set_community_model {mizer}

Deprecated function for setting up parameters for a community-type model

Description

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

Usage

set_community_model(
  max_w = 1e+06,
  min_w = 0.001,
  min_w_pp = 1e-10,
  z0 = 0.1,
  alpha = 0.2,
  h = 10,
  beta = 100,
  sigma = 2,
  q = 0.8,
  n = 2/3,
  kappa = 1000,
  lambda = 2 + q - n,
  f0 = 0.7,
  r_pp = 10,
  gamma = NA,
  knife_edge_size = 1000,
  knife_is_min = TRUE,
  recruitment = kappa * min_w^-lambda,
  rec_mult = 1,
  ...
)

Arguments

max_w

The maximum size of the community. The w_inf of the species used to represent the community is set to this value. The default value is 1e6.

min_w

The minimum size of the community. Default value is 1e-3.

min_w_pp

The smallest size of the resource spectrum.

z0

The background mortality of the community. Default value is 0.1.

alpha

The assimilation efficiency of the community. Default value 0.2

h

The maximum food intake rate. Default value is 10.

beta

The preferred predator prey mass ratio. Default value is 100.

sigma

The width of the prey preference. Default value is 2.0.

q

The search volume exponent. Default value is 0.8.

n

The scaling of the intake. Default value is 2/3.

kappa

The carrying capacity of the resource spectrum. Default value is 1000.

lambda

The exponent of the resource spectrum. Default value is 2 + q - n.

f0

The average feeding level of individuals who feed on a power-law spectrum. This value is used to calculate the search rate parameter gamma (see the package vignette). Default value is 0.7.

r_pp

Growth rate parameter for the resource spectrum. Default value is 10.

gamma

Volumetric search rate. Estimated using h, f0 and kappa if not supplied.

knife_edge_size

The size at the edge of the knife-selectivity function. Default value is 1000.

knife_is_min

Is the knife-edge selectivity function selecting above (TRUE) or below (FALSE) the edge. Default is TRUE.

recruitment

The constant recruitment in the smallest size class of the community spectrum. This should be set so that the community spectrum continues the resource spectrum. Default value = kappa * min_w^-lambda.

rec_mult

Additional multiplier for the constant recruitment. Default value is 1.

...

Other arguments to pass to the MizerParams constructor.

Details

This functions creates a MizerParams object so that community-type models can be easily set up and run. A community model has several features that distinguish it from the food-web type models. Only one 'species' is resolved, i.e. one 'species' is used to represent the whole community. The resource spectrum only extends to the start of the community spectrum. Recruitment to the smallest size in the community spectrum is constant and set by the user. As recruitment is constant, the proportion of energy invested in reproduction (the slot psi of the returned MizerParams object) is set to 0. Standard metabolism has been turned off (the parameter ks is set to 0). Consequently, the growth rate is now determined solely by the assimilated food (see the package vignette for more details).

The function has many arguments, all of which have default values. The main arguments that the users should be concerned with are z0, recruitment, alpha and f0 as these determine the average growth rate of the community.

Fishing selectivity is modelled as a knife-edge function with one parameter, knife_edge_size, which determines the size at which species are selected.

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 keep a small time step size dt of around 0.1 to avoid any instabilities with the solver. You can check for these numerical instabilities by plotting the biomass or abundance through time after the projection.

Value

An object of type MizerParams

References

K. H. Andersen,J. E. Beyer and P. Lundberg, 2009, Trophic and individual efficiencies of size-structured communities, Proceedings of the Royal Society, 276, 109-114

Examples

params <- set_community_model(f0=0.7, z0=0.2, recruitment=3e7)
# This is now achieved with
params <- newCommunityParams(f0 = 0.7, z0 = 0.2)
sim <- project(params, effort = 0, t_max = 100, dt=0.1)
plotBiomass(sim)
plotSpectra(sim)