Title:	Length-Based Multispecies Analysis by Numerical Simulation
Version:	0.1.2
Date:	2019-12-09
Description:	Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.
Depends:	R (≥ 3.5.0)
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.0.0
LinkingTo:	Rcpp, RcppArmadillo
Imports:	Rcpp, abind, methods
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
Collate:	'NS_par.R' 'RcppExports.R' 'calc_M1.R' 'calc_Q.R' 'calc_growth.R' 'calc_indicators.R' 'calc_mature.R' 'param_setup.R' 'run_LeMans.R' 'calc_output.R' 'calc_phi.R' 'calc_prefs.R' 'calc_ration_growthfac.R' 'calc_recruits.R' 'calc_suit_vect.R' 'get_Catch.R' 'get_SSB.R' 'get_indicators.R' 'other.R' 'plot_SSB.R' 'plot_indicators.R'
NeedsCompilation:	yes
Packaged:	2019-12-09 13:49:56 UTC; MS23
Author:	Michael A. Spence [aut, cre], Hayley J. Bannister [aut], Johnathan E. Ball [aut], Paul J. Dolder [aut], Robert B. Thorpe [aut], Christopher A. Griffiths [ctb]
Maintainer:	Michael A. Spence <michael.spence@cefas.co.uk>
Repository:	CRAN
Date/Publication:	2019-12-09 15:10:02 UTC

A constructor for the LeMansParam class

Description

A constructor for the LeMansParam class.

Usage

LeMansParam(df, gdf, ...)

## S4 method for signature 'ANY,ANY'
LeMansParam(
  df,
  gdf,
  nfish = nrow(df),
  nsc = 32,
  pred_mu = -2.25,
  pred_sigma = 0.5,
  other = 1e+12,
  bounds = NULL,
  calc_phi_min = FALSE,
  phi_min = 0.1,
  vary_growth = TRUE,
  growth_eff = 0.5,
  growth_eff_decay = 0.11,
  eps = 1e-05,
  force_mature = TRUE,
  species_names = paste("species", 1:nfish, sep = "_"),
  kappa = rep(10, nfish),
  tau = matrix(1, nfish, nfish),
  rec_fun = rep("hockey-stick", nfish),
  recruit_params = list(a = 18.835 - 4.133 * df$Linf, b = rep(1000/nfish, nfish)),
  natmort_opt = rep("std_RNM", nfish),
  Nmort = rep(0.8, nfish),
  prop = rep(3/4, nfish),
  curve = rep("logistic", nfish),
  catch_species = ((0:(length(curve) - 1))%%nfish) + 1,
  max_catchability = rep(1, length(curve)),
  gear_name = paste("gear_", 1:length(curve), sep = ""),
  custom = NULL,
  ...
)

## S4 method for signature 'missing,ANY'
LeMansParam(
  gdf,
  nfish = length(Linf),
  nsc = 32,
  pred_mu = -2.25,
  pred_sigma = 0.5,
  other = 1e+12,
  bounds = NULL,
  calc_phi_min = FALSE,
  phi_min = 0.1,
  vary_growth = FALSE,
  growth_eff = 0.5,
  growth_eff_decay = 0.11,
  eps = 1e-05,
  force_mature = TRUE,
  Linf,
  k,
  W_a,
  W_b,
  Lmat,
  species_names = paste("species", 1:nfish, sep = "_"),
  kappa = rep(10, nfish),
  tau = matrix(1, nfish, nfish),
  rec_fun = rep("hockey-stick", nfish),
  recruit_params = list(a = 18.835 - 4.133 * Linf, b = rep(1000/nfish, nfish)),
  natmort_opt = rep("std_RNM", nfish),
  Nmort = rep(0.8, nfish),
  prop = rep(3/4, nfish),
  curve = rep("logistic", nfish),
  catch_species = ((0:(length(curve) - 1))%%nfish) + 1,
  max_catchability = rep(1, length(curve)),
  gear_name = paste("gear_", 1:length(curve), sep = ""),
  custom = NULL,
  ...
)

## S4 method for signature 'missing,missing'
LeMansParam(
  df,
  gdf,
  nfish = length(Linf),
  nsc = 32,
  pred_mu = -2.25,
  pred_sigma = 0.5,
  other = 1e+12,
  bounds = NULL,
  calc_phi_min = TRUE,
  phi_min = 0.1,
  vary_growth = FALSE,
  growth_eff = 0.5,
  growth_eff_decay = 0.11,
  eps = 1e-05,
  force_mature = TRUE,
  species_names = paste("species", 1:nfish, sep = "_"),
  Linf,
  k,
  W_a,
  W_b,
  Lmat,
  kappa = rep(10, nfish),
  tau = matrix(1, nfish, nfish),
  rec_fun = rep("hockey-stick", nfish),
  recruit_params = list(a = 18.835 - 4.133 * Linf, b = rep(1000/nfish, nfish)),
  natmort_opt = rep("std_RNM", nfish),
  Nmort = rep(0.8, nfish),
  prop = rep(3/4, nfish),
  curve = rep("logistic", nfish),
  catch_species = ((0:(length(curve) - 1))%%nfish) + 1,
  max_catchability = rep(1, length(curve)),
  gear_name = paste("gear_", 1:length(curve), sep = ""),
  custom = NULL,
  ...
)

Arguments

Details

Converts objects of class data frame or vector to class LeMansParams for use in the LeMans model. Linf, W_a, W_b, k and Lmat are required as either a data frame or as vectors.

Value

An object of class LeMansParam for use in the LeMans model.

See Also

run_LeMans, LeMans_param

Examples

# To run the model with all inputs specified explicitly:
# Set up species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50% of individuals are mature (cm).

NS_params <- LeMansParam(species_names=NS_par$species_names, Linf=Linf, k=k, W_a=W_a, W_b=W_b,
Lmat=Lmat, tau=NS_tau, recruit_params=list(a=NS_par$a, b=NS_par$b), eta=rep(0.25, 21), L50=Lmat,
other=NS_other)

###############################################
# Alternatively:
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=NS_other)

An S4 class representing the outputs of the LeMans model

Description

An S4 class representing the outputs of the LeMans model

Slots

N

An array with dimensions nsc, nfish and tot_time representing the number of individuals in each length class at each time step of the model.

Catch

An array with dimensions nsc, nfish and tot_time representing the biomass of each species that is caught in each length class at each time step of the model.

M2

An array with dimensions nsc, nfish and tot_time representing the predation mortality of each species in each length class at each time step of the model.

R

A matrix with dimensions tot_time and nfish representing the number of recruits of each species at each time step of the model.

An S4 class representing the inputs of the LeMans model

Description

An S4 class representing the inputs of the LeMans model

Details

Sets the class of a LeMansParam object, which is then used to run the LeMans model.

Slots

nsc

A numeric value representing the number of length classes in the model.

nfish

A numeric value representing the number of fish species in the model.

phi_min

A numeric value representing the time step of the model.

l_bound

A numeric vector of length nsc representing the lower bounds of the length classes.

u_bound

A numeric vector of length nsc representing the upper bounds of the length classes.

mid

A numeric vector of length nfish representing the mid-point of the length classes.

species_names

A numeric or character vector of length nfish that denotes the names of the species in the model.

Linf

A numeric vector of length nfish representing the mid-point of the length classes.

W_a

A numeric vector representing the parameter a in the length-weight conversion.

W_b

A numeric vector representing the parameter b in the length-weight conversion.

k

k A numeric vector of length nfish representing the von Bertalanffy growth parameter (1/yr) for each species.

Lmat

A numeric vector of length nsc representing the length at which 50% of the individuals are mature.

mature

A matrix with dimensions nsc and nfish with elements in the range 0-1 representing the proportion of mature individuals of each species in each length class.

sc_Linf

A numeric vector of length nsc representing the length class at which each species reaches its asymptotic length.

wgt

A matrix with dimensions nsc and nfish representing the weight of each species in each length class.

phi

A matrix with dimensions nsc and nfish representing the proportion of individuals that leave each length class.

ration

A matrix with dimensions nsc and nfish representing the amount of food required for fish of a given species and length class to grow according to the von Bertalanffy growth curve in a time step.

other

A numeric value representing the amount of other food (g) available from prey that is not explicitly represented in the model.

M1

A matrix of dimensions nsc and nfish representing the natural mortality of each species for each length class.

suit_M2

A list object of length nfish. Each element in the list is an array of dimensions nsc, nsc and nfish containing a value between 0 and 1 representing prey preference and prey suitability for each species and length class.

Qs

An array of dimensions nsc, nfish and length(gear) representing the catchability of each species by each of the fishing gears.

stored_rec_funs

A list object of length rec_fun where each element includes the stock recruitment function for a given species. If an invalid recruitment function is selected, NULL is returned and a warning message is shown.

eps

A numeric value specifying a numerical offset. The default value is 1e-5.

recruit_params

A list object of length nfish specifying the parameters for the recruitment function.

The length at 50% of the maximum catchability of the catchability curve

Description

The length at 50% of the maximum catchability of the catchability curve associated with NS_mixed_fish.

Usage

NS_L50

Format

A numeric value representing the length at 50% of the maximum catchability of the catchability curve.

References

Thorpe, R.B., Dolder, P.J. , Reeves, S., Robinson, P., Jennings, S. (2015). Assessing fishery and ecological consequences of alternative management options for multispecies fisheries ICES Journal of Marine Science, 73(6):1503-1512.

The steepness of the slope of the catchability curve

Description

The steepness of the slope of the catchability curve associated with NS_mixed_fish.

Usage

NS_eta

Format

A numeric value representing the steepness of the slope of the catchability curve.

References

Thorpe, R.B., Dolder, P.J. , Reeves, S., Robinson, P., Jennings, S. (2015). Assessing fishery and ecological consequences of alternative management options for multispecies fisheries ICES Journal of Marine Science, 73(6):1503-1512.

Gear selectivity data frame

Description

A gear selectivity data frame for the 21 species in NS_par.

Usage

NS_mixed_fish

Format

A data frame with 21 rows and 4 variables, including:

catch_species

A character string describing the species to apply the catchability curve to.

curve

A character vector describing the type of curve to be used to determine the catchability of each species by the fishing gear.

gear_name

A character string describing the name of the gear.

max_catchability

A numeric vector describing the maximum catchability for each catchability curve.

References

Thorpe, R.B., Dolder, P.J. , Reeves, S., Robinson, P., Jennings, S. (2015). Assessing fishery and ecological consequences of alternative management options for multispecies fisheries ICES Journal of Marine Science, 73(6):1503-1512.

Other food for the North Sea

Description

Other food for the North Sea dataset, NS_par.

Usage

NS_other

Format

A numerical value representing other food. To be used with NS_par.

North Sea data

Description

Data for the 21 species in the North Sea version of the LeMans model.

Usage

NS_par

Format

A data frame with 21 rows and 8 variables, including:

species_names

A numeric or character vector of length nfish that denotes the names of the species in the model.

Linf

A numeric vector of length nfish representing the asymptotic length of each species (cm).

W_a

A numeric vector of length nfish representing the parameter a in the length-weight conversion.

W_b

A numeric vector of length nfish representing the parameter b in the length-weight conversion.

k

A numeric vector of length nfish representing the von Bertalanffy growth parameter (1/yr) for each species.

Lmat

A numeric vector of length nsc representing the length at which 50% of the individuals are mature (cm).

a

A numeric value representing the density independent part of the hockey-stick recruitment curve.

b

A numeric value representing the density dependent part of the hockey-stick recruitment curve.

References

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

North Sea interaction matrix

Description

A predator-prey interaction matrix for the 21 species in NS_par.

Usage

NS_tau

Format

A matrix with 21 rows and 21 columns:

Row indices represent predators and column indices represent prey. A value of 1 at location i, j indicates prey j is eaten by predator i.

References

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

Calculate community indicators

Description

Calculates the Large Fish Indicator (LFI), Typical Length (TyL) or Length Quantile (LQ) for the community or a subset of the species.

Usage

calc_LFI(wgt, N, bry, prop)

calc_LQ(wgt, u_bound, N, prob)

calc_TyL(wgt, mid, N)

Arguments

Value

calc_LFI returns a numeric value or vector representing the proportion of the biomass in the length classes above bry and in prop of the biomass in the length class bry.

calc_TYL returns a numeric value or vector representing the biomass-weighted geometric mean length of the community.

calc_LQ returns a numeric value or vector representing the length at which biomass exceeds a given proportion prob of the total biomass.

See Also

get_indicators

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e11)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate the LFI for 40cm
bry <- which(NS_params@l_bound<=40 & NS_params@u_bound>40)
length_LFI <- 40
prop <- (length_LFI-NS_params@l_bound[bry])/(NS_params@u_bound[bry]-NS_params@l_bound[bry])
LFI <- calc_LFI(model_run@N[,,101], NS_params@wgt, bry, prop)

# Calculate TyL for the final time step
calc_TyL(wgt=NS_params@wgt, mid=NS_params@mid, N=model_run@N[,,101])

# Calculate the LQ for the final time step
calc_LQ(wgt=NS_params@wgt, u_bound=NS_params@u_bound, N=model_run@N[,,101], prob=0.5)

Calculate background mortality

Description

Calculates the background mortality of each species in each length class per time step.

Usage

calc_M1(
  nsc,
  sc_Linf,
  phi_min,
  natmort_opt = rep("std_RNM", length(sc_Linf)),
  Nmort = rep(0.8, length(sc_Linf)),
  prop = rep(0.75, length(sc_Linf))
)

get_M1(nsc, sc_Linf, natmort_opt, Nmort, prop)

calc_M1_stdRNM(nsc, sc_Linf, Nmort = 0.8, prop = 0.75)

calc_M1_lin(nsc, sc_Linf, Nmort = 0.8)

calc_M1_constRNM(nsc, sc_Linf, Nmort = 0.8)

Arguments

Details

The background mortality is defined as the number of individuals that die per year, but not from predation or fishing

N*exp(-M1)

where N is the total number of individuals. This function allows three different models for background mortality: (1) "constant", which gives M1=Nmort for all length classes up to and including the sc_Linf-th class; (2) "std_RNM", which gives M1=Nmort for all length classes between floor(sc_Linf*prop) and the sc_Linf-th class and M1=0 for the rest; and (3) "linear", which gives M1=0 for the first length class, followed by a linear increase in M1 up to and including the sc_Linf-th length class M1=Nmort and M1=0 for the rest.

Value

calc_M1 returns a matrix of dimensions nsc and nfish representing the background mortality of each species for each length class.

get_M1 returns a numeric vector of length nsc representing the background mortality for each length class.

calc_M1_stdRNM returns a numeric vector of length nsc representing the background mortality for each length class. M1=Nmort for all length classes between floor(sc_Linf*prop) and the sc_Linf-th class and M1=0 for the rest.

calc_M1_lin returns a numeric vector of length nsc representing the background mortality for each length class. M1=0 for the first length class. M1 then increases linearly up to and including the sc_Linf-th length class. For all length classes above sc_Linf, M1=0.

calc_M1_constRNM returns a numeric vector of length nsc representing the background mortality for each length class. M1=Nmort for all length classes up to and including the sc_Linf-th class. For all length classes above sc_Linf, M1=0.

References

Thorpe, R.B., Jennings, S., Dolder, P.J. (2017). Risks and benefits of catching pretty good yield in multispecies mixed fisheries. ICES Journal of Marine Science, 74(8):2097-2106.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf

# Calculate background mortality with natmort_opt="std_RNM", Nmort=0.8 and prop=0.75 for all species
M1 <- calc_M1(nsc, sc_Linf, phi_min)

# Get background mortality with natmort_opt="std_RNM", Nmort=0.8 and prop=0.75 for all species
natmort_opt <- "std_RNM"
Nmort <- 0.8
prop <- 0.75
get_M1(nsc, sc_Linf, natmort_opt, Nmort, prop)

# Calculate standard residual background mortality
M1_stdRNM <- calc_M1_stdRNM(nsc, sc_Linf)

# Calculate linear background mortality
M1_lin <- calc_M1_lin(nsc, sc_Linf,Nmort=0.3)

# Calculate constant residual background mortality
M1_constRNM <- calc_M1_constRNM(nsc, sc_Linf)

Calculate predation mortality

Description

Calculates the predation mortality for each species in each length class.

Usage

calc_M2(N, ration, wgt, nfish, nsc, other, sc_Linf, suit_M2)

Arguments

Details

The predation mortality of the ith species in the jth length class is

sum_m(sum_n(I[j,i]*N[j,i]*suit_M2[[m]][n,j,i]/

(sum_k(sum_l(suit_M2[[m]][n,l,k]wgt[l,k]N[l,k]))+other)))

where sum_m represents the sum over all m, sum_n represents the sum over all n, sum_l represents the sum over all l and sum_k represents the sum over all k. This equation corresponds to a Holling type-II functional response. See equation 8 of Hall et al. (2006) for more details.

Value

A matrix with dimensions nsc and nfish representing the the predation mortality for each species in each length class.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
ration <- tmp$ration
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt
g_eff <- tmp$g_eff

# Calculate predator-prey size preferences
prefs <- calc_prefs(pred_mu=-2.25, pred_sigma=0.5, wgt, sc_Linf)

# Calculate prey preference and prey suitability
suit_M2 <- calc_suit_vect(nsc, nfish, sc_Linf, prefs, NS_tau)


# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the predation mortality
M2 <- calc_M2(N0, ration, wgt, nfish, nsc, other=1e12, sc_Linf, suit_M2)

Calculate gear catchability

Description

Calculates the catchability for each fishing gear.

Usage

calc_Q(
  curve = rep("logistic", nfish),
  species = ((0:(length(curve) - 1))%%nfish) + 1,
  max_catchability = rep(1, length(curve)),
  gear_name = paste("gear_", 1:length(curve), sep = ""),
  custom = NULL,
  nsc,
  nfish,
  l_bound,
  u_bound,
  mid,
  eps = 1e-05,
  species_names,
  ...
)

get_Q(curve, species, gear_name, nsc, nfish, l_bound, u_bound, mid, eps, ...)

logistic_catch(species, nsc, nfish, mid, eps, L50 = 50, eta = 0.25, ...)

log_gaussian_catch(species, nsc, nfish, mid, eps, Lmu = 50, Lsigma = 1, ...)

knife_edge_catch(species, nsc, nfish, l_bound, u_bound, Lmin = 50, ...)

Arguments

Details

This function allows three different models for catchability, all of which are rescaled so that their maximum catchability is equal to one:

(1) "logistic" is proportional to

1/(1+exp(-eta*(mid-L50)));

(2) "log-gaussian" is proportional to

dlnorm(mid, log(Lmu), Lsigma);

and (3) "knife-edge" is equal to 1 for the length classes indexed by max(which(l_bound<Lmin)):nsc and 0 for all other length classes. This means that all of the individuals in the length class containing Lmin will have a catchability of 1.

In calc_Q the catchability is rescaled so that the maximum catchability is one.

Value

calc_Q returns an array of dimensions nsc, nfish and gear representing the catchability of each species by each of the fishing gears, scaled from 0 to max_catchability.

get_Q returns a catchability curve for a given species and gear scaled from 0 to 1. If an invalid catchability curve is selected, NULL is returned and a warning message is shown.

logistic_catch returns a matrix with dimensions nsc and nfish with all elements set to zero excluding one column that represents the logistic catchability curve for the selected species scaled from 0 to 1.

log_gaussian_catch returns a matrix with dimensions nsc and nfish with all elements set to zero excluding one column that represents the log-gaussian catchability curve for the selected species scaled from 0 to 1.

knife_edge_catch returns a matrix with dimensions nsc and nfish with all elements set to zero excluding one column that represents the knife-edge catchability curve for the selected species scaled from 0 to 1.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Thorpe, R.B., Jennings, S., Dolder, P.J. (2017). Risks and benefits of catching pretty good yield in multispecies mixed fisheries. ICES Journal of Marine Science, 74(8):2097-2106.

See Also

dlnorm

Examples

# Set up the inputs to the function
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Calculate gear catchability
Qs <- calc_Q(curve=rep("logistic", nfish), species=NS_par$species_names,
             max_catchability=rep(1, nfish), gear_name=NS_par$species_names,
             nsc=nsc, nfish=nfish, mid=mid, l_bound=l_bound, u_bound=u_bound,
             species_names=NS_par$species_names, eta=rep(0.25, nfish), L50=Lmat)

# Calculate logistic catchability for the first species
logistic_catch(species=1, nsc, nfish, mid, eps=1e-5,L50=Lmat[1], eta=0.25)

# Calculate log-gaussian catchability for the first species
log_gaussian_catch(species=1, nsc, nfish, mid, eps=1e-5, Lmu=50, Lsigma=1)

# Calculate knife-edge catchability for the first species
knife_edge_catch(species=1, nsc, nfish, l_bound, u_bound, Lmin=50)

Calculate Spawning Stock Biomass (SSB) or total biomass

Description

Calculates the spawning stock biomass (SSB) or total biomass of each species in the model.

Usage

calc_SSB(wgt, mature, N)

calc_biomass(wgt, N)

Arguments

Details

The Spawning Stock Biomass is equal to colSums(mature*N*wgt) and the total biomass is equal to colSums(mature*N*wgt).

Value

calc_SSB returns a numeric vector of length nfish representing the SSB of each species (g).

calc_biomass returns a numeric vector of length length(species) where the jth element is the biomass (g) of the jth species.

See Also

calc_recruits

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate SSB
calc_SSB(wgt=NS_params@wgt, mature=NS_params@mature, N=model_run@N[,,101])

# Calculate biomass in the last time step
calc_biomass(wgt=NS_params@wgt, N=model_run@N[,,101])

Calculate growth

Description

Calculates the number of individuals of each species in each length class for the next time step.

Usage

calc_growth(N, phi, nfish, nsc)

Arguments

Value

A matrix with dimensions nsc and nfish representing the number of individuals of each species in each length class for the next time step.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf) * 1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate growth
growth <- calc_growth(N0, phi, nfish, nsc)

Calculate the proportion of mature individuals

Description

Calculates the proportion of individuals that are mature for each species and length class.

Usage

calc_mature(Lmat, nfish, mid, kappa, sc_Linf, eps = 1e-05, force_mature = TRUE)

Arguments

Details

The proportion of individuals in the jth length class of the ith species that are mature is described by a logistic model

1/(1+exp(-kappa[i]*(mid[j]-Lmat[i])))

Value

A matrix with dimensions nsc and nfish and elements in the range 0-1 representing the proportion of individuals that are mature for each species and length class.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf

# Calculate the proportion of mature individuals
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

Calculate the proportion of individuals that leave each length class for each species each length class each time step.

Description

Calculate the proportion of individuals of each species that leave each length class at each time step.

Usage

calc_phi(
  k,
  Linf,
  nsc,
  nfish,
  u_bound,
  l_bound,
  calc_phi_min = FALSE,
  phi_min = 0.1
)

Arguments

Details

Calculates the time (yrs) for an average fish to grow from the lower to the upper bound of a length class assuming von Bertalanffy growth. The values are scaled to the fastest growing fish and length class combination in order to calculate the proportion of individuals leaving each length class in a time step.

Value

A list object containing phi and phi_min. phi is a matrix of dimensions nsc and nfish representing the proportion of individuals of each species that leave each length class. phi_min is a numeric value representing the time step of the model.

References

Hilborn, R. & Walters, C.J. (1992). Quantitative Fisheries Stock Assessment. Springer.

von Bertalanffy, L. (1957). Quantitative Laws in Metabolism and Growth. The Quarterly Review of Biology, 32:217-231.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Calculate the proportion of individuals that leave each length class
# with and without a fixed value for phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=TRUE) # without fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

Calculate predator-prey size preferences

Description

Calculates the size preference of each predator species in each length class for each prey species in each length class.

Usage

calc_prefs(pred_mu, pred_sigma, wgt, sc_Linf)

Arguments

Details

A predator of species i in length class j has a size preference for species k in length class l equal to

exp(-(log10(wgt[l, k]/wgt[j, i])-pred_mu)^2/(2*pred_sigma)).

Value

An array of dimensions nsc, nfish, nsc and nfish. The first and second dimensions represent the prey species whereas the third and fourth dimensions represent the predator species.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Ursin, E. (1973). On the prey size preferences of cod and dab. Meddelelser fra Danmarks Fiskeri-og Havundersgelser, 7:85-98.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate predator-prey size preferences
prefs <- calc_prefs(pred_mu=-2.25, pred_sigma=0.5, wgt, sc_Linf)

Calculate growth increments

Description

Calculates the amount of food required for fish of a given species and length class to grow according to the von Bertalanffy growth curve in a time step.

Usage

calc_ration_growthfac(
  k,
  Linf,
  nsc,
  nfish,
  l_bound,
  u_bound,
  mid,
  W_a,
  W_b,
  phi_min,
  vary_growth = TRUE,
  growth_eff = 0.5,
  growth_eff_decay = 0.11
)

Arguments

Details

The weight increments of the ith species in the jth length class is calculated by determining the amount an individual will grow in one time step, phi_min, if it were to follow the von Bertalanffy growth curve

L22=(Linf[i]-mid[j])*(1-exp(-k[i]*phi_min)).

The weight of a fish at the mid-point of the size class is calculated using the length-weight relationship

wgt[j,i] = a[i]*mid[j]^b[i],

and similarly the expected change in weight of the the fish is calculated as

growth_inc = (W_a[i]*L22^W_b[i]).

It also has a growth efficiency

g_eff[j, i]=(1-(wgt[j,i]/(W_a[i]*Linf[i]^W_b[i]))^growth_eff_decay)*growth_eff

if vary_growth==TRUE or g_eff[j, i]=growth_eff otherwise.

ration is then calculated by

growth_inc*(1/g_eff[j, i]).

Value

A list object containing ration, sc_Linf, wgt and g_eff. ration is a matrix with dimensions nsc and nfish representing the amount of food required for fish of a given species and length class to grow according to the von Bertalanffy growth curve in a time step. sc_Linf is a numeric vector of length nfish representing the length class at which each species reaches Linf. wgt is a matrix with dimensions nsc and nfish representing the weight of each species in each length class. g_eff is a matrix with dimensions nsc and nfish representing the growth efficiency of each species in each length class.

References

von Bertalanffy, L. (1957). Quantitative Laws in Metabolism and Growth. The Quarterly Review of Biology, 32:217-231

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
ration <- tmp$ration
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt
g_eff <- tmp$g_eff

Calculate the number of recruits

Description

Calculates the number of recruits of each species in a year.

Usage

calc_recruits(SSB, stored_rec_funs, rec_args)

Arguments

Value

A numeric vector of length nfish representing the number of new recruits of each species.

References

Barrowman, N.J., Myers, R.A. (2000). Still more spawner-recruit curves: the hockey stick and its generalisations. Canadian Journal of Fisheries and Aquatic Science, 57:665–676.

Beverton, R.J.H., Holt, S.J. (1957). On the Dynamics of Exploited Fish Populations, volume 19 of Fisheries Investigations (Series 2). United Kingdom Ministry of Agriculture and Fisheries.

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Ogle, D.H. (2016). Introductory Fisheries Analyses with R. CRC Press.

Ricker, W.E. (1954). Stock and recruitment. Journal of the Fisheries Research Board of Canada, 11:559-623.

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

See Also

get_rec_fun, make_rec_fun, rec_BH, rec_Ricker, rec_hockey, rec_const, rec_linear and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

# Calculate the number of recruits
R <- calc_recruits(SSB, stored_rec_funs, recruit_params)

Combines prey preference and prey suitability

Description

Calculates a combined value for prey preference and prey suitability standardised to a value between 0 and 1.

Usage

calc_suit_vect(nsc, nfish, sc_Linf, prefs, tau)

Arguments

Details

tau values are assigned to an array of dimensions nsc, nfish, nsc and nfish and multiplied by the array prefs. This creates an array of dimensions nsc, nfish, nsc and nfish indicating prey suitability. Prey suitability is then standardised to sum to 1 for each predator species in each length class.

Value

A list object of length nfish. Each element in the list is an array of dimensions nsc, nsc and nfish containing a value between 0 and 1 that represents prey preference and prey suitability for each species and length class.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

See Also

calc_M2.

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate predator-prey size preferences
prefs <- calc_prefs(pred_mu=-2.25, pred_sigma=0.5, wgt, sc_Linf)

# Calculate prey preference and prey suitability
suit_M2 <- calc_suit_vect(nsc, nfish, sc_Linf, prefs, NS_tau)

Combine two LeMans_outputs objects

Description

Combines two LeMans_outputs objects.

Usage

comb_LeMans_run(LeMans_run_x, LeMans_run_y, cont = TRUE)

Arguments

Details

If cont==T, the first years output from LeMans_run_y is removed as this will be the same as the last year of LeMans_run_x.

Value

A LeMans_outputs object.

See Also

LeMans_outputs, run_LeMans

Examples

# Set up the inputs to the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=NS_other)

# Define fishing effort
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])

# Run the model for the first time
model_run1 <- run_LeMans(NS_params, years=10, effort=effort)

# Run the model for another 12 years
effort1 <- matrix(0.5, 12, dim(NS_params@Qs)[3])
model_run2 <- run_LeMans(N=model_run1@N[,,101], NS_params, years=12, effort=effort1)

# Combine the two model runs into a single run
out <- comb_LeMans_run(model_run1, model_run2, cont=TRUE)

Generate a starting value for N

Description

Generate a starting value for N, which represents the number of individuals in each length class for each species.

Usage

get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept = 1e+10, slope = -5)

Arguments

Details

The total number of individuals in the community in each length class is equal to intercept*mid^slope. Within each length class, the number of individuals of each species is determined using the proportion of each species' biomass that is found in that length class.

Value

A matrix with dimensions nsc and nfish representing the number of individuals in each length class.

References

Andersen, K.H., Blanchard, J.L., Fulton, E.A., Gislason, H., Jacobsen, N.S., van Kooten, T. (2016). Assumptions behind size-based ecosystem models are realistic. ICES Journal of Marine Science, 73(6):1651-1655.

See Also

run_LeMans

Examples

# Set up the inputs to the function
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Get an initial population
get_N0(nsc, nfish, mid, wgt, sc_Linf)

Calculate Spawning Stock Biomass (SSB) or total biomass

Description

Calculates the Spawning Stock Biomass (SSB) or the total biomass of each species in the model.

Usage

get_SSB(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_SSB(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3]
)

## S4 method for signature 'LeMans_param,missing'
get_SSB(inputs, N, species = 1:dim(N)[2], time_steps = 1:dim(N)[3])

## S4 method for signature 'missing,LeMans_outputs'
get_SSB(
  wgt,
  mature,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL
)

## S4 method for signature 'missing,missing'
get_SSB(
  wgt,
  mature,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL
)

get_biomass(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_biomass(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3]
)

## S4 method for signature 'LeMans_param,missing'
get_biomass(inputs, N, species = 1:dim(N)[2], time_steps = 1:dim(N)[3])

## S4 method for signature 'missing,LeMans_outputs'
get_biomass(
  wgt,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL
)

## S4 method for signature 'missing,missing'
get_biomass(
  wgt,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL
)

Arguments

Details

The SSB for each species in species is equal to:

colSums(N[, species]*wgt[, species]*mature[, species]).

The biomass for each species in species is equal to:

colSums(N[, species]*wgt[, species])

Value

If length(species)>1, get_SSB returns a matrix with dimensions length(time_steps) by length(species) where the i,jth element is the SSB (g) of the jth species in the ith time_steps. If length(species)==1, the function will return a numeric vector of length length(time_steps).

If length(species)>1, get_biomass returns a matrix with dimensions length(time_steps) by length(species) where the i,jth element is the biomass (g) of the jth species in the ith time_steps. If length(species)==1, the function will return a numeric vector of length length(time_steps).

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate SSB
get_SSB(inputs=NS_params, outputs=model_run)

# Calculate biomass
get_biomass(inputs=NS_params, outputs=model_run)

Get annual catch for each species, catch per unit effort or catch per gear

Description

Get annual catch for each species, the Catch Per Unit Effort (CPUE) or the Catch Per Gear (CPG)

Usage

get_annual_catch(inputs, outputs, ...)

## S4 method for signature 'missing,missing'
get_annual_catch(
  Catch,
  years = (dim(Catch)[3] + (inc_first - 1)) * phi_min,
  phi_min = 0.1,
  inc_first = FALSE
)

## S4 method for signature 'LeMans_param,missing'
get_annual_catch(
  inputs,
  Catch,
  years = (dim(Catch)[3] + (inc_first - 1)) * inputs@phi_min,
  inc_first = FALSE
)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_annual_catch(
  inputs,
  outputs,
  years = (dim(outputs@Catch)[3] + (inc_first - 1)) * inputs@phi_min,
  inc_first = FALSE
)

## S4 method for signature 'missing,LeMans_outputs'
get_annual_catch(
  outputs,
  years = (dim(outputs@Catch)[3] + (inc_first - 1)) * phi_min,
  phi_min = 0.1,
  inc_first = FALSE
)

get_CPUE(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_CPUE(inputs, outputs, effort, years = nrow(effort), inc_first = FALSE)

## S4 method for signature 'LeMans_param,missing'
get_CPUE(inputs, Catch, effort, years = nrow(effort), inc_first = FALSE)

## S4 method for signature 'missing,LeMans_outputs'
get_CPUE(
  outputs,
  Qs,
  effort,
  years = nrow(effort),
  phi_min = 0.1,
  inc_first = FALSE
)

## S4 method for signature 'missing,missing'
get_CPUE(
  Catch,
  Qs,
  effort,
  years = nrow(effort),
  phi_min = 0.1,
  inc_first = FALSE
)

get_CPG(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_CPG(inputs, outputs, effort, years = nrow(effort), inc_first = FALSE)

## S4 method for signature 'LeMans_param,missing'
get_CPG(inputs, Catch, effort, years = nrow(effort), inc_first = FALSE)

## S4 method for signature 'missing,LeMans_outputs'
get_CPG(
  outputs,
  Qs,
  effort,
  years = nrow(effort),
  phi_min = 0.1,
  inc_first = FALSE
)

## S4 method for signature 'missing,missing'
get_CPG(
  Catch,
  Qs,
  effort,
  years = nrow(effort),
  phi_min = 0.1,
  inc_first = FALSE
)

Arguments

Value

get_annual_catch returns a matrix with dimensions years and length(species) where the i,jth element represents the total catch (g) of the jth species in the ith year.

get_CPUE returns a matrix with dimensions tot_time and nfish where the i,jth element represents the CPUE in the ith time step for the jth species.

get_CPG returns an array with dimensions nfish, dim(Qs[3]) and the number of time steps, where the i,j,kth element denotes the total catch of the ith species by the jth gear in the kth time step.

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Get annual catch
get_annual_catch(inputs=NS_params, outputs=model_run)

# Calculate the CPUE
get_CPUE(inputs=NS_params, outputs=model_run, effort=effort)

# Calculate Catch Per Gear (CPG)
get_CPG(inputs=NS_params, outputs=model_run, effort=effort)

Calculate indicators

Description

Calculates Mean Maximum Length (MML), the Large Fish Indicator (LFI), Typical Length (TyL) and Length Quantiles (LQ) of the whole community or a subset of the species.

Usage

get_indicators(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_indicators(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  prob = 0.5,
  length_LFI = 40
)

## S4 method for signature 'LeMans_param,missing'
get_indicators(
  inputs,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  prob = 0.5,
  length_LFI = 40
)

## S4 method for signature 'missing,LeMans_outputs'
get_indicators(
  wgt,
  mid,
  l_bound,
  u_bound,
  Linf,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL,
  prob = 0.5,
  length_LFI = 40
)

## S4 method for signature 'missing,missing'
get_indicators(
  wgt,
  mid,
  l_bound,
  u_bound,
  Linf,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL,
  prob = 0.5,
  length_LFI = 40
)

get_LFI(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_LFI(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  length_LFI = 40
)

## S4 method for signature 'LeMans_param,missing'
get_LFI(
  inputs,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  length_LFI = 40
)

## S4 method for signature 'missing,LeMans_outputs'
get_LFI(
  wgt,
  l_bound,
  u_bound,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL,
  length_LFI = 40
)

## S4 method for signature 'missing,missing'
get_LFI(
  wgt,
  l_bound,
  u_bound,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL,
  length_LFI = 40
)

get_MML(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_MML(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3]
)

## S4 method for signature 'LeMans_param,missing'
get_MML(inputs, N, species = 1:dim(N)[2], time_steps = 1:dim(N)[3])

## S4 method for signature 'missing,LeMans_outputs'
get_MML(
  wgt,
  Linf,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL
)

## S4 method for signature 'missing,missing'
get_MML(
  wgt,
  Linf,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL
)

get_TyL(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_TyL(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3]
)

## S4 method for signature 'LeMans_param,missing'
get_TyL(inputs, N, species = 1:dim(N)[2], time_steps = 1:dim(N)[3])

## S4 method for signature 'missing,LeMans_outputs'
get_TyL(
  wgt,
  mid,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL
)

## S4 method for signature 'missing,missing'
get_TyL(
  wgt,
  mid,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL
)

get_LQ(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
get_LQ(
  inputs,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  prob = 0.5
)

## S4 method for signature 'LeMans_param,missing'
get_LQ(inputs, N, species = 1:dim(N)[2], time_steps = 1:dim(N)[3], prob = 0.5)

## S4 method for signature 'missing,LeMans_outputs'
get_LQ(
  wgt,
  u_bound,
  outputs,
  species = 1:dim(outputs@N)[2],
  time_steps = 1:dim(outputs@N)[3],
  species_names = NULL,
  prob = 0.5
)

## S4 method for signature 'missing,missing'
get_LQ(
  wgt,
  u_bound,
  N,
  species = 1:dim(N)[2],
  time_steps = 1:dim(N)[3],
  species_names = NULL,
  prob = 0.5
)

Arguments

Details

The LFI represents the proportion of biomass with a length larger than length_LFI. The MML is the biomass weighted mean of Linf:

sum(biomass[species]*Linf[species])/sum(biomass[species])

where biomass is a numeric vector of length nfish representing the biomass of each species. TyL is the biomass-weighted geometric mean length of the community:

exp(sum(biomass_*log(mid))/sum(Bio_l))

where biomass_ is a numeric vector of length nsc representing the biomass of all the species in each length class. The LQ is the length at which the biomass exceeds a given proportion prob of the total biomass.

Value

get_indicators returns a list object with names 'LFI', 'MML', 'TYL' and 'LQ'. If length(length_LFI)>1, 'LFI' is a matrix with dimensions length(time_steps) by length(length_LFI) where the i,jth element represents the LFI using the jth length_LFI in the ith time_steps. If length(length_LFI)==1, the function will return a numeric vector of length length(time_steps). 'MML' is a numeric vector of length time_steps where each element is the MML for the species in species. 'TYL' is a numeric vector of length time_steps where each element is the TyL for the species in species. If length(prob)==1, 'LQ' is a matrix with dimensions length(time_steps) by length(prob) where the i,jth element is the LQ using thejth prob in the ith time_steps. If length(prob)==1, the function will return a numeric vector of length length(time_steps).

If length(length_LFI)==1, get_LFI returns a matrix with dimensions length(time_steps) by length(length_LFI) where the i,jth element is the LFI using the jth length_LFI in the ith time_steps. If length(length_LFI)==1, the function will return a numeric vector of length length(time_steps).

get_MML returns a numeric vector of length time_steps where each element is the MML for the species in species.

If length(prob)>1, get_LQ returns a matrix with dimensions length(time_steps) and length(prob) where the i,jth element is the LQ using the the jth prob in the ith time_steps. If length(prob)==1, the function will return a numeric vector of length length(time_steps).

See Also

plot_indicators

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate the indicators
get_indicators(inputs=NS_params, outputs=model_run)

# Calculate the LFI
get_LFI(inputs=NS_params, outputs=model_run)

# Calculate MML
get_MML(inputs=NS_params, outputs=model_run)

# Calculate TyL
get_TyL(inputs=NS_params, outputs=model_run)

# Calculate LQs
get_LQ(inputs=NS_params, outputs=model_run)

Collate the stock recruitment functions

Description

Collates the stock recruitment functions for all of the species in the model.

Usage

get_rec_fun(rec_fun = "hockey-stick")

Arguments

Details

For "Beverton-Holt", the stock recruitment function is defined as a*SSB/(1+b*SSB); for "Ricker" it is defined as a*SSB*exp(-b*SSB); for "hockey-stick" it is defined as min(a*SSB, b); for "constant" it is defined as a, and for "linear" it is defined as a*SSB. In all cases, SSB is the Spawning Stock Biomass in 1000s of tonnes and a and b are parameters of the specific stock recruitment functions.

Value

A list object of length rec_fun where each element includes the stock recruitment function for a given species. If an invalid recruitment function is selected, NULL is returned and a warning message is shown.

References

Barrowman, N.J., Myers, R.A. (2000). Still more spawner-recruit curves: the hockey stick and its generalisations. Canadian Journal of Fisheries and Aquatic Science, 57:665–676.

Beverton, R.J.H., Holt, S.J. (1957). On the Dynamics of Exploited Fish Populations, volume 19 of Fisheries Investigations (Series 2). United Kingdom Ministry of Agriculture and Fisheries.

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Ogle, D.H. (2016). Introductory Fisheries Analyses with R. CRC Press.

Ricker, W.E. (1954). Stock and recruitment. Journal of the Fisheries Research Board of Canada, 11:559-623.

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

See Also

calc_recruits, make_rec_fun, rec_BH, rec_Ricker, rec_hockey, rec_const, rec_linear and calc_SSB

Examples

nfish <- nrow(NS_par)
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))

Generate the stock recruitment functions

Description

Generates the stock recruitment function for a given species.

Usage

make_rec_fun(rec_fun = "hockey-stick")

Arguments

Details

For "Beverton-Holt", the stock recruitment function is defined as a*SSB/(1+b*SSB); for "Ricker" it is defined as a*SSB*exp(-b*SSB); for "hockey-stick" it is defined as min(a*SSB, b); for "constant" it is defined as a, and for "linear" it is defined as a*SSB. In all cases, SSB is the Spawning Stock Biomass in 1000s of tonnes and a and b are parameters of the specific stock recruitment functions.

Value

The stock recruitment function for a given species. If an invalid recruitment function is selected, NULL is returned and a warning message is shown.

References

Barrowman, N.J., Myers, R.A. (2000). Still more spawner-recruit curves: the hockey stick and its generalisations. Canadian Journal of Fisheries and Aquatic Science, 57:665–676.

Beverton, R.J.H., Holt, S.J. (1957). On the Dynamics of Exploited Fish Populations, volume 19 of Fisheries Investigations (Series 2). United Kingdom Ministry of Agriculture and Fisheries.

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Ogle, D.H. (2016). Introductory Fisheries Analyses with R. CRC Press.

Ricker, W.E. (1954). Stock and recruitment. Journal of the Fisheries Research Board of Canada, 11:559-623.

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

See Also

calc_recruits, get_rec_fun, rec_BH, rec_Ricker, rec_hockey, rec_const, rec_linear and calc_SSB

Examples

# Run the function
make_rec_fun("Ricker")

Plot Spawning Stock Biomass (SSB)

Description

Plots community and/or species-specific Spawning Stock Biomass (SSB) or total biomass.

Usage

plot_SSB(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_SSB(inputs, outputs, species, time_steps, species_names, SSB, ...)

## S4 method for signature 'LeMans_param,missing'
plot_SSB(inputs, N, species, time_steps, species_names, SSB, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_SSB(wgt, mature, outputs, species, time_steps, species_names, SSB, ...)

## S4 method for signature 'missing,missing'
plot_SSB(
  wgt,
  mature,
  N,
  species,
  species_names,
  time_steps,
  SSB,
  full_plot_only = TRUE,
  units = "g",
  ...
)

plot_biomass(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_biomass(inputs, outputs, species, time_steps, species_names, biomass, ...)

## S4 method for signature 'LeMans_param,missing'
plot_biomass(inputs, N, species, time_steps, biomass, species_names, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_biomass(wgt, outputs, species, time_steps, biomass, species_names, ...)

## S4 method for signature 'missing,missing'
plot_biomass(
  wgt,
  N,
  species,
  time_steps,
  species_names,
  biomass,
  full_plot_only = TRUE,
  units = "g",
  ...
)

Arguments

Value

plot_SSB returns line plots of the in SSB of the selected species through time.

plot_biomass returns line plots of the changes in biomass of the selected species through time.

See Also

get_SSB, get_biomass

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate SSB
SSB <- get_SSB(NS_params, model_run)

# Plot SSB
plot_SSB(SSB=SSB)

# Calculate biomass
biomass <- get_biomass(NS_params, model_run)

# Plot biomass
plot_biomass(biomass=biomass)

Plot indicators

Description

Plots Mean Maximum Length (MML), the Large Fish Indicator (LFI), Typical Length (TyL) and the Length Quantiles (LQ) of the whole community or a subset of the species.

Usage

plot_indicators(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_indicators(inputs, outputs, species, time_steps, prob, length_LFI, ...)

## S4 method for signature 'LeMans_param,missing'
plot_indicators(inputs, N, species, time_steps, prob, length_LFI, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_indicators(
  wgt,
  mid,
  l_bound,
  u_bound,
  Linf,
  species,
  outputs,
  time_steps,
  species_names = NULL,
  prob,
  length_LFI,
  ...
)

## S4 method for signature 'missing,missing'
plot_indicators(
  wgt,
  mid,
  l_bound,
  u_bound,
  Linf,
  N,
  species,
  time_steps,
  species_names = NULL,
  prob,
  length_LFI,
  MML,
  TyL,
  LFI,
  LQ,
  units = "cm",
  ...
)

plot_LFI(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_LFI(
  inputs,
  outputs,
  species,
  time_steps,
  species_names,
  length_LFI,
  LFI,
  ...
)

## S4 method for signature 'LeMans_param,missing'
plot_LFI(inputs, N, species, time_steps, species_names, length_LFI, LFI, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_LFI(
  wgt,
  l_bound,
  u_bound,
  outputs,
  species,
  time_steps,
  species_names,
  length_LFI,
  LFI,
  ...
)

## S4 method for signature 'missing,missing'
plot_LFI(
  wgt,
  l_bound,
  u_bound,
  N,
  species,
  time_steps,
  species_names,
  length_LFI,
  LFI,
  units = "cm",
  ...
)

plot_MML(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_MML(inputs, outputs, species, species_names, time_steps, MML, ...)

## S4 method for signature 'LeMans_param,missing'
plot_MML(inputs, N, species, time_steps, species_names, MML, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_MML(wgt, Linf, outputs, species, time_steps, species_names, MML, ...)

## S4 method for signature 'missing,missing'
plot_MML(
  wgt,
  Linf,
  N,
  species,
  time_steps,
  species_names,
  MML,
  units = "cm",
  ...
)

plot_TyL(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_TyL(inputs, outputs, species, time_steps, species_names, TyL, ...)

## S4 method for signature 'LeMans_param,missing'
plot_TyL(inputs, N, species, time_steps, species_names, TyL, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_TyL(wgt, mid, outputs, species, time_steps, species_names, TyL, ...)

## S4 method for signature 'missing,missing'
plot_TyL(
  wgt,
  mid,
  N,
  species,
  time_steps,
  species_names,
  TyL,
  units = "cm",
  ...
)

plot_LQ(inputs, outputs, ...)

## S4 method for signature 'LeMans_param,LeMans_outputs'
plot_LQ(inputs, outputs, species, time_steps, species_names, LQ, prob, ...)

## S4 method for signature 'LeMans_param,missing'
plot_LQ(inputs, N, species, species_names, time_steps, LQ, prob, ...)

## S4 method for signature 'missing,LeMans_outputs'
plot_LQ(
  wgt,
  u_bound,
  outputs,
  species,
  time_steps,
  species_names,
  LQ,
  prob,
  ...
)

## S4 method for signature 'missing,missing'
plot_LQ(
  wgt,
  u_bound,
  N,
  species,
  time_steps,
  species_names,
  LQ,
  prob,
  units = "cm",
  ...
)

Arguments

Value

plot_indicators returns a set of four line plots depicting changes in the MML, LFI, TyL and LQ(s) of the community (including only the selected species) over time.

plot_LFI returns a line plot depicting the LFI of the community (including only the selected species) over time.

plot_MML returns a line plot depicting the MML of the community (including only the selected species) over time.

plot_TYL returns a line plot depicting the TyL of the community (including only the selected species) over time.

plot_LQ returns a line plot depicting the LQ(s) of the community (including only the selected species) over time.

See Also

get_indicators

Examples

# Set up and run the model
NS_params <- LeMansParam(NS_par, tau=NS_tau, eta=rep(0.25, 21), L50=NS_par$Lmat, other=1e12)
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])
model_run <- run_LeMans(NS_params, years=10, effort=effort)

# Calculate the indicators
tmp <- get_indicators(NS_params, model_run)
MML <- tmp$MML
LFI <- tmp$LFI
TyL <- tmp$TYL
LQ <- tmp$LQ

# Plot the indicators
plot_indicators(MML=MML, LFI=LFI, TyL=TyL, LQ=LQ)

# Plot the LFI
plot_LFI(LFI=LFI)

# Plot MML
plot_MML(MML=MML)

# Plot the TyL
plot_TyL(TyL=TyL)

# Plot the LQs
plot_LQ(LQ=LQ)

The Beverton-Holt stock recruitment function

Description

Calculates the number of recruits as given by the Beverton-Holt stock recruitment function.

Usage

rec_BH(SSB, rec_args)

Arguments

Details

The Beverton-Holt stock recruitment function is defined as a*(SSB/1e9))/(1+b*(SSB/1e9)).

Value

A numeric value representing the number of recruits of a given species.

References

Beverton, R.J.H., Holt, S.J. (1957). On the Dynamics of Exploited Fish Populations, volume 19 of Fisheries Investigations (Series 2). United Kingdom Ministry of Agriculture and Fisheries.

See Also

calc_recruits, make_rec_fun, get_rec_fun, rec_Ricker, rec_hockey, rec_const, rec_linear and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

# Run the function
rec_BH(SSB[1], recruit_params[[1]])

The Ricker stock recruitment function

Description

Calculates the number of recruits as given by the Ricker stock recruitment function.

Usage

rec_Ricker(SSB, rec_args)

Arguments

Details

The Ricker stock recruitment function is defined as a*(SSB/1e9))*exp(-b*(SSB/1e9)).

Value

A numeric value representing the number of recruits of a given species.

References

Hall, S. J., Collie, J. S., Duplisea, D. E., Jennings, S., Bravington, M., & Link, J. (2006). A length-based multispecies model for evaluating community responses to fishing. Canadian Journal of Fisheries and Aquatic Sciences, 63(6):1344-1359.

Ricker, W.E. (1954). Stock and recruitment. Journal of the Fisheries Research Board of Canada, 11:559-623.

See Also

calc_recruits, make_rec_fun, get_rec_fun, rec_BH, rec_hockey, rec_const, rec_linear and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

rec_Ricker(SSB[1], recruit_params[[1]])

The constant stock recruitment function

Description

Calculates the number of recruits as given by the constant stock recruitment function.

Usage

rec_const(SSB, rec_args)

Arguments

Details

The number of recruits is defined as the value of a.

Value

A numeric value representing the number of recruits of a given species.

See Also

calc_recruits, make_rec_fun, get_rec_fun, rec_BH, rec_Ricker, rec_hockey, rec_linear and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

rec_const(SSB[1], recruit_params[[1]])

The hockey-stick stock recruitment function

Description

Calculates the number of recruits as given by the hockey-stick stock recruitment function.

Usage

rec_hockey(SSB, rec_args)

Arguments

Details

The stock recruitment function is defined as min(a*(SSB/1e9), b).

Value

A numeric value representing the number of recruits of a given species.

References

Barrowman, N.J., Myers, R.A. (2000). Still more spawner-recruit curves: the hockey stick and its generalisations. Canadian Journal of Fisheries and Aquatic Science, 57:665–676.

Thorpe, R.B., Le Quesne, W.J.F., Luxford, F., Collie, J.S., Jennings, S. (2015). Evaluation and management implications of uncertainty in a multispecies size-structured model of population and community responses to fishing. Methods in Ecology and Evolution, 6:49-58.

See Also

calc_recruits, make_rec_fun, get_rec_fun, rec_BH, rec_Ricker, rec_const, rec_linear and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

rec_hockey(SSB[1], recruit_params[[1]])

The density-independent stock recruitment function

Description

Calculates the number of recruits as given by the density-independent stock recruitment function.

Usage

rec_linear(SSB, rec_args)

Arguments

Details

The number of recruits is defined as a*(SSB/1e9).

Value

A numeric value representing the number of recruits of a given species.

References

Ogle, D.H. (2016). Introductory Fisheries Analyses with R. CRC Press.

See Also

calc_recruits, make_rec_fun, get_rec_fun, rec_BH, rec_Ricker, rec_hockey, rec_const and calc_SSB

Examples

# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi_min <- tmp$phi_min

# Run calc_ration_growthfac()
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)

# Calculate the SSB
SSB <- calc_SSB(mature, N0, wgt)

rec_linear(SSB[1], recruit_params[[1]])

Project the LeMans model

Description

Project the LeMans model forward in time.

Usage

run_LeMans(params, ...)

## S4 method for signature 'missing'
run_LeMans(
  N0,
  Fs,
  tot_time,
  nsc,
  nfish,
  phi_min,
  mature,
  sc_Linf,
  wgt,
  phi,
  ration,
  other,
  M1,
  suit_M2,
  stored_rec_funs,
  recruit_params,
  eps = 1e-05
)

## S4 method for signature 'LeMans_param'
run_LeMans(
  params,
  years = 10,
  N0 = NULL,
  effort = matrix(0, years, dim(params@Qs)[3]),
  Fs,
  intercept = 1e+10,
  slope = -5,
  tot_time
)

Arguments

Value

An object of class LeMans_outputs.

See Also

LeMans_outputs, LeMans_param, LeMansParam

Examples

# Run the model with all inputs specified explicitly:
# Set up the inputs to the function - species-independent parameters
nfish <- nrow(NS_par)
nsc <- 32
maxsize <- max(NS_par$Linf)*1.01 # the biggest size is 1% bigger than the largest Linf
l_bound <- seq(0, maxsize, maxsize/nsc); l_bound <- l_bound[-length(l_bound)]
u_bound <- seq(maxsize/nsc, maxsize, maxsize/nsc)
mid <- l_bound+(u_bound-l_bound)/2

# Set up the inputs to the function - species-specific parameters
Linf <- NS_par$Linf # the von-Bertalanffy asymptotic length of each species (cm).
W_a <- NS_par$W_a # length-weight conversion parameter.
W_b <- NS_par$W_b # length-weight conversion parameter.
k <- NS_par$k # the von-Bertalnaffy growth parameter.
Lmat <- NS_par$Lmat # the length at which 50\% of individuals are mature (cm).

# Get phi_min
tmp <- calc_phi(k, Linf, nsc, nfish, u_bound, l_bound, calc_phi_min=FALSE,
                  phi_min=0.1) # fixed phi_min
phi <- tmp$phi
phi_min <- tmp$phi_min

# Calculate growth increments
tmp <- calc_ration_growthfac(k, Linf, nsc, nfish, l_bound, u_bound, mid, W_a, W_b, phi_min)
ration <- tmp$ration
sc_Linf <- tmp$sc_Linf
wgt <- tmp$wgt
g_eff <- tmp$g_eff

# Calculate maturity
mature <- calc_mature(Lmat, nfish, mid, kappa=rep(10, nfish), sc_Linf)

# Create recruitment functions
stored_rec_funs <- get_rec_fun(rep("hockey-stick", nfish))
recruit_params <- do.call("Map", c(c, list(a=NS_par$a, b=NS_par$b)))

# Calculate background mortality
M1 <- calc_M1(nsc, sc_Linf, phi_min)

# Calculate predator-prey size preferences
prefs <- calc_prefs(pred_mu=-2.25, pred_sigma=0.5, wgt, sc_Linf)

# Calculate prey preference and prey suitability
suit_M2 <- calc_suit_vect(nsc, nfish, sc_Linf, prefs, NS_tau)

# Calculate catchability
Qs <- calc_Q(curve=rep("logistic", nfish), species=NS_par$species_names,
             max_catchability=rep(1, nfish), gear_name=NS_par$species_names,
             nsc=nsc, nfish=nfish, mid=mid, l_bound=l_bound, u_bound=u_bound,
             species_names=NS_par$species_names, eta=rep(0.25, nfish), L50=Lmat)

# Get an initial population
N0 <- get_N0(nsc, nfish, mid, wgt, sc_Linf, intercept=1e10, slope=-5)
years <- 10 # run the model for 10 years
tot_time <- years*phi_min # total number of time steps

# Define fishing effort to be 0.5 for all species
effort <- matrix(0.5, tot_time, dim(Qs)[3])

# Calculate F
Fs <- array(0, dim=c(nsc, nfish, tot_time))
for (j in 1:ncol(effort)) {
  for (ts in 1:tot_time) {
    Fs[,,ts] <- Fs[,,ts]+effort[ts, j]*Qs[,,j]
  }
}

# Run the model
model_run <- run_LeMans(N0=N0, tot_time=tot_time, Fs=Fs, nsc=nsc, nfish=nfish,
                        phi_min=phi_min, mature=mature, sc_Linf=sc_Linf, wgt=wgt,
                        phi=phi, ration=ration, other=NS_other, M1=M1, suit_M2=suit_M2,
                        stored_rec_funs=stored_rec_funs, recruit_params=recruit_params,
                        eps=1e-05)

##############################################
# Alternatively:
NS_params <- LeMansParam(NS_par,tau=NS_tau,eta=rep(0.25,21),L50=NS_par$Lmat,other=NS_other)

# Define fishing effort
effort <- matrix(0.5, 10, dim(NS_params@Qs)[3])

# Run the model
model_run <- run_LeMans(NS_params, years=10, effort=effort)