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SSMSE: Management Strategy Evaluation for Stock Synthesis

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SSMSE build status

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Current support level for SSMSE

SSMSE is not currently being actively developed. SSMSE is being maintained for bugs fixes only - bug fixes will be addressed, but suggestions for new features and enhancements will not. Changes in staffing in the future could support more active development.

Disclaimer

“The United States Department of Commerce (DOC) GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.”

Motivation for developing SSMSE

This package was developed to increase the ease of using the stock assessment software Stock Synthesis (SS3) directly as an operating model (OM) in Management Strategy Evaluation (MSE). The approach requires a conditioned Stock Synthesis mode to use as the OM. The intended users are those who conduct MSEs.

Below, we’ll work through a simple example MSE as a way of introducing the SSMSE package. An additional example is included in the Journal Of Open Source Software SSMSE paper.

How to cite SSMSE

Doering et al., (2023). SSMSE: An R package for Management Strategy Evaluation with Stock Synthesis Operating Models. Journal of Open Source Software, 8(90), 4937, https://doi.org/10.21105/joss.04937

Need Help?

Installing the SSMSE R package

To install SSMSE from github:

remotes::install_github("nmfs-fish-tools/SSMSE")

You can read the help files with

?SSMSE

Note that when SSMSE is installed, Stock Synthesis binaries are included in the SSMSE/bin/ folder downloaded to the user’s local R library. Currently, the Stock Synthesis 3.30.18 is included. Separate binaries are included for Windows, Mac and Linux. Users may swap out these binaries, but SSMSE is not tested with other versions of SS3.

Troubleshooting Installation

Here are some tips:

Still having trouble installing SSMSE? Please don’t hesitate to post in discussions about the issue.

An SSMSE example

Suppose we want to look at how well we are able to achieve a performance metric under uncertainty in the operating model (OM). We will look 2 scenarios, one where Steepness (h) is specified correctly and one where it is specified incorrectly in an estimation model (EM):

Scenario 1. h-ctl: Cod operating model (h = 0.65) with correctly specified cod model EM (fixed h = 0.65). The OM is the same as the EM.

Scenario 2. h-1: Cod operating model (h = 1) with misspecified cod model EM (fixed h = 0.65); The OM is not the same as the EM.

Note that this is a simple example where the OM and EM structures for both scenarios are identical, except for different steepness between the OM and EM in scenario 2 and some process error we will include in the operating model. We will assume we want to run the MSE loop for 6 years, with a stock assessment occuring every 3 years (and forecasting catch to maintain 40% of unfished spawning stock biomass). The cod model’s last year is 100, so the OM is initially conditioned through year 100. Then, after conditioning the operating model through year 100, assessments will occur in years 100 and 103. The operating model runs through year 106. We chose not to run the assessment in year 106, as there was no need for its output in this example.

Setup R workspace folders

First, we will load the SSMSE package and create a folder in which to run the example:

library(SSMSE) # load the package
## Welcome to SSMSE version 0.2.8 using Stock Synthesis 3.30.18.
library(r4ss) # install using remotes::install_github("r4ss/r4ss)
## 
## Attaching package: 'r4ss'
## The following object is masked from 'package:stats':
## 
##     profile
## The following object is masked from 'package:base':
## 
##     jitter
# library(foreach) #if using run_parallel = TRUE
# library(doParallel) #if using run_parallel = TRUE

# Create a folder for the output in the working directory.
run_SSMSE_dir <- file.path("run_SSMSE-ex")
dir.create(run_SSMSE_dir)

Create the operating models (OMs)

Specify alternative values for h

The cod model with h = 0.65 (as in scenario 1) is included as external package data in SSMSE. However, we will need to modify it to use as an operating model with h = 1 (as in scenario 2). Note in this case that refit_OM is false, so the model is not being refit, just run through without fitting. To condition the new model on the same data as the input model, refit_OM should be TRUE.

First, we identify where the base cod model is stored, modify it such that the steepness parameter is 1, and save the modified cod OM for scenario 2 in a new folder in the run_SSMSE_dir directory.

cod_mod_path <- system.file("extdata", "models", "cod", package = "SSMSE")
# develop_OMs will save a model called "cod_SR_BH_steep_1" in the out_dir
# specified
develop_OMs(
  OM_name = "cod", out_dir = run_SSMSE_dir, par_name = "SR_BH_steep",
  par_vals = 1, refit_OMs = FALSE, hess = FALSE
)
# OM model for scenario 2
cod_1_path <- file.path(run_SSMSE_dir, "cod_SR_BH_steep_1")

Adding process error through recruitment deviations and time-varying selectivity

Recruitment deviations, implementation error, and changes in parameters in the projection period of the OM can be added through the future_om_list input to run_SSMSE.

First, we’ll set up the list to add recruitment deviations in the projection period. The same recruitment deviation patterns are used across scenarios, but different patterns are use across iterations in the same scenario. We also want these deviations to have the same standard deviations as the historical deviations with 0 mean (the assumed default).

# Start from a list created by a helper function
template_mod_change <- create_future_om_list()
# add recruitment deviations
rec_dev_specify <- template_mod_change[[1]]
rec_dev_specify$pars <- "rec_devs" # apply change to rec devs
rec_dev_specify$scen <- c("replicate", "all")
# using 1 to 100 means the sd or mean will be calculated by taking the sd across years
# from 1 to 100
rec_dev_specify$input$first_yr_averaging <- 1
rec_dev_specify$input$last_yr_averaging <- 100
# The following 2 lines suggest that this change is immediately applied in year
# 101, with no transitory period for using sd 0 to the new sd.
rec_dev_specify$input$last_yr_orig_val <- 100
rec_dev_specify$input$first_yr_final_val <- 101
rec_dev_specify$input$ts_param <- "sd" # this change is for the sd
# no input value needed since it will be calclated from the historical rec devs.
rec_dev_specify$input$value <- NA
rec_dev_specify
## $pars
## [1] "rec_devs"
## 
## $scen
## [1] "replicate" "all"      
## 
## $pattern
## [1] "model_change"
## 
## $input
##   first_yr_averaging last_yr_averaging last_yr_orig_val first_yr_final_val
## 1                  1               100              100                101
##   ts_param   method value
## 1       sd absolute    NA

Next, suppose we want to allow selectivity to vary annually for 1 selectivity parameter of the fishery throughout the projection period. The following specifies that the value for selectivity varies randomly around the base value with a sd of 0.2.

# put together the change for selectivity (random values around the orig val, with
# an sd of 0.2)
mod_change_sel <- template_mod_change[[1]]
mod_change_sel$scen[2] <- "all" # apply to all scenarios
# The following 2 lines suggest that this change is immediately applied in year
# 101, with no transitory period for using sd 0 to the new sd.
# historical values are NA in this case, because they are not used to determine
# the sd to use.
mod_change_sel$input$last_yr_orig_val <- 100
mod_change_sel$input$first_yr_final_val <- 101
mod_change_sel$input$ts_param <- "sd" # this change is for the sd
mod_change_sel$input$value <- 0.2 # se to use in the projection period
mod_change_sel
## $pars
## [1] "SizeSel_P_3_Fishery(1)"
## 
## $scen
## [1] "replicate" "all"      
## 
## $pattern
## [1] "model_change"
## 
## $input
##   first_yr_averaging last_yr_averaging last_yr_orig_val first_yr_final_val
## 1                 NA                NA              100                101
##   ts_param   method value
## 1       sd absolute   0.2

Finally, add these two changes together into an object to pass to run_SSMSE

future_om_list_recdevs_sel <- list(
  rec_dev_specify,
  mod_change_sel
)

Add observation error through sampling from OM

The argument sample_struct specifies the structure for sampling from the OM (and passing to the EM). The function create_sample_struct can be used to construct a simple sampling structure consistent with an input data file:

datfile <- system.file("extdata", "models", "cod", "ss3.dat", package = "SSMSE")
sample_struct_1_scen <- create_sample_struct(dat = datfile, nyrs = 6) # note warning
## Warning in FUN(X[[i]], ...): Pattern not found for lencomp: FltSvy 1, Seas 1.
## Returning NA for Yr in this dataframe.
sample_struct_1_scen
## $catch
##    Yr Seas FltSvy    SE
## 1 101    1      1 0.005
## 2 102    1      1 0.005
## 3 103    1      1 0.005
## 4 104    1      1 0.005
## 5 105    1      1 0.005
## 6 106    1      1 0.005
## 
## $CPUE
##    Yr Seas FltSvy  SE
## 1 105    7      2 0.2
## 
## $lencomp
##   Yr Seas FltSvy Sex Part Nsamp
## 1 NA    1      1   0    0   125
## 
## $agecomp
##    Yr Seas FltSvy Sex Part Ageerr Lbin_lo Lbin_hi Nsamp
## 1 105    1      2   0    0      1      -1      -1   500
## 
## $meanbodywt
## [1] NA
## 
## $MeanSize_at_Age_obs
## [1] NA

By default, create_sample_struct identifies sampling patterns from the historical period of the OM and replicates those patterns in the projection period. In our cod example, the sample structure specifies that catch will be added to the estimation model every year (years 101 to 106), but an index of abundance (i.e., CPUE) and age composition (i.e., agecomp) will only be added in year 105. We will use the same sampling scheme for both scenarios, but it is possible to specify different sampling for each scenario. The user could modify this sampling strategy (for example, maybe age composition should also be sampled from FltSvy 2 in Yr 102; the user could add another line to the dataframe in sample_struct$agecomp).

Note that length comp (lencomp) includes an NA value for year. This is because no consistent pattern was identified, so the user must define their own input. In this case, we will remove sampling length comps all together:

sample_struct_1_scen$lencomp <- NULL # don't use length sampling

The same sampling structure will be used for both scenarios, which we define in a list below:

sample_struct_list_all <- list("h-ctl" = sample_struct_1_scen, "h-1" = sample_struct_1_scen)

Examine the management procedure used

We will use the same management procedure for both scenarios:

  1. Conduct a stock assessment every 3 years to get stock status.
  2. Project from this stock assessment using the SS3 forecast file to get projected future catch.
  3. Put this projected catch (without implementation error, in the case of this example) back into the OM. Extend the OM forward in time to get the true values for the population.

Let’s take a look at step 2 in the management procedure, which is implemented using the forecasting module in SS. We will examine the forecast file for the estimation model to better understand how catches will be forecasted from the assessment. We will use the same management procedure in both of these scenarios, although for a full MSE analysis, it is likely that multiple management procedures would be compared.

fore <- r4ss::SS_readforecast(
  system.file("extdata", "models", "cod", "forecast.ss", package = "SSMSE"),
  verbose = FALSE
)
fore$Forecast
## [1] 3
fore$Btarget
## [1] 0.4

fore$Forecast = 3 means our forecasts from the assessment will use fishing mortality (F) to attmpt to achieve a relative (to unfished) spawning stock biomass. Based on fore$Btarget, the relative biomass target is 40% of unfished spawning stock biomass. Note also that the control rule fore$BforconstantF and fore$BfornoF values are set low to make it unlikely that they will be used (these parameters are used for a ramp harvest control rule, which we do not want to use here):

fore$BforconstantF
## [1] 0.03
fore$BfornoF
## [1] 0.01

Futhermore, fore$Flimitfraction is set to 1 so that the forecasted catch is set equal to the overfishing limit (for simplicity):

fore$Flimitfraction
## [1] 1

Note that the number of forecast years is 1:

fore$Nforecastyrs
## [1] 1

However, an assessment will be conducted every 3 years and thus 3 years of projections is required. SSMSE will automatically modify this value in the estimation model to the appropriate number of forecasting years.

More information on using the forecast module in SS3 to forecast catches is available in the Stock Synthesis users manual.

Users can also specify their own custom management procedures.

Run SSMSE

Now, we create a directory to store our results, and use run_SSMSE to run the MSE analysis loop (note this will take some time to run, ~ 20 min):

run_res_path <- file.path(run_SSMSE_dir, "results")
dir.create(run_res_path)
res <- run_SSMSE(
  scen_name_vec = c("h-ctl", "h-1"), # name of the scenario
  out_dir_scen_vec = run_res_path, # directory in which to run the scenario
  iter_vec = c(5, 5), # run with 5 iterations each
  OM_name_vec = NULL, # specify directories instead
  OM_in_dir_vec = c(cod_mod_path, normalizePath(cod_1_path)), # OM files
  EM_name_vec = c("cod", "cod"), # cod is included in package data
  MS_vec = c("EM", "EM"), # The management strategy is specified in the EM
  nyrs_vec = c(6, 6), # Years to project OM forward
  nyrs_assess_vec = c(3, 3), # Years between assessments
  future_om_list = future_om_list_recdevs_sel,
  run_parallel = FALSE, # Run iterations in parallel
  sample_struct_list = sample_struct_list_all, # How to sample data for running the EM.
  sample_struct_hist_list = NULL, # because this is null, will just use sampling
  # as in the current OM data file for the historical period.
  seed = 12345
) # Set a fixed integer seed that allows replication

See ?run_SSMSE for more details on function arguments. In a real MSE analysis, running 100+ iterations to reflect the full range of uncertainty (given observation and process errors) in the results would be preferred. However, we are only running 5 iterations per scenario in this demonstration to reduce computing time.

run_SSMSE output

run_SSMSE will create new folders in the folders specified in out_dir_scen_vec (note that in this case, we are running both scenarios in the same folder). After is complete, there will be a folder for each scenario in run_res_path (since out_dir_scen_vec = run_res_path in this example):

list.dirs(run_res_path, recursive = FALSE)
## [1] "run_SSMSE-ex/results/h-1"   "run_SSMSE-ex/results/h-ctl"

Within each scenario is a folder for each scenario:

# See folders for scenario 1.
list.dirs(file.path(run_res_path, "h-ctl"), recursive = FALSE)
## [1] "run_SSMSE-ex/results/h-ctl/1" "run_SSMSE-ex/results/h-ctl/2"
## [3] "run_SSMSE-ex/results/h-ctl/3" "run_SSMSE-ex/results/h-ctl/4"
## [5] "run_SSMSE-ex/results/h-ctl/5"

And within each scenario folder, there are folders containing the SS3 models that were run by run_SSMSE.

# see folders for the first iteration of scenario 1
list.dirs(file.path(run_res_path, "h-ctl", "1"), recursive = FALSE)
## [1] "run_SSMSE-ex/results/h-ctl/1/cod_EM_103" 
## [2] "run_SSMSE-ex/results/h-ctl/1/cod_EM_init"
## [3] "run_SSMSE-ex/results/h-ctl/1/cod_OM"

There should be 1 folder for the OM, which is run multiple times in this same folder during the MSE analysis. There are multiple folders for the EMs, as a new folder is created each time an assessment is done. The first run is the folder with a name ending in init; then, each assessment after is named for the updated end year of the model.

With many iterations, the number of files adds up; in the future, we hope to add options to save less output.

Performance metrics

Quantitative performance metrics should be specified before conducting an MSE. Typically, a suite of performance metrics will be examined; however, for simplicity in this example, we will only look at what the achieved relative biomass was for the last 3 years of projection in the MSE to determine how it compares to the intended management target of 40% of unfished Spawning Stock Biomass. Note that we are only running our MSE projection for 6 years, but longer projections are typical in MSE analyses.

Summarize results

The function SSMSE_summary_all can be used to summarize the model results in a list of 3 dataframes, one for scalar outputs (named scalar), one for timeseries outputs (ts), one for derived quantities (dq). This function also creates summary csv files in the folder where the results are stored.

# Summarize 1 iteration of output
summary <- SSMSE_summary_all(run_res_path)
## Extracting results from 2 scenarios
## Starting h-1 with 5 iterations
## Starting h-ctl with 5 iterations

Plotting and data manipulation can then be done with these summaries. For example, SSB over time by model can be plotted. The models include the Operating Model (cod_OM), Estimation model (EM) for the historical period of years 0-100 (cod_EM_init), and the EM run with last year of data in year 103 (cod_EM_103). The operating models are shown in blue or black (depending on the scenario), and the estimation model runs are shown in orange, and the scenarios are shown on different subplots:

library(ggplot2) # use install.packages("ggplot2") to install package if needed
library(tidyr) # use install.packages("tidyr") to install package if needed
library(dplyr) # use install.packages("dplyr") to install package if needed
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Simple Convergence Check

Check there are no params on bounds or SSB that is way too small or way too large

check_convergence <- function(summary, min_yr = 101, max_yr = 120, n_EMs = 5) {
  require(dplyr) # note: not the best way to do this
  if (any(!is.na(summary$scalar$params_on_bound))) {
    warning("Params on bounds")
  } else {
    message("No params on bounds")
  }
  summary$ts$model_type <- ifelse(grepl("_EM_", summary$ts$model_run), "EM", "OM")
  calc_SSB <- summary$ts %>%
    filter(year >= min_yr & year <= max_yr) %>%
    select(iteration, scenario, year, model_run, model_type, SpawnBio)
  OM_vals <- calc_SSB %>%
    filter(model_type == "OM") %>%
    rename(SpawnBio_OM = SpawnBio) %>%
    select(iteration, scenario, year, SpawnBio_OM)
  EM_vals <- calc_SSB %>%
    filter(model_type == "EM") %>%
    rename(SpawnBio_EM = SpawnBio) %>%
    select(iteration, scenario, year, model_run, SpawnBio_EM)
  bind_vals <- full_join(EM_vals, OM_vals, by = c("iteration", "scenario", "year")) %>%
    mutate(SSB_ratio = SpawnBio_EM / SpawnBio_OM)
  filter_SSB <- bind_vals %>%
    filter(SSB_ratio > 2 | SSB_ratio < 0.5)
  if (nrow(filter_SSB) > 0) {
    warning("Some large/small SSBs relative to OM")
  } else {
    message("All SSBs in EM are no greater than double and no less than half SSB vals in the OM")
  }
  return_val <- bind_vals
}
values <- check_convergence(summary = summary, min_yr = 101, max_yr = 106, n_EMs = 5)
## No params on bounds
## All SSBs in EM are no greater than double and no less than half SSB vals in the OM

Plot Spawning Stock Biomass (SSB)

This plot shows that SSB estimated does not match perfectly with the operating model. A similar plot could be made for any parameter of interest.

# plot SSB by year and model run
ggplot2::ggplot(
  data = subset(summary$ts, model_run %in% c("cod_OM", "cod_SR_BH_steep_1_OM", "cod_EM_103")),
  ggplot2::aes(x = year, y = SpawnBio)
) +
  ggplot2::geom_vline(xintercept = 100, color = "gray") +
  ggplot2::geom_line(ggplot2::aes(linetype = as.character(iteration), color = model_run)) +
  ggplot2::scale_color_manual(values = c("#D65F00", "black", "blue")) +
  ggplot2::scale_linetype_manual(values = rep("solid", 50)) +
  ggplot2::guides(linetype = FALSE) +
  ggplot2::facet_wrap(. ~ scenario) +
  ggplot2::theme_classic()
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

<!-- -->

Now, we calculate and plot the performance metric, which is average spawning stock biomass (SSB) from years 104 to 106.

# get_SSB_avg calculates the SSB in each year for each
# iteration of the operating model, then takes the average over the years from
# min_yr, to max_year. It uses the summary object as input to do these
# calculations.
get_SSB_avg <- function(summary, min_yr, max_yr) {
  OM_vals <- unique(summary$ts$model_run)
  OM_vals <- grep("_OM$", OM_vals, value = TRUE)
  SSB_yr <- summary$ts %>%
    filter(year >= min_yr & year <= max_yr) %>%
    filter(model_run %in% OM_vals) %>%
    select(iteration, scenario, year, SpawnBio) %>%
    group_by(iteration, scenario) %>%
    summarize(avg_SSB = mean(SpawnBio), .groups = "keep") %>%
    ungroup()
  SSB_yr
}
avg_SSB <- get_SSB_avg(summary, min_yr = 104, max_yr = 106)

# function to summarize data in plot
data_summary <- function(x) {
  m <- mean(x)
  ymin <- m - sd(x)
  ymax <- m + sd(x)
  return(c(y = m, ymin = ymin, ymax = ymax))
}
# Now, plot the average relative spawning stock biomass for years 104 - 106
ggplot(data = avg_SSB, aes(x = scenario, y = avg_SSB)) +
  stat_summary(
    fun.data = data_summary,
    position = position_dodge(width = 0.9), color = "blue"
  ) +
  labs(
    title = "Long-term average  SSB\n(years 104-106)",
    x = "Scenario", y = "SSB"
  ) +
  theme_classic()

<!-- -->

From the above plot, we see differences in the average SSb between the 2 scenarios.

Example MSE Results

We can see from the performance metric that mis-specifying the value of steepness will results in higher realized relative spawning stock biomass than correctly specifying it. This gives us some idea of the consequences of misspecifying steepness in the stock assessment.

Delete the files

If you wish to delete the files created from this example, you can use:

unlink(run_SSMSE_dir, recursive = TRUE)

Advanced options: use a custom management strategy/procedure

Users can outline a custom managment strategy as an R function to use. As long as the correct inputs and outputs are used, any estimation method and management procedure can be used. For example, here is a simple function that just sets future catches as half the sampled catches in a specified year:

constant_catch_MS <- function(OM_dat, nyrs_assess, catch_yr = 100,
                              frac_catch = 0.5, ...) { # need to include ... to allow function to work
  # set catch the same as the previous year (sampled catch).
  # catch is in the same units as the operating model, in this case it is in
  # biomass.
  catch <- data.frame(
    year = (OM_dat$endyr + 1):(OM_dat$endyr + nyrs_assess), # the years to project the model forward
    seas = 1, # hard coded from looking at model
    fleet = 1, # hard coded from looking at model
    catch = OM_dat$catch[OM_dat$catch$year == catch_yr, "catch"] * frac_catch,
    catch_se = 0.05
  ) # hard coded from looking at model
  catch_bio <- catch # catch in biomass. In this case, catch is in biomass for both. Could also be left as NULL
  catch_F <- NULL # catch in terms of F, can be left as NULL.
  discards <- NULL # discards can be left as NULL if there are no discards
  catch_list <- list(
    catch = catch,
    catch_bio = catch_bio,
    catch_F = catch_F,
    discards = discards
  )
}

The function should be created in a separate file. In this case, assume this function is available in a file custom_funs.R.

This function can then be used in a call to run_SSMSE:

run_result_custom <- run_SSMSE(
  scen_name_vec = "constant-catch", # name of the scenario
  out_dir_scen_vec = run_res_path, # directory in which to run the scenario
  iter_vec = 1,
  OM_name_vec = "cod", # specify directories instead
  OM_in_dir_vec = NULL,
  MS_vec = "constant_catch_MS", # use the custom function
  custom_MS_source = "custom_funs.R", # File where the custom function is available.
  nyrs_vec = 6, # Years to project OM forward
  nyrs_assess_vec = 3, # Years between assessments
  future_om_list = future_om_list_recdevs_sel,
  sample_struct_list = list(sample_struct_list[[1]]), # How to sample data for running the MS.
  seed = 12345
) # Set a fixed integer seed that allows replication

How can I contribute to SSMSE?

Currently, feedback on how using SSMSE is working or not would be most hopeful! Please open an issue if a problem is found. The developers can also be contacted through github discussions or by emailing nmfs.stock.synthesis@noaa.gov.

If you are interested in contributing, please read the NMFS Fisheries Toolbox R Contribution Guide. This project and everyone participating in it is governed by the NMFS Fisheries Toolbox Code of Conduct. By participating, you are expected to uphold this code.

Roadmap: Where is SSMSE headed next?

SSMSE will be applied to a number of problems. If you are interested in using SSMSE, please don’t hesitate to reach out to the developers via github discussions or by emailing nmfs.stock.synthesis@noaa.gov.

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