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Catch, effort and nominal CPUE: by fleet or aggregated

Source: R/aggregate.R
catch_table.Rd

  • catch_table(): observed catch (t) by fleet, summed over fleets ("aggregate"), or summed over fleets and species ("combined").

  • effort_table(): observed effort (fishing days) by fleet or summed over fleets.

  • nominal_cpue(): catch / effort (kg per fishing day) at the chosen level, and as an index relative to its mean. "aggregate" divides the catch of all fleets by the effort of all fleets, ignoring that a day of trap fishing and a day of linefishing catch differently; "combined" also sums the catch of all species.

Usage

catch_table(data, species = NULL, by = c("fleet", "aggregate", "combined"))

effort_table(data, by = c("fleet", "aggregate"))

nominal_cpue(data, species = NULL, by = c("fleet", "aggregate", "combined"))

Arguments

data

a floss_data.

species

species names; default all (for "combined", the species summed).

by

"fleet", "aggregate" or "combined" (catch, CPUE).

Value

a data frame with species, iter, year, source (fleet or "all fleets") and the values (observed and true).

Details

Nominal CPUE uses all years with catch and effort, unlike the CPUE index of sample_data() (which is a standardised index for the sampled years). With changing fleet mix or effort creep, nominal CPUE drifts away from the stock: compare it with cpue_true (true catch / true effort) and the true biomass.

Examples

# \donttest{
x <- simulate_fishery(nsim = 2, iters = 1)
head(nominal_cpue(x$data, by = "aggregate"))
#>                     species iter year     source  catch_t effort     cpue
#> 1 Carcharhinus melanopterus    1 1971 all fleets 26.34111    582 45.25963
#> 2 Carcharhinus melanopterus    1 1972 all fleets 29.08215    516 56.36076
#> 3 Carcharhinus melanopterus    1 1973 all fleets 33.97667    706 48.12560
#> 4 Carcharhinus melanopterus    1 1974 all fleets 42.85550    797 53.77102
#> 5 Carcharhinus melanopterus    1 1975 all fleets 48.48520   1054 46.00114
#> 6 Carcharhinus melanopterus    1 1976 all fleets 50.29837    930 54.08426
#>       index catch_true effort_true cpue_true index_true
#> 1 0.9997929   26.38079    522.3684  50.50227   1.147047
#> 2 1.2450185   27.87265    541.4572  51.47712   1.169188
#> 3 1.0631026   35.56634    688.7058  51.64228   1.172939
#> 4 1.1878109   39.43281    839.1149  46.99334   1.067349
#> 5 1.0161728   50.33827    941.0162  53.49352   1.214986
#> 6 1.1947305   49.83039    905.2078  55.04857   1.250306
plot_aggregate(x$data, "Lutjanus bohar", what = "cpue", by = "fleet")

# }