NWFSC Case Study Petrale Sole

The setup

Code

# Names of required packages
packages <- c("dplyr", "tidyr", "ggplot2", "TMB", "reshape2", "here", "remotes", "lubridate")

# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages], repos = "http://cran.us.r-project.org")
}

remotes::install_github("kaskr/TMB_contrib_R/TMBhelper")
remotes::install_github("NOAA-FIMS/FIMS")
remotes::install_github("r4ss/r4ss")

# Load packages
invisible(lapply(packages, library, character.only = TRUE))

library(FIMS)
library(TMBhelper)

R_version <- version$version.string
TMB_version <- packageDescription("TMB")$Version
FIMS_commit <- substr(packageDescription("FIMS")$GithubSHA1, 1, 7)

Code

theme_set(theme_bw())

R version: R version 4.4.1 (2024-06-14)
TMB version: 1.9.15
FIMS commit: f0d4e76
Stock name: West Coast Petrale Sole
Region: NWFSC
Analyst: Ian G. Taylor

Simplifications to the original assessment

The operational petrale sole stock assessment used Stock Synthesis (SS3) and included numerous data types and population dynamic assumptions that are not yet available in FIMS. A simplified SS3 model was also developed to provide a closer comparison but is still a work in progress. This is intended as a demonstration and nothing more.

For both the FIMS model and the simplified SS3 model, I made the following changes:

Remove data
- Remove lengths
- Remove male ages
- Remove discard fractions and discard comps
Simplify selectivity
- Remove length-based retention functions
- Convert to age-based logistic (from length-based double-normal, fixed asymptotic)
Remove parameter priors (on M and h)
Use female mean weight at age as calculated from the parametric growth curves
Varying Index CV to constant over time
Varying Catch ESS to constant over time

Script to prepare data for building FIMS object

Code

# read SS3 input files from petrale sole assessment on github
petrale_input <- r4ss::SS_read("https://raw.githubusercontent.com/pfmc-assessments/petrale/main/models/2023.a034.001/")

# # reading SS3 output doesn't work from github, so just using hard-wired values from model output
# petrale_output <- r4ss::SS_output("https://raw.githubusercontent.com/pfmc-assessments/petrale/main/models/2023.a034.001/")

# generic names for SS3 data and control files could be useful in future generalized version of this code
ss3dat <- petrale_input$dat
ss3ctl <- petrale_input$ctl

# define the dimensions
years <- seq(ss3dat$styr, ss3dat$endyr)
nyears <- length(years)
nseasons <- 1
# ages <- 0:ss3dat$Nages # population ages in SS3, starts at age 0
ages <- 1:17 # same as data bins
nages <- length(ages)

# source function to simplify data and convert from SS3 format
source("R/get_ss3_data.R")

# fleet 4 used conditional age-at-length data with marginal observations
# entered as fleet == -4 (to exclude from likelihood due to redundancy)
# using only marginals for FIMS and exclude CAAL data by filtering out
# the fleet 4 age data
# similarly, some early ages were excluded from fleet 1 in the original model
# by assigning to fleet -1, these get filtered by get_ss3_data()

# only include age comps with fleet = -4 or 1:
ss3dat$agecomp <- ss3dat$agecomp |> dplyr::filter(fleet %in% c(-4, 1))
ss3dat$agecomp$fleet <- abs(ss3dat$agecomp$fleet)

# convert SS3 data into FIMS format using function defined in the R directory
mydat <- get_ss3_data(ss3dat, fleets = c(1,4), ages = ages)

# rename fleet4 as fleet2 (fleets 2 and 3 already removed above)
mydat <- mydat |>
  dplyr::mutate(name = dplyr::case_when(
    name == "fleet1" ~ name,
    name == "fleet4" ~ "fleet2" # change fleet4 to fleet2
  ))

Setup FIMS model

Code

## set up FIMS data objects
clear()
clear_logs()

# I don't know what these commands are doing
age_frame <- FIMS::FIMSFrame(mydat) 
fishery_catch <- FIMS::m_landings(age_frame) # filtering for the landings only
fishery_agecomp <- FIMS::m_agecomp(age_frame, "fleet1") # filtering for ages from fleet 1
survey_index <- FIMS::m_index(age_frame, "fleet2") # filtering for index data from fleet 2
survey_agecomp <- FIMS::m_agecomp(age_frame, "fleet2") # filtering for ages from fleet 2

fish_index <- methods::new(Index, nyears)
fish_index$index_data <- fishery_catch
fish_age_comp <- methods::new(AgeComp, nyears, nages)
# Q: I'm confused about FIMSFrame being set up with age comps in proportions
#   vs here needing age comps in numbers
# A: It's just not sorted out in FIMS yet, in the future this could be made simpler
fish_age_comp$age_comp_data <- age_frame@data |>
  dplyr::filter(type == "age" & name == "fleet1") |>
  dplyr::mutate(n = value * uncertainty) |>
  dplyr::pull(n) |>
  round(1)


# switches to turn on or off estimation
estimate_fish_selex <- TRUE
estimate_survey_selex <- TRUE
estimate_q <- TRUE
estimate_F <- TRUE
estimate_recdevs <- TRUE
estimate_init_naa <- FALSE
estimate_log_rzero <- TRUE

### set up fishery
## methods::show(DoubleLogisticSelectivity)
fish_selex <- methods::new(LogisticSelectivity)

# SS3 model had length-based selectivity which leads to sex-specific
# age-based selectivity due to sexually-dimorphic growth.
# I didn't bother to calculate an age-based inflection point averaged over sexes
fish_selex$inflection_point$value <- 10
fish_selex$inflection_point$is_random_effect <- FALSE
fish_selex$inflection_point$estimated <- estimate_fish_selex
fish_selex$slope$value <- 2
fish_selex$slope$is_random_effect <- FALSE
fish_selex$slope$estimated <- estimate_fish_selex

## create fleet object for fishing fleet
fish_fleet <- methods::new(Fleet)
fish_fleet$nages <- nages
fish_fleet$nyears <- nyears
fish_fleet$log_Fmort <- log(rep(0.00001, nyears))
fish_fleet$estimate_F <- estimate_F
fish_fleet$random_F <- FALSE
fish_fleet$log_q <- 0
fish_fleet$estimate_q <- FALSE
fish_fleet$random_q <- FALSE
fish_fleet$log_obs_error <- rep(log(sqrt(log(0.01^2 + 1))), nyears)

# Set Index, AgeComp, and Selectivity using the IDs from the modules defined above
fish_fleet$SetAgeCompLikelihood(1)
fish_fleet$SetIndexLikelihood(1)
fish_fleet$SetObservedIndexData(fish_index$get_id())
fish_fleet$SetObservedAgeCompData(fish_age_comp$get_id())
fish_fleet$SetSelectivity(fish_selex$get_id())

## Setup survey
survey_fleet_index <- methods::new(Index, nyears)
survey_age_comp <- methods::new(AgeComp, nyears, nages)
survey_fleet_index$index_data <- survey_index
survey_age_comp$age_comp_data <- mydat |>
  dplyr::filter(type == "age" & name == "fleet2") |>
  dplyr::mutate(n = value * uncertainty) |>
  dplyr::pull(n)

## survey selectivity: ascending logistic
## methods::show(DoubleLogisticSelectivity)
survey_selex <- new(LogisticSelectivity)
survey_selex$inflection_point$value <- 6
survey_selex$inflection_point$is_random_effect <- FALSE
survey_selex$inflection_point$estimated <- estimate_survey_selex
survey_selex$slope$value <- 2
survey_selex$slope$is_random_effect <- FALSE
survey_selex$slope$estimated <- estimate_survey_selex

## create fleet object for survey
survey_fleet <- methods::new(Fleet)
survey_fleet$is_survey <- TRUE
survey_fleet$nages <- nages
survey_fleet$nyears <- nyears
survey_fleet$estimate_F <- FALSE
survey_fleet$random_F <- FALSE
survey_fleet$log_q <- 1.4 # petrale sole catchability estimated ~4.0 = exp(1.4)
survey_fleet$estimate_q <- estimate_q
survey_fleet$random_q <- FALSE
# Q: why can't the index uncertainty come from FIMSFrame?
survey_fleet$log_obs_error <- age_frame@data |>
  dplyr::filter(type == "index" & name == "fleet2") |>
  dplyr::pull(uncertainty) |>
  log()

survey_fleet$SetAgeCompLikelihood(1)
survey_fleet$SetIndexLikelihood(1)
survey_fleet$SetSelectivity(survey_selex$get_id())
survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
survey_fleet$SetObservedAgeCompData(survey_age_comp$get_id())

# Population module

# recruitment
recruitment <- methods::new(BevertonHoltRecruitment)
# methods::show(BevertonHoltRecruitment)

# petrale sigmaR is 0.5
recruitment$log_sigma_recruit$value <- log(ss3ctl$SR_parms["SR_sigmaR", "INIT"])
# petrale log(R0) is around 9.6 (where R0 is in thousands)
recruitment$log_rzero$value <- ss3ctl$SR_parms["SR_LN(R0)", "INIT"]
# Q: do we need to account for SS3 R0 in thousands?
# A: formula below is thanks to Bai Li
#    in https://github.com/NOAA-FIMS/case-studies/commit/d7c0d645a18766d030f632e5818d91764e2297ef
# but did not produce good results (perhaps due to some other issue) 
# recruitment$log_rzero$value <- log(exp(ss3ctl$SR_parms["SR_LN(R0)", "INIT"])*1000)
recruitment$log_rzero$is_random_effect <- FALSE
recruitment$log_rzero$estimated <- estimate_log_rzero
# petrale steepness is fixed at 0.8
steep <- ss3ctl$SR_parms["SR_BH_steep", "INIT"]
recruitment$logit_steep$value <- -log(1.0 - steep) + log(steep - 0.2)
recruitment$logit_steep$is_random_effect <- FALSE
recruitment$logit_steep$estimated <- FALSE

recruitment$estimate_log_devs <- estimate_recdevs
# Q: why are parameters "log_devs" when output is "report$log_recruit_dev"?
# and are they multipliers, not deviations from zero?
# needed to change from 1 to 0 to get stable population
recruitment$log_devs <- rep(0, nyears) # set to no deviations (multiplier) to start

# growth
ewaa_growth <- methods::new(EWAAgrowth)
ewaa_growth$ages <- ages
# NOTE: getting weight-at-age vector from
# petrale_output$wtatage |>
#   dplyr::filter(Sex == 1 & Fleet == -1 & Yr == 1876) |>
#   dplyr::select(paste(0:40)) |>
#   round(4)
ewaa_growth$weights <- c(
  # 0.0010,  # age 0
  0.0148, 0.0617, 0.1449, 0.2570, 0.3876, 0.5260, 0.6640, 0.7957, 0.9175,
  1.0273, 1.1247, 1.2097, 1.2831, 1.3460, 1.3994, 1.4446, 1.4821
)

# maturity
maturity <- new(LogisticMaturity)
# approximate age-based equivalent to length-based maturity in petrale model
# based on looking at model$endgrowth |> dplyr::filter(Sex == 1) |> dplyr::select(Age_Beg, Len_Mat)
maturity$inflection_point$value <- 6.5
maturity$inflection_point$is_random_effect <- FALSE
maturity$inflection_point$estimated <- FALSE
maturity$slope$value <- 2 # arbitrary guess
maturity$slope$is_random_effect <- FALSE
maturity$slope$estimated <- FALSE

# population
population <- new(Population)
# petrale natural mortality is estimated around 0.14
M_value <- ss3ctl$MG_parms["NatM_p_1_Fem_GP_1", "INIT"]
population$log_M <- rep(log(M_value), nages * nyears)
population$estimate_M <- FALSE
# initial numbers at age based on R0 + mortality
init_naa <- exp(recruitment$log_rzero$value) * exp(-(ages - 1) * M_value)
init_naa[nages] <- init_naa[nages] / M_value # sum of infinite series
population$log_init_naa <- log(init_naa)
population$estimate_init_naa <- estimate_init_naa
population$nages <- nages
population$ages <- ages
population$nfleets <- 2 # fleets plus surveys
population$nseasons <- nseasons
population$nyears <- nyears

population$SetMaturity(maturity$get_id())
population$SetGrowth(ewaa_growth$get_id())
population$SetRecruitment(recruitment$get_id())

Run FIMS model

Code

# make FIMS model
success <- CreateTMBModel()
parameters <- list(p = get_fixed())
obj <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE)
opt <- nlminb(obj$par, obj$fn, obj$gr,
  control = list(eval.max = 10000, iter.max = 10000)
)
print(opt)
# sdr <- TMB::sdreport(obj)
# sdr_fixed <- summary(sdr, "fixed")
# print(sdr_fixed)

Get FIMS output and plot results

Code

report <- obj$report(obj$env$last.par.best)
# copy input data to use as basis for results
results_frame <- age_frame@data
results_frame$expected <- NA
# convert date string to numeric year
results_frame <- results_frame |>
  dplyr::mutate(year = lubridate::as_date(datestart) |> lubridate::year())
# add expected index to data frame
results_frame$expected[results_frame$type == "index" & results_frame$name == "fleet2"] <-
  report$exp_index[[2]]
# add estimated catch to data frame
results_frame$expected[results_frame$type == "landings" & results_frame$name == "fleet1"] <-
  report$exp_catch[[1]]
# add estimated age comps to data frame
for (fleet in 1:2) {
  # copy Cole's approach to rescaling expected comps to proportions
  x1 <- matrix(report$cnaa[[fleet]], ncol = nages, byrow = TRUE)
  x1 <- x1 / rowSums(x1)
  dimnames(x1) <- list(year = years, age = ages)
  x1 <- reshape2::melt(x1, value.name = "paa") |>
    dplyr::mutate(type = "age", name = paste0("fleet", fleet))
  # add expected proportions into results_frame
  results_frame <-
    # add paa for age comps (will be NA for all other types)
    dplyr::left_join(x = results_frame, y = x1) |>
    # replace value column with paa for age data within this fleet (when not NA)
    dplyr::mutate(expected = dplyr::case_when(is.na(paa) ~ expected, TRUE ~ paa)) |>
    dplyr::select(-paa) # remove temporary paa column
}

# plot catch fit
results_frame |>
  dplyr::filter(type == "landings" & value != -999) |>
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  xlab("Year") +
  ylab("Catch (mt)") +
  geom_line(aes(x = year, y = expected), color = "blue") +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_fit_catch.png")

# plot index fit
results_frame |>
  dplyr::filter(type == "index" & value != -999) |>
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  xlab("Year") +
  ylab("Index") +
  geom_line(aes(x = year, y = expected), color = "blue") +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_fit_index.png")

# plot age comp fits
# age comps for fleet 1
results_frame |>
  dplyr::filter(type == "age" & name == "fleet1" & value != -999) |>
  ggplot(aes(x = age, y = value)) +
  # note: dir = "v" sets vertical direction to fill the facets which
  # makes comparison of progression of cohorts easier to see
  facet_wrap(vars(year), dir = "v") +
  geom_point() +
  xlab("Age") +
  ylab("Proportion") +
  geom_line(aes(x = age, y = expected), color = "blue") +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_fit_comps_fleet1.png")
# age comps for fleet 2
results_frame |>
  dplyr::filter(type == "age" & name == "fleet2" & value != -999) |>
  ggplot(aes(x = age, y = value)) +
  # note: dir = "v" sets vertical direction to fill the facets which
  # makes comparison of progression of cohorts easier to see
  facet_wrap(vars(year), dir = "v") +
  geom_point() +
  xlab("Age") +
  ylab("Proportion") +
  geom_line(aes(x = age, y = expected), color = "blue") +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_fit_comps_fleet2.png")

# assemble time series output
timeseries <- rbind(
  data.frame(
    year = c(years, max(years) + 1),
    type = "ssb",
    value = report$ssb[[1]]
  ),
  data.frame(
    year = c(years, max(years) + 1),
    type = "biomass",
    value = report$biomass[[1]]
  ),
  data.frame(
    year = c(years),
    type = "recruitment",
    value = report$recruitment[[1]][1:nyears] # final value was 0
  ),
  data.frame(
    year = c(years),
    type = "F_mort",
    value = report$F_mort[[1]]
  )
)
# plot time series of 4 quantities
timeseries |>
  ggplot(aes(x = year, y = value)) +
  facet_wrap(vars(type), scales = "free") +
  geom_line() +
  expand_limits(y = 0) +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_time_series.png")

Compare results to SS3 models (still a work in progress)

Code

# load saved time series
timeseries_compare <- readRDS("data_files/NWFSC-petrale-SS3-timeseries.rds")
# add FIMS output to time series table
timeseries_compare <- timeseries |>
  dplyr::mutate(platform = "FIMS") |>
  rbind(timeseries_compare)
# make plot comparing time series
timeseries_compare |>
  ggplot(aes(year, value, color = platform)) +
  geom_line() +
  facet_wrap("type", scales = "free") +
  ylim(0, NA) +
  labs(x = NULL, y = NULL) +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_timeseries_comparison.png")
# numbers at age as a matrix
naa <- matrix(report$naa[[1]], ncol = nages, byrow = TRUE)
# numbers at age as a long data frame
naa_df <- tidyr::expand_grid(year = c(years, max(years) + 1), age = ages) |>
  data.frame(naa = report$naa[[1]])
# bubble plot of numbers at age
naa_df |>
  ggplot(aes(x = year, y = age, size = naa)) +
  geom_point(alpha = 0.2) +
  theme_bw()

Code

ggsave("figures/NWFSC-petrale_numbers_at_age.png")
clear()
# end "if (FALSE)" block to only run if model is working

Plots

Catch fit

Fit to catch ### Index fit Fit to index ### Age comp fits Fit to fishery age comps Fit to survey age comps ### Time series comparisons with SS3 model Time series comparison with SS3 models