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Case Study: West Coast Sablefish

Source: vignettes/ac_wc_sablefish_case_study.Rmd
ac_wc_sablefish_case_study.Rmd

Overview

This case study reproduces the 2025 West Coast sablefish assessment, run in Stock Synthesis 3, in SPoRC.

The model is single region, two sex, single season, with six catch fleets and four trawl surveys:

Source Years Observations Likelihood
Catch, three gears and their discard fleets 1890–2024 620 Lognormal, CV 0.01
Triennial early and late, NWFSC slope, WCGBTS 1980–2024 35 Lognormal
Fishery age compositions, six fleets 1983–2024 198 Multinomial
Survey age compositions, four surveys 1983–2024 52 Multinomial

The assessment fits one further data source that this bridge does not, an index of the recruitment deviations themselves over 2020–2024, and the section on it below says what leaving it out costs.

Ages run a=0a = 0 to a+=70a_{+} = 70 with compositions binned to 00 to 5050, and model years run y1=1890y_{1} = 1890 to yend=2024y_{\text{end}} = 2024. Recruitment is age 0 and comes off the same year’s spawning biomass, on a Beverton-Holt curve with steepness fixed at 0.75 and σR=1.4\sigma_{R} = 1.4.

library(SPoRC)
library(dplyr)
library(ggplot2)
data("sgl_rg_wc_sablefish_data")

dat <- sgl_rg_wc_sablefish_data
yrs <- dat$years
n_yrs <- length(yrs)
n_ages <- length(dat$ages)
n_fish <- dat$n_fish_fleets
n_srv <- dat$n_srv_fleets

What Stock Synthesis says differently

Seven conventions hold the whole bridge, and each of them can easily forgotten if missed. Age 0 fish spawn in the equilibrium year only. Spawning biomass is female numbers times fecundity at age, formed before the year’s recruits settle, so the age 0 cell is empty in every year the model runs. The unfished equilibrium is different: it already holds R0/2R_{0}/2 at age 0, and that is what SSBy1SSB_{y_{1}} and S0S_{0} are built from. Dropping those fish everywhere moves S0S_{0} by 0.009 percent, which the Beverton-Holt then propagates into recent recruitment. SPoRC forms the first year’s spawning biomass from the initial numbers and every later year’s from the survivors, so a maturity at age 0 that is the fecundity ratio in y1y_{1} and zero afterwards is exact.

The recruitment deviation is not the deviation. Stock Synthesis generates recruitment as

Ry=f(SSBy)⋅exp⁡(εy−12byσR2)R_{y} = f(SSB_{y}) \cdot \exp\left(\varepsilon_{y} - \tfrac{1}{2}b_{y}\sigma_{R}^{2}\right)

with byb_{y} the bias ramp, so SPoRC’s deviation is the assessment’s less the bias term. The ramp’s four breakpoints (1974, 1979, 2023, 2024) are given to bias_year in deviation index space rather than as calendar years.

Fishing mortality is solved, not estimated. Under the hybrid method Stock Synthesis conditions FF on the catch, while SPoRC estimates it against a lognormal catch likelihood at the data file’s CV of 0.01. The two agree to six digits, which is as close as that likelihood’s CV of 0.01 holds them.

Extra survey standard deviation is added, not combined in quadrature. For each survey, σy=σyinput+σextra\sigma_{y} = \sigma_{y}^{\text{input}} + \sigma^{\text{extra}}. SPoRC currently has no parameter for it, so it is kept at the assessment’s estimate.

Two fleets have two composition data sources. The trawl fishery and the West Coast groundfish bottom trawl survey each have sexed and unsexed compositions in the same years. SPoRC holds one composition type per fleet, so each gets a second fleet sharing the first’s selectivity: a fishery fleet companion with no catch observation, and a survey fleet companion with no index.

Model dimensions

Single region, single season and two sexes, so the population, region and season subscripts collapse to one and are dropped throughout.

input_list <- Setup_Mod_Dim(
  years = yrs,
  ages = dat$ages,
  lens = dat$lens,
  n_regions = dat$n_regions,
  n_sexes = dat$n_sexes,
  n_fish_fleets = n_fish,
  n_srv_fleets = n_srv,
  n_seas = dat$n_seas,
  n_pop = dat$n_pop,
  natal_region = dat$natal_region,
  verbose = FALSE
)

Recruitment

Recruitment is age 0 off the same year’s spawning biomass, which is rec_lag = 0, on a Beverton-Holt curve at fixed steepness. The model starts from an unfished equilibrium with no initial deviations and no initial fishing mortality, so InitDevs_spec = "fix" and the initial age structure is the bare geometric series.

input_list <- Setup_Mod_Rec(
  input_list = input_list,
  rec_model = "bh_rec",
  rec_lag = 0,
  SR_ref_yr = 1,
  t_spawn = dat$t_spawn,
  h_spec = "fix",
  steepness_h = array(qlogis((dat$steepness - 0.2) / 0.8), dim = c(1, 1)),
  sigmaR_spec = "fix",
  ln_sigmaR = array(log(dat$sigmaR), dim = c(2, 1, 1)),
  sigmaR_switch = 1,
  do_rec_bias_ramp = 1,
  bias_year = dat$bias_year,
  max_bias_ramp_fct = dat$max_bias_adj,
  RecDevs_pen_center = "fixed",
  dont_est_recdev_last = 0,
  init_age_strc = 2,
  equil_init_age_strc = 0,
  InitDevs_spec = "fix",
  ln_global_R0 = dat$mle$ln_R0
)

Steepness is bounded on a logit over (0.2,1)(0.2, 1), so the fixed value goes in transformed. SR_ref_yr = 1 puts the unfished spawning biomass per recruit on the first year’s weight and fecundity at age, which is where the assessment computes S0S_{0}.

bias_year is the one argument here that is easy to get wrong. It is indexed in deviation space, not in calendar years, and the assessment’s four breakpoints are the last_early_yr_nobias_adj, first_yr_fullbias_adj, last_yr_fullbias_adj and first_recent_yr_nobias_adj of its control file. The bridge stage below checks the resulting ramp against the assessment’s own biasadjuster column, which is the cheapest way to know it went in right.

Biological dynamics

Weight at age and fecundity at age are empirical and year specific, read from the assessment’s wtatage.ss. Maturity is the ratio of the two, so spawning biomass is numbers times fecundity, with the age 0 cell with its value in the equilibrium year alone.

input_list <- Setup_Mod_Biologicals(
  input_list = input_list,
  WAA = dat$WAA,
  WAA_fish = array(dat$WAA, dim = c(dim(dat$WAA), n_fish)),
  WAA_srv = array(dat$WAA, dim = c(dim(dat$WAA), n_srv)),
  MatAA = dat$MatAA,
  fit_lengths = 0,
  AgeingError = dat$AgeingError,
  M_spec = "est_ln_M",
  Use_M_prior = 1,
  M_prior = dat$M_prior,
  addtocomp = dat$addtocomp,
  comp_const_obs = 1,
  addtosrvidx = 0,
  addtofishidx = 0
)

# the age 0 fish are spawners in the equilibrium year only, and setup refuses a
# non-zero maturity at the recruit age under rec_lag = 0
input_list$data$MatAA[1, 1, 1, 1, 1, 1] <- dat$mat_age0_yr1

Natural mortality is one value for both sexes, the assessment’s male parameter being set equal to the female’s, under a lognormal prior with median e−2.631e^{-2.631} and standard deviation 0.31 on the log scale.

The ageing error matrix maps the 71 modeled ages onto the 51 composition bins, the last of which accumulates. The assessment prints it with the observed bins descending, so it is transposed on the way in.

Movement and tagging

Neither is used.

input_list <- Setup_Mod_Movement(input_list = input_list, use_fixed_movement = 1,
                                 Fixed_Movement = NA, do_recruits_move = 0)

input_list <- Setup_Mod_Tagging(input_list = input_list, use_conv_fish_tagging = 0)

Catch and fishing mortality 

input_list <- Setup_Mod_Catch_and_F(
  input_list = input_list,
  ObsCatch = dat$ObsCatch,
  UseCatch = dat$UseCatch,
  Use_F_pen = 0,
  ln_F_mean_spec = "fix",
  sigmaC_spec = "fix",
  ln_sigmaC = array(log(dat$sigmaC), dim = c(1, n_yrs, 1, n_fish))
)

The assessment has no fishing mortality penalty and no mean fishing mortality parameter, so Use_F_pen = 0 and ln_F_mean_spec = "fix" leave the deviations with all of log⁡F\log F. Fixing the mean matters: with the deviations unpenalized the mean and their average are exactly redundant, and fixing one of them is what keeps that ridge from going off the rails.

Years before a fleet’s first landing are true fishery closures, entered as a zero catch with UseCatch = 0, which forces FF to zero and drops the deviation. The trawl fleet’s composition companion is different: it has no catch observation at all, entered as NA, which leaves its fishing mortality estimated. Seeded at 10−1210^{-12} it takes nothing out of the population, so its catch at age is the trawl fleet’s numbers at age scaled by the same selectivity, which is what makes its compositions the right expectation. This is needed because one fleet can’t have two sets of composition data.

Fishery compositions 

input_list <- Setup_Mod_FishIdx_and_Comps(
  input_list = input_list,
  ObsFishIdx = array(NA_real_, dim = c(1, n_yrs, 1, n_fish)),
  ObsFishIdx_SE = array(NA_real_, dim = c(1, n_yrs, 1, n_fish)),
  UseFishIdx = array(0, dim = c(1, n_yrs, 1, n_fish)),
  fish_idx_type = rep("none", n_fish),
  FishIdx_LikeType = rep("lognormal", n_fish),
  ObsFishAgeComps = dat$ObsFishAgeComps,
  UseFishAgeComps = dat$UseFishAgeComps,
  ISS_FishAgeComps = dat$ISS_FishAgeComps,
  ObsFishLenComps = array(NA_real_, dim = c(1, n_yrs, 1, 1, dat$n_sexes, n_fish)),
  UseFishLenComps = array(0, dim = c(1, n_yrs, 1, n_fish)),
  ISS_FishLenComps = array(0, dim = c(1, n_yrs, 1, dat$n_sexes, n_fish)),
  FishAgeComps_LikeType = rep("Multinomial", n_fish),
  FishLenComps_LikeType = rep("none", n_fish),
  FishAgeComps_Type = paste0(ifelse(dat$fish_sex == 3, "spltRjntS", "agg"),
                             "_Year_1-terminal_Fleet_", seq_len(n_fish)),
  FishLenComps_Type = paste0("none_Year_1-terminal_Fleet_", seq_len(n_fish))
)

FishAgeComps_Type is where the two composition data sources show up. The three gear fleets have sexed compositions, which is spltRjntS, females and males in one vector normalized together. The three discard fleets and the trawl companion have compositions with the sexes pooled, which is agg.

The sample sizes were built as

ISSy=Ny⋅νf/(1+nbins⋅0.001)\text{ISS}_{y} = N_{y} \cdot \nu_{f} \big/ \left(1 + n_{\text{bins}} \cdot 0.001\right)

with νf\nu_{f} the assessment’s variance adjustment for that fleet, which run from 0.005 to 0.97 here, and nbinsn_{\text{bins}} 102 for a sexed composition and 51 for a pooled one.

Survey indices and compositions 

t_srv <- array(dat$t_srv, dim = c(1, 1, n_srv))

# The assessment reports each survey's standard error with its estimated extra
# standard deviation already added. Subtracting it recovers what went in, which
# is what SPoRC wants, since the extra component is kept as a parameter.
input_se <- function(dat) {
  se <- dat$ObsSrvIdx_SE
  for(f in seq_along(dat$mle$extra_sd)) se[,,,f] <- se[,,,f] - dat$mle$extra_sd[f]
  se
}

input_list <- Setup_Mod_SrvIdx_and_Comps(
  input_list = input_list,
  ObsSrvIdx = dat$ObsSrvIdx,
  # The assessment reports its survey standard errors with an estimated extra
  # standard deviation already added, so subtracting it recovers the input errors
  # and the extra component is kept as a parameter instead. Fleets 1 and 4 sit
  # at the assessment's lower bound, which is it asking for none, so they are kept.
  ObsSrvIdx_SE = input_se(dat),
  sigmaSrvIdx_spec = "est_additive",
  sigmaSrvIdx_map = c(NA, 1, 2, NA, NA),
  ln_sigmaSrvIdx = log(pmax(c(dat$mle$extra_sd, 1e-8), 1e-8)),
  UseSrvIdx = dat$UseSrvIdx,
  # fleets 1-4 are the trawl surveys and 5 the unsexed compositions of the last
  # of them, which carries no index
  srv_idx_type = c(rep("biom", n_srv - 1), "none"),
  SrvIdx_LikeType = rep("lognormal", n_srv),
  ObsSrvAgeComps = dat$ObsSrvAgeComps,
  UseSrvAgeComps = dat$UseSrvAgeComps,
  ISS_SrvAgeComps = dat$ISS_SrvAgeComps,
  ObsSrvLenComps = array(NA_real_, dim = c(1, n_yrs, 1, 1, dat$n_sexes, n_srv)),
  UseSrvLenComps = array(0, dim = c(1, n_yrs, 1, n_srv)),
  ISS_SrvLenComps = array(0, dim = c(1, n_yrs, 1, dat$n_sexes, n_srv)),
  SrvAgeComps_LikeType = rep("Multinomial", n_srv),
  SrvLenComps_LikeType = rep("none", n_srv),
  SrvAgeComps_Type = paste0(ifelse(dat$srv_sex == 3, "spltRjntS", "agg"),
                            "_Year_1-terminal_Fleet_", seq_len(n_srv)),
  SrvLenComps_Type = paste0("none_Year_1-terminal_Fleet_", seq_len(n_srv)),
  t_srv = t_srv
)

All four surveys are in month 7, so t_srv = 0.5 and the index is formed from numbers decayed half a year. The standard errors already hold the extra standard deviation added to them.

Selectivity

Every fleet is on the age based double normal, dbnrml, six parameters controlling the start of the plateau, its end, the ascending and descending widths, and the selectivity at the first and last bins. Three things about how the assessment arranges them have to come across.

The trawl and hook and line fleets change in 2002 and the two trawl and hook and line discard fleets in 2011 and 2019, which are time blocks. The pot fleet mirrors hook and line outright, and each composition companion mirrors its parent, which is est_shared_f_x. And the hook and line male curve is offsets on the female’s, with a fifth male parameter scaling the whole curve, which is fish_sel_sex_offset = "par_apical".

# selectivity blocks are named by the years the assessment's surfaces change
blk_string <- function(blocks, fleet) {
  st <- blocks[[fleet]]$blk_yr
  en <- c(st[-1] - 1, n_yrs)
  paste0("Block_", seq_along(st), "_Year_", st, "-",
         ifelse(en == n_yrs, "terminal", en), "_Fleet_", fleet)
}

input_list <- Setup_Mod_Fishsel_and_Q(
  input_list = input_list,
  fish_sel_model = paste0("dbnrml_Fleet_", seq_len(n_fish)),
  cont_tv_fish_sel = paste0("none_Fleet_", seq_len(n_fish)),
  fish_sel_blocks = unlist(lapply(seq_len(n_fish), function(f) blk_string(dat$fish_sel_blocks_ss3, f))),
  fish_q_blocks = paste0("none_Fleet_", seq_len(n_fish)),
  fish_fixed_sel_pars_spec = replace(rep("est_all", n_fish), c(3, n_fish),
                                     c("est_shared_f_2", "est_shared_f_1")),
  fish_sel_sex_offset = replace(rep("par", n_fish), c(2, 3), "par_apical"),
  fish_q_spec = rep("fix", n_fish)
)

input_list <- Setup_Mod_Srvsel_and_Q(
  input_list = input_list,
  srv_sel_model = paste0("dbnrml_Fleet_", seq_len(n_srv)),
  cont_tv_srv_sel = paste0("none_Fleet_", seq_len(n_srv)),
  srv_sel_blocks = unlist(lapply(seq_len(n_srv), function(sf) blk_string(dat$srv_sel_blocks_ss3, sf))),
  srv_q_blocks = paste0("none_Fleet_", seq_len(n_srv)),
  srv_fixed_sel_pars_spec = replace(rep("est_all", n_srv), n_srv,
                                    paste0("est_shared_f_", n_srv - 1)),
  srv_sel_sex_offset = rep("par", n_srv),
  srv_q_spec = c(rep("est_all", n_srv - 1), "fix"),
  t_srv = t_srv
)

Catchability floats in the assessment, meaning it is solved rather than estimated, which reaches the same optimum as estimating it. The composition companion of the bottom trawl survey has no index, so its catchability stays out of the parameter vector.

All six parameters are on the same scale in both, so the assessment’s values go straight in as starting values, and the parameter count comes out at the assessment’s own 32.

fish_sel_sex_offset = "par_apical" is what holds the male curve. The par half makes the male slots offsets on the female’s, which is how the assessment writes the peak and the selectivity at the last bin. The apical half is the assessment’s fifth male parameter: the height the male ascending and descending limbs are built up to and the plateau sits at, with the two ends left where their own parameters put them. That is a different statement from a "scale" offset, which multiplies the finished curve and holds the ends along with it, and the difference is not cosmetic: the two agree on the curve at any one point but disagree about how it moves, so under a scale offset the assessment’s estimate is not a stationary point and the fit walks away from it. The male ascending and descending widths take no offset and the selectivity at the male first bin is read from the female’s parameter, exactly as the assessment does, so those three offsets are zero.

Weighting 

input_list <- Setup_Mod_Weighting(
  input_list = input_list,
  Wt_Catch = 1, Wt_FishIdx = 0, Wt_SrvIdx = 1, Wt_Rec = 1, Wt_F = 1, Wt_Tagging = 0,
  Wt_FishAgeComps = array(1, dim = c(1, n_yrs, 1, dat$n_sexes, n_fish)),
  Wt_FishLenComps = array(1, dim = c(1, n_yrs, 1, dat$n_sexes, n_fish)),
  Wt_SrvAgeComps = array(1, dim = c(1, n_yrs, 1, dat$n_sexes, n_srv)),
  Wt_SrvLenComps = array(1, dim = c(1, n_yrs, 1, dat$n_sexes, n_srv))
)

data <- input_list$data
parameters <- input_list$par
mapping <- input_list$map

The assessment’s composition weights are already in the sample sizes, so every weight here is one.

The recruitment index this bridge leaves out

The assessment carries one more survey fleet, Stock Synthesis fleet 11, which is not an index of the population at all. Its five observations over 2020–2024 are units 36, a normal likelihood on the recruitment deviation εy\varepsilon_{y} itself, so its prediction would be q(εy−μy)q\left(\varepsilon_{y} - \mu_{y}\right) with μy\mu_{y} the center the recruitment penalty asserts for that year. That is an observation of a parameter rather than of anything the population dynamics produce: the fleet would read no numbers at age, so its selectivity, survey timing and weight at age would never be used, and its likelihood would have to be normal rather than lognormal because a deviation is signed.

SPoRC has no index type for it, so it is not one of the five survey fleets above. What the assessment made of it is still in dat$ss3, whose likelihood and predicted index tables are its report as it stands, and that is what the comparisons below name and subtract. The same information can be brought in through machinery the package already has, as a covariate on recruitment in a DSEM (see vignettes("ag_dsem")), which is a statement about the process rather than a survey of it.

Starting at the assessment’s estimate

Every parameter is set to the assessment’s maximum likelihood estimate, read from ss3.par at twelve significant digits rather than from the report file, which prints six. The bridge helper does this in one call; the pieces are the recruitment deviations less their bias correction, log fishing mortality as a fixed mean plus deviations, the four catchabilities, natural mortality, and the selectivity parameters in SPoRC’s transforms.

source(system.file("tests", "testthat", "helper-bridge_wc_sablefish.R", package = "SPoRC"))

input_list <- seed_wc_sablefish_mle(build_wc_sablefish_input(dat), dat)
seed <- fit_model(input_list$data, input_list$par, input_list$map,
                  do_optim = FALSE, silent = TRUE)

The comparison before anything is optimized, against a report file with six significant digits:

numbers at age                     5.8e-04 %
spawning biomass                   4.5e-04 %
recruitment                        3.9e-04 %
total biomass, January 1           4.9e-04 %
predicted catch                    4.8e-04 %
survey indices                     5.1e-04 %
bias ramp                          0        (absolute)
selectivity, all eleven fleets     5.0e-07  (absolute)
expected age compositions          6.1e-07  (absolute)

SPoRC writes each likelihood component as a proper density where the assessment drops normalizing constants, so the comparison subtracts exactly the constants it omits:

Component SPoRC less constants Assessment Difference
Catch 0.0000072 0.0000000 7.2×10−67.2\times10^{-6}
Triennial early index -9.5934212 -9.5934200 −1.2×10−6-1.2\times10^{-6}
Triennial late index -4.8821916 -4.8822100 1.8×10−51.8\times10^{-5}
NWFSC slope index -4.6967965 -4.6968000 3.5×10−63.5\times10^{-6}
WCGBTS index -25.5881300 -25.5880000 −1.3×10−4-1.3\times10^{-4}
Trawl ages 262.1262400 262.1260000 2.4×10−42.4\times10^{-4}
Hook and line ages 303.7876300 303.7880000 −3.7×10−4-3.7\times10^{-4}
Pot ages 86.7638400 86.7638000 4.0×10−54.0\times10^{-5}
Trawl discard ages 248.4356900 248.4360000 −3.1×10−4-3.1\times10^{-4}
Hook and line discard ages 54.7879350 54.7879000 3.5×10−53.5\times10^{-5}
Pot discard ages 65.7715190 65.7715000 1.9×10−51.9\times10^{-5}
Triennial early ages 12.2708500 12.2709000 −5.0×10−5-5.0\times10^{-5}
Triennial late ages 73.2284320 73.2284000 3.2×10−53.2\times10^{-5}
NWFSC slope ages 35.3020090 35.3020000 8.7×10−68.7\times10^{-6}
WCGBTS ages 199.0197000 199.0200000 −3.0×10−4-3.0\times10^{-4}
Recruitment deviations 22.4041010 22.4041400 −3.9×10−5-3.9\times10^{-5}
Natural mortality prior 0.2040659 0.2040690 −3.1×10−6-3.1\times10^{-6}

The assessment’s recruitment index, which is not fit here, is a further 1.2484100 of its total. The comparison is therefore against that total less that row: 1334.5231 against 1334.5232, a difference of 4.4×10−54.4\times10^{-5}, which is the report file’s own print precision.

Fitting and comparison 

est <- fit_model(input_list$data, input_list$par, input_list$map,
                 do_optim = TRUE, newton_loops = 3, silent = TRUE)
est$sdrep <- RTMB::sdreport(est, hessian.fixed = est$he(est$optim$par))

710 parameters, 32 of them selectivity. The objective falls by 0.0095 from the assessment’s estimate to -880.6833, and the fitted selectivity stays within 1.8×10−41.8\times10^{-4} of the assessment’s on every one of the twelve fleets.

Median Maximum
Spawning biomass -0.069 % 0.100 %
Recruitment -0.133 % 8.13 %
SPoRC Assessment
ln⁡R0\ln R_{0} 10.163776 10.165108
Natural mortality 0.0877404 0.0877770
Triennial early catchability 1.023289 1.022873
Triennial late catchability 2.770884 2.770076
NWFSC slope catchability 0.509935 0.509779
WCGBTS catchability 1.049972 1.049699

Why the two differ

Everything in the population dynamics, the observation model and the selectivity agrees at the assessment report’s own print precision, and so does every likelihood component the two share. What moves is the fit, and three things account for it.

The first is the recruitment index, the one data source this bridge does not carry. It spoke to five year classes and to nothing else, so that is where its absence shows. No deviation before 2020 moves by more than 0.005 on the log scale, and of the five it observed only 2024 moves at all far.

Year Observation SE SPoRC deviation Assessment deviation Recruitment
2020 0.300 1.192 1.4420 1.4410 -0.01 %
2021 0.682 1.185 1.0900 1.0892 -0.04 %
2022 -0.912 1.216 -1.4772 -1.4968 1.86 %
2023 0.382 1.181 1.4953 1.4934 0.07 %
2024 0.680 1.187 0.4234 0.5070 -8.13 %

2024 is the terminal year, whose deviation the age compositions have not yet seen a single fish of, so the index was the only observation of it and dropping it leaves the deviation to the recruitment penalty alone. That is the whole of the 8 percent maximum in the recruitment row above, and it propagates into spawning biomass only as far as a single unfished year class does, which is a tenth of a percent.

The same holds for uncertainty. Against the assessment’s own standard errors, the coefficient of variation on recruitment is unchanged to four significant figures in every year but two: 2024 widens from 0.926 to 0.968, and 2022 is marginally tighter at 0.397 against 0.403, the deviation there having been the one the index disagreed with. Spawning biomass is unchanged to four figures in every year, 2024 included. Five observations on a parameter, weighted at a standard error of about 1.2 on a series whose σR\sigma_{R} is 1.4, are worth roughly four percent on the terminal year class and nothing else.

The rest of the recruitment series moves too, but as a level rather than a shape: releasing 2024 lets ln⁡R0\ln R_{0} settle 0.0013 lower, which carries every year class from 1890 to 2021 down by about 0.04 percent together. That uniform shift, not any single year, is what the median rows in the two tables above are reporting.

The other two causes are conventions rather than data, and they are the same two the other bridges carry. The assessment solves fishing mortality from the catch where SPoRC estimates it against a lognormal catch likelihood at CV 0.01, so the two agree only to the precision that likelihood enforces; and the extra survey standard deviation is kept at the assessment’s estimate, because SPoRC has no parameter for it. Neither moves the fit at the assessment’s own estimate, where every likelihood component the two share agrees to the report file’s print precision, but the second does mean the standard errors here do not have that source of uncertainty.