On this page we present the details of the mizer model, taking care to separate the essential features of the model that are hard-coded and the various possible specialisations for which mizer provides setup functions. We will provide links to the functions that can be used to set or change the various model parameters (all collected together in `setParams()`

) as well as to the functions that calculate the various ecological rates in the model (all collected together in `mizerRates()`

) because the help pages of these functions will provide useful additional details.

And clicking again will hide the details again.

The model assumes that, to a first approximation, an individual can be characterized by its weight \(w\) and its species number \(i\) only. The aim of the model is to calculate the size spectrum \(N_i(w)\), which is the *density* of individuals of species \(i\) such that \(\int_w^{w+dw}N_i(w)dw\) is the *number* of individuals of species \(i\) in the size interval \([w,w+dw]\). In other words: the number of individuals in a size range is the area under the number density \(N_i(w)\).

Here is a plot of an example size spectrum for two species with \(N_i(w)\) on the vertical axis for \(i=1,2\) and \(w\) on the horizontal axis.

```
library(mizer)
params <- newTraitParams(no_sp = 2, min_w = 1e-3)
plotSpectra(params, resource = FALSE, power = 0)
```

To represent this continuous size spectrum in the computer, the size variable \(w\) is discretized into a vector `w`

of discrete weights, providing a grid of sizes spanning the range from the smallest egg size to the largest asymptotic size. These grid values divide the full size range into a finite number of size bins. The size bins should be chosen small enough to avoid the discretisation errors from becoming too big. You can fetch this vector with `w()`

and the vector of bin sizes with `dw()`

.

The weight grid is set up to be logarithmically spaced, so that `w[j]=w[1]*10^(j*dx)`

for some fixed `dx`

. This means that the bin widths increase with size: `dw[j] = w[j+1] - w[j] = w[j] * (10^dx - 1)`

. This grid is set up automatically when creating a MizerParams object.

In the code the size spectrum is stored as an array `N`

such that `N[i, a]`

holds the density \(N_i(w_a)\) at weights \(w_a=\)`w[a]`

, or, if time dependence is included, an array such that `N[i, a, u]`

holds \(N_i(w_a,t_u)\). See `N()`

.

Note that, contrary to what one might have expected, `N[i, a]`

is not the *number* of individuals in a size bin but the *density* at a grid point. The number of individuals in the size bin between `w[a]`

and `w[a+1]=w[a]+dw[a]`

is only approximately given as `N[i, a]*dw[a]`

, where `dw[a]= w[a+1]-w[a]`

.

Of course all these calculations with discrete sizes and size bins are only giving approximations to the continuous values, and these approximations get better the smaller the size bins are, i.e., the more size bins are used. This is why the functions setting up MizerParams objects allow you to choose the number of size bins `no_w`

.

A good way to think about the time evolution of the number density \(N_i(w)\) is to consider the familiar situation of traffic density on the roads. A fish’s life is a journey along the size axis from egg size to size at death. There are many other fish making the same journey. The speed with which the fish move along the size axis is their growth rate. This is quite analogous to the speed of traffic on a road. Just as the speed of cars depends on the density of other cars on the road, the growth rate of fish depends on the density of other fish. When the density of cars on a stretch of road is high, their speed decreases, and this leads to a pile-up and can lead to traffic jams. Similarly when the density of fish in a size interval is high, their growth rate goes down due to competition for the same food sources, and this too can lead to pile-ups and bottlenecks. So one just has to replace the space variable in a traffic model by size to get an equation for the fish size spectra.

The additional feature in the evolution of fish densities that is not present in road traffic density is that fish can die while they are growing up, and this death rate is also dependent on the density of other fish.

The time evolution of the number density \(N_i(w)\) is described by the McKendrick-von Foerster equation, which is a transport equation (as one would use for traffic density) but with an additional loss term due to fish mortality:

\[\begin{equation} \label{eq:MvF} \frac{\partial N_i(w)}{\partial t} + \frac{\partial g_i(w) N_i(w)}{\partial w} = -\mu_i(w) N_i(w), \end{equation}\]

where individual growth \(g_i(w)\) and mortality \(\mu_i(w)\) will be described below. There is no need for you to understand the mathematical notation used in this equation to understand its origin: it just says that the rate at which the number of fish in a size bracket changes is the rate at which fish grow into the size bracket from a smaller size minus the rate at which fish grow out of it to a larger size minus the rate at which the fish in the size bracket die. So to simulate the size spectrum dynamics we need to specify the growth rates and the mortality rates. This we will do below. The important point is that these rates depend on the density of other fish of other sizes, making the size-spectrum dynamics non-linear and non-local in very interesting ways. The resulting effects are too complicated to disentangle by pure thought. This is where simulations with the mizer package come in.

This McKendrick-von Foerster equation is approximated in mizer by a finite-difference method. This allows the `project()`

function in mizer to project the size spectrum forwards in time: Given the spectrum at one time the `project()`

function calculates it at a set of later times.

Of course there also needs to be reproduction into the smallest size class, otherwise there would be no small fish any more after a while. So for the smallest size class instead of a rate of growth into the size class there is a rate of reproduction of new individuals into that size class. This reproduction will be described further below.

Besides the fish spectrum there is also a resource spectrum \(N_R(w)\), representing for example the phytoplankton. This spectrum starts at a smaller size than the fish spectrum, in order to provide food also for the smallest individuals (larvae) of the fish spectrum. By default the time evolution of the resource spectrum is described by a semi-chemostat equation.

The semichemostat dynamics are given by \[\begin{equation}
\label{eq:nb}
\frac{\partial N_R(w,t)}{\partial t}
= r_R(w) \Big[ c_R (w) - N_R(w,t) \Big] - \mu_R(w) N_R(w,t).
\end{equation}\] Here \(r_R(w)\) is the resource regeneration rate and \(c_R(w)\) is the carrying capacity in the absence of predation. These parameters are changed with `setResource()`

. By default mizer assumes allometric forms \[r_R(w)= r_R\, w^{n-1}.\] \[c_R(w)=\kappa\, w^{-\lambda}.\] You can retrieve these with `getResourceRate()`

and `getResourceCapacity()`

respectively. It is also possible to implement other resource dynamics, as described in the help page for `setResource()`

. The mortality \(\mu_R(w)\) is due to predation by consumers and is described in the subsection Resource mortality.

Because the resource spectrum spans a larger range of sizes these sizes Because the resource spectrum spans a larger range of sizes these sizes Because the resource spectrum spans a larger range of sizes these sizes are discretized into a different vector of weights

The resource spectrum is then represented by a vector `NResource`

such that `NResource[c]`

=\(N_R(\)`w_full[c]`

\()\).

Consumers can grow only by consuming prey. In the next few subsections we will build towards determining the growth rate resulting from predation. We will discuss how we model the predator-prey encounter rate, the resulting rate of consumption, the rate of loss due to metabolism, and the partitioning of the remaining energy into reproduction and growth.

The rate at which a predator of species \(i\) and weight \(w\) encounters food (mass per time) is determined by summing over all prey species and the resource spectrum and integrating over all prey sizes \(w_p\), weighted by the selectivity factors: \[\begin{equation}
\label{eq:1}
E_{i}(w) = \gamma_i(w) \int \left(\sum_{j} \theta_{ij} N_j(w_p) +
\theta_{iR} N_R(w_p) \right)
\phi_i(w,w_p) w_p \, dw_p.
\end{equation}\] This is calculated by `getEncounter()`

. The overall prefactor \(\gamma_i(w)\) sets the predation power of the predator. It could be interpreted as a search volume. It is set by `setSearchVolume()`

. By default it is assumed to scale allometrically as \(\gamma_i(w) = \gamma_i\, w^q.\)

The \(\theta\) matrix sets the interaction strength between predators and the various prey species and resource. It is changed with `setInteraction()`

.

The size selectivity is encoded in the predation kernel \(\phi_i(w,w_p)\). This is changed with `setPredKernel()`

.

An important simplification occurs when the predation kernel \(\phi_i(w,w_p)\) depends on the size of the prey **only** through the predator/prey size ratio \(w_p/w\), \[\phi_i(w, w_p)=\tilde{\phi}_i(w/w_p).\] This is assumed by default but can be overruled. The default for the predation kernel is the truncated log-normal function \[
\label{eq:4}
\tilde{\phi}_i(x) = \begin{cases}
\exp \left[ \dfrac{-(\ln(x / \beta_i))^2}{2\sigma_i^2} \right]
&\text{ if }x\in\left[0,\beta_i\exp(3\sigma_i)\right]\\
0&\text{ otherwise,}
\end{cases}
\] where \(\beta_i\) is the preferred predator-prey mass ratio and \(\sigma_i\) sets the width of the predation kernel.

The integral in the expression for the encounter rate is approximated by a Riemann sum over all weight brackets: \[ {\tt encounter}[i,a] = {\tt search\_vol}[i,a]\sum_{k} \left( n_{R}[k] + \sum_{j} \theta[i,j] n[j,k] \right) \phi_i\left(w[a],w[k]\right) w[k]\, dw[k]. \] In the case of a predation kernel that depends on \(w/w_p\) only, this becomes a convolution sum and can be evaluated efficiently via fast Fourier transform.

The encountered food is consumed subjected to a standard Holling functional response type II to represent satiation. This determines the *feeding level* \(f_i(w)\), which is a dimensionless number between 0 (no food) and 1 (fully satiated) so that \(1-f_i(w)\) is the proportion of the encountered food that is consumed. The feeding level is given by

\[\begin{equation} \label{eq:f} f_i(w) = \frac{E_i(w)}{E_i(w) + h_i(w)}, \end{equation}\]

where \(h_i(w)\) is the maximum consumption rate. This is changed with `setMaxIntakeRate()`

. By default mizer assumes an allometric form \(h_i(w) = h_i\, w^n.\) The feeding level is calculated with the function `getFeedingLevel()`

.

The rate at which food is consumed is then \[\begin{equation} (1-f_i(w))E_{i}(w)=f_i(w)\, h_i(w). \end{equation}\]

Some of the consumed food is used to fuel the needs for metabolism and activity and movement, at a rate \({\tt metab}_i(w)\). By default this is made up out of standard metabolism, scaling with exponent \(p\), and loss due to activity and movement, scaling with exponent \(1\): \[{\tt metab}_i(w) = k_{s.i}\,w^p + k_i\,w.\] See the help page for `setMetabolicRate()`

.

The remaining rate, if any, is assimilated with an efficiency \(\alpha_i\) and is then available for growth and reproduction. So the rate at which energy becomes available for growth and reproduction is \[\begin{equation}
\label{eq:Er}
E_{r.i}(w) = \max(0, \alpha_i f_i(w)\, h_i(w) - {\tt metab}_i(w))
\end{equation}\] This is calculated with the `getEReproAndGrowth()`

function.

A proportion \(\psi_i(w)\) of the energy available for growth and reproduction is used for reproduction. This proportion should change from zero below the weight \(w_{m.i}\) of maturation to one at the asymptotic weight \(w_{\infty.i}\), where all available energy is used for reproduction. This function is changed with `setReproduction()`

. Mizer provides a default form for the function which you can however overrule.

What is left over after metabolism and reproduction is taken into account is invested in somatic growth. Thus the growth rate is \[\begin{equation}
\label{eq:growth}
g_i(w) = E_{r.i}(w)\left(1-\psi_i(w)\right).
\end{equation}\] It is calculated by the `getEGrowth()`

function.

When food supply does not cover the requirements of metabolism and activity, growth and reproduction stops, i.e. there is no negative growth. The individual should then be subjected to a starvation mortality, but starvation mortality is not implemented in mizer at the moment.

The mortality rate of an individual \(\mu_i(w)\) has three sources: predation mortality \(\mu_{p.i}(w)\), background mortality \(\mu_{ext.i}(w)\) and fishing mortality \(\mu_{f.i}(w)\).

Predation mortality is calculated such that all that is eaten translates into corresponding predation mortalities on the ingested prey individuals. Recalling that \(1-f_j(w)\) is the proportion of the food encountered by a predator of species \(j\) and weight \(w\) that is actually consumed, the rate at which all predators of species \(j\) consume prey of size \(w_p\) is \[\begin{equation}
\label{eq:pred_rated}
{\tt pred\_rate}_j(w_p) = \int \phi_j(w,w_p) (1-f_j(w))
\gamma_j(w) N_j(w) \, dw.
\end{equation}\] This predation rate is calculated by the function `getPredRate()`

.

The integral is approximated by a Riemann sum over all fish weight brackets. \[ {\tt pred\_rate}[j,c] = \sum_{a} {\tt pred_kernel}[j,a,c]\,(1-{\tt feeding_level}[j,a])\, \gamma[j,a]\,n[j,a]\,dw[a]. \]

The mortality rate due to predation is then obtained as \[\begin{equation}
\label{eq:mup}
\mu_{p.i}(w_p) = \sum_j {\tt pred\_rate}_j(w_p)\, \theta_{ji}.
\end{equation}\] This predation mortality rate is calculated by the function `getPredMort()`

.

External mortality \(\mu_{ext.i}(w)\) is independent of the abundances and is changed with `setExtMort()`

. By default mizer assumes that the external mortality for each species is a constant \(z0_i\) independent of size. The value of \(z0_i\) is either specified as a species parameter or it is assumed to depend allometrically on the asymptotic size: \[z0_i = z0_{pre} w_{\infty.i}^{1-n}.\]

The fishing parameters for the model are set up with `setFishing()`

, where you can find the details of how to set up gears with different selectivities and the capabilities of different species. Fishing mortality \(\mu_{f.i}(w)\) is calculated with the function `getFMort()`

.

The total mortality rate \[\mu_i(w)=\mu_{p.i}(w)+\mu_{ext,u}(w)+\mu_{f.i}(w)\] is calculated with the function `getMort()`

.

The predation mortality rate on resource is given by a similar expression as the predation mortality on fish: \[\begin{equation}
\label{eq:mupp}
\mu_{p}(w_p) = \sum_j {\tt pred\_rate}_j(w_p)\, \theta_{jp}.
\end{equation}\] This is the only mortality on resource currently implemented in mizer. It is calculated with the function `getResourceMort()`

.

The total rate of investment into reproduction (grams/year) is found by integrating the contribution from all individuals of species \(i\), each of which invests a proportion \(\psi_i(w)\) of their consumption. This total rate of energy investment can then be converted to a total rate of egg production \(R_{p.i}\) (numbers per year): \[\begin{equation}
\label{eq:Rp}
R_{p.i} = \frac{\epsilon}{2 w_0} \int N_i(w) E_{r.i}(w) \psi_i(w) \, dw,
\end{equation}\] Here the total rate of investment is multiplied by an efficiency factor \(\epsilon\) and then dividing by the egg weight \(w_0\) to convert the energy into number of eggs. The result is multiplied by a factor \(1/2\) to take into account that only females reproduce. This rate of potential egg production is calculated with `getRDI()`

.

Three important density-dependent mechanisms widely assumed in fisheries models are automatically captured in the mizer model that lead to an emergent stock-recruitment relationship:

- High density of spawners leads to a reduced food income of the spawners and consequently reduced per-capita reproduction.
- High density of larvae leads to slower growth of larvae due to food competition, exposing the larvae to high mortality for a longer time, thereby decreasing the survivorship to recruitment size.
- High density of fish leads to more predation on eggs and fish larvae by other fish species or by cannibalism.

However there are other sources of density dependence that are not explicitly modelled mechanistically in mizer. An example would be a limited carrying capacity of suitable spawning grounds and other spatial effects.

This requires additional phenomenological density dependent contributions to the stock-recruitment. In mizer this type of density dependence is modelled through constraints on egg production and survival. The default functional form of this density dependence is represented by a reproduction rate \(R_i\) (numbers per time) that approaches a maximum as the energy invested in reproduction increases, modelled mathematically it is analogous to a *Beverton-Holt* type function:

\[\begin{equation}
\label{eq:R}
R_i = R_{\max.i} \frac{R_{p.i}}{R_{p.i} + R_{\max.i}},
\end{equation}\] where \(R_{\max.i}\) is the maximum reproduction rate of each trait class. This final rate of reproduction is calculated with `getRDD()`

.

This default *Beverton-Holt* type is implemented by `BervertonHoldRDD()`

but mizer also provides alternatives `RickerRDD()`

, `SheperdRDD()`

, `constantRDD()`

and `noRDD()`

. Also, users are able to write their own functions, e.g. *hockey-stick*. See `setReproduction()`

for details.