FIMS Case Study of South Atlantic Scamp (SEFSC)

Setup description

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: 1.9.14
  • FIMS commit: 081972c
  • Stock name: South Atlantic scamp grouper
  • Region: SEFSC
  • Analyst: Kyle Shertzer
  • Analyses completed on 1 July 2024

Simplifications or modifications to the original assessment

The original stock assessment (SEDAR-68OA) was conducted using the Beaufort Assessment Model (BAM). That assessment included details not yet available in FIMS, and so the following simplifications or modifications were made to the BAM configuration to allow more direct comparisons with FIMS output. These assessments and comparisons are for demonstration only.

Simplifications or modifications:

  • Set SSB calculations to occur on Jan. 1, rather than time of peak spawning
  • Set abundance calculations for matching index data to occur on Jan. 1, rather than mid-year sampling
  • Dropped time blocks on fleets’ selectivities
  • Dropped all length compositions
  • Dropped estimation of the variation in size at age, as this parameter is not estimable without length comps.
  • Converted index data and predictions to occur in weight rather than numbers
  • Converted SSB to be female only. Because scamp are a protogynous hermaphrodite, the original assessment accounted for males implicitly by computing SSB as the sum of total (male + female) mature biomass. One way to match that accounting would be to assume that the female maturity vector equals that of both sexes combined, and then further assume that the population is 100% female. However, current FIMS (1 July 2024) does not allow deviation from a 50:50 sex ratio. Thus, for this example, FIMS and BAM use the maturity vector of both sexes combined, but apply that vector only to females and assume a 50:50 sex ratio when computing SSB.
  • Female maturity at age modeled as a logistic function, rather than empirical.
  • Extended estimates of rec devs forward in time to the terminal year. Based on likelihood profiling, values from the last two years were not estimable, and thus were fixed in the original assessment.
  • Extended estimates of rec devs backward in time to the initial year, 1969. The original assessment started estimating rec devs in 1980, when age composition data become available.
  • Dropped the fishery dependent growth curve and set mean size at age of the landings equal to the population growth curve.
  • Converted all observed and predicted landings to weight (mt). The original assessment used units native to the data collection: commercial in pounds and recreational in numbers.
  • Replaced Dirichlet-multinomial with standard multinomial distribution for fitting age comps.
  • Extended age comps to include ages 1-20+, as modeled in the population. The original assessment modeled ages 1-20+ in the population, but fitted ages 1-15+ in the age compositions because of many zeros in the 16-20 age range.
  • Dropped fishery dependent indices. This was for simplicity, as modeling those indices would require mirroring a fleet’s selectivity, which is not something I currently know how to do in FIMS.

Add your script that sets up and runs the model

Code 
###############################################
# South Atlantic scamp grouper example assessment
# Compare output from FIMS and a simplified version of BAM
##############################################

# clear memory
clear()
graphics.off()

sca <- dget("data_files/scamp32o.rdat") # get scamp data and output from simplified version of BAM

# Set dimensions
styr <- dplyr::first(sca$t.series$year)
endyr <- dplyr::last(sca$t.series$year)
years <- styr:endyr
nyears <- endyr - styr + 1 # the number of years which we have data for
nseasons <- 1 # the number of seasons in each year. FIMS currently defaults to 1
ages <- sca$a.series$age # age vector.
nages <- length(ages) # the number of age groups.

# Prepare data; initialize all values with -999 (missing)
# fleet1 is commercial, fleet2 is recreational
fleet1_landings <- fleet2_landings <- rep(-999, nyears)
fleet1_landings_cv <- fleet2_landings_cv <- rep(-999, nyears)
survey_index <- rep(-999, nyears)
survey_index_cv <- rep(-999, nyears)
fleet1_ac <- fleet2_ac <- survey_ac <- matrix(-999, nrow = nyears, ncol = nages)
fleet1_ac_n <- fleet2_ac_n <- survey_ac_n <- rep(-999, nyears)

fleet1_landings <- sca$t.series$L.COM.ob
fleet1_landings_cv <- sca$t.series$cv.L.COM
fleet1_landings_logSD <- log(sqrt(log(1.0 + fleet1_landings_cv^2)))
fleet2_landings <- sca$t.series$L.REC.ob
fleet2_landings_cv <- sca$t.series$cv.L.REC
fleet2_landings_logSD <- log(sqrt(log(1.0 + fleet2_landings_cv^2)))

survey_index <- sca$t.series$U.CVT.ob
survey_index <- replace_na(survey_index, -999)
survey_index_cv <- sca$t.series$cv.U.CVT
survey_index_logSD <- log(sqrt(log(1.0 + survey_index_cv^2)))
survey_index_logSD <- replace_na(survey_index_logSD, -999)
survey_index_logSD[nyears - 1] <- -999 # manually replacing the 2020 CV as a missing value


# COMMENT: These multinomial entries are not whole numbers and many <1.This is not technically
# correct, but it does not seem to make a difference based on my testing.
fleet1_ac_n <- sca$t.series$acomp.COM.n
fleet1_ac_n <- replace_na(fleet1_ac_n, -999)
fleet2_ac_n <- sca$t.series$acomp.REC.n
fleet2_ac_n <- replace_na(fleet2_ac_n, -999)
survey_ac_n <- sca$t.series$acomp.CVT.n
survey_ac_n <- replace_na(survey_ac_n, -999)

fleet1_ac[!is.na(sca$t.series$acomp.COM.n), ] <- sca$comp.mats$acomp.COM.ob * fleet1_ac_n[!is.na(sca$t.series$acomp.COM.n)]
fleet2_ac[!is.na(sca$t.series$acomp.REC.n), ] <- sca$comp.mats$acomp.REC.ob * fleet2_ac_n[!is.na(sca$t.series$acomp.REC.n)]
survey_ac[!is.na(sca$t.series$acomp.CVT.n), ] <- sca$comp.mats$acomp.CVT.ob * survey_ac_n[!is.na(sca$t.series$acomp.CVT.n)]



## put data into fims friendly form
res <- data.frame(
  type = character(),
  name = character(),
  age = integer(),
  datestart = character(),
  dateend = character(),
  value = double(),
  unit = character(),
  uncertainty = double()
)

fleet1_landings_df <- data.frame(
  type = "landings",
  name = "fleet1",
  age = NA,
  datestart = paste0(seq(styr, endyr), "-01-01"),
  dateend = paste0(seq(styr, endyr), "-12-31"),
  value = as.numeric(fleet1_landings),
  unit = "mt",
  uncertainty = fleet1_landings_logSD
)

fleet2_landings_df <- data.frame(
  type = "landings",
  name = "fleet2",
  age = NA,
  datestart = paste0(seq(styr, endyr), "-01-01"),
  dateend = paste0(seq(styr, endyr), "-12-31"),
  value = as.numeric(fleet2_landings),
  unit = "mt",
  uncertainty = fleet2_landings_logSD
)

survey_index_df <- data.frame(
  type = "index",
  name = "survey1",
  age = NA,
  datestart = paste0(seq(styr, endyr), "-01-01"),
  dateend = paste0(seq(styr, endyr), "-12-31"),
  value = as.numeric(survey_index),
  unit = "",
  uncertainty = survey_index_logSD
)

fleet1_ac_df <- data.frame(
  type = "age",
  name = "fleet1",
  age = rep(seq(1, nages), nyears),
  datestart = rep(paste0(seq(styr, endyr), "-01-01"), each = nages),
  dateend = rep(paste0(seq(styr, endyr), "-12-31"), each = nages),
  value = as.numeric(t(fleet1_ac)),
  unit = "",
  uncertainty = rep(fleet1_ac_n, each = nages)
)

fleet2_ac_df <- data.frame(
  type = "age",
  name = "fleet2",
  age = rep(seq(1, nages), nyears),
  datestart = rep(paste0(seq(styr, endyr), "-01-01"), each = nages),
  dateend = rep(paste0(seq(styr, endyr), "-12-31"), each = nages),
  value = as.numeric(t(fleet2_ac)),
  unit = "",
  uncertainty = rep(fleet2_ac_n, each = nages)
)

survey_ac_df <- data.frame(
  type = "age",
  name = "survey1",
  age = rep(seq(1, nages), nyears),
  datestart = rep(paste0(seq(styr, endyr), "-01-01"), each = nages),
  dateend = rep(paste0(seq(styr, endyr), "-12-31"), each = nages),
  value = as.numeric(t(survey_ac)),
  unit = "",
  uncertainty = rep(survey_ac_n, each = nages)
)

landings <- rbind(fleet1_landings_df, fleet2_landings_df)
index <- survey_index_df
agecomps <- rbind(fleet1_ac_df, fleet2_ac_df, survey_ac_df)

res <- rbind(res, landings, index, agecomps)

fims_frame <- FIMS::FIMSFrame(res)
# COMMENT: m_landings concatenates fleets' landings, and does not take fleet as an argument, as m_index or m_agecomp do
# This seems error-prone as it requires specifying landings through hard indexing (see lines 221 and 223) 
fims_fleets_landings <- FIMS::m_landings(fims_frame)
fims_survey1_index <- FIMS::m_index(fims_frame, "survey1")
fims_fleet1_agecomp <- FIMS::m_agecomp(fims_frame, "fleet1")
fims_fleet2_agecomp <- FIMS::m_agecomp(fims_frame, "fleet2")
fims_survey1_agecomp <- FIMS::m_agecomp(fims_frame, "survey1")

####################################################################################
# COMMENT: It's confusing to specify landings as an Index (lines 220 and 222)
spp_fleet1_landings <- methods::new(Index, nyears)
spp_fleet1_landings$index_data <- fims_fleets_landings[1:nyears] # NOTE: This poor coding appears necessary bc m_landings doesn't take fleet as an argument
spp_fleet2_landings <- methods::new(Index, nyears)
spp_fleet2_landings$index_data <- fims_fleets_landings[(nyears + 1):(2 * nyears)] # NOTE: See note two lines above.
spp_survey1_index <- methods::new(Index, nyears)
spp_survey1_index$index_data <- fims_survey1_index

spp_fleet1_ac <- methods::new(AgeComp, nyears, nages)
spp_fleet1_ac$age_comp_data <- fims_fleet1_agecomp
spp_fleet2_ac <- methods::new(AgeComp, nyears, nages)
spp_fleet2_ac$age_comp_data <- fims_fleet2_agecomp
spp_survey1_ac <- methods::new(AgeComp, nyears, nages)
spp_survey1_ac$age_comp_data <- fims_survey1_agecomp

####################################################################################
# set up selectivities for fleets and survey
spp_fleet1_selectivity <- methods::new(LogisticSelectivity)
spp_fleet1_selectivity$inflection_point$value <- sca$parm.cons$selpar_A50_COM2[8]
spp_fleet1_selectivity$inflection_point$is_random_effect <- FALSE
spp_fleet1_selectivity$inflection_point$estimated <- TRUE
spp_fleet1_selectivity$slope$value <- sca$parm.cons$selpar_slope_COM2[8]
spp_fleet1_selectivity$slope$is_random_effect <- FALSE
spp_fleet1_selectivity$slope$estimated <- TRUE

spp_fleet2_selectivity <- methods::new(LogisticSelectivity)
spp_fleet2_selectivity$inflection_point$value <- sca$parm.cons$selpar_A50_REC2[8]
spp_fleet2_selectivity$inflection_point$is_random_effect <- FALSE
spp_fleet2_selectivity$inflection_point$estimated <- TRUE
spp_fleet2_selectivity$slope$value <- sca$parm.cons$selpar_slope1_REC2[8]
spp_fleet2_selectivity$slope$is_random_effect <- FALSE
spp_fleet2_selectivity$slope$estimated <- TRUE

spp_survey1_selectivity <- methods::new(LogisticSelectivity)
spp_survey1_selectivity$inflection_point$value <- sca$parm.cons$selpar_A501_CVT[8]
spp_survey1_selectivity$inflection_point$is_random_effect <- FALSE
spp_survey1_selectivity$inflection_point$estimated <- TRUE
spp_survey1_selectivity$slope$value <- sca$parm.cons$selpar_slope1_CVT[8]
spp_survey1_selectivity$slope$is_random_effect <- FALSE
spp_survey1_selectivity$slope$estimated <- TRUE

####################################################################################
# Create the fleet1 object
# See all fields with show(Fleet1)
spp_fleet1 <- methods::new(Fleet)
# Set nyears and nages
spp_fleet1$nages <- nages
spp_fleet1$nyears <- nyears
# Set values for log_Fmort
spp_fleet1$log_Fmort <- log(sca$t.series$F.COM) # rep(0, nyears)
# Turn on estimation for F
spp_fleet1$estimate_F <- TRUE
spp_fleet1$random_F <- FALSE
spp_fleet1$log_obs_error <- fleet1_landings_logSD
spp_fleet1$estimate_obs_error <- FALSE
# Next two lines not currently used by FIMS
spp_fleet1$SetAgeCompLikelihood(1)
spp_fleet1$SetIndexLikelihood(1)
# Set Index, AgeComp, and Selectivity using the IDs from the modules defined above
spp_fleet1$SetObservedIndexData(spp_fleet1_landings$get_id())
spp_fleet1$SetObservedAgeCompData(spp_fleet1_ac$get_id())
spp_fleet1$SetSelectivity(spp_fleet1_selectivity$get_id())

####################################################################################
# Create the fleet2 object
# See all fields with show(fleet2)
spp_fleet2 <- methods::new(Fleet)
# Set nyears and nages
spp_fleet2$nages <- nages
spp_fleet2$nyears <- nyears
# Set values for log_Fmort
spp_fleet2$log_Fmort <- log(sca$t.series$F.REC) # rep(0, nyears)
# Turn on estimation for F
spp_fleet2$estimate_F <- TRUE
spp_fleet2$random_F <- FALSE
spp_fleet2$log_obs_error <- fleet2_landings_logSD
spp_fleet2$estimate_obs_error <- FALSE
# Next two lines not currently used by FIMS
spp_fleet2$SetAgeCompLikelihood(1)
spp_fleet2$SetIndexLikelihood(1)
# Set Index, AgeComp, and Selectivity using the IDs from the modules defined above
spp_fleet2$SetObservedIndexData(spp_fleet2_landings$get_id())
spp_fleet2$SetObservedAgeCompData(spp_fleet2_ac$get_id())
spp_fleet2$SetSelectivity(spp_fleet2_selectivity$get_id())

####################################################################################
# Create the survey object
spp_survey1 <- methods::new(Fleet) # COMMENT: it is confusing to specify surveys as "Fleet" (line 306)
spp_survey1$is_survey <- TRUE
spp_survey1$nages <- nages
spp_survey1$nyears <- nyears
spp_survey1$estimate_F <- FALSE
spp_survey1$random_F <- FALSE
spp_survey1$log_q <- log(sca$parms$q.CVT)
spp_survey1$estimate_q <- TRUE
spp_survey1$random_q <- FALSE
spp_survey1$log_obs_error <- survey_index_logSD
spp_survey1$estimate_obs_error <- FALSE
spp_survey1$SetAgeCompLikelihood(1)
spp_survey1$SetIndexLikelihood(1)
spp_survey1$SetSelectivity(spp_survey1_selectivity$get_id())
spp_survey1$SetObservedIndexData(spp_survey1_index$get_id())
spp_survey1$SetObservedAgeCompData(spp_survey1_ac$get_id())

####################################################################################
# Create population

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

recruitment$log_sigma_recruit$value <- log(sca$parm.cons$rec_sigma[8])
recruitment$log_rzero$value <- sca$parm.cons$log_R0[8]
recruitment$log_rzero$is_random_effect <- FALSE
recruitment$log_rzero$estimated <- TRUE
recruitment$logit_steep$value <- 0.999 # Scamp used the null recruitment model -log(1.0 - 0.75) + log(0.75 - 0.2)
recruitment$logit_steep$is_random_effect <- FALSE
recruitment$logit_steep$estimated <- FALSE

recruitment$estimate_log_devs <- TRUE
recruitment$log_devs <- sca$t.series$logR.dev # rep(0, nyears)

# Growth (here, empirical weight at age)
# NOTE: Use the same units as landings and ssb (here, mt)
ewaa_growth <- methods::new(EWAAgrowth)
ewaa_growth$ages <- ages
ewaa_growth$weights <- sca$a.series$wgt.mt


# Maturity
# NOTE, to match FIMS for a protogynous stock, these maturity values were obtained by fitting a logistic fcn to the age vector,
# mat.female*prop.female + mat.male*prop.male and then assuming an all female population 

maturity <- new(LogisticMaturity)
maturity$inflection_point$value <- 2.254187
maturity$inflection_point$is_random_effect <- FALSE
maturity$inflection_point$estimated <- FALSE
maturity$slope$value <- 1.659077
maturity$slope$is_random_effect <- FALSE
maturity$slope$estimated <- FALSE

# Population
population <- new(Population)

# M is vector of age1 M X nyrs then age2 M X nyrs
population$log_M <-
  log(as.numeric(matrix(
    rep(sca$a.series$M, each = nyears),
    nrow = nyears
  )))

population$estimate_M <- FALSE
population$log_init_naa <- log(sca$N.age[1, ])
population$estimate_init_naa <- FALSE
population$nages <- nages
population$ages <- ages
population$nfleets <- 3 # 2 fleets and 1 survey
population$nseasons <- nseasons
population$nyears <- nyears

# Link recruitment, growth, and maturity modules to this new popn module
population$SetMaturity(maturity$get_id())
population$SetGrowth(ewaa_growth$get_id())
population$SetRecruitment(recruitment$get_id())

####################################################################################
# Put it all together, creating the FIMS model and making the TMB fcn
success <- CreateTMBModel()
parameters <- list(p = get_fixed())
obj <- MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE)

# Fitting the model
opt <- nlminb(obj$par, obj$fn, obj$gr,
  control = list(eval.max = 800, iter.max = 800)
) # , method = "BFGS",
#   control = list(maxit=1000000, reltol = 1e-15))

# print(opt)


# TMB reporting
sdr <- TMB::sdreport(obj)
sdr_fixed <- summary(sdr, "fixed")
report <- obj$report(obj$env$last.par.best)

# print(sdr_fixed)

######################################################################
# Plot results
library(colorspace)
cols <- sequential_hcl(5, "Viridis")
out.folder <- "figures"
dir.create(out.folder, showWarnings = FALSE)
plot.type <- "png"

selex.bam.fleet1 <- 1 / (1 + exp(-sca$parm.cons$selpar_slope_COM2[8] * (ages - sca$parm.cons$selpar_A50_COM2[8])))
selex.fims.fleet1 <- 1 / (1 + exp(-opt$par[2] * (ages - opt$par[1])))
selex.bam.fleet2 <- 1 / (1 + exp(-sca$parm.cons$selpar_slope1_REC2[8] * (ages - sca$parm.cons$selpar_A50_REC2[8])))
selex.fims.fleet2 <- 1 / (1 + exp(-opt$par[4] * (ages - opt$par[3])))
selex.bam.survey <- 1 / (1 + exp(-sca$parm.cons$selpar_slope1_CVT[8] * (ages - sca$parm.cons$selpar_A501_CVT[8])))
selex.fims.survey <- 1 / (1 + exp(-opt$par[6] * (ages - opt$par[5])))


index_results_allyr <- data.frame(
  yr = styr:endyr,
  observed = spp_survey1_index$index_data,
  fims.expected = report$exp_index[[3]],
  bam.expected = sca$t.series$U.CVT.pr
)
index_results <- index_results_allyr %>% filter(observed != -999.00)
fleet1_landings_results <- data.frame(
  yr = styr:endyr,
  observed = spp_fleet1_landings$index_data,
  fims.expected = report$exp_index[[1]],
  bam.expected = sca$t.series$L.COM.pr
)
fleet2_landings_results <- data.frame(
  yr = styr:endyr,
  observed = spp_fleet2_landings$index_data,
  fims.expected = report$exp_index[[2]],
  bam.expected = sca$t.series$L.REC.pr
)

fleet1_F_results <- data.frame(
  yr = styr:endyr,
  fims.F.fleet1 = report$F_mort[[1]],
  bam.F.fleet1 = sca$t.series$F.COM
)
fleet2_F_results <- data.frame(
  yr = styr:endyr,
  fims.F.fleet2 = report$F_mort[[2]],
  bam.F.fleet2 = sca$t.series$F.REC
)

# Dropping the last (extra) year from FIMS output, assuming it is a projection yr (not an initialization yr)
fims.naa <- matrix(report$naa[[1]], ncol = nages, byrow = TRUE)
fims.naa <- fims.naa[-54, ]
popn_results <- data.frame(
  yr = styr:endyr,
  fims.ssb = report$ssb[[1]][1:nyears],
  fims.recruits = report$recruitment[[1]][1:nyears] / 1000,
  fims.biomass = report$biomass[[1]][1:nyears],
  fims.abundance = rowSums(fims.naa) / 1000,
  bam.ssb = sca$t.series$SSB,
  bam.recruits = sca$t.series$recruits / 1000,
  bam.biomass = sca$t.series$B,
  bam.abundance = sca$t.series$N / 1000
)

yr.ind <- 1:nyears

yr.fleet1.ind <- yr.ind[fleet1_ac_n >= 0]
yr.fleet1.ac <- years[yr.fleet1.ind]
fims.fleet1.ncaa <- matrix(report$cnaa[[1]], ncol = nages, byrow = TRUE)
fims.fleet1.ncaa <- fims.fleet1.ncaa[yr.fleet1.ind, ]
fims.fleet1.caa <- fims.fleet1.ncaa / rowSums(fims.fleet1.ncaa)
bam.fleet1.caa <- sca$comp.mats$acomp.COM.pr
obs.fleet1.caa <- sca$comp.mats$acomp.COM.ob

yr.fleet2.ind <- yr.ind[fleet2_ac_n >= 0]
yr.fleet2.ac <- years[yr.fleet2.ind]
fims.fleet2.ncaa <- matrix(report$cnaa[[2]], ncol = nages, byrow = TRUE)
fims.fleet2.ncaa <- fims.fleet2.ncaa[yr.fleet2.ind, ]
fims.fleet2.caa <- fims.fleet2.ncaa / rowSums(fims.fleet2.ncaa)
bam.fleet2.caa <- sca$comp.mats$acomp.REC.pr
obs.fleet2.caa <- sca$comp.mats$acomp.REC.ob

yr.survey.ind <- yr.ind[survey_ac_n >= 0]
yr.survey.ac <- years[yr.survey.ind]
fims.survey.ncaa <- matrix(report$cnaa[[3]], ncol = nages, byrow = TRUE)
fims.survey.ncaa <- fims.survey.ncaa[yr.survey.ind, ]
fims.survey.caa <- fims.survey.ncaa / rowSums(fims.survey.ncaa)
bam.survey.caa <- sca$comp.mats$acomp.CVT.pr
obs.survey.caa <- sca$comp.mats$acomp.CVT.ob
######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_tseries_fits.", plot.type, sep = ""), width = 8, height = 10, units="in", res=72)
mat <- matrix(1:3, ncol = 1)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(4.1, 4.25, 1.0, 0.5), cex = 1)

plot(index_results$yr, index_results$observed,
  ylim = c(0, max(index_results[, -1])),
  pch = 16, col = cols[1], ylab = "Index", xlab = ""
)
lines(index_results$yr, index_results$bam.expected, lwd = 3, col = cols[2])
lines(index_results$yr, index_results$fims.expected, lwd = 3, col = cols[4])
legend("topright",
  legend = c("observed", "BAM expected", "FIMS expected"),
  pch = c(16, -1, -1), lwd = c(-1, 3, 3), col = c(cols[1], cols[2], cols[4])
)

plot(fleet1_landings_results$yr, fleet1_landings_results$observed,
  ylim = c(0, max(fleet1_landings_results[, -1])),
  pch = 16, col = cols[1], ylab = "Fleet1 landings (mt)", xlab = ""
)
lines(fleet1_landings_results$yr, fleet1_landings_results$bam.expected, lwd = 3, col = cols[2])
lines(fleet1_landings_results$yr, fleet1_landings_results$fims.expected, lwd = 3, col = cols[4])

plot(fleet2_landings_results$yr, fleet2_landings_results$observed,
  ylim = c(0, max(fleet2_landings_results[, -1])),
  pch = 16, col = cols[1], ylab = "Fleet2 landings (mt)", xlab = ""
)
lines(fleet2_landings_results$yr, fleet2_landings_results$bam.expected, lwd = 3, col = cols[2])
lines(fleet2_landings_results$yr, fleet2_landings_results$fims.expected, lwd = 3, col = cols[4])

dev.off()

######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_tseries_F.", plot.type, sep = ""), width = 8, height = 8, units="in", res=72)
mat <- matrix(1:2, ncol = 1)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(4.1, 4.25, 1.0, 0.5), cex = 1)

plot(fleet1_F_results$yr, fleet1_F_results$bam.F.fleet1,
  ylim = c(0, max(fleet1_F_results[, -1])),
  type = "l", lwd = 3, col = cols[2], ylab = "Fleet 1 F", xlab = ""
)
lines(fleet1_F_results$yr, fleet1_F_results$fims.F.fleet1, lwd = 3, col = cols[4])
legend("topleft",
  legend = c("BAM predicted", "FIMS predicted"),
  lwd = c(3, 3), col = c(cols[2], cols[4])
)
plot(fleet2_F_results$yr, fleet2_F_results$bam.F.fleet2,
  ylim = c(0, max(fleet2_F_results[, -1])),
  type = "l", lwd = 3, col = cols[2], ylab = "Fleet 2 F", xlab = ""
)
lines(fleet2_F_results$yr, fleet2_F_results$fims.F.fleet2, lwd = 3, col = cols[4])

dev.off()

######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_selex.", plot.type, sep = ""), width = 8, height = 10, units="in", res=72)
mat <- matrix(1:3, ncol = 1)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(4.1, 4.25, 1.0, 0.5), cex = 1)

plot(ages, selex.bam.fleet1, lwd = 3, col = cols[2], type = "l", xlab = "", ylab = "Fleet1 selectivity")
lines(ages, selex.fims.fleet1, lwd = 3, col = cols[4])
legend("bottomright",
  legend = c("BAM predicted", "FIMS predicted"),
  lwd = c(3, 3), col = c(cols[2], cols[4])
)
plot(ages, selex.bam.fleet2, lwd = 3, col = cols[2], type = "l", xlab = "", ylab = "Fleet2 selectivity")
lines(ages, selex.fims.fleet2, lwd = 3, col = cols[4])
plot(ages, selex.bam.survey, lwd = 3, col = cols[2], type = "l", xlab = "Age", ylab = "Survey selectivity")
lines(ages, selex.fims.survey, lwd = 3, col = cols[4])

dev.off()



######################################################################

png(filename = paste(out.folder, "/SEFSC_scamp_tseries_popn.", plot.type, sep = ""), width = 8, height = 7, units="in", res=72)
mat <- matrix(1:4, ncol = 2)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(4.1, 4.25, 1.0, 0.5), cex = 1)

plot(popn_results$yr, popn_results$bam.ssb,
  ylim = c(0, max(popn_results[, c(2, 6)])),
  type = "l", lwd = 3, col = cols[2], ylab = "SSB (mt)", xlab = ""
)
lines(popn_results$yr, popn_results$fims.ssb, lwd = 3, col = cols[4])
legend("topleft",
  legend = c("BAM predicted", "FIMS predicted"),
  lwd = c(3, 3), col = c(cols[2], cols[4])
)

plot(popn_results$yr, popn_results$bam.biomass,
  ylim = c(0, max(popn_results[, c(4, 8)])),
  type = "l", lwd = 3, col = cols[2], ylab = "Biomass (mt)", xlab = ""
)
lines(popn_results$yr, popn_results$fims.biomass, lwd = 3, col = cols[4])

plot(popn_results$yr, popn_results$bam.recruits,
  ylim = c(0, max(popn_results[, c(3, 7)])),
  type = "l", lwd = 3, col = cols[2], ylab = "Recruits (1000s)", xlab = ""
)
lines(popn_results$yr, popn_results$fims.recruits, lwd = 3, col = cols[4])

plot(popn_results$yr, popn_results$bam.abundance,
  ylim = c(0, max(popn_results[, c(5, 9)])),
  type = "l", lwd = 3, col = cols[2], ylab = "Abundance (1000s)", xlab = ""
)
lines(popn_results$yr, popn_results$fims.abundance, lwd = 3, col = cols[4])

dev.off()

######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_caa_fleet1.", plot.type, sep = ""), width = 8, height = 11, units="in", res=72)
mat <- matrix(1:18, ncol = 3)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(2.2, 2.7, 0.5, 0.5), cex = 0.75)

for (i in 1:nrow(obs.fleet1.caa))
{
  plot(1:nages, obs.fleet1.caa[i, ], col = cols[1], xlab = "", ylab = "", pch = 16)
  lines(1:nages, bam.fleet1.caa[i, ], lwd = 3, col = cols[2])
  lines(1:nages, fims.fleet1.caa[i, ], lwd = 3, col = cols[4])
  if (i > 1) legend("topright", legend = yr.fleet1.ac[i], cex = 1, bty = "n")
  if (i == 1) {
    legend("topright",
      legend = c("Fleet1 Age Comps", "observed", "BAM expected", "FIMS expected"),
      pch = c(-1, 16, -1, -1), lwd = c(-1, -1, 3, 3), col = c(cols[1], cols[1], cols[2], cols[4]), cex = 0.7
    )
    legend("right", legend = yr.fleet1.ac[i], cex = 1, bty = "n")
  }
}

dev.off()

######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_caa_fleet2.", plot.type, sep = ""), width = 8, height = 11, units="in", res=72)

mat <- matrix(1:28, ncol = 4)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(2.2, 2.7, 0.5, 0.5), cex = 0.75)

for (i in 1:nrow(obs.fleet2.caa))
{
  plot(1:nages, obs.fleet2.caa[i, ], col = cols[1], xlab = "", ylab = "", pch = 16)
  lines(1:nages, bam.fleet2.caa[i, ], lwd = 3, col = cols[2])
  lines(1:nages, fims.fleet2.caa[i, ], lwd = 3, col = cols[4])
  if (i > 1) legend("topright", legend = yr.fleet2.ac[i], cex = 1, bty = "n")
  if (i == 1) {
    legend("topright",
      legend = c("Fleet2 Age Comps", "observed", "BAM expected", "FIMS expected"),
      pch = c(-1, 16, -1, -1), lwd = c(-1, -1, 3, 3), col = c(cols[1], cols[1], cols[2], cols[4]), cex = 0.5
    )
    legend("right", legend = yr.fleet2.ac[i], cex = 1, bty = "n")
  }
}

dev.off()

######################################################################
png(filename = paste(out.folder, "/SEFSC_scamp_caa_survey.", plot.type, sep = ""), width = 8, height = 11, units="in", res=72)

mat <- matrix(1:30, ncol = 5)
layout(mat = mat, widths = rep.int(1, ncol(mat)), heights = rep.int(1, nrow(mat)))
par(las = 1, mar = c(2.2, 2.7, 0.5, 0.5), cex = 0.75)

for (i in 1:nrow(obs.survey.caa))
{
  plot(1:nages, obs.survey.caa[i, ], col = cols[1], xlab = "", ylab = "", pch = 16)
  lines(1:nages, bam.survey.caa[i, ], lwd = 3, col = cols[2])
  lines(1:nages, fims.survey.caa[i, ], lwd = 3, col = cols[4])
  if (i > 1) legend("topright", legend = yr.survey.ac[i], cex = 1, bty = "n")
  if (i == 1) {
    legend("topright",
      legend = c("Survey Age Comps", "observed", "BAM expected", "FIMS expected"),
      pch = c(-1, 16, -1, -1), lwd = c(-1, -1, 3, 3), col = c(cols[1], cols[1], cols[2], cols[4]), cex = 0.5
    )
    legend("right", legend = yr.survey.ac[i], cex = 1, bty = "n")
  }
}

dev.off()

clear()

Comparison figures

Fits-tseries

Fits-CAA-fleet1

Fits-CAA-fleet2

Fits-CAA-survey

Predicted-selex

Predicted-tseries-F

Predicted-tseries-population

What was your experience using FIMS? What could we do to improve usability?

  • Great joy when it finally worked! But, it took a lot of time to first simplify the corresponding BAM implementation and then to run FIMS successfully.
  • Data input seems complex for now. I was able to mimic previous examples, but would not likely have been successful starting from scratch. A user interface will presumably help simplify the process.
  • Clear distinction between “fleet” and “index” would be helpful. Examples of this confusion include 1) landings are defined using methods::new(Index, nyears) and spp_fleet1$SetObservedIndexData(spp_fleet1_landings$get_id()), and 2) surveys are defined using methods::new(Fleet).
  • For simplification of the scamp example, I dropped two fishery dependent indices. My impression is that, in FIMS, fishery dependent indices would need to be defined as their own “fleet” and then somehow mirror the selectivity of the corresponding fishing fleet, presumably using TMB’s mapping feature. I think it would be more straightforward if a fishery dependent index could be defined as an object linked to the fleet, similar to how landings and age comp data are handled.
  • When a model has multiple fleets, the function FIMS::m_landings concatenates all fleets’ landings into one long vector. Thus, one must specify correct indexing values when pulling landings by fleet from that concatenation. This seems clunky and error-prone, and it would be preferable if FIMS::m_landings took fleet as an argument, similar to FIMS::m_agecomp, so that landings by fleet could be obtained by fleet reference.

List any issues that you ran into or found

The FIMS feature to assign proportion female at age is not yet functional and it is hard-coded using a 50:50 sex ratio. Many stocks in the southeast are protogynous hermphrodites, such that individuals start life as females and later convert to males. This life history creates a sex ratio that is tilted toward females for younger ages and males for older ages.

What features are most important to add based on this case study?

  • Discards. I chose this assessment as a case study because it did not explicitly model discards, and I knew that capability was not yet available in FIMS. However, most assessments in the Southeast model dead discards.
  • Fitting to length composition data.

Acknowledgments

Thanks to Nathan Vaughan and Ian Taylor, who helped troubleshoot earlier versions of the code.