R/parameters.R
In vquennessen/densityratio: Using Density Ratios to Help Manage Fisheries In And Around Marine Reserves
Defines functions
parameters
Documented in
parameters
#' Life history parameters
#'
#' \code{parameters} loads the life history parameters for a specific fished
#' stock.
#'
#' @param Species character value, the species to analyze. Species names are in
#' the format species abbreviation_state or stock_year assessed. Example:
#' BR.CA.2003 is the California black rockfish stock assessed in 2003.
#'
#' @return a list of numeric values and vectors required to run the base model
#' and supporting functions.
#' @export
#'
#' @examples
#' parameters(Species = 'BR_CA_2003')
parameters = function(Species) {
species_list <- c('BR_CA_2003', 'BR_OR_2015', 'CAB_CA_2005', 'CAB_OR_2019',
'LING_OW_2017', 'CR_OR_2015', 'China_OR_2015',
'BR_OR_2015_overfished', 'CAB_OR_2019_overfished',
'LING_OW_2017_overfished', 'CR_OR_2015_overfished',
'BR_OR_2015_SSS', 'CAB_OR_2019_SSS',
'LING_OW_2017_SSS', 'CR_OR_2015_SSS')
###### Error handling ########################################################
if (!is.character(Species)) {
stop('Study species must be a character string.')
}
if (!is.element(Species, species_list)) {
stop('Desired stock parameters are not known. Try a different species,
region, and/or year.')
}
##### Black Rockfish (CA) 2003 assessment #####
# source: Ralston & Dick 2003
if (Species == 'BR_CA_2003') {
Max_age <- 35 # maximum age
M <- 0.14 # natural mortality
Rec_age <- 2 # age at recruitment
WA <- 1.68e-5; WB <- 3 # weight at length parameters (f)
A1 <- 5; L1 <- 32.21; # growth parameters (f)
A2 <- 15; L2 <- 47.95;
K <- 0.2022
L50 <- 39.53 # length at 50% maturity
K_mat <- -0.4103 # slope of maturity curve
H <- 0.65 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.488 # depletion
Fb <- 0.07 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('sport', 'hook', 'trawl') # names of fleets
Alpha <- c(0.33, 0.6, 0.64) # slope of upcurve per fleet
Beta <- c(1.2, 0.6, 0) # slope of downcurve per fleet
F_fin <- c(0.25, 0.06, 1) # final selectivity if dome-shaped
A50_up <- c(2, 5, 10) # A50 value for upcurve
A50_down <- c(6, 16, 35) # A50 value for downcurve
Cf <- c(0.71, 0.28, 0.01) # fraction of fishery caught / fleet
}
##### Black Rockfish (OR) 2015 assessment, overfished #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015_overfished') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.2 # depletion
Fb <- 0.16 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Black Rockfish (OR) 2015 assessment, set stock status #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015_SSS') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.05 # fishing mortality to cause
# overfishing (D = 0.)
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Cabezon (OR) 2019 assessment, overfished #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019_overfished') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.44 # depletion
Fb <- 0.24 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Cabezon (OR) 2019 assessment, set stock status #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019_SSS') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.15 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Lingcod (OR and WA) 2017 assessment, overfished #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017_overfished') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.37 # depletion
Fb <- 0.16 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Lingcod (OR and WA) 2017 assessment, set stock status #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017_SSS') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.08 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Canary Rockfish (OR) 2015 assessment, overfished #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015_overfished') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.4 # depletion
Fb <- 0.04 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### Canary Rockfish (OR) 2015 assessment, set stock status #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015_SSS') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.02 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### China Rockfish (Northern OR) 2015 assessment #####
# source: Cope et al. 2015
if (Species == 'China_OR_2015') {
Max_age <- 79 # maximum age
M <- 0.07 # natural mortality
Rec_age <- 5 # age at recruitment
WA <- 7.79E-06; WB <- 3.177 # weight at length parameters (f) #
A1 <- 10; L1 <- 30.5; # growth parameters (f)
A2 <- 30; L2 <- 36.85;
K <- 0.147
L50 <- 27 # length at 50% maturity
K_mat <- -0.467 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.2 # adult movement proportion
D <- 0.6149 # depletion
Fb <- 0.07 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('Commercial', 'Rec_PC',
'Rec_PR') # names of fleets
Alpha <- c(1.05, 0.45, 0.45) # slope of upcurve per fleet
Beta <- c(1, 1, 1) # slope of downcurve per fleet
F_fin <- c(1, 1, 1) # final selectivity if dome-shaped
A50_up <- c(10, 12, 12) # A50 value for upcurve
A50_down <- c(79, 79, 79) # A50 value for downcurve
Cf <- c(0.02, 0.54, 0.44) # fraction of fishery caught / fleet
}
##### Black Rockfish (OR) 2015 assessment, actual #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.604 # depletion
Fb <- 0.05 # fishing mortality to cause
# overfishing (D = 0.)
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Cabezon (OR) 2019 assessment #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.528 # depletion
Fb <- 0.17 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Lingcod (OR and WA) 2017 assessment #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.579 # depletion
Fb <- 0.08 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Canary Rockfish (OR) 2015 assessment #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.555 # depletion
Fb <- 0.02 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### final output ###########################################################
output <- list(Max_age, M, Rec_age, WA, WB, A1, L1, A2, L2, K, L50, K_mat, H,
Sigma_R, Rho_R, AMP, D, Fb, P, X, SP, Fleets, Alpha, Beta,
F_fin, A50_up, A50_down, Cf)
return(output)
}
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Defines functions parameters
Documented in parameters
#' Life history parameters
#'
#' \code{parameters} loads the life history parameters for a specific fished
#' stock.
#'
#' @param Species character value, the species to analyze. Species names are in
#' the format species abbreviation_state or stock_year assessed. Example:
#' BR.CA.2003 is the California black rockfish stock assessed in 2003.
#'
#' @return a list of numeric values and vectors required to run the base model
#' and supporting functions.
#' @export
#'
#' @examples
#' parameters(Species = 'BR_CA_2003')
parameters = function(Species) {
species_list <- c('BR_CA_2003', 'BR_OR_2015', 'CAB_CA_2005', 'CAB_OR_2019',
'LING_OW_2017', 'CR_OR_2015', 'China_OR_2015',
'BR_OR_2015_overfished', 'CAB_OR_2019_overfished',
'LING_OW_2017_overfished', 'CR_OR_2015_overfished',
'BR_OR_2015_SSS', 'CAB_OR_2019_SSS',
'LING_OW_2017_SSS', 'CR_OR_2015_SSS')
###### Error handling ########################################################
if (!is.character(Species)) {
stop('Study species must be a character string.')
}
if (!is.element(Species, species_list)) {
stop('Desired stock parameters are not known. Try a different species,
region, and/or year.')
}
##### Black Rockfish (CA) 2003 assessment #####
# source: Ralston & Dick 2003
if (Species == 'BR_CA_2003') {
Max_age <- 35 # maximum age
M <- 0.14 # natural mortality
Rec_age <- 2 # age at recruitment
WA <- 1.68e-5; WB <- 3 # weight at length parameters (f)
A1 <- 5; L1 <- 32.21; # growth parameters (f)
A2 <- 15; L2 <- 47.95;
K <- 0.2022
L50 <- 39.53 # length at 50% maturity
K_mat <- -0.4103 # slope of maturity curve
H <- 0.65 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.488 # depletion
Fb <- 0.07 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('sport', 'hook', 'trawl') # names of fleets
Alpha <- c(0.33, 0.6, 0.64) # slope of upcurve per fleet
Beta <- c(1.2, 0.6, 0) # slope of downcurve per fleet
F_fin <- c(0.25, 0.06, 1) # final selectivity if dome-shaped
A50_up <- c(2, 5, 10) # A50 value for upcurve
A50_down <- c(6, 16, 35) # A50 value for downcurve
Cf <- c(0.71, 0.28, 0.01) # fraction of fishery caught / fleet
}
##### Black Rockfish (OR) 2015 assessment, overfished #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015_overfished') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.2 # depletion
Fb <- 0.16 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Black Rockfish (OR) 2015 assessment, set stock status #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015_SSS') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.05 # fishing mortality to cause
# overfishing (D = 0.)
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Cabezon (OR) 2019 assessment, overfished #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019_overfished') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.44 # depletion
Fb <- 0.24 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Cabezon (OR) 2019 assessment, set stock status #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019_SSS') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.15 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Lingcod (OR and WA) 2017 assessment, overfished #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017_overfished') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.37 # depletion
Fb <- 0.16 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Lingcod (OR and WA) 2017 assessment, set stock status #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017_SSS') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.08 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Canary Rockfish (OR) 2015 assessment, overfished #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015_overfished') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.4 # depletion
Fb <- 0.04 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### Canary Rockfish (OR) 2015 assessment, set stock status #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015_SSS') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.5665 # depletion
Fb <- 0.02 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### China Rockfish (Northern OR) 2015 assessment #####
# source: Cope et al. 2015
if (Species == 'China_OR_2015') {
Max_age <- 79 # maximum age
M <- 0.07 # natural mortality
Rec_age <- 5 # age at recruitment
WA <- 7.79E-06; WB <- 3.177 # weight at length parameters (f) #
A1 <- 10; L1 <- 30.5; # growth parameters (f)
A2 <- 30; L2 <- 36.85;
K <- 0.147
L50 <- 27 # length at 50% maturity
K_mat <- -0.467 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.2 # adult movement proportion
D <- 0.6149 # depletion
Fb <- 0.07 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('Commercial', 'Rec_PC',
'Rec_PR') # names of fleets
Alpha <- c(1.05, 0.45, 0.45) # slope of upcurve per fleet
Beta <- c(1, 1, 1) # slope of downcurve per fleet
F_fin <- c(1, 1, 1) # final selectivity if dome-shaped
A50_up <- c(10, 12, 12) # A50 value for upcurve
A50_down <- c(79, 79, 79) # A50 value for downcurve
Cf <- c(0.02, 0.54, 0.44) # fraction of fishery caught / fleet
}
##### Black Rockfish (OR) 2015 assessment, actual #####
# source: Cope et al. 2016
if (Species == 'BR_OR_2015') { #####
Max_age <- 40 # maximum age
M <- 0.17 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.6e-5; WB <- 2.88 # weight at length parameters (f)
A1 <- 1; L1 <- 20.32 # growth parameters (f)
A2 <- 40; L2 <- 49.67
K <- 0.21
L50 <- 43.69 # length at 50% maturity
K_mat <- -0.66 # slope of maturity curve
H <- 0.77 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.604 # depletion
Fb <- 0.05 # fishing mortality to cause
# overfishing (D = 0.)
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'live', 'dead', # names of fleets
'ocean', 'shore')
Alpha <- c(0.325, 0.4, 0.35,
0.65, 0.425) # slope of upcurve per fleet
Beta <- c(0.25, 0.5, 0.4, 1.1, 0.5) # slope of downcurve per fleet
F_fin <- c(0.325, 0.05, -0.11,
-0.025, 0.135) # final selectivity if dome-shaped
A50_up <- c(7, 5, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 13, 13, 12, 6) # A50 value for downcurve
Cf <- c(0.0001, 0.1679, 0.0982, # fraction of fishery caught / fleet
0.6979, 0.0358)
}
##### Cabezon (OR) 2019 assessment #####
# source: Cope et al. 2019
if (Species == 'CAB_OR_2019') {
Max_age <- 20 # maximum age
M <- 0.26 # natural mortality
Rec_age <- 4 # age at recruitment
WA <- 1.90e-5; WB <- 2.99 # weight at length parameters (f)
A1 <- 4; L1 <- 44.30 # growth parameters (f)
A2 <- 20; L2 <- 63.35
K <- 0.225
L50 <- 43 # length at 50% maturity
K_mat <- -0.7 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.528 # depletion
Fb <- 0.17 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('live', 'dead', 'ocean', # names of fleets
'shore')
Alpha <- c(0.4, 0.33, 0.35, 0.9) # slope of upcurve per fleet
Beta <- c(0.35, 0, 0, 0.2) # slope of downcurve per fleet
F_fin <- c(0.7, 1, 1, 0.07) # final select. if dome-shaped
A50_up <- c(3, 4, 2, 1) # A50 value for upcurve
A50_down <- c(17, 1, 1, 3) # A50 value for downcurve
Cf <- c(0.6033, 0.0415, 0.3423, # fraction of fishery
0.0130)
}
##### Lingcod (OR and WA) 2017 assessment #####
# source: Haltuch et al. 2018
if (Species == 'LING_OW_2017') {
Max_age <- 25 # maximum age
M <- 0.28 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 2.76e-6; WB <- 3.28 # weight at length parameters (f)
A1 <- 1; L1 <- 17.28; # growth parameters (f)
A2 <- 20; L2 <- 120;
K <- 0.128
L50 <- 56.7 # length at 50% maturity
K_mat <- -0.27 # slope of maturity curve
H <- 0.7 # steepness
Sigma_R <- 0.55 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.579 # depletion
Fb <- 0.08 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'fixed_gear', # names of fleets
'WArec', 'ORrec')
Alpha <- c(0.25, 0.25, 0.55, 1) # slope of upcurve per fleet
Beta <- c(0.09, 0.3, 0.17, 0.15) # slope of downcurve per fleet
F_fin <- c(0.07, 0, 0, 0) # final select. if dome-shaped
A50_up <- c(3, 5, 5, 3) # A50 value for upcurve
A50_down <- c(15, 12, 10, 9) # A50 value for downcurve
Cf <- c(0.2872, 0.1379, 0.3253, # fraction of fishery
0.2496)
}
##### Canary Rockfish (OR) 2015 assessment #####
# source: Thorson & Wetzel 2015
if (Species == 'CR_OR_2015') {
Max_age <- 84 # maximum age
M <- 0.0521 # natural mortality
Rec_age <- 3 # age at recruitment
WA <- 1.18e-5; WB <- 3.094 # weight at length parameters (f)
A1 <- 1; L1 <- 9.05; # growth parameters (f)
A2 <- 20; L2 <- 60.05;
K <- 0.129
L50 <- 42 # length at 50% maturity
K_mat <- -0.25 # slope of maturity curve
H <- 0.773 # steepness
Sigma_R <- 0.5 # recruitment standard deviation
Rho_R <- 0 # recruitment autocorrelation
AMP <- 0.1 # adult movement proportion
D <- 0.555 # depletion
Fb <- 0.02 # fishing mortality to cause D
P <- 0.77 # Proportion of positive transects
# in PISCO monitoring data
X <- 15.42 # mean of positive transects
SP <- 16.97 # std of positive transects
Fleets <- c('trawl', 'non-trawl', # names of fleets
'rec', 'hake', 'research')
Alpha <- c(0.3, 0.6, 1, 1, 1) # slope of upcurve per fleet
Beta <- c(1, 0, 1, 1, 0.08) # slope of downcurve per fleet
F_fin <- c(0.36, 1, 0.175, 0.65, 0.8)# final selectivity if dome-shaped
A50_up <- c(5, 5, 4, 8, 1) # A50 value for upcurve
A50_down <- c(10, 50, 7, 11, 30) # A50 value for downcurve
Cf <- c(0.3908, 0.3122, 0.2246, # fraction of fishery caught / fleet
0.0295, 0.0429) # from upcurve to 1
}
##### final output ###########################################################
output <- list(Max_age, M, Rec_age, WA, WB, A1, L1, A2, L2, K, L50, K_mat, H,
Sigma_R, Rho_R, AMP, D, Fb, P, X, SP, Fleets, Alpha, Beta,
F_fin, A50_up, A50_down, Cf)
return(output)
}
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