'Opakapaka Length Frequency Data

Length frequency composition data extracted from a Stock Synthesis 3 (SS3) model and prepared for RTMB (R Template Model Builder) stock assessment modeling. Observations are organized by fishery, year, month, and 5 cm length bins.

Usage

opaka_lf

Format

A data.table with 7 columns: year (integer), month (integer), ts (integer), fishery (numeric), bin (numeric), value (numeric), week (numeric). Multiple rows per fishery-year (one per 5 cm length bin). Total rows = 1394 (82 observation-years x 17 bins).

Source

Extracted from Stock Synthesis 3 data file (data_echo.ss_new). Historical period only (1949-2023).

Details

Data workflow

Preparation steps:

Parse length composition observations from SS3 data file (data_echo.ss_new)
Extract proportions and sample sizes by fleet-year
Convert proportions to pseudo-counts (proportion x Nsamp)
Reshape to long format (one row per fishery-year-length bin)
Truncate to historical period (1949-2023)

Output columns

year: Calendar year (1949-2023). Integer.
month: Observation month (1 for all observations). Integer.
ts: Model timestep number (1-75). Integer.
fishery: Fleet index: 1 = Comm (75 obs), 3 = ResFish (7 obs). Numeric.
bin: Length bin lower edge (cm). 5 cm bins: 5, 10, 15, ..., 85. Numeric.
value: Pseudo-count (proportion x Nsamp) in this length bin. Numeric.
week: Temporal indicator (always 1). Numeric.

Fleet coverage

Fleet 1 (Comm): 75 observation-years (1949-2023). Sample sizes range from ~23 to ~755.
Fleet 2 (Non_comm): No length composition data available.
Fleet 3 (ResFish): 7 observation-years (2017-2023). Fixed sample size of 60 per year.

Notes

Data length bins are 5 cm wide (5, 10, 15, ..., 85 cm), giving 17 bins
Values are pseudo-counts derived from SS3 proportions x Nsamp
Sum of values across bins per fishery-year gives the effective sample size
Very small pseudo-counts (< 1e-10) represent SS3's numerical zeros and should be treated as absent observations

Examples

if (FALSE) { # \dontrun{
  data(opaka_lf)
  str(opaka_lf)
  # View sample data
  head(opaka_lf)
  # Get effective sample size per fishery-year
  opaka_lf[, .(total_n = sum(value)), by = .(year, fishery)]
  # Count observations by fleet
  opaka_lf[, uniqueN(paste(year, month)), by = fishery]
} # }