In pacific-hake/hake-assessment: Stock Assessment for Pacific Hake

Changes from `r last_assess_yr`

Changes from the `r last_assess_yr` assessment

Update Stock Synthesis to version r ss_version
Incrementally update catch, weight-at-age, age-1 index, and fishery age-composition data from years to 2023
Incrementally add 2023 catch, weight-at-age, biomass survey, survey age-composition, age-1 index, and fishery age-composition data \pause
Change the error distribution associated with the age-1 index from lognormal to Student's t-distribution
Use the model-based matrix of input weight-at-age data
Incorporate model-based estimates and predictions of time-varying maturity ogives into the calculation of time-varying fecundity \pause
All model runs in the assessment use MCMC

Bridging: Model Updates and Additions (biomass)

p <- list()
p[[1]] <-
  plot_biomass(d_obj = d_obj_bridge_biomass[[1]],
               point_size = 1.5,
               show_arrows = TRUE,
               dodge_bo = 0.75,
               leg_ncol = 2,
               leg_font_size = 5,
               x_labs_mod = 10,
               wrap_y_label = TRUE,
               tick_prop = 2,
               leg_pos = c(0.5, 0.9))
p[[2]] <-
  plot_rel_biomass(d_obj = d_obj_bridge_rel_biomass[[1]],
                   ylim = c(0, 2.5),
                   x_labs_mod = 10,
                   wrap_y_label = TRUE,
                   leg_pos = "none")

plot_grid(plotlist = p,
          nrow = 1,
          ncol = 2)

Bridging: Model Updates and Additions (recruitment)

  plot_recdevs(d_obj = d_obj_bridge_recdev[[1]],
               x_labs_mod = 10,
               leg_ncol = 2,
               leg_font_size = 6,
               line_width = 0.1,
               rev_colors = TRUE,
               leg_pos = c(0.22, 0.9))

Bridging: Model Updates and Additions (survey)

p <- list()
p[[1]] <-
  plot_survey_index_fits(base_model,
                         d_obj = d_obj_bridge_age2_index[[1]],
                         survey_type = "age2",
                         rev_colors = TRUE,
                         leg_pos = "none",
                         xlim = c(1995, end_yr))

p[[2]] <-
  plot_survey_index_fits(base_model,
                         d_obj = d_obj_bridge_age1_index[[1]],
                         survey_type = "age1",
                         xlim = c(1995, end_yr),
                         ylim = c(0, 11),
                         leg_ncol = 2,
                         leg_font_size = 5,
                         leg_pos = c(0.45, 0.9),
                         y_breaks = seq(0, 11, by = 2),
                         rev_colors = TRUE)

plot_grid(plotlist = p,
          nrow = 1,
          ncol = 2)

Bridging: Model Structure (biomass)

p <- list()
p[[1]] <-
  plot_biomass(d_obj = d_obj_bridge_biomass[[2]],
               point_size = 1.5,
               show_arrows = TRUE,
               dodge_bo = 0.75,
               leg_ncol = 2,
               leg_font_size = 4.5,
               x_labs_mod = 10,
               wrap_y_label = TRUE,
               tick_prop = 2,
               leg_pos = c(0.52, 0.9))
p[[2]] <-
  plot_rel_biomass(d_obj = d_obj_bridge_rel_biomass[[2]],
                   ylim = c(0, 2.5),
                   x_labs_mod = 10,
                   wrap_y_label = TRUE,
                   leg_pos = "none")

plot_grid(plotlist = p,
          nrow = 1,
          ncol = 2)

Bridging: Model Structure (recruitment)

  plot_recdevs(d_obj = d_obj_bridge_recdev[[2]],
               x_labs_mod = 10,
               leg_ncol = 2,
               leg_font_size = 5,
               leg_pos = c(0.2, 0.9),
               rev_colors = TRUE,
               line_width = 0.1)

Bridging: Model Structure (survey)

p <- list()
p[[1]] <-
  plot_survey_index_fits(base_model,
                         d_obj = d_obj_bridge_age2_index[[2]],
                         survey_type = "age2",
                         rev_colors = FALSE,
                         leg_pos = "none",
                         xlim = c(1995, end_yr))

p[[2]] <-
  plot_survey_index_fits(base_model,
                         d_obj = d_obj_bridge_age1_index[[2]],
                         survey_type = "age1",
                         xlim = c(1995, end_yr),
                         ylim = c(0, 11),
                         leg_ncol = 1,
                         leg_font_size = 5,
                         leg_pos = c(0.25, 0.82),
                         y_breaks = seq(0, 11, by = 2),
                         rev_colors = TRUE)

plot_grid(plotlist = p,
          nrow = 1,
          ncol = 2)

Bridging: Summary

\bsmall

Updating data previous to r last_data_yr had very little effect on the stock trajectory
Adding the r last_data_yr survey biomass estimate led to a downward shift in stock trajectory (and fit to biomass survey) going back to 2017
Adding the r last_data_yr survey age compositions increased stock biomass from 2023 to 2024 with increases in 2020 and 2021 recruitment
Adding the age-1 index resulted in a slight increase in 2022 recruitment
Adding r last_assess_yr fishery age composition data shifted the stock trajectory upwards slightly in recent years and increased 2020 and 2021 recruitment (decreased 2019 and 2022) \pause
Model-based weight-at-age altered stock size and status in the 1970s and 1980s (and equilibrium B~0~), but had little net effect since that time
Time-varying maturity did not influence general trends in stock size or status, but:
There was a reduction in spawning biomass in recent years; and
The transition to spawning biomass was delayed for up to a year in some cases