mizer (version 2.3.0)

newTraitParams: Set up parameters for a trait-based multispecies model

Description

This functions creates a MizerParams object describing a trait-based model. This is a simplification of the general size-based model used in mizer in which the species-specific parameters are the same for all species, except for the asymptotic size, which is considered the most important trait characterizing a species. Other parameters are related to the asymptotic size. For example, the size at maturity is given by w_inf * eta, where eta is the same for all species. For the trait-based model the number of species is not important. For applications of the trait-based model see Andersen & Pedersen (2010). See the mizer website for more details and examples of the trait-based model.

Usage

newTraitParams(
  no_sp = 11,
  min_w_inf = 10,
  max_w_inf = 10^4,
  min_w = 10^(-3),
  max_w = max_w_inf,
  eta = 10^(-0.6),
  min_w_mat = min_w_inf * eta,
  no_w = round(log10(max_w_inf/min_w) * 20 + 1),
  min_w_pp = 1e-10,
  w_pp_cutoff = min_w_mat,
  n = 2/3,
  p = n,
  lambda = 2.05,
  r_pp = 0.1,
  kappa = 0.005,
  alpha = 0.4,
  h = 40,
  beta = 100,
  sigma = 1.3,
  f0 = 0.6,
  fc = 0.25,
  ks = NA,
  gamma = NA,
  ext_mort_prop = 0,
  reproduction_level = 1/4,
  R_factor = deprecated(),
  gear_names = "knife_edge_gear",
  knife_edge_size = 1000,
  egg_size_scaling = FALSE,
  resource_scaling = FALSE,
  perfect_scaling = FALSE
)

Arguments

no_sp

The number of species in the model.

min_w_inf

The asymptotic size of the smallest species in the community. This will be rounded to lie on a grid point.

max_w_inf

The asymptotic size of the largest species in the community. This will be rounded to lie on a grid point.

min_w

The size of the the egg of the smallest species. This also defines the start of the community size spectrum.

max_w

The largest size in the model. By default this is set to the largest asymptotic size max_w_inf. Setting it to something larger only makes sense if you plan to add larger species to the model later.

eta

Ratio between maturity size and asymptotic size of a species. Ignored if min_w_mat is supplied. Default is 10^(-0.6), approximately 1/4.

min_w_mat

The maturity size of the smallest species. Default value is eta * min_w_inf. This will be rounded to lie on a grid point.

no_w

The number of size bins in the community spectrum. These bins will be equally spaced on a logarithmic scale. Default value is such that there are 20 bins for each factor of 10 in weight.

min_w_pp

The smallest size of the resource spectrum. By default this is set to the smallest value at which any of the consumers can feed.

w_pp_cutoff

The largest size of the resource spectrum. Default value is min_w_inf unless perfect_scaling = TRUE when it is Inf.

n

Scaling exponent of the maximum intake rate.

p

Scaling exponent of the standard metabolic rate. By default this is equal to the exponent n.

lambda

Exponent of the abundance power law.

r_pp

Growth rate parameter for the resource spectrum.

kappa

Coefficient in abundance power law.

alpha

The assimilation efficiency.

h

Maximum food intake rate.

beta

Preferred predator prey mass ratio.

sigma

Width of prey size preference.

f0

Expected average feeding level. Used to set gamma, the coefficient in the search rate. Ignored if gamma is given explicitly.

fc

Critical feeding level. Used to determine ks if it is not given explicitly.

ks

Standard metabolism coefficient. If not provided, default will be calculated from critical feeding level argument fc.

gamma

Volumetric search rate. If not provided, default is determined by get_gamma_default() using the value of f0.

ext_mort_prop

The proportion of the total mortality that comes from external mortality, i.e., from sources not explicitly modelled. A number in the interval [0, 1).

reproduction_level

A number between 0 an 1 that determines the level of density dependence in reproduction, see setBevertonHolt().

R_factor

Use reproduction_level = 1 / R_factor instead.

gear_names

The names of the fishing gears for each species. A character vector, the same length as the number of species.

knife_edge_size

The minimum size at which the gear or gears select fish. A single value for each gear or a vector with one value for each gear.

egg_size_scaling

If TRUE, the egg size is a constant fraction of the maximum size of each species. This fraction is min_w / min_w_inf. If FALSE, all species have the egg size w_min.

resource_scaling

If TRUE, the carrying capacity for larger resource is reduced to compensate for the fact that fish eggs and larvae are present in the same size range.

perfect_scaling

If TRUE then parameters are set so that the community abundance, growth before reproduction and death are perfect power laws. In particular all other scaling corrections are turned on.

Value

An object of type MizerParams

Details

The function has many arguments, all of which have default values. Of particular interest to the user are the number of species in the model and the minimum and maximum asymptotic sizes.

The characteristic weights of the smallest species are defined by min_w (egg size), min_w_mat (maturity size) and min_w_inf (asymptotic size). The asymptotic sizes of the no_sp species are logarithmically evenly spaced, ranging from min_w_inf to max_w_inf. Similarly the maturity sizes of the species are logarithmically evenly spaced, so that the ratio eta between maturity size and asymptotic size is the same for all species. If egg_size_scaling = TRUE then also the ratio between asymptotic size and egg size is the same for all species. Otherwise all species have the same egg size.

In addition to setting up the parameters, this function also sets up an initial condition that is close to steady state.

The search rate coefficient gamma is calculated using the expected feeding level, f0.

The option of including fishing is given, but the steady state may loose its natural stability if too much fishing is included. In such a case the user may wish to include stabilising effects (like reproduction_level) to ensure the steady state is stable. Fishing selectivity is modelled as a knife-edge function with one parameter, knife_edge_size, which is the size at which species are selected. Each species can either be fished by the same gear (knife_edge_size has a length of 1) or by a different gear (the length of knife_edge_size has the same length as the number of species and the order of selectivity size is that of the asymptotic size).

The resulting MizerParams object can be projected forward using project() like any other MizerParams object. When projecting the model it may be necessary to reduce dt below 0.1 to avoid any instabilities with the solver. You can check this by plotting the biomass or abundance through time after the projection.

See Also

Other functions for setting up models: newCommunityParams(), newMultispeciesParams(), newSingleSpeciesParams()

Examples

# NOT RUN {
params <- newTraitParams()
sim <- project(params, t_max = 5, effort = 0)
plotSpectra(sim)
# }