paradox_pe_sim: Simulate biomass and effort trajectories
In jdyeakel/paradox: Portfolio effect paradox

Description Usage Arguments Value Author(s) Examples

Simulate biomass and effort trajectories

  paradox_pe_sim(t_end = 1000L, num_pop = 10L,
    alpha = numeric(), beta = 1/150, m = 0.01, n = 1,
    sigma = 0.05, q = 0.01, cpar = 1.4, p = 0.5,
    effort_init = 10, biomass_init = 50, vuln_thresh = 0.1,
    burnin = 500L, return_ts = FALSE)

`t_end`	Number of time steps to simulate over
`num_pop`	Number of populations
`alpha`	alpha parameter in the Shepherd stock-recruit function (density-independent growth). Entered as a numeric vector with one alpha per population.
`beta`	beta parameter in the Shepherd stock-recruit function (capacity parameter)
`m`	Natural mortality
`n`	n parameter in the Shepherd stock-recruit function. Controls compensation level.
`sigma`	Standard deviation on the simulated alpha values
`q`	Effectiveness parameter in the effort function
`cpar`	Operational costs parameter in the effort function
`p`	Intrinsic value of the fishery parameter in the effort function
`effort_init`	Initial effort.
`biomas_init`	Initial population biomass. Recycled across all populations.
`vuln_threshold`	The vulnerability threshold. A proportion (between 0 and 1) indicating the fraction of the mean biomass of a subpopulation before that subpopulation is declared "vulnerable".
`burnin`	The number of years to discard as burnin.
`return_ts`	Logical indicating whether the time series should be returned as part of the output. `FALSE` by default to save memory if running many repetitions.

A list object. $performance contains the performance attributes. If return_ts = TRUE: $biomass contains the biomass matrix (time is incremented along the columns and populations down the rows without the burnin period removed); $effort contains the effort in a numeric vector.

Original model developed by Justin Yeakel. C++ version originally ported by Sean Anderson.

out <- paradox_pe_sim(alpha = rep(0.5, 10), return_ts = TRUE)
names(out)
print(out$performance)
op <- par(mfrow = c(2, 1), mar = c(4, 4, .5, .5))
matplot(t(out$biomass[, -c(1:500)]), type = "l", lty = 1, xlab = "Year",
  ylab = "Biomass")
plot(out$effort[-c(1:500)], type = "l", ylab = "Effort", xlab = "Year")
par(op)