scenario_low <- create_scenario(h = 0.6, "Low, h=0.6")
scenario_high <- create_scenario(h = 0.9, "High, h=0.9")
scenarios <- list(scenario_low, scenario_high)
ref_low <- scenario_low$ref_points
ref_high <- scenario_high$ref_points
# Yield curves
p_yield <- plot_yield_comparison(scenarios, colors = c("blue", "red")) +
labs(title = "Productivity shifts move Fmsy and yield")
# Risk calibration (small table rendered as text)
target_risk <- 0.05
b0_frac <- 0.2
n_sims_risk <- 200
seed_low <- 2200
seed_high <- 3200
calc_terminal_b0_risk <- function(scenario, F_target, B0, n_sims, seed_base, b0_frac) {
params <- scenario$params
n_years <- params$n_years
below <- logical(n_sims)
for (i in 1:n_sims) {
sim <- run_simulation_hcr(
params,
F_target = F_target,
B0 = B0,
catch_cap = NULL,
seed = seed_base + i
)
below[i] <- sim$SSB[n_years] < (b0_frac * B0)
}
mean(below)
}
combined_b0_risk <- function(F_target) {
risk_low <- calc_terminal_b0_risk(
scenario_low,
F_target,
ref_low$SSB0,
n_sims_risk,
seed_low,
b0_frac
)
risk_high <- calc_terminal_b0_risk(
scenario_high,
F_target,
ref_high$SSB0,
n_sims_risk,
seed_high,
b0_frac
)
mean(c(risk_low, risk_high))
}
F_grid <- seq(0.05, 1.0, by = 0.05)
risk_grid <- sapply(F_grid, combined_b0_risk)
if (target_risk <= min(risk_grid)) {
Ftarget <- F_grid[which.min(risk_grid)]
} else if (target_risk >= max(risk_grid)) {
Ftarget <- F_grid[which.max(risk_grid)]
} else {
ordered <- data.frame(F_grid, risk_grid)[order(risk_grid), ]
Ftarget <- approx(ordered$risk_grid, ordered$F_grid, xout = target_risk)$y
}
risk_label <- paste0("Calibrated Ftarget = ", round(Ftarget, 3))
p_yieldGraphical Abstract
Risk‑calibrated HCR under productivity shifts
Question: Can a single transparent HCR remain robust when steepness changes?
Approach: Two steepness regimes (h = 0.6, 0.9) in a pollock‑like model; calibrate one Ftarget so mean risk P(SSB < 0.2 B0) = 0.05 across scenarios.
Result: A single risk‑calibrated Ftarget provides consistent biomass protection across steepness regimes while preserving catch potential in high productivity states.
Implication: A transparent, SPR‑based HCR can be made robust without time‑varying covariates.