Jack Mackerel Assessment SC11
02 February 2024
This document was compiled in RMarkdown, and shows the
incremental results of the Chilean Jack Mackerel (Trachurus
murphyi) stock assessment update in 2024. The files associated with
this document can be found on Github.
The code to create the input files for this assessment and to run
these models can be found in jjm/assessment/R/SC11_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/srcfolder in your Terminal, and using themakecommand - 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.
- This can be done by navigating to the
- Updated the
jjmRpackage- This can be done in
Rusing the commandremotes::install_github("SPRFMO/jjmR")
- This can be done in
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 2022 (single stock h1 and two-stock
h2) model and data set (model 1.02) from SC10. |
| 0.01 | As 0.00 but with revised catches through 2022 (currently still estimates) |
| 0.02 | As 0.01 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 |
| 0.03 | As 0.02 but with updated 2022 weight at age data for all fisheries and their associated CPUE indices |
| 0.04 | As 0.03 but replaced offshore CPUE up to 2022 |
| 0.05 | As 0.04 but with 2023 catch projections |
| 0.06 | As 0.05 but with updated 2023 fishery age composition data for N_Chile, SC_Chile, and Offshore_Trawl, and updated 2023 fishery length composition data for FarNorth |
| 0.07 | As 0.06 but with updated 2023 weight at age data for N_Chile, SC_Chile, and FarNorth fleets, and for their associated CPUE indices |
| 0.08 | As 0.07 but replaced SC_Chile_CPUE index (traditional absolute scaled CPUE by trip) |
| 0.09 | As 0.08 but replaced Peru_CPUE index |
| 0.10 | As 0.09 but updated AcousN 2023 index, with associated age composition and weight at age |
| ———– | ————– |
| Models 1.x | Updated Model and Sensitivities |
| 1.00 | As 0.10 but with updated model (selectivity changes, recruitment) to 2023; 0.10 data file |
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()
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)
}
finmodname <- "1.07"2 Re-running Last Year’s Model (a sanity check)
Re-running the 2023 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).
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).
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).
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).
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).
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).
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 2023 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 2023.
3.1.1.2 Recruitment
Plot comparing recruitment estimated by last year’s model (h1_0.00) with data updated to 2023.
3.1.1.3 Fishing Mortality
Plot comparing fishing mortality estimated by last year’s model (h1_0.00) with data updated to 2023.
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 2023.
3.1.2.2 Recruitment
Plot comparing recruitment estimated by last year’s model (h2_0.00) with data updated to 2023.
3.1.2.3 Fishing Mortality
Plot comparing fishing mortality estimated by last year’s model (h2_0.00) with data updated to 2023.
3.2 Updating this year’s data
The data updated to 2024 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 2024.
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 2024.
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 2024.
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 2024.
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 2024.
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 2024.
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 2024 (h1_0.10).
4.1.2 Recruitment
Plot comparing recruitment estimated by last year’s model (h1_0.00) with data updated to 2024 (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 2024 (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 2024 (h2_0.10).
4.2.2 Recruitment
Plot comparing recruitment estimated by last year’s model (h2_0.00) with data updated to 2024 (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 2024 (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 in previous years.
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).
5.1.2 Recruitment
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).
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).
5.2.2 Recruitment
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).
6 Since the Web Meeting
The code used to run the following sensitivities (1.01 onwards) can
be found in assessment/R/SC11.R.
| Model | Description |
|---|---|
| 1.00 | As 0.10 but with updated model (selectivity changes, recruitment) to 2023; 0.10 data file |
| 1.01 | As 1.00 but with updated Acoustic_CS data (2020, 2021, 2023) |
| 1.02 | As 1.01 but with a break in selectivity in 2020 |
| 1.03 | As 1.00 but with updated ageing error matrix from Chile (SC11-JM05) (NOT RUN) |
| 1.04 | As 1.00 but with proposed Chile CPUE index incorporating effort creep based on fisher interviews (SC11-JM06) (NOT RUN; comparison shown in SC11-JM06) |
| 1.05 | As 1.00 but with proposed Chile CPUE index from SC11-JM07 |
| 1.06 | As 1.00 but with updated Peruvian weight-at-age data (run by Peru) |
| 1.07 | As 1.06 but with downweighted 2022 offshore CPUE index |
| 1.08 | As 1.07 but with Peruvian high seas catch allocated to the offshore fleet instead of fleet 3 |
- Re-run 0.05-1.00 onwards with updated catch projections for Chile
(80,466 t for Fleet 1 and 736,292 t for Fleet 2; Ignacio Paya 30/08/23)
and Peru (210,000 t; Ana Alegre 01/09/23)
- Thanks to Criscely and Josymar (Peru) for cross-checking and finding bugs
- Acoustic Central South data obtained (Ignacio Paya 10/09/23)
- Chilean updates to informed effort creep estimates sensitivity has already been done in SC11-JM06_corrected
- Variable weight-at-age from Peru (thanks to Peru)
- The mean-wight-at-age for the FarNorth fleet has not been updated since 2015, even though those data were reported. Peru indicated that these data do change over time due to annually-estimated weight-length parameters, albeit with minor variation.
6.1 Updating the Chilean Acoustic Central-South Survey
The Central-South Acoustic survey has not been updated since 2009, due to inconsistencies in survey timing and methodology in subsequent years (?). Since 2020, the survey has been conducted for three years- 2020, 2021, and 2023. During the JMWG Web Meeting in August this year (G128-2023), Chile requested that the SC consider including these updated data points in the assessment model. Two versions were proposed- one with just the data updated and a second with a break allowed in selectivity estimates (selectivity change penalty and catchability in 2019) to account for changes since 2009.
- The 2020 data have not been submitted to the Jack Mackerel Data Repository
- Should the weighting of the new data points (2020, 2021, and 2023)
remain the same for the entire time series (historical series ending in
2009)?
- Index CV is 0.3, compared to AcousN CV of 0.5
- Age composition sample size 6.8 compared to AcousN 12.4 (Francis Weights from 2022 benchmark)
6.1.1 Single-stock hypothesis
6.1.1.1 Biomass
Plot comparing biomass estimates from updating the Chilean Central South Acoustic Survey (h1_1.01) with a regime change in selectivity in 2020 (h1_1.02).
6.1.1.2 Recruitment
Plot comparing recruitment estimates from updating the Chilean Central South Acoustic Survey (h1_1.01) with a regime change in selectivity in 2020 (h1_1.02).
6.1.1.3 Fishing Mortality
Plot comparing fishing mortality estimates from updating the Chilean Central South Acoustic Survey (h1_1.01) with a regime change in selectivity in 2020 (h1_1.02).
6.1.1.4 Fit to Survey
6.1.1.5 Fit to Age Composition
6.1.2 Two-stock hypothesis
6.1.2.1 Biomass
Plot comparing biomass estimated from updating the Chilean Central South Acoustic Survey (h2_1.01) with a regime change in selectivity in 2020 (h2_1.02).
6.1.2.2 Recruitment
Plot comparing recruitment estimates from updating the Chilean Central South Acoustic Survey (h2_1.01) with a regime change in selectivity in 2020 (h2_1.02).
6.1.2.3 Fishing Mortality
Plot comparing fishing mortality estimated from updating the Chilean Central South Acoustic Survey (h2_1.01) with a regime change in selectivity in 2020 (h2_1.02).
6.1.2.4 Fit to Survey
6.1.2.5 Fit to Age Composition
6.2 Proposed changes to Chilean CPUE Index
JM06: informed technological creep factor
JM07: spatiotemporal model using INLA
JM12: SPDE-based GLM
JM10 not included as the values were not provided in the paper
Plot comparing z-scored Chilean CPUE indices proposed in SC11-JM06, SC11-JM07, and SC11-JM12.10
6.2.1 CPUE Index from JM07 (INPESCA)
6.2.1.0.1 Single-stock hypothesis
6.2.1.0.1.1 Biomass
Plot comparing biomass estimates from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.0.1.2 Recruitment
Plot comparing recruitment estimates from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.0.1.3 Fishing Mortality
Plot comparing fishing mortality estimates from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.0.1.4 Fit to Survey
6.2.1.1 Two-stock hypothesis
6.2.1.1.1 Biomass
Plot comparing biomass estimated from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.1.2 Recruitment
Plot comparing recruitment estimates from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.1.3 Fishing Mortality
Plot comparing fishing mortality estimated from updating the Chilean CPUE index to that from SC11-JM07.
6.2.1.1.3.1 Fit to Survey
6.3 Annual variation in FarNorth mean weight-at-age
The Peruvian mean weight-at-age vectors have not been updated since 2015 due to low fishing levels (?). These data have historically been provided to the SC but they have not been used in the assessment.
6.3.1 Single-stock hypothesis
6.3.1.1 Biomass
Plot comparing biomass estimates from updating the FarNorth mean weight-at-age from 2015-2023.
6.3.1.2 Recruitment
Plot comparing recruitment estimates from updating the FarNorth mean weight-at-age from 2015-2023.
6.3.1.3 Fishing Mortality
Plot comparing fishing mortality estimates from updating the FarNorth mean weight-at-age from 2015-2023.
6.3.2 Two-stock hypothesis
6.3.2.1 Biomass
Plot comparing biomass estimated from updating the FarNorth mean weight-at-age from 2015-2023.
6.3.2.2 Recruitment
Plot comparing recruitment estimates from updating the FarNorth mean weight-at-age from 2015-2023.
6.3.2.3 Fishing Mortality
Plot comparing fishing mortality estimated from updating the FarNorth mean weight-at-age from 2015-2023.
6.4 Downweighting the final year of the Offshore CPUE
The offshore CPUE series has an unusually high final year value, likely due to reasons unrelated to the stock size. It was suggested to downweight this final value to reduce its impact on the assessment.
6.4.1 Single-stock hypothesis
6.4.1.1 Biomass
Plot comparing biomass estimates from downweighting the 2022 offshore CPUE value.
6.4.1.2 Recruitment
Plot comparing recruitment estimates from downweighting the 2022 offshore CPUE value.
6.4.1.3 Fishing Mortality
Plot comparing fishing mortality estimates from downweighting the 2022 offshore CPUE value.
6.4.2 Two-stock hypothesis
6.4.2.1 Biomass
Plot comparing biomass estimated from downweighting the 2022 offshore CPUE value.
6.4.2.2 Recruitment
Plot comparing recruitment estimates from downweighting the 2022 offshore CPUE value.
6.4.2.3 Fishing Mortality
Plot comparing fishing mortality estimated from downweighting the 2022 offshore CPUE value.
6.5 Allocating Peruvian high seas catch to the offshore fleet
In 2023, the Peruvian fleet fished in the SPRFMO Convention Area, amounting to 20,056 tons of catch. The SC decided to incorporate those catches to Fleet 3 due to two factors, namely that 1) the fishery composition data were not separated by area, and 2) the fleet characteristics (e.g., gear) are that of the far-north fleet rather than that of the offshore fleet.
6.5.1 Single-stock hypothesis
6.5.1.1 Biomass
Plot comparing biomass estimates from allocating the Peruvian high seas catch to the offshore fleet.
6.5.1.2 Recruitment
Plot comparing recruitment estimates from allocating the Peruvian high seas catch to the offshore fleet.
6.5.1.3 Fishing Mortality
Plot comparing fishing mortality estimates from allocating the Peruvian high seas catch to the offshore fleet.
6.5.2 Two-stock hypothesis
6.5.2.1 Biomass
Plot comparing biomass estimated from allocating the Peruvian high seas catch to the offshore fleet.
6.5.2.2 Recruitment
Plot comparing recruitment estimates from allocating the Peruvian high seas catch to the offshore fleet.
6.5.2.3 Fishing Mortality
Plot comparing fishing mortality estimated allocating the Peruvian high seas catch to the offshore fleet.
7 Updated Model Diagnostics
Model 1.07 was selected as the final model for 2024.
7.1 Single-Stock Hypothesis
The \(B_{MSY}\) for this year (an average of the most recent ten years) will be 8.088 million tons.
7.1.1 Fits to Data
7.1.1.1 Fishery Age Composition
7.1.1.2 Fishery Length Composition
7.1.1.3 Survey Age Composition
7.1.1.4 Index Data
7.1.2 Projections
Projections done under a conservative recruitment regime (steepness 0.65, recruitment curve estimated from 2001:2015).
7.1.2.1 SSB
7.1.2.2 Catch
7.1.3 Kobe Plot
BMSY fixed at 8.088 million tons.
7.1.4 Summary Plot
BMSY fixed at 8.088 million tons.
7.2 Two-Stock Hypothesis
The \(B_{MSY}\) for this year (an average of the most recent ten years) will be 6.822 million tons for the south stock and 0.246 million tons for the north stock.
7.2.1 Fits to Data
7.2.1.1 Fishery Age Composition
7.2.1.2 Fishery Length Composition
7.2.1.3 Survey Age Composition
7.2.1.4 Index Data
7.2.2 Projections
Projections done under a conservative recruitment regime (steepness 0.65, recruitment curve estimated from 2001:2015).
7.2.2.1 SSB
7.2.2.2 Catch
7.2.3 Kobe Plot
BMSY fixed at 6.822 million tons for the south stock and 0.246.
7.2.4 Summary Plot
7.3 Likelihood Table
| h1_1.07 | h2_1.07 | |
|---|---|---|
| catch_like | 0.94 | 1.20 |
| age_like_fsh | 244.84 | 228.37 |
| length_like_fsh | 463.10 | 460.50 |
| sel_like_fsh | 306.83 | 184.40 |
| ind_like | 191.60 | 187.07 |
| age_like_ind | 63.01 | 68.38 |
| length_like_ind | 0.00 | 0.00 |
| sel_like_ind | 26.81 | 29.57 |
| rec_like | 2.22 | 8.39 |
| fpen | 0.01 | 0.04 |
| post_priors_indq | 0.23 | 0.21 |
| post_priors | 0.00 | 0.00 |
| residual | 0.03 | 0.14 |
| total | 1299.64 | 1168.26 |