
'Opakapaka Stock Assessment Data
opaka_data.RdA comprehensive list containing biological parameters, catch and CPUE data, and Bayesian prior specifications prepared for RTMB (R Template Model Builder) stock assessment modeling. Extracted from a Stock Synthesis 3 (SS3) model and converted to opal data conventions.
Format
A list with 22 named elements combining dimensions, biological parameters, observed data (catch and CPUE), and Bayesian priors for RTMB model initialization.
Source
Extracted from Stock Synthesis 3 (SS3 v3.30.19) model input files
(data_echo.ss_new, control.ss_new). Historical data only
(1949-2023), forecast period excluded.
Details
Data structure (22 list elements)
Age and time dimensions:
- age_a
Vector of integer ages (1:44). Length = 44. Internal opal ages mapping to SS3 ages 0-43 (opal age
i= SS3 agei - 1).- n_age
Number of age classes (44).
- n_season
Number of seasons per year (1).
- n_fishery
Number of fisheries/fleets (3).
- n_year
Number of years in model (75, representing 1949-2023).
- first_yr
First year designation in model (1, representing 1949).
- last_yr
Last year designation in model (75, representing 2023).
- years
Vector of model years (1:75).
Length structure:
- len_bin_start
First length bin lower edge (5 cm).
- len_bin_width
Width of each length bin (5 cm).
- n_len
Total number of length bins (17).
Catch data:
- first_yr_catch
First year with catch data (1).
- catch_units_f
Numeric vector of unit codes by fishery (3 elements). All fisheries use units = 1 (biomass).
- catch_obs_ysf
Three-dimensional array of observed catch (75 years x 1 season x 3 fisheries). Dimensions follow (year, season, fishery). Fleet 3 (ResFish survey) has zero catch.
CPUE data:
- cpue_switch
Indicator for CPUE inclusion (1 = included).
- cpue_data
Data.table with 82 rows and 8 columns containing CPUE observations from two fleets:
year: Calendar year (1949-2023)
month: Observation month (7 for Comm fleet, 1 for ResFish)
ts: Sequential timestep (1-75)
fishery: Fleet identifier (1 = Comm, 3 = ResFish)
index: Integer index identifier (1 for Comm, 2 for ResFish)
metric: Data type ("cpue")
units: Unit code (1 for biomass)
value: CPUE observations
se: Log-space standard error (0.2 for Comm, 0.15 for ResFish)
Comm fleet (fleet 1) covers 1949-2023 (75 obs). ResFish survey (fleet 3) covers 2017-2023 (7 obs).
Biological parameters:
- lw_a
Length-weight allometric coefficient (1.75e-05).
- lw_b
Length-weight allometric exponent (2.99).
- maturity
Maturity at length vector (17 elements). Logistic maturity probabilities at each data length bin midpoint (Mat50 = 40.7 cm, slope = -2.26).
- fecundity
Fecundity at length vector (17 elements). Fecundity equals weight at each length bin midpoint (fecundity option: eggs = Wt).
- M
Natural mortality at age vector (44 elements). Constant instantaneous rate (0.135) across all ages.
Growth reference ages (Schnute parameterization):
- A1
Reference age for L1 (1). Corresponds to SS3 age 0.
- A2
Reference age for L2 (44 = n_age). Corresponds to SS3 age 43.
Selectivity:
- sel_type_f
Selectivity function type by fishery (3 elements). Integer codes: 1 = logistic, 2 = double-normal, 3 = double Richards.
Priors:
- priors
List of 8 Bayesian prior specifications, each containing:
type: Prior distribution type ("none" = uninformative)
par1: First parameter (NA when type = "none")
par2: Second parameter (NA when type = "none")
index: Parameter index in the model
Priors cover: log_B0, log_cpue_q, par_sel, log_L1, log_L2, log_k, log_CV1, log_CV2.
Fleet definitions
- Fleet 1 (Comm)
Commercial fishery. Catch fleet with logistic selectivity. CPUE and length composition data available 1949-2023.
- Fleet 2 (Non_comm)
Non-commercial fishery. Catch fleet with logistic selectivity (fixed parameters). Catch data only, no CPUE or compositions.
- Fleet 3 (ResFish)
Research fishery survey. Survey fleet with double-normal selectivity. CPUE and length composition data available 2017-2023.
Growth conversion
Growth parameters were converted from the SS3 standard von Bertalanffy parameterization (L_at_Amin = 6.0 at age 0, Linf = 67.5 at age 999, k = 0.242) to the opal Schnute parameterization with A1 = 1 and A2 = n_age. The conversion is exact to machine precision (~1e-14). The VB growth coefficient k is unchanged between parameterizations.
See also
opaka_parameters for starting parameter values, opaka_lf for length frequency composition data.
Examples
if (FALSE) { # \dontrun{
data(opaka_data)
str(opaka_data)
# Access catch data
head(opaka_data$cpue_data)
# Get biological parameters
opaka_data$M
# Check dimensions
cat("Ages:", opaka_data$n_age, "\n")
cat("Years:", opaka_data$n_year, "\n")
cat("Fleets:", opaka_data$n_fishery, "\n")
cat("Length bins:", opaka_data$n_len, "\n")
} # }