The previous sections have used wrapper functions to set up
MizerParams objects that are appropriate for single-species, community-
and trait-based models. We now turn our attention to multispecies, or
species-specific, models. These are potentially more complicated than
the community and trait-based models and use the full power of the
mizer package.
In multispecies type models multiple species are resolved. However, unlike in the trait-based model which also resolves multiple species, the species parameters will be those of real-world species. There are several advantages to this approach. As well as investigating the community as a whole (as was done for the community and trait-based models), we are able to investigate the dynamics of individual species. This means that species specific management rules can be tested and species specific metrics, such as yield, can be compared to reference levels.
A multispecies model can take more effort to set up. For example, each species will have different life-history parameters; there may be multiple gear types with different selectivities targeting different groups of species; the fishing effort of each gear may change with time instead of just being constant (which has been the case in the simulations we have looked at so far); the interactions between the species needs to be considered.
In later sections we build up a multispecies model for the North Sea.
To effectively use mizer for a multispecies model we are
going to have to take a closer look at the MizerParams
class and the project() function. This will all be done in
the context of examples so hopefully everything will be clear.
We also take a closer look at some of the summary plots and analyses that can be performed, for example, calculating a range of size-based indicators.
Setting up a multispecies model
Overview
The MizerParams class is used for storing model parameters. We have already met the MizerParams class when we looked at community and trait-based models. However, to set up a multispecies model we will need to specify many more parameters.This is probably the most complicated part of using the mizer package, so we will take it slowly.
A MizerParams object stores the:
- life-history parameters of the species in the community, such as maximum size \(w_{max}\);
- size-based biological parameters for the species, such as the search volume;
- density-dependent reproduction functions and parameters of each species;
- an interaction matrix to describe the spatial overlap of pairs of species;
- parameters relating to the growth and dynamics of the resource spectrum;
- fishing gear parameters: selectivity and catchability.
Note that the MizerParams class does not store any
parameters that can vary through time, such as fishing effort or
population abundance. These are stored in the MizerSim
class which we will come to later in the section on running a
simulation.
Although the MizerParams class contains a lot of
information, it is relatively straightforward to set up and use. Objects
of class MizerParams are created using the constructor
method newMultispeciesParams() (this constructor method was
called MizerParams() in previous version of mizer). This constructor
method can take many arguments. However, creation is simplified because
many of the arguments have default values.
In the rest of this section we look at the main arguments to the
newMultispeciesParams() function. To help understand how
the constructor is used and how the MizerParams class
relates to the equations given in the
model description section, there is an example section where we
create example parameter objects using data that comes with the
mizer package.
The species parameters
Although many of the arguments used when creating a
MizerParams object are optional, there is one argument that
must be supplied by the user: the species specific parameters.
These are stored in a single data.frame object. The
data.frame is arranged species by parameter, so each column
is a parameter and each row has the parameters for one of the species in
the model. Although it is possible to create the data.frame by hand in
R, it is probably easier to create the data externally as a .csv file
(perhaps using a suitable open source spreadsheet such as LibreOffice)
and then read the data into R.
For each species in the model community there are certain parameters that are essential and that do not have default values. The user must provide values for these parameters. There are also some essential parameters that have default values, such as the selectivity function parameters, and some that are calculated internally using default relationships if not explicitly provided. These defaults are used if the parameters are not found in the data.frame.
The essential columns of the species parameters data.frame that have
no default values are: species, the names of the species in
the community and w_max, the maximum mass of the
species.
The gear parameters
In mizer, fishing mortality is imposed on species by
fishing gears. The total fishing mortality is obtained by summing over
the mortality from all gears, \[\begin{equation}
% {#eq:muf}
\mu_{f.i}(w) = \sum_g F_{g,i}(w),
\end{equation}\] where the fishing mortality \(F_{g,i}(w)\) imposed by gear \(g\) on species \(i\) at size \(w\) is calculated as: \[\begin{equation}
% {#eq:sel}
F_{g,i}(w) = S_{g,i}(w) Q_{g,i} E_{g}
\end{equation}\] where \(S\) is
the selectivity by species, gear and size, \(Q\) is the catchability by species and gear
and \(E\) is the fishing effort by
gear. The selectivity at size has a range between 0 (not selected at
that size) to 1 (fully selected at that size). Catchability is used as
an additional scalar to make the link between gear selectivity, fishing
effort and fishing mortality. For example, it can be set so that an
effort of 1 gives a desired fishing mortality. In this way effort can
then be specified relative to a ‘base effort’, e.g. the effort in a
particular year.
Selectivity and catchability are stored as arrays in the MizerParams
object. However the user does not have to create these arrays by hand if
they provide a data frame with the necessary information. In particular
the selectivity can be calculate by specifying functions for the
selectivity curves. Mizer provides a range of such selectivity functions
and the user just needs to specify their parameters for each gear and
each species in the gear_params data frame. All the details
can be found on the help page for setFishing().
Fishing effort is not stored in the MizerParams object. Instead, effort is set when the simulation is run and can vary through time (see the section on running a simulation).
Example of making MizerParams objects
As mentioned in the preceding sections, an object of
MizerParams is created by using the
newMultispeciesParams() constructor method.
The first step is to prepare the species specific parameter data.frame. As mentioned above, one way of doing this is to use a spreadsheet and save it as a .csv file. We will use this approach here. An example .csv file has been included in the package. This contains the species parameters for a multispecies North Sea model. The location of the file can be found by running
params_location <- system.file("extdata", "NS_species_params.csv",
package = "mizer")This file can be opened with most spreadsheets or a text editor for you to inspect. This can be loaded into R with
species_params <- read.csv(params_location)This reads the .csv file into R in the form of a data.frame. You can
check this with the class:
class(species_params)## [1] "data.frame"Let’s have a look at the data frame:
species_params## species w_inf w_mat beta sigma R_max k_vb
## 1 Sprat 33 13 51076 0.8 7.38e+11 0.681
## 2 Sandeel 36 4 398849 1.9 4.10e+11 1.000
## 3 N.pout 100 23 22 1.5 1.05e+13 0.849
## 4 Herring 334 99 280540 3.2 1.11e+12 0.606
## 5 Dab 324 21 191 1.9 1.12e+10 0.536
## 6 Whiting 1192 75 22 1.5 5.48e+11 0.323
## 7 Sole 866 78 381 1.9 3.87e+10 0.284
## 8 Gurnard 668 39 283 1.8 1.65e+12 0.266
## 9 Plaice 2976 105 113 1.6 4.08e+14 0.122
## 10 Haddock 3485 165 558 2.1 1.84e+12 0.271
## 11 Cod 40044 1606 66 1.3 8.26e+09 0.216
## 12 Saithe 16856 1076 40 1.1 1.12e+11 0.175You can see that there are \(12\)
species and \(7\) columns of
parameters: species,
w_max,w_mat,beta,sigma,R_max
and k_vb.
Of these parameters, species and w_max are
essential and have no default values (as described in the
section on species parameters). w_max is the maximum
size of the species, w_mat is its maturity size, and
beta and sigma are parameters of the predation
kernel (by default mizer uses a log-normal predation kernel).
R_max is a parameter introducing additional density
dependence into reproduction parameter using a Beverton-Holt type
function (see setReproduction() for details). The final
column, k_vb, will be used to calculate values for
h and then gamma. This column is only
essential here because the h and gamma are not
included in the data.frame. It would also have been possible to include
h and gamma columns in the data.frame and not
include the k_vb column.
The values of the non-essential species specific parameters, like for
example alpha, k, ks,
z0, w_min and erepro, were not
included in the data.frame. This means that the default values will be
automatically used when we create the MizerParams
object.
For this example we will not set up gear parameters. There are no
columns describing the fishing selectivity. There is no
sel_func column to determine the selectivity function. This
means that the default selectivity function, knife_edge,
will be used. As mentioned in the section on fishing
gears, this function also needs another argument,
knife_edge_size. This is not present in the data.frame and
so it will be set to the default value of w_mat. Also,
there is no catchability column so a default value for
catchability of 1 will be used for all gears and
species.
To create the MizerParams object we pass the species
parameter data.frame into the newMultispeciesParams()
constructor method:
params <- newMultispeciesParams(species_params)## Warning in validSpeciesParams(species_params): The species parameter data frame
## is missing a `w_max` column. I am copying over the values from the `w_inf`
## column. But note that `w_max` should be the maximum size of the largest
## individual, not the asymptotic size of an average indivdidual.
## Warning in validSpeciesParams(species_params): The species parameter data frame
## is missing a `w_max` column. I am copying over the values from the `w_inf`
## column. But note that `w_max` should be the maximum size of the largest
## individual, not the asymptotic size of an average indivdidual.## Because you have n != p, the default value for `h` is not very good.
## Because the age at maturity is not known, I need to fall back to using
## von Bertalanffy parameters, where available, and this is not reliable.
## No ks column so calculating from critical feeding level.
## Using z0 = z0pre * w_max ^ z0exp for missing z0 values.
## Using f0, h, lambda, kappa and the predation kernel to calculate gamma.We have just created a MizerParams object:
class(params)## [1] "MizerParams"
## attr(,"package")
## [1] "mizer"The MizerParams object also stores a copy of the species parameter
data frame that we provided. We can look at it with
species_params():
species_params(params)## species w_inf w_mat beta sigma R_max k_vb n p w_max
## Sprat Sprat 33 13 51076 0.8 7.38e+11 0.681 0.6666667 0.7 33
## Sandeel Sandeel 36 4 398849 1.9 4.10e+11 1.000 0.6666667 0.7 36
## N.pout N.pout 100 23 22 1.5 1.05e+13 0.849 0.6666667 0.7 100
## Herring Herring 334 99 280540 3.2 1.11e+12 0.606 0.6666667 0.7 334
## Dab Dab 324 21 191 1.9 1.12e+10 0.536 0.6666667 0.7 324
## Whiting Whiting 1192 75 22 1.5 5.48e+11 0.323 0.6666667 0.7 1192
## Sole Sole 866 78 381 1.9 3.87e+10 0.284 0.6666667 0.7 866
## Gurnard Gurnard 668 39 283 1.8 1.65e+12 0.266 0.6666667 0.7 668
## Plaice Plaice 2976 105 113 1.6 4.08e+14 0.122 0.6666667 0.7 2976
## Haddock Haddock 3485 165 558 2.1 1.84e+12 0.271 0.6666667 0.7 3485
## Cod Cod 40044 1606 66 1.3 8.26e+09 0.216 0.6666667 0.7 40044
## Saithe Saithe 16856 1076 40 1.1 1.12e+11 0.175 0.6666667 0.7 16856
## w_min alpha interaction_resource pred_kernel_type h k ks
## Sprat 0.001 0.6 1 lognormal 14.51026 0 1.598545
## Sandeel 0.001 0.6 1 lognormal 28.36951 0 3.250607
## N.pout 0.001 0.6 1 lognormal 30.69918 0 3.318311
## Herring 0.001 0.6 1 lognormal 31.20041 0 3.212332
## Dab 0.001 0.6 1 lognormal 34.68295 0 3.760307
## Whiting 0.001 0.6 1 lognormal 32.78322 0 3.406676
## Sole 0.001 0.6 1 lognormal 24.90951 0 2.585095
## Gurnard 0.001 0.6 1 lognormal 22.29126 0 2.367448
## Plaice 0.001 0.6 1 lognormal 17.71691 0 1.820523
## Haddock 0.001 0.6 1 lognormal 40.62144 0 4.111691
## Cod 0.001 0.6 1 lognormal 74.81794 0 7.019866
## Saithe 0.001 0.6 1 lognormal 43.50194 0 4.136466
## z0 q gamma w_mat25 m erepro
## Sprat 0.18705957 0.7166667 5.765885e-11 11.647460 1 1
## Sandeel 0.18171206 0.7166667 4.267142e-11 3.583834 1 1
## N.pout 0.12926608 0.7166667 9.749884e-11 20.607045 1 1
## Herring 0.08647736 0.7166667 2.812559e-11 88.699888 1 1
## Dab 0.08735805 0.7166667 7.663981e-11 18.815128 1 1
## Whiting 0.05658819 0.7166667 1.041177e-10 67.196884 1 1
## Sole 0.06294752 0.7166667 5.308445e-11 69.884760 1 1
## Gurnard 0.06863713 0.7166667 5.091838e-11 34.942380 1 1
## Plaice 0.04171321 0.7166667 4.774060e-11 94.075638 1 1
## Haddock 0.03957464 0.7166667 7.679024e-11 147.833146 1 1
## Cod 0.01753768 0.7166667 2.549664e-10 1438.909287 1 1
## Saithe 0.02340093 0.7166667 1.797143e-10 964.051303 1 1We can see that this returns the original species data.frame (with
w_max and so on), plus any default values that may not have
been included in the original data.frame. For example, we can see that
there are now columns for alpha and h and
gamma etc.
Also note how the default fishing gears have been set up. Even though we did not provide a gear parameter data frame, the MizerParams object has one that we can access with
gear_params(params)## species gear sel_func knife_edge_size
## Sprat, knife_edge_gear Sprat knife_edge_gear knife_edge 13
## Sandeel, knife_edge_gear Sandeel knife_edge_gear knife_edge 4
## N.pout, knife_edge_gear N.pout knife_edge_gear knife_edge 23
## Herring, knife_edge_gear Herring knife_edge_gear knife_edge 99
## Dab, knife_edge_gear Dab knife_edge_gear knife_edge 21
## Whiting, knife_edge_gear Whiting knife_edge_gear knife_edge 75
## Sole, knife_edge_gear Sole knife_edge_gear knife_edge 78
## Gurnard, knife_edge_gear Gurnard knife_edge_gear knife_edge 39
## Plaice, knife_edge_gear Plaice knife_edge_gear knife_edge 105
## Haddock, knife_edge_gear Haddock knife_edge_gear knife_edge 165
## Cod, knife_edge_gear Cod knife_edge_gear knife_edge 1606
## Saithe, knife_edge_gear Saithe knife_edge_gear knife_edge 1076
## catchability
## Sprat, knife_edge_gear 1
## Sandeel, knife_edge_gear 1
## N.pout, knife_edge_gear 1
## Herring, knife_edge_gear 1
## Dab, knife_edge_gear 1
## Whiting, knife_edge_gear 1
## Sole, knife_edge_gear 1
## Gurnard, knife_edge_gear 1
## Plaice, knife_edge_gear 1
## Haddock, knife_edge_gear 1
## Cod, knife_edge_gear 1
## Saithe, knife_edge_gear 1All species are caught by a gear called “knife_edge_gear”. The
selectivity function for each fishing gear has been set in the
sel_func column to the default function,
knife_edge(). A catchability column has been
added with a default value of 1 for each of the species that the gear
catches. An example of setting the catchability by hand can be seen in
the section on the
North Sea.
There is a summary() method for MizerParams
objects which prints a useful summary of the model parameters:
summary(params)## An object of class "MizerParams"
## Consumer size spectrum:
## minimum size: 0.001
## maximum size: 40044
## no. size bins: 100
## Resource size spectrum:
## minimum size: 8.6774e-13
## maximum size: 9.84582
## no. size bins: 171 (218 size bins in total)
## Species details:
## species w_max w_mat w_min beta sigma
## 1 Sprat 33 13 0.001 51076 0.8
## 2 Sandeel 36 4 0.001 398849 1.9
## 3 N.pout 100 23 0.001 22 1.5
## 4 Herring 334 99 0.001 280540 3.2
## 5 Dab 324 21 0.001 191 1.9
## 6 Whiting 1192 75 0.001 22 1.5
## 7 Sole 866 78 0.001 381 1.9
## 8 Gurnard 668 39 0.001 283 1.8
## 9 Plaice 2976 105 0.001 113 1.6
## 10 Haddock 3485 165 0.001 558 2.1
## 11 Cod 40044 1606 0.001 66 1.3
## 12 Saithe 16856 1076 0.001 40 1.1
##
## Fishing gear details:
## Gear Effort Target species
## ----------------------------------
## knife_edge_gear 0.00 Sprat, Sandeel, N.pout, Herring, Dab, Whiting, Sole, Gurnard, Plaice, Haddock, Cod, SaitheAs well as giving a summary of the species in the model and what gear
is fishing what species, it gives a summary of the size structure of the
community. For example there are \(100\) size classes in the community,
ranging from \(0.001\) to \(4\times 10^{4}\) . These values are
controlled by the arguments no_w, min_w and
max_w respectively. For example, if we wanted 200 size
classes in the model we would use:
params200 <- newMultispeciesParams(species_params, no_w = 200)
summary(params200)Setting the interaction matrix
So far we have created a MizerParams object by passing
in only the species parameter data.frame argument. We did not specify an
interaction matrix. The interaction matrix describes the interaction of
each pair of species in the model. This can be viewed as a proxy for
spatial interaction e.g. to model predator-prey interaction that is not
size based. The values in the interaction matrix are used to scale the
encountered food in [getEncounter()] and the predation mortality rate in
[getPredMort()] (see the
section on predator-prey encounter rate and on predation mortality).
The entries of the interaction matrix are dimensionless numbers taking values are between 0 (species do not overlap and therefore do not interact with each other) to 1 (species overlap perfectly). By default mizer sets all values to 1, implying that all species fully interact with each other, i.e. the species are spread homogeneously across the model area.
getInteraction(params)## Warning: `getInteraction()` was deprecated in mizer 2.4.0.
## ℹ Please use `interaction_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## prey
## predator Sprat Sandeel N.pout Herring Dab Whiting Sole Gurnard Plaice Haddock
## Sprat 1 1 1 1 1 1 1 1 1 1
## Sandeel 1 1 1 1 1 1 1 1 1 1
## N.pout 1 1 1 1 1 1 1 1 1 1
## Herring 1 1 1 1 1 1 1 1 1 1
## Dab 1 1 1 1 1 1 1 1 1 1
## Whiting 1 1 1 1 1 1 1 1 1 1
## Sole 1 1 1 1 1 1 1 1 1 1
## Gurnard 1 1 1 1 1 1 1 1 1 1
## Plaice 1 1 1 1 1 1 1 1 1 1
## Haddock 1 1 1 1 1 1 1 1 1 1
## Cod 1 1 1 1 1 1 1 1 1 1
## Saithe 1 1 1 1 1 1 1 1 1 1
## prey
## predator Cod Saithe
## Sprat 1 1
## Sandeel 1 1
## N.pout 1 1
## Herring 1 1
## Dab 1 1
## Whiting 1 1
## Sole 1 1
## Gurnard 1 1
## Plaice 1 1
## Haddock 1 1
## Cod 1 1
## Saithe 1 1For the North Sea this is not the case and so the model would be improved by also including an interaction matrix which describes the spatial overlap between species.
An example interaction matrix for the North Sea has been included in
mizer as a .csv file. The location of the file can be found
by running:
inter_location <- system.file("extdata", "NS_interaction.csv",
package = "mizer")Take a look at it in a spreadsheet if you want. As mentioned above,
to read this file into R we can make use of the read.csv()
function. However, this time we want the first column of the .csv file
to be the row names. We therefore use an additional argument to the
read.csv() function: row.names.
inter <- read.csv(inter_location, row.names = 1)
inter## Sprat Sandeel N.pout Herring Dab Whiting
## Sprat 0.72912919 0.03408440 0.06354517 0.27416982 0.36241552 0.26525924
## Sandeel 0.03408440 0.68119882 0.04892432 0.05888214 0.09736663 0.07510011
## N.pout 0.06354517 0.04892432 0.79660429 0.29755069 0.09088798 0.29989886
## Herring 0.27416982 0.05888214 0.29755069 0.65890104 0.28963957 0.37373927
## Dab 0.36241552 0.09736663 0.09088798 0.28963957 0.80817768 0.33389727
## Whiting 0.26525924 0.07510011 0.29989886 0.37373927 0.33389727 0.70928230
## Sole 0.29795558 0.06020860 0.01679020 0.20014139 0.38047464 0.19227455
## Gurnard 0.17515576 0.05992649 0.30624141 0.27510627 0.22041200 0.37109904
## Plaice 0.37065975 0.07801855 0.07855818 0.27791867 0.56492206 0.29503807
## Haddock 0.08135547 0.09395730 0.54917554 0.34835469 0.13168065 0.39164787
## Cod 0.33757969 0.09943453 0.32502256 0.40477930 0.41647801 0.44060879
## Saithe 0.01681321 0.01609022 0.29498937 0.12620591 0.03138197 0.10228168
## Sole Gurnard Plaice Haddock Cod Saithe
## Sprat 0.29795558 0.17515576 0.37065975 0.08135547 0.33757969 0.01681321
## Sandeel 0.06020860 0.05992649 0.07801855 0.09395730 0.09943453 0.01609022
## N.pout 0.01679020 0.30624141 0.07855818 0.54917554 0.32502256 0.29498937
## Herring 0.20014139 0.27510627 0.27791867 0.34835469 0.40477930 0.12620591
## Dab 0.38047464 0.22041200 0.56492206 0.13168065 0.41647801 0.03138197
## Whiting 0.19227455 0.37109904 0.29503807 0.39164787 0.44060879 0.10228168
## Sole 0.71558049 0.10677895 0.39137317 0.03447799 0.25761229 0.01242055
## Gurnard 0.10677895 0.88010500 0.16492120 0.35735444 0.35183282 0.12351994
## Plaice 0.39137317 0.16492120 0.71922391 0.11248513 0.35043671 0.03294939
## Haddock 0.03447799 0.35735444 0.11248513 0.85830725 0.39577341 0.26167470
## Cod 0.25761229 0.35183282 0.35043671 0.39577341 0.78654705 0.20894496
## Saithe 0.01242055 0.12351994 0.03294939 0.26167470 0.20894496 0.66383553We can set the interaction matrix in our existing MizerParams object
params with the setInteraction() function:
params <- setInteraction(params, interaction = inter)Alternatively, instead of changing the interaction matrix in the
existing MizerParams object, we could have created a new object from
scratch with our interaction matrix by passing it to
newMultispeciesParams():
params_new <- newMultispeciesParams(species_params, interaction = inter)## Warning in validSpeciesParams(species_params): The species parameter data frame
## is missing a `w_max` column. I am copying over the values from the `w_inf`
## column. But note that `w_max` should be the maximum size of the largest
## individual, not the asymptotic size of an average indivdidual.
## Warning in validSpeciesParams(species_params): The species parameter data frame
## is missing a `w_max` column. I am copying over the values from the `w_inf`
## column. But note that `w_max` should be the maximum size of the largest
## individual, not the asymptotic size of an average indivdidual.## Because you have n != p, the default value for `h` is not very good.
## Because the age at maturity is not known, I need to fall back to using
## von Bertalanffy parameters, where available, and this is not reliable.
## No ks column so calculating from critical feeding level.
## Using z0 = z0pre * w_max ^ z0exp for missing z0 values.
## Using f0, h, lambda, kappa and the predation kernel to calculate gamma.Note that the first argument must be the species parameters data.frame. The remaining arguments can be in any order but should be named. We are using the default values for all other parameters.
We now have all we need to start running projections. Before we get to that though, we’ll take a quick look at how different fishing gears can be set up.
Setting different gears
In the above example, each species is caught by the same gear (named “knife_edge_gear”). This is the default when no gear information is provided.
gear_params(params)## species gear sel_func knife_edge_size
## Sprat, knife_edge_gear Sprat knife_edge_gear knife_edge 13
## Sandeel, knife_edge_gear Sandeel knife_edge_gear knife_edge 4
## N.pout, knife_edge_gear N.pout knife_edge_gear knife_edge 23
## Herring, knife_edge_gear Herring knife_edge_gear knife_edge 99
## Dab, knife_edge_gear Dab knife_edge_gear knife_edge 21
## Whiting, knife_edge_gear Whiting knife_edge_gear knife_edge 75
## Sole, knife_edge_gear Sole knife_edge_gear knife_edge 78
## Gurnard, knife_edge_gear Gurnard knife_edge_gear knife_edge 39
## Plaice, knife_edge_gear Plaice knife_edge_gear knife_edge 105
## Haddock, knife_edge_gear Haddock knife_edge_gear knife_edge 165
## Cod, knife_edge_gear Cod knife_edge_gear knife_edge 1606
## Saithe, knife_edge_gear Saithe knife_edge_gear knife_edge 1076
## catchability
## Sprat, knife_edge_gear 1
## Sandeel, knife_edge_gear 1
## N.pout, knife_edge_gear 1
## Herring, knife_edge_gear 1
## Dab, knife_edge_gear 1
## Whiting, knife_edge_gear 1
## Sole, knife_edge_gear 1
## Gurnard, knife_edge_gear 1
## Plaice, knife_edge_gear 1
## Haddock, knife_edge_gear 1
## Cod, knife_edge_gear 1
## Saithe, knife_edge_gear 1Here, we look at an example where we set up four different gears:
Industrial, Pelagic, Beam and Otter trawl, that catch different
combinations of species. We can achieve that by only changing the
gear column in the gear_params data frame.
gear_params(params)$gear <- c("Industrial", "Industrial", "Industrial",
"Pelagic", "Beam", "Otter",
"Beam", "Otter", "Beam",
"Otter", "Otter", "Otter")You can see the result by calling summary() on the
params object.
summary(params)## An object of class "MizerParams"
## Consumer size spectrum:
## minimum size: 0.001
## maximum size: 40044
## no. size bins: 100
## Resource size spectrum:
## minimum size: 8.6774e-13
## maximum size: 9.84582
## no. size bins: 171 (218 size bins in total)
## Species details:
## species w_max w_mat w_min beta sigma
## 1 Sprat 33 13 0.001 51076 0.8
## 2 Sandeel 36 4 0.001 398849 1.9
## 3 N.pout 100 23 0.001 22 1.5
## 4 Herring 334 99 0.001 280540 3.2
## 5 Dab 324 21 0.001 191 1.9
## 6 Whiting 1192 75 0.001 22 1.5
## 7 Sole 866 78 0.001 381 1.9
## 8 Gurnard 668 39 0.001 283 1.8
## 9 Plaice 2976 105 0.001 113 1.6
## 10 Haddock 3485 165 0.001 558 2.1
## 11 Cod 40044 1606 0.001 66 1.3
## 12 Saithe 16856 1076 0.001 40 1.1
##
## Fishing gear details:
## Gear Effort Target species
## ----------------------------------
## Industrial 0.00 Sprat, Sandeel, N.pout
## Pelagic 0.00 Herring
## Beam 0.00 Dab, Sole, Plaice
## Otter 0.00 Whiting, Gurnard, Haddock, Cod, SaitheIn this example the same gear now catches multiple stocks. For example, the Industrial gear catches Sprat, Sandeel and Norway Pout. Why would we want to set up the gears like this? In the next section on running a multispecies model we will see that to project the model through time you can specify the fishing effort for each gear through time. By setting the gears up in this way you can run different management scenarios of changing the efforts of the fishing gears rather than on individual species. It also means that after a simulation has been run you can examine the catches by gear.
Setting to steady state
Once the MizerParams object has been properly set up, it
may be the case that one wishes put the system in steady state.
Sometimes this can be done simply by running the model using
project() until it reaches steady state. However, this
method is not guaranteed to work, and there is a function called
steady() that is more reliable. The function
steady() must be supplied with a MizerParams object. It
takes that MizerParams object, looks at the initial system state,
computes the levels of reproduction of the different species, hold them
fixed, and evolves the system until a steady state is reached (or more
precisely, until the amount that the population abundances change during
a time-step is below some small tolerance level). After this, the
reproductive efficiency of each species is altered so that when the
reproduction dynamics are turned back on (i.e., when we stop holding
recruitment levels fixed), the values of the reproduction levels which
we held the system fixed at will be realized. The steady function is not
sure to converge, and the way it re-tunes the reproductive efficiency
values may not be realistic, but the idea is to alter the other
parameters in the system until steady() does arrive at a
steady state with sensible reproductive efficiency values.
Now that we know how to create a multispecies model we shall discuss how to run a multispecies model.