Description Usage Arguments Details Value References See Also Examples

View source: R/wrapper_functions.R

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`

. All the species-specific parameters are the same, 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`

vignette for more details and examples
of the trait-based model.

1 2 3 4 5 6 7 | ```
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,
no_w_pp = NA, 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", ...)
``` |

`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` |
The largest size of the community spectrum. Default value is the largest w_inf in the community x 1.1. |

`min_w_pp` |
The smallest size of the background spectrum. |

`no_w_pp` |
Obsolete argument that is no longer used because the number of plankton size bins is determined because all size bins have to be logarithmically equally spaced. |

`w_pp_cutoff` |
The cut off size of the background 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 |

`r_pp` |
Growth rate of the primary productivity. Default value is 4. |

`kappa` |
Carrying capacity of the resource spectrum. Default value is 0.005. |

`lambda` |
Exponent of the resource spectrum. 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` |
Volumetric search rate. Estimated using |

`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 |

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 &
Pederson, 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.

An object of type `MizerParams`

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.

MizerParams

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Not run:
trait_params <- set_trait_model(no_sp = 15)
init_pop <- get_initial_n(trait_params, n0_mult = 0.001)
sim <- project(trait_params, effort = 0, t_max = 50, dt=0.2,
initial_n = init_pop, t_save = 1)
plot(sim)
## Set up industrial fishery that only fishes on species with w_inf <= 500 g
## And where the selectivity of the industrial fishery = w_inf * 0.05
no_sp <- 10
min_w_inf <- 10
max_w_inf <- 1e5
w_inf <- 10^seq(from=log10(min_w_inf), to = log10(max_w_inf), length=no_sp)
knife_edges <- w_inf * 0.05
industrial_gears <- w_inf <= 500
other_gears <- w_inf > 500
gear_names <- rep("Industrial", no_sp)
gear_names[other_gears] <- "Other"
params_gear <- set_trait_model(no_sp = no_sp, min_w_inf = min_w_inf,
max_w_inf = max_w_inf, knife_edge_size = knife_edges, gear_names = gear_names)
## Only turn on Industrial fishery. Set effort of the Other gear to 0
sim <- project(params_gear, t_max = 20, effort = c(Industrial = 1, Other = 0))
## End(Not run)
``` |

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