From Ecopath with Ecosim to Fisheries Integrated Modeling System

Introduction

Ecosystem-based fishery management (EBFM) is crucial for addressing the complex, dynamic challenges posed by non-stationary climate, ecological, and economic conditions. However, translating outputs from ecosystem models into products suitable for fishery stock assessment models remains a key challenge.

This vignette demonstrates an end-to-end simulation workflow implemented in the {ecosystemom} package for linking ecosystem model outputs with fisheries estimation models. The workflow includes functions to:

load_model() — import and standardize output from ecosystem operating models (OM). For example, this function can be used to read and standardize Ecopath with Ecosim (EwE) Ecosim outputs for downstream analyses within {ecosystemom}.
calc_truth() — extract “true” population quantities from the OM, including annual or monthly biomass trajectories and biomass-at-age.
sample_*() — generate sampled observations from OM outputs for use in estimation models such as the Fisheries Integrated Modeling System (FIMS).
create_dsem_inputs() — prepare environmental covariates or diet composition data from the OM to support candidate model specifications for dynamic structural equation models (DSEMs) and related ecosystem-informed analyses.

library(ecosystemom)
library(FIMS)
set.seed(1234)

Load EwE Model Outputs

We begin by identifying and parsing raw comma-separated output matrices exported from a baseline EwE model run of the Northwest Atlantic. The critical target files extracted by {ecosystemom} include:

basic_estimates.csv
biomass_monthly.csv
catch_monthly.csv
diet_composition.csv
mortality_monthly.csv
weight_monthly.csv

Get functional groups from the EwE model

# Locate package internal files
ewe_nwatlantic_path <- system.file(
  "extdata", "ewe_ecosim_base_nwatlantic",
  package = "ecosystemom"
)

model_years <- 1985:2017

# Load functional groups
functional_groups <- get_functional_groups(
  file_path = fs::path(ewe_nwatlantic_path, "basic_estimates.csv")
)

functional_groups |>
  scroll_table()

functional_group	species	group	functional_group_snake_case
striped bass 0	striped bass	0	striped_bass_0
striped bass 2-5	striped bass	2-5	striped_bass_2_5
striped bass 6+	striped bass	6+	striped_bass_6_plus
menhaden 0	menhaden	0	menhaden_0
menhaden 1	menhaden	1	menhaden_1
menhaden 2	menhaden	2	menhaden_2
menhaden 3	menhaden	3	menhaden_3
menhaden 4	menhaden	4	menhaden_4
menhaden 5	menhaden	5	menhaden_5
menhaden 6+	menhaden	6+	menhaden_6_plus
spiny dogfish	spiny dogfish	NA	spiny_dogfish
bluefish juv	bluefish	juv	bluefish_juv
bluefish adult	bluefish	adult	bluefish_adult
weakfish juv	weakfish	juv	weakfish_juv
weakfish adult	weakfish	adult	weakfish_adult
Atlantic herring 0-1	Atlantic herring	0-1	atlantic_herring_0_1
Atlantic herring 2+	Atlantic herring	2+	atlantic_herring_2_plus
anchovies	anchovies	NA	anchovies
benthos	benthos	NA	benthos
zooplankton	zooplankton	NA	zooplankton
phytoplankton	phytoplankton	NA	phytoplankton
Detritus	Detritus	NA	detritus

Load EwE Ecosim model

# Load and standardize EwE outputs
data_om <- load_model(
  directory = ewe_nwatlantic_path,
  functional_groups = functional_groups,
  type = "ewe_ecosim",
  unit = c(
    "biomass" = "1e^6 mt",
    "catch" = "1e^6 mt",
    "landings" = "1e^6 mt",
    "total_mortality" = "year^-1",
    "weight" = "kg"
  )
)

data_om |>
  head(n = 10) |>
  dplyr::mutate(file_name = "./biomass_monthly.csv") |>
  scroll_table()

file_name	type	year	month	functional_group	value	species	group	functional_group_snake_case	unit
./biomass_monthly.csv	biomass	1985	1	striped bass 0	0.0084961	striped bass	0	striped_bass_0	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	striped bass 2-5	0.0362939	striped bass	2-5	striped_bass_2_5	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	striped bass 6+	0.0186583	striped bass	6+	striped_bass_6_plus	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 0	0.2910449	menhaden	0	menhaden_0	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 1	0.9729747	menhaden	1	menhaden_1	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 2	0.7663768	menhaden	2	menhaden_2	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 3	0.3195014	menhaden	3	menhaden_3	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 4	0.1119810	menhaden	4	menhaden_4	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 5	0.0409002	menhaden	5	menhaden_5	1e^6 mt
./biomass_monthly.csv	biomass	1985	1	menhaden 6+	0.0314173	menhaden	6+	menhaden_6_plus	1e^6 mt


data_om |> 
  dplyr::distinct(type, species) |>
  scroll_table()

type	species
biomass	striped bass
biomass	menhaden
biomass	spiny dogfish
biomass	bluefish
biomass	weakfish
biomass	Atlantic herring
biomass	anchovies
biomass	benthos
biomass	zooplankton
biomass	phytoplankton
biomass	Detritus
catch	striped bass
catch	menhaden
catch	spiny dogfish
catch	bluefish
catch	weakfish
catch	Atlantic herring
catch	anchovies
catch	benthos
catch	zooplankton
catch	phytoplankton
catch	Detritus
total_mortality	striped bass
total_mortality	menhaden
total_mortality	spiny dogfish
total_mortality	bluefish
total_mortality	weakfish
total_mortality	Atlantic herring
total_mortality	anchovies
total_mortality	benthos
total_mortality	zooplankton
total_mortality	phytoplankton
total_mortality	Detritus
weight	striped bass
weight	menhaden
weight	spiny dogfish
weight	bluefish
weight	weakfish
weight	Atlantic herring
weight	anchovies
weight	benthos
weight	zooplankton
weight	phytoplankton
weight	Detritus
fishing_mortality	striped bass
fishing_mortality	menhaden
fishing_mortality	spiny dogfish
fishing_mortality	bluefish
fishing_mortality	weakfish
fishing_mortality	Atlantic herring
fishing_mortality	anchovies
fishing_mortality	benthos
fishing_mortality	zooplankton
fishing_mortality	phytoplankton
fishing_mortality	Detritus
natural_mortality	striped bass
natural_mortality	menhaden
natural_mortality	spiny dogfish
natural_mortality	bluefish
natural_mortality	weakfish
natural_mortality	Atlantic herring
natural_mortality	anchovies
natural_mortality	benthos
natural_mortality	zooplankton
natural_mortality	phytoplankton
natural_mortality	Detritus

Extract “Truth” from the OM

Using calc_truth(), we synthesize raw EwE time series into a structured tibble. This “truth” represents the true state of the ecosystem against which our estimation model will be tested. For this demonstration, our focal species is “menhaden”. To isolate a target stock assessment from the broader ecosystem web, we subset a single focal species from the OM. In this vignette, we track Atlantic Menhaden (Brevoortia tyrannus), a key forage fish species. We extract “true” indices and age compositions using calc_truth().

# Extract "true" values for menhaden
truth_om <- calc_truth(
  data = data_om,
  species_name = "menhaden"
)

truth_om |>
  dplyr::select(-truth_om) |>
  scroll_table()

species_name	truth_label	truth_type	truth_time_step
menhaden	biomass	index	monthly
menhaden	biomass	index	yearly
menhaden	biomass	agecomp	monthly
menhaden	biomass	agecomp	yearly
menhaden	catch	index	monthly
menhaden	catch	index	yearly
menhaden	catch	agecomp	monthly
menhaden	catch	agecomp	yearly
menhaden	fishing_mortality	index	monthly
menhaden	fishing_mortality	index	yearly
menhaden	fishing_mortality	agecomp	monthly
menhaden	fishing_mortality	agecomp	yearly
menhaden	natural_mortality	agecomp	monthly
menhaden	natural_mortality	agecomp	yearly
menhaden	numbers	index	monthly
menhaden	numbers	index	yearly
menhaden	numbers	agecomp	monthly
menhaden	numbers	agecomp	yearly
menhaden	total_mortality	agecomp	monthly
menhaden	total_mortality	agecomp	yearly
menhaden	weight	agecomp	monthly
menhaden	weight	agecomp	yearly


# Extract and unnest annual catch
catch_index_om <- truth_om |> 
  dplyr::filter(
    truth_label == "catch",
    truth_type == "index",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om)) |>
  dplyr::mutate(
    truth_value = truth_value * 1000000, 
    truth_unit = "mt"
  ) 
  
catch_index_om |>
  scroll_table()

species_name	truth_label	truth_type	truth_time_step	truth_year	truth_unit	truth_value
menhaden	catch	index	yearly	1985	mt	16436.46
menhaden	catch	index	yearly	1986	mt	16565.34
menhaden	catch	index	yearly	1987	mt	15939.05
menhaden	catch	index	yearly	1988	mt	12596.12
menhaden	catch	index	yearly	1989	mt	25993.96
menhaden	catch	index	yearly	1990	mt	321014.86
menhaden	catch	index	yearly	1991	mt	258893.40
menhaden	catch	index	yearly	1992	mt	365211.37
menhaden	catch	index	yearly	1993	mt	600358.51
menhaden	catch	index	yearly	1994	mt	659307.57
menhaden	catch	index	yearly	1995	mt	604887.34
menhaden	catch	index	yearly	1996	mt	907276.10
menhaden	catch	index	yearly	1997	mt	771183.60
menhaden	catch	index	yearly	1998	mt	878034.15
menhaden	catch	index	yearly	1999	mt	981962.22
menhaden	catch	index	yearly	2000	mt	961678.94
menhaden	catch	index	yearly	2001	mt	926563.53
menhaden	catch	index	yearly	2002	mt	933937.94
menhaden	catch	index	yearly	2003	mt	435965.57
menhaden	catch	index	yearly	2004	mt	292324.12
menhaden	catch	index	yearly	2005	mt	66979.80
menhaden	catch	index	yearly	2006	mt	58767.23
menhaden	catch	index	yearly	2007	mt	36368.17
menhaden	catch	index	yearly	2008	mt	41439.10
menhaden	catch	index	yearly	2009	mt	42480.31
menhaden	catch	index	yearly	2010	mt	42963.63
menhaden	catch	index	yearly	2011	mt	47277.22
menhaden	catch	index	yearly	2012	mt	91043.37
menhaden	catch	index	yearly	2013	mt	87008.47
menhaden	catch	index	yearly	2014	mt	120764.99
menhaden	catch	index	yearly	2015	mt	119770.21
menhaden	catch	index	yearly	2016	mt	110616.90
menhaden	catch	index	yearly	2017	mt	175165.13


# Extract and unnest annual weight-at-age
weight_agecomp_om <- truth_om |>
  dplyr::filter(
    truth_label == "weight",
    truth_type == "agecomp",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om)) |>
  dplyr::mutate(
    truth_value = truth_value / 1000,
    truth_unit = "mt"
  )

# Extract and unnest annual catch-at-age in numbers
catch_agecomp_om <- truth_om |> 
  dplyr::filter(
    truth_label == "catch",
    truth_type == "agecomp",
    truth_time_step == "yearly"
  ) |>
  tidyr::unnest(cols = c(truth_om)) |>
  dplyr::mutate(
    truth_value = truth_value * 1000000,
    truth_unit = "mt"
  ) |>
  dplyr::left_join(
    weight_agecomp_om |>
      dplyr::select(-species_name, -truth_label, -truth_type, -truth_time_step, -truth_unit), 
    by = c("truth_year", "truth_group"),
    suffix = c("_catch", "_weight")
  ) |>
  dplyr::mutate(
    truth_value = ceiling(truth_value_catch / truth_value_weight), 
    truth_unit = "numbers"
  ) |>
  dplyr::select(-truth_value_catch, -truth_value_weight)

# Extract and unnest annual biomass
biomass_index_om <- truth_om |>
  dplyr::filter(
    truth_label == "biomass",
    truth_type == "index",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om)) |>
  dplyr::mutate(
    truth_value = truth_value * 1000000, 
    truth_unit = "mt"
  )

# Extract and unnest annual number-at-age
number_agecomp_om <- truth_om |>
  dplyr::filter(
    truth_label == "numbers",
    truth_type == "agecomp",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om)) |>
  dplyr::mutate(
    truth_value = ceiling(truth_value * 1000000 * 1000),
    truth_unit = "numbers"
  )

# Extract and unnest annual natural mortality by age
natural_mortality_agecomp_om <- truth_om |>
  dplyr::filter(
    truth_label == "natural_mortality",
    truth_type == "agecomp",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om))

# Extract and unnest annual fishing mortality by age
fishing_mortality_agecomp_om <- truth_om |>
  dplyr::filter(
    truth_label == "fishing_mortality",
    truth_type == "agecomp",
    truth_time_step == "yearly"
  ) |> 
  tidyr::unnest(cols = c(truth_om))

# Extract and unnest annual fishing mortality: apical F
fishing_mortality_index_om <- truth_om |>
  dplyr::filter(
    truth_label == "fishing_mortality",
    truth_type == "index",
    truth_time_step == "yearly"
  ) |>
  tidyr::unnest(cols = c(truth_om))

Generate Sampled Data

To mimic observed fisheries data, sampled observations are generated from the OM “truth” while incorporating observation error.

Fishery-Dependent Data

Observed catch data are simulated by applying lognormal observation error to the “true” catch values with a standard deviation of 0.05. Age composition data are generated using a multinomial sampling distribution with an effective sample size of N=200.

catch_index_sd <- 0.05
catch_index_sampled <- catch_index_om |> 
  dplyr::mutate(
    sampled_value = sample_lognormal(
      x = truth_value, 
      sd = catch_index_sd
    )
  )

catch_index_sampled |>
  scroll_table()

species_name	truth_label	truth_type	truth_time_step	truth_year	truth_unit	truth_value	sampled_value
menhaden	catch	index	yearly	1985	mt	16436.46	15454.48
menhaden	catch	index	yearly	1986	mt	16565.34	16775.75
menhaden	catch	index	yearly	1987	mt	15939.05	16806.13
menhaden	catch	index	yearly	1988	mt	12596.12	11188.14
menhaden	catch	index	yearly	1989	mt	25993.96	26524.54
menhaden	catch	index	yearly	1990	mt	321014.86	328829.77
menhaden	catch	index	yearly	1991	mt	258893.40	251245.21
menhaden	catch	index	yearly	1992	mt	365211.37	354920.81
menhaden	catch	index	yearly	1993	mt	600358.51	582922.58
menhaden	catch	index	yearly	1994	mt	659307.57	629822.64
menhaden	catch	index	yearly	1995	mt	604887.34	589887.95
menhaden	catch	index	yearly	1996	mt	907276.10	862019.15
menhaden	catch	index	yearly	1997	mt	771183.60	740898.60
menhaden	catch	index	yearly	1998	mt	878034.15	879768.17
menhaden	catch	index	yearly	1999	mt	981962.22	1028932.91
menhaden	catch	index	yearly	2000	mt	961678.94	955195.83
menhaden	catch	index	yearly	2001	mt	926563.53	902060.99
menhaden	catch	index	yearly	2002	mt	933937.94	891227.94
menhaden	catch	index	yearly	2003	mt	435965.57	417571.04
menhaden	catch	index	yearly	2004	mt	292324.12	329443.53
menhaden	catch	index	yearly	2005	mt	66979.80	67346.13
menhaden	catch	index	yearly	2006	mt	58767.23	57271.33
menhaden	catch	index	yearly	2007	mt	36368.17	35531.40
menhaden	catch	index	yearly	2008	mt	41439.10	42349.40
menhaden	catch	index	yearly	2009	mt	42480.31	40980.84
menhaden	catch	index	yearly	2010	mt	42963.63	39912.67
menhaden	catch	index	yearly	2011	mt	47277.22	48594.79
menhaden	catch	index	yearly	2012	mt	91043.37	86392.70
menhaden	catch	index	yearly	2013	mt	87008.47	86834.03
menhaden	catch	index	yearly	2014	mt	120764.99	115099.74
menhaden	catch	index	yearly	2015	mt	119770.21	126398.53
menhaden	catch	index	yearly	2016	mt	110616.90	107882.56
menhaden	catch	index	yearly	2017	mt	175165.13	168849.39


catch_agecomp_sample_size <- 150
catch_agecomp_sampled <- catch_agecomp_om |> 
  dplyr::group_by(truth_year) |> 
  dplyr::mutate(
    sampled_value = sample_multinomial(
      x = truth_value,
      sample_size = catch_agecomp_sample_size
    )
  ) |> 
  dplyr::ungroup()

Fishery-Independent Survey Data

Fishery-independent survey observations are simulated by applying a double-logistic selectivity and catchability coefficient (q) to the “true” number-at-age matrix from the OM. Observed survey index is simulated by applying lognormal observation error to the “true” values with a standard deviation of 0.1. Age composition data are generated using a multinomial sampling distribution with an effective sample size of N=200.

ages <- 0:6
names(ages) <- functional_groups |>
  dplyr::filter(species == "menhaden") |>
  dplyr::pull(group)

# Define explicit survey catchability
catchability_survey <- 0.05
selectivity_inflection_point_asc <- 1.2
selectivity_slope_asc <- 2.1
selectivity_inflection_point_desc <- 3.8
selectivity_slope_desc <- 1.2
selectivity_ascending  <- 1 / (1 + exp(-selectivity_slope_asc * (ages - selectivity_inflection_point_asc)))
selectivity_descending <- 1 / (1 + exp(-selectivity_slope_desc * (ages - selectivity_inflection_point_desc)))
selectivity_survey   <- selectivity_ascending * (1 - selectivity_descending)
names(selectivity_survey) <- functional_groups |>
  dplyr::filter(species == "menhaden") |>
  dplyr::pull(group)

# Survey index
survey_data <- number_agecomp_om |>
  dplyr::left_join(
    weight_agecomp_om |>
      dplyr::select(-species_name, -truth_label, -truth_type, -truth_time_step, -truth_unit), 
    by = c("truth_year", "truth_group"),
    suffix = c("_number", "_weight")
  ) |>
  dplyr::mutate(
    selectivity = selectivity_survey[truth_group],
    truth_value_selected_number = ceiling(truth_value_number * selectivity * catchability_survey),
    truth_value_selected_biomass = truth_value_selected_number * truth_value_weight
  )

# Generate observed survey biomass indices with lognormal error (SD = 0.1)
survey_index_sd <- 0.1
survey_index_sampled <- survey_data |> 
  dplyr::select(
    -truth_value_number, -truth_value_weight, -selectivity, -truth_value_selected_number
  ) |>
  dplyr::mutate(
    truth_label = "biomass",
    truth_unit = "mt"
  ) |>
  # Aggregate all ages/groups into one annual value
  dplyr::group_by(truth_year) |>
  dplyr::summarise(
    truth_value = sum(truth_value_selected_biomass),
    .groups = "drop"
  ) |>
  dplyr::mutate(
    species_name = "menhaden",
    truth_label = "biomass",
    truth_type = "index",
    truth_time_step = "yearly",
    truth_unit = "mt"
  ) |>
  dplyr::mutate(
    sampled_value = sample_lognormal(
      x = truth_value, 
      sd = survey_index_sd
    )
  )

# Survey agecomp
survey_agecomp_sample_size <- 150
survey_agecomp_sampled <- survey_data |> 
  dplyr::group_by(truth_year) |> 
  dplyr::mutate(
    sampled_value = sample_multinomial(
      x = truth_value_selected_number,
      sample_size = survey_agecomp_sample_size
    )
  ) |> 
  dplyr::ungroup() |>
  dplyr::select(
    -truth_value_number, -truth_value_weight, -selectivity,
    -truth_value_selected_biomass
  ) |>
  dplyr::rename(truth_value = truth_value_selected_number)

Prepare FIMS-compatible data

The sampled fishery-dependent and fishery-independent observations are next reformatted into a FIMSFrame object for use with the FIMS. This includes annual landings, survey biomass indices, age composition observations, and weight-at-age information.

The resulting FIMSFrame object provides the standardized data structure required for model configuration and estimation in FIMS.

Click to expand/collapse code

fishing_fleet_name <- "fishing_fleet"
survey_fleet_name <- "survey_fleet"

landings_data <- data.frame(
  type = "landings",
  name = fishing_fleet_name,
  age = NA, 
  timing = model_years,
  value = catch_index_sampled[["sampled_value"]],
  unit = "mt",
  uncertainty = catch_index_sd
)

index_data <- data.frame(
  type = "index",
  name = survey_fleet_name,
  age = NA,
  timing = model_years,
  value = survey_index_sampled[["sampled_value"]],
  unit = "mt",
  uncertainty = survey_index_sd
)

age_data <- rbind(
  data.frame(
    type = "age_comp",
    name = fishing_fleet_name,
    age = unname(ages[catch_agecomp_sampled[["truth_group"]]]),
    timing = catch_agecomp_sampled[["truth_year"]],
    value = catch_agecomp_sampled[["sampled_value"]],
    unit = "number",
    uncertainty = catch_agecomp_sample_size
  ),
  data.frame(
    type = "age_comp",
    name = survey_fleet_name,
    age = unname(ages[survey_agecomp_sampled[["truth_group"]]]),
    timing = survey_agecomp_sampled[["truth_year"]],
    value = survey_agecomp_sampled[["sampled_value"]],
    unit = "number",
    uncertainty = survey_agecomp_sample_size
  )
)

weight_at_age <- data.frame(
  type = "weight_at_age",
  name = fishing_fleet_name,
  age = unname(ages[weight_agecomp_om[["truth_group"]]]),
  timing = weight_agecomp_om[["truth_year"]],
  value = weight_agecomp_om[["truth_value"]],
  unit = "mt",
  uncertainty = NA
)

weight_year_plus <- weight_at_age |>
  dplyr::filter(timing == max(model_years)) |>
  dplyr::mutate(timing = timing + 1)

weight_at_age_data <- dplyr::bind_rows(
  weight_at_age, 
  weight_year_plus
)

data_fims <- rbind(landings_data, index_data, age_data, weight_at_age_data) |>
  dplyr::mutate(
    length = NA, 
    .after = "age"
  ) |>
  FIMS::FIMSFrame()

methods::show(data_fims)
#> # A tibble: 6 × 8
#>   type     name            age length timing value unit   uncertainty
#>   <chr>    <chr>         <int>  <dbl>  <dbl> <dbl> <chr>        <dbl>
#> 1 age_comp fishing_fleet     0     NA   1985    22 number         150
#> 2 age_comp fishing_fleet     1     NA   1985    49 number         150
#> 3 age_comp fishing_fleet     2     NA   1985    67 number         150
#> 4 age_comp fishing_fleet     3     NA   1985    12 number         150
#> 5 age_comp fishing_fleet     4     NA   1985     0 number         150
#> 6 age_comp fishing_fleet     5     NA   1985     0 number         150
#> additional slots include the following:fleets:
#> [1] "fishing_fleet" "survey_fleet" 
#> n_years:
#> [1] 33
#> ages:
#> [1] 0 1 2 3 4 5 6
#> n_ages:
#> [1] 7
#> lengths:
#> numeric(0)
#> n_lengths:
#> [1] 0
#> start_year:
#> [1] 1985
#> end_year:
#> [1] 2017

Configure the FIMS estimation model

FIMS models are initialized using a set of default configurations and parameter values derived from the input data. In this section, we customize those defaults to better align the FIMS estimation model with the underlying ecosystem OM.

Key modifications include:

Replacing the default selectivity formulation with a double-logistic selectivity function for both the fishing fleet and survey fleet.
Initializing fishing mortality, survey catchability, recruitment, and natural mortality parameters using values derived from the OM “truth”.
Specifying maturity-at-age using a logistic maturity curve.
Initializing numbers-at-age in the first model year directly from the OM population state.

These steps provide informed starting values that improve consistency between the OM and the estimation model.

Click to expand/collapse code

# Generate default FIMS model configurations
default_configurations <- FIMS::create_default_configurations(
  data = data_fims
)

default_configurations |>
  tidyr::unnest(cols = data) |>
  scroll_table()

model_family	module_name	fleet_name	module_type	distribution_type	distribution
catch_at_age	Data	fishing_fleet	AgeComp	Data	Dmultinom
catch_at_age	Data	fishing_fleet	Landings	Data	Dlnorm
catch_at_age	Selectivity	fishing_fleet	Logistic	NA	NA
catch_at_age	Data	survey_fleet	AgeComp	Data	Dmultinom
catch_at_age	Data	survey_fleet	Index	Data	Dlnorm
catch_at_age	Selectivity	survey_fleet	Logistic	NA	NA
catch_at_age	Growth	NA	EWAA	NA	NA
catch_at_age	Maturity	NA	Logistic	NA	NA
catch_at_age	Recruitment	NA	BevertonHolt	process	Dnorm


# Replace the default selectivity model with a double-logistic function
updated_configurations <- default_configurations |>
  tidyr::unnest(cols = data) |>
  dplyr::rows_update(
    y = tibble::tibble(
      module_name = "Selectivity",
      module_type = "DoubleLogistic"
    ),
    by = c("module_name")
  )

updated_configurations |>
  scroll_table()

model_family	module_name	fleet_name	module_type	distribution_type	distribution
catch_at_age	Data	fishing_fleet	AgeComp	Data	Dmultinom
catch_at_age	Data	fishing_fleet	Landings	Data	Dlnorm
catch_at_age	Selectivity	fishing_fleet	DoubleLogistic	NA	NA
catch_at_age	Data	survey_fleet	AgeComp	Data	Dmultinom
catch_at_age	Data	survey_fleet	Index	Data	Dlnorm
catch_at_age	Selectivity	survey_fleet	DoubleLogistic	NA	NA
catch_at_age	Growth	NA	EWAA	NA	NA
catch_at_age	Maturity	NA	Logistic	NA	NA
catch_at_age	Recruitment	NA	BevertonHolt	process	Dnorm


# Create default parameter values from the updated model configuration
default_parameters <- FIMS::create_default_parameters(
  configurations = updated_configurations,
  data = data_fims
) |>
  tidyr::unnest(cols = data)

# Fishing fleet selectivity
# Option 1: Estimate selectivity from OM fishing mortality-at-age
# Mismatch note: the ecosystem model OM scales fishing selectivity (double 
# logistic) to a maximum of 1, where FIMS does not.
catch_selectivity <- estimate_true_selectivity(
  data = fishing_mortality_agecomp_om,
  ages = ages,
  functional_form = "double_logistic"
) |>
  dplyr::mutate(fleet_name = fishing_fleet_name)

# Option 2: Explicit fishing fleet selectivity parameter values
catch_selectivity_inflection_point_asc <- 1.8
catch_selectivity_slope_asc <- 3.1
catch_selectivity_inflection_point_desc <- 0.01
catch_selectivity_slope_desc <- 0.88

# Estimate maturity parameters
maturity_parameters <- estimate_true_maturity(
  ages = ages,
  spawning_proportion = c(0, 0.1, 0.5, 0.9, 1, 1, 1),
  functional_form = "logistic"
)

# Estimate recruitment log_sd
recruitment_ewe <- number_agecomp_om |>
  dplyr::filter(truth_group == "0") |>
  dplyr::pull(truth_value)

log_sd_proxy <- (sd(log(recruitment_ewe) - mean(log(recruitment_ewe)))) |>
  log()


# Update parameter values using OM-derived truth information
updated_parameters <- default_parameters |>
  # dplyr::filter(!(module_name == "Selectivity" & fleet_name == fishing_fleet_name)) |>
  # dplyr::bind_rows(catch_selectivity) |>
  dplyr::rows_update(
    y = tibble::tibble(
      fleet_name = fishing_fleet_name,
      label = c(
        "inflection_point_asc", "slope_asc", 
        "inflection_point_desc", "slope_desc"
      ),
      value = c(
        catch_selectivity_inflection_point_asc,
        catch_selectivity_slope_asc,
        catch_selectivity_inflection_point_desc,
        catch_selectivity_slope_desc
      )
    ),
    by = c("fleet_name", "label")
  ) |>
  dplyr::rows_update(
    y = tibble::tibble(
      fleet_name = fishing_fleet_name,
      label = "log_Fmort",
      time = fishing_mortality_index_om[["truth_year"]],
      value = fishing_mortality_index_om[["truth_value"]] |>
        log()
    ), 
    by = c("fleet_name", "label", "time")
  ) |> 
  dplyr::rows_update(
    y = tibble::tibble(
      fleet_name = survey_fleet_name,
      label = c(
        "inflection_point_asc", "slope_asc", 
        "inflection_point_desc", "slope_desc", 
        "log_q"
      ),
      value = c(
        selectivity_inflection_point_asc,
        selectivity_slope_asc,
        selectivity_inflection_point_desc,
        selectivity_slope_desc,
        log(catchability_survey)
      )
    ),
    by = c("fleet_name", "label")
  ) |>
  dplyr::rows_update(
    y = tibble::tibble(
      label = "log_rzero", 
      module_type = "BevertonHolt",
      value = number_agecomp_om |>
        dplyr::filter(truth_group == "0") |>
        dplyr::pull(truth_value) |>
        (\(x) mean(log(x)))()
        # dplyr::filter(truth_group  == "0", truth_year == model_years[1]) |>
        # dplyr::pull(truth_value) |>
        # log()
    ),
    by = c("label", "module_type")
  ) |>
  dplyr::rows_update(
    y = tibble::tibble(
      label = "logit_steep", 
      module_type = "BevertonHolt",
      # calculate from vulnerability matrix: v / (v + 1)
      # v = 411.23 + 1.02 + 191.58 + 2 + 1016.36 + 12.18 + 2 + 403.26 = 2039.63
      # h = v / (v + 1) = 0.99
      value = -log(1.0 - 0.99) + log(0.99 - 0.2)
    ),
    by = c("label", "module_type")
  ) |>
  dplyr::rows_update(
    y = tibble::tibble(
      label = "log_sd",
      module_type = "BevertonHolt",
      value = log_sd_proxy,
      estimation_type = "constant"
    ),
    by = c("label", "module_type")
  ) |>
  dplyr::filter(!(module_name == "Maturity")) |>
  dplyr::bind_rows(maturity_parameters) |>
  dplyr::rows_update(
    y = tibble::tibble(
      label = "log_M", 
      age = unname(ages[natural_mortality_agecomp_om[["truth_group"]]]),
      time = natural_mortality_agecomp_om[["truth_year"]],
      value = log(natural_mortality_agecomp_om[["truth_value"]])
    ),
    by = c("label", "age", "time")
  ) |>
  dplyr::rows_update(
    y = tibble::tibble(
      label = "log_init_naa",
      age = number_agecomp_om |>
        dplyr::filter(truth_year == model_years[1]) |>
        dplyr::pull(truth_group) |>
        (\(x) unname(ages[x]))(),
      value = number_agecomp_om |>
        dplyr::filter(truth_year == model_years[1]) |>
        dplyr::pull(truth_value) |>
        log()
    ),
    by = c("label", "age")
  )

# Display updated parameter table
updated_parameters |>
  scroll_table()

model_family	module_name	fleet_name	module_type	label	age	length	time	value	estimation_type	distribution_type	distribution
catch_at_age	Selectivity	fishing_fleet	DoubleLogistic	inflection_point_asc	NA	NA	NA	1.8000000	fixed_effects	NA	NA
catch_at_age	Selectivity	fishing_fleet	DoubleLogistic	slope_asc	NA	NA	NA	3.1000000	fixed_effects	NA	NA
catch_at_age	Selectivity	fishing_fleet	DoubleLogistic	inflection_point_desc	NA	NA	NA	0.0100000	fixed_effects	NA	NA
catch_at_age	Selectivity	fishing_fleet	DoubleLogistic	slope_desc	NA	NA	NA	0.8800000	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_q	NA	NA	NA	0.0000000	constant	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1985	-4.3883506	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1986	-4.4365488	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1987	-4.5062927	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1988	-4.7597733	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1989	-4.0244071	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1990	-1.4276610	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1991	-1.5789096	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1992	-1.2008085	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1993	-0.6559324	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1994	-0.5195762	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1995	-0.5837083	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1996	-0.0562434	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1997	-0.1307473	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1998	0.0898828	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	1999	0.3755226	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2000	0.5622899	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2001	0.8092934	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2002	1.5348651	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2003	1.2316391	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2004	1.8614121	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2005	0.6861542	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2006	0.6069573	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2007	0.2939806	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2008	0.0769286	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2009	-0.0688433	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2010	-0.3206130	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2011	-0.6751821	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2012	-0.3278062	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2013	-0.7579312	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2014	-0.8353783	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2015	-1.1794289	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2016	-1.6155008	fixed_effects	NA	NA
catch_at_age	Fleet	fishing_fleet	NA	log_Fmort	NA	NA	2017	-1.4445852	fixed_effects	NA	NA
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1985	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1986	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1987	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1988	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1989	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1990	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1991	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1992	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1993	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1994	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1995	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1996	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1997	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1998	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	1999	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2000	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2001	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2002	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2003	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2004	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2005	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2006	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2007	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2008	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2009	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2010	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2011	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2012	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2013	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2014	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2015	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2016	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	Landings	log_sd	NA	NA	2017	-2.9957323	constant	Data	Dlnorm
catch_at_age	Data	fishing_fleet	AgeComp	NA	NA	NA	NA	NA	NA	Data	Dmultinom
catch_at_age	Selectivity	survey_fleet	DoubleLogistic	inflection_point_asc	NA	NA	NA	1.2000000	fixed_effects	NA	NA
catch_at_age	Selectivity	survey_fleet	DoubleLogistic	slope_asc	NA	NA	NA	2.1000000	fixed_effects	NA	NA
catch_at_age	Selectivity	survey_fleet	DoubleLogistic	inflection_point_desc	NA	NA	NA	3.8000000	fixed_effects	NA	NA
catch_at_age	Selectivity	survey_fleet	DoubleLogistic	slope_desc	NA	NA	NA	1.2000000	fixed_effects	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_q	NA	NA	NA	-2.9957323	fixed_effects	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1985	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1986	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1987	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1988	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1989	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1990	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1991	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1992	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1993	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1994	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1995	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1996	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1997	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1998	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	1999	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2000	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2001	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2002	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2003	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2004	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2005	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2006	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2007	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2008	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2009	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2010	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2011	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2012	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2013	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2014	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2015	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2016	-200.0000000	constant	NA	NA
catch_at_age	Fleet	survey_fleet	NA	log_Fmort	NA	NA	2017	-200.0000000	constant	NA	NA
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1985	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1986	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1987	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1988	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1989	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1990	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1991	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1992	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1993	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1994	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1995	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1996	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1997	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1998	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	1999	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2000	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2001	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2002	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2003	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2004	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2005	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2006	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2007	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2008	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2009	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2010	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2011	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2012	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2013	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2014	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2015	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2016	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	Index	log_sd	NA	NA	2017	-2.3025851	constant	Data	Dlnorm
catch_at_age	Data	survey_fleet	AgeComp	NA	NA	NA	NA	NA	NA	Data	Dmultinom
catch_at_age	Recruitment	NA	BevertonHolt	log_rzero	NA	NA	NA	24.1027090	fixed_effects	NA	NA
catch_at_age	Recruitment	NA	BevertonHolt	logit_steep	NA	NA	NA	4.3694479	constant	NA	NA
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1986	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1987	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1988	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1989	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1990	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1991	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1992	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1993	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1994	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1995	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1996	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1997	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1998	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	1999	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2000	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2001	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2002	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2003	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2004	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2005	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2006	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2007	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2008	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2009	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2010	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2011	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2012	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2013	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2014	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2015	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2016	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_devs	NA	NA	2017	0.0000000	random_effects	process	Dnorm
catch_at_age	Recruitment	NA	BevertonHolt	log_sd	NA	NA	NA	0.3322577	constant	process	Dnorm
catch_at_age	Population	NA	NA	log_M	0	NA	1985	0.5510554	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1985	0.2578511	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1985	0.2345560	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1985	0.3162692	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1985	-0.0214885	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1985	-0.0588443	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1985	-0.3368522	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1986	0.5498290	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1986	0.2525224	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1986	0.2368321	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1986	0.3225341	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1986	-0.0169781	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1986	-0.0477069	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1986	-0.3378550	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1987	0.5429496	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1987	0.2532065	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1987	0.2310666	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1987	0.3265500	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1987	-0.0133847	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1987	-0.0459568	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1987	-0.3330032	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1988	0.5342818	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1988	0.2533493	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1988	0.2334027	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1988	0.3244762	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1988	-0.0091122	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1988	-0.0393908	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1988	-0.3246372	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1989	0.5299623	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1989	0.2549291	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1989	0.2359440	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1989	0.3294850	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1989	-0.0085674	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1989	-0.0287585	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1989	-0.3062291	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1990	0.5248457	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1990	0.2575867	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1990	0.2474677	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1990	0.3450687	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1990	0.0062393	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1990	-0.0158175	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1990	-0.2765721	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1991	0.5245231	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1991	0.2536110	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1991	0.2388876	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1991	0.3361421	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1991	0.0102276	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1991	0.0010650	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1991	-0.2498855	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1992	0.5489156	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1992	0.2515150	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1992	0.2415777	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1992	0.3392345	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1992	0.0117635	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1992	0.0135985	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1992	-0.2218991	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1993	0.5676137	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1993	0.2598213	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1993	0.2440548	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1993	0.3493315	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1993	0.0189946	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1993	0.0185022	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1993	-0.1989036	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1994	0.5733348	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1994	0.2650077	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1994	0.2451121	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1994	0.3383036	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1994	0.0203143	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1994	0.0232880	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1994	-0.1878561	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1995	0.5714939	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1995	0.2671489	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1995	0.2452425	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1995	0.3310723	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1995	0.0075400	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1995	0.0219000	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1995	-0.1841973	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1996	0.5737462	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1996	0.2712608	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1996	0.2720812	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1996	0.3682440	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1996	0.0228084	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1996	0.0197111	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1996	-0.1800214	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1997	0.5681622	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1997	0.2709746	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1997	0.2456691	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1997	0.3458910	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1997	0.0284636	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1997	0.0244087	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1997	-0.1819947	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1998	0.5619796	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1998	0.2733790	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1998	0.2647729	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1998	0.3547694	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1998	0.0273642	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1998	0.0323325	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1998	-0.1822804	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	1999	0.5515537	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	1999	0.2780025	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	1999	0.2777461	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	1999	0.3887215	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	1999	0.0377142	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	1999	0.0187607	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	1999	-0.1874319	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2000	0.5340019	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2000	0.2819094	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2000	0.2818811	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2000	0.3971050	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2000	0.0613504	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2000	0.0140518	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2000	-0.2027708	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2001	0.5088359	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2001	0.2904512	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2001	0.3150982	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2001	0.4372885	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2001	0.0845208	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2001	0.0249441	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2001	-0.2176771	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2002	0.5031093	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2002	0.3388435	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2002	0.6235324	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2002	0.8869642	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2002	0.3265030	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2002	0.0966963	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2002	-0.2268668	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2003	0.4731706	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2003	0.3343495	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2003	0.2232572	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2003	0.3743130	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2003	0.2736178	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2003	0.1090028	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2003	-0.2664116	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2004	0.4398723	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2004	0.4214778	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2004	0.9795473	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2004	1.0558503	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2004	0.4349309	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2004	0.2781599	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2004	-0.2591358	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2005	0.4287877	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2005	0.2797446	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2005	-0.0241177	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2005	-0.0363912	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2005	0.0010457	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2005	0.0430499	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2005	-0.3152792	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2006	0.3269996	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2006	0.2875417	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2006	0.3426944	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2006	0.1217517	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2006	-0.2883387	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2006	-0.0708458	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2006	-0.3230885	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2007	0.3674750	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2007	0.2006637	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2007	0.2011350	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2007	0.3437272	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2007	-0.2176963	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2007	-0.2891413	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2007	-0.3316298	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2008	0.3573618	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2008	0.2473479	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2008	0.1530368	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2008	0.2744374	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2008	-0.0342365	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2008	-0.2498623	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2008	-0.3518946	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2009	0.3300643	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2009	0.2240159	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2009	0.2212610	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2009	0.2515276	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2009	-0.0698555	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2009	-0.0999613	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2009	-0.4366274	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2010	0.3474680	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2010	0.2053755	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2010	0.1749986	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2010	0.2994620	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2010	-0.1000761	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2010	-0.1293331	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2010	-0.4334877	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2011	0.3447916	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2011	0.2190391	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2011	0.1678352	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2011	0.2626725	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2011	-0.0575752	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2011	-0.1569189	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2011	-0.4347730	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2012	0.3490304	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2012	0.2116800	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2012	0.2033567	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2012	0.2896446	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2012	-0.0715796	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2012	-0.1124122	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2012	-0.4560552	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2013	0.3732705	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2013	0.2141775	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2013	0.1687402	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2013	0.2823266	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2013	-0.0679503	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2013	-0.1312684	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2013	-0.4359907	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2014	0.3781329	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2014	0.2243395	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2014	0.1910518	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2014	0.2695222	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2014	-0.0632346	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2014	-0.1246131	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2014	-0.4365002	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2015	0.4026229	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2015	0.2201120	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2015	0.1964566	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2015	0.2830671	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2015	-0.0779602	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2015	-0.1204480	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2015	-0.4337887	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2016	0.4379455	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2016	0.2269621	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2016	0.1974454	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2016	0.2881298	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2016	-0.0641154	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2016	-0.1316616	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2016	-0.4270618	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	0	NA	2017	0.4742664	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	1	NA	2017	0.2358421	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	2	NA	2017	0.2108008	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	3	NA	2017	0.2968401	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	4	NA	2017	-0.0521981	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	5	NA	2017	-0.1120706	constant	NA	NA
catch_at_age	Population	NA	NA	log_M	6	NA	2017	-0.4277903	constant	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	0	NA	NA	25.3696876	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	1	NA	NA	23.7255009	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	2	NA	NA	22.3631177	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	3	NA	NA	20.8751140	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	4	NA	NA	19.4450038	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	5	NA	NA	18.2081170	fixed_effects	NA	NA
catch_at_age	Population	NA	NA	log_init_naa	6	NA	NA	17.6518987	fixed_effects	NA	NA
catch_at_age	Growth	NA	EWAA	NA	NA	NA	NA	NA	NA	NA	NA
catch_at_age	Maturity	NA	Logistic	inflection_point	NA	NA	NA	1.9999914	constant	NA	NA
catch_at_age	Maturity	NA	Logistic	slope	NA	NA	NA	2.2306407	constant	NA	NA

Fit the FIMS Model

After configuring model structure and parameter values, the FIMS estimation model can be initialized and optimized.

The workflow below:

Initializes the FIMS model object using the prepared data and parameter values.
Runs optimization to estimate model parameters.
Extracts estimated quantities for downstream comparison with the OM “truth”.
Clears the FIMS interface to reset the modeling environment.

# Initialize and fit the FIMS estimation model
fit_fims <- updated_parameters |>
  FIMS::initialize_fims((data = data_fims)) |>
  FIMS::fit_fims(
    optimize = TRUE,
    control = list(
      # eval.max = 20000,
      eval.max = 20000,
      iter.max = 10000,
      trace = 0
    )
  )

# Extract estimates
estimates_fims <- FIMS::get_estimates(fit_fims)

FIMS::clear()

Compare OM and FIMS

Note: FIMS has not converged yet :( and I am still working on investigation!

The fitted FIMS model can now be compared against the ecosystem OM “truth” used to generate the simulated observations.

Although the temporal trends between the OM and FIMS are similar, the absolute scales differ substantially due to structural differences between the ecosystem operating model and the single-species estimation model. In this example, important sources of scale mismatch include:

differences in spawning biomass definitions between EwE and FIMS and fixed female proportion in FIMS (0.5).
differences in double-logistic selectivity parameterization and scaling.

Create DSEM inputs

In addition to supporting fisheries stock assessment workflows, {ecosystemom} can also be used to generate candidate inputs for Dynamic Structural Equation Models (DSEMs). These models provide a flexible framework for evaluating ecosystem linkages, environmental drivers, and trophic interactions through time.

The create_dsem_inputs() function:

extracts and reshapes ecosystem time-series data,
generates candidate SEM pathways based on trophic interactions, and
filters trophic links using a user-defined diet composition threshold.

In this example, trophic links are derived from the static Ecopath diet composition matrix, where diet_composition_threshold controls the minimum diet proportion retained in the candidate SEM structure.

# Load diet composition data
data_diet_composition <- load_diet_composition(
  file.path(ewe_nwatlantic_path , "diet_composition.csv")
)

data <- tibble::tibble(
  data_om = list(data_om),
  data_diet_composition = list(data_diet_composition)
)

sem <- create_dsem_inputs(
  data = data,
  focal_functional_group = "menhaden 0",
  diet_composition_threshold = 0.05
)

sem[["sem_tibble"]][[1]] |>
  scroll_table()

driver	target	type	param_name
zooplankton	menhaden_0	bottom_up	zooplankton_menhaden_0
phytoplankton	menhaden_0	bottom_up	phytoplankton_menhaden_0
detritus	menhaden_0	bottom_up	detritus_menhaden_0
striped_bass_2_5	menhaden_0	top_down	striped_bass_2_5_menhaden_0
striped_bass_6_plus	menhaden_0	top_down	striped_bass_6_plus_menhaden_0


sem[["sem_lines"]]
#> [1] "zooplankton -> menhaden_0, 0, zooplankton_menhaden_0\nphytoplankton -> menhaden_0, 0, phytoplankton_menhaden_0\ndetritus -> menhaden_0, 0, detritus_menhaden_0\nstriped_bass_2_5 -> menhaden_0, 0, striped_bass_2_5_menhaden_0\nstriped_bass_6_plus -> menhaden_0, 0, striped_bass_6_plus_menhaden_0"

# Fit Dynamic Structural Equation Model (DSEM)
fit_dsem <- dsem::dsem(
  sem = sem[["sem_lines"]],
  tsdata = sem[["data_time_series_sem"]][[1]],
  control = dsem::dsem_control(quiet = TRUE)
)
#> Warning in dsem::dsem(sem = sem[["sem_lines"]], tsdata =
#> sem[["data_time_series_sem"]][[1]], : The ratio of maximum and minimum Hessian
#> eigenvalues is high. Some parameters might not be identifiable.

# Display model summary
fit_dsem |>
  summary() |>
  dplyr::select(path, Estimate, Std_Error, p_value) |>
  knitr::kable(digits = 3)

path	Estimate	Std_Error
zooplankton -> menhaden_0	-1.478	0.038
phytoplankton -> menhaden_0	-2.993	0.261
detritus -> menhaden_0	-5.461	0.201
striped_bass_2_5 -> menhaden_0	-1.353	0.080
striped_bass_6_plus -> menhaden_0	0.944	0.044
year <-> year	9.534	0.339
month <-> month	3.456	0.123
zooplankton <-> zooplankton	-0.173	0.006
phytoplankton <-> phytoplankton	-0.026	0.001
detritus <-> detritus	0.009	0.000
striped_bass_2_5 <-> striped_bass_2_5	-0.026	0.001
striped_bass_6_plus <-> striped_bass_6_plus	0.035	0.001
menhaden_0 <-> menhaden_0	0.006	0.000

Next steps

Future development of {ecosystemom} will focus on expanding helper functions for initializing and configuring FIMS models directly from ecosystem operating model outputs, and better diagnosing structural matches and mismatches between modeling frameworks. Additional planned extensions include support for a broader suite of ecosystem operating models beyond Ecosim, such as Ecospace, individual-based implementations of Ecospace, and Atlantis, enabling more flexible and general ecosystem-to-assessment simulation workflows across modeling platforms.

An Ecosystem-to-Stock-Assessment Simulation Workflow using {ecosystemom}