optim.stockSim: Optimization of fishing mortality

In marchtaylor/fishdynr: Fisheries science related population dynamics models

Description

Sets up an exploration of optimum fishing policy using the function stockSim (i.e. optimal time series of fishing mortalities, Ft, in order to maximize time series yield, Yt). The routine is described in further detail in Walters and Martell (2004).

Usage

optim.stockSim(Ft = 0, params, nyears = 100, env_at = 1,
  env_bt = 1, opt = "sum", disc.rate = 0.02)

Arguments

Ft	time series vector for fishing mortality. If a single value, then the function assumes a constant fishing mortality for the entire simulation (default=0)
params	List of parameters to for the population. Applies a single cohort simulation for the initial population state using cohortSim function. See stockSim for details.
nyears	number of years in the simulation
env_at	time series vector for environmental effects to maximum recruitment (e.g. srrFecBH_a in srrFecBH) (default=1).
env_bt	time series vector for environmental effects to maximum recruitment (e.g. srrFecBH_b in srrFecBH) (default=1).
opt	what should be optimized ("sum", "sum.log", "sum.disc")
disc.rate	discount rate (default = 0.02)

References

Walters, C. J., Martell, S. J., 2004. Fisheries ecology and management. Princeton University Press.

Examples

set.seed(1)
data(tilapia)
params <- tilapia
params$knife_edge_size <- 20
params$N0 <- 1e9
nyears <- 50
Ft <- rep(0.5, nyears)
set.seed(1)
env_at <- runif(nyears, min=0.5, max=1.5)
env_bt <- rep(1, nyears); env_bt[20:35] <- 0.5


# Optimization of Ft (will take some time to reach cost function mimimum)
out <- optim(
  par = Ft,  # initial guess
  fn = optim.stockSim,
  params = params,
  nyears = nyears,
  env_at = env_at,
  env_bt = env_bt,
  opt = "sum.log", # optimize the sum of log yield
  method = "L-BFGS-B",
  lower = 0,
  upper = 2,
  control = list(fnscale=-1, trace=4)
)

# optimum Ft series
plot(out$par, t="l")

# optimum Yt series
tmp <- stockSim(params, nyears=nyears, Ft=out$par, env_at=env_at, env_bt=env_bt)
plot(Yt ~ t, tmp, t="l")
sum(tmp$Yt/1e6, na.rm=TRUE)

# optimum yield versus stock biomass
plot(Yt ~ Bt, tmp)
fit <- lm(Yt ~ Bt, tmp)
abline(fit)
summary(fit)

op <- par(mar=c(4,4,1,4))
plot(Bt ~ t, tmp, t="l", ylim=c(0, max(tmp$Bt)))
par(new=TRUE)
plot(Yt ~ t, tmp, col=2, t="l", axes=FALSE, xlab="", ylab="")
axis(4, col=2)
mtext("Yt", side=4, line=3, col=2)
par(op)

marchtaylor/fishdynr documentation built on May 21, 2019, 11:27 a.m.