In Bai-Li-NOAA/IFA4EBFM: Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

knitr::opts_chunk$set(echo = TRUE)

devtools::load_all()

required_pkg <- c(
  "RColorBrewer", "ggplot2", "tidyverse",
  "reshape2", "here"
)

pkg_to_install <- required_pkg[!(required_pkg %in%
  installed.packages()[, "Package"])]
if (length(pkg_to_install)) install.packages(pkg_to_install, repos = "http://cran.us.r-project.org")

invisible(lapply(required_pkg, library, character.only = TRUE))

options(digits = 2)

EwE scenario

We used the output data from EwE AMO_PCP scenario for generating single species stock assessment input data. The AMO_PCP scenario incorporates Atlantic Multidecadal Oscillation and precipitation information in the ecosystem modeling work and it creates a strong response.

devtools::load_all()

set.seed(999)

# specify working directories ---------------------------------------------

project_path <- here::here()
ewe_output_path <- file.path(project_path, "data", "ewe", "7ages", "ecosim_with_environmental_driver", "amo_pcp")
menhadenSA_output_path <- file.path(project_path, "data", "AtlanticMenhadenSA")

# read Atlantic menhaden Beaufort Assessment Model output data --------

menhadenSA_output <- dget(file.path(menhadenSA_output_path, "am019.rdat"))

years <- 1985:2017
ages <- 0:6

skip_nrows <- 8
species <- 4:10
species_labels <- paste0("age", ages)
species_vec <- paste0("X", species)

# Load biomass data
temp <- scan(file.path(ewe_output_path, "biomass_annual.csv"), what = "", sep = "\n")
data <- temp[-c(1:skip_nrows)]
data <- read.table(
  text = as.character(data),
  sep = ",",
  col.names = read.table(text = temp[skip_nrows], sep = ",")
)

data <- data[, c("year.group", species_vec)]
data[, species_vec] <- data[, species_vec] * 1000000  # biomass in mt
colnames(data) <- c("year", species_labels)

#data[, species_labels] <- data[, species_labels] / rowSums(data[, species_labels])
baa <- reshape2::melt(data, id.vars = c("year"))
colnames(baa) <- c("Year", "Age", "Biomass")

p <- ggplot(data=baa, aes(x=Age, fill=Age, y=Biomass)) +
      geom_bar(position='dodge', stat='identity') +
      facet_wrap(~Year) +
      labs(
        title = "Biomass-at-age",
        x = "Age",
        y = "Proportion"
      )+
      theme_bw()+
      theme(axis.text.x = element_text(angle = 45))
  print(p)

`IFA4EBFM::create_fishery()` is used to simulate fishery data

Input data
EwE annual catch-at-age and weight-at-age data
BAM annual Coefficient of variation (CV) of catch and sample size data.
Output data
Ecosystem operating model total catch in biomass, total catch in abundance, catch-at-age in abundance, catch-at-age in biomass, CV of catch, sample size, and weight-at-age data
Observed total catch in biomass and catch-at-age in proportion for one fleet
Units information

# Create fishery ----------------------------------------------------------

fishery_sample_num <- cbind(
  menhadenSA_output$t.series$acomp.cRs.n[which(menhadenSA_output$t.series$year %in% years)],
  menhadenSA_output$t.series$acomp.cBs.n[which(menhadenSA_output$t.series$year %in% years)],
  menhadenSA_output$t.$acomp.cBn.n[which(menhadenSA_output$t.series$year %in% years)],
  menhadenSA_output$t.series$acomp.cRn.n[which(menhadenSA_output$t.series$year %in% years)]
)
fishery_sample_num[fishery_sample_num==-99999] <- 0

fishery <- create_fishery(
  file_path = file.path(ewe_output_path, "catch_annual.csv"),
  skip_nrows = 8,
  species = 4:10,
  species_labels = paste0("age", ages),
  ewe_years = 0:32,
  data_years = years,
  fleet_num = 1,
  selectivity = NULL,
  CV = rep(0.05, length(years)),
  sample_num = apply(fishery_sample_num, 1, sum),
  waa_path = file.path(ewe_output_path, "weight_annual.csv")
)

Catch biomass and catch-at-age in proportion over time

# Total catch in biomass
om <- data.frame (
  Year = years, 
  Catch = fishery$om_total_catch_biomass,
  Model = "OM"
)

obs <- data.frame (
  Year = years, 
  Catch = fishery$obs_total_catch_biomass$fleet1,
  Model = "OBS"
)

year_id <- which(menhadenSA_output$t.series$year %in% years)
bam <- data.frame (
  Year = years, 
  Catch = (menhadenSA_output$t.series$L.cBs.ob[year_id] + menhadenSA_output$t.series$L.cBn.ob[year_id] + menhadenSA_output$t.series$L.cRs.ob[year_id] + menhadenSA_output$t.series$L.cRn.ob[year_id]) * 1000,
  Model = "BAM"
)

#data <- rbind(om, obs, bam)
data <- rbind(om, obs)
ggplot(data, aes(x = Year, y = Catch, color = Model)) +
  geom_line() +
  labs(
    title = "Comparison between models",
    x = "Year",
    y = "Total Catch (mt)"
  ) +
  theme_bw()

# Catch-at-age in number
om <- fishery$om_caa_abundance/rowSums(fishery$om_caa_abundance)
om$Year <- years
om$Model <- "OM"
om <- reshape2::melt(om, id.vars = c("Year", "Model"))
colnames(om) <- c("Year", "Model", "Age", "Catch")

obs <- fishery$obs_caa_prop$fleet1
obs$Year <- years
obs$Model <- "OBS"
obs <- reshape2::melt(obs, id.vars = c("Year", "Model"))
colnames(obs) <- c("Year", "Model", "Age", "Catch")

bam_crn <- as.data.frame(menhadenSA_output$comp.mats$acomp.cRn.ob[paste(years), ])
colnames(bam_crn) <- paste0("age", ages)
bam_crn$Year <- years
bam_crn$Model <- "BAM_cRn"
bam_crn <- reshape2::melt(bam_crn, id.vars = c("Year", "Model"))
colnames(bam_crn) <- c("Year", "Model", "Age", "Catch")

bam_crs <- as.data.frame(menhadenSA_output$comp.mats$acomp.cRs.ob[paste(years), ])
colnames(bam_crs) <- paste0("age", ages)
bam_crs$Year <- years
bam_crs$Model <- "BAM_cRs"
bam_crs <- reshape2::melt(bam_crs, id.vars = c("Year", "Model"))
colnames(bam_crs) <- c("Year", "Model", "Age", "Catch")

bam_cbn <- as.data.frame(menhadenSA_output$comp.mats$acomp.cBn.ob[paste(years), ])
colnames(bam_cbn) <- paste0("age", ages)
bam_cbn$Year <- years
bam_cbn$Model <- "BAM_cBn"
bam_cbn <- reshape2::melt(bam_cbn, id.vars = c("Year", "Model"))
colnames(bam_cbn) <- c("Year", "Model", "Age", "Catch")

bam_cbs <- as.data.frame(menhadenSA_output$comp.mats$acomp.cBs.ob[paste(years), ])
colnames(bam_cbs) <- paste0("age", ages)
bam_cbs$Year <- years
bam_cbs$Model <- "BAM_cBs"
bam_cbs <- reshape2::melt(bam_cbs, id.vars = c("Year", "Model"))
colnames(bam_cbs) <- c("Year", "Model", "Age", "Catch")

data <- list(om=om, obs=obs, bam_crn=bam_crn, bam_crs=bam_crs, bam_cbn=bam_cbn, bam_cbs=bam_cbs)
title <- paste(c("OM", "OBS", "BAM_cRn", "BAM_cRs", "BAM_cBn", "BAM_cBs"), "catch-at-age")

for (i in 1:2){
#for (i in 1:length(data)){
  p <- ggplot(data=data[[i]], aes(x=Age, fill=Age, y=Catch)) +
      geom_bar(position='dodge', stat='identity') +
      facet_wrap(~Year) +
      labs(
        title = title[i],
        x = "Age",
        y = "Proportion"
      )+
      theme_bw()+
      theme(axis.text.x = element_text(angle = 45))
  print(p)
}

`IFA4EBFM::create_survey()` is used to simulate survey data

Input data
EwE monthly biomass-at-age and weight-at-age data
BAM survey number, time, selectivity, catchability, CV, sample size data
Output data
Ecosystem operating model biomass-at-age, abundance-at-age, abundance index, CV of surveys, sample size, weight-at-age, and length-at-age data
Observed abundance indices, length composition in proportion/in number using different approaches
Units information
Survey information
survey1 survey: Oct from 1990 - 2017; length composition data from 1990 - 2017
survey2 survey: April from 1985 - 2017; length composition data from 2013 - 2017
survey3 survey: April from 1990 - 2017; no length composition data
survey4 survey: June from 1985 - 2017; no length composition data

# Create survey ----------------------------------------------------------

# selectivity settings
survey_num <- 4

survey_name <- c("survey1", "survey2", "survey3", "survey4")

# set up survey time
# Need to check Table 26 from BAM assessment: Length cutoffs used to distinguish age-0 from age-1+ Atlantic menhaden at different regions.

survey1_year <- 1990:2017 # Adult Index (survey1): age 1+ fish; September - January; Time of the year when menhaden were most abundant in this region

survey2_year <- 1985:2017 # Adult Index (survey2): age 1+ fish; March - May

survey3_year <- 1990:2017 # Adult Index (survey3): age 1+ fish: April - July

survey4_year <- 1985:2017 # YOY Index (survey4): covered all months, many surveys starts at July

survey_time <- list(
  survey1 = data.frame(
    year = survey1_year,
    month = rep(10, length(survey1_year)) # Oct 15
  ),
  survey2 = data.frame(
    year = survey2_year,
    month = rep(4, length(survey2_year)) # April 15
  ),
  survey3 = data.frame(
    year = survey3_year,
    month = rep(4, length(survey3_year)) # April 15
  ),
  survey4 = data.frame(
    year = survey4_year,
    month = rep(6, length(survey4_year)) # June 1
  )
)

# set up survey selectivity
survey1_sel <- IFA4EBFM::logistic(
  pattern = "simple_logistic",
  x = ages,
  slope_asc = 2.2,
  location_asc = 3.0
)

survey2_sel <- IFA4EBFM::logistic(
  pattern = "double_logistic",
  x = ages,
  slope_asc = 4.3,
  location_asc = 2.3,
  slope_desc = 3.5,
  location_desc = 2.3
)

survey3_sel <- IFA4EBFM::logistic(
  pattern = "double_logistic",
  x = ages,
  slope_asc = 7.0,
  location_asc = 0.3,
  slope_desc = 7.0,
  location_desc = 2.0
)

survey_selectivity <- list(
  survey1 = as.data.frame(
    matrix(rep(survey1_sel, times = length(years)), ncol = length(ages), byrow = TRUE),
    row.names = years
  ),
  survey2 = as.data.frame(
    matrix(rep(survey2_sel, times = length(years)), ncol = length(ages), byrow = TRUE),
    row.names = years,
  ),
  survey3 = as.data.frame(
    matrix(rep(survey3_sel, times = length(years)), ncol = length(ages), byrow = TRUE),
    row.names = years,
  ),
  survey4 = as.data.frame(
    matrix(rep(c(1, rep(0, 6)), times = length(years)), ncol = length(ages), byrow = TRUE),
    row.names = years,
  )
)

survey_selectivity <- lapply(survey_selectivity, setNames, paste("age", ages))

# set up catchability
yr_catchability_change_survey4 <- 1986

survey_catchability <- list(
  survey1 = menhadenSA_output$t.series$q.nad[which(menhadenSA_output$t.series$year %in% years)],
  survey2 = menhadenSA_output$t.series$q.mad[which(menhadenSA_output$t.series$year %in% years)],
  survey3 = menhadenSA_output$t.series$q.sad[which(menhadenSA_output$t.series$year %in% years)],
  survey4 = c(menhadenSA_output$t.series$q.jai[which(menhadenSA_output$t.series$year %in% c(years[1]:yr_catchability_change_survey4))], menhadenSA_output$t.series$q2.jai[which(menhadenSA_output$t.series$year %in% c((yr_catchability_change_survey4 + 1):tail(years, n = 1)))])
)

survey_catchability <- lapply(survey_catchability, setNames, years)

# set up CV
survey_CV <- list(
  survey1 = menhadenSA_output$t.series$cv.U.nad[which(menhadenSA_output$t.series$year %in% years)],
  survey2 = menhadenSA_output$t.series$cv.U.mad[which(menhadenSA_output$t.series$year %in% years)],
  survey3 = menhadenSA_output$t.series$cv.U.sad[which(menhadenSA_output$t.series$year %in% years)],
  survey4 = menhadenSA_output$t.series$cv.U.jai[which(menhadenSA_output$t.series$year %in% years)]
)
survey_CV <- lapply(survey_CV, setNames, years)

# set up sample number
# survey_sample_num <- list(
#   survey1 = menhadenSA_output$t.series$lcomp.nad.nfish[which(menhadenSA_output$t.series$year %in% years)],
#   survey2 = menhadenSA_output$t.series$lcomp.mad.nfish[which(menhadenSA_output$t.series$year %in% years)],
#   survey3 = rep(NA, length = length(years)),
#   survey4 = rep(NA, length = length(years))
# )

survey_sample_num <- list(
    survey1 = rep(800, length= length(years)),
    survey2 = rep(800, length= length(years)),
    survey3 = rep(800, length= length(years)),
    survey4 = rep(800, length= length(years))
)
survey_sample_num <- lapply(survey_sample_num, setNames, years)

for (i in 1:length(survey_sample_num)){
  survey_sample_num[[i]][survey_sample_num[[i]] == -99999] <- NA
}

# set up age-length population structure
length_bin <- seq(10.0, 400, 10)/10 # in cm
mid_length_bin <- seq(10.5, 405, 10)/10 # in cm
nbin <- length(length_bin)
bin_width <- 1

length_CV <- list(
  survey1 = 0.12,
  survey2 = 0.17,
  survey3 = NA,
  survey4 = NA
)

# Create survey
survey <- IFA4EBFM::create_survey(
  file_path = file.path(ewe_output_path, "biomass_monthly.csv"),
  skip_nrows = 8,
  species = 4:10,
  species_labels = paste0("age", ages),
  years = years,
  survey_num = survey_num,
  survey_time = survey_time,
  selectivity = survey_selectivity,
  catchability = survey_catchability,
  CV = survey_CV,
  sample_num = survey_sample_num,
  waa_path = file.path(ewe_output_path, "weight_monthly.csv"),
  length_bin = length_bin,
  mid_length_bin = mid_length_bin,
  nbin = nbin,
  bin_width = bin_width,
  length_CV = length_CV
)

Survey selectivity patterns

plot(0:6, as.numeric(survey_selectivity$survey1[1, ]), 
     type="o", pch=1, col=1, lty=1,
     xlab = "Age", ylab = "Selectivity")
lines(0:6, as.numeric(survey_selectivity$survey2[1, ]), pch=2, col=2, lty=2, type="o")
lines(0:6, as.numeric(survey_selectivity$survey3[1,]), pch=3, col=3, lty=3, type="o")
lines(0:6, as.numeric(survey_selectivity$survey4[1, ]), pch=4, col=4, lty=4, type="o")
legend("topright",
       names(survey_selectivity),
       pch= 1:4,
       col = 1:4, 
       lty=1:4,
       bty="n"
       )

Survey index over time

om <- data.frame(matrix(ncol=4, nrow=0))
for (i in 1:length(survey$om_abundance_index)){
  temp <- data.frame(
  Year = as.numeric(names(survey$om_abundance_index[[i]])), 
  Index = survey$om_abundance_index[[i]],
  Model = "OM",
  Survey = names(survey$om_abundance_index)[i]
)
  om <- rbind(om, temp)
}

obs <- data.frame(matrix(ncol=4, nrow=0))
for (i in 1:length(survey$obs_abundance_index)){
  temp <- data.frame(
  Year = as.numeric(names(survey$obs_abundance_index[[i]])), 
  Index = survey$obs_abundance_index[[i]],
  Model = "OBS",
  Survey = names(survey$obs_abundance_index)[i]
)
  obs <- rbind(obs, temp)
}

data <- rbind(om, obs)
ggplot(data, aes(x = Year, y = Index, color = Model)) +
  geom_line() +
  facet_wrap(~Survey, scales = "free_y") +
  labs(
    title = "Comparison between models",
    x = "Year",
    y = "Abundance Index"
  ) +
  theme_bw()

Survey length composition over time

SS3 approach

for (i in 1:length(survey$obs_lencomp_proportion_ss3)){
  obs <- as.data.frame(survey$obs_lencomp_proportion_ss3[[i]])
  obs$Year <- as.numeric(row.names(survey$obs_lencomp_proportion_ss3[[i]]))
  obs$Model <- "OBS"
  obs$Survey <- names(survey$obs_lencomp_proportion_ss3)[i]

  obs <- na.omit(obs)

  obs <- reshape2::melt(obs, id.vars = c("Year", "Model", "Survey"))
  colnames(obs) <- c("Year", "Model", "Survey", "Length_Bin", "Value")

  if (nrow(obs) != 0) {
    obs$Length_Bin <- as.numeric(as.character(obs$Length_Bin))
    p <- ggplot(data=obs, aes(x=Length_Bin, fill=Length_Bin, y=Value)) +
      geom_bar(position='dodge', stat='identity') +
      facet_wrap(~Year) +
      labs(
        title = names(survey$obs_lencomp_proportion_ss3)[i],
        x = "Length Bins (cm)",
        y = "Proportion"
      )+
      theme_bw()
  print(p)
  }
}

BAM approach

for (i in 1:length(survey$obs_lencomp_proportion_bam)){
  obs <- as.data.frame(survey$obs_lencomp_proportion_bam[[i]])
  obs$Year <- as.numeric(row.names(survey$obs_lencomp_proportion_bam[[i]]))
  obs$Model <- "OBS"
  obs$Survey <- names(survey$obs_lencomp_proportion_bam)[i]

  obs <- na.omit(obs)

  obs <- reshape2::melt(obs, id.vars = c("Year", "Model", "Survey"))
  colnames(obs) <- c("Year", "Model", "Survey", "Length_Bin", "Value")

  if (nrow(obs) != 0) {
    obs$Length_Bin <- as.numeric(as.character(obs$Length_Bin))
    p <- ggplot(data=obs, aes(x=Length_Bin, fill=Length_Bin, y=Value)) +
      geom_bar(position='dodge', stat='identity') +
      facet_wrap(~Year) +
      labs(
        title = names(survey$obs_lencomp_proportion_bam)[i],
        x = "Year",
        y = "Proportion"
      )+
      theme_bw()
  print(p)
  }
}

Key findings

Tried two approaches to calculate age-length probability matrix
use SS3 approach using pnorm in R
rewrite ADMB function in R based on BAM code
the two approaches produce almost identical results, so will only use SS3 approach for further analysis
Observed a very high proportion of fish in length bin 10.5 cm
need to check selectivity-at-age 0 and perhaps modify selectivity pattern for survey survey1
expand length bins. According to EwE outputs, mean length-at-age 0 is around 6 cm, which is below the current first length bin 10.5 cm.

Modify selectivity pattern for survey survey1 and update length bins

Change slope_asc from 2.2 to 3.0:
selectivity-at-age from original scenario: 0.003, 0.030, 0.240, 1.00, 0.400, 0.027, 0.001
updated selectivity-at-age: 0.0003, 0.0060, 0.1147, 1.0000, 0.4251, 0.0268, 0.0014
Change the first length bin from 10 cm to 1 cm and the last length bin from 40.5 cm to 46.5 cm

survey1_sel <- IFA4EBFM::logistic(
  pattern = "simple_logistic",
  x = ages,
  slope_asc = 3.0,
  location_asc = 3.0
)

survey1 = as.data.frame(
    matrix(rep(survey1_sel, times = length(years)), ncol = length(ages), byrow = TRUE),
    row.names = years
  )
colnames(survey1) <- paste0("ages", ages)

survey_selectivity$survey1 <- survey1

# set up age-length population structure
length_bin <- seq(10, 500, 10)/10 # in cm
mid_length_bin <- seq(15, 505, 10)/10 # in cm
nbin <- length(length_bin)
bin_width <- 1

# Create survey
survey <- IFA4EBFM::create_survey(
  file_path = file.path(ewe_output_path, "biomass_monthly.csv"),
  skip_nrows = 8,
  species = 4:10,
  species_labels = paste0("age", ages),
  years = years,
  survey_num = survey_num,
  survey_time = survey_time,
  selectivity = survey_selectivity,
  catchability = survey_catchability,
  CV = survey_CV,
  sample_num = survey_sample_num,
  waa_path = file.path(ewe_output_path, "weight_monthly.csv"),
  length_bin = length_bin,
  mid_length_bin = mid_length_bin,
  nbin = nbin,
  bin_width = bin_width,
  length_CV = length_CV
)

Updated survey length composition over time

om <- data.frame(matrix(ncol=4, nrow=0))
for (i in 1:length(survey$om_abundance_index)){
  temp <- data.frame(
  Year = as.numeric(names(survey$om_abundance_index[[i]])), 
  Index = survey$om_abundance_index[[i]],
  Model = "OM",
  Survey = names(survey$om_abundance_index)[i]
)
  om <- rbind(om, temp)
}

obs <- data.frame(matrix(ncol=4, nrow=0))
for (i in 1:length(survey$obs_abundance_index)){
  temp <- data.frame(
  Year = as.numeric(names(survey$obs_abundance_index[[i]])), 
  Index = survey$obs_abundance_index[[i]],
  Model = "OBS",
  Survey = names(survey$obs_abundance_index)[i]
)
  obs <- rbind(obs, temp)
}

data <- rbind(om, obs)
for (i in 1:length(survey$obs_lencomp_proportion_ss3)){
  obs <- as.data.frame(survey$obs_lencomp_proportion_ss3[[i]])
  obs$Year <- as.numeric(row.names(survey$obs_lencomp_proportion_ss3[[i]]))
  obs$Model <- "OBS"
  obs$Survey <- names(survey$obs_lencomp_proportion_ss3)[i]

  obs <- na.omit(obs)

  obs <- reshape2::melt(obs, id.vars = c("Year", "Model", "Survey"))
  colnames(obs) <- c("Year", "Model", "Survey", "Length_Bin", "Value")

  if (nrow(obs) != 0) {
    obs$Length_Bin <- as.numeric(as.character(obs$Length_Bin))
    p <- ggplot(data=obs, aes(x=Length_Bin, fill=Length_Bin, y=Value)) +
      geom_bar(position='dodge', stat='identity') +
      facet_wrap(~Year) +
      labs(
        title = names(survey$obs_lencomp_proportion_ss3)[i],
        x = "Length Bins (cm)",
        y = "Proportion"
      )+
      theme_bw()
  print(p)
  }
}

`IFA4EBFM::create_biodata()` is used to prepare biological data for a stock assessment

biodata <- create_biodata(nsex=1, narea=1, ages=ages, years=years,
                          length_bin=length_bin, mid_length_bin=mid_length_bin,
                          nbin=nbin, bin_width=bin_width, length_CV=length_CV,
                          annual_weight_path=file.path(ewe_output_path, "weight_annual.csv"),
                          monthly_weight_path=file.path(ewe_output_path, "weight_monthly.csv"),
                          species=4:10,
                          species_labels=paste0("age", ages),
                          skip_nrows=8,
                          lw_a=0.01, lw_b=3,
                          k=0.331,
                          t0 = -0.1,
                          winf = 0.237,
                          maturity_at_age=c(0.0, 0.1, 0.5, 0.9, 1.0, 1.0, 1.0), # From both BAM and EwE
                          natural_mortality_at_age=c(1.76, 1.31, 1.03, 0.9, 0.81, 0.76, 0.72) # From both BAM and EwE
                          )

Key findings

EwE Von Bertalanffy Growth model uses a specialized equation. Carrying capacity parameter k from the model needs to be carefully compared before using the value. The default values used in the EwE weight-at-age calculation are different compared to the BAM input values, so the weight-at-age matrices would be different from different models.

Bai-Li-NOAA/IFA4EBFM documentation built on April 5, 2024, 6:41 p.m.

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Bai-Li-NOAA/IFA4EBFM
Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

In Bai-Li-NOAA/IFA4EBFM: Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

EwE scenario

`IFA4EBFM::create_fishery()` is used to simulate fishery data

Catch biomass and catch-at-age in proportion over time

`IFA4EBFM::create_survey()` is used to simulate survey data

Survey selectivity patterns

Survey index over time

Survey length composition over time

Key findings

Modify selectivity pattern for survey survey1 and update length bins

Updated survey length composition over time

`IFA4EBFM::create_biodata()` is used to prepare biological data for a stock assessment

Key findings

R Package Documentation

Browse R Packages

We want your feedback!

Bai-Li-NOAA/IFA4EBFM Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

In Bai-Li-NOAA/IFA4EBFM: Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

EwE scenario

IFA4EBFM::create_fishery() is used to simulate fishery data

Catch biomass and catch-at-age in proportion over time

IFA4EBFM::create_survey() is used to simulate survey data

Survey selectivity patterns

Survey index over time

Survey length composition over time

Key findings

Modify selectivity pattern for survey survey1 and update length bins

Updated survey length composition over time

IFA4EBFM::create_biodata() is used to prepare biological data for a stock assessment

Key findings

R Package Documentation

Browse R Packages

We want your feedback!

Bai-Li-NOAA/IFA4EBFM
Interdisciplinary Forecasting Approach for Ecosystem-Based Fisheries Management

`IFA4EBFM::create_fishery()` is used to simulate fishery data

`IFA4EBFM::create_survey()` is used to simulate survey data

`IFA4EBFM::create_biodata()` is used to prepare biological data for a stock assessment