This functions creates a MizerParams
object so
that traitbasedtype models can be easily set up and
run. The traitbased size spectrum model can be derived
as a simplification of the general sizebased model used
in mizer
. All the speciesspecific 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
traitbased model the number of species is not important.
For applications of the traitbased model see Andersen &
Pedersen (2010). See the mizer
vignette for more
details and examples of the traitbased model.
1 2 3 4 5 6 7 8 9  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 = 1e10,
no_w_pp = round(no_w) * 0.3, 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 
The number of the extra size bins in the background spectrum (i.e. the difference between the number of sizes bins in the community spectrum and the full spectrum). 
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+qn). 
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 ^ (n1). 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
BevertonHolt 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 knifeedge 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, 795802.
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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