,f,,] <- FleetPars[[p]][[f]]$V_real
VBF[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$VBiomass
MPA[p,f,,] <- FleetPars[[p]][[f]]$MPA
CB[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Removals
CB_ret[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Landings
}
}
# need to make a copy because R is doing weird things with elements with similar names
HistFleetPars <- FleetPars
Snames <- SIL(Stocks,"Name")
Fnames <- matrix(make.unique(SIL(Fleets,"Name")),nrow=nf)
# ---- Begin loop over MPs ----
mm <- 1 # for debugging
TAC_A <- array(NA,c(nsim,np,nf)) # Temporary store of the TAC
TAE_A <- array(NA,c(nsim,np,nf)) # Temporary store of the TAE
MPrecs_A_blank<-list() # Temporary Hierarchical list of MPrec objects
for(p in 1:np) MPrecs_A_blank[[p]]<-list()
LastTAE <- histTAE <- Effort_pot <-LastAllocat <-LastCatch <-TACused <-
array(NA,c(nsim,np,nf))
LastSpatial <- array(NA,c(nareas,np,nf,nsim))
# temporary vulnerability for MSY calcs combined over fleets
V_Pt <- array(NA,c(nsim,nf,n_age,nyears+proyears))
for (mm in 1:nMP) {
if(!silent){
message(" ----- ", mm, "/", nMP, " MPs, Running MSE for: ") # print a progress report
for(p in 1:np){
MPrep<-data.frame(MPrefs[mm,,p])
row.names(MPrep)<-Fnames[,p]
names(MPrep)=Snames[p]
print(MPrep)
}
message(" --------------------------------- ")
}
checkNA <- array(0,c(np,nf,proyears)) # save number of NAs
# reset StockPars (MICE), selectivity & retention parameters for projections
for (p in 1:np) {
for (f in 1:nf) {
# reset selectivity parameters for projections
FleetPars[[p]][[f]]$L5_P <- HistFleetPars[[p]][[f]]$L5
FleetPars[[p]][[f]]$LFS_P <- HistFleetPars[[p]][[f]]$LFS
FleetPars[[p]][[f]]$Vmaxlen_P <- HistFleetPars[[p]][[f]]$Vmaxlen
# selectivity at length array - projections
FleetPars[[p]][[f]]$SLarray_P <- HistFleetPars[[p]][[f]]$SLarray_real
# selectivity at age array - projections
FleetPars[[p]][[f]]$V_P <- HistFleetPars[[p]][[f]]$V
FleetPars[[p]][[f]]$V_P_real <- HistFleetPars[[p]][[f]]$V_real # selectivity at age array - realized selectivity (max may be less than 1)
FleetPars[[p]][[f]]$V_P_real_2 <- HistFleetPars[[p]][[f]]$V_real_2 # selectivity at age array - realized selectivity (max = 1)
# reset retention parameters for projections
FleetPars[[p]][[f]]$LR5_P <- HistFleetPars[[p]][[f]]$LR5
FleetPars[[p]][[f]]$LFR_P <- HistFleetPars[[p]][[f]]$LFR
FleetPars[[p]][[f]]$Rmaxlen_P <- HistFleetPars[[p]][[f]]$Rmaxlen
# retention at age array - projections
FleetPars[[p]][[f]]$retA_P <- HistFleetPars[[p]][[f]]$retA # retention at age array - projections
FleetPars[[p]][[f]]$retA_P_real <- HistFleetPars[[p]][[f]]$retA_real # retention at age array - realized selectivity (max may be less than 1)
FleetPars[[p]][[f]]$retA_P_real_2 <- HistFleetPars[[p]][[f]]$retA_real_2 # asymptote at 1
# retention at length array - projections
FleetPars[[p]][[f]]$retL_P <- HistFleetPars[[p]][[f]]$retL_real
# Discard ratio for projections
FleetPars[[p]][[f]]$DR_P <- HistFleetPars[[p]][[f]]$DR
FleetPars[[p]][[f]]$FM_P <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
FleetPars[[p]][[f]]$FM_Pret <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
# stores prospective F before reallocation to new areas
FleetPars[[p]][[f]]$FM_nospace <- array(NA,
dim = c(nsim, n_age,
proyears, nareas))
# last apical F
FleetPars[[p]][[f]]$FML <- array(NA, dim = c(nsim, nareas))
FleetPars[[p]][[f]]$Z_P <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
FleetPars[[p]][[f]]$CB_P <- array(NA,
dim = c(nsim,n_age, proyears, nareas))
# retained catch
FleetPars[[p]][[f]]$CB_Pret <- array(NA,
dim = c(nsim,n_age, proyears, nareas))
}
StockPars[[p]] <- multiHist[[p]][[1]]@SampPars$Stock
# Discard mortality for projections
StockPars[[p]]$Fdisc_P <- StockPars[[p]]$Fdisc
StockPars[[p]]$N_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$Biomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$VBiomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$SSN_P <-array(NA, dim = c(nsim,n_age, proyears, nareas))
StockPars[[p]]$SSB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
}
# projection arrays
N_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
Biomass_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
VBiomass_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
SSN_P <-array(NA, dim = c(nsim,np, n_age, proyears, nareas))
SSB_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
FMt_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
Z_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
FM_P <- array(NA, dim = c(nsim, np,nf,n_age, proyears, nareas))
FMret_P <- array(NA, dim = c(nsim,np,nf, n_age, proyears, nareas))
VBF_P<-array(NA, dim = c(nsim,np,nf, n_age, proyears, nareas))
# 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 (!silent) {
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))
}
}
#### Time-invariant parameters to project one-time step forward. Potential time-varying parameters inside local() call
# Matrix nsim x np
hs <- sapply(1:np, function(p) StockPars[[p]]$hs)
SRrel <- sapply(1:np, function(p) StockPars[[p]]$SRrel)
R0 <- sapply(1:np, function(p) StockPars[[p]]$R0)
SSB0array <- sapply(1:np, function(p) StockPars[[p]]$SSB0)
B0array <- sapply(1:np, function(p) StockPars[[p]]$B0)
# Vector length p
a_y <- sapply(1:np, function(p) StockPars[[p]]$a)
b_y <- sapply(1:np, function(p) StockPars[[p]]$b)
# Array nsim x np x nareas
aR <- sapply(1:np, function(p) StockPars[[p]]$aR, simplify = "array") %>% aperm(c(1, 3, 2))
bR <- sapply(1:np, function(p) StockPars[[p]]$bR, simplify = "array") %>% aperm(c(1, 3, 2))
R0a <- sapply(1:np, function(p) StockPars[[p]]$R0a, simplify = "array") %>% aperm(c(1, 3, 2))
SSBpR <- sapply(1:np, function(p) StockPars[[p]]$SSBpR, simplify = "array") %>% aperm(c(1, 3, 2))
Asize <- sapply(1:np, function(p) StockPars[[p]]$Asize, simplify = "array") %>% aperm(c(1, 3, 2))
# Array nsim x np x n_age x nareas x nareas x allyears
mov <- sapply(1:np, function(p) StockPars[[p]]$mov, simplify = "array") %>% aperm(c(1, 6, 2:5))
# Array nsim x np x nf
Spat_targ <- sapply(1:np, function(p) {
sapply(1:nf, function(f) HistFleetPars[[p]][[f]]$Spat_targ)
}, simplify = "array") %>% aperm(c(1, 3, 2))
# Predict abundance, spawning, recruitment at the beginning of y = nyears+1.
# F, M from y = nyears and growth, maturity, recdev, etc. in y = nyears+1
# MICE rel updates growth, Perr_y for nyears+1, M for nyears
#
# note that Fcur is apical F but, in popdynOneMICE it is DIVIDED in future
# years between the two areas depending on vulnerable biomass.
# So to get Fcur you need to sum over areas (a bit weird)
NextYrN <- local({
# Matrix nsim x np
Perr <- sapply(1:np, function(p) StockPars[[p]]$Perr_y[, nyears + maxage + 1])
Knext <- sapply(1:np, function(p) StockPars[[p]]$Karray[, nyears + 1])
Linfnext <- sapply(1:np, function(p) StockPars[[p]]$Linfarray[, nyears + 1])
t0next <- sapply(1:np, function(p) StockPars[[p]]$t0array[, nyears + 1])
M <- sapply(1:np, function(p) StockPars[[p]]$Marray[, nyears])
# Array nsim x np x n_age
M_agecur <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[, , nyears], simplify = "array") %>% aperm(c(1, 3, 2))
Mat_agenext <- sapply(1:np, function(p) StockPars[[p]]$Mat_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Fec_agenext <- sapply(1:np, function(p) StockPars[[p]]$Fec_Age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Len_agenext <- sapply(1:np, function(p) StockPars[[p]]$Len_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Wt_agenext <- sapply(1:np, function(p) StockPars[[p]]$Wt_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
SRRfun_p <- lapply(1:np, function(p) StockPars[[p]]$SRRfun)
sapply(1:nsim, function(x) {
SRRpars_p <- lapply(1:np, function(p) StockPars[[p]]$SRRpars[[x]])
SexPars_y <- SexPars
SexPars_y$Herm <- subsetHerm(SexPars_y$Herm, y = nyears+1)
popdynOneMICE(np = np, nf = nf, nareas = nareas, maxage = maxage,
Ncur = array(N[x,,,nyears,], c(np, n_age, nareas)),
Bcur = array(Biomass[x,,,nyears,], c(np, n_age, nareas)),
SSBcur = array(SSB[x,,,nyears,], c(np, n_age, nareas)),
Vcur = array(VF[x,,,,nyears],c(np, nf, n_age)),
FMretx = array(FMret[x,,,,nyears,],c(np,nf,n_age,nareas)),
FMx = array(FM[x,,,,nyears,], c(np, nf, n_age, nareas)),
PerrYrp = Perr[x, ], hsx = hs[x, ],
aRx = array(aR[x, , ], c(np, nareas)),
bRx = array(bR[x, , ], c(np, nareas)),
movy = array(mov[x,,,,,nyears+1], c(np, n_age, nareas, nareas)),
Spat_targ = array(Spat_targ[x, , ], c(np, nf)), SRrelx = SRrel[x, ],
M_agecur = array(M_agecur[x, , ], c(np, n_age)),
Mat_agenext = array(Mat_agenext[x, , ], c(np, n_age)),
Fec_agenext = array(Fec_agenext[x, , ], c(np, n_age)),
Asizex = array(Asize[x, , ], c(np, nareas)),
Kx = Knext[x, ], Linfx = Linfnext[x, ], t0x = t0next[x, ], Mx = M[x, ],
R0x = R0[x, ], R0ax = array(R0a[x, , ], c(np, nareas)),
SSBpRx = array(SSBpR[x, , ], c(np, nareas)), ax = a_y, bx = b_y,
Rel = list(), # Do not use MICE. Parameters were updated in last time step of SimulateMOM!
SexPars = SexPars_y, x = x,
plusgroup = plusgroup, SSB0x = SSB0array[x, ], B0x = B0array[x, ],
Len_agenext = array(Len_agenext[x, , ], c(np, n_age)),
Wt_agenext = array(Wt_agenext[x, , ], c(np, n_age)),
SRRfun=SRRfun_p, SRRpars=SRRpars_p, ycur = nyears)
})
})
N_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[1,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
Biomass_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[23,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSN_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[24,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSB_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[25,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBiomass_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[19,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBF_P[,,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[20,], use.names=FALSE)),
dim=c(np, nf, n_age, nareas, nsim)), c(5,1,2,3,4))
FML <- apply(array(FM[, ,,, nyears, ],c(nsim,np,nf,n_age,nareas)),
c(1, 3), max)
for (p in 1:np) {
StockPars[[p]]$N_P <- N_P[,p,,,]
StockPars[[p]]$Biomass_P <- Biomass_P[,p,,,]
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
StockPars[[p]]$VBiomass_P <- VBiomass_P[,p,,,]
}
# ---- Update true abundance ----
# - used for FMSY ref methods so that FMSY is applied to current abundance
for (p in 1:np) {
for (f in 1:nf) {
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
} # end fleets
} # end stocks
# --- Apply MP in initial projection year ----
# - Combined MP -
if(MPcond[mm]=="MMP"){
# returns a hierarchical list object stock then fleet of Data objects
# DataList<-getDataList(MSElist,mm)
DataList<-getDataList(MSElist,mm)
# returns a hierarchical list object stock then fleet then slot type of Rec
MPRecs_A <- applyMMP(DataList, MP = MPs[mm], reps = 1, silent = TRUE, parallel = parallel_MPs[mm])
Data_p_A <- MPrecs_A_blank
for(p in 1:np)for(f in 1:nf){
Data_p_A[[p]][[f]]<-MSElist[[p]][[f]][[mm]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # record TAC rec in Data
Data_p_A[[p]][[f]]@Misc <- MPRecs_A[[p]][[f]]$Misc
}
}else if(MPcond[mm]=="complex"){
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
# need this for aggregating data and distributing TACs over stocks
realVB<-apply(VBiomass[,,,1:nyears,, drop=FALSE],c(1,2,4),sum,na.rm=T)
curdat<-multiDataS(MSElist,Real.Data.Map,np,mm,nf,realVB)
runMP <- applyMP(curdat, MPs = MPs[mm], parallel=parallel_MPs[mm],
reps = 1, silent=TRUE) # Apply MP
chk_run <- try(runMP[[1]][[1]], silent=TRUE)
if (methods::is(chk_run, 'try-error')) {
warning('Error applying MP. Returning Data object that failed')
out <- list()
out$runMP <- runMP
out$Data <- curdat
out$MP <- MPs[mm]
return(out)
}
Stock_Alloc<-realVB[,,nyears, drop=FALSE]/apply(realVB[,,nyears, drop=FALSE],1,sum)
for(p in 1:np) {
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-runMP[[1]][[1]]$TAC*MOM@Allocation[[p]][,f]*
Stock_Alloc[,p,1]
MPRecs_A[[p]][[f]]$Effort<-runMP[[1]][[1]]$Effort*MOM@Efactor[[p]][,f]
if(length(MPRecs_A[[p]][[f]]$Effort)>0)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(length(MPRecs_A[[p]][[f]]$TAC)>0)
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC
}
}
}else{
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
for(p in 1:np){
if(MPcond[mm]=="bystock"){
if(nf>1){
curdat<-multiData(MSElist,StockPars,p,mm,nf)
}else{
curdat<-MSElist[[p]][[f]][[mm]]
}
runMP <- applyMP(curdat, MPs = MPs[[p]][mm], reps = 1,
parallel = parallel_MPs[match(MPs[[p]][mm], names(parallel_MPs))],
silent=TRUE) # Apply MP
# Do allocation calcs
TAC_A[,p,] <- array(as.vector(unlist(runMP[[1]][[1]]$TAC))*
MOM@Allocation[[p]],c(nsim,nf))
TAE_A[,p,] <- array(as.vector(unlist(runMP[[1]][[1]]$Effort))*
MOM@Efactor[[p]],c(nsim,nf))
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-matrix(TAC_A[,p,f],nrow=1) # copy allocated TAC
MPRecs_A[[p]][[f]]$Effort<-matrix(TAE_A[,p,f],nrow=1)
# This next line is to make the NULL effort recommendations of an
# output control MP compatible with CalcMPdynamics (expects a null matrix)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # copy allocated tAC
}
}else if(MPcond[mm]=="byfleet"){
for(f in 1:nf){
curdat<-MSElist[[p]][[f]][[mm]]
runMP <- applyMP(curdat, MPs = MPrefs[mm,f,p], reps = 1,
parallel = parallel_MPs[match(MPrefs[mm,f,p], names(parallel_MPs))],
silent=TRUE) # Apply MP
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC <- MPRecs_A[[p]][[f]]$TAC
}
}
} # end of stocks
}
# Update Misc slot in Data
for (p in 1:np) {
for (f in 1:nf) {
MSElist[[p]][[f]][[mm]]@Misc <- Data_p_A[[p]][[f]]@Misc
}
}
# Update M if there is a MICE rel for CalcMPDynamics
StockPars_MPCalc <- StockPars
if (length(Rel) && any(DV_MICE == "M")) {
M_MICE <- sapply(1:nsim, function(x) {
Responses <- ResFromRel(Rel[DV_MICE == "M"],
Bcur = array(Biomass_P[x, , , 1, ], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x, , , 1, ], c(np, n_age, nareas)),
Ncur = array(N_P[x, , , 1, ], c(np, n_age, nareas)),
SSB0 = SSB0array[x, ], B0 = B0array[x, ],
seed = 1, x = x, y = nyears + 1)
Dmodnam <- sapply(Responses, getElement, "modnam")
Dp <- sapply(Responses, getElement, "Dp")
Dval <- lapply(Responses, getElement, "value")
Dage <- lapply(Responses, getElement, "age")
Dmult <- sapply(Responses, getElement, "mult")
oldM_ageArray <- M_ageArray <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[x, , nyears+1]) %>% t()
oldMx <- Mx <- sapply(1:np, function(p) StockPars[[p]]$Marray[x, nyears+1])
# Ensure this is consistent with code in popdynOneMICE
Rel_txt <- sapply(1:length(Responses), function(r) {
if (all(!is.na(Dage[[r]]))) { # Age-specific M
Rel_var <- paste0("M_ageArray[", Dp[r], ", Dage[[r]] + 1]")
} else {
Rel_var <- paste0(Dmodnam[r], "[", Dp[r], "]")
}
if (Dmult[r]) {
Rel_val <- paste("Dval[[r]] *", Rel_var)
} else {
Rel_val <- "Dval[[r]]"
}
paste(Rel_var, "<-", Rel_val)
})
for (r in 1:length(Responses)) {
eval(parse(text = Rel_txt[r]))
}
if (all(oldM_ageArray == M_ageArray)) M_ageArray <- oldM_ageArray * Mx/oldMx
return(M_ageArray)
}, simplify = "array")
for(p in 1:np) StockPars_MPCalc[[p]]$M_ageArray[, , nyears+1] <- t(M_MICE[p, , ])
}
MPCalcs_list <- vector('list', np)
for(p in 1:np) {
MPCalcs_list[[p]] <- vector('list', nf)
for(f in 1:nf) {
TACused[,p,f] <- apply(Data_p_A[[p]][[f]]@TAC, 2, quantile,
p = MOM@pstar, na.rm = T)
checkNA[p,f,y] <- sum(is.na(TACused[,p,f]))
LastTAE[,p,f] <- rep(NA, nsim) # no current TAE exists
histTAE[,p,f] <- rep(NA, nsim) # no existing TAE
LastSpatial[,p,f,] <- array(MPA[p,f,nyears,], dim=c(nareas, nsim)) #
# default assumption of reallocation of effort to open areas
LastAllocat[,p,f] <- rep(1, nsim)
LastCatch[,p,f] <- apply(CB[,p,f,,nyears,], 1, sum)
Effort_pot[,p,f] <- rep(NA, nsim) # No bio-economic model
MPCalcs <- CalcMPDynamics(MPRecs=MPRecs_A[[p]][[f]], y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[, p, f, , , ],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]],
control=control)
if(length(SexPars)>0) MPCalcs<- MPCalcsNAs(MPCalcs) # Zeros caused by SexPars
TACa[,p,f, mm, y] <- TACused[,p,f]#MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE # TAE set by MP
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # TAE
LastCatch[,p,f] <- MPCalcs$TACrec # TAC et by MP
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FMret_P[,p,f,,,]<- MPCalcs$FM_Pret
#FretA[,p,f,,]<- MPCalcs$FM_Pret
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
MPCalcs_list[[p]][[f]] <- MPCalcs
}
}
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
spawn_time_frac <- array(0, dim=c(nsim, np, n_age, nareas))
for (p in 1:np) {
spawn_time_frac[,p,,] <-StockPars[[p]]$spawn_time_frac
F_age <- array(0, dim=c(nsim, n_age, nareas))
for (f in 1:nf) {
F_age <- F_age + FleetPars[[p]][[f]]$FM_P[,,y,]
}
M_age <- replicate(nareas,StockPars[[p]]$M_ageArray[,,nyears+y])
Z_age <- M_age+F_age
N_Psp <- StockPars[[p]]$N_P[,,y,]
for (a in 1:n_age) {
N_Psp[,a,] <- N_Psp[,a,] * exp(-(Z_age[,a,]*spawn_time_frac[,p,a,]))
}
SSB_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Fec_Age[,,nyears+y])
SSN_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Mat_age[,,nyears+y])
}
# recruitment
for (p in 1:np) {
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
if (length(SexPars$SSBfrom)) { # use SSB from another stock to predict recruitment
SSBcurr <- local({
SSBfrom <- array(SexPars$SSBfrom[p, ], c(np, nsim, n_age, nareas)) %>%
aperm(c(2, 1, 3, 4))
apply(SSBfrom * SSB_P[,,,y,], c(1, 2), sum) # nsim x np; contribution of each stock p' to p
})
} else {
SSBcurr <- apply(SSB_P[, p, , y, ], c(1, 3), sum) # nsim x nareas
}
recdev <- StockPars[[p]]$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars[[p]]$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars[[p]]$hs,
aR= StockPars[[p]]$aR, bR=StockPars[[p]]$bR, R0a=StockPars[[p]]$R0a,
SSBpR=StockPars[[p]]$SSBpR,
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars)
StockPars[[p]]$N_P[,1,y,] <- N_P[,p,1,y,] <- t(rec_area)
}
# the years in which there are updates
upyrs <- 1 + (0:(floor(proyears/interval[mm]) - 1)) * interval[mm]
# --- Begin projection years ----
for (y in 2:proyears) {
if (!silent) setTxtProgressBar(pb, y) # Works with pbapply
# -- Calculate MSY stats for this year ----
# if selectivity has changed
for (p in 1:np) {
for (f in 1:nf) {
SelectChanged <- FALSE
if (any(
FleetPars[[p]][[f]]$retA_P[,,nyears+y] - FleetPars[[p]][[f]]$retA_P[,,nyears+y] !=0)) SelectChanged <- TRUE
if (any(
FleetPars[[p]][[f]]$V_P[,,nyears+y] - FleetPars[[p]][[f]]$V_P[,,nyears+y] !=0)) SelectChanged <- TRUE
# recalculate MSY ref points because selectivity has changed
V_Pt[,f,,]<-FleetPars[[p]][[f]]$V_P*
apply(CB[,p,f,,nyears,], 1, sum) # Weighted by catch frac
}
if (SelectChanged) {
#summed over fleets and normalized to 1
V_P<-nlz(apply(V_Pt,c(1,3,4),sum),c(1,3),"max")
y1 <- nyears + y
MSYrefsYr <- sapply(1:nsim, optMSY_eq,
yr.ind=y1,
StockPars=StockPars[[p]],
V=V_P)
MSY_y[,p,mm,y1] <- MSYrefsYr[1,]
FMSY_y[,p,mm,y1] <- MSYrefsYr[2,]
SSBMSY_y[,p,mm,y1] <- MSYrefsYr[3,]
BMSY_y[,p,mm,y1] <- MSYrefsYr[6,]
VBMSY_y[,p,mm,y1] <- MSYrefsYr[7,]
}
} # end of annual MSY
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))
# Predict abundance at the beginning of y with F, M from y-1 and
# growth, maturity, recdev, etc. in y
#
# note that Fcur is apical F but, in popdynOneMICE it is DIVIDED in future
# years between the two areas depending on vulnerable biomass. So to get
# Fcur you need to sum over areas (a bit weird)
NextYrN <- local({ # Calculate SSB in year y from abundance and mortality in y-1
# Matrix nsim x np
Perr <- sapply(1:np, function(p) StockPars[[p]]$Perr_y[, nyears + maxage + y])
Knext <- sapply(1:np, function(p) StockPars[[p]]$Karray[, nyears + y])
Linfnext <- sapply(1:np, function(p) StockPars[[p]]$Linfarray[, nyears + y])
t0next <- sapply(1:np, function(p) StockPars[[p]]$t0array[, nyears + y])
M <- sapply(1:np, function(p) StockPars[[p]]$Marray[, nyears + y - 1])
# Array nsim x np x n_age
M_agecur <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[, , nyears + y - 1], simplify = "array") %>% aperm(c(1, 3, 2))
Mat_agenext <- sapply(1:np, function(p) StockPars[[p]]$Mat_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Fec_agenext <- sapply(1:np, function(p) StockPars[[p]]$Fec_Age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Len_agenext <- sapply(1:np, function(p) StockPars[[p]]$Len_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Wt_agenext <- sapply(1:np, function(p) StockPars[[p]]$Wt_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
SRRfun_p <- lapply(1:np, function(p) StockPars[[p]]$SRRfun)
sapply(1:nsim, function(x) {
SRRpars_p <- lapply(1:np, function(p) StockPars[[p]]$SRRpars[[x]])
SexPars_y <- SexPars
SexPars_y$Herm <- subsetHerm(SexPars_y$Herm, y = nyears+y)
popdynOneMICE(np = np, nf = nf, nareas = nareas, maxage = maxage,
Ncur = array(N_P[x,,,y-1,], c(np, n_age, nareas)),
Bcur = array(Biomass_P[x,,,y-1,], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x,,,y-1,], c(np, n_age, nareas)),
Vcur = array(VF[x,,,,nyears+y-1],c(np, nf, n_age)),
FMretx = array(FMret_P[x,,,,y-1,],c(np,nf,n_age,nareas)),
FMx = array(FM_P[x,,,,y-1,], c(np, nf, n_age, nareas)),
PerrYrp = Perr[x, ], hsx = hs[x, ],
aRx = array(aR[x, , ], c(np, nareas)),
bRx = array(bR[x, , ], c(np, nareas)),
movy = array(mov[x,,,,,nyears+y], c(np, n_age, nareas, nareas)),
Spat_targ = array(Spat_targ[x, , ], c(np, nf)), SRrelx = SRrel[x, ],
M_agecur = array(M_agecur[x, , ], c(np, n_age)),
Mat_agenext = array(Mat_agenext[x, , ], c(np, n_age)),
Fec_agenext = array(Fec_agenext[x, , ], c(np, n_age)),
Asizex = array(Asize[x, , ], c(np, nareas)),
Kx = Knext[x, ], Linfx = Linfnext[x, ], t0x = t0next[x, ], Mx = M[x, ],
R0x = R0[x, ], R0ax = array(R0a[x, , ], c(np, nareas)),
SSBpRx = array(SSBpR[x, , ], c(np, nareas)), ax = a_y, bx = b_y,
Rel = Rel,
SexPars = SexPars_y, x = x,
plusgroup = plusgroup, SSB0x = SSB0array[x, ], B0x = B0array[x, ],
Len_agenext = array(Len_agenext[x, , ], c(np, n_age)),
Wt_agenext = array(Wt_agenext[x, , ], c(np, n_age)),
SRRfun=SRRfun_p, SRRpars=SRRpars_p, ycur = nyears+y-1)
})
})
N_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[1,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
Biomass_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[23,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSN_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[24,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSB_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[25,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBiomass_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[19,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBF_P[,,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[20,], use.names=FALSE)),
dim=c(np, nf, n_age, nareas, nsim)), c(5,1,2,3,4))
FML <- apply(array(FM_P[, ,,, y-1, ],c(nsim,np,nf,n_age,nareas)),
c(1, 3), max)
if (length(Rel)) {
Linfarray <- aperm(array(as.numeric(unlist(NextYrN[8,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Karray <- aperm(array(as.numeric(unlist(NextYrN[9,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
t0array <- aperm(array(as.numeric(unlist(NextYrN[10,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Len_age <- aperm(array(as.numeric(unlist(NextYrN[14,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Wt_age <- aperm(array(as.numeric(unlist(NextYrN[15,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Fec_Age <- aperm(array(as.numeric(unlist(NextYrN[26,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
M_ageArray <- aperm(array(as.numeric(unlist(NextYrN[2,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Marray <- aperm(array(as.numeric(unlist(NextYrN[11, ], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Perr_y <- aperm(array(as.numeric(unlist(NextYrN[27, ], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
}
for (p in 1:np) {
StockPars[[p]]$N_P <- N_P[,p,,,]
StockPars[[p]]$Biomass_P <- Biomass_P[,p,,,]
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
StockPars[[p]]$VBiomass_P <- VBiomass_P[,p,,,]
#for(f in 1:nf)FleetPars[[p]][[f]]$FML<-FML[]
if (length(Rel)) {
StockPars[[p]]$Linfarray[, nyears + y] <- Linfarray[, p]
StockPars[[p]]$Karray[, nyears + y] <- Karray[, p]
StockPars[[p]]$t0array[, nyears + y] <- t0array[, p]
StockPars[[p]]$Len_age[, , nyears + y] <- Len_age[, p, ]
StockPars[[p]]$Wt_age[, , nyears + y] <- Wt_age[, p, ]
StockPars[[p]]$Fec_Age[, , nyears + y] <- Fec_Age[, p, ]
StockPars[[p]]$M_ageArray[, , nyears + y - 1] <- M_ageArray[, p, ]
StockPars[[p]]$Marray[, nyears + y - 1] <- Marray[, p]
StockPars[[p]]$Perr_y[, nyears + maxage + y] <- Perr_y[, p]
}
}
# Update M in year y (one step ahead) if there is a MICE rel for CalcMPDynamics (not used in updateData)
StockPars_MPCalc <- StockPars
if (length(Rel) && any(DV_MICE == "M")) {
M_MICE <- sapply(1:nsim, function(x) {
Responses <- ResFromRel(Rel[DV_MICE == "M"],
Bcur = array(Biomass_P[x, , , 1, ], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x, , , 1, ], c(np, n_age, nareas)),
Ncur = array(N_P[x, , , 1, ], c(np, n_age, nareas)),
SSB0 = SSB0array[x, ], B0 = B0array[x, ],
seed = 1, x = x, y = nyears+y)
Dmodnam <- sapply(Responses, getElement, "modnam")
Dp <- sapply(Responses, getElement, "Dp")
Dval <- lapply(Responses, getElement, "value")
Dage <- lapply(Responses, getElement, "age")
Dmult <- sapply(Responses, getElement, "mult")
oldM_ageArray <- M_ageArray <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[x, , nyears + y]) %>% t()
oldMx <- Mx <- sapply(1:np, function(p) StockPars[[p]]$Marray[x, nyears + y])
Rel_txt <- sapply(1:length(Responses), function(r) {
if (all(!is.na(Dage[[r]]))) { # Age-specific M
Rel_var <- paste0("M_ageArray[", Dp[r], ", Dage[[r]] + 1]")
} else {
Rel_var <- paste0(Dmodnam[r], "[", Dp[r], "]")
}
if (Dmult[r]) {
Rel_val <- paste("Dval[[r]] *", Rel_var)
} else {
Rel_val <- "Dval[[r]]"
}
paste(Rel_var, "<-", Rel_val)
})
for (r in 1:length(Responses)) {
eval(parse(text = Rel_txt[r]))
}
if (all(oldM_ageArray == M_ageArray)) M_ageArray <- oldM_ageArray * Mx/oldMx
return(M_ageArray)
}, simplify = "array")
for(p in 1:np) StockPars_MPCalc[[p]]$M_ageArray[, , nyears + y] <- t(M_MICE[p, , ])
}
#
CBret_P <- array(0, dim=c(nsim, np, nf, n_age, proyears, nareas))
for (p in 1:np) {
for(f in 1:nf) {
if (!is.null(control$TAC)) {
if (control$TAC=="removals") {
CBret_P[,p,f,,,] <-FleetPars[[p]][[f]]$CB_P
}
} else {
CBret_P[,p,f,,,] <-FleetPars[[p]][[f]]$CB_Pret
}
}
}
# --- An update year ----
if (y %in% upyrs) {
# --- Update Data object ----
for (p in 1:np) {
for (f in 1:nf) {
OM <- suppressMessages(new('OM', docheck=FALSE)) # temporary while MSEtool::makeData requires this
OM@nyears <- nyears
OM@hbiascv <- MOM@Obs[[p]][[f]]@hbiascv
OM@maxF <- MOM@maxF
OM@CurrentYr <- MSElist[[1]][[1]][[1]]@LHYear
OM@reps <- MOM@reps
OM@nsim <- nsim
OM@BMSY_B0biascv <- MOM@Obs[[p]][[f]]@BMSY_B0biascv
OM@proyears <- proyears
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- MSElist[[p]][[f]][[mm]]@OM
MSElist[[p]][[f]][[mm]] <- new('Data')
MSElist[[p]][[f]][[mm]]@OM <- om
} else {
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ MPCalcs_list[[i]][[f]]$TACrec
}
MSElist[[p]][[f]][[mm]] <- updateData_MS(Data=MSElist[[p]][[f]][[mm]],
OM,
MPCalcs=MPCalcs_list[[p]][[f]],
Effort=Effort[,p,f,, ,drop=FALSE],
Biomass=Biomass,
N=N,
Biomass_P=Biomass_P,
CB_Pret=CBret_P,
N_P=N_P,
SSB=SSB,
SSB_P=SSB_P,
VBiomass=VBF,
VBiomass_P=VBF_P,
StockPars,
FleetPars,
ObsPars,
ImpPars,
upyrs=upyrs,
interval=interval,
y=y,
mm=mm,
Misc=MSElist[[p]][[f]][[mm]]@Misc,
RealData=multiHist[[p]][[f]]@Data,
p, f,
map.stocks)
}
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
# ---- Update true abundance ----
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
if (!is.null(MPrec)) MSElist[[p]][[f]][[mm]]@MPrec <- MPrec
} # end of fleet
} # end of stock
if(MPcond[mm]=="MMP"){
# returns a hierarchical list object stock then fleet of Data objects
DataList <- getDataList(MSElist,mm)
# # returns a hierarchical list object stock then fleet then slot type of Rec
MPRecs_A <- applyMMP(DataList, MP = MPs[mm], reps = 1, silent = TRUE, parallel = parallel_MPs[mm])
Data_p_A <- MPrecs_A_blank
for(p in 1:np)for(f in 1:nf){
Data_p_A[[p]][[f]]<-MSElist[[p]][[f]][[mm]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # record TAC rec in Data
Data_p_A[[p]][[f]]@Misc <- MPRecs_A[[p]][[f]]$Misc
}
}else if(MPcond[mm]=="complex"){
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
# need this for aggregating data and distributing TACs over stocks
realVB<-abind::abind(apply(VBiomass[,,,1:nyears,, drop=FALSE],c(1,2,4),sum,na.rm=T),
apply(VBiomass_P[,,,1:(y-1), , drop=FALSE],c(1,2,4),sum,na.rm=T),
along=3)
curdat <- multiDataS(MSElist,Real.Data.Map,np,mm,nf,realVB)
runMP <- applyMP(curdat, MPs = MPs[mm], reps = 1, parallel=parallel_MPs[mm], silent=TRUE) # Apply MP
Stock_Alloc <- realVB[,,nyears, drop=FALSE]/
apply(realVB[,,nyears, drop=FALSE],1,sum)
chk_run <- try(runMP[[1]][[1]], silent=TRUE)
if (methods::is(chk_run, 'try-error')) {
warning('Error applying MP. Returning Data object that failed')
out <- list()
out$runMP <- runMP
out$Data <- curdat
out$MP <- MPs[mm]
return(out)
}
for(p in 1:np) for(f in 1:nf){
MPRecs_A[[p]][[f]] <- runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC <- runMP[[1]][[1]]$TAC *
MOM@Allocation[[p]][,f] * Stock_Alloc[,p,1]
MPRecs_A[[p]][[f]]$Effort <- runMP[[1]][[1]]$Effort * MOM@Efactor[[p]][,f]
if(length(MPRecs_A[[p]][[f]]$Effort)>0)
if(all(is.na(MPRecs_A[[p]][[f]]$Effort[1,])))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(length(MPRecs_A[[p]][[f]]$TAC)>0)
if(all(is.na(MPRecs_A[[p]][[f]]$TAC[1,])))
MPRecs_A[[p]][[f]]$TAC <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC
}
} else {
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
for(p in 1:np){
if(MPcond[mm]=="bystock"){
if(nf>1){
curdat<-multiData(MSElist,StockPars,p,mm,nf)
}else{
curdat<-MSElist[[p]][[f]][[mm]]
}
runMP <- applyMP(curdat, MPs = MPs[[p]][mm], reps = MOM@reps,
parallel = parallel_MPs[match(MPs[[p]][mm], names(parallel_MPs))],
silent=TRUE) # Apply MP
# Do allocation calcs
TAC_A[,p,]<-array(as.vector(unlist(runMP[[1]][[1]]$TAC))*MOM@Allocation[[p]],c(nsim,nf))
TAE_A[,p,]<-array(as.vector(unlist(runMP[[1]][[1]]$Effort))*MOM@Efactor[[p]],c(nsim,nf))
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-matrix(TAC_A[,p,f],nrow=1) # Just pass the allocated TAC
MPRecs_A[[p]][[f]]$Effort<-matrix(TAE_A[,p,f],nrow=1)
# This next line is to make the NULL effort recommendations of
# an output control MP compatible with CalcMPdynamics (expects a null matrix)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # Copy the allocated TAC
}
} else if(MPcond[mm]=="byfleet"){
for(f in 1:nf){
curdat<-MSElist[[p]][[f]][[mm]]
runMP <- applyMP(curdat, MPs = MPrefs[mm,f,p],
parallel = parallel_MPs[match(MPrefs[mm,f,p], names(parallel_MPs))],
reps = MOM@reps, silent=TRUE) # Apply MP
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC <- MPRecs_A[[p]][[f]]$TAC
}
} # end of MPcond conditional
} # end of stock loop
} # end of MMP
# Update Misc slot in Data
for (p in 1:np) {
for (f in 1:nf) {
MSElist[[p]][[f]][[mm]]@Misc <- Data_p_A[[p]][[f]]@Misc
}
}
for(p in 1:np){
for(f in 1:nf){
# calculate pstar quantile of TAC recommendation dist
TACused[,p,f] <- apply(Data_p_A[[p]][[f]]@TAC, 2, quantile,
p = MOM@pstar, na.rm = T)
checkNA[p,f,y] <-checkNA[p,f,y] + sum(is.na(TACused[,p,f]))
MPCalcs <- CalcMPDynamics(MPRecs=MPRecs_A[[p]][[f]], y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[,p,f,,,],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]], control=control)
# Zeros caused by SexPars
if(length(SexPars)>0) MPCalcs<-MPCalcsNAs(MPCalcs)
TACa[,p,f, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE # adjustment to TAE
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # TAE
LastCatch[,p,f] <- MPCalcs$TACrec
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
MPCalcs_list[[p]][[f]] <- MPCalcs
} # end of fleets
} # end of stocks
# end of update year
} else {
# ---- Not an update year ----
for(p in 1:np){
for(f in 1:nf){
NoMPRecs <- MPRecs_A[[p]][[f]]
NoMPRecs$Spatial <- NA
MPCalcs <- CalcMPDynamics(MPRecs=NoMPRecs, y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[,p,f,,,],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]], control=control)
if(length(SexPars)>0)
MPCalcs <- MPCalcsNAs(MPCalcs) # Zeros caused by SexPars
TACa[,p,f, mm, y] <- TACused[,p,f] # recommended TAC
#TACa[,p,f, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # recommended TAE
# LastEi[,p,f] <- MPCalcs$Ei # adjustment to effort
LastCatch[,p,f] <- MPCalcs$TACrec
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FMret_P[,p,f,,,]<- MPCalcs$FM_Pret
#FretA[,p,f,,]<- MPCalcs$FM_Pret
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
} # end of fleets
} # end of stocks
} # end of not update year
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
for (p in 1:np) {
spawn_time_frac[,p,,] <-StockPars[[p]]$spawn_time_frac
F_age <- array(0, dim=c(nsim, n_age, nareas))
for (f in 1:nf) {
F_age <- F_age + FleetPars[[p]][[f]]$FM_P[,,y,]
}
M_age <- replicate(nareas,StockPars[[p]]$M_ageArray[,,nyears+y])
Z_age <- M_age+F_age
N_Psp <- StockPars[[p]]$N_P[,,y,]
for (a in 1:n_age) {
N_Psp[,a,] <- N_Psp[,a,] * exp(-(Z_age[,a,]*spawn_time_frac[,p,a,]))
}
SSB_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Fec_Age[,,nyears+y])
SSN_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Mat_age[,,nyears+y])
}
# recruitment
for (p in 1:np) {
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
if (length(SexPars$SSBfrom)) { # use SSB from another stock to predict recruitment
SSBcurr <- local({
SSBfrom <- array(SexPars$SSBfrom[p, ], c(np, nsim, n_age, nareas)) %>%
aperm(c(2, 1, 3, 4))
apply(SSBfrom * SSB_P[,,,y,], c(1, 2), sum) # nsim x np; contribution of each stock p' to p
})
} else {
SSBcurr <- apply(SSB_P[, p, , y, ], c(1, 3), sum) # nsim x nareas
}
recdev <- StockPars[[p]]$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars[[p]]$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars[[p]]$hs,
aR= StockPars[[p]]$aR, bR=StockPars[[p]]$bR, R0a=StockPars[[p]]$R0a,
SSBpR=StockPars[[p]]$SSBpR,
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars)
StockPars[[p]]$N_P[,1,y,] <- N_P[,p,1,y,] <- t(rec_area)
}
} # end of projection years
if(!silent) close(pb) # use pbapply::closepb(pb) for shiny related stuff
if (max(upyrs) < proyears) { # One more call to complete Data object
# --- Update Data object ----
upyrs2 <- upyrs[upyrs>0]
upyrs2 <- c(upyrs2, proyears)
if (length(upyrs2) < 3 && !silent) message("Updating the Data object for the final time...")
interval[mm] <- proyears
for (p in 1:np) {
for (f in 1:nf) {
OM <- suppressMessages(new('OM', docheck=FALSE))
OM@nyears <- nyears
OM@hbiascv <- MOM@Obs[[p]][[f]]@hbiascv
OM@maxF <- MOM@maxF
OM@CurrentYr <- MSElist[[1]][[1]][[1]]@LHYear
OM@reps <- MOM@reps
OM@nsim <- nsim
OM@BMSY_B0biascv <- MOM@Obs[[p]][[f]]@BMSY_B0biascv
OM@proyears <- proyears
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- MSElist[[p]][[f]][[mm]]@OM
MSElist[[p]][[f]][[mm]] <- new('Data')
MSElist[[p]][[f]][[mm]]@OM <- om
} else {
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ MPCalcs_list[[i]][[f]]$TACrec
}
MSElist[[p]][[f]][[mm]] <- updateData_MS(Data=MSElist[[p]][[f]][[mm]],
OM,
MPCalcs=MPCalcs_list[[p]][[f]],
Effort=Effort[,p,f,, ,drop=FALSE],
Biomass=Biomass,
N=N,
Biomass_P=Biomass_P,
CB_Pret=CBret_P,
N_P=N_P,
SSB=SSB,
SSB_P=SSB_P,
VBiomass=VBF,
VBiomass_P=VBF_P,
StockPars,
FleetPars,
ObsPars,
ImpPars,
upyrs=upyrs2,
interval=interval,
y=y,
mm=mm,
Misc=MSElist[[p]][[f]][[mm]]@Misc,
RealData=multiHist[[p]][[f]]@Data,
p, f,
map.stocks)
}
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
# ---- Update true abundance ----
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
} # end of fleet
} # end of stock
}
# SSB relative to SSBMSY
SB_SBMSYa[, ,mm, ] <- apply(SSB_P, c(1,2, 4), sum, na.rm=TRUE)/array(SSBMSY_y[,,mm,(nyears+1):(nyears+proyears)],
c(nsim,np,proyears))
# for(p in 1:np) for(f in 1:nf)
# FMa[,p,f, mm, ] <- -log(1 - apply(FleetPars[[p]][[f]]$CB_P, c(1, 3), sum,
# na.rm=TRUE)/apply(VBiomass_P[,p,,,]+
# FleetPars[[p]][[f]]$CB_P,
# c(1, 3), sum, na.rm=TRUE))
for(f in 1:nf)
F_FMSYa[, ,f,mm, ] <- FMa[,,f, mm, ]/FMSY_y[,,mm,(nyears+1):(nyears+proyears)]
Ba[, ,mm, ] <- apply(Biomass_P, c(1, 2,4), sum, na.rm=TRUE) # biomass
SSBa[, ,mm, ] <- apply(SSB_P, c(1, 2,4), sum, na.rm=TRUE) # spawning stock biomass
VBa[, ,mm, ] <- apply(VBiomass_P, c(1, 2, 4), sum, na.rm=TRUE) # vulnerable biomass
N_P_mp[,,, mm,,] <- N_P
for(p in 1:np) for(f in 1:nf)
Ca[, p,f,mm, ] <- apply(FleetPars[[p]][[f]]$CB_P, c(1, 3),
sum, na.rm=TRUE) # removed
for(p in 1:np) for(f in 1:nf)
CaRet[, p,f,mm, ] <- apply(FleetPars[[p]][[f]]$CB_Pret, c(1, 3),
sum, na.rm=TRUE) # retained catch
#if (!silent) message("Calculating spawning potential ratio")
for(p in 1:np) {
SPReqa[, p, mm, ] <- CalcSPReq(FM = sapply(FleetPars[[p]], getElement, "FM_P", simplify = "array") %>% apply(1:4, sum),
StockPars_MPCalc[[p]], n_age, nareas, nyears, proyears, nsim)
SPRdyna[, p, mm, ] <- local({
FMh <- array(FM[, p, , , , ], c(nsim, nf, n_age, nyears, nareas)) %>% apply(c(1, 3:5), sum)
FMp <- sapply(FleetPars[[p]], getElement, "FM_P", simplify = "array") %>% apply(1:4, sum)
CalcSPRdyn(abind::abind(FMh, FMp, along = 3),
StockPars_MPCalc[[p]], n_age, nareas, nyears, proyears, nsim)
})
}
if (!silent) {
if (all(checkNA != nsim) & !all(checkNA == 0)) {
# print number of NAs
# message(checkNA)
# message(checkNA[upyrs])
ntot <- sum(checkNA[,,upyrs])
totyrs <- sum(checkNA[,,upyrs] >0)
nfrac <- round(ntot/(length(upyrs)*nsim),2)*100
cat("\n")
message(totyrs, ' years had TAC = NA for some simulations (', nfrac, "% of total simulations)")
message('Used TAC_y = TAC_y-1')
}
if("progress"%in%names(control))
if(control$progress)
shiny::incProgress(1/nMP, detail = round(mm*100/nMP))
}
if (length(Rel)) {
for (r in names(StockPars_MICE)) {
if (r == "Perr_y") {
StockPars_MICE[[r]][, , mm, ] <- sapply(1:np, function(p) {
StockPars_MPCalc[[p]][[r]][, maxage + nyears + 1:proyears]
}, simplify = "array") %>% aperm(c(1, 3, 2))
} else {
StockPars_MICE[[r]][, , , mm, ] <- sapply(1:np, function(p) {
StockPars_MPCalc[[p]][[r]][, , nyears + 1:proyears]
}, simplify = "array") %>% aperm(c(1, 4, 2, 3))
}
}
}
} # end of MP loop
OM<-Obsout<-list()
for(p in 1:np) {
OM[[p]]<-Obsout[[p]]<-list()
for(f in 1:nf) {
OM[[p]][[f]]<-MSElist[[p]][[f]][[1]]@OM
Obsout[[p]][[f]]<-MSElist[[p]][[f]][[1]]@Obs
# remove MSElist Misc
if (!extended) {
for (mm in 1:nMP) {
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- NULL
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- NULL
MSElist[[p]][[f]][[mm]]@Misc$ReferencePoints <- NULL
}
}
}
}
Misc <- list()
# Misc$Data <-MSElist
Misc[['MOM']]<-MOM
if (length(Rel)) {
if (!silent) message("Returning updated parameters from MICE relationship in 'Misc$MICE'")
Misc[["MICE"]] <- StockPars_MICE # Update for potential values updated by MICE
}
if (extended) {
if (!silent) message("Returning complete abundance series in 'Misc$extended$N")
Misc[["extended"]] <- list(
N = local({
histN <- replicate(nMP, StockPars[[1]]$N) %>% aperm(c(1,2,3,6,4,5)) # nsim x np x n_age x nyears x nareas x nMP
abind::abind(histN, N_P_mp, along=5) # nsim x np x n_age x nMP x proyears x nareas
})
)
}
# need to reformat MMP and complex mode to work with MSEout slot
if(methods::is(MPs,"character")) MPs<-list(MPs)
# ---- Create MMSE Object ----
# add all reference points from multiHist to MMSE@RefPoint
# and remove from multiHist
# this is done so MMSE@multiHist can be dropped to save a ton of memory
# while still preserving ref points in the MMSE object
RefPoint <- list()
RefPoint$ByYear <- list(MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
BMSY=BMSY_y,
VBMSY=VBMSY_y)
# add other ref points
nms <- names(multiHist[[1]][[1]]@Ref$ByYear)
nms <- nms[!nms %in% names( RefPoint$ByYear)]
# drop SPR, Fcrash etc - not updated in projection and could change if selectivity changes
nms <- c(nms[grepl('0', nms)], 'h')
nms <- nms[!nms=="F01_YPR"]
for (nm in nms) {
RefPoint$ByYear[[nm]] <- array(NA, dim=c(nsim, np, nMP, proyears+nyears))
for (p in 1:np) {
for(mm in 1:nMP) {
RefPoint$ByYear[[nm]][,p,mm,] <- multiHist[[p]][[1]]@Ref$ByYear[[nm]]
}
}
}
RefPoint$Dynamic_Unfished <- list()
nms <- names(multiHist[[1]][[1]]@Ref$Dynamic_Unfished)
# add Dynamic_Unfished
### NOTE: Should be updated by MICE rel, e.g., M/Perr_y/growth have changed in the projection !
for (nm in nms) {
RefPoint$Dynamic_Unfished[[nm]] <- array(NA, dim=c(nsim, np, proyears+nyears))
for (p in 1:np) {
RefPoint$Dynamic_Unfished[[nm]][,p,] <- multiHist[[p]][[1]]@Ref$Dynamic_Unfished[[nm]]
}
}
# remove Ref from MultiHist
for (p in 1:np) {
for(f in 1:nf) {
multiHist[[p]][[f]]@Ref <- list('Now in MMSE@RefPoint')
}
}
if (dropHist) {
multiHist <- list('multiHist dropped (dropHist=TRUE). Reference points available in MMSE@RefPoint')
}
MSEout <- new("MMSE",
Name = MOM@Name,
nyears,
proyears,
nMPs=nMP,
MPs=MPs,
MPcond=MPcond,
MPrefs=MPrefs,
nsim,
nstocks=np,
nfleets=nf,
Snames=Snames,
Fnames=Fnames,
Stocks=Stocks,
Fleets=Fleets,
Obss=Obs,
Imps=Imps,
OM=OM,
Obs=Obsout,
SB_SBMSY=SB_SBMSYa,
F_FMSY=F_FMSYa,
N=N_P_mp, # apply(N_P_mp, c(1,2,4,5), 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(),
RefPoint=RefPoint,
multiHist=multiHist,
PPD=MSElist,
Misc=Misc)
MSEout
}
#' Run a multi-fleet multi-stock Management Strategy Evaluation
#'
#' Functions for running a multi-stock and/or multi-fleet Management
#' Strategy Evaluation (closed-loop simulation) for a specified operating model
#'
#' @param MOM A multi-fleet multi-stock operating model (class [MSEtool::MOM-class])
#' @param MPs A matrix of methods (nstock x nfleet) (character string) of class MP
#' @param Hist Should model stop after historical simulations? Returns a list
#' 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? See Details for more information.
#' @param checkMPs Logical. Check if the specified MPs exist and can be run on `SimulatedData`?
#' @param dropHist Logical. Drop the (very large) `multiHist` object from the returned `MMSE` object?
#' The `multiHist` object can be (re-)created using `SimulateMOM` or kept in `MMSE@multiHist` if
#' `dropHist=FALSE`
#' @param extended Logical. Return extended projection results?
#' if TRUE, `MMSE@Misc$extended` is a named list with extended data
#' (including historical and projected abundance by area).
#' @describeIn multiMSE Run a multi-stock, multi-fleet MSE
#'
#' @details
#' ## Running MPs in parallel
#'
#' For most MPs, running in parallel can actually lead to an increase in computation time, due to the overhead in sending the
#' information over to the cores. Consequently, by default the MPs will not be run in parallel if `parallel=TRUE`
#' (although other internal code will be run in parallel mode).
#'
#' To run MPs in parallel, specify a named list with the name of the MP(s) assigned as TRUE. For example,`parallel=list(AvC=TRUE`)
#' will run the `AvC` MP in parallel mode.
#
#' @return Functions return objects of class `MMSE` and `multiHist`
#' #' \itemize{
#' \item SimulateMOM - An object of class `multiHist`
#' \item ProjectMOM - An object of class `MMSE`
#' \item multiMSE - An object of class `MMSE`
#' }
#' @author T. Carruthers and A. Hordyk
#' @export
multiMSE <- function(MOM=MSEtool::Albacore_TwoFleet,
MPs=list(list(c("AvC","DCAC"),c("FMSYref","curE"))),
Hist=FALSE,
silent=FALSE,
parallel=TRUE,
checkMPs=FALSE,
dropHist=TRUE,
extended=FALSE) {
# ---- Initial Checks and Setup ----
if (methods::is(MOM,'MOM')) {
if (MOM@nsim <=1) stop("MOM@nsim must be > 1", call.=FALSE)
} else if ('multiHist' %in% class(MOM)) {
stop("You must specify an operating model of class `MOM`")
# if (!silent) message("Using `multiHist` object to reproduce historical dynamics")
#
# # --- Extract cpars from Hist object ----
# nstocks <- length(MOM)
# nfleets <- length(MOM[[1]])
#
# cpars <- list()
# cpars[[1]] <- list()
#
# for (s in 1:nstocks) {
# for (f in 1:nfleets) {
# cpars[[s]][[f]] <- c(MOM[[s]][[f]]@SampPars$Stock,
# MOM[[s]][[f]]@SampPars$Fleet,
# MOM[[s]][[f]]@SampPars$Obs,
# MOM[[s]][[f]]@SampPars$Imp,
# MOM[[s]][[f]]@OMPars,
# MOM[[s]][[f]]@OM@cpars)
# }
# }
#
# # --- Populate a new OM object ----
# newMOM <- MOM[[1]][[1]]@Misc$MOM
# newMOM@cpars <- cpars
# MOM <- newMOM
} else {
stop("You must specify an operating model of class `MOM`")
}
if (checkMPs) {
allMPs <- unique(unlist(MPs))
CheckMPs(MPs=allMPs, silent=silent)
}
multiHist <- SimulateMOM(MOM, parallel, silent)
if (Hist) {
if(!silent) message("Returning historical simulations")
return(multiHist)
}
if(!silent) message("Running forward projections")
MSEout <- try(ProjectMOM(multiHist=multiHist, MPs, parallel, silent, checkMPs=FALSE,
dropHist=dropHist,extended=extended), silent=TRUE)
if (methods::is(MSEout,'try-error')) {
message('The following error occured when running the forward projections: ',
crayon::red(attributes(MSEout)$condition))
message('Returning the historical simulations (class `multiHist`). To avoid re-running spool up, ',
'the forward projections can be run with ',
'`ProjectMOM(multiHist, MPs, ...)`')
return(multiHist)
}
MSEout
}
Blue-Matter/MSEtool documentation built on Nov. 7, 2024, 11:38 p.m.
R Package Documentation
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Defines functions multiMSE SimulateMOM
Documented in multiMSE SimulateMOM
#' @describeIn multiMSE Simulate historical dynamics for multi-OM
#'
#' @export
#'
SimulateMOM <- function(MOM=MSEtool::Albacore_TwoFleet, parallel=TRUE, silent=FALSE) {
# ---- Initial Checks and Setup ----
if (methods::is(MOM,'MOM')) {
if (MOM@nsim <=1) stop("MOM@nsim must be > 1", call.=FALSE)
} else {
stop("You must specify an operating model of class `MOM`")
}
# ---- Set up parallel processing ----
ncpus <- set_parallel(parallel)
# ---- Set pbapply functions ----
if (requireNamespace("pbapply", quietly = TRUE) && !silent) {
.lapply <- pbapply::pblapply
.sapply <- pbapply::pbsapply
# Argument to pass parallel cluster (if running)
formals(.lapply)$cl <- formals(.sapply)$cl <- substitute(if (snowfall::sfIsRunning()) snowfall::sfGetCluster() else NULL)
} else if (snowfall::sfIsRunning()) {
.lapply <- snowfall::sfLapply
.sapply <- snowfall::sfSapply
} else {
.lapply <- base::lapply
.sapply <- base::sapply
}
set.seed(MOM@seed) # set seed for reproducibility
nsim <- MOM@nsim
nyears <- MOM@Fleets[[1]][[1]]@nyears # number of historical years
proyears <- MOM@proyears
allyears <- nyears+proyears
Stocks <- MOM@Stocks
Fleets <- MOM@Fleets
Obs <- MOM@Obs
Imps <- MOM@Imps
Rel <- MOM@Rel
SexPars <- MOM@SexPars
Complexes <- MOM@Complexes
CatchFrac <- MOM@CatchFrac
np <- length(Stocks)
nf <- length(Fleets[[1]])
if (MOM@CatchFrac[[1]] %>% nrow() != nsim) { # re-calculate CatchFrac
CatchFrac <- lapply(MOM@CatchFrac, function(x) x[1:nsim, , drop = FALSE])
MOM@CatchFrac <- CatchFrac
}
if (!length(Rel) && np == 1 && nf == 1) {
if (!silent) message_info("You have specified only a single stock and fleet with no MICE relationships. ",
"You should really be using the function MSEtool::runMSE()")
} else if(np > 1 && !length(MOM@Rel) && !length(MOM@SexPars)) {
if (!silent) message_info("You have specified more than one stock but no MICE relationships ",
"(slot MOM@Rel) or sex-specific relationships (slot MOM@SexPars) among these. ",
"As they are independent, consider doing MSE for one stock at a time for ",
"computational efficiency.")
}
maxF <- MOM@maxF
Snames <- SIL(Stocks, "Name")
Fnames <- SIL(Fleets, "Name") %>% make.unique() %>% matrix(nrow = nf)
cpars <- MOM@cpars
# ---- Custom Parameters (cpars) Options ----
control <- cpars$control; cpars$control <- NULL
# Ignore MICE in historical period
if (!is.null(control$HistRel) && !control$HistRel) {
HistRel <- list()
} else {
HistRel <- Rel
}
# Option to optimize depletion for vulnerable biomass instead of spawning biomass
optVB <- FALSE
if (!is.null(control$D) && control$D == "VB") optVB <- TRUE
# Allocation
if(!length(MOM@Allocation)) {
MOM@Allocation <- CatchFrac
if (!silent) message_info("Slot @Allocation of MOM object not specified. Setting slot ",
"@Allocation equal to slot @CatchFrac - current catch fractions")
}
if(!length(MOM@Efactor)) {
MOM@Efactor <- lapply(1:np, function(x) matrix(1, nsim, nf))
if (!silent) message_info("Slot @Efactor of MOM object not specified. Setting slot @Efactor ",
"to current effort for all fleets")
}
# All stocks and sampled parameters must have compatible array sizes (maxage)
maxage_s <- unique(SIL(Stocks, "maxage"))
maxage <- max(maxage_s)
if (length(maxage_s) > 1) {
if (!silent) message_info(paste("Stocks of varying maximum ages have been specified,",
"all simulations will run to",max(maxage_s),"ages"))
Stocks <- lapply(Stocks, function(x) {
x@maxage <- max(maxage)
return(x)
})
}
if (!silent) message("Loading operating model")
StockPars <- FleetPars <- ObsPars <- ImpPars <- SampCpars <- new('list')
plusgroup <- rep(1, np)
for (p in 1:np) {
# Check for plusgroup now, then remove from cpars before SampleCpars call (see Simulate)
if (!is.null(cpars[[p]][[1]]$plusgroup) && all(!cpars[[p]][[1]]$plusgroup)) {
plusgroup[p] <- 0
for (f in 1:nf) cpars[[p]][[f]]$plusgroup <- NULL
}
SampCpars[[p]] <- lapply(1:nf, function(f) {
if (length(cpars) && length(cpars[[p]][[f]])) {
if (!silent) message("Sampling custom parameters for ", Snames[p], Fnames[f, p])
SampleCpars(cpars[[p]][[f]], nsim, silent=silent)
} else {
list()
}
})
set.seed(MOM@seed) # set seed again after cpars has been sampled
# --- Sample Stock Parameters ----
StockPars[[p]] <- SampleStockPars(Stock = Stocks[[p]], nsim, nyears,
proyears, cpars = SampCpars[[p]][[1]],
msg = !silent)
StockPars[[p]]$plusgroup <- plusgroup[p]
StockPars[[p]]$maxF <- MOM@maxF
StockPars[[p]]$n_age <- StockPars[[p]]$maxage+1
# --- custom SRR function ---
# not implemented
StockPars[[p]] <- Check_custom_SRR(StockPars[[p]], SampCpars[[p]][[1]], nsim)
#if (any(StockPars[[p]]$SRrel>2))
# stop('Custom stock recruit function not supported in `multiMSE`')
# --- Sample Fleet Parameters ----
FleetPars[[p]] <- lapply(1:nf, function(f) {
SampleFleetPars(Fleet = Fleets[[p]][[f]],
Stock = StockPars[[p]],
nsim, nyears, proyears,
cpars = SampCpars[[p]][[f]],
msg=!silent)
})
# --- Sample Obs Parameters ----
ObsPars[[p]] <- lapply(1:nf, function(f) {
SampleObsPars(MOM@Obs[[p]][[f]], nsim,
cpars = SampCpars[[p]][[f]],
Stock = StockPars[[p]],
nyears, proyears)
})
# --- Sample Imp Parameters ----
ImpPars[[p]] <- lapply(1:nf, function(f) {
SampleImpPars(MOM@Imps[[p]][[f]], nsim,
cpars = SampCpars[[p]][[f]],
nyears, proyears)
})
}
# --- Update Parameters for two-sex stocks ----
# Depletion, stock-recruit parameters, recdevs, Fleet, Obs, and Imp copied
# from females to males
if (length(SexPars)) {
if (length(SexPars$Herm)) {
SexPars$Herm <- checkHerm(SexPars$Herm, maxage, nsim, nyears, proyears)
}
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) {
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
StockPars_t <- StockPars
FleetPars_t <- FleetPars
slot_s <- c("D", "hs", "AC", "R0", "R0a", "Perr_y")
# slot_f <- c("Esd", "Find", "dFFinal", "Spat_targ", "qinc", "qcv", "qvar", "FinF")
slot_f <- c("Esd", "Spat_targ", "qinc", "qcv", "qvar")
if (!silent) message_info("You have specified sex-specific dynamics,",
"these parameters will be mirrored across sex types according to SexPars$SSBfrom:\n",
paste(c(slot_s, slot_f), collapse = ", "),
", all observation and implementation parameters")
for (p in 1:np) {
StockPars[[p]][slot_s] <- StockPars_t[[parcopy[p]]][slot_s]
for (f in 1:nf) {
FleetPars[[p]][[f]][slot_f] <- FleetPars_t[[parcopy[p]]][[f]][slot_f]
ObsPars[[p]][[f]] <- ObsPars[[parcopy[p]]][[f]]
ImpPars[[p]][[f]] <- ImpPars[[parcopy[p]]][[f]]
}
}
}
} # end of sexpars
nareas_s <- NIL(StockPars, "nareas", lev1 = TRUE)
nareas <- unique(nareas_s)
if(length(unique(nareas_s)) != 1) {
stop("Stocks must have the same specified number of areas - check cpars$mov",
" for each stock object")
}
# ---- Bio-Economic Parameters ----
# TODO
# ---- Initialize arrays ----
n_age <- maxage + 1 # number of age classes (starting at age-0)
N <- Biomass <- Z<- VBiomass<- SSN <- SSB <- array(NA,
dim = c(nsim, np, n_age,
nyears, nareas))
VF <- FretA <- array(NA, dim = c(nsim, np, nf, n_age, allyears))
VBF <- FM <- FMret <- array(NA, dim = c(nsim, np, nf, n_age, nyears, nareas))
SPR <- array(NA, dim = c(nsim, np, n_age, nyears)) # store the Spawning Potential Ratio
MPA <- array(1,c(np,nf, nyears+proyears,nareas))
Agearray <- array(rep(1:n_age, each = nsim), dim = c(nsim, n_age)) # Age array
# ---- Hermaphroditism -----
# (this is the fraction to be kept (after sex change))
# E.g. protygynous (Female to male) is H_1_2 where 1 is female 2 is male
# [sim, stock, maxage] Defaults to all 1s if length(SexPars$Herm)==0
if (!is.null(control$HermEq) && !control$HermEq) {
HermFrac <- expandHerm(list(), maxage = maxage, np = np, nsim = nsim)
} else {
HermFrac <- expandHerm(SexPars$Herm, maxage = maxage, np = np, nsim = nsim)
}
Unfished_Equilibrium <- list()
for(p in 1:np){
# --- Pre Equilibrium calcs ----
# Set up some array indexes sim (S) age (A) year (Y) region/area (R)
SPAYR <- as.matrix(expand.grid(1:nareas, 1, 1:n_age, p, 1:nsim)[5:1])
SPA <- SPAYR[,1:3]
SAY <- SPAYR[, c(1,3,4)]
SAR <- SPAYR[, c(1,3,5)]
SA <- Sa <- SPAYR[, c(1,3)]
SR <- SPAYR[, c(1,5)]
S <- SPAYR[, 1]
SY <- SPAYR[, c(1, 4)]
Sa[,2] <- n_age-Sa[,2]+1 # This is the process error index for initial year
# Calculate initial distribution if mov provided in cpars
# ---- Calculate initial distribution if mov provided in cpars ----
if (is.null(StockPars[[p]]$initdist)) {
# mov has been passed in cpars - initdist hasn't been defined
StockPars[[p]]$initdist <- CalcDistribution(StockPars=StockPars[[p]],
FleetPars=FleetPars[[p]][[1]],
SampCpars=SampCpars[[p]][[1]],
nyears, maxF,
plusgroup[p], checks=FALSE)
}
#*HermFrac[,p,1] # !!!! INITDIST OF AGE 1. Unfished recruitment by area
R0a <- matrix(StockPars[[p]]$R0, nrow=nsim, ncol=nareas, byrow=FALSE) *
StockPars[[p]]$initdist[,1,]
# ---- Unfished Equilibrium calcs ----
# unfished survival for every year
surv <- lapply(1:nsim, calc_survival, StockPars=StockPars[[p]],
plusgroup=plusgroup[p], inc_spawn_time=FALSE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
# unfished survival (spawning)
SBsurv <- lapply(1:nsim, calc_survival, StockPars=StockPars[[p]],
plusgroup=plusgroup[p], inc_spawn_time=TRUE) %>%
abind(., along=3) %>% aperm(., c(3,1,2))
Nfrac <- surv * StockPars[[p]]$Mat_age # predicted numbers of mature ages in all years
# indices for all years
SAYR_a <- as.matrix(expand.grid(1:nareas, 1:(nyears+proyears), 1:n_age, 1:nsim)[4:1])
SAY_a <- SAYR_a[, 1:3]
SAR_a <- SAYR_a[, c(1,2,4)]
SA_a <- SAYR_a[, 1:2]
SR_a <- SAYR_a[, c(1, 4)]
S_a <- SAYR_a[, 1]
SY_a <- SAYR_a[, c(1, 3)]
# arrays for unfished biomass for all years
SSN_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
N_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
Biomass_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
SSB_a <- array(NA, dim = c(nsim, n_age, nyears+proyears, nareas))
# Calculate initial spawning stock numbers for all years
SSN_a[SAYR_a] <- Nfrac[SAY_a] * StockPars[[p]]$R0[S_a] * StockPars[[p]]$initdist[SAR_a]
N_a[SAYR_a] <- StockPars[[p]]$R0[S_a] * surv[SAY_a] * StockPars[[p]]$initdist[SAR_a]
Biomass_a[SAYR_a] <- N_a[SAYR_a] * StockPars[[p]]$Wt_age[SAY_a] # Calculate initial stock biomass
# SSB_a[SAYR_a] <- SSN_a[SAYR_a] * StockPars[[p]]$Wt_age[SAY_a] # Calculate spawning stock biomass
SSB_a[SAYR_a] <- SBsurv[SAY_a] * StockPars[[p]]$R0[S_a] * StockPars[[p]]$initdist[SAR_a] * StockPars[[p]]$Fec_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
Vraw <- array(NIL(listy=FleetPars[[p]],namey="V_real"),c(nsim,n_age,allyears,nf))
Vind <- as.matrix(expand.grid(1:nsim,p,1:nf,1:n_age,1:allyears))
VF[Vind] <- Vraw[Vind[,c(1,4,5,3)]]
Fretraw <- array(NIL(listy=FleetPars[[p]],namey="retA_real"),c(nsim,n_age,allyears,nf))
FretA[Vind] <- Fretraw[Vind[,c(1,4,5,3)]]
if(nf==1){
V <- VF[,p,1,,] #<-SOL(FleetPars[[p]],"V")
}else{
#Weight by catch fraction
V <- array(0,c(nsim,n_age,allyears))
for(f in 1:nf){
V <- V+VF[,p,f,,]*CatchFrac[[p]][,f]
}
#V<-nlz(V,c(1,3),"max") # currently assume unfished vulnerability is equally weighted among fleets
# V includes discards
}
# unfished vulnerable biomass for each year
VB0_a <- apply(apply(Biomass_a, c(1,2,3), sum) * V, c(1,3), sum)
# ---- Unfished Reference Points ----
SSBpRa <- array(SSB0_a/matrix(StockPars[[p]]$R0, nrow=nsim, ncol=nyears+proyears),
dim = c(nsim, nyears+proyears))
UnfishedRefs <- sapply(1:nsim, CalcUnfishedRefs, ageM=StockPars[[p]]$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[[p]] <- 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,V)
)
# 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[[p]]$hs)/(0.8 * SSB0a), nrow=nsim) # Ricker SR params
aR <- matrix(exp(bR * SSB0a)/SSBpR, nrow=nsim) # Ricker SR params
# --- Optimize for Initial Depletion ----
# currently done in SS2MOM
# --- Non-equilibrium Initial Year ----
SSN[SPAYR] <- Nfrac[SAY] * StockPars[[p]]$R0[S] * StockPars[[p]]$initdist[SAR] *
StockPars[[p]]$Perr_y[Sa]
# Calculate initial stock numbers
N[SPAYR] <- StockPars[[p]]$R0[S] * surv[SAY] * HermFrac[SPA] *
StockPars[[p]]$initdist[SAR] * StockPars[[p]]$Perr_y[Sa]
# Calculate initial stock biomass
Biomass[SPAYR] <- N[SPAYR] * StockPars[[p]]$Wt_age[SAY]
# Calculate spawning stock biomass
SSB[SPAYR] <- SBsurv[SAY] * StockPars[[p]]$R0[S] * StockPars[[p]]$initdist[SAR] * StockPars[[p]]$Fec_Age[SAY]
# Calculate vunerable biomass
VBiomass[SPAYR] <- Biomass[SPAYR] * V[SAY]
# Assign stock parameters to StockPars object
StockPars[[p]]$SSBpR <- SSBpR
StockPars[[p]]$aR <- aR
StockPars[[p]]$bR <- bR
StockPars[[p]]$SSB0 <- SSB0
StockPars[[p]]$SSN0 <- SSN0
StockPars[[p]]$VB0 <- VB0
StockPars[[p]]$R0a <- R0a
StockPars[[p]]$surv <- surv
StockPars[[p]]$B0 <- B0
StockPars[[p]]$N0 <- N0
# loop over fleets
for(f in 1:nf) {
FleetPars[[p]][[f]]$V_real<-VF[,p,f,,] # update fleet vulnerability for this stock
# --- Historical Spatial closures ----
if (!is.null(SampCpars[[p]][[f]]$MPA)) {
thisMPA <- SampCpars[[p]][[f]]$MPA
if (any(dim(thisMPA) != c(nyears+proyears, nareas))) {
stop('cpars$MPA must be a matrix with dimensions `nyears+proyears, nareas`', .call = FALSE)
}
if (any(thisMPA != 1 & thisMPA != 0)) {
stop('values in cpars$MPA must be either 0 (closed) or open (1)', .call = FALSE)
}
if (any(thisMPA != 1)) {
for (a in 1:nareas) {
yrs <- which(thisMPA[, a] == 0)
if (length(yrs)) {
if (!silent) {
message_info('Spatial closure detected for Stock ' , p, ' and Fleet ', f,
' in area ', a, ' in years ',
paste(findIntRuns(yrs), collapse=", "))
}
}
}
}
MPA[p, f,, ] <- thisMPA
} else {
if (!is.na(FleetPars[[p]][[f]]$MPA) && all(FleetPars[[p]][[f]]$MPA==TRUE)) {
MPA[p, f,, 1] <- 0
if (!silent) message_info('Historical MPA in Area 1 for all years')
}
}
FleetPars[[p]][[f]]$MPA <- MPA[p,f,,]
} # end of loop over fleets
} # end of loop over stocks
# ---- SexPars - Update SSB0 and Ricker SRR parameters for male stock ----
# Other parameters have been updated (R0, h, rec devs) earlier
if (length(SexPars)) {
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) {
if (!silent) message_info("You have specified sex-specific dynamics, unfished spawning biomass",
"and specified stock depletion will be mirrored across sex types according ",
"to SexPars$SSBfrom")
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
StockPars_t <- StockPars # need to store a temporary object for copying to/from
SSB0s <- matrix(NIL(StockPars_t, "SSB0"), nsim, np) # sim, p
for (p in 1:np) {
StockPars[[p]]$SSB0 <- apply(matrix(SexPars$SSBfrom[p, ], nsim, np, byrow = TRUE) * SSB0s, 1, sum)
StockPars[[p]]$SSBpR <- array(StockPars[[p]]$SSB0/StockPars_t[[p]]$R0,c(nsim,nareas)) # SSBpR hardwired to be the same among areas !!!!
# Ricker SR params
SSB0a <- StockPars[[p]]$SSB0 * StockPars_t[[p]]$R0a/apply(StockPars_t[[p]]$R0a, 1, sum)
StockPars[[p]]$bR <- matrix(log(5 * StockPars_t[[p]]$hs)/(0.8 * SSB0a), nrow=nsim)
StockPars[[p]]$aR <- matrix(exp(StockPars[[p]]$bR * SSB0a)/StockPars[[p]]$SSBpR, nrow=nsim)
}
}
if (length(SexPars$Herm)) {
if (!silent) message_info("You have specified sequential hermaphroditism (SexPars$Herm).",
"Unfished stock numbers will be calculated from this vector of fractions ",
"at age. Population dynamics will move individuals from one sex to another.")
}
} # end of SexPars loop
# --- Optimize catchability (q) to fit depletion ----
optD <- TRUE
# skip optimization if qs are provided in cpars
qs <- matrix(NA, nrow = nsim, ncol = np)
qfrac <- array(1, dim = c(nsim, np, nf))
for (p in 1:np) {
for (f in 1:nf) {
if (!is.null(SampCpars[[p]][[f]]$qs)) {
optD <- FALSE
qs[,p] <- SampCpars[[p]][[f]]$qs
if (all(SampCpars[[p]][[f]]$qs==0)) qfrac[, p, f] <- 0
FleetPars[[p]][[f]]$qs <- qs[, p] * qfrac[, p, f]
}
}
}
qs[qs==0] <- 1
bounds <- c(0.0001, 15) # q bounds for optimizer
if (optD) {
exp.time <- (np * nf)/(9*ncpus) * nsim
exp.time <- round(exp.time,2)
if(!silent)
message("Optimizing for user-specified depletion ",
'for ', nsim, 'simulations,', np, ' stocks, and ', nf, 'fleets',
"(could take a while!)")
out <- .lapply(1:nsim, getq_multi_MICE, StockPars, FleetPars, np, nf, nareas,
maxage, nyears, N, VF, FretA, maxF=MOM@maxF,
MPA,CatchFrac, bounds=bounds,tol=1E-6,HistRel,SexPars,
plusgroup=plusgroup, optVB=optVB, silent=silent)
qs <- NIL(out,"qtot") %>% matrix(nsim, np, byrow = TRUE)
qfrac <- NIL(out,"qfrac") %>% array(c(np, nf, nsim)) %>% aperm(c(3, 1, 2))
for (p in 1:np) {
for (f in 1:nf) {
FleetPars[[p]][[f]]$qs <- qs[, p] * qfrac[, p, f]
}
}
}
# --- Check that q optimizer has converged ----
# bounds for q (catchability). Flag if bounded optimizer hits the bounds
LimBound <- c(1.1, 0.9)*range(bounds)
probQ <- which(apply(qs > max(LimBound) | qs < min(LimBound),1,sum)>0)
Nprob <- length(probQ)
# If q has hit bound, re-sample depletion and try again.
# Tries 'ntrials' times and then alerts user
fracD <- 0.05; ntrials <- 50
if (!is.null(control$ntrials)) ntrials <- control$ntrials
if (!is.null(control$fracD)) fracD <- control$fracD
if (length(probQ) > 0 & optD) {
Err <- TRUE
if(!silent) message(Nprob,
' simulations have final biomass that is not ',
'close to sampled depletion')
if(!silent) message('Re-sampling depletion, recruitment error and fishing effort')
count <- 0
MOM2 <- MOM
while (Err & count < ntrials) {
Nprob <- length(probQ)
SampCpars2 <- vector("list", nf)
for(p in 1:np){
for(f in 1:nf){
if(length(cpars)>0 && length(cpars[[p]][[f]])>0){
# check each list object has the same length and if not stop and error report
ncparsim <- cparscheck(cpars[[p]][[f]])
SampCpars2[[f]] <- SampleCpars(cpars[[p]][[f]], Nprob, silent=silent)
}
}
ResampStockPars <- SampleStockPars(MOM2@Stocks[[p]],
nsim=Nprob,nyears=nyears,
proyears=proyears,
cpars=SampCpars2[[1]],
msg=FALSE)
ResampStockPars$CAL_bins <- StockPars[[p]]$CAL_bins
ResampStockPars$CAL_binsmid <- StockPars[[p]]$CAL_binsmid
ResampStockPars$nCALbins <- length(StockPars[[p]]$CAL_binsmid )
# Re-sample depletion
StockPars[[p]]$D[probQ] <- ResampStockPars$D
# Re-sample recruitment deviations
StockPars[[p]]$procsd[probQ] <- ResampStockPars$procsd
StockPars[[p]]$AC[probQ] <- ResampStockPars$AC
StockPars[[p]]$Perr_y[probQ,] <- ResampStockPars$Perr_y
StockPars[[p]]$hs[probQ] <- ResampStockPars$hs
} # end of P
# Re-sample historical fishing effort
ResampFleetPars<- vector("list", nf)
for(p in 1:np){
for(f in 1:nf){
ResampFleetPars <- SampleFleetPars(MOM2@Fleets[[p]][[f]],
Stock=ResampStockPars,
nsim=Nprob,
nyears=nyears,
proyears=proyears,
cpars=SampCpars2[[f]],
msg=FALSE)
FleetPars[[p]][[f]]$Esd[probQ] <- ResampFleetPars$Esd
FleetPars[[p]][[f]]$Find[probQ, ] <- ResampFleetPars$Find
FleetPars[[p]][[f]]$dFfinal[probQ] <- ResampFleetPars$dFfinal
}
}
out2 <- .lapply(probQ,getq_multi_MICE,StockPars, FleetPars, np,nf, nareas,
maxage, nyears, N, VF, FretA, maxF=MOM@maxF,
MPA,CatchFrac, bounds= bounds,tol=1E-6,HistRel,SexPars,
plusgroup=plusgroup, optVB=optVB, silent=silent)
qs2<-t(matrix(NIL(out2,"qtot"),nrow=np))
qout2<-array(NIL(out2,"qfrac"),c(np,nf,nsim))
qfrac2<-array(NA,c(Nprob,np,nf))
qind2<-TEG(dim(qfrac2))
qfrac2[qind2]<-qout2[qind2[,c(2,3,1)]]
qfrac[probQ,,]<-qfrac2
qs[probQ,]<-qs2
probQ <- which(apply(qs > max(LimBound) | qs < min(LimBound),1,sum)>0)
count <- count + 1
if (length(probQ) == 0) Err <- FALSE
} # end of while loop
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)
if (!silent) message(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0)
if (!silent) message(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent)
message("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 ntrials")
} else {
if (length(tooLow) > 0)
if (!silent) message(tooLow, " sims can't get down to the lower bound on depletion")
if (length(tooHigh) > 0)
if (!silent) message(tooHigh, " sims can't get to the upper bound on depletion")
if(!silent)
message("More than ", 100-fracD*100, "% simulations can get to the ",
"sampled depletion.\nContinuing")
}
}
for(p in 1:np)for(f in 1:nf) FleetPars[[p]][[f]]$qs<-qs[,p]*qfrac[,p,f]
} # end of re-optimization conditional
if(!silent)
message("Calculating historical stock and fishing dynamics")
# ---- Run Historical Simulations ----
histYrs <- .sapply(1:nsim, HistMICE,
StockPars=StockPars,
FleetPars=FleetPars,
np=np,
nf=nf,
nareas=nareas,
maxage=maxage,
nyears=nyears,
N=N,
VF=VF,
FretA=FretA,
maxF=MOM@maxF,
MPA=MPA,
Rel=HistRel,
SexPars=SexPars,
qs=qs,
qfrac=qfrac,
plusgroup=plusgroup
)
N <- aperm(array(as.numeric(unlist(histYrs[1,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
Biomass <- aperm(array(as.numeric(unlist(histYrs[2,], use.names=FALSE)),
dim=c(np ,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
SSN <- aperm(array(as.numeric(unlist(histYrs[3,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
SSB <- aperm(array(as.numeric(unlist(histYrs[4,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
VBiomass <- aperm(array(as.numeric(unlist(histYrs[5,], use.names=FALSE)),
dim=c(np, n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
FM <- aperm(array(as.numeric(unlist(histYrs[6,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
FMret <- aperm(array(as.numeric(unlist(histYrs[7,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
Linfarray <- aperm(array(as.numeric(unlist(histYrs[8,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
Karray <- aperm(array(as.numeric(unlist(histYrs[9,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
t0array <- aperm(array(as.numeric(unlist(histYrs[10,], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
Len_age <- aperm(array(as.numeric(unlist(histYrs[11,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Wt_age <- aperm(array(as.numeric(unlist(histYrs[12,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Fec_Age <- aperm(array(as.numeric(unlist(histYrs[21,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
VBF <- aperm(array(as.numeric(unlist(histYrs[17,], use.names=FALSE)),
dim=c(np,nf,n_age, nyears, nareas, nsim)), c(6,1,2,3,4,5))
Z <- aperm(array(as.numeric(unlist(histYrs[18,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
FMt<-aperm(array(as.numeric(unlist(histYrs[19,], use.names=FALSE)),
dim=c(np,n_age, nyears, nareas, nsim)), c(5,1,2,3,4))
M_ageArray <- aperm(array(as.numeric(unlist(histYrs[20,], use.names=FALSE)),
dim=c(np, n_age, nyears+1, nsim)), c(4,1,2,3))
Marray <- aperm(array(as.numeric(unlist(histYrs[13, ], use.names=FALSE)),
dim=c(np, nyears+1, nsim)), c(3,1,2))
spat_targ_out <- array(as.numeric(unlist(histYrs[22,], use.names=FALSE)),
dim=c(np, nf, nsim))
# update StockPars (MICE)
for (p in 1:np) {
StockPars[[p]]$Linfarray[, 0:nyears + 1] <- Linfarray[, p, ]
StockPars[[p]]$Karray[, 0:nyears + 1] <- Karray[, p, ]
StockPars[[p]]$t0array[, 0:nyears + 1] <- t0array[, p, ]
StockPars[[p]]$Len_age[, , 0:nyears + 1] <- Len_age[, p, , ]
StockPars[[p]]$Wt_age[, , 0:nyears + 1] <- Wt_age[, p, , ]
StockPars[[p]]$Fec_Age[, , 0:nyears + 1] <- Fec_Age[, p, , ]
StockPars[[p]]$M_ageArray[, , 1:nyears] <- M_ageArray[, p, , 1:nyears]
StockPars[[p]]$Marray[, 1:nyears] <- Marray[, p, 1:nyears]
# update Spat_targ
for (fl in 1:nf) {
FleetPars[[p]][[fl]]$Spat_targ <- spat_targ_out[p,fl,]
}
}
# TODO - selectivity-at-age should update if growth changes
# Depletion check
SSB0_specified <- array(NIL(StockPars,'SSB0'),c(nsim, np))
D_specified <- array(NIL(StockPars,'D'), c(nsim, np))
if (optVB) {
VB0_specified <- array(NIL(StockPars,'VB0'),c(nsim,np))
Depletion <- apply(VBiomass[,,,nyears,,drop = FALSE], 1:2, sum)/ VB0_specified
} else {
Depletion <- apply(SSB[,,,nyears,,drop = FALSE], 1:2, sum)/ SSB0_specified
}
if (length(SexPars)) {
if (is.null(SexPars$share_par) || SexPars$share_par == TRUE) { # need to copy over depletion for a sex-specific model
sexmatches <- sapply(1:nrow(SexPars$SSBfrom), function(x) paste(SexPars$SSBfrom[x, ], collapse = "_"))
parcopy <- match(sexmatches, sexmatches)
Depletion[, 1:np] <- Depletion[, parcopy]
}
}
for(p in 1:np) StockPars[[p]]$Depletion <- Depletion[, p] # add actual Depletion to StockPars
if (!is.null(control$checks)) {
if (prod(round(Depletion,2)/ round(D_specified,2)) != 1) {
print(cbind(round(Depletion, 4), round(D_specified, 4)) %>%
structure(dimnames = list(Sim = 1:nsim, Depletion = c("Specified", "Estimated"))))
warning("Possible problem in depletion calculations")
}
}
# --- Calculate MSY statistics for each year ----
# ignores spatial closures
# assumes all vulnerable fish are caught - ie no discarding
if(!silent) message("Calculating MSY and per-recruit reference points for each year")
# average life-history parameters over ageM years
SPR_hist <- list()
for (p in 1:np) {
V <- local({
FMt_future <- aperm(replicate(proyears, FMt[,,,nyears,, drop = FALSE]), c(1,2,3,4,6,5)) # Terminal year sel
FMt_all <- abind::abind(FMt[,p,,,], FMt_future[,p,,1,,], along = 3)
V <- apply(FMt_all, 1:3, sum) # sum across years and areas, works if areas are evenly distributed
V[V<=0] <- tiny
nlz(V, c(1, 3), "max")
})
MSYrefsYr <- lapply(1:nsim, function(x) {
sapply(1:(nyears+proyears), function(y) {
optMSY_eq(x,
yr.ind=y,
StockPars=StockPars[[p]],
V=V)
})
})
# --- Annual reference points ----
StockPars[[p]]$MSY_y <- sapply(MSYrefsYr, function(x) x["Yield", ]) %>% t()
StockPars[[p]]$FMSY_y <- sapply(MSYrefsYr, function(x) x["F", ]) %>% t()
StockPars[[p]]$SSBMSY_y <- sapply(MSYrefsYr, function(x) x["SB", ]) %>% t()
StockPars[[p]]$BMSY_y <- sapply(MSYrefsYr, function(x) x["B", ]) %>% t()
StockPars[[p]]$VBMSY_y <- sapply(MSYrefsYr, function(x) x["VB", ]) %>% t()
StockPars[[p]]$R0_y <- sapply(MSYrefsYr, function(x) x["R0", ]) %>% t()
StockPars[[p]]$h_y <- sapply(MSYrefsYr, function(x) x["h", ]) %>% t()
StockPars[[p]]$N0_y <- sapply(MSYrefsYr, function(x) x["N0", ]) %>% t()
StockPars[[p]]$SN0_y <- sapply(MSYrefsYr, function(x) x["SN0", ]) %>% t()
StockPars[[p]]$B0_y <- sapply(MSYrefsYr, function(x) x["B0", ]) %>% t()
StockPars[[p]]$SSB0_y <- sapply(MSYrefsYr, function(x) x["SB0", ]) %>% t()
StockPars[[p]]$VB0_y <- sapply(MSYrefsYr, function(x) x["VB", ]/x["VB_VB0", ]) %>% t()
# --- MSY reference points ----
MSYRefPoints <- sapply(1:nsim, CalcMSYRefs,
MSY_y=StockPars[[p]]$MSY_y,
FMSY_y= StockPars[[p]]$FMSY_y,
SSBMSY_y=StockPars[[p]]$SSBMSY_y,
BMSY_y=StockPars[[p]]$BMSY_y,
VBMSY_y=StockPars[[p]]$VBMSY_y,
ageM=StockPars[[p]]$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$M # ratio of true FMSY to natural mortality rate M
SSBMSY_SSB0 <- SSBMSY/SSB0 # SSBMSY relative to unfished (SSB)
BMSY_B0 <- BMSY/B0 # Biomass relative to unfished (B0)
VBMSY_VB0 <- VBMSY/StockPars[[p]]$VB0 # VBiomass relative to unfished (VB0)
StockPars[[p]]$MSY <- MSY
StockPars[[p]]$FMSY <- FMSY
StockPars[[p]]$SSBMSY <- SSBMSY
StockPars[[p]]$BMSY <- BMSY
StockPars[[p]]$VBMSY <- VBMSY
StockPars[[p]]$UMSY <- UMSY
StockPars[[p]]$FMSY_M <- FMSY_M
StockPars[[p]]$SSBMSY_SSB0 <- SSBMSY_SSB0
StockPars[[p]]$FMSY_M <- StockPars[[p]]$FMSY/StockPars[[p]]$M
StockPars[[p]]$BMSY_B0 <- BMSY_B0
StockPars[[p]]$VBMSY_VB0 <- VBMSY_VB0
# --- Dynamic Unfished Reference Points ---- assumes no MICE rel
Unfished <- sapply(1:nsim, function(x)
popdynCPP(nareas, StockPars[[p]]$maxage,
Ncurr=N[x,p,,1,],
nyears+proyears,
M_age=StockPars[[p]]$M_ageArray[x,,],
Asize_c=StockPars[[p]]$Asize[x,],
MatAge=StockPars[[p]]$Mat_age[x,,],
WtAge=StockPars[[p]]$Wt_age[x,,],
FecAge=StockPars[[p]]$Fec_Age[x,,],
Vuln=FleetPars[[p]][[1]]$V_real[x,,],
Retc=FleetPars[[p]][[1]]$retA_real[x,,],
Prec=StockPars[[p]]$Perr_y[x,],
movc=split_along_dim(StockPars[[p]]$mov[x,,,,],4),
SRrelc=StockPars[[p]]$SRrel[x],
Effind=rep(0, nyears+proyears),
Spat_targc=FleetPars[[p]][[1]]$Spat_targ[x],
hc=StockPars[[p]]$hs[x],
R0c=StockPars[[p]]$R0a[x,],
SSBpRc=StockPars[[p]]$SSBpR[x,],
aRc=StockPars[[p]]$aR[x,],
bRc=StockPars[[p]]$bR[x,],
Qc=0,
Fapic=0,
MPA=MPA[p,f,,],
maxF=maxF,
control=1,
SSB0c=StockPars[[p]]$SSB0[x],
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars[[x]],
plusgroup=StockPars[[p]]$plusgroup,
spawn_time_frac =StockPars[[p]]$spawn_time_frac[x]))
N_unfished <- aperm(array(as.numeric(unlist(Unfished[1,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
Biomass_unfished <- aperm(array(as.numeric(unlist(Unfished[2,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSN_unfished <- aperm(array(as.numeric(unlist(Unfished[3,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
SSB_unfished <- aperm(array(as.numeric(unlist(Unfished[4,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
VBiomass_unfished <- aperm(array(as.numeric(unlist(Unfished[5,], use.names=FALSE)),
dim=c(n_age, nyears+proyears, nareas, nsim)), c(4,1,2,3))
# ---- Calculate per-recruit reference points ----
StockPars[[p]]$SSB <- SSB[,p,,,]
StockPars[[p]]$N <- N[,p,,,]
F01_YPR_y <- array(0, dim=c(nsim, nyears+proyears)) # store F01 for each sim, and year
Fmax_YPR_y <- F01_YPR_y # store Fmax for each sim, and year
Fcrash_y <- F01_YPR_y # store Fcrash for each sim, and year
SPRcrash_y <- F01_YPR_y # store SPRcrash for each sim, and year
Fmed_y <- F01_YPR_y # store Fmed (F that generates the median historical SSB/R) for each sim, and year
SPR_target <- seq(0.2, 0.6, 0.05)
F_SPR_y <- array(0, dim = c(nsim, length(SPR_target), nyears + proyears)) %>%
structure(dimnames = list(NULL, paste0("F_", 100*SPR_target, "%"), NULL)) #array of F-SPR% by sim, SPR%, year
per_recruit_F <- lapply(1:nsim, function(x) {
lapply(1:(nyears+proyears), function(y) {
per_recruit_F_calc(x,
yr.ind=y,
StockPars=StockPars[[p]],
V=V,
SPR_target=SPR_target)
})
})
F_SPR_y[] <- lapply(per_recruit_F, function(x) sapply(x, getElement, 1)) %>%
simplify2array() %>% aperm(c(3, 1, 2))
F01_YPR_y[] <- sapply(per_recruit_F, function(x) sapply(x, function(y) y$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[[p]] <- list()
SPR_hist[[p]]$Equilibrium <- CalcSPReq(FMt[,p,,,], StockPars[[p]],
n_age, nareas, nyears, proyears, nsim,
Hist = TRUE)
SPR_hist[[p]]$Dynamic <- CalcSPRdyn(FMt[,p,,,], StockPars[[p]],
n_age, nareas, nyears, proyears, nsim, Hist = TRUE)
# ---- Calculate Mean Generation Time ----
MarrayArea <- replicate(nareas, StockPars[[p]]$M_ageArray[,,1:nyears])
Mnow<-apply(MarrayArea[,,nyears,]*N[,p,,nyears,],1:2,sum)/apply(N[,p,,nyears,],1:2,sum)
MGTsurv<-t(exp(-apply(Mnow,1,cumsum)))
StockPars[[p]]$MGT<-apply(Agearray*(StockPars[[p]]$Mat_age[,,nyears]*MGTsurv),1,sum)/apply(StockPars[[p]]$Mat_age[,,nyears]*MGTsurv,1,sum)
# ---- Calculate Reference Yield ----
# if(!silent) message("Calculating reference yield - best fixed F strategy")
## TODO - add RefY calcs
StockPars[[p]]$RefY <- StockPars[[p]]$MSY
# ---- Store Reference Points ----
StockPars[[p]]$ReferencePoints <- list(
ByYear=list(
N0=StockPars[[p]]$N0_y,
SN0=StockPars[[p]]$SN0_y,
B0=StockPars[[p]]$B0_y,
SSB0=StockPars[[p]]$SSB0_y,
VB0=StockPars[[p]]$VB0_y,
R0=StockPars[[p]]$R0_y,
h=StockPars[[p]]$h_y,
MSY=StockPars[[p]]$MSY_y,
FMSY=StockPars[[p]]$FMSY_y,
SSBMSY=StockPars[[p]]$SSBMSY_y,
BMSY=StockPars[[p]]$BMSY_y,
VBMSY=StockPars[[p]]$VBMSY_y,
F01_YPR=F01_YPR_y,
Fmax_YPR=Fmax_YPR_y,
F_SPR=F_SPR_y,
Fcrash=Fcrash_y,
Fmed=Fmed_y,
SPRcrash=SPRcrash_y
),
Dynamic_Unfished=list(
N0=apply(N_unfished, c(1,3), sum),
B0=apply(Biomass_unfished, c(1,3), sum),
SN0=apply(SSN_unfished, c(1,3), sum),
SSB0=apply(SSB_unfished, c(1,3), sum),
VB0=apply(VBiomass_unfished, c(1,3), sum),
Rec=apply(N_unfished[,1,,], c(1,2), sum)
),
ReferencePoints=data.frame(
N0=StockPars[[p]]$N0,
B0=StockPars[[p]]$B0,
SSB0=StockPars[[p]]$SSB0,
SSN0=StockPars[[p]]$SSN0,
VB0=StockPars[[p]]$VB0,
MSY=StockPars[[p]]$MSY,
FMSY=StockPars[[p]]$FMSY,
SSBMSY=StockPars[[p]]$SSBMSY,
BMSY=StockPars[[p]]$BMSY,
VBMSY=StockPars[[p]]$VBMSY,
UMSY=StockPars[[p]]$UMSY,
FMSY_M=StockPars[[p]]$FMSY_M,
SSBMSY_SSB0=StockPars[[p]]$SSBMSY_SSB0,
BMSY_B0=StockPars[[p]]$BMSY_B0,
VBMSY_VB0=StockPars[[p]]$VBMSY_VB0,
RefY=StockPars[[p]]$RefY,
MGT=StockPars[[p]]$MGT
)
)
}
# --- Calculate Historical Catch ----
# Calculate catch-at-age
# empirical weight-at-age for the catch
Wt_age_C <- array(NA,c(nsim,np,nf,n_age,nyears,nareas))
for (p in 1:np) {
for (f in 1:nf){
Wt_age_C[,p,f,,,] <- replicate(nareas, FleetPars[[p]][[f]]$Wt_age_C[,,1:nyears])
}
}
CB <- CBret <- Cret <- array(NA,c(nsim,np,nf,n_age,nyears,nareas))
CNind <- TEG(dim(CB))
Nind<-CNind[,c(1,2,4,5,6)] # sim, stock, n_age, nyears, nareas
Biomass_C <- array(0, dim=dim(FMret))
Biomass_C[CNind] <- N[Nind] * Wt_age_C[CNind]
CB[CNind] <- Biomass_C[CNind]*(1-exp(-Z[Nind]))*(FM[CNind]/Z[Nind])
CB[!is.finite(CB)] <- 0 # fix for Z=0
if(!is.null(control$checks)) {
for(p in 1:np){
Cp <- local({
num <- apply(CB[, p, , , nyears, , drop = FALSE], c(1, 3), sum) # nsim x nf
num/rowSums(num)
})
if(prod(round(CatchFrac[[p]], 4)/round(Cp, 4)) != 1) {
warning("Possible problem in catch fraction calculations")
print("Possible problem in catch fraction calculations")
print(Snames[p])
print(cbind(CatchFrac[[p]], round(Cp, 4)) %>%
structure(dimnames = list(Sim = 1:nsim, CatchFrac = c("Specified", "Estimated"))))
}
}
}
# Calculate retained-at-age
Cret[CNind] <- N[Nind] * (1-exp(-Z[Nind])) * (FMret[CNind]/Z[Nind]) #apply(Cret,1:5,sum)
Cret[!is.finite(Cret)] <- 0
CBret[CNind] <- Biomass_C[CNind] * (1-exp(-Z[Nind])) * (FMret[CNind]/Z[Nind])
CBret[!is.finite(CBret)] <- 0
# Add to FleetPars
for (p in 1:np) {
for (f in 1:nf){
FleetPars[[p]][[f]]$CBret <- CBret[,p,f,,,]
FleetPars[[p]][[f]]$CB <- CB[,p,f,,,]
FleetPars[[p]][[f]]$Fishing_Mortality <- apply(FM[,p,f,,,], c(1,3), max)
}
}
# --- Sampling by area ----
valNames <- c("Catch", 'BInd', 'SBInd', 'VInd', 'RecInd',
'CAA', 'CAL')
Sample_Area_array <- array(1, dim=c(nsim, nyears+proyears, nareas))
Sample_Area <- rep(list(Sample_Area_array), length(valNames))
names(Sample_Area) <- valNames
# Not currently working
# if (!is.null(OM@cpars$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))
}
}
# --- Populate Data object with Historical Data ----
CurrentYr <- MOM@Fleets[[1]][[1]]@CurrentYr
DataList <- new('list')
for (p in 1:np) {
DataList[[p]] <- vector('list', nf)
if (!is.null(control$skipdata)) {
for (f in 1:nf) {
Data <- new('Data')
# ---- Add Stock & Fleet Dynamics to Data ----
StockPars[[p]]$N <- N
StockPars[[p]]$SSB <- SSB
StockPars[[p]]$Biomass <- Biomass
Data@Misc$StockPars <- StockPars[[p]]
Data@Misc$FleetPars <- FleetPars[[p]][[f]]
Data@Misc$ReferencePoints <- StockPars[[p]]$ReferencePoints
DataList[[p]][[f]] <- Data
}
} else {
if (!silent) message('Generating historical data for', Snames[p])
for (f in 1:nf) {
if (!silent) message('Generating historical data for', Fnames[f])
ObsPars[[p]][[f]]$Sample_Area <- Sample_Area # add to Obs Pars
Data <- makeData(Biomass=Biomass[,p,,,],
CBret=CBret[,p,f,,,],
Cret=Cret[,p,f,,,],
N=N[,p,,,],
SSB=SSB[,p,,,],
VBiomass=VBF[,p,f,,,],
StockPars=StockPars[[p]],
FleetPars=FleetPars[[p]][[f]],
ObsPars=ObsPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]],
RefPoints=StockPars[[p]]$ReferencePoints$ReferencePoints,
SampCpars=SampCpars[[p]][[f]],
StockPars[[p]]$initD,
Sample_Area,
Name=paste(Snames[p], Fnames[f, p]),
nyears,
proyears,
nsim,
nareas,
MOM@reps,
CurrentYr,
silent=TRUE,
control)
# ---- Add Stock & Fleet Dynamics to Data ----
StockPars[[p]]$N <- N
StockPars[[p]]$SSB <- SSB
StockPars[[p]]$Biomass <- Biomass
Data@Misc$StockPars <- StockPars[[p]]
Data@Misc$FleetPars <- FleetPars[[p]][[f]]
Data@Misc$ReferencePoints <- StockPars[[p]]$ReferencePoints
DataList[[p]][[f]] <- Data
}
}
}
# ---- Condition Simulated Data on input Data object (if it exists) & calculate error stats ----
Real.Data.Map <- matrix(rep(1:np), nrow=nf, ncol=np, byrow=TRUE)
if (!is.null(SampCpars[[1]][[1]]$Real.Data.Map)) {
Real.Data.Map.t <- SampCpars[[1]][[1]]$Real.Data.Map
# check dimensions
if (any(dim(Real.Data.Map.t) != c(nf, np))) {
message_warn('cpars$Real.Data.Map does not have dimensions n.fleet x n.stock. Not being used')
Real.Data.Map.t <- Real.Data.Map
}
Real.Data.Map <- Real.Data.Map.t
}
# Aggregate comp effective and sample size
# (only used if fleet/stock data are mapped to each other)
CAA_ESS_array <- array(0, dim=c(nsim, np, nf))
CAL_ESS_array <- CAA_nsamp_array <- CAL_nsamp_array <- CAA_ESS_array
for (p in 1:np) {
for (f in 1:nf) {
CAA_ESS_array[,p,f] <- ObsPars[[p]][[f]]$CAA_ESS
CAL_ESS_array[,p,f] <- ObsPars[[p]][[f]]$CAL_ESS
CAA_nsamp_array[,p,f] <- ObsPars[[p]][[f]]$CAA_nsamp
CAL_nsamp_array[,p,f] <- ObsPars[[p]][[f]]$CAL_nsamp
}
}
for (p in 1:np) {
for (f in 1:nf) {
if (methods::is(SampCpars[[p]][[f]]$Data,"Data")) {
# real data has been passed in cpars
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- DataList[[p]][[f]]@OM
DataList[[p]][[f]] <- new('Data')
DataList[[p]][[f]]@OM <- om
ObsPars[[p]][[f]] <- ObsPars[[mapped.to]][[f]]
} else {
ObsPars[[p]][[f]]$CAA_ESS <- apply(CAA_ESS_array[,map.stocks,, drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAL_ESS <- apply(CAL_ESS_array[,map.stocks, ,drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAA_nsamp <- apply(CAA_nsamp_array[,map.stocks,,drop=FALSE], 1, sum)
ObsPars[[p]][[f]]$CAL_nsamp <- apply(CAL_nsamp_array[,map.stocks,,drop=FALSE], 1, sum)
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ DataList[[i]][[f]]@MPrec
}
# update Data and calculate observation error
updatedData <- AddRealData_MS(SimData=DataList[[p]][[f]],
RealData=SampCpars[[p]][[f]]$Data,
StockPars=StockPars,
FleetPars=FleetPars,
ObsPars=ObsPars,
SampCpars=SampCpars,
map.stocks,
CBret,
VBF,
p,
f,
nsim,
nyears,
proyears,
msg=!silent,
control)
updatedData$ObsPars[[1]][[1]]$VIerr_y[1,1:69]
if (!is.null(MPrec)) {
if (is.na(SampCpars[[p]][[f]]$Data@MPrec)) {
# don't update if MPrec provided in real data
updatedData$Data@MPrec <- MPrec
}
}
DataList[[p]][[f]] <- updatedData$Data
ObsPars <- updatedData$ObsPar
}
}
}
}
multiHist <- vector('list', np)
stock.names <- names(MOM@Stocks)
fleet.names <- names(MOM@Fleets[[1]])
if (is.null(stock.names)) stock.names <- paste('Stock', rep(1:length(MOM@Stocks)))
if (is.null(fleet.names)) fleet.names <- paste('Fleet', rep(1:length(MOM@Fleets[[1]])))
for (p in 1:np) {
multiHist[[p]] <- vector('list', nf)
names(multiHist) <- stock.names
for (f in 1:nf) {
Hist <- new("Hist")
# Data@Misc <- list()
Hist@Data <- DataList[[p]][[f]]
Hist@Data@Obs <- data.frame() # remove
ind <- which(lapply(ObsPars[[p]][[f]], length) == nsim)
obs <- data.frame(ObsPars[[p]][[f]][ind])
ind <- which(lapply(ImpPars[[p]][[f]], length) == nsim)
imp <- data.frame(ImpPars[[p]][[f]][ind])
OMPars <- DataList[[p]][[f]]@OM
if (nrow(OMPars)<1) {
OMPars <- data.frame(obs, imp)
} else {
OMPars <- data.frame(OMPars, obs, imp)
}
Hist@OMPars <- OMPars
if (f==1) {
Hist@AtAge <- list(Length=StockPars[[p]]$Len_age,
Weight=StockPars[[p]]$Wt_age,
Select=FleetPars[[p]][[f]]$V_real_2,
Retention=FleetPars[[p]][[f]]$retA_real,
Maturity=StockPars[[p]]$Mat_age,
N.Mortality=StockPars[[p]]$M_ageArray,
Z.Mortality=Z[,p,,,],
F.Mortality=FM[,p,f,,,],
Fret.Mortality=FMret[,p,f,,,],
Number=N[,p,,,],
Biomass=Biomass[,p,,,],
VBiomass=VBF[,p,f,,,],
SBiomass=SSB[,p,,,],
Removals=CB[,p,f,,,],
Landings=CBret[,p,f,,,],
Discards=CB[,p,f,,,]-CBret[,p,f,,,]
)
} else {
Hist@AtAge <- list(Select=FleetPars[[p]][[f]]$V_real_2,
Retention=FleetPars[[p]][[f]]$retA_real,
Z.Mortality=Z[,p,,,],
F.Mortality=FM[,p,f,,,],
Fret.Mortality=FMret[,p,f,,,],
VBiomass=VBF[,p,f,,,],
Removals=CB[,p,f,,,],
Landings=CBret[,p,f,,,],
Discards=CB[,p,f,,,]-CBret[,p,f,,,]
)
}
if (f==1) {
Hist@TSdata <- list(
Number=apply(N[,p,,,],c(1,3,4), sum),
Biomass=apply(Biomass[,p,,,],c(1,3,4), sum),
VBiomass=apply(VBF[,p,f,,,],c(1,3,4), sum),
SBiomass=apply(SSB[,p,,,],c(1,3,4), sum),
Removals=apply(CB[,p,f,,,], c(1,3,4), sum),
Landings=apply(CBret[,p,f,,,],c(1,3,4), sum),
Discards=apply(CB[,p,f,,,]-CBret[,p,f,,,],c(1,3,4), sum),
Find=FleetPars[[p]][[f]]$Find,
RecDev=StockPars[[p]]$Perr_y,
SPR=SPR_hist[[p]],
Unfished_Equilibrium=Unfished_Equilibrium[[p]]
)
} else {
Hist@TSdata <- list(
VBiomass=apply(VBF[,p,f,,,],c(1,3,4), sum),
Removals=apply(CB[,p,f,,,], c(1,3,4), sum),
Landings=apply(CBret[,p,f,,,],c(1,3,4), sum),
Discards=apply(CB[,p,f,,,]-CBret[,p,f,,,],c(1,3,4), sum),
Find=FleetPars[[p]][[f]]$Find
)
}
if (f==1) {
Hist@Ref <- StockPars[[p]]$ReferencePoints
}
if (f==1) {
Hist@SampPars <- list(
Stock=StockPars[[p]],
Fleet=FleetPars[[p]][[f]],
Obs=ObsPars[[p]][[f]],
Imp=ImpPars[[p]][[f]]
)
} else {
Hist@SampPars <- list(
Fleet=FleetPars[[p]][[f]],
Obs=ObsPars[[p]][[f]],
Imp=ImpPars[[p]][[f]]
)
}
if (f==1) {
temp <- MOM@cpars$Data
MOM@cpars <- list()
MOM@cpars$control <- control
MOM@cpars$Data <- temp
} else {
MOM@cpars <- list()
}
Hist@Misc <- list(
MOM=MOM,
Stock=stock.names[p],
Fleet=fleet.names[f]
)
multiHist[[p]][[f]] <- Hist
}
names(multiHist[[p]]) <- fleet.names
}
class(multiHist) <- c('list', 'multiHist')
attr(multiHist, "version") <- packageVersion("MSEtool")
attr(multiHist, "date") <- date()
attr(multiHist, "R.version") <- R.version
attr(multiHist, "Stocks") <- stock.names
attr(multiHist, "Fleets") <- fleet.names
multiHist[[1]][[1]]@Misc$Real.Data.Map <- Real.Data.Map
multiHist
}
#' @describeIn multiMSE Run Forward Projections for a `MOM` object
#' @param multiHist An Historical Simulation object (class `multiHist`)
#' @export
ProjectMOM <- function (multiHist=NULL, MPs=NA, parallel=FALSE, silent=FALSE,
checkMPs=FALSE, dropHist=FALSE, extended=FALSE) {
# ---- Setup ----
if (! 'multiHist' %in% class(multiHist))
stop('Must provide an object of class `multiHist`')
# Unpack historical simulation data
MOM <- multiHist[[1]][[1]]@Misc$MOM
set.seed(MOM@seed) # set seed for reproducibility
nsim <- MOM@nsim # number of simulations
nyears <- MOM@Fleets[[1]][[1]]@nyears # number of historical years
proyears <- MOM@proyears # number of projection years
interval <- MOM@interval # management interval (annual)
maxF <- MOM@maxF # maximum apical F
pstar <- MOM@pstar # Percentile of the sample of the TAC for each MP
reps <- MOM@reps # Number of samples of the management recommendation for each MP
allyears <- nyears+proyears
Stocks <- MOM@Stocks
Fleets <- MOM@Fleets
Obs <- MOM@Obs
Imps <- MOM@Imps
Rel <- MOM@Rel
SexPars <- MOM@SexPars
Complexes <- MOM@Complexes
CatchFrac <- MOM@CatchFrac
control <- MOM@cpars$control; MOM@cpars$control <- NULL
np <- length(Stocks)
nf <- length(Fleets[[1]])
Real.Data.Map <- multiHist[[1]][[1]]@Misc$Real.Data.Map
if (is.null(Real.Data.Map))
Real.Data.Map <- matrix(rep(1:np), nrow=nf, ncol=np, byrow=TRUE)
# ---- Detect MP Specification ----
MPcond <- "unknown"
if (!length(Rel) && np == 1 && nf == 1) {
if (!silent)
message_info("runMSE checking: you have specified a single stock and fleet with no MICE relationships.",
"For analysis you should be using runMSE(). Use this only for debugging",
"against runMSE.")
if (!methods::is(MPs,"character")) {
stop('`MPs` must be specified as a vector of character names if there is only',
' 1 stock and 1 fleet. `MPs` is currently a ', class(MPs))
}
MPcond <- "complex"
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- unlist(MPs)
# check class of MPs
tt <- try(sapply(MPs, getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
if (!all(MP_classes == MP_classes[1]))
stop('All MPs must be same class (`MP`)')
}
if (methods::is(MPs,'character') && MPcond == 'unknown') {
# check class of MPs
tt <- try(sapply(MPs, getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
if (any(!MP_classes %in% c('MP', 'MMP'))) {
inval <- unique(MP_classes[!MP_classes %in% c('MP', 'MMP')])
stop('Cannot mix MPs of class ', paste0(inval, collapse=","), ' with MPs of class `MP` and `MMP`')
}
MP_class <- unique(MP_classes)
MPcond <- rep(NA, length(MPs))
if ('MMP' %in% MP_class) {
if (!silent) message_info("MMP mode:\nYou have specified multi-fleet, multi-stock MPs of",
"class MMP. This class of MP accepts all data objects (stocks x fleets)",
"to simultaneously make a recommendation specific to each stock and fleet")
MPcond[which(MP_classes == 'MMP')] <- "MMP"
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- MPs
}
if ('MP' %in% MP_class) {
if (!silent) message_info("Complex mode:\nYou have specified a vector of MPs rather than a",
"list of MPs, one list position for MP type. The same MP will",
"be applied to the aggregate data for all stocks and fleets.",
"The MP will, for example, be used to set a single TAC for all",
"stocks and fleets combined. This will be allocated among fleets",
"according to recent catches and among stocks according to",
"available, vulnerable biomass")
MPcond[which(MP_classes == 'MP')] <- "complex"
MPtemp <- MPs
nMP <- length(MPs)
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[] <- unlist(MPs)
}
}
if (methods::is(MPs,'list') && 'unknown' %in% MPcond) {
if(identical(ldim(MPs), ldim(Fleets))){
if (!silent) message_info("Byfleet mode: you have specified an MP for each stock and fleet.",
"Only fleet-specific data (e.g. catches and indices) will be used to set",
"advice for each fleet for each stock")
MPcond <- "byfleet"
nMP <- length(MPs[[1]][[1]])
MPrefs <- array(NA,c(nMP,nf,np))
MPrefs[]<-unlist(MPs)
} else if (ldim(MPs)==ldim(Fleets)[1]) { # not a two-tier list
if (!silent) message_info("Bystock mode: you have specified a vector of MPs for each stock,",
"but not a vector of MPs for each stock and fleet. The catch data for these",
" fleets will be combined, a single MP will be used to set a single TAC",
"for all fleets combined that will be allocated between the fleets",
"according to recent catches")
MPcond<-"bystock"
checkN <- unlist(lapply(MPs, length))
if (!all(checkN == checkN[1]))
stop('Must have the same number of MPs for each stock')
nMP<-length(MPs[[1]])
MPrefs<-array(NA,c(nMP,nf,np))
for(p in 1:np)MPrefs[,,p]<-MPs[[p]]
}
tt <- try(sapply(unlist(MPs), getMP), silent=TRUE)
if (methods::is(tt,'try-error'))
stop('Error in the MPs -', strsplit(tt,':')[[1]][2])
MP_classes <- sapply(tt, class)
}
if ('unknown' %in% MPcond)
stop('`MPs` is not a vector or list with correct dimensions. See `?multiMSE`')
if (length(MPcond) != nMP) MPcond <- rep(MPcond, nMP)
if (methods::is(MPs,"list")) {
allMPs<-unlist(MPs)
}else{
allMPs<-MPs
}
if (nMP < 1) stop("No valid MPs found", call.=FALSE)
# ---- Set up parallel processing of MPs ---
runparallel <- FALSE
if (any(parallel==TRUE)) runparallel <- TRUE
if (methods::is(parallel, 'list')) runparallel <- TRUE
isrunning <- snowfall::sfIsRunning()
if (!runparallel & isrunning) snowfall::sfStop()
# Don't run MPs in parallel unless specified
parallel_MPs <- rep(FALSE, length(allMPs))
names(parallel_MPs) <- allMPs
if (methods::is(parallel, 'list')) {
parallel <- parallel[parallel==TRUE]
ind <- match(names(parallel), allMPs)
ind <- ind[!is.na(ind)]
parallel_MPs[ind] <- TRUE
names(parallel_MPs) <- allMPs
}
if (runparallel & any(parallel_MPs)) {
if (!isrunning) setup()
Export_customMPs(allMPs)
if (!silent) message("MPs running in parallel: ", paste0(names(parallel), collapse = ", "))
}
ncpus <- set_parallel(runparallel, msg=!silent)
# ---- Check MPs ----
if (checkMPs && all(MP_classes=='MP')) {
CheckMPs(MPs=allMPs, silent=silent)
}
# ---- Set Management Interval for each MP ----
# TODO - make same structure as MPs argument
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)) {
if (!silent) message(df$MP[i], 'has management interval:', df$interval[i], ifelse(i == nrow(df), "\n", ""))
}
}
# --- Store MSY statistics for each projection year ----
MSY_y <- FMSY_y <- SSBMSY_y <- BMSY_y <- VBMSY_y <- array(NA, dim=c(nsim,np, nMP, nyears+proyears))
# MSY stats from historical simulations
for (p in 1:np) {
MSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$MSY), c(1,3,2))
FMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$FMSY), c(1,3,2))
SSBMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$SSBMSY), c(1,3,2))
BMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$BMSY), c(1,3,2))
VBMSY_y[,p,,] <- aperm(replicate(nMP, multiHist[[p]][[1]]@Ref$ByYear$VBMSY), c(1,3,2))
}
# ---- Set-up arrays and objects for projections ----
# create a data object for each method
# (they have identical historical data and branch in projected years)
MSElist <- list('list')
for (p in 1:np) {
MSElist[[p]] <- lapply(1:nf, function(f) list(multiHist[[p]][[f]]@Data)[rep(1, nMP)]) # Historical data for this stock and fleet
}
# TODO - update names of stored values
SB_SBMSYa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected SB_SBMSY
Ba <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected Biomass
SSBa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected SSB
VBa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the projected vulnerable biomass
FMa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected fishing mortality rate
F_FMSYa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected F_FMSY
Ca <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected removed catch
CaRet <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected retained catch
TACa <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected TAC recommendation
TAE_out <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the projected TAE recommendation
Effort <- array(NA, dim = c(nsim, np, nf, nMP, proyears)) # store the Effort
SPReqa <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the equilibrium Spawning Potential Ratio
SPRdyna <- array(NA, dim = c(nsim, np, nMP, proyears)) # store the dynamic Spawning Potential Ratio
# ---- Grab Stock, Fleet, Obs and Imp values from Hist ----
StockPars <- FleetPars <- ObsPars <- ImpPars <- list()
for(p in 1:np) {
StockPars[[p]] <- multiHist[[p]][[1]]@SampPars$Stock
FleetPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Fleet)
ObsPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Obs)
ImpPars[[p]] <- lapply(1:nf, function(f) multiHist[[p]][[f]]@SampPars$Imp)
}
nareas <- StockPars[[1]]$nareas
maxage <- StockPars[[1]]$maxage
n_age <- maxage + 1
plusgroup <- sapply(1:np, function(p) StockPars[[p]]$plusgroup) # a vector
# projection arrays for storing all info (by simulation, stock, age, MP, years, areas)
N_P_mp <- array(NA, dim = c(nsim, np, n_age, nMP, proyears, nareas))
# store M, growth, length at age, rec dev by MP due to MICE rel.
if (length(Rel)) {
DV_MICE <- sapply(1:length(Rel), function(r) get_Dnam(Rel[[r]][["terms"]][1]))
DVarray <- c("M" = "M_ageArray", "a" = "Wt_age", "b" = "Wt_age", #"R0", "hs",
"K" = "Len_age", "Linf" = "Len_age", "t0" = "Len_age", "Perr_y" = "Perr_y")
StockPars_MICE <- lapply(unique(DV_MICE), function(r) {
if (r == "Perr_y") {
array(NA_real_, dim = c(nsim, np, nMP, proyears))
} else {
array(NA_real_, dim = c(nsim, np, n_age, nMP, proyears))
}
}) %>%
structure(names = DVarray[unique(DV_MICE)])
}
# ---- Grab Historical N-at-age etc ----
N <- array(NA, dim=c(nsim, np, n_age, nyears, nareas))
Biomass <- SSB <- VBiomass <- N
FM <- FMret <- array(NA, dim=c(nsim, np, nf, n_age, nyears, nareas))
VBF <- array(NA, dim=c(nsim, np, nf, n_age, nyears, nareas))
VF<- array(NA, dim=c(nsim, np, nf, n_age, nyears+proyears))
CB <- CB_ret <- FM
MPA <- array(NA, dim=c(np, nf, nyears+proyears, nareas))
for (p in 1:np) {
N[,p,,,] <- multiHist[[p]][[1]]@AtAge$Number
Biomass[,p,,,] <- multiHist[[p]][[1]]@AtAge$Biomass
SSB[,p,,,] <- multiHist[[p]][[1]]@AtAge$SBiomass
VBiomass[,p,,,] <- multiHist[[p]][[1]]@AtAge$VBiomass
for (f in 1:nf) {
FM[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$F.Mortality
FMret[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Fret.Mortality
VF[,p,f,,] <- FleetPars[[p]][[f]]$V_real
VBF[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$VBiomass
MPA[p,f,,] <- FleetPars[[p]][[f]]$MPA
CB[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Removals
CB_ret[,p,f,,,] <- multiHist[[p]][[f]]@AtAge$Landings
}
}
# need to make a copy because R is doing weird things with elements with similar names
HistFleetPars <- FleetPars
Snames <- SIL(Stocks,"Name")
Fnames <- matrix(make.unique(SIL(Fleets,"Name")),nrow=nf)
# ---- Begin loop over MPs ----
mm <- 1 # for debugging
TAC_A <- array(NA,c(nsim,np,nf)) # Temporary store of the TAC
TAE_A <- array(NA,c(nsim,np,nf)) # Temporary store of the TAE
MPrecs_A_blank<-list() # Temporary Hierarchical list of MPrec objects
for(p in 1:np) MPrecs_A_blank[[p]]<-list()
LastTAE <- histTAE <- Effort_pot <-LastAllocat <-LastCatch <-TACused <-
array(NA,c(nsim,np,nf))
LastSpatial <- array(NA,c(nareas,np,nf,nsim))
# temporary vulnerability for MSY calcs combined over fleets
V_Pt <- array(NA,c(nsim,nf,n_age,nyears+proyears))
for (mm in 1:nMP) {
if(!silent){
message(" ----- ", mm, "/", nMP, " MPs, Running MSE for: ") # print a progress report
for(p in 1:np){
MPrep<-data.frame(MPrefs[mm,,p])
row.names(MPrep)<-Fnames[,p]
names(MPrep)=Snames[p]
print(MPrep)
}
message(" --------------------------------- ")
}
checkNA <- array(0,c(np,nf,proyears)) # save number of NAs
# reset StockPars (MICE), selectivity & retention parameters for projections
for (p in 1:np) {
for (f in 1:nf) {
# reset selectivity parameters for projections
FleetPars[[p]][[f]]$L5_P <- HistFleetPars[[p]][[f]]$L5
FleetPars[[p]][[f]]$LFS_P <- HistFleetPars[[p]][[f]]$LFS
FleetPars[[p]][[f]]$Vmaxlen_P <- HistFleetPars[[p]][[f]]$Vmaxlen
# selectivity at length array - projections
FleetPars[[p]][[f]]$SLarray_P <- HistFleetPars[[p]][[f]]$SLarray_real
# selectivity at age array - projections
FleetPars[[p]][[f]]$V_P <- HistFleetPars[[p]][[f]]$V
FleetPars[[p]][[f]]$V_P_real <- HistFleetPars[[p]][[f]]$V_real # selectivity at age array - realized selectivity (max may be less than 1)
FleetPars[[p]][[f]]$V_P_real_2 <- HistFleetPars[[p]][[f]]$V_real_2 # selectivity at age array - realized selectivity (max = 1)
# reset retention parameters for projections
FleetPars[[p]][[f]]$LR5_P <- HistFleetPars[[p]][[f]]$LR5
FleetPars[[p]][[f]]$LFR_P <- HistFleetPars[[p]][[f]]$LFR
FleetPars[[p]][[f]]$Rmaxlen_P <- HistFleetPars[[p]][[f]]$Rmaxlen
# retention at age array - projections
FleetPars[[p]][[f]]$retA_P <- HistFleetPars[[p]][[f]]$retA # retention at age array - projections
FleetPars[[p]][[f]]$retA_P_real <- HistFleetPars[[p]][[f]]$retA_real # retention at age array - realized selectivity (max may be less than 1)
FleetPars[[p]][[f]]$retA_P_real_2 <- HistFleetPars[[p]][[f]]$retA_real_2 # asymptote at 1
# retention at length array - projections
FleetPars[[p]][[f]]$retL_P <- HistFleetPars[[p]][[f]]$retL_real
# Discard ratio for projections
FleetPars[[p]][[f]]$DR_P <- HistFleetPars[[p]][[f]]$DR
FleetPars[[p]][[f]]$FM_P <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
FleetPars[[p]][[f]]$FM_Pret <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
# stores prospective F before reallocation to new areas
FleetPars[[p]][[f]]$FM_nospace <- array(NA,
dim = c(nsim, n_age,
proyears, nareas))
# last apical F
FleetPars[[p]][[f]]$FML <- array(NA, dim = c(nsim, nareas))
FleetPars[[p]][[f]]$Z_P <- array(NA,
dim = c(nsim, n_age, proyears, nareas))
FleetPars[[p]][[f]]$CB_P <- array(NA,
dim = c(nsim,n_age, proyears, nareas))
# retained catch
FleetPars[[p]][[f]]$CB_Pret <- array(NA,
dim = c(nsim,n_age, proyears, nareas))
}
StockPars[[p]] <- multiHist[[p]][[1]]@SampPars$Stock
# Discard mortality for projections
StockPars[[p]]$Fdisc_P <- StockPars[[p]]$Fdisc
StockPars[[p]]$N_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$Biomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$VBiomass_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
StockPars[[p]]$SSN_P <-array(NA, dim = c(nsim,n_age, proyears, nareas))
StockPars[[p]]$SSB_P <- array(NA, dim = c(nsim, n_age, proyears, nareas))
}
# projection arrays
N_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
Biomass_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
VBiomass_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
SSN_P <-array(NA, dim = c(nsim,np, n_age, proyears, nareas))
SSB_P <- array(NA, dim = c(nsim,np, n_age, proyears, nareas))
FMt_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
Z_P <- array(NA, dim = c(nsim, np, n_age, proyears, nareas))
FM_P <- array(NA, dim = c(nsim, np,nf,n_age, proyears, nareas))
FMret_P <- array(NA, dim = c(nsim,np,nf, n_age, proyears, nareas))
VBF_P<-array(NA, dim = c(nsim,np,nf, n_age, proyears, nareas))
# indexes
SAYRL <- as.matrix(expand.grid(1:nsim, 1:n_age, nyears, 1:nareas)) # Final historical year
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, 1 + nyears, 1:nareas)) # Trajectory year
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, 1, 1:nareas))
SYt <- SAYRt[, c(1, 3)]
SAYt <- SAYRt[, 1:3]
SR <- SAYR[, c(1, 4)]
SA1 <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SY1 <- SAYR[, c(1, 3)]
SAY1 <- SAYRt[, 1:3]
SYA <- as.matrix(expand.grid(1:nsim, 1, 1:n_age)) # Projection year
SY <- SYA[, 1:2]
SA <- SYA[, c(1, 3)]
SAY <- SYA[, c(1, 3, 2)]
S <- SYA[, 1]
# -- First projection year ----
y <- 1
if (!silent) {
if (requireNamespace("pbapply", quietly = TRUE)) {
pb <- pbapply::timerProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
} else {
pb <- txtProgressBar(min = 1, max = proyears, style = 3, width = min(getOption("width"), 50))
}
}
#### Time-invariant parameters to project one-time step forward. Potential time-varying parameters inside local() call
# Matrix nsim x np
hs <- sapply(1:np, function(p) StockPars[[p]]$hs)
SRrel <- sapply(1:np, function(p) StockPars[[p]]$SRrel)
R0 <- sapply(1:np, function(p) StockPars[[p]]$R0)
SSB0array <- sapply(1:np, function(p) StockPars[[p]]$SSB0)
B0array <- sapply(1:np, function(p) StockPars[[p]]$B0)
# Vector length p
a_y <- sapply(1:np, function(p) StockPars[[p]]$a)
b_y <- sapply(1:np, function(p) StockPars[[p]]$b)
# Array nsim x np x nareas
aR <- sapply(1:np, function(p) StockPars[[p]]$aR, simplify = "array") %>% aperm(c(1, 3, 2))
bR <- sapply(1:np, function(p) StockPars[[p]]$bR, simplify = "array") %>% aperm(c(1, 3, 2))
R0a <- sapply(1:np, function(p) StockPars[[p]]$R0a, simplify = "array") %>% aperm(c(1, 3, 2))
SSBpR <- sapply(1:np, function(p) StockPars[[p]]$SSBpR, simplify = "array") %>% aperm(c(1, 3, 2))
Asize <- sapply(1:np, function(p) StockPars[[p]]$Asize, simplify = "array") %>% aperm(c(1, 3, 2))
# Array nsim x np x n_age x nareas x nareas x allyears
mov <- sapply(1:np, function(p) StockPars[[p]]$mov, simplify = "array") %>% aperm(c(1, 6, 2:5))
# Array nsim x np x nf
Spat_targ <- sapply(1:np, function(p) {
sapply(1:nf, function(f) HistFleetPars[[p]][[f]]$Spat_targ)
}, simplify = "array") %>% aperm(c(1, 3, 2))
# Predict abundance, spawning, recruitment at the beginning of y = nyears+1.
# F, M from y = nyears and growth, maturity, recdev, etc. in y = nyears+1
# MICE rel updates growth, Perr_y for nyears+1, M for nyears
#
# note that Fcur is apical F but, in popdynOneMICE it is DIVIDED in future
# years between the two areas depending on vulnerable biomass.
# So to get Fcur you need to sum over areas (a bit weird)
NextYrN <- local({
# Matrix nsim x np
Perr <- sapply(1:np, function(p) StockPars[[p]]$Perr_y[, nyears + maxage + 1])
Knext <- sapply(1:np, function(p) StockPars[[p]]$Karray[, nyears + 1])
Linfnext <- sapply(1:np, function(p) StockPars[[p]]$Linfarray[, nyears + 1])
t0next <- sapply(1:np, function(p) StockPars[[p]]$t0array[, nyears + 1])
M <- sapply(1:np, function(p) StockPars[[p]]$Marray[, nyears])
# Array nsim x np x n_age
M_agecur <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[, , nyears], simplify = "array") %>% aperm(c(1, 3, 2))
Mat_agenext <- sapply(1:np, function(p) StockPars[[p]]$Mat_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Fec_agenext <- sapply(1:np, function(p) StockPars[[p]]$Fec_Age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Len_agenext <- sapply(1:np, function(p) StockPars[[p]]$Len_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
Wt_agenext <- sapply(1:np, function(p) StockPars[[p]]$Wt_age[, , nyears + 1], simplify = "array") %>% aperm(c(1, 3, 2))
SRRfun_p <- lapply(1:np, function(p) StockPars[[p]]$SRRfun)
sapply(1:nsim, function(x) {
SRRpars_p <- lapply(1:np, function(p) StockPars[[p]]$SRRpars[[x]])
SexPars_y <- SexPars
SexPars_y$Herm <- subsetHerm(SexPars_y$Herm, y = nyears+1)
popdynOneMICE(np = np, nf = nf, nareas = nareas, maxage = maxage,
Ncur = array(N[x,,,nyears,], c(np, n_age, nareas)),
Bcur = array(Biomass[x,,,nyears,], c(np, n_age, nareas)),
SSBcur = array(SSB[x,,,nyears,], c(np, n_age, nareas)),
Vcur = array(VF[x,,,,nyears],c(np, nf, n_age)),
FMretx = array(FMret[x,,,,nyears,],c(np,nf,n_age,nareas)),
FMx = array(FM[x,,,,nyears,], c(np, nf, n_age, nareas)),
PerrYrp = Perr[x, ], hsx = hs[x, ],
aRx = array(aR[x, , ], c(np, nareas)),
bRx = array(bR[x, , ], c(np, nareas)),
movy = array(mov[x,,,,,nyears+1], c(np, n_age, nareas, nareas)),
Spat_targ = array(Spat_targ[x, , ], c(np, nf)), SRrelx = SRrel[x, ],
M_agecur = array(M_agecur[x, , ], c(np, n_age)),
Mat_agenext = array(Mat_agenext[x, , ], c(np, n_age)),
Fec_agenext = array(Fec_agenext[x, , ], c(np, n_age)),
Asizex = array(Asize[x, , ], c(np, nareas)),
Kx = Knext[x, ], Linfx = Linfnext[x, ], t0x = t0next[x, ], Mx = M[x, ],
R0x = R0[x, ], R0ax = array(R0a[x, , ], c(np, nareas)),
SSBpRx = array(SSBpR[x, , ], c(np, nareas)), ax = a_y, bx = b_y,
Rel = list(), # Do not use MICE. Parameters were updated in last time step of SimulateMOM!
SexPars = SexPars_y, x = x,
plusgroup = plusgroup, SSB0x = SSB0array[x, ], B0x = B0array[x, ],
Len_agenext = array(Len_agenext[x, , ], c(np, n_age)),
Wt_agenext = array(Wt_agenext[x, , ], c(np, n_age)),
SRRfun=SRRfun_p, SRRpars=SRRpars_p, ycur = nyears)
})
})
N_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[1,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
Biomass_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[23,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSN_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[24,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSB_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[25,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBiomass_P[,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[19,],
use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBF_P[,,,,1,] <- aperm(array(as.numeric(unlist(NextYrN[20,], use.names=FALSE)),
dim=c(np, nf, n_age, nareas, nsim)), c(5,1,2,3,4))
FML <- apply(array(FM[, ,,, nyears, ],c(nsim,np,nf,n_age,nareas)),
c(1, 3), max)
for (p in 1:np) {
StockPars[[p]]$N_P <- N_P[,p,,,]
StockPars[[p]]$Biomass_P <- Biomass_P[,p,,,]
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
StockPars[[p]]$VBiomass_P <- VBiomass_P[,p,,,]
}
# ---- Update true abundance ----
# - used for FMSY ref methods so that FMSY is applied to current abundance
for (p in 1:np) {
for (f in 1:nf) {
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
} # end fleets
} # end stocks
# --- Apply MP in initial projection year ----
# - Combined MP -
if(MPcond[mm]=="MMP"){
# returns a hierarchical list object stock then fleet of Data objects
# DataList<-getDataList(MSElist,mm)
DataList<-getDataList(MSElist,mm)
# returns a hierarchical list object stock then fleet then slot type of Rec
MPRecs_A <- applyMMP(DataList, MP = MPs[mm], reps = 1, silent = TRUE, parallel = parallel_MPs[mm])
Data_p_A <- MPrecs_A_blank
for(p in 1:np)for(f in 1:nf){
Data_p_A[[p]][[f]]<-MSElist[[p]][[f]][[mm]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # record TAC rec in Data
Data_p_A[[p]][[f]]@Misc <- MPRecs_A[[p]][[f]]$Misc
}
}else if(MPcond[mm]=="complex"){
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
# need this for aggregating data and distributing TACs over stocks
realVB<-apply(VBiomass[,,,1:nyears,, drop=FALSE],c(1,2,4),sum,na.rm=T)
curdat<-multiDataS(MSElist,Real.Data.Map,np,mm,nf,realVB)
runMP <- applyMP(curdat, MPs = MPs[mm], parallel=parallel_MPs[mm],
reps = 1, silent=TRUE) # Apply MP
chk_run <- try(runMP[[1]][[1]], silent=TRUE)
if (methods::is(chk_run, 'try-error')) {
warning('Error applying MP. Returning Data object that failed')
out <- list()
out$runMP <- runMP
out$Data <- curdat
out$MP <- MPs[mm]
return(out)
}
Stock_Alloc<-realVB[,,nyears, drop=FALSE]/apply(realVB[,,nyears, drop=FALSE],1,sum)
for(p in 1:np) {
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-runMP[[1]][[1]]$TAC*MOM@Allocation[[p]][,f]*
Stock_Alloc[,p,1]
MPRecs_A[[p]][[f]]$Effort<-runMP[[1]][[1]]$Effort*MOM@Efactor[[p]][,f]
if(length(MPRecs_A[[p]][[f]]$Effort)>0)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(length(MPRecs_A[[p]][[f]]$TAC)>0)
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC
}
}
}else{
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
for(p in 1:np){
if(MPcond[mm]=="bystock"){
if(nf>1){
curdat<-multiData(MSElist,StockPars,p,mm,nf)
}else{
curdat<-MSElist[[p]][[f]][[mm]]
}
runMP <- applyMP(curdat, MPs = MPs[[p]][mm], reps = 1,
parallel = parallel_MPs[match(MPs[[p]][mm], names(parallel_MPs))],
silent=TRUE) # Apply MP
# Do allocation calcs
TAC_A[,p,] <- array(as.vector(unlist(runMP[[1]][[1]]$TAC))*
MOM@Allocation[[p]],c(nsim,nf))
TAE_A[,p,] <- array(as.vector(unlist(runMP[[1]][[1]]$Effort))*
MOM@Efactor[[p]],c(nsim,nf))
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-matrix(TAC_A[,p,f],nrow=1) # copy allocated TAC
MPRecs_A[[p]][[f]]$Effort<-matrix(TAE_A[,p,f],nrow=1)
# This next line is to make the NULL effort recommendations of an
# output control MP compatible with CalcMPdynamics (expects a null matrix)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # copy allocated tAC
}
}else if(MPcond[mm]=="byfleet"){
for(f in 1:nf){
curdat<-MSElist[[p]][[f]][[mm]]
runMP <- applyMP(curdat, MPs = MPrefs[mm,f,p], reps = 1,
parallel = parallel_MPs[match(MPrefs[mm,f,p], names(parallel_MPs))],
silent=TRUE) # Apply MP
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC <- MPRecs_A[[p]][[f]]$TAC
}
}
} # end of stocks
}
# Update Misc slot in Data
for (p in 1:np) {
for (f in 1:nf) {
MSElist[[p]][[f]][[mm]]@Misc <- Data_p_A[[p]][[f]]@Misc
}
}
# Update M if there is a MICE rel for CalcMPDynamics
StockPars_MPCalc <- StockPars
if (length(Rel) && any(DV_MICE == "M")) {
M_MICE <- sapply(1:nsim, function(x) {
Responses <- ResFromRel(Rel[DV_MICE == "M"],
Bcur = array(Biomass_P[x, , , 1, ], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x, , , 1, ], c(np, n_age, nareas)),
Ncur = array(N_P[x, , , 1, ], c(np, n_age, nareas)),
SSB0 = SSB0array[x, ], B0 = B0array[x, ],
seed = 1, x = x, y = nyears + 1)
Dmodnam <- sapply(Responses, getElement, "modnam")
Dp <- sapply(Responses, getElement, "Dp")
Dval <- lapply(Responses, getElement, "value")
Dage <- lapply(Responses, getElement, "age")
Dmult <- sapply(Responses, getElement, "mult")
oldM_ageArray <- M_ageArray <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[x, , nyears+1]) %>% t()
oldMx <- Mx <- sapply(1:np, function(p) StockPars[[p]]$Marray[x, nyears+1])
# Ensure this is consistent with code in popdynOneMICE
Rel_txt <- sapply(1:length(Responses), function(r) {
if (all(!is.na(Dage[[r]]))) { # Age-specific M
Rel_var <- paste0("M_ageArray[", Dp[r], ", Dage[[r]] + 1]")
} else {
Rel_var <- paste0(Dmodnam[r], "[", Dp[r], "]")
}
if (Dmult[r]) {
Rel_val <- paste("Dval[[r]] *", Rel_var)
} else {
Rel_val <- "Dval[[r]]"
}
paste(Rel_var, "<-", Rel_val)
})
for (r in 1:length(Responses)) {
eval(parse(text = Rel_txt[r]))
}
if (all(oldM_ageArray == M_ageArray)) M_ageArray <- oldM_ageArray * Mx/oldMx
return(M_ageArray)
}, simplify = "array")
for(p in 1:np) StockPars_MPCalc[[p]]$M_ageArray[, , nyears+1] <- t(M_MICE[p, , ])
}
MPCalcs_list <- vector('list', np)
for(p in 1:np) {
MPCalcs_list[[p]] <- vector('list', nf)
for(f in 1:nf) {
TACused[,p,f] <- apply(Data_p_A[[p]][[f]]@TAC, 2, quantile,
p = MOM@pstar, na.rm = T)
checkNA[p,f,y] <- sum(is.na(TACused[,p,f]))
LastTAE[,p,f] <- rep(NA, nsim) # no current TAE exists
histTAE[,p,f] <- rep(NA, nsim) # no existing TAE
LastSpatial[,p,f,] <- array(MPA[p,f,nyears,], dim=c(nareas, nsim)) #
# default assumption of reallocation of effort to open areas
LastAllocat[,p,f] <- rep(1, nsim)
LastCatch[,p,f] <- apply(CB[,p,f,,nyears,], 1, sum)
Effort_pot[,p,f] <- rep(NA, nsim) # No bio-economic model
MPCalcs <- CalcMPDynamics(MPRecs=MPRecs_A[[p]][[f]], y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[, p, f, , , ],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]],
control=control)
if(length(SexPars)>0) MPCalcs<- MPCalcsNAs(MPCalcs) # Zeros caused by SexPars
TACa[,p,f, mm, y] <- TACused[,p,f]#MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE # TAE set by MP
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # TAE
LastCatch[,p,f] <- MPCalcs$TACrec # TAC et by MP
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FMret_P[,p,f,,,]<- MPCalcs$FM_Pret
#FretA[,p,f,,]<- MPCalcs$FM_Pret
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
MPCalcs_list[[p]][[f]] <- MPCalcs
}
}
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
spawn_time_frac <- array(0, dim=c(nsim, np, n_age, nareas))
for (p in 1:np) {
spawn_time_frac[,p,,] <-StockPars[[p]]$spawn_time_frac
F_age <- array(0, dim=c(nsim, n_age, nareas))
for (f in 1:nf) {
F_age <- F_age + FleetPars[[p]][[f]]$FM_P[,,y,]
}
M_age <- replicate(nareas,StockPars[[p]]$M_ageArray[,,nyears+y])
Z_age <- M_age+F_age
N_Psp <- StockPars[[p]]$N_P[,,y,]
for (a in 1:n_age) {
N_Psp[,a,] <- N_Psp[,a,] * exp(-(Z_age[,a,]*spawn_time_frac[,p,a,]))
}
SSB_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Fec_Age[,,nyears+y])
SSN_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Mat_age[,,nyears+y])
}
# recruitment
for (p in 1:np) {
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
if (length(SexPars$SSBfrom)) { # use SSB from another stock to predict recruitment
SSBcurr <- local({
SSBfrom <- array(SexPars$SSBfrom[p, ], c(np, nsim, n_age, nareas)) %>%
aperm(c(2, 1, 3, 4))
apply(SSBfrom * SSB_P[,,,y,], c(1, 2), sum) # nsim x np; contribution of each stock p' to p
})
} else {
SSBcurr <- apply(SSB_P[, p, , y, ], c(1, 3), sum) # nsim x nareas
}
recdev <- StockPars[[p]]$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars[[p]]$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars[[p]]$hs,
aR= StockPars[[p]]$aR, bR=StockPars[[p]]$bR, R0a=StockPars[[p]]$R0a,
SSBpR=StockPars[[p]]$SSBpR,
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars)
StockPars[[p]]$N_P[,1,y,] <- N_P[,p,1,y,] <- t(rec_area)
}
# the years in which there are updates
upyrs <- 1 + (0:(floor(proyears/interval[mm]) - 1)) * interval[mm]
# --- Begin projection years ----
for (y in 2:proyears) {
if (!silent) setTxtProgressBar(pb, y) # Works with pbapply
# -- Calculate MSY stats for this year ----
# if selectivity has changed
for (p in 1:np) {
for (f in 1:nf) {
SelectChanged <- FALSE
if (any(
FleetPars[[p]][[f]]$retA_P[,,nyears+y] - FleetPars[[p]][[f]]$retA_P[,,nyears+y] !=0)) SelectChanged <- TRUE
if (any(
FleetPars[[p]][[f]]$V_P[,,nyears+y] - FleetPars[[p]][[f]]$V_P[,,nyears+y] !=0)) SelectChanged <- TRUE
# recalculate MSY ref points because selectivity has changed
V_Pt[,f,,]<-FleetPars[[p]][[f]]$V_P*
apply(CB[,p,f,,nyears,], 1, sum) # Weighted by catch frac
}
if (SelectChanged) {
#summed over fleets and normalized to 1
V_P<-nlz(apply(V_Pt,c(1,3,4),sum),c(1,3),"max")
y1 <- nyears + y
MSYrefsYr <- sapply(1:nsim, optMSY_eq,
yr.ind=y1,
StockPars=StockPars[[p]],
V=V_P)
MSY_y[,p,mm,y1] <- MSYrefsYr[1,]
FMSY_y[,p,mm,y1] <- MSYrefsYr[2,]
SSBMSY_y[,p,mm,y1] <- MSYrefsYr[3,]
BMSY_y[,p,mm,y1] <- MSYrefsYr[6,]
VBMSY_y[,p,mm,y1] <- MSYrefsYr[7,]
}
} # end of annual MSY
TACa[,,, mm, y] <- TACa[,,, mm, y-1] # TAC same as last year unless changed
SAYRt <- as.matrix(expand.grid(1:nsim, 1:n_age, y + nyears, 1:nareas)) # Trajectory year
SAYt <- SAYRt[, 1:3]
SAYtMP <- cbind(SAYt, mm)
SYt <- SAYRt[, c(1, 3)]
SAY1R <- as.matrix(expand.grid(1:nsim, 1:n_age, y - 1, 1:nareas))
SAYR <- as.matrix(expand.grid(1:nsim, 1:n_age, y, 1:nareas))
SY <- SAYR[, c(1, 3)]
SA <- SAYR[, 1:2]
S1 <- SAYR[, 1]
SAY <- SAYR[, 1:3]
S <- SAYR[, 1]
SR <- SAYR[, c(1, 4)]
SA2YR <- as.matrix(expand.grid(1:nsim, 2:n_age, y, 1:nareas))
SA1YR <- as.matrix(expand.grid(1:nsim, 1:(n_age - 1), y -1, 1:nareas))
# Predict abundance at the beginning of y with F, M from y-1 and
# growth, maturity, recdev, etc. in y
#
# note that Fcur is apical F but, in popdynOneMICE it is DIVIDED in future
# years between the two areas depending on vulnerable biomass. So to get
# Fcur you need to sum over areas (a bit weird)
NextYrN <- local({ # Calculate SSB in year y from abundance and mortality in y-1
# Matrix nsim x np
Perr <- sapply(1:np, function(p) StockPars[[p]]$Perr_y[, nyears + maxage + y])
Knext <- sapply(1:np, function(p) StockPars[[p]]$Karray[, nyears + y])
Linfnext <- sapply(1:np, function(p) StockPars[[p]]$Linfarray[, nyears + y])
t0next <- sapply(1:np, function(p) StockPars[[p]]$t0array[, nyears + y])
M <- sapply(1:np, function(p) StockPars[[p]]$Marray[, nyears + y - 1])
# Array nsim x np x n_age
M_agecur <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[, , nyears + y - 1], simplify = "array") %>% aperm(c(1, 3, 2))
Mat_agenext <- sapply(1:np, function(p) StockPars[[p]]$Mat_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Fec_agenext <- sapply(1:np, function(p) StockPars[[p]]$Fec_Age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Len_agenext <- sapply(1:np, function(p) StockPars[[p]]$Len_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
Wt_agenext <- sapply(1:np, function(p) StockPars[[p]]$Wt_age[, , nyears + y], simplify = "array") %>% aperm(c(1, 3, 2))
SRRfun_p <- lapply(1:np, function(p) StockPars[[p]]$SRRfun)
sapply(1:nsim, function(x) {
SRRpars_p <- lapply(1:np, function(p) StockPars[[p]]$SRRpars[[x]])
SexPars_y <- SexPars
SexPars_y$Herm <- subsetHerm(SexPars_y$Herm, y = nyears+y)
popdynOneMICE(np = np, nf = nf, nareas = nareas, maxage = maxage,
Ncur = array(N_P[x,,,y-1,], c(np, n_age, nareas)),
Bcur = array(Biomass_P[x,,,y-1,], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x,,,y-1,], c(np, n_age, nareas)),
Vcur = array(VF[x,,,,nyears+y-1],c(np, nf, n_age)),
FMretx = array(FMret_P[x,,,,y-1,],c(np,nf,n_age,nareas)),
FMx = array(FM_P[x,,,,y-1,], c(np, nf, n_age, nareas)),
PerrYrp = Perr[x, ], hsx = hs[x, ],
aRx = array(aR[x, , ], c(np, nareas)),
bRx = array(bR[x, , ], c(np, nareas)),
movy = array(mov[x,,,,,nyears+y], c(np, n_age, nareas, nareas)),
Spat_targ = array(Spat_targ[x, , ], c(np, nf)), SRrelx = SRrel[x, ],
M_agecur = array(M_agecur[x, , ], c(np, n_age)),
Mat_agenext = array(Mat_agenext[x, , ], c(np, n_age)),
Fec_agenext = array(Fec_agenext[x, , ], c(np, n_age)),
Asizex = array(Asize[x, , ], c(np, nareas)),
Kx = Knext[x, ], Linfx = Linfnext[x, ], t0x = t0next[x, ], Mx = M[x, ],
R0x = R0[x, ], R0ax = array(R0a[x, , ], c(np, nareas)),
SSBpRx = array(SSBpR[x, , ], c(np, nareas)), ax = a_y, bx = b_y,
Rel = Rel,
SexPars = SexPars_y, x = x,
plusgroup = plusgroup, SSB0x = SSB0array[x, ], B0x = B0array[x, ],
Len_agenext = array(Len_agenext[x, , ], c(np, n_age)),
Wt_agenext = array(Wt_agenext[x, , ], c(np, n_age)),
SRRfun=SRRfun_p, SRRpars=SRRpars_p, ycur = nyears+y-1)
})
})
N_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[1,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
Biomass_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[23,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSN_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[24,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
SSB_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[25,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBiomass_P[,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[19,], use.names=FALSE)),
dim=c(np,n_age, nareas, nsim)), c(4,1,2,3))
VBF_P[,,,,y,]<-aperm(array(as.numeric(unlist(NextYrN[20,], use.names=FALSE)),
dim=c(np, nf, n_age, nareas, nsim)), c(5,1,2,3,4))
FML <- apply(array(FM_P[, ,,, y-1, ],c(nsim,np,nf,n_age,nareas)),
c(1, 3), max)
if (length(Rel)) {
Linfarray <- aperm(array(as.numeric(unlist(NextYrN[8,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Karray <- aperm(array(as.numeric(unlist(NextYrN[9,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
t0array <- aperm(array(as.numeric(unlist(NextYrN[10,], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Len_age <- aperm(array(as.numeric(unlist(NextYrN[14,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Wt_age <- aperm(array(as.numeric(unlist(NextYrN[15,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Fec_Age <- aperm(array(as.numeric(unlist(NextYrN[26,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
M_ageArray <- aperm(array(as.numeric(unlist(NextYrN[2,], use.names=FALSE)),
dim=c(np, n_age, nsim)), c(3,1,2))
Marray <- aperm(array(as.numeric(unlist(NextYrN[11, ], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
Perr_y <- aperm(array(as.numeric(unlist(NextYrN[27, ], use.names=FALSE)),
dim=c(np, nsim)), c(2,1))
}
for (p in 1:np) {
StockPars[[p]]$N_P <- N_P[,p,,,]
StockPars[[p]]$Biomass_P <- Biomass_P[,p,,,]
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
StockPars[[p]]$VBiomass_P <- VBiomass_P[,p,,,]
#for(f in 1:nf)FleetPars[[p]][[f]]$FML<-FML[]
if (length(Rel)) {
StockPars[[p]]$Linfarray[, nyears + y] <- Linfarray[, p]
StockPars[[p]]$Karray[, nyears + y] <- Karray[, p]
StockPars[[p]]$t0array[, nyears + y] <- t0array[, p]
StockPars[[p]]$Len_age[, , nyears + y] <- Len_age[, p, ]
StockPars[[p]]$Wt_age[, , nyears + y] <- Wt_age[, p, ]
StockPars[[p]]$Fec_Age[, , nyears + y] <- Fec_Age[, p, ]
StockPars[[p]]$M_ageArray[, , nyears + y - 1] <- M_ageArray[, p, ]
StockPars[[p]]$Marray[, nyears + y - 1] <- Marray[, p]
StockPars[[p]]$Perr_y[, nyears + maxage + y] <- Perr_y[, p]
}
}
# Update M in year y (one step ahead) if there is a MICE rel for CalcMPDynamics (not used in updateData)
StockPars_MPCalc <- StockPars
if (length(Rel) && any(DV_MICE == "M")) {
M_MICE <- sapply(1:nsim, function(x) {
Responses <- ResFromRel(Rel[DV_MICE == "M"],
Bcur = array(Biomass_P[x, , , 1, ], c(np, n_age, nareas)),
SSBcur = array(SSB_P[x, , , 1, ], c(np, n_age, nareas)),
Ncur = array(N_P[x, , , 1, ], c(np, n_age, nareas)),
SSB0 = SSB0array[x, ], B0 = B0array[x, ],
seed = 1, x = x, y = nyears+y)
Dmodnam <- sapply(Responses, getElement, "modnam")
Dp <- sapply(Responses, getElement, "Dp")
Dval <- lapply(Responses, getElement, "value")
Dage <- lapply(Responses, getElement, "age")
Dmult <- sapply(Responses, getElement, "mult")
oldM_ageArray <- M_ageArray <- sapply(1:np, function(p) StockPars[[p]]$M_ageArray[x, , nyears + y]) %>% t()
oldMx <- Mx <- sapply(1:np, function(p) StockPars[[p]]$Marray[x, nyears + y])
Rel_txt <- sapply(1:length(Responses), function(r) {
if (all(!is.na(Dage[[r]]))) { # Age-specific M
Rel_var <- paste0("M_ageArray[", Dp[r], ", Dage[[r]] + 1]")
} else {
Rel_var <- paste0(Dmodnam[r], "[", Dp[r], "]")
}
if (Dmult[r]) {
Rel_val <- paste("Dval[[r]] *", Rel_var)
} else {
Rel_val <- "Dval[[r]]"
}
paste(Rel_var, "<-", Rel_val)
})
for (r in 1:length(Responses)) {
eval(parse(text = Rel_txt[r]))
}
if (all(oldM_ageArray == M_ageArray)) M_ageArray <- oldM_ageArray * Mx/oldMx
return(M_ageArray)
}, simplify = "array")
for(p in 1:np) StockPars_MPCalc[[p]]$M_ageArray[, , nyears + y] <- t(M_MICE[p, , ])
}
#
CBret_P <- array(0, dim=c(nsim, np, nf, n_age, proyears, nareas))
for (p in 1:np) {
for(f in 1:nf) {
if (!is.null(control$TAC)) {
if (control$TAC=="removals") {
CBret_P[,p,f,,,] <-FleetPars[[p]][[f]]$CB_P
}
} else {
CBret_P[,p,f,,,] <-FleetPars[[p]][[f]]$CB_Pret
}
}
}
# --- An update year ----
if (y %in% upyrs) {
# --- Update Data object ----
for (p in 1:np) {
for (f in 1:nf) {
OM <- suppressMessages(new('OM', docheck=FALSE)) # temporary while MSEtool::makeData requires this
OM@nyears <- nyears
OM@hbiascv <- MOM@Obs[[p]][[f]]@hbiascv
OM@maxF <- MOM@maxF
OM@CurrentYr <- MSElist[[1]][[1]][[1]]@LHYear
OM@reps <- MOM@reps
OM@nsim <- nsim
OM@BMSY_B0biascv <- MOM@Obs[[p]][[f]]@BMSY_B0biascv
OM@proyears <- proyears
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- MSElist[[p]][[f]][[mm]]@OM
MSElist[[p]][[f]][[mm]] <- new('Data')
MSElist[[p]][[f]][[mm]]@OM <- om
} else {
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ MPCalcs_list[[i]][[f]]$TACrec
}
MSElist[[p]][[f]][[mm]] <- updateData_MS(Data=MSElist[[p]][[f]][[mm]],
OM,
MPCalcs=MPCalcs_list[[p]][[f]],
Effort=Effort[,p,f,, ,drop=FALSE],
Biomass=Biomass,
N=N,
Biomass_P=Biomass_P,
CB_Pret=CBret_P,
N_P=N_P,
SSB=SSB,
SSB_P=SSB_P,
VBiomass=VBF,
VBiomass_P=VBF_P,
StockPars,
FleetPars,
ObsPars,
ImpPars,
upyrs=upyrs,
interval=interval,
y=y,
mm=mm,
Misc=MSElist[[p]][[f]][[mm]]@Misc,
RealData=multiHist[[p]][[f]]@Data,
p, f,
map.stocks)
}
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
# ---- Update true abundance ----
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
if (!is.null(MPrec)) MSElist[[p]][[f]][[mm]]@MPrec <- MPrec
} # end of fleet
} # end of stock
if(MPcond[mm]=="MMP"){
# returns a hierarchical list object stock then fleet of Data objects
DataList <- getDataList(MSElist,mm)
# # returns a hierarchical list object stock then fleet then slot type of Rec
MPRecs_A <- applyMMP(DataList, MP = MPs[mm], reps = 1, silent = TRUE, parallel = parallel_MPs[mm])
Data_p_A <- MPrecs_A_blank
for(p in 1:np)for(f in 1:nf){
Data_p_A[[p]][[f]]<-MSElist[[p]][[f]][[mm]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # record TAC rec in Data
Data_p_A[[p]][[f]]@Misc <- MPRecs_A[[p]][[f]]$Misc
}
}else if(MPcond[mm]=="complex"){
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
# need this for aggregating data and distributing TACs over stocks
realVB<-abind::abind(apply(VBiomass[,,,1:nyears,, drop=FALSE],c(1,2,4),sum,na.rm=T),
apply(VBiomass_P[,,,1:(y-1), , drop=FALSE],c(1,2,4),sum,na.rm=T),
along=3)
curdat <- multiDataS(MSElist,Real.Data.Map,np,mm,nf,realVB)
runMP <- applyMP(curdat, MPs = MPs[mm], reps = 1, parallel=parallel_MPs[mm], silent=TRUE) # Apply MP
Stock_Alloc <- realVB[,,nyears, drop=FALSE]/
apply(realVB[,,nyears, drop=FALSE],1,sum)
chk_run <- try(runMP[[1]][[1]], silent=TRUE)
if (methods::is(chk_run, 'try-error')) {
warning('Error applying MP. Returning Data object that failed')
out <- list()
out$runMP <- runMP
out$Data <- curdat
out$MP <- MPs[mm]
return(out)
}
for(p in 1:np) for(f in 1:nf){
MPRecs_A[[p]][[f]] <- runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC <- runMP[[1]][[1]]$TAC *
MOM@Allocation[[p]][,f] * Stock_Alloc[,p,1]
MPRecs_A[[p]][[f]]$Effort <- runMP[[1]][[1]]$Effort * MOM@Efactor[[p]][,f]
if(length(MPRecs_A[[p]][[f]]$Effort)>0)
if(all(is.na(MPRecs_A[[p]][[f]]$Effort[1,])))
MPRecs_A[[p]][[f]]$Effort <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(length(MPRecs_A[[p]][[f]]$TAC)>0)
if(all(is.na(MPRecs_A[[p]][[f]]$TAC[1,])))
MPRecs_A[[p]][[f]]$TAC <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial <- matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC
}
} else {
# A temporary blank hierarchical list object stock by fleet
MPRecs_A <- Data_p_A <- MPrecs_A_blank
for(p in 1:np){
if(MPcond[mm]=="bystock"){
if(nf>1){
curdat<-multiData(MSElist,StockPars,p,mm,nf)
}else{
curdat<-MSElist[[p]][[f]][[mm]]
}
runMP <- applyMP(curdat, MPs = MPs[[p]][mm], reps = MOM@reps,
parallel = parallel_MPs[match(MPs[[p]][mm], names(parallel_MPs))],
silent=TRUE) # Apply MP
# Do allocation calcs
TAC_A[,p,]<-array(as.vector(unlist(runMP[[1]][[1]]$TAC))*MOM@Allocation[[p]],c(nsim,nf))
TAE_A[,p,]<-array(as.vector(unlist(runMP[[1]][[1]]$Effort))*MOM@Efactor[[p]],c(nsim,nf))
for(f in 1:nf){
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
MPRecs_A[[p]][[f]]$TAC<-matrix(TAC_A[,p,f],nrow=1) # Just pass the allocated TAC
MPRecs_A[[p]][[f]]$Effort<-matrix(TAE_A[,p,f],nrow=1)
# This next line is to make the NULL effort recommendations of
# an output control MP compatible with CalcMPdynamics (expects a null matrix)
if(is.na(MPRecs_A[[p]][[f]]$Effort[1,1]))
MPRecs_A[[p]][[f]]$Effort<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$Effort))
if(is.na(MPRecs_A[[p]][[f]]$TAC[1,1]))
MPRecs_A[[p]][[f]]$TAC<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
if(is.na(MPRecs_A[[p]][[f]]$Spatial[1,1]))
MPRecs_A[[p]][[f]]$Spatial<-matrix(NA,
nrow=0,
ncol=ncol(MPRecs_A[[p]][[f]]$TAC))
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC<-MPRecs_A[[p]][[f]]$TAC # Copy the allocated TAC
}
} else if(MPcond[mm]=="byfleet"){
for(f in 1:nf){
curdat<-MSElist[[p]][[f]][[mm]]
runMP <- applyMP(curdat, MPs = MPrefs[mm,f,p],
parallel = parallel_MPs[match(MPrefs[mm,f,p], names(parallel_MPs))],
reps = MOM@reps, silent=TRUE) # Apply MP
MPRecs_A[[p]][[f]]<-runMP[[1]][[1]]
Data_p_A[[p]][[f]]<-runMP[[2]]
Data_p_A[[p]][[f]]@TAC <- MPRecs_A[[p]][[f]]$TAC
}
} # end of MPcond conditional
} # end of stock loop
} # end of MMP
# Update Misc slot in Data
for (p in 1:np) {
for (f in 1:nf) {
MSElist[[p]][[f]][[mm]]@Misc <- Data_p_A[[p]][[f]]@Misc
}
}
for(p in 1:np){
for(f in 1:nf){
# calculate pstar quantile of TAC recommendation dist
TACused[,p,f] <- apply(Data_p_A[[p]][[f]]@TAC, 2, quantile,
p = MOM@pstar, na.rm = T)
checkNA[p,f,y] <-checkNA[p,f,y] + sum(is.na(TACused[,p,f]))
MPCalcs <- CalcMPDynamics(MPRecs=MPRecs_A[[p]][[f]], y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[,p,f,,,],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]], control=control)
# Zeros caused by SexPars
if(length(SexPars)>0) MPCalcs<-MPCalcsNAs(MPCalcs)
TACa[,p,f, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE # adjustment to TAE
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # TAE
LastCatch[,p,f] <- MPCalcs$TACrec
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
MPCalcs_list[[p]][[f]] <- MPCalcs
} # end of fleets
} # end of stocks
# end of update year
} else {
# ---- Not an update year ----
for(p in 1:np){
for(f in 1:nf){
NoMPRecs <- MPRecs_A[[p]][[f]]
NoMPRecs$Spatial <- NA
MPCalcs <- CalcMPDynamics(MPRecs=NoMPRecs, y,
nyears, proyears, nsim,
Biomass_P=StockPars[[p]]$Biomass_P,
VBiomass_P=VBF_P[,p,f,,,],
LastTAE=LastTAE[,p,f],
histTAE=histTAE[,p,f],
LastSpatial=LastSpatial[,p,f,],
LastAllocat=LastAllocat[,p,f],
LastTAC=LastCatch[,p,f],
TACused=TACused[,p,f],
maxF=maxF,
LR5_P=FleetPars[[p]][[f]]$LR5_P,
LFR_P=FleetPars[[p]][[f]]$LFR_P,
Rmaxlen_P=FleetPars[[p]][[f]]$Rmaxlen_P,
retL_P=FleetPars[[p]][[f]]$retL_P,
retA_P=FleetPars[[p]][[f]]$retA_P,
retA_P_real=FleetPars[[p]][[f]]$retA_P_real,
retA_P_real_2=FleetPars[[p]][[f]]$retA_P_real_2,
L5_P=FleetPars[[p]][[f]]$L5_P,
LFS_P=FleetPars[[p]][[f]]$LFS_P,
Vmaxlen_P=FleetPars[[p]][[f]]$Vmaxlen_P,
SLarray_P=FleetPars[[p]][[f]]$SLarray_P,
V_P=FleetPars[[p]][[f]]$V_P,
V_P_real=FleetPars[[p]][[f]]$V_P_real,
V_P_real_2=FleetPars[[p]][[f]]$V_P_real_2,
Fdisc_P=StockPars[[p]]$Fdisc_P,
DR_P=FleetPars[[p]][[f]]$DR_P,
FM_P=FleetPars[[p]][[f]]$FM_P,
FM_Pret=FleetPars[[p]][[f]]$FM_Pret,
Z_P=FleetPars[[p]][[f]]$Z_P,
CB_P=FleetPars[[p]][[f]]$CB_P,
CB_Pret=FleetPars[[p]][[f]]$CB_Pret,
Effort_pot=Effort_pot[,p,f],
StockPars=StockPars_MPCalc[[p]],
FleetPars=FleetPars[[p]][[f]],
ImpPars=ImpPars[[p]][[f]], control=control)
if(length(SexPars)>0)
MPCalcs <- MPCalcsNAs(MPCalcs) # Zeros caused by SexPars
TACa[,p,f, mm, y] <- TACused[,p,f] # recommended TAC
#TACa[,p,f, mm, y] <- MPCalcs$TACrec # recommended TAC
LastSpatial[,p,f,] <- MPCalcs$Si
LastAllocat[,p,f] <- MPCalcs$Ai
LastTAE[,p,f] <- MPCalcs$TAE
TAE_out[,p,f, mm, y] <- MPCalcs$TAE # recommended TAE
# LastEi[,p,f] <- MPCalcs$Ei # adjustment to effort
LastCatch[,p,f] <- MPCalcs$TACrec
Effort[,p,f, mm, y] <- rep(MPCalcs$Effort,nsim)[1:nsim]
FleetPars[[p]][[f]]$CB_P <- MPCalcs$CB_P # removals
FleetPars[[p]][[f]]$CB_Pret <- MPCalcs$CB_Pret # retained catch
FleetPars[[p]][[f]]$FM_P <- MPCalcs$FM_P # fishing mortality
FM_P[,p,f,,,]<- MPCalcs$FM_P
FleetPars[[p]][[f]]$FM_Pret <- MPCalcs$FM_Pret # retained fishing mortality
FMret_P[,p,f,,,]<- MPCalcs$FM_Pret
#FretA[,p,f,,]<- MPCalcs$FM_Pret
FleetPars[[p]][[f]]$Z_P <- MPCalcs$Z_P # total mortality
FleetPars[[p]][[f]]$retA_P <- MPCalcs$retA_P # retained-at-age
FleetPars[[p]][[f]]$retA_P_real <- MPCalcs$retA_P_real
FleetPars[[p]][[f]]$retA_P_real_2 <- MPCalcs$retA_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
FleetPars[[p]][[f]]$V_P <- MPCalcs$V_P # vulnerable-at-age
FleetPars[[p]][[f]]$V_P_real <- MPCalcs$V_P_real
FleetPars[[p]][[f]]$V_P_real_2 <- MPCalcs$V_P_real_2
FleetPars[[p]][[f]]$retL_P <- MPCalcs$retL_P # retained-at-length
VF[,p,f,,]<- MPCalcs$V_P
FleetPars[[p]][[f]]$SLarray_P <- MPCalcs$SLarray_P # vulnerable-at-length
FMa[,p,f,mm,y] <- MPCalcs$Ftot # Total fishing mortality (by stock & fleet)
} # end of fleets
} # end of stocks
} # end of not update year
# ---- Account for timing of spawning (if spawn_time_frac >0) ----
for (p in 1:np) {
spawn_time_frac[,p,,] <-StockPars[[p]]$spawn_time_frac
F_age <- array(0, dim=c(nsim, n_age, nareas))
for (f in 1:nf) {
F_age <- F_age + FleetPars[[p]][[f]]$FM_P[,,y,]
}
M_age <- replicate(nareas,StockPars[[p]]$M_ageArray[,,nyears+y])
Z_age <- M_age+F_age
N_Psp <- StockPars[[p]]$N_P[,,y,]
for (a in 1:n_age) {
N_Psp[,a,] <- N_Psp[,a,] * exp(-(Z_age[,a,]*spawn_time_frac[,p,a,]))
}
SSB_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Fec_Age[,,nyears+y])
SSN_P[,p,,y,] <- N_Psp * replicate(nareas,StockPars[[p]]$Mat_age[,,nyears+y])
}
# recruitment
for (p in 1:np) {
StockPars[[p]]$SSN_P <- SSN_P[,p,,,]
StockPars[[p]]$SSB_P <- SSB_P[,p,,,]
if (length(SexPars$SSBfrom)) { # use SSB from another stock to predict recruitment
SSBcurr <- local({
SSBfrom <- array(SexPars$SSBfrom[p, ], c(np, nsim, n_age, nareas)) %>%
aperm(c(2, 1, 3, 4))
apply(SSBfrom * SSB_P[,,,y,], c(1, 2), sum) # nsim x np; contribution of each stock p' to p
})
} else {
SSBcurr <- apply(SSB_P[, p, , y, ], c(1, 3), sum) # nsim x nareas
}
recdev <- StockPars[[p]]$Perr_y[, y+nyears+n_age-1]
rec_area <- sapply(1:nsim, calcRecruitment, SRrel=StockPars[[p]]$SRrel,
SSBcurr=SSBcurr,
recdev=recdev, hs=StockPars[[p]]$hs,
aR= StockPars[[p]]$aR, bR=StockPars[[p]]$bR, R0a=StockPars[[p]]$R0a,
SSBpR=StockPars[[p]]$SSBpR,
SRRfun=StockPars[[p]]$SRRfun,
SRRpars=StockPars[[p]]$SRRpars)
StockPars[[p]]$N_P[,1,y,] <- N_P[,p,1,y,] <- t(rec_area)
}
} # end of projection years
if(!silent) close(pb) # use pbapply::closepb(pb) for shiny related stuff
if (max(upyrs) < proyears) { # One more call to complete Data object
# --- Update Data object ----
upyrs2 <- upyrs[upyrs>0]
upyrs2 <- c(upyrs2, proyears)
if (length(upyrs2) < 3 && !silent) message("Updating the Data object for the final time...")
interval[mm] <- proyears
for (p in 1:np) {
for (f in 1:nf) {
OM <- suppressMessages(new('OM', docheck=FALSE))
OM@nyears <- nyears
OM@hbiascv <- MOM@Obs[[p]][[f]]@hbiascv
OM@maxF <- MOM@maxF
OM@CurrentYr <- MSElist[[1]][[1]][[1]]@LHYear
OM@reps <- MOM@reps
OM@nsim <- nsim
OM@BMSY_B0biascv <- MOM@Obs[[p]][[f]]@BMSY_B0biascv
OM@proyears <- proyears
# -- check real data mirroring across stocks ---
map.stocks <- which(Real.Data.Map[f,] ==p)
if (length(map.stocks)==0) {
# stock data have been mapped to another one
mapped.to <- Real.Data.Map[f,p]
om <- MSElist[[p]][[f]][[mm]]@OM
MSElist[[p]][[f]][[mm]] <- new('Data')
MSElist[[p]][[f]][[mm]]@OM <- om
} else {
# update MPrec (map last catch across mapped stock)
MPrec <- rep(0, nsim)
for (i in map.stocks) {
MPrec <- MPrec+ MPCalcs_list[[i]][[f]]$TACrec
}
MSElist[[p]][[f]][[mm]] <- updateData_MS(Data=MSElist[[p]][[f]][[mm]],
OM,
MPCalcs=MPCalcs_list[[p]][[f]],
Effort=Effort[,p,f,, ,drop=FALSE],
Biomass=Biomass,
N=N,
Biomass_P=Biomass_P,
CB_Pret=CBret_P,
N_P=N_P,
SSB=SSB,
SSB_P=SSB_P,
VBiomass=VBF,
VBiomass_P=VBF_P,
StockPars,
FleetPars,
ObsPars,
ImpPars,
upyrs=upyrs2,
interval=interval,
y=y,
mm=mm,
Misc=MSElist[[p]][[f]][[mm]]@Misc,
RealData=multiHist[[p]][[f]]@Data,
p, f,
map.stocks)
}
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- StockPars[[p]]
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- FleetPars[[p]][[f]]
# ---- Update true abundance ----
M_array <- array(0.5*StockPars[[p]]$M_ageArray[,,nyears+y],
dim=c(nsim, n_age, nareas))
Atemp <- apply(StockPars[[p]]$VBiomass_P[, , y, ] *
exp(-M_array), 1, sum) # Abundance (mid-year before fishing)
MSElist[[p]][[f]][[mm]]@OM$A <- Atemp
} # end of fleet
} # end of stock
}
# SSB relative to SSBMSY
SB_SBMSYa[, ,mm, ] <- apply(SSB_P, c(1,2, 4), sum, na.rm=TRUE)/array(SSBMSY_y[,,mm,(nyears+1):(nyears+proyears)],
c(nsim,np,proyears))
# for(p in 1:np) for(f in 1:nf)
# FMa[,p,f, mm, ] <- -log(1 - apply(FleetPars[[p]][[f]]$CB_P, c(1, 3), sum,
# na.rm=TRUE)/apply(VBiomass_P[,p,,,]+
# FleetPars[[p]][[f]]$CB_P,
# c(1, 3), sum, na.rm=TRUE))
for(f in 1:nf)
F_FMSYa[, ,f,mm, ] <- FMa[,,f, mm, ]/FMSY_y[,,mm,(nyears+1):(nyears+proyears)]
Ba[, ,mm, ] <- apply(Biomass_P, c(1, 2,4), sum, na.rm=TRUE) # biomass
SSBa[, ,mm, ] <- apply(SSB_P, c(1, 2,4), sum, na.rm=TRUE) # spawning stock biomass
VBa[, ,mm, ] <- apply(VBiomass_P, c(1, 2, 4), sum, na.rm=TRUE) # vulnerable biomass
N_P_mp[,,, mm,,] <- N_P
for(p in 1:np) for(f in 1:nf)
Ca[, p,f,mm, ] <- apply(FleetPars[[p]][[f]]$CB_P, c(1, 3),
sum, na.rm=TRUE) # removed
for(p in 1:np) for(f in 1:nf)
CaRet[, p,f,mm, ] <- apply(FleetPars[[p]][[f]]$CB_Pret, c(1, 3),
sum, na.rm=TRUE) # retained catch
#if (!silent) message("Calculating spawning potential ratio")
for(p in 1:np) {
SPReqa[, p, mm, ] <- CalcSPReq(FM = sapply(FleetPars[[p]], getElement, "FM_P", simplify = "array") %>% apply(1:4, sum),
StockPars_MPCalc[[p]], n_age, nareas, nyears, proyears, nsim)
SPRdyna[, p, mm, ] <- local({
FMh <- array(FM[, p, , , , ], c(nsim, nf, n_age, nyears, nareas)) %>% apply(c(1, 3:5), sum)
FMp <- sapply(FleetPars[[p]], getElement, "FM_P", simplify = "array") %>% apply(1:4, sum)
CalcSPRdyn(abind::abind(FMh, FMp, along = 3),
StockPars_MPCalc[[p]], n_age, nareas, nyears, proyears, nsim)
})
}
if (!silent) {
if (all(checkNA != nsim) & !all(checkNA == 0)) {
# print number of NAs
# message(checkNA)
# message(checkNA[upyrs])
ntot <- sum(checkNA[,,upyrs])
totyrs <- sum(checkNA[,,upyrs] >0)
nfrac <- round(ntot/(length(upyrs)*nsim),2)*100
cat("\n")
message(totyrs, ' years had TAC = NA for some simulations (', nfrac, "% of total simulations)")
message('Used TAC_y = TAC_y-1')
}
if("progress"%in%names(control))
if(control$progress)
shiny::incProgress(1/nMP, detail = round(mm*100/nMP))
}
if (length(Rel)) {
for (r in names(StockPars_MICE)) {
if (r == "Perr_y") {
StockPars_MICE[[r]][, , mm, ] <- sapply(1:np, function(p) {
StockPars_MPCalc[[p]][[r]][, maxage + nyears + 1:proyears]
}, simplify = "array") %>% aperm(c(1, 3, 2))
} else {
StockPars_MICE[[r]][, , , mm, ] <- sapply(1:np, function(p) {
StockPars_MPCalc[[p]][[r]][, , nyears + 1:proyears]
}, simplify = "array") %>% aperm(c(1, 4, 2, 3))
}
}
}
} # end of MP loop
OM<-Obsout<-list()
for(p in 1:np) {
OM[[p]]<-Obsout[[p]]<-list()
for(f in 1:nf) {
OM[[p]][[f]]<-MSElist[[p]][[f]][[1]]@OM
Obsout[[p]][[f]]<-MSElist[[p]][[f]][[1]]@Obs
# remove MSElist Misc
if (!extended) {
for (mm in 1:nMP) {
MSElist[[p]][[f]][[mm]]@Misc$StockPars <- NULL
MSElist[[p]][[f]][[mm]]@Misc$FleetPars <- NULL
MSElist[[p]][[f]][[mm]]@Misc$ReferencePoints <- NULL
}
}
}
}
Misc <- list()
# Misc$Data <-MSElist
Misc[['MOM']]<-MOM
if (length(Rel)) {
if (!silent) message("Returning updated parameters from MICE relationship in 'Misc$MICE'")
Misc[["MICE"]] <- StockPars_MICE # Update for potential values updated by MICE
}
if (extended) {
if (!silent) message("Returning complete abundance series in 'Misc$extended$N")
Misc[["extended"]] <- list(
N = local({
histN <- replicate(nMP, StockPars[[1]]$N) %>% aperm(c(1,2,3,6,4,5)) # nsim x np x n_age x nyears x nareas x nMP
abind::abind(histN, N_P_mp, along=5) # nsim x np x n_age x nMP x proyears x nareas
})
)
}
# need to reformat MMP and complex mode to work with MSEout slot
if(methods::is(MPs,"character")) MPs<-list(MPs)
# ---- Create MMSE Object ----
# add all reference points from multiHist to MMSE@RefPoint
# and remove from multiHist
# this is done so MMSE@multiHist can be dropped to save a ton of memory
# while still preserving ref points in the MMSE object
RefPoint <- list()
RefPoint$ByYear <- list(MSY=MSY_y,
FMSY=FMSY_y,
SSBMSY=SSBMSY_y,
BMSY=BMSY_y,
VBMSY=VBMSY_y)
# add other ref points
nms <- names(multiHist[[1]][[1]]@Ref$ByYear)
nms <- nms[!nms %in% names( RefPoint$ByYear)]
# drop SPR, Fcrash etc - not updated in projection and could change if selectivity changes
nms <- c(nms[grepl('0', nms)], 'h')
nms <- nms[!nms=="F01_YPR"]
for (nm in nms) {
RefPoint$ByYear[[nm]] <- array(NA, dim=c(nsim, np, nMP, proyears+nyears))
for (p in 1:np) {
for(mm in 1:nMP) {
RefPoint$ByYear[[nm]][,p,mm,] <- multiHist[[p]][[1]]@Ref$ByYear[[nm]]
}
}
}
RefPoint$Dynamic_Unfished <- list()
nms <- names(multiHist[[1]][[1]]@Ref$Dynamic_Unfished)
# add Dynamic_Unfished
### NOTE: Should be updated by MICE rel, e.g., M/Perr_y/growth have changed in the projection !
for (nm in nms) {
RefPoint$Dynamic_Unfished[[nm]] <- array(NA, dim=c(nsim, np, proyears+nyears))
for (p in 1:np) {
RefPoint$Dynamic_Unfished[[nm]][,p,] <- multiHist[[p]][[1]]@Ref$Dynamic_Unfished[[nm]]
}
}
# remove Ref from MultiHist
for (p in 1:np) {
for(f in 1:nf) {
multiHist[[p]][[f]]@Ref <- list('Now in MMSE@RefPoint')
}
}
if (dropHist) {
multiHist <- list('multiHist dropped (dropHist=TRUE). Reference points available in MMSE@RefPoint')
}
MSEout <- new("MMSE",
Name = MOM@Name,
nyears,
proyears,
nMPs=nMP,
MPs=MPs,
MPcond=MPcond,
MPrefs=MPrefs,
nsim,
nstocks=np,
nfleets=nf,
Snames=Snames,
Fnames=Fnames,
Stocks=Stocks,
Fleets=Fleets,
Obss=Obs,
Imps=Imps,
OM=OM,
Obs=Obsout,
SB_SBMSY=SB_SBMSYa,
F_FMSY=F_FMSYa,
N=N_P_mp, # apply(N_P_mp, c(1,2,4,5), sum),
B=Ba,
SSB=SSBa,
VB=VBa,
FM=FMa,
SPR=list(Equilibrium = SPReqa, Dynamic = SPRdyna),
Catch=CaRet,
Removals=Ca,
Effort = Effort,
TAC=TACa,
TAE=TAE_out,
BioEco=list(),
RefPoint=RefPoint,
multiHist=multiHist,
PPD=MSElist,
Misc=Misc)
MSEout
}
#' Run a multi-fleet multi-stock Management Strategy Evaluation
#'
#' Functions for running a multi-stock and/or multi-fleet Management
#' Strategy Evaluation (closed-loop simulation) for a specified operating model
#'
#' @param MOM A multi-fleet multi-stock operating model (class [MSEtool::MOM-class])
#' @param MPs A matrix of methods (nstock x nfleet) (character string) of class MP
#' @param Hist Should model stop after historical simulations? Returns a list
#' containing all historical data
#' @param silent Should messages be printed out to the console?
#' @param parallel Logical or a named list. Should MPs be run using parallel processing? See Details for more information.
#' @param checkMPs Logical. Check if the specified MPs exist and can be run on `SimulatedData`?
#' @param dropHist Logical. Drop the (very large) `multiHist` object from the returned `MMSE` object?
#' The `multiHist` object can be (re-)created using `SimulateMOM` or kept in `MMSE@multiHist` if
#' `dropHist=FALSE`
#' @param extended Logical. Return extended projection results?
#' if TRUE, `MMSE@Misc$extended` is a named list with extended data
#' (including historical and projected abundance by area).
#' @describeIn multiMSE Run a multi-stock, multi-fleet MSE
#'
#' @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.
#
#' @return Functions return objects of class `MMSE` and `multiHist`
#' #' \itemize{
#' \item SimulateMOM - An object of class `multiHist`
#' \item ProjectMOM - An object of class `MMSE`
#' \item multiMSE - An object of class `MMSE`
#' }
#' @author T. Carruthers and A. Hordyk
#' @export
multiMSE <- function(MOM=MSEtool::Albacore_TwoFleet,
MPs=list(list(c("AvC","DCAC"),c("FMSYref","curE"))),
Hist=FALSE,
silent=FALSE,
parallel=TRUE,
checkMPs=FALSE,
dropHist=TRUE,
extended=FALSE) {
# ---- Initial Checks and Setup ----
if (methods::is(MOM,'MOM')) {
if (MOM@nsim <=1) stop("MOM@nsim must be > 1", call.=FALSE)
} else if ('multiHist' %in% class(MOM)) {
stop("You must specify an operating model of class `MOM`")
# if (!silent) message("Using `multiHist` object to reproduce historical dynamics")
#
# # --- Extract cpars from Hist object ----
# nstocks <- length(MOM)
# nfleets <- length(MOM[[1]])
#
# cpars <- list()
# cpars[[1]] <- list()
#
# for (s in 1:nstocks) {
# for (f in 1:nfleets) {
# cpars[[s]][[f]] <- c(MOM[[s]][[f]]@SampPars$Stock,
# MOM[[s]][[f]]@SampPars$Fleet,
# MOM[[s]][[f]]@SampPars$Obs,
# MOM[[s]][[f]]@SampPars$Imp,
# MOM[[s]][[f]]@OMPars,
# MOM[[s]][[f]]@OM@cpars)
# }
# }
#
# # --- Populate a new OM object ----
# newMOM <- MOM[[1]][[1]]@Misc$MOM
# newMOM@cpars <- cpars
# MOM <- newMOM
} else {
stop("You must specify an operating model of class `MOM`")
}
if (checkMPs) {
allMPs <- unique(unlist(MPs))
CheckMPs(MPs=allMPs, silent=silent)
}
multiHist <- SimulateMOM(MOM, parallel, silent)
if (Hist) {
if(!silent) message("Returning historical simulations")
return(multiHist)
}
if(!silent) message("Running forward projections")
MSEout <- try(ProjectMOM(multiHist=multiHist, MPs, parallel, silent, checkMPs=FALSE,
dropHist=dropHist,extended=extended), silent=TRUE)
if (methods::is(MSEout,'try-error')) {
message('The following error occured when running the forward projections: ',
crayon::red(attributes(MSEout)$condition))
message('Returning the historical simulations (class `multiHist`). To avoid re-running spool up, ',
'the forward projections can be run with ',
'`ProjectMOM(multiHist, MPs, ...)`')
return(multiHist)
}
MSEout
}
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