# Set up parameters for a trait-based multispecies model

Source:`R/wrapper_functions.R`

`newTraitParams.Rd`

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
- 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.

## 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

```
if (FALSE) {
params <- newTraitParams()
sim <- project(params, t_max = 5, effort = 0)
plotSpectra(sim)
}
```