Define a treatment

A treatment is one or more controlled changes applied to an operating model from a given year. Run with run_treatment(), the baseline and the treated operating models share every random number (effort noise, recruitment deviations, sampling), so all differences come from the treatment.

Usage

treatment(
  name = "treatment",
  effort = NULL,
  recruitment = NULL,
  selectivity = NULL,
  creep = NULL,
  M = NULL
)

Arguments

name

label.

effort, recruitment, selectivity, creep, M

treatment components.

Value

a list of class floss_treatment.

Details

Components (each optional, NULL = no change):

• effort: list of fleet, from, mult: nominal effort of the fleet is multiplied by mult from year from (mult = 0: fleet leaves; a value0 for a fleet with no effort before has no effect, so use the fisherytrend to add a fleet).

• recruitment: list with type = "pulse" (years, mult), "trend" (from, rate per year, e.g. -0.05) or "regime" (from, mult): multiplies the recruitment deviations.

• selectivity: list of fleet, from, sel (a selectivity()): the fleet's selectivity changes from year from (e.g. a gear switch or a minimum size).

• creep: list of fleet, from, rate: efficiency increases by rate per year from from (in addition to the fishery's own creep).

• M: list of from, mult: natural mortality is multiplied from from.

Several entries of one component are given as a list of lists.

Examples

treatment("halve linefish effort",
          effort = list(fleet = "linefish", from = 2010, mult = 0.5))
#> FLOSS treatment: halve linefish effort 
#>   effort: linefish x 0.5 from 2010 
treatment("strong 2015 year class",
          recruitment = list(type = "pulse", years = 2015, mult = 5))
#> FLOSS treatment: strong 2015 year class 
#>   recruitment: pulse x 5 in 2015