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The sbt data

Source: vignettes/data.qmd

Introduction

The sbt software is an R package (R Core Team 2024) that contains the CCSBT operating model (OM) coded using RTMB Kristensen (2025). This page provides examples using the sbt model.

Load inputs

If you have not done so already, you will need to install the packages below:

The sbt RTMB model is loaded along with several R functions using library(sbt). The tidyverse and reshape2 packages are used for data manipulation and plotting. The code theme_set(theme_bw()) alters the plot aesthetics of the entire article

The data list is defined below and then the get_data function is used to set up some additional inputs that are required before the data list can be passed to MakeADFun.

data <- list(
  last_yr = 2022, age_increase_M = 25, length_m50 = 150, length_m95 = 180, 
  catch_UR_on = 0, catch_surf_case = 1, catch_LL1_case = 1, 
  scenarios_surf = data_csv1$scenarios_surface, 
  scenarios_LL1 = data_csv1$scenarios_LL1,
  removal_switch_f = c(0, 0, 0, 1, 0, 0), # 0=harvest rate, 1=direct removals
  sel_min_age_f = c(2, 2, 2, 8, 6, 0, 2),
  sel_max_age_f = c(17, 9, 17, 22, 25, 7, 17),
  sel_end_f = c(1, 0, 1, 1, 1, 0, 1),
  sel_LL1_yrs = c(1952, 1957, 1961, 1965, 1969, 1973, 1977, 1981, 1985, 1989, 
                  1993, 1997, 2001, 2006, 2007, 2008, 2011, 2014, 2017, 2020),
  sel_LL2_yrs = c(1969, 2001, 2005, 2008, 2011, 2014, 2017, 2020),
  sel_LL3_yrs = c(1954, 1961, 1965, 1969, 1970, 1971, 2005, 2006, 2007),
  sel_LL4_yrs = c(1953),
  sel_Ind_yrs = c(1976, 1995, 1997, 1999, 2002, 2004, 2006, 2008, 2010, 2012, 
                  2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022),
  sel_Aus_yrs = c(1952, 1969, 1973, 1977, 1981, 1985, 1989, 1993, 1997, 1998, 
                  1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 
                  2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
                  2019, 2020, 2021, 2022),
  sel_CPUE_yrs = c(1969, 1973, 1977, 1981, 1985, 1989, 1993, 1997, 2001, 2006, 
                   2007, 2008, 2011, 2014, 2017, 2020),
  af_switch = 9,
  lf_switch = 9, lf_minbin = c(1, 1, 1, 11),
  cpue_switch = 1, cpue_a1 = 5, cpue_a2 = 17,
  troll_switch = 1, 
  aerial_switch = 4, aerial_tau = 0.3,
  tag_switch = 1, tag_var_factor = 1.82,
  hsp_switch = 1, hsp_false_negative = 0.7467647, 
  pop_switch = 1, 
  gt_switch = 1
)

data <- get_data(data_in = data)
names(data)
 [1] "last_yr"             "age_increase_M"      "length_m50"
 [4] "length_m95"          "removal_switch_f"    "sel_min_age_f"
 [7] "sel_max_age_f"       "sel_end_f"           "af_switch"
[10] "lf_switch"           "lf_minbin"           "cpue_switch"
[13] "cpue_a1"             "cpue_a2"             "troll_switch"
[16] "aerial_switch"       "aerial_tau"          "tag_switch"
[19] "tag_var_factor"      "hsp_switch"          "hsp_false_negative"
[22] "pop_switch"          "gt_switch"           "first_yr"
[25] "n_year"              "n_season"            "min_age"
[28] "max_age"             "n_age"               "n_length"
[31] "n_fishery"           "age_a"               "length_mu_ysa"
[34] "length_sd_a"         "weight_fya"          "first_yr_catch"
[37] "first_yr_catch_f"    "n_catch"             "catch_year"
[40] "catch_obs_ysf"       "sel_change_year_fy"  "pop_obs"
[43] "paly"                "hsp_obs"             "gt_obs"
[46] "aerial_survey"       "troll_years"         "troll_obs"
[49] "troll_sd"            "cpue_years"          "cpue_obs"
[52] "cpue_sd"             "af_min_age"          "af_max_age"
[55] "n_af"                "af_year"             "af_fishery"
[58] "af_obs"              "af_n"                "dl_yal"
[61] "alk_ysal"            "lf_year"             "lf_fishery"
[64] "lf_season"           "lf_obs"              "lf_n"
[67] "lf_slices"           "af_sliced"           "af_sliced_ysfa"
[70] "cpue_lfs"            "cpue_n"              "tag_shed_immediate"
[73] "tag_shed_continuous" "tag_rep_rates_ya"    "tag_rel_min_age"
[76] "tag_rel_max_age"     "tag_release_cta"     "tag_recap_ctaa"
[79] "tag_recap_max_age"   "min_K"               "n_K"
[82] "n_T"                 "n_I"                 "n_J"

Input tables

The fleets (Table 1).

Table 1: Summary of the 6 Fishery Fleets in the CCSBT Operating Model
Fishery fleet Primary Nations / Components Gear type Key characteristics in assessment
LL1 Japan Longline Provides the primary standardised CPUE index for adult abundance.
LL2 Korea & Taiwan Longline Combined data for Northeast Asian longline fleets; distinct selectivity.
LL3 Other Longline Longline Catch from smaller fleets and non-member effort not in LL1, LL2, or LL4.
LL4 New Zealand Longline Specifically the NZ domestic and charter longline fishery data.
Indonesian Indonesia Longline Operates on spawning grounds (Area 1); catches oldest mature fish.
Australian surface Australia Purse Seine Targets juveniles (ages 2–4) in the GAB for ranching/farming.
Fishery Description Season
LL1 Japanese LL areas 4-9 and other longline catches 2
LL2 Taiwanese albacore LL and gillnet 2
LL3 Japanese LL in Area 2 1
LL4 Japanese spawning (Area 1) 1
Indonesian Indonesian spawning 1
Australian Surface Surface fishery 1
CPUE block Selectivity-only block used in the CPUE likelihood 2

Below are examples of some of the model inputs in tabular form. Mean lengths by year, season, and age are shown in Table 2. The parent offspring pair data are shown in Table 3. The unaccounted catch data are shown in Table 6.

Table 2: Mean length (cm) by year, season, and age. Only a subset of years and ages 1-10 are shown.
Age
Year Season 0 1 2 3 4 5 6 7 8 9 10
1931 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
1932 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
1933 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
1934 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
1935 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
1936 1 45 57.1 74.1 88.8 101.8 113.9 124.3 133.3 141.0 147.6 153.3
2017 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
2018 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
2019 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
2020 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
2021 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
2022 2 50 68.9 91.6 105.5 117.3 127.2 135.6 142.7 148.7 153.8 158.1
Table 3: The parent offspring pair (POP) data set.
Cohort CaptureYear CaptureCov CaptureSwitch NPOPS Comps
2007 2012 15 1 0 1315
2008 2012 15 1 0 963
2009 2012 15 1 0 876
2010 2012 15 1 0 903
2011 2012 15 1 0 899
2003 2013 15 1 0 1317
2004 2013 15 1 0 1325
2005 2013 15 1 0 1356
2006 2013 15 1 0 1347
2007 2013 15 1 0 1315
2008 2013 15 1 0 963
2009 2013 15 1 0 876
2010 2013 15 1 0 903
2011 2013 15 1 0 899
2012 2013 15 1 0 953
Table 4: The halft sibling pairs (HSP) data set.
cohort1 cohort2 nC nK
2013 2014 809592 4
2013 2015 645624 1
2013 2016 1237446 0
2013 2017 1291248 1
2013 2018 1181936 0
2014 2015 716688 2
2014 2016 1373652 1
2014 2017 1433376 4
2014 2018 1312032 3
2015 2016 1095444 2
2015 2017 1143072 1
2015 2018 1046304 3
2016 2017 2190888 4
2016 2018 2005416 5
2017 2018 2092608 2
Table 5: The gene tagging (GT) data set.
Release year Release age Recapture year Number released Number sampled Number of matches
2016 2 2017 2952 15389 20
2017 2 2018 6480 11932 67
2018 2 2019 6295 11980 66
2019 2 2020 4242 11109 31
2021 2 2022 6401 10742 41
Table 6: The unaccounted catch (tonnes) data set.
Year LL1 LL2 LL3 LL4 Indonesia Australia
2008 72 0 0 0 0 0
2009 152 0 0 0 0 0
2010 271 0 0 0 0 0
2011 151 0 0 0 0 0
2012 275 0 0 0 0 0
2013 432 0 0 0 0 0
2014 121 0 0 0 0 0
2015 326 0 0 0 0 0
2016 756 0 0 0 0 0
2017 984 0 0 0 0 0
2018 1511 0 0 0 0 0
2019 1155 0 0 0 0 0
2020 1160 0 0 0 0 0
2021 1160 0 0 0 0 0
2022 1160 0 0 0 0 0

Input plots

Length at age (Figure 1). Weight at age (Figure 2).

plot_length_at_age(data = data, years = c(1931, 2022))
Figure 1: Length (cm) at age during each season. 
plot_weight_at_age(data = data, years = c(1931, 2000, 2010, 2022))
Figure 2: Weight (kg) at age for each fishery.

References

Kristensen, Kasper. 2025. RTMB: ’R’ Bindings for ’TMB’. https://doi.org/10.32614/CRAN.package.RTMB.
Kristensen, Kasper, Anders Nielsen, Casper W. Berg, Hans Skaug, and Bradley M. Bell. 2016. “TMB: Automatic Differentiation and Laplace Approximation.” Journal of Statistical Software 70 (5): 1–21. https://doi.org/10.18637/jss.v070.i05.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/.