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

Annoted data files can be found in the Excel spreadsheet SC10_datfiles.xlsx on jjm/assessment/data.

The code to create the input files for this assessment and to run these models can be found in jjm/assessment/R/SC10_Bridging.R. Should you choose to run the models, please ensure that you have:

  • 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
Models 0.x Data introductions
0.00 Exact 2021 (single stock h1 and two-stock h2) model and data set (model 1.14) from benchmark SCW14.
0.01 As 0.00 but with revised catches through 2021 (currently still estimates)
0.02 As 0.01 but with updated 2021 fishery age composition data for N_Chile, SC_Chile, and Offshore_Trawl, and updated 2021 fishery length composition data for FarNorth
0.03 As 0.02 but with updated 2021 weight at age data for all fisheries and their associated CPUE indices
0.04 As 0.03 but replaced offshore CPUE up to 2021
0.05 As 0.04 but with updated AcousN 2021 index, with associated age composition and weight at age
0.06 As 0.05 but with 2022 catch projections
0.07 As 0.06 but with updated 2022 fishery age composition data for N_Chile, SC_Chile, and Offshore_Trawl, and updated 2022 fishery length composition data for FarNorth
0.08 As 0.07 but with updated 2022 weight at age data for N_Chile, SC_Chile, and FarNorth fleets, and for their associated CPUE indices
0.09 As 0.08 but replaced SC_Chile_CPUE index (traditional absolute scaled CPUE by trip)
0.10 As 0.09 but replaced Peru_CPUE index
———– ————–
Models 1.x Updated Model and Sensitivities
1.00 Update model (selectivity changes, recruitment) to 2022; 0.10 data file
1.01 As 1.00 but with correct growth parameters to reflect FL (Linf=73.56; L0=13.56; SC10-Doc27 Peru National Report - ANJ)
1.02 As 1.01 but with added flexibility for offshore fleet 
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, "h1"))
  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 <- "1.02"

2 Re-running Benchmark Model (a sanity check)

Re-running the 2021 model and comparing the results with that year’s SC meeting.

2.1 Single-stock hypothesis

2.1.1 Biomass

Plot comparing biomass estimated by last year's model (mod_prev) with a re-run of the model this year (h1_0.00).

Plot comparing biomass estimated by last year’s model (mod_prev) with a re-run of the model this year (h1_0.00).

2.1.2 Recruitment

Plot comparing recruitment estimated by last year's model (mod_prev) with a re-run of the model this year (h1_0.00).

Plot comparing recruitment estimated by last year’s model (mod_prev) with a re-run of the model this year (h1_0.00).

2.1.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year's model (mod_prev) with a re-run of the model this year (h1_0.00).

Plot comparing fishing mortality estimated by last year’s model (mod_prev) with a re-run of the model this year (h1_0.00).

2.2 Two-stock hypothesis

2.2.1 Biomass

Plot comparing biomass estimated by last year's model (mod_prev) with a re-run of the model this year (h2_0.00).

Plot comparing biomass estimated by last year’s model (mod_prev) with a re-run of the model this year (h2_0.00).

2.2.2 Recruitment

Plot comparing recruitment estimated by last year's model (mod_prev) with a re-run of the model this year (h2_0.00).

Plot comparing recruitment estimated by last year’s model (mod_prev) with a re-run of the model this year (h2_0.00).

2.2.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year's model (mod_prev) with a re-run of the model this year (h2_0.00).

Plot comparing fishing mortality estimated by last year’s model (mod_prev) with a re-run of the model this year (h2_0.00).

3 Incremental Data Updates

The most updated table of model runs can be found on Github.

3.1 Updating last year’s data

The data updated to 2021 include catch estimates, age and length compositions, and indices of abundance.

3.1.1 Single-stock hypothesis

3.1.1.1 Biomass

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

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

3.1.1.2 Recruitment

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

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

3.1.1.3 Fishing Mortality

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

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

3.1.2 Two-stock hypothesis

3.1.2.1 Biomass

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

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

3.1.2.2 Recruitment

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

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

3.1.2.3 Fishing Mortality

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

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

3.2 Updating this year’s data

The data updated to 2022 include projected catch estimates, age and length compositions, and indices of abundance.

3.2.1 Single-stock hypothesis

3.2.1.1 Biomass

Plot comparing biomass estimated with last year's data using the same model (h1_0.05) but with data updated to 2022.

Plot comparing biomass estimated with last year’s data using the same model (h1_0.05) but with data updated to 2022.

3.2.1.2 Recruitment

Plot comparing recruitment estimated with last year's data using the same model (h1_0.05) but with data updated to 2022.

Plot comparing recruitment estimated with last year’s data using the same model (h1_0.05) but with data updated to 2022.

3.2.1.3 Fishing Mortality

Plot comparing fishing mortality estimated with last year's data using the same model (h1_0.05) but with data updated to 2022.

Plot comparing fishing mortality estimated with last year’s data using the same model (h1_0.05) but with data updated to 2022.

3.2.2 Two-stock hypothesis

3.2.2.1 Biomass

Plot comparing biomass estimated with last year's data using the same model (h2_0.05) but with data updated to 2022.

Plot comparing biomass estimated with last year’s data using the same model (h2_0.05) but with data updated to 2022.

3.2.2.2 Recruitment

Plot comparing recruitment estimated with last year's data using the same model (h2_0.05) but with data updated to 2022.

Plot comparing recruitment estimated with last year’s data using the same model (h2_0.05) but with data updated to 2022.

3.2.2.3 Fishing Mortality

Plot comparing fishing mortality estimated with last year's data using the same model (h2_0.05) but with data updated to 2022.

Plot comparing fishing mortality estimated with last year’s data using the same model (h2_0.05) but with data updated to 2022.

3.3 Stepping through the CPUE replacements

3.3.1 SC Chile CPUE

3.3.1.1 Single-stock hypothesis

3.3.1.1.1 Biomass

3.3.1.1.2 Recruitment

3.3.1.1.3 Fishing Mortality

3.3.1.2 Two-stock hypothesis

3.3.1.2.1 Biomass

3.3.1.2.2 Recruitment

3.3.1.2.3 Fishing Mortality

3.3.2 Peru CPUE

3.3.2.1 Single-stock hypothesis

3.3.2.1.1 Biomass

3.3.2.1.2 Recruitment

3.3.2.1.3 Fishing Mortality

3.3.2.2 Two-stock hypothesis

3.3.2.2.1 Biomass

3.3.2.2.2 Recruitment

3.3.2.2.3 Fishing Mortality

3.3.3 Offshore CPUE

3.3.3.1 Single-stock hypothesis

3.3.3.1.1 Biomass

3.3.3.1.2 Recruitment

3.3.3.1.3 Fishing Mortality

3.3.3.2 Two-stock hypothesis

3.3.3.2.1 Biomass

3.3.3.2.2 Recruitment

3.3.3.2.3 Fishing Mortality

4 Final Data Update

This just shows the final data update, using the exact same model (i.e., with the same control files).

4.1 Single-stock hypothesis

4.1.1 Biomass

Plot comparing biomass estimated by last year's model (h1_0.00) with data updated to 2022 (h1_0.10).

Plot comparing biomass estimated by last year’s model (h1_0.00) with data updated to 2022 (h1_0.10).

4.1.2 Recruitment

Plot comparing recruitment estimated by last year's model (h1_0.00) with data updated to 2022 (h1_0.10).

Plot comparing recruitment estimated by last year’s model (h1_0.00) with data updated to 2022 (h1_0.10).

4.1.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year's model (h1_0.00) with data updated to 2022 (h1_0.10).

Plot comparing fishing mortality estimated by last year’s model (h1_0.00) with data updated to 2022 (h1_0.10).

4.2 Two-stock hypothesis

4.2.1 Biomass

Plot comparing fishing mortality estimated by last year's model (h2_0.00) with data updated to 2022 (h2_0.10).

Plot comparing fishing mortality estimated by last year’s model (h2_0.00) with data updated to 2022 (h2_0.10).

4.2.2 Recruitment

Plot comparing recruitment estimated by last year's model (h2_0.00) with data updated to 2022 (h2_0.10).

Plot comparing recruitment estimated by last year’s model (h2_0.00) with data updated to 2022 (h2_0.10).

4.2.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year's model (h2_0.00) with data updated to 2022 (h2_0.10).

Plot comparing fishing mortality estimated by last year’s model (h2_0.00) with data updated to 2022 (h2_0.10).

5 Model Update

These results are from updating the model to include selectivity changes in the most recent year, and to extend the recruitment regime shift time series. This was the same procedure that was applied to the previous year.

5.1 Single-stock hypothesis

5.1.1 Biomass

Plot comparing biomass estimates from last year's model (h1_0.00) to this year's (h1_1.00).

Plot comparing biomass estimates from last year’s model (h1_0.00) to this year’s (h1_1.00).

5.1.2 Recruitment

Plot comparing recruitment estimates from last year's model (h1_0.00) to this year's (h1_1.00).

Plot comparing recruitment estimates from last year’s model (h1_0.00) to this year’s (h1_1.00).

5.1.3 Fishing Mortality

Plot comparing fishing mortality estimates from last year's model (h1_0.00) to this year's (h1_1.00).

Plot comparing fishing mortality estimates from last year’s model (h1_0.00) to this year’s (h1_1.00).

5.2 Two-stock hypothesis

5.2.1 Biomass

Plot comparing biomass estimated by last year's model (h2_0.00) to this year's (h2_1.00).

Plot comparing biomass estimated by last year’s model (h2_0.00) to this year’s (h2_1.00).

5.2.2 Recruitment

Plot comparing recruitment estimated by last year's model (h2_0.00) to this year's (h2_1.00).

Plot comparing recruitment estimated by last year’s model (h2_0.00) to this year’s (h2_1.00).

5.2.3 Fishing Mortality

Plot comparing fishing mortality estimated by last year's model (h2_0.00) to this year's (h2_1.00).

Plot comparing fishing mortality estimated by last year’s model (h2_0.00) to this year’s (h2_1.00).

6 Correcting Growth Parameters

Peru found a mistake in the growth parameters- we had been applying growth parameters for Total Length to Fork Length data. Model 1.01 corrects this error.

6.1 Single-stock hypothesis

6.1.1 Biomass

Plot comparing biomass estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

Plot comparing biomass estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

6.1.2 Recruitment

Plot comparing recruitment estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

Plot comparing recruitment estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

6.1.3 Fishing Mortality

Plot comparing fishing mortality estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

Plot comparing fishing mortality estimates using TL growth parameters (h1_1.00) to using FL parameters (h1_1.01).

6.2 Two-stock hypothesis

6.2.1 Biomass

Plot comparing biomass estimated using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

Plot comparing biomass estimated using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

6.2.2 Recruitment

Plot comparing recruitment using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

Plot comparing recruitment using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

6.2.3 Fishing Mortality

Plot comparing fishing mortality estimated using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

Plot comparing fishing mortality estimated using TL growth parameters (h2_1.00) to using FL parameters (h2_1.01).

7 Sensitivity Analyses

Downweighting the selectivity change penalty and upweighting the final year age comp data for offshore fleet.

7.1 Single-stock hypothesis

7.1.1 Biomass

Plot comparing biomass estimates for h1_1.01 and h1_1.02.

Plot comparing biomass estimates for h1_1.01 and h1_1.02.

7.1.2 Recruitment

Plot comparing recruitment estimates for h1_1.01 and h1_1.02.

Plot comparing recruitment estimates for h1_1.01 and h1_1.02.

7.1.3 Fishing Mortality

Plot comparing fishing mortality estimates for h1_1.01 and h1_1.02.

Plot comparing fishing mortality estimates for h1_1.01 and h1_1.02.

7.2 Two-stock hypothesis

7.2.1 Biomass

Plot comparing biomass estimated for h2_1.01 and h2_1.02.

Plot comparing biomass estimated for h2_1.01 and h2_1.02.

7.2.2 Recruitment

Plot comparing recruitment for h2_1.01 and h2_1.02.

Plot comparing recruitment for h2_1.01 and h2_1.02.

7.2.3 Fishing Mortality

Plot comparing fishing mortality estimated for h2_1.01 and h2_1.02.

Plot comparing fishing mortality estimated for h2_1.01 and h2_1.02.

8 Final Model

Model 1.02 was selected as the final model for 2022. This model incorporates:

8.1 Single-Stock Hypothesis

8.1.1 Fits to Data

8.1.1.1 Fishery Age Composition

8.1.1.2 Fishery Length Composition

8.1.1.3 Survey Age Composition

8.1.1.4 Index Data

8.1.2 Projections

8.1.2.1 SSB

8.1.2.2 Catch

8.1.3 Risk Table

Multiplier of \(F_{2022}\) \(B_{2024}\) P(\(B_{2024}\) > \(B_{MSY}\)) % \(B_{2028}\) P(\(B_{2028}\) > \(B_{MSY}\)) % \(B_{2032}\) P(\(B_{2032}\) > \(B_{MSY}\)) % Catch 2023 (kt) Catch 2024 (kt)
0 16447 100 17978 100 17868 99 0 0
FMSY 10568 95 6908 32 6112 20 3120 2659
0.75 14813 100 13485 98 12541 92 764 844
1 14323 100 12409 95 11404 87 1006 1083
1.25 13856 100 11484 91 10462 81 1243 1305

8.1.4 Kobe Plot

The BMSY for this year (an average of the most recent ten years) will be 7819kt.

8.1.5 Summary Plot

8.1.6 Retrospective

8.1.6.1 Model Retrospective

8.1.6.1.1 SSB

8.1.6.1.2 Recruitment

8.1.6.2 Historical Retrospective

8.1.6.2.1 Derived Quantities
8.1.6.2.2 Reference Points

8.2 Two-Stock Hypothesis

8.2.1 Fits to Data

8.2.1.1 Fishery Age Composition

8.2.1.2 Fishery Length Composition

8.2.1.3 Survey Age Composition

8.2.1.4 Index Data

8.2.2 Projections

8.2.2.1 SSB

8.2.2.2 Catch

8.2.3 Risk Table

8.2.3.1 Stock_1

Multiplier of \(F_{2022}\) \(B_{2024}\) P(\(B_{2024}\) > \(B_{MSY}\)) % \(B_{2028}\) P(\(B_{2028}\) > \(B_{MSY}\)) % \(B_{2032}\) P(\(B_{2032}\) > \(B_{MSY}\)) % Catch 2023 (kt) Catch 2024 (kt)
0 14976 100 16498 100 16371 100 0 0
FMSY 9994 100 6680 74 5865 58 2528 2175
0.75 13556 100 12531 99 11594 98 645 705
1 13128 100 11563 99 10558 96 849 905
1.25 12721 100 10724 98 9696 93 1048 1091

8.2.3.2 Stock_2

Multiplier of \(F_{2022}\) \(B_{2024}\) P(\(B_{2024}\) > \(B_{MSY}\)) % \(B_{2028}\) P(\(B_{2028}\) > \(B_{MSY}\)) % \(B_{2032}\) P(\(B_{2032}\) > \(B_{MSY}\)) % Catch 2023 (kt) Catch 2024 (kt)
0 1460 100 1374 100 1290 99 0 0
FMSY 1202 99 682 59 417 1 187 154
0.75 1352 99 1031 95 840 82 72 72
1 1321 99 947 92 734 67 94 91
1.25 1292 99 874 86 644 49 116 108

8.2.4 Kobe Plot

8.2.5 Summary Plot

8.2.6 Retrospective

8.2.6.1 Model Retrospective

8.2.6.1.1 SSB

8.2.6.1.2 Recruitment

8.3 Likelihood Table

h1_1.02 h2_1.02
catch_like 0.86 1.08
age_like_fsh 242.06 226.07
length_like_fsh 458.58 450.50
sel_like_fsh 300.27 180.96
ind_like 190.95 183.75
age_like_ind 57.75 63.52
length_like_ind 0.00 0.00
sel_like_ind 26.36 28.27
rec_like 0.35 7.65
fpen 0.01 0.03
post_priors_indq 0.23 0.21
post_priors 0.00 0.00
residual 0.03 0.08
total 1277.46 1142.12

9 Other Business

9.1 General

9.2 Other models to run