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
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.
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 = 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, R_factor = 4, gear_names = "knife_edge_gear", knife_edge_size = 1000, egg_size_scaling = FALSE, resource_scaling = FALSE, perfect_scaling = FALSE )
The number of species in the model.
The asymptotic size of the smallest species in the community. This will be rounded to lie on a grid point.
The asymptotic size of the largest species in the community. This will be rounded to lie on a grid point.
The size of the the egg of the smallest species. This also defines the start of the community size spectrum.
The largest size in the model. By default this is set to the
largest asymptotic size
Ratio between maturity size and asymptotic size of a species.
The maturity size of the smallest species. Default value is
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.
The smallest size of the resource spectrum. By default this is set to the smallest value at which any of the consumers can feed.
The largest size of the resource spectrum. Default value
is min_w_inf unless
Scaling exponent of the maximum intake rate.
Scaling exponent of the standard metabolic rate. By default this is
equal to the exponent
Exponent of the abundance power law.
Growth rate parameter for the resource spectrum.
Coefficient in abundance power law.
The assimilation efficiency of the community.
Maximum food intake rate.
Preferred predator prey mass ratio.
Width of prey size preference.
Expected average feeding level. Used to set
Critical feeding level. Used to determine
Standard metabolism coefficient. If not provided, default will be
calculated from critical feeding level argument
Volumetric search rate. If not provided, default is determined
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).
The factor such that
The names of the fishing gears for each species. A character vector, the same length as the number of species.
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.
An object of type
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
are logarithmically evenly spaced, ranging from
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.
Although the trait based model's steady state is often stable without
imposing additional density-dependence, the function can set a Beverton-Holt
type density-dependence that imposes a maximum for the reproduction rate that
is a multiple of the reproduction rate at steady state. That multiple is set
by the argument
The search rate coefficient
gamma is calculated using the expected
The option of including fishing is given, but the steady state may lose its
natural stability if too much fishing is included. In such a case the user
may wish to include stabilising effects (like
R_factor) 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
knife_edge_size has a length of 1) or by a different gear (the
knife_edge_size has the same length as the number of species
and the order of selectivity size is that of the asymptotic size).
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.