Deprecated function for setting up parameters for a trait-based model

This function has been deprecated in favour of the function newTraitParams() that sets better default values.

Usage

set_trait_model(
  no_sp = 10,
  min_w_inf = 10,
  max_w_inf = 1e+05,
  no_w = 100,
  min_w = 0.001,
  max_w = max_w_inf * 1.1,
  min_w_pp = 1e-10,
  w_pp_cutoff = 1,
  k0 = 50,
  n = 2/3,
  p = 0.75,
  q = 0.9,
  eta = 0.25,
  r_pp = 4,
  kappa = 0.005,
  lambda = 2 + q - n,
  alpha = 0.6,
  ks = 4,
  z0pre = 0.6,
  h = 30,
  beta = 100,
  sigma = 1.3,
  f0 = 0.5,
  gamma = NA,
  knife_edge_size = 1000,
  gear_names = "knife_edge_gear",
  ...
)

Arguments

no_sp: The number of species in the model. The default value is 10. The more species, the longer takes to run.
min_w_inf: The asymptotic size of the smallest species in the community.
max_w_inf: The asymptotic size of the largest species in the community.
no_w: The number of size bins in the community spectrum.
min_w: The smallest size of the community spectrum.
max_w: Obsolete argument because the maximum size of the consumer spectrum is set to max_w_inf.
min_w_pp: Obsolete argument because the smallest resource size is set to the smallest size at which the consumers feed.
w_pp_cutoff: The cut off size of the resource spectrum. Default value is 1.
k0: Multiplier for the maximum recruitment. Default value is 50.
n: Scaling of the intake. Default value is 2/3.
p: Scaling of the standard metabolism. Default value is 0.75.
q: Exponent of the search volume. Default value is 0.9.
eta: Factor to calculate w_mat from asymptotic size.
r_pp: Growth rate parameter for the resource spectrum. Default value is 4.
kappa: Coefficient in abundance power law. Default value is 0.005.
lambda: Exponent of the abundance power law. Default value is (2+q-n).
alpha: The assimilation efficiency of the community. The default value is 0.6
ks: Standard metabolism coefficient. Default value is 4.
z0pre: The coefficient of the background mortality of the community. z0 = z0pre * w_inf ^ (n-1). The default value is 0.6.
h: Maximum food intake rate. Default value is 30.
beta: Preferred predator prey mass ratio. Default value is 100.
sigma: Width of prey size preference. Default value is 1.3.
f0: Expected average feeding level. Used to set gamma, the factor for the search volume. The default value is 0.5.
gamma: Volumetric search rate. Estimated using h, f0 and kappa if not supplied.
knife_edge_size: The minimum size at which the gear or gears select species. Must be of length 1 or no_sp.
gear_names: The names of the fishing gears. A character vector, the same length as the number of species. Default is 1 - no_sp.
...: Other arguments to pass to the MizerParams constructor.

Value

An object of type MizerParams

Details

This functions creates a MizerParams object so that trait-based-type models can be easily set up and run. The trait-based size spectrum model can be derived as a simplification of the general size-based model used in mizer. 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_max * 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 vignette for more details and examples of the trait-based model.

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 asymptotic sizes of the species are spread evenly on a logarithmic scale within this range.

The stock recruitment relationship is the default Beverton-Holt style. The maximum recruitment is calculated using equilibrium theory (see Andersen & Pedersen, 2010) and a multiplier, k0. Users should adjust k0 to get the spectra they want.

The factor for the search volume, gamma, is calculated using the expected feeding level, f0.

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 community model it may be necessary to reduce dt to 0.1 to avoid any instabilities with the solver. You can check this by plotting the biomass or abundance through time after the projection.

References

K. H. Andersen and M. Pedersen, 2010, Damped trophic cascades driven by fishing in model marine ecosystems. Proceedings of the Royal Society V, Biological Sciences, 1682, 795-802.