Jack Mackerel Assessment SCW14

This document was compiled in RMarkdown, and shows the incremental results of the Chilean Jack Mackerel (Trachurus murphyi) stock assessment benchmark in 2022. The files associated with this document can be found on Github.

Annoted data files can be found in the Excel spreadsheet newAge.xlsx on jjm/assessment/data.

• Compiled the latest jjm/src/jjms.tpl
  • This can be done by navigating to the jjm/src folder in your Terminal, and using the make command
  • You will need to have ADMB installed to do this.
  • This does not need to be done if you’re only re-compiling the R Markdown document.
• Updated the jjmR package
  • This can be done in R using the command remotes::install_github("SPRFMO/jjmR")

1 Model Naming Convention

File naming conventions have been changed to reflect the stock structure hypotheses associated with each run. The h1 denotes the single-stock hypothesis, while h2 denotes the two-stock one.

Model	Description
0.00	Exact 2021 (single stock h1 and two-stock h2) model and data set through 2020 (mod1.0 from SC09)
0.01	As 0.00 but with the model beginning at age-0.
0.02	As 0.01 but replacing previous age data with updated data; mean stock weight at age calculated as a mean from SC_Chile 4th quarter, 1995-2020; natural mortality set to 0.25; updated growth rates to new Chilean values; downweighting DEPM.
0.03	As 0.00 but replacing previous age data with updated data; mean stock weight at age calculated as a mean from SC_Chile 4th quarter, 1995-2020; natural mortality set to 0.25; updated growth rates to new Chilean values; downweighting DEPM.
—-	—-
1.00	As 0.03
1.01	As 1.00 but with Peruvian growth parameters for the single-stock model. (ED)
1.02	As 1.00 but with age-varying M (Gislason method; scaled to 0.25). (NH)
1.03	As 1.00 but with the new offshore CPUE index that incorporates effort creep. (MP)
1.04	As 1.00 but with pre-weighted sample sizes for composition data. (IP)
1.05	As 1.04 but with sample sizes for composition data and CVs of index data based on expert judgement.

if(!'devtools' %in% installed.packages()) install.packages('devtools')
devtools::install_github("SPRFMO/jjmR")

if(!'kableExtra' %in% installed.packages()) install.packages('kableExtra')

You’ll need to be in the jjm/assessment directory in order for the code here to run.

library(jjmR)
library(tidyverse)
library(kableExtra)

pwd <- getwd()
# if (!grepl(basename(pwd), "assessment", ignore.case = TRUE)) {
#   stop(paste("Set working directory to jjm/assessment"))
# }

geth <- function(mod,h=hyp) paste0(h,"_", mod)

fn.plotind <- function(mods2compare, indname) {
  fn.seldata <- function(x) {
  x$data$Index[,i] %>%
    bind_rows() %>%
    pivot_longer(everything(), names_to="year") %>%
    drop_na() %>%
    mutate(year=as.numeric(year),
            assessment_year=max(year))
  }

  mods <- compareModels(geth(mods2compare, "h2"))
  i <- grep(indname,mods[[1]]$data$Inames)
  dat2use <- list()
  for(m in 1:length(mods)) {
    dat2use[[m]] <- fn.seldata(mods[[m]])
  }

  p <- map_dfr(dat2use, ~as_tibble(.)) %>%
        mutate(assessment_year=as.factor(assessment_year)) %>%
        ggplot(aes(x=year,y=value,colour=assessment_year)) +
        geom_line() +
        theme_minimal() + 
        scale_x_continuous(breaks= scales::pretty_breaks())
  print(p)
}

fixed_bmsy <- function(mod,refpt=5500){
  old_rat <- (mod[[1]]$output[[1]]$msy_mt[,13])
  new_rat <- (mod[[1]]$output[[1]]$msy_mt[,12]/ refpt)
  mod[[1]]$output[[1]]$msy_mt[,13] <- new_rat
  mod[[1]]$output[[1]]$msy_mt[,10] <- refpt
  return(mod)
}

FinModName <- "1.05"

2 Updated Age Data

2.1 Single-stock hypothesis

2.1.1 Biomass

Plot comparing biomass estimated by last year’s model (h1_0.00) with data updated to 2021.

2.1.2 Recruitment

Plot comparing recruitment estimated by last year’s model (h1_0.00) with data updated to 2021.

2.1.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year’s model (h1_0.00) with data updated to 2021.

2.2 Two-stock hypothesis

2.2.1 Biomass

Plot comparing biomass estimated by last year’s model (h2_0.00) with data updated to 2021.

2.2.2 Recruitment

Plot comparing recruitment estimated by last year’s model (h2_0.00) with data updated to 2021.

2.2.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year’s model (h2_0.00) with data updated to 2021.

3 Correcting Growth Parameters

3.1 Single-stock hypothesis

3.1.1 Biomass

Plot comparing biomass estimated by last year’s model (h1_0.03) with data updated to 2021.

3.1.2 Recruitment

Plot comparing recruitment estimated by last year’s model (h1_0.03) with data updated to 2021.

3.1.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year’s model (h1_0.03) with data updated to 2021.

3.2 Two-stock hypothesis

3.2.1 Biomass

Plot comparing biomass estimated by last year’s model (h2_0.03) with data updated to 2021.

3.2.2 Recruitment

Plot comparing recruitment estimated by last year’s model (h2_0.03) with data updated to 2021.

3.2.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year’s model (h2_0.03) with data updated to 2021.