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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/"
)
```
<img src="man/figures/mizer.png" style='height: 100%; width: 100%; object-fit: contain'/>
[](https://cran.r-project.org/package=mizer)
[](https://cran.r-project.org/package=mizer)
[](https://cran.r-project.org/package=mizer)
[](https://codecov.io/github/sizespectrum/mizer?branch=master)
Mizer is an R package to run
[dynamic multi-species size-spectrum models](#dynamic-multi-species-size-spectrum-models)
of fish communities. The package has been developed to model marine ecosystems
that are subject to fishing. However, it may also be appropriate for other
ecosystems.
The package contains routines and functions to allow users to set up an ecosystem
model, and then project it through time under different fishing strategies.
Methods are included to explore the results, including plots and calculation of
community indicators such as the slope of the size spectrum. Size-based models
can be complicated so mizer contains many default options that can be easily
changed by the user.
<!-- Mizer can also be used to create web apps that allow users to explore models -->
<!-- without the need to install R. An [example of such an -->
<!-- app](https://mizer.shinyapps.io/selectivity/) investigates the effect of -->
<!-- switching to a gear with a T90 extension net to reduce the catches of undersize -->
<!-- hake and red mullet -->
Mizer has been supporting research in marine ecology and fisheries science
since 2014
([see publications](https://sizespectrum.org/mizer/articles/publications.html)).
Mizer is still under active
development. Version 2.0 has increased the user-friendliness and the
flexibility of the framework. Contributions from the user community are very
welcome. There is a sister package called
[mizerExperimental](https://sizespectrum.org/mizerExperimental/) where user
contributions can be checked out and receive feedback from the community.
Example mizer models can be contributed to
[mizerExamples](https://sizespectrum.org/mizerExamples/).
Does your project or publication use mizer? If so, we would love to know. You can
also join our Google Discussion group here:
https://groups.google.com/forum/#!forum/size-spectrum-models
Recent work on mizer was funded by the European
Commission Horizon 2020 Research and Innovation Programme under Grant Agreement
No 634495 for the project MINOUW (http://minouw-project.eu/) and the Australian
Research Council Discovery Project [Rewiring Marine Food Webs](https://marinesocioecology.org/projects/rewiring-marine-food-webs-predicting-consequences-of-species-distribution-shifts-on-marine-communities/).
## Installation
The package is on [CRAN](https://cran.r-project.org/package=mizer) and therefore
available from R's built-in package manager.
```{r, eval = FALSE}
# Install release version from CRAN
install.packages("mizer")
# Alternatively, install development version from GitHub
devtools::install_github("sizespectrum/mizer")
```
## Example
The following code loads the mizer package, loads some information about species
in the North Sea that comes as an example with the package, sets up the
parameters for the mizer model, and runs a simulation for 10 years.
```{r, message = FALSE}
library(mizer)
params <- newMultispeciesParams(NS_species_params, inter)
sim <- project(params, t_max = 10, effort = 0)
```
The results of the simulation can then be analysed, for example via plots:
```{r}
plot(sim)
```
See the accompanying
[Get started](https://sizespectrum.org/mizer/articles/mizer.html) page
for more details on how the package works, including detailed examples.
## Dynamic multi-species size-spectrum models
A mizer model captures the interactions between multiple species. The growth
rates of fish are determined by the availability of prey and the death rates
are influenced by the abundance of predators, as well as fishing. The model
starts with the individual-level physiological rates for each species, as well
as the predation preferences, and deduces the population-level dynamics from
these. Thus quantities like fish diets and fisheries yields emerge dynamically
and can be projected into the future.
Because a mizer model tracks the size of individuals as they grow up over
several orders of magnitude from their egg size to their maximum size, it
correctly tracks the ontogenetic diet shifts. An individual typically moves
through several trophic levels during its life time. This is often not
correctly captured in other multi-species models.
A mizer model can be set up with only a small amount of information
because it uses allometric scaling relations and size-based feeding rules to
choose sensible defaults for unknown parameters.
Setting up a new multi-species mizer model is a two-step process, similar to
what may be familiar from Ecopath with Ecosim: First one calibrates the model
to describe a steady state that is in agreement with current observations
(as in Ecopath), then one chooses the additional parameters that determine the
dynamics away from the steady state (as in Ecosim). This model can then be
used to investigate future effects of changes in fishing policy or of
environmental stressors.
## Modelling environmental change
A mizer model is a good tool for studying the effect of environmental changes,
like climate change, because it is a mechanistic model that can deduce the
complex population-level changes that one is interested in from the simpler
changes in the physiological rates and feeding interactions of individual fish
species.
## Smooth traffic on the biomass highway
It is interesting to think of the marine ecosystem as a transport system that
moves biomass from the size of primary producers (mostly unicellular
plankton) up to the sizes of fish that humans like to consume.
Each fish that grows up from egg size to maturity by eating smaller
individuals is like a car on this biomass highway. The yield of our
fisheries depend on this traffic flowing smoothly and without traffic jams.
An analogy with road traffic may be helpful:
In road traffic, if traffic density gets too high in a section of the highway,
drivers slow down, which leads to a pile-up producing even higher traffic
density, leading to further slow-down in a potentially vicious cycle known as a
traffic jam. Traffic management that ignores how the traffic density affects
traffic speed fails. Luckily our mathematical understanding of transport
equations has made practical contributions to managing traffic in ways that
produce smoother traffic flow and hence higher throughput.
Mizer implements the transport equations for marine ecosystems. The
potential for traffic jams is the same: if for example there is a high density
of predators of a particular size, which all have preference for prey of a
particular smaller size, then due to competition for that prey the growth of
those predators slows down, leading to a pile-up which leads to further
depletion of prey, leading to further slow-down, in a potentially vicious cycle.
Luckily the natural ecosystem has evolved to facilitate very smooth traffic
on the biomass highway with resultant high productivity. This state is
characterised by an approximate power-law shape of the biomass size spectrum.
The purpose of mizer is to allow us to understand how various stressors, like
fishing or climate change, affect the size spectrum and hence flow of biomass
and the productivity and resilience of the marine ecosystem. Mizer allows us to
investigate how size-based fisheries management strategies can be used to keep
the ecosystem close to its natural productive state.
## A model one can understand
One big advantage of a mizer model is that it is based on a strong mathematical
foundation. This allows a degree of a priori understanding of the behaviour of
the model that is absent in many other multi-species model. This theoretical
foundation is well presented in the book "Fish Ecology, Evolution, and
Exploitation" by Ken Andersen.