Nominal effort \(E_{f,y}\) is the trend times multiplicative lognormal noise with mean one, \(\exp(\epsilon - \sigma_E^2/2)\), \(\epsilon\sim N(0,\sigma_E^2)\). The noise is drawn once per fishery and iteration, so all species share it. Efficiency (effort creep) is \(\eta_{f,y} = (1 + c_f)^{\max(0, y - y^c_f)}\); it scales fishing mortality but not the nominal effort index.
Arguments
- fishery
a
fishery().- nsim
number of iterations.
- seed
random seed; default the fishery's seed, so repeated calls give the same effort.
Value
a list with FLQuants
effort (nominal) and efficiency, one
element per fleet, each FLQuant(age = "all", year, iter = nsim).
Examples
eff <- effort_sim(reef_fishery(), nsim = 5)
eff$effort$traps
#> An object of class "FLQuant"
#> iters: 5
#>
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1969 1970 1971 1972
#> all 0.0534(0.00446) 0.0937(0.00403) 0.1755(0.02321) 0.1812(0.01962)
#> year
#> age 1973 1974 1975 1976
#> all 0.2118(0.02675) 0.2598(0.02272) 0.3235(0.02534) 0.3087(0.01308)
#> year
#> age 1977 1978 1979 1980
#> all 0.3802(0.01899) 0.4350(0.06873) 0.5969(0.08622) 0.5138(0.04779)
#> year
#> age 1981 1982 1983 1984
#> all 0.6073(0.12820) 0.6090(0.03531) 0.6216(0.05445) 0.8377(0.15440)
#> year
#> age 1985 1986 1987 1988
#> all 0.7749(0.07345) 0.8666(0.20188) 0.8599(0.09929) 0.8441(0.02612)
#> year
#> age 1989 1990 1991 1992
#> all 0.8044(0.07432) 1.0161(0.07032) 0.8512(0.10233) 0.8809(0.11608)
#> year
#> age 1993 1994 1995 1996
#> all 0.9993(0.20834) 0.8778(0.19351) 1.0141(0.03099) 0.8683(0.02573)
#> year
#> age 1997 1998 1999 2000
#> all 0.7439(0.06951) 1.0222(0.10233) 0.9082(0.10478) 0.7968(0.01082)
#> year
#> age 2001 2002 2003 2004
#> all 0.8912(0.12565) 0.8939(0.06216) 0.8589(0.09223) 0.8199(0.17360)
#> year
#> age 2005 2006 2007 2008
#> all 0.8596(0.20169) 0.9702(0.06401) 0.7754(0.02942) 0.7472(0.09957)
#> year
#> age 2009 2010 2011 2012
#> all 0.7770(0.06449) 0.7668(0.01229) 0.7321(0.11540) 0.7481(0.09850)
#> year
#> age 2013 2014 2015 2016
#> all 0.9159(0.05832) 0.7404(0.03249) 0.7509(0.06097) 0.6858(0.04250)
#> year
#> age 2017 2018 2019 2020
#> all 0.7857(0.15346) 0.7812(0.08217) 0.8217(0.06849) 0.7940(0.04931)
#> year
#> age 2021 2022 2023 2024
#> all 0.8703(0.05584) 0.7491(0.03471) 0.8382(0.04207) 0.7601(0.11255)
#> year
#> age 2025
#> all 0.7659(0.05825)
#>
#> units: relative