2. HCRs and MSEs: an introduction

Fitting OMs

An example MSE using the northern rockfish stock assessment.

library(Rceattle)
data("NorthernRockfish2022")
nrdata <- NorthernRockfish2022
nrdata$fleet_control$proj_F_prop <- 1

om <- Rceattle::fit_mod(
  data_list = nrdata,
  estimateMode = 0,  # Estimate
  initMode = 1,      # Assume unfished equilibrium
  M1Fun = build_M1(
    M1_model = "sex_age_invariant",
    linkages = list(
      log_M1 = linkage_spec(
        formula = ~ 1,
        priors = list(
          "(Intercept)" = normal(log(0.06), 0.05)
        )
      )
    )
  ),
  fit_control = fit_control(phase = TRUE)
)

Fitting EMs (Tier 3 HCR)

We can use the HCR = build_hcr() argument in fit_mod to specify the HCR and BRPs of our management strategy. A variety of HCRs and BRPs are included and can be found by ?build_hcr.

em <- Rceattle::fit_mod(
  data_list = nrdata,
  estimateMode = 0, # Estimate
  initMode = 1,      # Assume unfished equilibrium
  M1Fun = build_M1(
    M1_model = "sex_age_invariant",
    linkages = list(
      log_M1 = linkage_spec(
        formula = ~ 1,
        priors = list(
          "(Intercept)" = normal(log(0.06), 0.05)
        )
      )
    )
  ),
  HCR = build_hcr(HCR = "NPFMC", # Tier3 HCR
                  Ftarget = 0.4, # F40%
                  Flimit = 0.35, # F35%
                  Plimit = 0.2,  # No fishing when SB<SB20
                  Alpha = 0.05),
  fit_control = fit_control(verbose = 1, phase = TRUE)
)

Running MSEs

mse1 <- run_mse(
  om = om, em = em,
  nsim = 10,
  assessment_period = 2,
  # Fishery = fleet 1 (annual), BTS = fleet 2 (biannual)
  sampling_period = c(1, 2),
  seed = 666 # default; per-sim seed = seed + sim
)

Reproducibility

run_mse() accepts two seed arguments, both with default seed = 666:

seed controls the random draws inside each simulated trajectory (recruitment, observation error, composition resampling). Each simulation worker calls set.seed(seed + sim), so simulation 1 uses seed 667, simulation 2 uses 668, and so on. Re-running the same run_mse() call with the same seed reproduces the trajectory bit-for-bit.
regenerate_seed (defaults to seed) controls the optional regenerate_past = TRUE step that re-fits the EM to OM-simulated historical data. Set this separately if you want to vary past-data realizations while holding the projection draws fixed.

The MSE uses a PSOCK cluster (parallel::parLapply) by default, so worker processes are clean and only see the explicit objects shipped by clusterExport() — there is no leakage from the parent’s .GlobalEnv RNG state. As a result the seed plumbing above is the only thing that controls reproducibility: there is no need to call set.seed() before run_mse() for the simulation results to be deterministic. (You may still want set.seed() for any plotting code that draws random colours or jitter.)

For management-facing runs, record packageVersion("Rceattle") and the seed alongside the saved .rds so a later reviewer can rerun the exact same trajectories from the same package tag.

Evaluating MSEs via plots

plot_depletionSSB(lapply(mse1, function(x) x$OM))

plot_depletionSSB(
  c(mse1$Sim_1$EM,           # EMs
    list(mse1$Sim_1$OM,      # OM
         mse1$Sim_1$OM_no_F  # OM w/ no fishing
    )),    
  line_col = c(
    paste0("grey", round(seq(80, 50, length.out = 15))),
    1, 3
  )
)

Summary statistics

mse_summ <- mse_summary(mse1)
knitr::kable(mse_summ, digits = 2)

Alternative scenarios

Different BRPs

em2 <- Rceattle::fit_mod(
  data_list = nrdata,
  estimateMode = 0, # Estimate
  initMode = 1,      # Assume unfished equilibrium
  M1Fun = build_M1(
    M1_model = "sex_age_invariant",
    linkages = list(
      log_M1 = linkage_spec(
        formula = ~ 1,
        priors = list(
          "(Intercept)" = normal(log(0.06), 0.05)
        )
      )
    )
  ),
  HCR = build_hcr(HCR = "NPFMC", # Tier3 HCR
                  Ftarget = 0.30,# F40%
                  Flimit = 0.10, # F35%
                  Plimit = 0,    # No limit
                  Alpha = 0.1),
  fit_control = fit_control(verbose = 1, phase = TRUE)
)

mse2 <- run_mse(
  om = om, em = em2, 
  nsim = 10, 
  assessment_period = 2,
  sampling_period = c(1, 2) # Fishery = fleet 1, BTS = fleet 2
)

Triannual BTS

mse3 <- run_mse(
  om = om, em = em, 
  nsim = 10, 
  assessment_period = 2,
  sampling_period = c(1, 3) # Fishery = fleet 1, BTS = fleet 2
)

4-year cycle and 50% sampling

mse4 <- run_mse(
  om = om, em = em, 
  nsim = 10, 
  fut_sample = 0.5,
  assessment_period = 4,
  sampling_period = c(1, 2) # Fishery = fleet 1, BTS = fleet 2
)

Climate MSE

Climate linked OM


# Fit OM
om2 <- Rceattle::fit_mod(
  data_list = nrdata,
  estimateMode = 0,  # Estimate
  initMode = 1,      # Assume unfished equilibrium
  M1Fun = build_M1(
    M1_model = "sex_age_invariant",
    linkages = list(
      log_M1 = linkage_spec(
        formula = ~ 1,
        priors = list(
          "(Intercept)" = normal(log(0.06), 0.05)
        )
      )
    )
  ),
  recFun = build_srr(
    srr_fun = "mean",    # Mean R with covariates
    linkages = list(
      log_R0 = linkage_spec(formula = ~ Temp)
    )),
  fit_control = fit_control(phase = TRUE)
)

plot_biomass(om2, incl_proj = TRUE)

Run MSE

mse5 <- run_mse(
  om = om2, em = em, 
  nsim = 10, 
  fut_sample = 0.5,
  assessment_period = 4,
  sampling_period = c(1, 2) # Fishery = fleet 1, BTS = fleet 2
)

Overall summary

mse_summ$MSE = 1
mse_summ2 <- mse_summary(mse2); mse_summ2$MSE = 2

knitr::kable(do.call("rbind", list(mse_summ, mse_summ2)) |>
               dplyr::relocate(MSE), digits = 2)

Grant Adams