man-roxygen/Stock_template.R

In Blue-Matter/MSEtool: Management Strategy Evaluation Toolkit

# This file is auto-generated by build_tools/write_class_definitions.R
# Do not edit by hand


#' @slot Name An identifying name for the Stock object. Single value. Character
#'  string.
#' @slot Common_Name Common name of the species. Character string.
#' @slot Species Scientific name of the species. Genus and species name.
#'  Character string.
#' @slot maxage The maximum age of individuals that is simulated. There are
#'  `maxage+1` (recruitment to age-0) age classes in the storage matrices.
#'  `maxage` is the 'plus group' where all age-classes > `maxage` are grouped,
#'  unless option switched off with \code{OM@cpars$plusgroup=0}. Single value.
#'  Positive integer.
#' @slot R0 Initial number of unfished recruits to age-0. This number is used
#'  to scale the size of the population to match catch or data, but does not affect
#'  any of the population dynamics unless the OM has been conditioned with data. As
#'  a result, for a data-limited fishery any number can be used for `R0`. In
#'  data-rich stocks `R0` may be estimated as part of a stock assessment, but for
#'  data limited stocks users can choose either an arbitrary number (say, 1000) or
#'  choose a number that produces simulated catches in recent historical years that
#'  are similar to real world catch data. Single value. Positive real number.
#' @slot M The instantaneous rate of natural mortality. For each simulation a
#'  single value is drawn from a uniform distribution specified by the upper and
#'  lower bounds provided. Uniform distribution lower and upper bounds.
#'  Non-negative real numbers.
#' @slot Msd Inter-annual variation in `M` expressed as a coefficient of
#'  variation of a log-normal distribution. For each simulation a single value is
#'  drawn from a uniform distribution specified by the upper and lower bounds
#'  provided. If this parameter is positive, yearly `M` is drawn from a log-normal
#'  distribution with a mean specified by `log(M)` drawn for that simulation and a
#'  standard deviation in log space specified by the value of `Msd` drawn for that
#'  simulation.  Uniform distribution lower and upper bounds. Non-negative real
#'  numbers
#' @slot h Steepness of the stock recruit relationship. Steepness governs the
#'  proportion of unfished recruits produced when the stock is at 20% of the
#'  unfished population size. For each simulation a single value is drawn from a
#'  uniform distribution specified by the upper and lower bounds provided. This
#'  value is the same in all years of a given simulation. Uniform distribution
#'  lower and upper bounds. Values from 1/5 to 1.
#' @slot SRrel Type of stock-recruit relationship. Use 1 to select a Beverton
#'  Holt relationship, 2 to select a Ricker relationship. Single value. Integer
#' @slot Perr Recruitment process error, which is defined as the standard
#'  deviation of the recruitment deviations in log space. For each simulation a
#'  single value is drawn from a uniform distribution specified by the upper and
#'  lower bounds provided. Uniform distribution lower and upper bounds.
#'  Non-negative real numbers.
#' @slot AC Autocorrelation in the recruitment deviations in log space. For
#'  each simulation a single value is drawn from a uniform distribution specified
#'  by the upper and lower bounds provided, and used to add lag-1 auto-correlation
#'  to the log recruitment deviations. Uniform distribution lower and upper bounds.
#'  Non-negative real numbers.
#' @slot Linf The von Bertalanffy growth parameter Linf, which specifies the
#'  average maximum size that would reached by adult fish if they lived
#'  indefinitely. For each simulation a single value is drawn from a uniform
#'  distribution specified by the upper and lower bounds provided. This value is
#'  the same in all years unless `Linfsd` is a positive number. Uniform
#'  distribution lower and upper bounds. Positive real numbers.
#' @slot Linfsd Inter-annual variation in Linf. For each simulation a single
#'  value is drawn from a uniform distribution specified by the upper and lower
#'  bounds provided. If this parameter has a positive value, yearly Linf is drawn
#'  from a log-normal distribution with a mean specified by the value of `Linf`
#'  drawn for that simulation and a standard deviation (in log space) specified by
#'  the value of `Linfsd` drawn for that simulation. Uniform distribution lower and
#'  upper bounds. Non-negative real numbers.
#' @slot K The von Bertalanffy growth parameter k, which specifies the average
#'  rate of growth. For each simulation a single value is drawn from a uniform
#'  distribution specified by the upper and lower bounds provided. This value is
#'  the same in all years unless `Ksd` is a positive number. Uniform distribution
#'  lower and upper bounds. Positive real numbers.
#' @slot Ksd Inter-annual variation in K. For each simulation a single value is
#'  drawn from a uniform distribution specified by the upper and lower bounds
#'  provided. If this parameter has a positive value, yearly K is drawn from a
#'  log-normal distribution with a mean specified by the value of `K` drawn for
#'  that simulation and a standard deviation (in log space) specified by the value
#'  of `Ksd` drawn for that simulation. Uniform distribution lower and upper
#'  bounds. Non-negative real numbers.
#' @slot t0 The von Bertalanffy growth parameter t0, which specifies the
#'  theoretical age at a size 0. For each simulation a single value is drawn from a
#'  uniform distribution specified by the upper and lower bounds provided. Uniform
#'  distribution lower and upper bounds. Non-positive real numbers.
#' @slot LenCV The coefficient of variation (defined as the standard deviation
#'  divided by mean) of the length-at-age.  For each simulation a single value is
#'  drawn from a uniform distribution specified by the upper and lower bounds
#'  provided to specify the distribution of observed length-at-age, and the CV of
#'  this distribution is constant for all age classes (i.e, standard deviation
#'  increases proportionally with the mean). Uniform distribution lower and upper
#'  bounds. Positive real numbers.
#' @slot L50 Length at 50% maturity. For each simulation a single value is
#'  drawn from a uniform distribution specified by the upper and lower bounds
#'  provided. The `L50` and `L50_95` parameters are converted to ages using the
#'  growth parameters provided and used to construct a logistic curve to determine
#'  the proportion of the population that is mature in each age class. Uniform
#'  distribution lower and upper bounds. Positive real numbers.
#' @slot L50_95 Difference in lengths between 50% and 95% maturity. For each
#'  simulation a single value is drawn from a uniform distribution specified by the
#'  upper and lower bounds provided. The value drawn is then added to the length at
#'  50% maturity to determine the length at 95% maturity. This parameterization is
#'  used `instead` of specifying the size at 95 percent maturity to avoid
#'  situations where the value drawn for the size at 95% maturity is smaller than
#'  that at 50% maturity. The `L50` and `L50_95` parameters are converted to ages
#'  using the growth parameters provided and used to construct a logistic curve to
#'  determine the proportion of the population that is mature in each age class.
#'  Uniform distribution lower and upper bounds. Positive real numbers.
#' @slot D Estimated current level of stock depletion, which is defined as the
#'  current spawning stock biomass divided by the unfished spawning stock biomass.
#'  For each simulation a single value is drawn from a uniform distribution
#'  specified by the upper and lower bounds provided. This parameter is used during
#'  model initialization to select a series of yearly historical recruitment values
#'  and fishing mortality rates that, based on the information provided, could have
#'  resulted in the specified depletion level in the simulated last historical
#'  year. Uniform distribution lower and upper bounds. Positive real numbers
#'  (typically < 1)
#' @slot a The alpha parameter in allometric length-weight relationship. Single
#'  value. Weight parameters are used to determine catch-at-age and
#'  population-at-age from the number of individuals in each age class and the
#'  length of each individual, which is drawn from a normal distribution determined
#'  by the `Linf`, `K`, `t0`, and `LenCV` parameters. As a result, they function as
#'  a way to scale between numbers at age and biomass, and are not stochastic
#'  parameters. Single value. Positive real number.
#' @slot b The beta parameter in allometric length-weight relationship. Single
#'  value. Weight parameters are used to determine catch-at-age and
#'  population-at-age from the number of individuals in each age class and the
#'  length of each individual, which is drawn from a normal distribution determine
#'  by the `Linf`, `K`, `t0`, and `LenCV` parameters. As a result, they function as
#'  a way to scale between numbers at age and biomass, and are not stochastic
#'  parameters. Single value. Positive real number.
#' @slot Size_area_1 The size of area 1 relative to area 2. The fraction of the
#'  unfished biomass in area 1. Please specify numbers between 0 and 1. For each
#'  simulation a single value is drawn from a uniform distribution specified by the
#'  upper and lower bounds provided. For example, if Size_area_1 is 0.2, then 20%
#'  of the total area is allocated to area 1. Fishing can occur in both areas, or
#'  can be turned off in one area to simulate the effects of a no take marine
#'  reserve. Uniform distribution lower and upper bounds. Positive real numbers.
#' @slot Frac_area_1 The fraction of the unfished biomass in area 1. Please
#'  specify numbers between 0 and 1. For each simulation a single value is drawn
#'  from a uniform distribution specified by the upper and lower bounds provided.
#'  For example, if Frac_area_1 is 0.5, then 50% of the unfished biomass is
#'  allocated to area 1, regardless of the size of area 1 (i.e, size and fraction
#'  in each area determine the density of fish, which may impact fishing spatial
#'  targeting). In each time step recruits are allocated to each area based on the
#'  proportion specified in Frac_area_1. Uniform distribution lower and upper
#'  bounds. Positive real numbers.
#' @slot Prob_staying The probability of individuals in area 1 remaining in
#'  area 1 over the course of one year. Please specify numbers between 0 and 1. For
#'  each simulation a single value is drawn from a uniform distribution specified
#'  by the upper and lower bounds provided. For example, in an area with a
#'  Prob_staying value of 0.95 each fish has a 95% probability of staying in that
#'  area in each time step, and a 5% probability of moving to the other area.
#'  Uniform distribution lower and upper bounds. Positive fraction.
#' @slot Fdisc The instantaneous discard mortality rate the stock experiences
#'  when fished using the gear type specified in the corresponding fleet object and
#'  discarded. For each simulation a single value is drawn from a uniform
#'  distribution specified by the upper and lower bounds provided. Uniform
#'  distribution lower and upper bounds. Non-negative real numbers.
#' @slot Source A reference to a website or article from which parameters were
#'  taken to define the stock object. Single value. Character string.