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Title:	Dynamic Multi-Species Size Spectrum Modelling
Date:	2025-11-16
Type:	Package
Description:	A set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment.
Maintainer:	Gustav Delius <gustav.delius@york.ac.uk>
Version:	2.5.4
License:	GPL-3
Imports:	assertthat, deSolve, dplyr, ggplot2 (≥ 3.4.0), ggrepel, grid,lubridate, methods, plotly, plyr, progress, Rcpp, reshape2,rlang, lifecycle
LinkingTo:	Rcpp
Depends:	R (≥ 3.1)
Suggests:	testthat (≥ 3.0.0), vdiffr, diffviewer, roxygen2, knitr,rmarkdown, pkgdown, covr, spelling
Collate:	'age_mat.R' 'helpers.R' 'MizerParams-class.R''MizerSim-class.R' 'reproduction.R' 'saveParams.R''species_params.R' 'setColours.R' 'setInteraction.R''setPredKernel.R' 'setSearchVolume.R' 'setMaxIntakeRate.R''setMetabolicRate.R' 'setMetadata.R' 'setExtMort.R''setExtEncounter.R' 'setReproduction.R' 'setResource.R''setFishing.R' 'setInitialValues.R' 'setBevertonHolt.R''upgrade.R' 'selectivity_funcs.R' 'pred_kernel_funcs.R''resource_dynamics.R' 'resource_semichemostat.R''resource_logistic.R' 'project.R' 'mizer-package.R''project_methods.R' 'rate_functions.R' 'summary_methods.R''plots.R' 'plotBiomassObservedVsModel.R''plotYieldObservedVsModel.R' 'animateSpectra.R''newMultispeciesParams.R' 'wrapper_functions.R''newSingleSpeciesParams.R' 'steady.R' 'extension.R' 'data.R''RcppExports.R' 'deprecated.R' 'get_initial_n.R''compareParams.R' 'customFunction.R' 'manipulate_species.R''calibrate.R' 'match.R' 'matchGrowth.R' 'steadySingleSpecies.R''defaults_edition.R' 'validSpeciesParams.R'
RoxygenNote:	7.3.3
Encoding:	UTF-8
LazyData:	true
URL:	https://sizespectrum.org/mizer/,https://github.com/sizespectrum/mizer
BugReports:	https://github.com/sizespectrum/mizer/issues
Language:	en-GB
RdMacros:	lifecycle
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2025-11-16 21:31:14 UTC; gustav
Author:	Gustav Delius [cre, aut, cph], Finlay Scott [aut, cph], Julia Blanchard [aut, cph], Ken Andersen [aut, cph], Richard Southwell [ctb, cph]
Repository:	CRAN
Date/Publication:	2025-11-16 21:50:02 UTC

mizer: Multi-species size-based modelling in R

Description

The mizer package implements multi-species size-based modelling in R. It hasbeen designed for modelling marine ecosystems.

Details

Usingmizer is relatively simple. There are three main stages:

1. Setting the model parameters. This is done by creating an object ofclassMizerParams. This includes model parameters such as thelife history parameters of each species, and the range of the size spectrum.There are several setup functions that help to create a MizerParams objectsfor particular types of models:

   • newSingleSpeciesParams()

   • newCommunityParams()

   • newTraitParams()

   • newMultispeciesParams()

2. Running a simulation. This is done by calling theproject() function with the model parameters. This produces anobject ofMizerSim that contains the results of the simulation.

3. Exploring results. After a simulation has been run, the results can beexplored using a range ofplotting_functions,summary_functions andindicator_functions.

See themizer website for full details ofthe principles behind mizer and how the package can be used to performsize-based modelling.

Author(s)

Maintainer: Gustav Deliusgustav.delius@york.ac.uk (ORCID) [copyright holder]

Authors:

• Finlay Scottdrfinlayscott@gmail.com [copyright holder]

• Julia Blanchardjulia.blanchard@utas.edu.au (ORCID) [copyright holder]

• Ken Andersenkha@aqua.dtu.dk (ORCID) [copyright holder]

Other contributors:

• Richard Southwellrichard.southwell@york.ac.uk [contributor, copyright holder]

See Also

Useful links:

• https://sizespectrum.org/mizer/

• https://github.com/sizespectrum/mizer

• Report bugs athttps://github.com/sizespectrum/mizer/issues

Beverton Holt function to calculate density-dependent reproduction rate

Description

Takes the density-independent ratesR_{di} of egg production (ascalculated bygetRDI()) and returnsreduced, density-dependent reproduction ratesR_{dd} given as

R_{dd} = R_{di}\frac{R_{max}}{R_{di} + R_{max}}

whereR_{max} are the maximum possible reproduction rates that must bespecified in a column in the species parameter dataframe.(All quantities in the above equation are species-specific but we droppedthe species index for simplicity.)

Usage

BevertonHoltRDD(rdi, species_params, ...)

Arguments

Details

This is only one example of a density-dependence. You can write your ownfunction based on this example, returning different density-dependentreproduction rates. Three other examples provided areRickerRDD(),SheperdRDD(),noRDD() andconstantRDD(). For more explanation seesetReproduction().

Value

Vector of density-dependent reproduction rates.

See Also

Other functions calculating density-dependent reproduction rate:RickerRDD(),SheperdRDD(),constantEggRDI(),constantRDD(),noRDD()

Alias forset_multispecies_model()

Description

An alias provided for backward compatibility with mizer version <= 1.0

Usage

MizerParams(  species_params,  interaction = matrix(1, nrow = nrow(species_params), ncol = nrow(species_params)),  min_w_pp = 1e-10,  min_w = 0.001,  max_w = NULL,  no_w = 100,  n = 2/3,  q = 0.8,  f0 = 0.6,  kappa = 1e+11,  lambda = 2 + q - n,  r_pp = 10,  ...)

Arguments

Value

A MizerParams object

A class to hold the parameters for a size based model.

Description

Although it is possible to build aMizerParams object by hand it isnot recommended and several constructors are available. Dynamic simulationsare performed usingproject() function on objects of this class. As auser you should never need to access the slots inside aMizerParams objectdirectly.

Details

TheMizerParams class is fairly complex with a large number ofslots, many of which are multidimensional arrays. The dimensions of thesearrays is strictly enforced so thatMizerParams objects are consistentin terms of number of species and number of size classes.

TheMizerParams class does not hold any dynamic information, e.g.abundances or harvest effort through time. These are held inMizerSim objects.

Slots

metadata

A list with metadata information. SeesetMetadata().

mizer_version

The package version of mizer (as returned bypackageVersion("mizer")) that created or upgraded the model.

extensions

A named vector of strings where each name is the name ofand extension package needed to run the model and each value is a stringgiving the information that the remotes package needs to install thecorrect version of the extension package, see https://remotes.r-lib.org/.

time_created

A POSIXct date-time object with the creation time.

time_modified

A POSIXct date-time object with the last modified time.

w

The size grid for the fish part of the spectrum. An increasingvector of weights (in grams) running from the smallest egg size to thelargest maximum size.

dw

The widths (in grams) of the size bins

w_full

The size grid for the full size range including the resourcespectrum. An increasing vector of weights (in grams) running from thesmallest resource size to the largest maximum size of fish. Thelast entries of the vector have to be equal to the content of the w slot.

dw_full

The width of the size bins for the full spectrum. The lastentries have to be equal to the content of the dw slot.

w_min_idx

A vector holding the index of the weight of the egg sizeof each species

maturity

An array (species x size) that holds the proportion ofindividuals of each species at size that are mature. This enters in thecalculation of the spawning stock biomass withgetSSB(). SetwithsetReproduction().

psi

An array (species x size) that holds the allocation to reproductionfor each species at size,\psi_i(w). Changed withsetReproduction().

intake_max

An array (species x size) that holds the maximum intake foreach species at size. Changed withsetMaxIntakeRate().

search_vol

An array (species x size) that holds the search volume foreach species at size. Changed withsetSearchVolume().

metab

An array (species x size) that holds the metabolismfor each species at size. Changed withsetMetabolicRate().

mu_b

An array (species x size) that holds the external mortality rate\mu_{ext.i}(w). Changed withsetExtMort().

ext_encounter

An array (species x size) that holds the external encounter rateE_{ext.i}(w). Changed withsetExtEncounter().

pred_kernel

An array (species x predator size x prey size) that holdsthe predation coefficient of each predator at size on each prey size. Ifthis is NA then the following two slots will be used. Changed withsetPredKernel().

ft_pred_kernel_e

An array (species x log of predator/prey size ratio)that holds the Fourier transform of the feeding kernel in a formappropriate for evaluating the encounter rate integral. If this is NAthen thepred_kernel will be used to calculate the availableenergy integral. Changed withsetPredKernel().

ft_pred_kernel_p

An array (species x log of predator/prey size ratio)that holds the Fourier transform of the feeding kernel in a formappropriate for evaluating the predation mortality integral. If this is NAthen thepred_kernel will be used to calculate the integral.Changed withsetPredKernel().

rr_pp

A vector the same length as the w_full slot. The size specificgrowth rate of the resource spectrum.

cc_pp

A vector the same length as the w_full slot. The size specificcarrying capacity of the resource spectrum.

resource_dynamics

Name of the function for projecting the resourceabundance density by one timestep.

other_dynamics

A named list of functions for projecting thevalues of other dynamical components of the ecosystem that may be modelledby a mizer extensions you have installed. The names of the list entriesare the names of those components.

other_encounter

A named list of functions for calculating thecontribution to the encounter rate from each other dynamical component.

other_mort

A named list of functions for calculating thecontribution to the mortality rate from each other dynamical components.

other_params

A list containing the parameters needed by any mizerextensions you may have installed to model other dynamical components ofthe ecosystem.

rates_funcs

A named list with the names of the functions that should beused to calculate the rates needed byproject(). By default this will beset to the names of the built-in rate functions.

sc

The community abundance of the scaling community

species_params

A data.frame to hold the species specific parameters.Seespecies_params() for details.

given_species_params

A data.frame to hold the species parameters thatwere given explicitly rather than obtained by default calculations.

gear_params

Data frame with parameters for gear selectivity. SeesetFishing() for details.

interaction

The species specific interaction matrix,\theta_{ij}.Changed withsetInteraction().

selectivity

An array (gear x species x w) that holds the selectivity ofeach gear for species and size,S_{g,i,w}. Changed withsetFishing().

catchability

An array (gear x species) that holds the catchability ofeach species by each gear,Q_{g,i}. Changed withsetFishing().

initial_effort

A vector containing the initial fishing effort for eachgear. Changed withsetFishing().

initial_n

An array (species x size) that holds the initial abundance ofeach species at each weight.

initial_n_pp

A vector the same length as the w_full slot that describesthe initial resource abundance at each weight.

initial_n_other

A list with the initial abundances of all otherecosystem components. Has length zero if there are no other components.

resource_params

List with parameters for resource.

A

Abundance multipliers.

linecolour

A named vector of colour values, named by species.Used to give consistent colours in plots.

linetype

A named vector of linetypes, named by species.Used to give consistent line types in plots.

ft_mask

An array (species x w_full) with zeros for weights larger thanthe maximum weight of each species. Used to efficiently minimizewrap-around errors in Fourier transform calculations.

See Also

project()MizerSim()emptyParams()newMultispeciesParams()newCommunityParams()newTraitParams()

Constructor for theMizerSim class

Description

A constructor for theMizerSim class. This is used byproject() to createMizerSim objects of the rightdimensions. It is not necessary for users to use this constructor.

Usage

MizerSim(params, t_dimnames = NA, t_max = 100, t_save = 1)

Arguments

Value

An object of typeMizerSim

A class to hold the results of a simulation

Description

A class that holds the results of projecting aMizerParamsobject through time usingproject().

Details

A newMizerSim object can be created with theMizerSim()constructor, but you will never have to do that because the object iscreated automatically byproject() when needed.

As a user you should never have to access the slots of a MizerSim objectdirectly. Instead there are a range of functions to extract the information.N() andNResource() return arrays with the saved abundances ofthe species and the resource population at size respectively.getEffort()returns the fishing effort of each gear through time.getTimes() returns the vector of times at which simulation resultswere stored andidxFinalT() returns the index with which to accessspecifically the value at the final time in the arrays returned by the otherfunctions.getParams() returns theMizerParams object that waspassed toproject(). There are also severalsummary_functions andplotting_functionsavailable to explore the contents of aMizerSim object.

The arrays all have named dimensions. The names of thetime dimensiondenote the time in years. The names of thew dimension are weights in gramsrounded to three significant figures. The names of thesp dimension are thesame as the species name in the order specified in the species_params dataframe. The names of thegear dimension are the names of the gears, in thesame order as specified when setting up theMizerParams object.

Extensions of mizer can use then_other slot to store the abundances ofother ecosystem components and these extensions should provide their ownfunctions for accessing that information.

TheMizerSim class has changed since previous versions of mizer. To useaMizerSim object created by a previous version, you need to upgrade itwithupgradeSim().

Slots

params

An object of typeMizerParams.

n

Three-dimensional array (time x species x size) that stores theprojected community number densities.

n_pp

An array (time x size) that stores the projected resource numberdensities.

n_other

A list array (time x component) that stores the projectedvalues for other ecosystem components.

effort

An array (time x gear) that stores the fishing effort by timeand gear.

Time series of size spectra

Description

Fetch the simulation results for the size spectra over time.

Usage

N(sim)NResource(sim)

Arguments

Value

ForN(): A three-dimensional array (time x species x size) with thenumber density of consumers

ForNResource(): An array (time x size) with the number density of resource

Examples

str(N(NS_sim))str(NResource(NS_sim))

Time series of other components

Description

Fetch the simulation results for other components over time.

Usage

NOther(sim)

Arguments

Value

A list array (time x component) that stores the projected values forother ecosystem components.

Example interaction matrix for the North Sea example

Description

The interaction coefficient between predator and prey speciesin the North Sea.

Usage

NS_interaction

Format

A 12 x 12 matrix.

Source

Blanchard et al.

Examples

params <- MizerParams(NS_species_params_gears,                      interaction = NS_interaction)

Example MizerParams object for the North Sea example

Description

A MizerParams object created from theNS_species_params_gears speciesparameters and theinter interaction matrix together with an initialcondition corresponding to the steady state obtained from fishing with anefforteffort = c(Industrial = 0, Pelagic = 1, Beam = 0.5, Otter = 0.5).

Usage

NS_params

Format

A MizerParams object

Source

Blanchard et al.

See Also

Other example parameter objects:NS_sim

Examples

sim = project(NS_params, effort = c(Industrial = 0, Pelagic = 1,                                     Beam = 0.5, Otter = 0.5))plot(sim)

Example MizerSim object for the North Sea example

Description

A MizerSim object containing a simulation with historical fishingmortalities from the North Sea, as created in the tutorial"A Multi-Species Model of the North Sea".

Usage

NS_sim

Format

A MizerSim object

Source

https://sizespectrum.org/mizer/articles/a_multispecies_model_of_the_north_sea.html

See Also

Other example parameter objects:NS_params

Examples

plotBiomass(NS_sim)

Example species parameter set based on the North Sea

Description

This data set is based on species in the North Sea (Blanchard et al.). It isa data.frame that contains all the necessary information to be used by theMizerParams() constructor. As there is no gear column, each species isassumed to be fished by a separate gear.

Usage

NS_species_params

Format

A data frame with 12 rows and 7 columns. Each row is a species.

species

Name of the species

w_max

Maximum size.

w_mat

Size at maturity

beta

Size preference ratio

sigma

Width of the size-preference

R_max

Maximum reproduction rate

k_vb

The von Bertalanffy k parameter

w_inf

The von Bertalanffy asymptotic size

Source

Blanchard et al.

Examples

params <- MizerParams(NS_species_params)

Example species parameter set based on the North Sea with different gears

Description

This data set is based on species in the North Sea (Blanchard et al.).It is similar to the data setNS_species_params except thatthis one has an additional column specifying the fishing gear thatoperates on each species.

Usage

NS_species_params_gears

Format

A data frame with 12 rows and 8 columns. Each row is a species.

species

Name of the species

w_max

Maximum size.

w_mat

Size at maturity

beta

Size preference ratio

sigma

Width of the size-preference

R_max

Maximum reproduction rate

k_vb

The von Bertalanffy k parameter

w_inf

The von Bertalanffy asymptotic size

gear

Name of the fishing gear

Source

Blanchard et al.

Examples

params <- MizerParams(NS_species_params_gears)

Ricker function to calculate density-dependent reproduction rate

Description

Takes the density-independent ratesR_{di} of egg production andreturns reduced, density-dependent ratesR_{dd} given as

R_{dd} = R_{di} \exp(- b R_{di})

Usage

RickerRDD(rdi, species_params, ...)

Arguments

Value

Vector of density-dependent reproduction rates.

See Also

Other functions calculating density-dependent reproduction rate:BevertonHoltRDD(),SheperdRDD(),constantEggRDI(),constantRDD(),noRDD()

Sheperd function to calculate density-dependent reproduction rate

Description

Takes the density-independent ratesR_{di} of egg production and returnsreduced, density-dependent ratesR_{dd} given as

R_{dd} = \frac{R_{di}}{1+(b\ R_{di})^c}

Usage

SheperdRDD(rdi, species_params, ...)

Arguments

Details

Withb = 1/R_{max} andc = 1 this reduces to the Beverton-Holtreproduction rate, seeBevertonHoltRDD().

Value

Vector of density-dependent reproduction rates.

See Also

Other functions calculating density-dependent reproduction rate:BevertonHoltRDD(),RickerRDD(),constantEggRDI(),constantRDD(),noRDD()

Add new species

Description

Takes aMizerParams object and adds additional species withgiven parameters to the ecosystem. It sets the initial values for these newspecies to their steady-state solution in the given initial state of theexisting ecosystem. This will be close to the true steady state if theabundances of the new species are sufficiently low. Hence the abundances ofthe new species are set so that they are at most 1/100th of the resourcepower law. Their reproductive efficiencies are set so as to keep them atthat low level.

Usage

addSpecies(  params,  species_params,  gear_params = data.frame(),  initial_effort,  interaction)

Arguments

Details

The resulting MizerParams object will use the same size grid wherepossible, but if one of the new species needs a larger range of w (eitherbecause a new species has an egg size smaller than those of existingspecies or a maximum size larger than those of existing species) then thegrid will be expanded and all arrays will be enlarged accordingly.

If any of the rate arrays of the existing species had been set by the userto values other than those calculated as default from the speciesparameters, then these will be preserved. Only the rates for the newspecies will be calculated from their species parameters.

After adding the new species, the background species are not retuned andthe system is not run to steady state. This could be done withsteady().The new species will have a reproduction level of 1/4, this can then bechanged withsetBevertonHolt()

Value

An object of typeMizerParams

See Also

removeSpecies()

Examples

params <- newTraitParams()species_params <- data.frame(    species = "Mullet",    w_max = 173,    w_mat = 15,    beta = 283,    sigma = 1.8,    h = 30,    a = 0.0085,    b = 3.11)params <- addSpecies(params, species_params)plotSpectra(params)

Calculate age at maturity

Description

Uses the growth rate and the size at maturity to calculate the age atmaturity

Usage

age_mat(params)

Arguments

Details

Using that by definition of the growth rateg(w) = dw/dt we have that

\mathrm{age_{mat}} = \int_0^{w_{mat}.}\frac{dw}{g(w)}

Value

A named vector. The names are the species names and the values arethe ages at maturity.

Examples

age_mat(NS_params)

Calculate age at maturity from von Bertalanffy growth parameters

Description

This is not a good way to determine the age at maturity because the vonBertalanffy growth curve is not reliable for larvae and juveniles. Howeverthis was used in previous versions of mizer and is supplied forbackwards compatibility.

Usage

age_mat_vB(object)

Arguments

Details

Uses the age at maturity that is implied by the von Bertalanffy growth curvespecified by thew_inf,k_vb,t0,a andb parameters in thespecies_params data frame.

If any ofk_vb is missing for a species, the function returns NA for thatspecies. Default values ofb = 3 andt0 = 0 are used if these aremissing. Ifw_inf is missing,w_max is used instead.

Value

A named vector. The names are the species names and the values arethe ages at maturity.

Animation of the abundance spectra

Description

Usage

animateSpectra(  sim,  species = NULL,  time_range,  wlim = c(NA, NA),  ylim = c(NA, NA),  power = 1,  total = FALSE,  resource = TRUE)

Arguments

Value

A plotly object

See Also

Other plotting functions:plot,MizerParams,missing-method,plot,MizerSim,missing-method,plotBiomass(),plotDiet(),plotFMort(),plotFeedingLevel(),plotGrowthCurves(),plotPredMort(),plotSpectra(),plotYield(),plotYieldGear(),plotting_functions

Examples

animateSpectra(NS_sim, power = 2, wlim = c(0.1, NA), time_range = 1997:2007)

Box predation kernel

Description

A predation kernel where the predator/prey mass ratio is uniformlydistributed on an interval.

Usage

box_pred_kernel(ppmr, ppmr_min, ppmr_max)

Arguments

Details

Writing the predator mass asw and the prey mass asw_p, thefeeding kernel is 1 ifw/w_p is betweenppmr_min andppmr_max and zero otherwise. The parameters need to be given in thespecies parameter dataframe in the columnsppmr_min andppmr_max.

Value

A vector giving the value of the predation kernel at each of thepredator/prey mass ratios in theppmr argument.

See Also

setPredKernel()

Other predation kernel:lognormal_pred_kernel(),power_law_pred_kernel(),truncated_lognormal_pred_kernel()

Examples

params <- NS_params# Set all required paramters before changing kernel typespecies_params(params)$ppmr_max <- 4000species_params(params)$ppmr_min <- 200species_params(params)$pred_kernel_type <- "box"plot(w_full(params), getPredKernel(params)["Cod", 10, ], type="l", log="x")

Calculate selectivity from gear parameters

Description

This function calculates the selectivity for each gear, species and size fromthe gear parameters. It is called bysetFishing() when theselectivity isnot set by the user.

Usage

calc_selectivity(params)

Arguments

Value

An array (gear x species x size) with the selectivity values

Examples

params <- NS_paramsstr(calc_selectivity(params))calc_selectivity(params)["Pelagic", "Herring", ]

Calibrate the model scale to match total observed biomass

Description

Given a MizerParams objectparams for which biomass observations areavailable for at least some species via thebiomass_observed column in thespecies_params data frame, this function returns an updated MizerParamsobject which is rescaled withscaleModel() so that the total biomass inthe model agrees with the total observed biomass.

Usage

calibrateBiomass(params)

Arguments

Details

Biomass observations usually only include individuals above a certain size.This size should be specified in a biomass_cutoff column of the speciesparameter data frame. If this is missing, it is assumed that all sizes areincluded in the observed biomass, i.e., it includes larval biomass.

After using this function the total biomass in the model will match thetotal biomass, summed over all species. However the biomasses of theindividual species will not match observations yet, with some specieshaving biomasses that are too high and others too low. So after thisfunction you may want to usematchBiomasses(). This is described in theblog post at https://bit.ly/2YqXESV.

If you have observations of the yearly yield instead of biomasses, you canusecalibrateYield() instead of this function.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$biomass_observed <-     c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)species_params(params)$biomass_cutoff <- 10params2 <- calibrateBiomass(params)plotBiomassObservedVsModel(params2)

Calibrate the model scale to match total observed number

Description

Given a MizerParams objectparams for which number observations areavailable for at least some species via thenumber_observed column in thespecies_params data frame, this function returns an updated MizerParamsobject which is rescaled withscaleModel() so that the total number inthe model agrees with the total observed number.

Usage

calibrateNumber(params)

Arguments

Details

Number observations usually only include individuals above a certain size.This size should be specified in a number_cutoff column of the speciesparameter data frame. If this is missing, it is assumed that all sizes areincluded in the observed number, i.e., it includes larval number.

After using this function the total number in the model will match thetotal number, summed over all species. However the numbers of theindividual species will not match observations yet, with some specieshaving numbers that are too high and others too low. So after thisfunction you may want to usematchNumbers(). This is described in theblog post at https://bit.ly/2YqXESV.

If you have observations of the yearly yield instead of numbers, you canusecalibrateYield() instead of this function.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$number_observed <-    c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)species_params(params)$number_cutoff <- 10params2 <- calibrateNumber(params)

Calibrate the model scale to match total observed yield

Description

Usage

calibrateYield(params)

Arguments

Details

This function has been deprecated and will be removed in the future unlessyou have a use case for it. If you do have a use case for it, please let thedevelopers know by creating an issue athttps://github.com/sizespectrum/mizer/issues.

Given a MizerParams objectparams for which yield observations areavailable for at least some species via theyield_observed column in thespecies_params data frame, this function returns an updated MizerParamsobject which is rescaled withscaleModel() so that the total yield inthe model agrees with the total observed yield.

After using this function the total yield in the model will match thetotal observed yield, summed over all species. However the yields of theindividual species will not match observations yet, with some specieshaving yields that are too high and others too low. So after thisfunction you may want to usematchYields().

If you have observations of species biomasses instead of yields, you canusecalibrateBiomass() instead of this function.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$yield_observed <-    c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)gear_params(params)$catchability <-    c(1.3, 0.065, 0.31, 0.18, 0.98, 0.24, 0.37, 0.46, 0.18, 0.30, 0.27, 0.39)params2 <- calibrateYield(params)plotYieldObservedVsModel(params2)

Compare two MizerParams objects and print out differences

Description

Usage

compareParams(params1, params2)

Arguments

Value

String describing the differences

Examples

params1 <- NS_paramsparams2 <- params1species_params(params2)$w_mat[1] <- 10compareParams(params1, params2)

Alias forvalidSpeciesParams()

Description

An alias provided for backward compatibility with mizer version <= 2.5.2

Usage

completeSpeciesParams(species_params)

Arguments

Details

validGivenSpeciesParams() checks the validity of the given speciesparameter It throws an error if

• thespecies column does not exist or contains duplicates

• the maximum size is not specified for all species

If a weight-based parameter is missing but the corresponding length-basedparameter is given, as well as thea andb parameters for length-weightconversion, then the weight-based parameters are added. If both length andweight are given, then weight is used and a warning is issued if the two areinconsistent.

If aw_inf column is given but now_max then the value fromw_inf isused. This is for backwards compatibility. But note that the von Bertalanffyparameterw_inf is not the maximum size of the largest individual, but theasymptotic size of an average individual.

Some inconsistencies in the size parameters are resolved as follows:

• Anyw_mat that is not smaller thanw_max is set tow_max / 4.

• Anyw_mat25 that is not smaller thanw_mat is set to NA.

• Anyw_min that is not smaller thanw_mat is set to0.001 orw_mat /10, whichever is smaller.

• Anyw_repro_max that is not larger thanw_mat is set to4 * w_mat.

The row names of the returned data frame will be the species names.Ifspecies_params was provided as a tibble it is converted back to anordinary data frame.

The function tests for some typical misspellings of parameter names, likewrong capitalisation or missing underscores and issues a warning if itdetects such a name.

validSpeciesParams() first callsvalidGivenSpeciesParams() but thengoes further by adding default values for species parameters that were notprovided. The function sets default values if any of the following speciesparameters are missing or NA:

• w_repro_max is set tow_max

• w_mat is set tow_max/4

• w_min is set to0.001

• alpha is set to0.6

• interaction_resource is set to1

• n is set to3/4

• p is set ton

Note that the species parameters returned by these functions are notguaranteed to produce a viable model. More checks of the parameters areperformed by the individual rate-setting functions (seesetParams() for thelist of these functions).

Value

ForvalidSpeciesParams(): A valid species parameter data frame withadditional parameters with default values.

ForvalidGivenSpeciesParams(): A valid species parameter data framewithout additional parameters.

See Also

species_params(),validGearParams(),validParams(),validSim()

Choose egg production to keep egg density constant

Description

The new egg production is set to compensate for the loss of individuals fromthe smallest size class through growth and mortality. The result should notbe modified by density dependence, so this should be used together withthenoRDD() function, see example.

Usage

constantEggRDI(params, n, e_growth, mort, ...)

Arguments

Value

Vector with the value for each species

See Also

Other functions calculating density-dependent reproduction rate:BevertonHoltRDD(),RickerRDD(),SheperdRDD(),constantRDD(),noRDD()

Examples

# choose an example params objectparams <- NS_params# We set the reproduction rate functionsparams <- setRateFunction(params, "RDI", "constantEggRDI")params <- setRateFunction(params, "RDD", "noRDD")# Now the egg density should stay fixed no matter how we fishsim <- project(params, effort = 10, progress_bar = FALSE)# To check that indeed the egg densities have not changed, we first construct# the indices for addressing the egg densitiesno_sp <- nrow(params@species_params)idx <- (params@w_min_idx - 1) * no_sp + (1:no_sp)# Now we can check equality between egg densities at the start and the endall.equal(finalN(sim)[idx], initialN(params)[idx])

Give constant reproduction rate

Description

Simply returns the value fromspecies_params$constant_reproduction.

Usage

constantRDD(rdi, species_params, ...)

Arguments

Value

Vectorspecies_params$constant_reproduction

See Also

Other functions calculating density-dependent reproduction rate:BevertonHoltRDD(),RickerRDD(),SheperdRDD(),constantEggRDI(),noRDD()

Helper function to keep other components constant

Description

Helper function to keep other components constant

Usage

constant_other(params, n_other, component, ...)

Arguments

Value

The current value of the component

Replace a mizer function with a custom version

Description

This function allows you to make arbitrary changes to how mizer works byallowing you to replace any mizer function with your own version. Youshould do this only as a last resort, when you find that you can not usethe standard mizer extension mechanism to achieve your goal.

Usage

customFunction(name, fun)

Arguments

Details

If the function you need to overwrite is one of the mizer rate functions,then you should usesetRateFunction() instead of this function. Similarlyyou should use⁠resource_dynamics()<-⁠ to change the resource dynamics andsetReproduction() to change the density-dependence in reproduction.You should also investigate whether you can achieve your goal by introducingadditional ecosystem components withsetComponent().

If you find that your goal really does require you to overwrite a mizerfunction, please also create an issue on the mizer issue tracker athttps://github.com/sizespectrum/mizer/issues todescribe your goal, because it will be interesting to the mizer communityand may motivate future improvements to the mizer functionality.

Note thatcustomFunction() only overwrites the function used by the mizercode. It does not overwrite the function that is exported by mizer. Thiswill become clear when you run the code in the Examples section.

This function does not in any way check that your replacement function iscompatible with mizer. Calling this function can totally break mizer.However you can always undo the effect by reloading mizer with

detach(package:mizer, unload = TRUE)library(mizer)

Value

No return value, called for side effects

Examples

## Not run: fake_project <- function(...) "Fake"customFunction("project", fake_project)mizer::project(NS_params) # This will print "Fake"project(NS_params) # This will still use the old project() function# To undo the effect:customFunction("project", project)mizer::project(NS_params) # This will again use the old project()## End(Not run)

Set defaults for predation kernel parameters

Description

If the predation kernel type has not been specified for a species, then itis set to "lognormal" and the default values are set for the parametersbeta andsigma.

Usage

default_pred_kernel_params(object)

Arguments

Value

Theobject with updated columns in the species params data frame.

Default editions

Description

Function to set and get which edition of default choices is being used.

Usage

defaults_edition(edition = NULL)

Arguments

Details

The mizer functions for creating new models make a lot of choices for defaultvalues for parameters that are not provided by the user. Sometimes we findbetter ways to choose the defaults and update mizer accordingly. When we dothis, we will increase the edition number.

If you calldefaults_edition() without an argument it returns thecurrently active edition. Otherwise it sets the active edition to thegiven value.

Users who want their existing code for creating models not to changebehaviour when run with future versions of mizer should explicitly set thedesired defaults edition at the top of their code.

The most recent edition is edition 2. It will become the default in thenext release. The current default is edition 1. The following defaultsare changed in edition 2:

• catchability = 0.3 instead of 1

• initial effort = 1 instead of 0

Value

The current edition number.

Check whether two objects are different

Description

Check whether two objects are numerically different, ignoring all attributes.

Usage

different(a, b)

Arguments

Details

We use this helper function in particular to see if a new value for a slotin MizerParams is different from the existing value in order to give theappropriate messages.

Value

TRUE or FALSE

Measure distance between current and previous state in terms of RDI

Description

This function can be used inprojectToSteady() to decide when sufficientconvergence to steady state has been achieved.

Usage

distanceMaxRelRDI(params, current, previous)

Arguments

Value

The largest absolute relative change in rdi:max(abs((current_rdi - previous_rdi) / previous_rdi))

See Also

Other distance functions:distanceSSLogN()

Measure distance between current and previous state in terms of fish abundances

Description

Calculates the sum squared difference between log(N) in current and previousstate. This function can be used inprojectToSteady() to decide whensufficient convergence to steady state has been achieved.

Usage

distanceSSLogN(params, current, previous)

Arguments

Value

The sum of squares of the difference in the logs of the (nonzero)fish abundances n:sum((log(current$n) - log(previous$n))^2)

See Also

Other distance functions:distanceMaxRelRDI()

Length based double-sigmoid selectivity function

Description

A hump-shaped selectivity function with a sigmoidal rise and an independentsigmoidal drop-off. This drop-off is what distinguishes this from thefunctionsigmoid_length() and it is intended to model the escape of largeindividuals from the fishing gear.

Usage

double_sigmoid_length(w, l25, l50, l50_right, l25_right, species_params, ...)

Arguments

Details

The selectivity is obtained as the product of two sigmoidal curves, onerising and one dropping. The sigmoidal rise is based on the two parametersl25 andl50 which determine the length at which 25% and 50% ofthe stock is selected respectively. The sigmoidal drop-off is based on thetwo parametersl50_right andl25_right which determine thelength at which the selectivity curve has dropped back to 50% and 25%respectively. The selectivity is given by the function

S(l) =\frac{1}{1 + \exp\left(\log(3)\frac{l50 -l}{l50 - l25}\right)}\frac{1}{1 +\exp\left(\log(3)\frac{l50_{right} -l}{l50_{right} -l25_{right}}\right)}

As the size-based model is weight based, and this selectivity function islength based, it uses the length-weight parametersa andb to convertbetween length and weight.

l = \left(\frac{w}{a}\right)^{1/b}

Value

Vector of selectivities at the given sizes.

See Also

gear_params() for setting the selectivity parameters.

Other selectivity functions:knife_edge(),sigmoid_length(),sigmoid_weight()

Create empty MizerParams object of the right size

Description

An internal function.Sets up a validMizerParams object with all the slotsinitialised and given dimension names, but with some slots left empty. Thisfunction is to be used by other functions to set up full parameter objects.

Usage

emptyParams(  species_params,  gear_params = data.frame(),  no_w = 100,  min_w = 0.001,  max_w = NA,  min_w_pp = 1e-12)

Arguments

Value

An empty but valid MizerParams object

Size grid

A size grid is created so thatthe log-sizes are equally spaced. The spacing is chosen so that there will beno_w fish size bins, with the smallest starting atmin_w and the largeststarting atmax_w. For the resource spectrum there is a larger set ofbins containing additional bins belowmin_w, with the same log size. The number of extra bins is such thatmin_w_pp comes to lie within the smallest bin.

Changes to species params

Thespecies_params slot of the returned MizerParams object may differfrom the data frame supplied as argument to this function becausedefault values are set for missing parameters.

See Also

SeenewMultispeciesParams() for a function that fillsthe slots left empty by this function.

Size spectra at end of simulation

Description

Size spectra at end of simulation

Usage

finalN(sim)finalNResource(sim)idxFinalT(sim)

Arguments

Value

ForfinalN(): An array (species x size) holding the consumernumber densities at the end of the simulation

ForfinalNResource(): A vector holding the resource numberdensities at the end of the simulation for all size classes

ForidxFinalT(): An integer giving the index for extracting theresults for the final time step

Examples

str(finalN(NS_sim))# This could also be obtained using `N()` and `idxFinalT()`identical(N(NS_sim)[idxFinalT(NS_sim), , ], finalN(NS_sim))str(finalNResource(NS_sim))idx <- idxFinalT(NS_sim)idx# This coincides withlength(getTimes(NS_sim))# and corresponds to the final timegetTimes(NS_sim)[idx]# We can use this index to extract the result at the final timeidentical(N(NS_sim)[idx, , ], finalN(NS_sim))identical(NResource(NS_sim)[idx, ], finalNResource(NS_sim))

Values of other ecosystem components at end of simulation

Description

Values of other ecosystem components at end of simulation

Usage

finalNOther(sim)

Arguments

Value

A named list holding the values of other ecosystem components at theend of the simulation

Gear parameters

Description

These functions allow you to get or set the gear parameters stored ina MizerParams object. These are used bysetFishing() to set up theselectivity and catchability and thus together with the fishing effortdetermine the fishing mortality.

Usage

gear_params(params)gear_params(params) <- value

Arguments

Details

Thegear_params data has one row for each gear-species pair and onecolumn for each parameter that determines how that gear interacts with thatspecies. The columns are:

• species The name of the species

• gear The name of the gear

• catchability A number specifying how strongly this gear selects thisspecies.

• sel_func The name of the function that calculates the selectivity curve.

• One column for each selectivity parameter needed by the selectivityfunctions.

For the details seesetFishing().

There can optionally also be a columnyield_observed that allows you tospecify for each gear and species the total annual fisheries yield.

The fishing effort, which is also needed to determine the fishing mortalityexerted by a gear is not set via thegear_params data frame but is setwithinitial_effort() or is specified when callingproject().

If you change a gear parameter, this will be used to recalculate theselectivity andcatchability arrays by callingsetFishing(),unless you have previously set these by hand.

⁠gear_params<-⁠ automatically sets the row names to contain the species nameand the gear name, separated by a comma and a space. The last example belowillustrates how this facilitates changing an individual gear parameter.

Value

Data frame with gear parameters

See Also

validGearParams()

Other functions for setting parameters:setExtEncounter(),setExtMort(),setFishing(),setInitialValues(),setInteraction(),setMaxIntakeRate(),setMetabolicRate(),setParams(),setPredKernel(),setReproduction(),setSearchVolume(),species_params()

Examples

params <- NS_params# gears set up in examplegear_params(params)# setting totally different gearsgear_params(params) <- data.frame(    gear = c("gear1", "gear2", "gear1"),    species = c("Cod", "Cod", "Haddock"),    catchability = c(0.5, 2, 1),    sel_fun = c("sigmoid_weight", "knife_edge", "sigmoid_weight"),    sigmoidal_weight = c(1000, NA, 800),    sigmoidal_sigma = c(100, NA, 100),    knife_edge_size = c(NA, 1000, NA)    )gear_params(params)# changing an individual entrygear_params(params)["Cod, gear1", "catchability"] <- 0.8

Calculate the total biomass of each species within a size range at each timestep.

Description

Calculates the total biomass through time within user defined size limits.The default option is to use the whole size range. You can specify minimumand maximum weight or length range for the species. Lengths take precedenceover weights (i.e. if both min_l and min_w are supplied, only min_l will beused).

Usage

getBiomass(object, ...)

Arguments

Value

If called with a MizerParams object, a vector with the biomass ingrams for each species in the model. If called with a MizerSim object, anarray (time x species) containing the biomass in grams at each time stepfor all species.

See Also

Other summary functions:getDiet(),getGrowthCurves(),getN(),getSSB(),getYield(),getYieldGear()

Examples

biomass <- getBiomass(NS_sim)biomass["1972", "Herring"]biomass <- getBiomass(NS_sim, min_w = 10, max_w = 1000)biomass["1972", "Herring"]

Calculate the slope of the community abundance

Description

Calculates the slope of the community abundance through time by performing alinear regression on the logged total numerical abundance at weight andlogged weights (natural logs, not log to base 10, are used). You can specifyminimum and maximum weight or length range for the species. Lengths takeprecedence over weights (i.e. if both min_l and min_w are supplied, onlymin_l will be used). You can also specify the species to be used in thecalculation.

Usage

getCommunitySlope(sim, species = NULL, biomass = TRUE, ...)

Arguments

Value

A data.frame with four columns: time step, slope, intercept and thecoefficient of determination R^2.

See Also

Other functions for calculating indicators:getMeanMaxWeight(),getMeanWeight(),getProportionOfLargeFish()

Examples

# Slope based on biomass, using all species and sizesslope_biomass <- getCommunitySlope(NS_sim)slope_biomass[1, ] # in 1976slope_biomass[idxFinalT(NS_sim), ] # in 2010# Slope based on numbers, using all species and sizesslope_numbers <- getCommunitySlope(NS_sim, biomass = FALSE)slope_numbers[1, ] # in 1976# Slope based on biomass, using all species and sizes between 10g and 1000gslope_biomass <- getCommunitySlope(NS_sim, min_w = 10, max_w = 1000)slope_biomass[1, ] # in 1976# Slope based on biomass, using only demersal species and # sizes between 10g and 1000gdem_species <- c("Dab","Whiting", "Sole", "Gurnard", "Plaice",                 "Haddock", "Cod", "Saithe")slope_biomass <- getCommunitySlope(NS_sim, species = dem_species,                                    min_w = 10, max_w = 1000)slope_biomass[1, ] # in 1976

Get information about other ecosystem components

Description

Get information about other ecosystem components

Usage

getComponent(params, component)

Arguments

Value

A list with the entriesinitial_value,dynamics_fun,encounter_fun,mort_fun,component_params for the requestedcomponent. If the requested component does not exist,NULL is returned.If nocomponent argument is given, then a list of lists for allcomponents is returned.

Get critical feeding level

Description

The critical feeding level is the feeding level at which the food intake isjust high enough to cover the metabolic costs, with nothing left over forgrowth or reproduction.

Usage

getCriticalFeedingLevel(params)

Arguments

Value

A matrix (species x size) with the critical feeding level

Examples

str(getFeedingLevel(NS_params))

Get diet of predator at size, resolved by prey species

Description

Calculates the rate at which a predator of a particular species and sizeconsumes biomass of each prey species, resource, and other components of theecosystem. Returns either the rates in grams/year or the proportion of thetotal consumption rate.

Usage

getDiet(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  proportion = TRUE)

Arguments

Details

The ratesD_{ij}(w) at which a predator of speciesiand sizew consumes biomass from prey speciesj arecalculated from the predation kernel\phi_i(w, w_p),the search volume\gamma_i(w), the feeding levelf_i(w), thespecies interaction matrix\theta_{ij} and the prey abundance densityN_j(w_p):

D_{ij}(w, w_p) = (1-f_i(w)) \gamma_i(w) \theta_{ij}\int N_j(w_p) \phi_i(w, w_p) w_p dw_p.

The prey indexj runs over all species and the resource.

Extra columns are added for the external encounter rate and for any extraecosystem components in your model for which you have defined an encounterrate function. These encounter rates are multiplied by1-f_i(w) to givethe rate of consumption of biomass from these extra components.

This function performs the same integration asgetEncounter() but does notaggregate over prey species, and multiplies by1-f_i(w) to get theconsumed biomass rather than the available biomass. Outside the range ofsizes for a predator species the returned rate is zero.

Value

An array (predator species x predator size x(prey species + resource + other components). Dimnames are "prey", "w",and "predator".

See Also

plotDiet()

Other summary functions:getBiomass(),getGrowthCurves(),getN(),getSSB(),getYield(),getYieldGear()

Examples

diet <- getDiet(NS_params)str(diet)

Get energy rate available for growth

Description

Calculates the energy rateg_i(w) (grams/year) available by species andsize for growth after metabolism, movement and reproduction have beenaccounted for.

Usage

getEGrowth(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A two dimensional array (prey species x prey size)

Your own growth rate function

By defaultgetEGrowth() callsmizerEGrowth(). However you canreplace this with your own alternative growth rate function. Ifyour function is called"myEGrowth" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "EGrowth", "myEGrowth")

Your function will then be called instead ofmizerEGrowth(), with thesame arguments.

See Also

getERepro(),getEReproAndGrowth()

Other rate functions:getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the energy at a particular time stepgrowth <- getEGrowth(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)# Growth rate at this time for Sprat of size 2ggrowth["Sprat", "2"]

Get energy rate available for reproduction

Description

Calculates the energy rate (grams/year) available for reproduction aftergrowth and metabolism have been accounted for.

Usage

getERepro(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A two dimensional array (prey species x prey size) holding

\psi_i(w)E_{r.i}(w)

whereE_{r.i}(w) is the rate at which energy becomes available forgrowth and reproduction, calculated withgetEReproAndGrowth(),and\psi_i(w) is the proportion of this energy that is used forreproduction. This proportion is taken from theparams object and isset withsetReproduction().

Your own reproduction rate function

By defaultgetERepro() callsmizerERepro(). However you canreplace this with your own alternative reproduction rate function. Ifyour function is called"myERepro" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ERepro", "myERepro")

Your function will then be called instead ofmizerERepro(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the rate at a particular time steperepro <- getERepro(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)# Rate at this time for Sprat of size 2gerepro["Sprat", "2"]

Get energy rate available for reproduction and growth

Description

Calculates the energy rateE_{r.i}(w) (grams/year) available forreproduction and growth after metabolism and movement have been accountedfor.

Usage

getEReproAndGrowth(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A two dimensional array (species x size) holding

E_{r.i}(w) = \max(0, \alpha_i\, (1 - {\tt feeding\_level}_i(w))\, {\tt encounter}_i(w) - {\tt metab}_i(w)).

Due to the form of the feeding level, calculated bygetFeedingLevel(), if the feeding level is nonzero this can also be expressed as

E_{r.i}(w) = \max(0, \alpha_i\, {\tt feeding\_level}_i(w)\, h_i(w) - {\tt metab}_i(w))

whereh_i is the maximum intake rate, set withsetMaxIntakeRate(). However this function is using the first equationabove so that it works also when the maximum intake rate is infinite, i.e.,there is no satiation.The assimilation rate\alpha_i is taken from the species parameterdata frame inparams. The metabolic ratemetab is taken fromparams and set withsetMetabolicRate().

The return value can be negative, which means that the energy intake does notcover the cost of metabolism and movement.

Your own energy rate function

By defaultgetEReproAndGrowth() callsmizerEReproAndGrowth(). However youcan replace this with your own alternative energy rate function. Ifyour function is called"myEReproAndGrowth" then you register it in aMizerParams objectparams with

params <- setRateFunction(params, "EReproAndGrowth", "myEReproAndGrowth")

Your function will then be called instead ofmizerEReproAndGrowth(), withthe same arguments.

See Also

The part of this energy rate that is invested into growth iscalculated withgetEGrowth() and the part that is invested intoreproduction is calculated withgetERepro().

Other rate functions:getEGrowth(),getERepro(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the energy at a particular time stepe <- getEReproAndGrowth(params, n = N(sim)[15, , ],                         n_pp = NResource(sim)[15, ], t = 15)# Rate at this time for Sprat of size 2ge["Sprat", "2"]

Alias forgetERepro()

Description

An alias provided for backward compatibility with mizer version <= 1.0

Usage

getESpawning(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A two dimensional array (prey species x prey size) holding

\psi_i(w)E_{r.i}(w)

whereE_{r.i}(w) is the rate at which energy becomes available forgrowth and reproduction, calculated withgetEReproAndGrowth(),and\psi_i(w) is the proportion of this energy that is used forreproduction. This proportion is taken from theparams object and isset withsetReproduction().

Your own reproduction rate function

By defaultgetERepro() callsmizerERepro(). However you canreplace this with your own alternative reproduction rate function. Ifyour function is called"myERepro" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ERepro", "myERepro")

Your function will then be called instead ofmizerERepro(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the rate at a particular time steperepro <- getERepro(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)# Rate at this time for Sprat of size 2gerepro["Sprat", "2"]

Fishing effort used in simulation

Description

Note that the array returned may not be exactly the same as theeffortargument that was passed in toproject(). This is because only the savedeffort is stored (the frequency of saving is determined by the argumentt_save).

Usage

getEffort(sim)

Arguments

Value

An array (time x gear) that contains the fishing effort by time andgear.

Examples

str(getEffort(NS_sim))

Get encounter rate

Description

Returns the rate at which a predator of speciesi andweightw encounters food (grams/year).

Usage

getEncounter(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A named two dimensional array (predator species x predator size) withthe encounter rates.

Predation encounter

The encounter rateE_i(w) at which a predator of speciesiand weightw encounters food has contributions from the encounter offish prey and of resource. This is determined by summing over all preyspecies and the resource spectrum and then integrating over all prey sizesw_p, weighted by predation kernel\phi(w,w_p):

E_i(w) = \gamma_i(w) \int\left( \theta_{ip} N_R(w_p) + \sum_{j} \theta_{ij} N_j(w_p) \right)\phi_i(w,w_p) w_p \, dw_p.

HereN_j(w) is the abundance density of speciesj andN_R(w) is the abundance density of resource.The overall prefactor\gamma_i(w) determines the predation power of thepredator. It could be interpreted as a search volume and is set with thesetSearchVolume() function. The predation kernel\phi(w,w_p) is set with thesetPredKernel() function. Thespecies interaction matrix\theta_{ij} is set withsetInteraction()and the resource interaction vector\theta_{ip} is taken from theinteraction_resource column inparams@species_params.

Details

The encounter rate is multiplied by1-f_0 to obtain the consumptionrate, wheref_0 is the feeding level calculated withgetFeedingLevel(). This is used by theproject() function for performingsimulations.

The function returns values also for sizes outside the size-range of thespecies. These values should not be used, as they are meaningless.

If your model contains additional components that you added withsetComponent() and for which you specified anencounter_fun function thenthe encounters of these components will be included in the returned value.

Your own encounter function

By defaultgetEncounter() callsmizerEncounter(). However you canreplace this with your own alternative encounter function. Ifyour function is called"myEncounter" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "Encounter", "myEncounter")

Your function will then be called instead ofmizerEncounter(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

encounter <- getEncounter(NS_params)str(encounter)

Get the total fishing mortality rate from all fishing gears by time, speciesand size.

Description

Calculates the total fishing mortality (in units 1/year) from all gears byspecies and size and possibly time. SeesetFishing() for details ofhow fishing gears are set up.

Usage

getFMort(object, effort, time_range, drop = TRUE)

Arguments

Details

The total fishing mortality is just the sum of the fishing mortalitiesimposed by each gear,F_i(w)=\sum_g F_{g,i,w}.The fishing mortality for each gear is obtained as catchability xselectivity x effort.

Value

An array. If the effort argument has a time dimension, or object isof classMizerSim, the output array has three dimensions (time xspecies x size). If the effort argument does not have a time dimension, theoutput array has two dimensions (species x size).

Theeffort argument is only used if aMizerParams object ispassed in. Theeffort argument can be a two dimensional array (time xgear), a vector of length equal to the number of gears (each gear has adifferent effort that is constant in time), or a single numeric value (eachgear has the same effort that is constant in time). The order of gears in theeffort argument must be the same as in theMizerParamsobject.

If the object argument is of classMizerSim then the effort slot oftheMizerSim object is used and theeffort argument is notused.

Your own fishing mortality function

By defaultgetFMort() callsmizerFMort(). However you canreplace this with your own alternative fishing mortality function. Ifyour function is called"myFMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "FMort", "myFMort")

Your function will then be called instead ofmizerFMort(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Get the total fishing mortality in the initial stateF <- getFMort(params, effort = 1)str(F)# Get the initial total fishing mortality when effort is different# between the four gears:F <- getFMort(params, effort = c(0.5,1,1.5,0.75))# Get the total fishing mortality when effort is different# between the four gears and changes with time:effort <- array(NA, dim = c(20,4))effort[, 1] <- seq(from = 0, to = 1, length = 20)effort[, 2] <- seq(from = 1, to = 0.5, length = 20)effort[, 3] <- seq(from = 1, to = 2, length = 20)effort[, 4] <- seq(from = 2, to = 1, length = 20)F <- getFMort(params, effort = effort)str(F)# Get the total fishing mortality using the effort already held in a # MizerSim object.sim <- project(params, t_max = 20, effort = 0.5)F <- getFMort(sim)F <- getFMort(sim, time_range = c(10, 20))

Get the fishing mortality by time, gear, species and size

Description

Calculates the fishing mortality rateF_{g,i,w} by gear, species andsize and possibly time (in units 1/year).

Usage

getFMortGear(object, effort, time_range)

Arguments

Value

An array. If the effort argument has a time dimension, or aMizerSim is passed in, the output array has four dimensions (time xgear x species x size). If the effort argument does not have a timedimension (i.e. it is a vector or a single numeric), the output array hasthree dimensions (gear x species x size).

Note

Here: fishing mortality = catchability x selectivity x effort.

Theeffort argument is only used if aMizerParams object ispassed in. Theeffort argument can be a two dimensional array (time xgear), a vector of length equal to the number of gears (each gear has adifferent effort that is constant in time), or a single numeric value (eachgear has the same effort that is constant in time). The order of gears in theeffort argument must be the same the same as in theMizerParamsobject. If theeffort argument is not supplied, its value is takenfrom the⁠@initial_effort⁠ slot in the params object.

If the object argument is of classMizerSim then the effort slot oftheMizerSim object is used and theeffort argument is notused.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <-NS_params# Get the fishing mortality in initial stateF <- getFMortGear(params, effort = 1)str(F)# Get the initial fishing mortality when effort is different# between the four gears:F <- getFMortGear(params, effort = c(0.5, 1, 1.5, 0.75))# Get the fishing mortality when effort is different# between the four gears and changes with time:effort <- array(NA, dim = c(20, 4))effort[, 1] <- seq(from=0, to = 1, length = 20)effort[, 2] <- seq(from=1, to = 0.5, length = 20)effort[, 3] <- seq(from=1, to = 2, length = 20)effort[, 4] <- seq(from=2, to = 1, length = 20)F <- getFMortGear(params, effort = effort)str(F)# Get the fishing mortality using the effort already held in a MizerSim object.sim <- project(params, t_max = 20, effort = 0.5)F <- getFMortGear(sim)F <- getFMortGear(sim, time_range = c(10, 20))

Get feeding level

Description

Returns the feeding level.By default this function usesmizerFeedingLevel() to calculatethe feeding level, but this can be overruled viasetRateFunction().

Usage

getFeedingLevel(object, n, n_pp, n_other, time_range, drop = FALSE, ...)

Arguments

Value

If aMizerParams object is passed in, the function returns a twodimensional array (predator species x predator size) based on theabundances also passed in.If aMizerSim object is passed in, the function returns a threedimensional array (time step x predator species x predator size) with thefeeding level calculated at every time step in the simulation.Ifdrop = TRUE then the dimension of length 1 will be removed fromthe returned array.

Feeding level

The feeding levelf_i(w) is theproportion of its maximum intake rate at which the predator is actuallytaking in fish. It is calculated from the encounter rateE_i and themaximum intake rateh_i(w) as

f_i(w) = \frac{E_i(w)}{E_i(w)+h_i(w)}.

The encounter rateE_i is passed as an argument or calculated withgetEncounter(). The maximum intake rateh_i(w) istaken from theparams object, and is set withsetMaxIntakeRate().As a consequence of the above expression for the feeding level,1-f_i(w) is the proportion of the food available to it that thepredator actually consumes.

Your own feeding level function

By defaultgetFeedingLevel() callsmizerFeedingLevel(). However you canreplace this with your own alternative feeding level function. Ifyour function is called"myFeedingLevel" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "FeedingLevel", "myFeedingLevel")

Your function will then be called instead ofmizerFeedingLevel(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Get initial feeding levelfl <- getFeedingLevel(params)# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the feeding level at all saved time stepsfl <- getFeedingLevel(sim)# Get the feeding level for years 15 - 20fl <- getFeedingLevel(sim, time_range = c(15, 20))

Get growth curves giving weight as a function of age

Description

Get growth curves giving weight as a function of age

Usage

getGrowthCurves(object, species = NULL, max_age = 20, percentage = FALSE)

Arguments

Value

An array (species x age) containing the weight in grams.

See Also

Other summary functions:getBiomass(),getDiet(),getN(),getSSB(),getYield(),getYieldGear()

Examples

growth_curves <- getGrowthCurves(NS_params, species = c("Cod", "Haddock"))str(growth_curves)library(ggplot2)ggplot(melt(growth_curves)) +  geom_line(aes(Age, value)) +  facet_wrap(~ Species, scales = "free") +  ylab("Size[g]") + xlab("Age[years]")

Deprecated function to get interaction matrix

Description

You should now useinteraction_matrix() instead.

Usage

getInteraction(params)

Arguments

Alias forgetPredMort()

Description

An alias provided for backward compatibility with mizer version <= 1.0

Usage

getM2(object, n, n_pp, n_other, time_range, drop = TRUE, ...)

Arguments

Value

If aMizerParams object is passed in, the function returns a twodimensional array (prey species x prey size) based on the abundances alsopassed in. If aMizerSim object is passed in, the function returns athree dimensional array (time step x prey species x prey size) with thepredation mortality calculated at every time step in the simulation.Dimensions may be dropped if they have length 1 unlessdrop = FALSE.

Your own predation mortality function

By defaultgetPredMort() callsmizerPredMort(). However you canreplace this with your own alternative predation mortality function. Ifyour function is called"myPredMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "PredMort", "myPredMort")

Your function will then be called instead ofmizerPredMort(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Predation mortality in initial stateM2 <- getPredMort(params)str(M2)# With constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get predation mortality at one time stepM2 <- getPredMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])# Get predation mortality at all saved time stepsM2 <- getPredMort(sim)str(M2)# Get predation mortality over the years 15 - 20M2 <- getPredMort(sim, time_range = c(15, 20))

Alias forgetResourceMort()

Description

An alias provided for backward compatibility with mizer version <= 1.0

Usage

getM2Background(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A vector of mortality rate by resource size.

Your own resource mortality function

By defaultgetResourceMort() callsmizerResourceMort(). However you canreplace this with your own alternative resource mortality function. Ifyour function is called"myResourceMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ResourceMort", "myResourceMort")

Your function will then be called instead ofmizerResourceMort(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates()

Examples

params <- NS_params# With constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get resource mortality at one time stepgetResourceMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])

Calculate the mean maximum weight of the community

Description

Calculates the mean maximum weight of the community through time. This can becalculated by numbers or biomass. The calculation is the sum of the w_max *abundance of each species, divided by the total abundance community, whereabundance is either in biomass or numbers. You can specify minimum andmaximum weight or length range for the species. Lengths take precedence overweights (i.e. if both min_l and min_w are supplied, only min_l will be used).You can also specify the species to be used in the calculation.

Usage

getMeanMaxWeight(sim, species = NULL, measure = "both", ...)

Arguments

Value

Depends on themeasure argument. Ifmeasure = “both”then you get a matrix with two columns, one with values by numbers, theother with values by biomass at each saved time step. Ifmeasure = “numbers” or“biomass” you get a vector of the respective values ateach saved time step.

See Also

Other functions for calculating indicators:getCommunitySlope(),getMeanWeight(),getProportionOfLargeFish()

Examples

mmw <- getMeanMaxWeight(NS_sim)years <- c("1967", "2010")mmw[years, ]getMeanMaxWeight(NS_sim, species=c("Herring","Sprat","N.pout"))[years, ]getMeanMaxWeight(NS_sim, min_w = 10, max_w = 5000)[years, ]

Calculate the mean weight of the community

Description

Calculates the mean weight of the community through time. This is simply thetotal biomass of the community divided by the abundance in numbers. You canspecify minimum and maximum weight or length range for the species. Lengthstake precedence over weights (i.e. if both min_l and min_w are supplied, onlymin_l will be used). You can also specify the species to be used in thecalculation.

Usage

getMeanWeight(sim, species = NULL, ...)

Arguments

Value

A vector containing the mean weight of the community through time

See Also

Other functions for calculating indicators:getCommunitySlope(),getMeanMaxWeight(),getProportionOfLargeFish()

Examples

mean_weight <- getMeanWeight(NS_sim)years <- c("1967", "2010")mean_weight[years]getMeanWeight(NS_sim, species = c("Herring", "Sprat", "N.pout"))[years]getMeanWeight(NS_sim, min_w = 10, max_w = 5000)[years]

Get total mortality rate

Description

Calculates the total mortality rate\mu_i(w) (in units 1/year) on eachspecies by size from predation mortality, background mortality and fishingmortality for a single time step.

Usage

getMort(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  effort = getInitialEffort(params),  t = 0,  ...)

Arguments

Details

If your model contains additional components that you added withsetComponent() and for which you specified amort_fun function thenthe mortality inflicted by these components will be included in the returnedvalue.

Value

A two dimensional array (prey species x prey size).

Your own mortality function

By defaultgetMort() callsmizerMort(). However you canreplace this with your own alternative mortality function. Ifyour function is called"myMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "Mort", "myMort")

Your function will then be called instead ofmizerMort(), with thesame arguments.

See Also

getPredMort(),getFMort()

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the total mortality at a particular time stepmort <- getMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ],                 t = 15, effort = 0.5)# Mortality rate at this time for Sprat of size 2gmort["Sprat", "2"]

Calculate the number of individuals within a size range

Description

Calculates the number of individuals within user-defined size limits. Thedefault option is to use the whole size range. You can specify minimum andmaximum weight or lengths for the species. Lengths take precedence overweights (i.e. if both min_l and min_w are supplied, only min_l will be used)

Usage

getN(object, ...)

Arguments

Value

If called with a MizerParams object, a vector with the numbers foreach species in the model. If called with a MizerSim object, an array (timex species) containing the numbers at each time step for all species.

See Also

Other summary functions:getBiomass(),getDiet(),getGrowthCurves(),getSSB(),getYield(),getYieldGear()

Examples

numbers <- getN(NS_sim)numbers["1972", "Herring"]# The above gave a huge number, because that included all the larvae.# The number of Herrings between 10g and 1kg is much smaller.numbers <- getN(NS_sim, min_w = 10, max_w = 1000)numbers["1972", "Herring"]

Extract the parameter object underlying a simulation

Description

Extract the parameter object underlying a simulation

Usage

getParams(sim)

Arguments

Value

The MizerParams object that was used to run the simulation

Examples

# This will be identical to the params object that was used to create the# simulationsim <- project(NS_params, t_max = 1)identical(getParams(sim), NS_params)

Get available energy

Description

This is deprecated and is no longer used by the mizer project() method.Calculates the amountE_{a,i}(w) of food exposed to each predator asa function of predator size.

Usage

getPhiPrey(object, n, n_pp, ...)

Arguments

Value

A two dimensional array (predator species x predator size)

See Also

project()

Examples

params <-  NS_paramssim <- project(params, t_max = 20, effort = 0.5)n <- sim@n[21,,]n_pp <- sim@n_pp[21,]getPhiPrey(params,n,n_pp)# ->getEncounter(params) / getSearchVolume(params)

Get total predation mortality rate

Description

Calculates the total predation mortality rate\mu_{p,i}(w_p) (in unitsof 1/year) on each prey species by prey size:

\mu_{p.i}(w_p) = \sum_j {\tt pred\_rate}_j(w_p)\, \theta_{ji}.

The predation ratepred_rate is returned bygetPredRate().

Usage

getPredMort(object, n, n_pp, n_other, time_range, drop = TRUE, ...)

Arguments

Value

If aMizerParams object is passed in, the function returns a twodimensional array (prey species x prey size) based on the abundances alsopassed in. If aMizerSim object is passed in, the function returns athree dimensional array (time step x prey species x prey size) with thepredation mortality calculated at every time step in the simulation.Dimensions may be dropped if they have length 1 unlessdrop = FALSE.

Your own predation mortality function

By defaultgetPredMort() callsmizerPredMort(). However you canreplace this with your own alternative predation mortality function. Ifyour function is called"myPredMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "PredMort", "myPredMort")

Your function will then be called instead ofmizerPredMort(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Predation mortality in initial stateM2 <- getPredMort(params)str(M2)# With constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get predation mortality at one time stepM2 <- getPredMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])# Get predation mortality at all saved time stepsM2 <- getPredMort(sim)str(M2)# Get predation mortality over the years 15 - 20M2 <- getPredMort(sim, time_range = c(15, 20))

Get predation rate

Description

Calculates the potential rate (in units 1/year) at which a prey individual ofa given sizew is killed by predators from speciesj. In formulas

{\tt pred\_rate}_j(w_p) = \int \phi_j(w,w_p) (1-f_j(w)) \gamma_j(w) N_j(w) \, dw.

This potential rate is used ingetPredMort() tocalculate the realised predation mortality rate on the prey individual.

Usage

getPredRate(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A two dimensional array (predator species x prey size),where the prey size runs over fish community plus resource spectrum.

Your own predation rate function

By defaultgetPredRate() callsmizerPredRate(). However you canreplace this with your own alternative predation rate function. Ifyour function is called"myPredRate" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "PredRate", "myPredRate")

Your function will then be called instead ofmizerPredRate(), withthe same arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Predation rate in initial statepred_rate <- getPredRate(params)str(pred_rate)# With constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the feeding level at one time steppred_rate <- getPredRate(params, n = N(sim)[15, , ],                          n_pp = NResource(sim)[15, ], t = 15)

Calculate the proportion of large fish

Description

Calculates the proportion of large fish through time in theMizerSimclass within user defined size limits. The default option is to use the wholesize range. You can specify minimum and maximum size ranges for the speciesand also the threshold size for large fish. Sizes can be expressed as weightor size. Lengths take precedence over weights (i.e. if both min_l and min_ware supplied, only min_l will be used). You can also specify the species tobe used in the calculation. This function can be used to calculate the LargeFish Index. The proportion is based on either abundance or biomass.

Usage

getProportionOfLargeFish(  sim,  species = NULL,  threshold_w = 100,  threshold_l = NULL,  biomass_proportion = TRUE,  ...)

Arguments

Value

A vector containing the proportion of large fish through time

See Also

Other functions for calculating indicators:getCommunitySlope(),getMeanMaxWeight(),getMeanWeight()

Examples

lfi <- getProportionOfLargeFish(NS_sim, min_w = 10, max_w = 5000,                                 threshold_w = 500)years <- c("1972", "2010")lfi[years]getProportionOfLargeFish(NS_sim)[years]getProportionOfLargeFish(NS_sim, species=c("Herring","Sprat","N.pout"))[years]getProportionOfLargeFish(NS_sim, min_w = 10, max_w = 5000)[years]getProportionOfLargeFish(NS_sim, min_w = 10, max_w = 5000,    threshold_w = 500, biomass_proportion = FALSE)[years]

Get density dependent reproduction rate

Description

Calculates the density dependent rate of egg productionR_i (units1/year) for each species. This is the flux entering the smallest size classof each species. The density dependent rate is the density independentrate obtained withgetRDI() after it has been put through thedensity dependence function. This is the Beverton-Holt functionBevertonHoltRDD() by default, but this can be changed. SeesetReproduction() for more details.

Usage

getRDD(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  rdi = getRDI(params, n = n, n_pp = n_pp, n_other = n_other, t = t),  ...)

Arguments

Value

A numeric vector the length of the number of species.

See Also

getRDI()

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the rate at a particular time stepgetRDD(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Get density independent rate of egg production

Description

Calculates the density-independent rate of total egg productionR_{di} (units 1/year) before density dependence, by species.

Usage

getRDI(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Details

This rate is obtained by taking the per capita rateE_r(w)\psi(w) atwhich energy is invested in reproduction, as calculated bygetERepro(),multiplying it by the number of individualsN(w) and integrating overall sizesw and then multiplying by the reproductive efficiency\epsilon and dividing by the egg sizew_min, and by a factor of twoto account for the two sexes:

R_{di} = \frac{\epsilon}{2 w_{min}} \int N(w) E_r(w) \psi(w) \, dw

Used bygetRDD() to calculate the actual, density dependent rate.SeesetReproduction() for more details.

Value

A numeric vector the length of the number of species.

Your own reproduction function

By defaultgetRDI() callsmizerRDI(). However you canreplace this with your own alternative reproduction function. Ifyour function is called"myRDI" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "RDI", "myRDI")

Your function will then be called instead ofmizerRDI(), with thesame arguments. For an example of an alternative reproduction functionseeconstantEggRDI().

See Also

getRDD()

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the density-independent reproduction rate at a particular time stepgetRDI(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Get all rates

Description

Calls other rate functions in sequence and collects the results in a list.

Usage

getRates(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  effort,  t = 0,  ...)

Arguments

Details

By default this function returns a list with the following components:

• encounter frommizerEncounter()

• feeding_level frommizerFeedingLevel()

• e frommizerEReproAndGrowth()

• e_repro frommizerERepro()

• e_growth frommizerEGrowth()

• pred_rate frommizerPredRate()

• pred_mort frommizerPredMort()

• f_mort frommizerFMort()

• mort frommizerMort()

• rdi frommizerRDI()

• rdd fromBevertonHoltRDD()

• resource_mort frommizerResourceMort()

However you can replace any of these rate functions by your own ratefunction if you wish, seesetRateFunction() for details.

Value

List of rates.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getResourceMort()

Examples

rates <- getRates(NS_params)names(rates)identical(rates$encounter, getEncounter(NS_params))

Get reproduction level

Description

The reproduction level is the ratio between the density-dependentreproduction rate and the maximal reproduction rate.

Usage

getReproductionLevel(params)

Arguments

Value

A named vector with the reproduction level for each species.

Examples

getReproductionLevel(NS_params)# The reproduction level can be changed without changing the steady state:params <- setBevertonHolt(NS_params, reproduction_level = 0.9)getReproductionLevel(params)# The result is the ratio of RDD and R_maxidentical(getRDD(params) / species_params(params)$R_max,          getReproductionLevel(params))

Deprecated functions for getting resource parameters

Description

Useresource_dynamics(),resource_level(),resource_rate() andresource_capacity() instead.

Usage

getResourceDynamics(params)getResourceLevel(params)getResourceRate(params)getResourceCapacity(params)

Arguments

Value

Name of the function that determines the resource dynamics

Get predation mortality rate for resource

Description

Calculates the predation mortality rate\mu_p(w) on the resourcespectrum by resource size (in units 1/year).

Usage

getResourceMort(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  t = 0,  ...)

Arguments

Value

A vector of mortality rate by resource size.

Your own resource mortality function

By defaultgetResourceMort() callsmizerResourceMort(). However you canreplace this with your own alternative resource mortality function. Ifyour function is called"myResourceMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ResourceMort", "myResourceMort")

Your function will then be called instead ofmizerResourceMort(), with thesame arguments.

See Also

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getMort(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates()

Examples

params <- NS_params# With constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get resource mortality at one time stepgetResourceMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])

Calculate the SSB of species

Description

Calculates the spawning stock biomass (SSB) through time of the species intheMizerSim class. SSB is calculated as the total mass of all matureindividuals.

Usage

getSSB(object)

Arguments

Value

If called with a MizerParams object, a vector with the SSB ingrams for each species in the model. If called with a MizerSim object, anarray (time x species) containing the SSB in grams at each time stepfor all species.

See Also

Other summary functions:getBiomass(),getDiet(),getGrowthCurves(),getN(),getYield(),getYieldGear()

Examples

ssb <- getSSB(NS_sim)ssb[c("1972", "2010"), c("Herring", "Cod")]

Times for which simulation results are available

Description

Times for which simulation results are available

Usage

getTimes(sim)

Arguments

Value

A numeric vectors of the times (in years) at which simulation resultshave been stored in the MizerSim object.

Examples

getTimes(NS_sim)

Calculate the rate at which biomass of each species is fished

Description

This yield rate is given in grams per year. It is calculated at each timestep saved in the MizerSim object.

Usage

getYield(object)

Arguments

Details

The yield ratey_i(t) for speciesi at timet is defined as

y_i(t)=\int\mu_{f.i}(w, t)N_i(w, t)w dw

where\mu_{f.i}(w, t) is the fishing mortality of an individual ofspeciesi and weightw at timet andN_i(w, t) is theabundance density of such individuals. The factor ofw converts theabundance density into a biomass density and the integral aggregates thecontribution from all sizes.

The total catch in a time period fromt_1 tot_2 is the integralof the yield rate over that period:

C = \int_{t_1}^{t2}y_i(t)dt

In practice, as the yield rate is only availableat the saved times, one can only approximate this integral by averaging overthe available yield rates during the time period and multiplying by the timeperiod. The less the yield changes between the saved values, the moreaccurate this approximation is. So the approximation can be improved bysaving simulation results at smaller intervals, using thet_save argumenttoproject(). But this is only a concern if abundances change quicklyduring the time period of interest.

Value

If called with a MizerParams object, a vector with the yield rate ingrams per year for each species in the model. If called with a MizerSimobject, an array (time x species) containing the yield rate at each timestep for all species.

See Also

getYieldGear()

Other summary functions:getBiomass(),getDiet(),getGrowthCurves(),getN(),getSSB(),getYieldGear()

Examples

yield <- getYield(NS_sim)yield[c("1972", "2010"), c("Herring", "Cod")]# Running simulation for another year, saving intermediate time stepsparams <- setInitialValues(getParams(NS_sim), NS_sim)sim <- project(params, t_save = 0.1, t_max = 1,                t_start = 2010, progress_bar = FALSE)# The yield rate for Herring decreases during the yeargetYield(sim)[, "Herring"]# We get the total catch in the year by averaging over the yearsum(getYield(sim)[1:10, "Herring"] / 10)

Calculate the rate at which biomass of each species is fished by each gear

Description

This yield rate is given in grams per year. It is calculated at each timestep saved in the MizerSim object.

Usage

getYieldGear(object)

Arguments

Details

For details of how the yield rate is defined see the help page ofgetYield().

Value

If called with a MizerParams object, an array (gear x species) withthe yield rate in grams per year from each gear for each species in themodel. If called with a MizerSim object, an array (time x gear x species)containing the yield rate at each time step.

See Also

getYield()

Other summary functions:getBiomass(),getDiet(),getGrowthCurves(),getN(),getSSB(),getYield()

Examples

yield <- getYieldGear(NS_sim)yield["1972", "Herring", "Herring"]# (In this example MizerSim object each species was set up with its own gear)

Alias forgetMort()

Description

An alias provided for backward compatibility with mizer version <= 1.0

Usage

getZ(  params,  n = initialN(params),  n_pp = initialNResource(params),  n_other = initialNOther(params),  effort = getInitialEffort(params),  t = 0,  ...)

Arguments

Details

If your model contains additional components that you added withsetComponent() and for which you specified amort_fun function thenthe mortality inflicted by these components will be included in the returnedvalue.

Value

A two dimensional array (prey species x prey size).

Your own mortality function

By defaultgetMort() callsmizerMort(). However you canreplace this with your own alternative mortality function. Ifyour function is called"myMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "Mort", "myMort")

Your function will then be called instead ofmizerMort(), with thesame arguments.

See Also

getPredMort(),getFMort()

Other rate functions:getEGrowth(),getERepro(),getEReproAndGrowth(),getEncounter(),getFMort(),getFMortGear(),getFeedingLevel(),getPredMort(),getPredRate(),getRDD(),getRDI(),getRates(),getResourceMort()

Examples

params <- NS_params# Project with constant fishing effort for all gears for 20 time stepssim <- project(params, t_max = 20, effort = 0.5)# Get the total mortality at a particular time stepmort <- getMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ],                 t = 15, effort = 0.5)# Mortality rate at this time for Sprat of size 2gmort["Sprat", "2"]

Get default value for f0

Description

Fills in any missing values for f0 so that if the prey abundance wasdescribed by the power law\kappa w^{-\lambda} then the encounter ratecoming from the givengamma parameter would lead to the feeding levelf_0. This is thus doing the inverse ofget_gamma_default().Only for internal use.

Usage

get_f0_default(params)

Arguments

Details

For species for which no value forgamma is specified in the speciesparameter data frame, thef0 values is kept as provided in the speciesparameter data frame or it is set to 0.6 if it is not provided.

Value

A vector with the values of f0 for all species

See Also

Other functions calculating defaults:get_gamma_default(),get_h_default(),get_ks_default()

Get default value for gamma

Description

Fills in any missing values for gamma so that fish feeding on a resourcespectrum described by the power law\kappa w^{-\lambda} achieve afeeding levelf_0. Only for internal use.

Usage

get_gamma_default(params)

Arguments

Value

A vector with the values of gamma for all species

See Also

Other functions calculating defaults:get_f0_default(),get_h_default(),get_ks_default()

Get default value for h

Description

Setsh so that the species reaches maturity sizew_mat at the maturityageage_mat if it feeds at feeding levelf0.

Usage

get_h_default(params)

Arguments

Details

Ifage_mat is missing in the species parameter data frame, then it iscalculated from the von Bertalanffy growth curve parametersk_vb and(optionallyt0) taken from the species parameter data frame. This is notreliable and a warning is issued.

If no growth information is given at all for a species, the default is settoh = 30.

Value

A vector with the values of h for all species

See Also

Other functions calculating defaults:get_f0_default(),get_gamma_default(),get_ks_default()

Calculate initial population abundances

Description

This function uses the model parameters and other parameters to calculateinitial values for the species number densities. These initialabundances are currently quite arbitrary and not close to the steady state.We intend to improve this in the future.

Usage

get_initial_n(params, n0_mult = NULL, a = 0.35)

Arguments

Value

A matrix (species x size) of population abundances.

Examples

init_n <- get_initial_n(NS_params)

Get default value forks

Description

Fills in any missing values forks so that the critical feeding level neededto sustain the species is as specified in thefc column in the speciesparameter data frame. If that column is not provided the default criticalfeeding levelf_c = 0.2 is used.

Usage

get_ks_default(params)

Arguments

Value

A vector with the values of ks for all species

See Also

Other functions calculating defaults:get_f0_default(),get_gamma_default(),get_h_default()

Get values from feeding kernel function

Description

This involves finding the feeding kernel function for each species, using thepred_kernel_type parameter in the species_params data frame, checking that itis valid and all its arguments are contained in the species_params dataframe, and then calling this function with the ppmr vector.

Usage

get_phi(species_params, ppmr)

Arguments

Value

An array (species x ppmr) with the values of the predation kernelfunction

Determine reproduction rate needed for initial egg abundance

Description

Determine reproduction rate needed for initial egg abundance

Usage

get_required_reproduction(params)

Arguments

Value

A vector of reproduction rates for all species

Get size range array

Description

Helper function that returns an array (species x size) of boolean valuesindicating whether that size bin is within the size limits specified by thearguments. Either the size limits can be the same for all species or theycan be specified as vectors with one value for each species in the model.

Usage

get_size_range_array(  params,  min_w = min(params@w),  max_w = max(params@w),  min_l = NULL,  max_l = NULL,  ...)

Arguments

Value

Boolean array (species x size)

Length to weight conversion

Ifmin_l is specified there is no need to specifymin_w and so on.However, if a length is specified (minimum or maximum) then it is necessaryfor the species parameter data.frame to include the parametersa andbthat determine the relation between lengthl and weightw by

w = a l^b.

It is possible to mix length and weight constraints, e.g. by supplying aminimum weight and a maximum length, but this must be done the same forall species. The default values are the minimum andmaximum weights of the spectrum, i.e., the full range of the size spectrum isused.

Get_time_elements

Description

Internal function to get the array element references of the time dimensionfor the time based slots of a MizerSim object.

Usage

get_time_elements(sim, time_range, slot_name = "n")

Arguments

Value

Named boolean vector indicating for each time whether it is includedin the range or not.

Description of indicator functions

Description

Mizer provides a range of functions to calculate indicatorsfrom a MizerSim object.

Details

A list of available indicator functions for MizerSim objects is given in the table below

See Also

summary_functions,plotting_functions

Initial values for fish spectra

Description

Values used as starting values for simulations withproject().

Usage

initialN(params) <- valueinitialN(object)

Arguments

Value

A matrix with dimensions species x size holding the initial numberdensities for the fish spectra.

See Also

initialNResource(),initialNOther()

Examples

# Doubling abundance of Cod in the initial state of the North Sea modelparams <- NS_paramsinitialN(params)["Cod", ] <- 2 * initialN(params)["Cod", ]# Calculating the corresponding initial biomassbiomass <- initialN(params)["Cod", ] * dw(NS_params) * w(NS_params)# Of course this initial state will no longer be a steady stateparams <- steady(params)

Initial values for other ecosystem components

Description

Values used as starting values for simulations withproject().

Usage

initialNOther(params) <- valueinitialNOther(object)

Arguments

Value

A named list with the initial values of other ecosystemcomponents

See Also

initialNResource(),initialN()

Initial value for resource spectrum

Description

Value used as starting value for simulations withproject().

Usage

initialNResource(params) <- valueinitialNResource(object)

Arguments

Value

A vector with the initial number densities for the resourcespectrum

See Also

initialN(),initialNOther()

Examples

# Doubling resource abundance in the initial state of the North Sea modelparams <- NS_paramsinitialNResource(params) <- 2 * initialNResource(params)# Of course this initial state will no longer be a steady stateparams <- steady(params)

Initial fishing effort

Description

The fishing effort is a named vector, specifying for each fishing gear theeffort invested into fishing with that gear. The effort value for each gearis multiplied by the catchability and the selectivity to determine thefishing mortality imposed by that gear, seesetFishing() for more details.The initial effort you have set can be overruled when running a simulationby providing aneffort argument toproject() which allows you tospecify a time-varying effort.

Usage

initial_effort(params)initial_effort(params) <- valuevalidEffortVector(effort, params)

Arguments

Details

A valid effort vector is a named vector with one effort value for each gear.However you can also supply the effort value in different ways:

• a scalar, which is then replicated for each gear

• an unnamed vector, which is then assumed to be in the same order as thegears in the params object

• a named vector in which the gear names have a different order than in theparams object. This is then sorted correctly.

• a named vector which only supplies values for some of the gears.The effort for the other gears is then set to zero.

These conversions are done by the functionvalidEffortVector().

Aneffort argument will lead to an error if it is either

• unnamed and of the wrong length

• named but where some names do not match any of the gears

• not numeric

Value

Effort vector

Alias forNS_interaction

Description

An alias provided for backward compatibility with mizer version <= 2.3

Usage

inter

Format

A 12 x 12 matrix.

Source

Blanchard et al.

Weight based knife-edge selectivity function

Description

A knife-edge selectivity function where weights greater or equal toknife_edge_size are fully selected and no fish smaller than this sizeare selected.

Usage

knife_edge(w, knife_edge_size, ...)

Arguments

Value

Vector of selectivities at the given sizes.

See Also

gear_params() for setting theknife_edge_size parameter.

Other selectivity functions:double_sigmoid_length(),sigmoid_length(),sigmoid_weight()

Length-weight conversion

Description

For each species, convert between length and weight using the relationship

w_i = a_i l_i^{b_i}

or

l_i = (w_i / a_i)^{1/b_i}

wherea andb are taken from the species parameter data frame andi is the species index.

Usage

l2w(l, species_params)w2l(w, species_params)

Arguments

Details

This is useful for converting a length-based species parameter to aweight-based species parameter.

If anya orb parameters are missing the default valuesa = 0.01 andb = 3 are used for the missing values.

Value

A vector with one entry for each species.l2w() returns a vectorof weights in grams andw2l() returns a vector of lengths in cm.

Helper function to produce nice breaks on logarithmic axes

Description

This is needed when the logarithmic y-axis spans less than one order ofmagnitude, in which case the ggplot2 default produces no ticks.

Usage

log_breaks(n = 6)

Arguments

Details

Thanks to Heather Turner athttps://stackoverflow.com/questions/14255533/pretty-ticks-for-log-normal-scale-using-ggplot2-dynamic-not-manual

Value

A function that can be used as the break argument in calls toscale_y_continuous() or scale_x_continuous()

Lognormal predation kernel

Description

This is the most commonly-used predation kernel. The log of the predator/preymass ratio is normally distributed.

Usage

lognormal_pred_kernel(ppmr, beta, sigma)

Arguments

Details

Writing the predator mass asw and the prey mass asw_p,the feeding kernel is given as

\phi_i(w, w_p) =\exp \left[ \frac{-(\ln(w / w_p / \beta_i))^2}{2\sigma_i^2} \right]

ifw/w_p is larger than 1 and zero otherwise. Here\beta_i is thepreferred predator-prey mass ratio and\sigma_i determines the width ofthe kernel. These two parameters need to be given in the species parameterdataframe in the columnsbeta andsigma.

This function is called fromsetPredKernel() to set up thepredation kernel slots in a MizerParams object.

Value

A vector giving the value of the predation kernel at each of thepredator/prey mass ratios in theppmr argument.

See Also

setPredKernel()

Other predation kernel:box_pred_kernel(),power_law_pred_kernel(),truncated_lognormal_pred_kernel()

Examples

params <- NS_paramsplot(w_full(params), getPredKernel(params)["Cod", 10, ], type="l", log="x")# The restriction that the kernel is zero for w/w_p < 1 is more# noticeable for larger sigmaspecies_params(params)$sigma <- 4plot(w_full(params), getPredKernel(params)["Cod", 10, ], type="l", log="x")

Match biomasses to observations

Description

The function adjusts the abundances of the species in the model so that theirbiomasses match with observations.

Usage

matchBiomasses(params, species = NULL)

Arguments

Details

The function works by multiplying for each species the abundance densityat all sizes by the same factor. This will of course not give a steadystate solution, even if the initial abundance densities were at steady state.So after using this function you may want to usesteady() to run the modelto steady state, after which of course the biomasses will no longer matchexactly. You could then iterate this process. This is described in theblog post at https://bit.ly/2YqXESV.

Before you can use this function you will need to have added abiomass_observed column to your model which gives the observed biomass ingrams. For species for which you have no observed biomass, you should setthe value in thebiomass_observed column to 0 or NA.

Biomass observations usually only include individuals above a certain size.This size should be specified in abiomass_cutoff column of the speciesparameter data frame. If this is missing, it is assumed that all sizes areincluded in the observed biomass, i.e., it includes larval biomass.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$biomass_observed <-     c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)species_params(params)$biomass_cutoff <- 10params <- calibrateBiomass(params)params <- matchBiomasses(params)plotBiomassObservedVsModel(params)

Adjust model to produce observed growth

Description

Scales the search volume, the maximum consumption rate, the metabolic rateand the external encounter rateall by the same factor in order to achieve a growth rate that allowsindividuals to reach their maturity size by their maturity age while keepingthe feeding level and the critical feeding level unchanged. Then recalculatesthe size spectra usingsteadySingleSpecies().

Usage

matchGrowth(params, species = NULL, keep = c("egg", "biomass", "number"))

Arguments

Details

Maturity size and age are taken from thew_mat andage_mat columns in thespecies_params data frame. Ifage_mat is missing, mizer calculates it fromthe von Bertalanffy growth curve parameters usingage_mat_vB(). If thoseare not available either for a species, the growth rate for that species willnot be changed.

Value

A modified MizerParams object with rescaled search volume, maximumconsumption rate and metabolic rate and rescaled species parametersgamma,h,ks andk.

Match numbers to observations

Description

The function adjusts the numbers of the species in the model so that theirnumbers match with observations.

Usage

matchNumbers(params, species = NULL)

Arguments

Details

The function works by multiplying for each species the number densityat all sizes by the same factor. This will of course not give a steadystate solution, even if the initial number densities were at steady state.So after using this function you may want to usesteady() to run the modelto steady state, after which of course the numbers will no longer matchexactly. You could then iterate this process. This is described in theblog post at https://bit.ly/2YqXESV.

Before you can use this function you will need to have added anumber_observed column to your model which gives the observed number ingrams. For species for which you have no observed number, you should setthe value in thenumber_observed column to 0 or NA.

Number observations usually only include individuals above a certain size.This size should be specified in anumber_cutoff column of the speciesparameter data frame. If this is missing, it is assumed that all sizes areincluded in the observed number, i.e., it includes larval number.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$number_observed <-    c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)species_params(params)$number_cutoff <- 10params <- calibrateNumber(params)params <- matchNumbers(params)

Match yields to observations

Description

This function has been deprecated and will be removed in the future unlessyou have a use case for it. If you do have a use case for it, please let thedevelopers know by creating an issue athttps://github.com/sizespectrum/mizer/issues.

Usage

matchYields(params, species = NULL)

Arguments

Details

If you want to match the yields to observations, you should use thematchYield() function from the mizerExperimental package instead, whichadjusts the catchability to match the yield rather than by adjusting thebiomass.

The function adjusts the abundances of the species in the model so that theiryearly yields under the given fishing mortalities match with observations.

The function works by multiplying for each species the abundance densityat all sizes by the same factor. This will of course not give a steadystate solution, even if the initial abundance densities were at steady state.So after using this function you may want to usesteady() to run the modelto steady state, after which of course the yields will no longer matchexactly. You could then iterate this process. This is described in theblog post at https://bit.ly/2YqXESV.

Before you can use this function you will need to have added ayield_observed column to your model which gives the observed yields ingrams per year. For species for which you have no observed biomass, youshould set the value in theyield_observed column to 0 or NA.

Value

A MizerParams object

Examples

params <- NS_paramsspecies_params(params)$yield_observed <-     c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)gear_params(params)$catchability <-    c(1.3, 0.065, 0.31, 0.18, 0.98, 0.24, 0.37, 0.46, 0.18, 0.30, 0.27, 0.39)params <- calibrateYield(params)params <- matchYields(params)plotYieldObservedVsModel(params)

Get energy rate available for growth needed to project standard mizer model

Description

Calculates the energy rateg_i(w) (grams/year) available by species andsize for growth after metabolism, movement and reproduction have beenaccounted for. Used byproject() for performing simulations.You would not usually call thisfunction directly but instead usegetEGrowth(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerEGrowth(params, n, n_pp, n_other, t, e_repro, e, ...)

Arguments

Value

A two dimensional array (species x size) with the growth rates.

Your own growth rate function

By defaultgetEGrowth() callsmizerEGrowth(). However you canreplace this with your own alternative growth rate function. Ifyour function is called"myEGrowth" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "EGrowth", "myEGrowth")

Your function will then be called instead ofmizerEGrowth(), with thesame arguments.

See Also

Other mizer rate functions:mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get energy rate available for reproduction needed to project standard mizermodel

Description

Calculates the energy rate (grams/year) available for reproduction aftergrowth and metabolism have been accounted for.You would not usually call thisfunction directly but instead usegetERepro(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerERepro(params, n, n_pp, n_other, t, e, ...)

Arguments

Value

A two dimensional array (species x size) holding

\psi_i(w)E_{r.i}(w)

whereE_{r.i}(w) is the rate at which energy becomes available forgrowth and reproduction, calculated withmizerEReproAndGrowth(),and\psi_i(w) is the proportion of this energy that is used forreproduction. This proportion is taken from theparams object and isset withsetReproduction().

Your own reproduction rate function

By defaultgetERepro() callsmizerERepro(). However you canreplace this with your own alternative reproduction rate function. Ifyour function is called"myERepro" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ERepro", "myERepro")

Your function will then be called instead ofmizerERepro(), with thesame arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get energy rate available for reproduction and growth needed to projectstandard mizer model

Description

Calculates the energy rateE_{r.i}(w) (grams/year) available to anindividual of species i and size w for reproduction and growth aftermetabolism and movement have been accounted for.You would not usually call this function directly but instead usegetEReproAndGrowth(), which then calls this function unless an alternativefunction has been registered, see below.

Usage

mizerEReproAndGrowth(  params,  n,  n_pp,  n_other,  t,  encounter,  feeding_level,  ...)

Arguments

Value

A two dimensional array (species x size) holding

E_{r.i}(w) = \max(0, \alpha_i\, (1 - {\tt feeding\_level}_i(w))\, {\tt encounter}_i(w) - {\tt metab}_i(w)).

Due to the form of the feeding level, calculated bygetFeedingLevel(), if the feeding level is nonzero this can also be expressed as

E_{r.i}(w) = \max(0, \alpha_i\, {\tt feeding\_level}_i(w)\, h_i(w) - {\tt metab}_i(w))

whereh_i is the maximum intake rate, set withsetMaxIntakeRate(). However this function is using the first equationabove so that it works also when the maximum intake rate is infinite, i.e.,there is no satiation.The assimilation rate\alpha_i is taken from the species parameterdata frame inparams. The metabolic ratemetab is taken fromparams and set withsetMetabolicRate().

The return value can be negative, which means that the energy intake does notcover the cost of metabolism and movement.

Your own energy rate function

By defaultgetEReproAndGrowth() callsmizerEReproAndGrowth(). However youcan replace this with your own alternative energy rate function. Ifyour function is called"myEReproAndGrowth" then you register it in aMizerParams objectparams with

params <- setRateFunction(params, "EReproAndGrowth", "myEReproAndGrowth")

Your function will then be called instead ofmizerEReproAndGrowth(), withthe same arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get encounter rate needed to project standard mizer model

Description

Calculates the rateE_i(w) at which a predator of speciesi andweightw encounters food (grams/year). You would not usually call thisfunction directly but instead usegetEncounter(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerEncounter(params, n, n_pp, n_other, t, ...)

Arguments

Value

A named two dimensional array (predator species x predator size) withthe encounter rates.

Predation encounter

The encounter rateE_i(w) at which a predator of speciesiand weightw encounters food has contributions from the encounter offish prey and of resource. This is determined by summing over all preyspecies and the resource spectrum and then integrating over all prey sizesw_p, weighted by predation kernel\phi(w,w_p):

E_i(w) = \gamma_i(w) \int\left( \theta_{ip} N_R(w_p) + \sum_{j} \theta_{ij} N_j(w_p) \right)\phi_i(w,w_p) w_p \, dw_p.

HereN_j(w) is the abundance density of speciesj andN_R(w) is the abundance density of resource.The overall prefactor\gamma_i(w) determines the predation power of thepredator. It could be interpreted as a search volume and is set with thesetSearchVolume() function. The predation kernel\phi(w,w_p) is set with thesetPredKernel() function. Thespecies interaction matrix\theta_{ij} is set withsetInteraction()and the resource interaction vector\theta_{ip} is taken from theinteraction_resource column inparams@species_params.

Details

The encounter rate is multiplied by1-f_0 to obtain the consumptionrate, wheref_0 is the feeding level calculated withgetFeedingLevel(). This is used by theproject() function for performingsimulations.

The function returns values also for sizes outside the size-range of thespecies. These values should not be used, as they are meaningless.

If your model contains additional components that you added withsetComponent() and for which you specified anencounter_fun function thenthe encounters of these components will be included in the returned value.

Your own encounter function

By defaultgetEncounter() callsmizerEncounter(). However you canreplace this with your own alternative encounter function. Ifyour function is called"myEncounter" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "Encounter", "myEncounter")

Your function will then be called instead ofmizerEncounter(), with thesame arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get the total fishing mortality rate from all fishing gears

Description

Calculates the total fishing mortality (in units 1/year) from all gears byspecies and size.The total fishing mortality is just the sum of the fishing mortalitiesimposed by each gear,\mu_{f.i}(w)=\sum_g F_{g,i,w}.You would not usually call thisfunction directly but instead usegetFMort(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerFMort(params, n, n_pp, n_other, t, effort, e_growth, pred_mort, ...)

Arguments

Value

An array (species x size) with the fishing mortality.

Your own fishing mortality function

By defaultgetFMort() callsmizerFMort(). However you canreplace this with your own alternative fishing mortality function. Ifyour function is called"myFMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "FMort", "myFMort")

Your function will then be called instead ofmizerFMort(), with thesame arguments.

Note

Here: fishing mortality = catchability x selectivity x effort.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get the fishing mortality needed to projectstandard mizer model

Description

Calculates the fishing mortality rateF_{g,i,w} by gear, species andsize.This is a helper function formizerFMort().

Usage

mizerFMortGear(params, effort)

Arguments

Value

An three dimensional array (gear x species x size) with thefishing mortality

Note

Here: fishing mortality = catchability x selectivity x effort.

See Also

setFishing()

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get feeding level needed to project standard mizer model

Description

You would not usually call this function directly but instead usegetFeedingLevel(), which then calls this function unless an alternativefunction has been registered, see below.

Usage

mizerFeedingLevel(params, n, n_pp, n_other, t, encounter, ...)

Arguments

Value

A two dimensional array (predator species x predator size) with thefeeding level.

Feeding level

The feeding levelf_i(w) is theproportion of its maximum intake rate at which the predator is actuallytaking in fish. It is calculated from the encounter rateE_i and themaximum intake rateh_i(w) as

f_i(w) = \frac{E_i(w)}{E_i(w)+h_i(w)}.

The encounter rateE_i is passed as an argument or calculated withgetEncounter(). The maximum intake rateh_i(w) istaken from theparams object, and is set withsetMaxIntakeRate().As a consequence of the above expression for the feeding level,1-f_i(w) is the proportion of the food available to it that thepredator actually consumes.

Your own feeding level function

By defaultgetFeedingLevel() callsmizerFeedingLevel(). However you canreplace this with your own alternative feeding level function. Ifyour function is called"myFeedingLevel" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "FeedingLevel", "myFeedingLevel")

Your function will then be called instead ofmizerFeedingLevel(), with thesame arguments.

See Also

The feeding level is used inmizerEReproAndGrowth() and inmizerPredRate().

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get total mortality rate needed to project standard mizer model

Description

Calculates the total mortality rate\mu_i(w) (in units 1/year) on eachspecies by size from predation mortality, background mortality and fishingmortality.You would not usually call thisfunction directly but instead usegetMort(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerMort(params, n, n_pp, n_other, t, f_mort, pred_mort, ...)

Arguments

Details

If your model contains additional components that you added withsetComponent() and for which you specified amort_fun function thenthe mortality inflicted by these components will be included in the returnedvalue.

Value

A named two dimensional array (species x size) with the totalmortality rates.

Your own mortality function

By defaultgetMort() callsmizerMort(). However you canreplace this with your own alternative mortality function. Ifyour function is called"myMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "Mort", "myMort")

Your function will then be called instead ofmizerMort(), with thesame arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get total predation mortality rate needed to project standard mizer model

Description

Calculates the total predation mortality rate\mu_{p,i}(w_p) (in unitsof 1/year) on each prey species by prey size:

\mu_{p.i}(w_p) = \sum_j {\tt pred\_rate}_j(w_p)\, \theta_{ji}.

You would not usually call thisfunction directly but instead usegetPredMort(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerPredMort(params, n, n_pp, n_other, t, pred_rate, ...)

Arguments

Value

A two dimensional array (prey species x prey size) with the predationmortality

Your own predation mortality function

By defaultgetPredMort() callsmizerPredMort(). However you canreplace this with your own alternative predation mortality function. Ifyour function is called"myPredMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "PredMort", "myPredMort")

Your function will then be called instead ofmizerPredMort(), with thesame arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredRate(),mizerRDI(),mizerRates(),mizerResourceMort()

Get predation rate needed to project standard mizer model

Description

Calculates the potential rate (in units 1/year) at which a prey individual ofa given sizew is killed by predators from speciesj. In formulas

{\tt pred\_rate}_j(w_p) = \int \phi_j(w,w_p) (1-f_j(w)) \gamma_j(w) N_j(w) \, dw.

This potential rate is used in the functionmizerPredMort() tocalculate the realised predation mortality rate on the prey individual.You would not usually call thisfunction directly but instead usegetPredRate(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerPredRate(params, n, n_pp, n_other, t, feeding_level, ...)

Arguments

Value

A named two dimensional array (predator species x prey size) with thepredation rate, where the prey size runs over fish community plus resourcespectrum.

Your own predation rate function

By defaultgetPredRate() callsmizerPredRate(). However you canreplace this with your own alternative predation rate function. Ifyour function is called"myPredRate" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "PredRate", "myPredRate")

Your function will then be called instead ofmizerPredRate(), withthe same arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerRDI(),mizerRates(),mizerResourceMort()

Get density-independent rate of reproduction needed to project standardmizer model

Description

Calculates the density-independent rate of total egg productionR_{di} (units 1/year) before density dependence, by species.You would not usually call thisfunction directly but instead usegetRDI(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerRDI(params, n, n_pp, n_other, t, e_growth, mort, e_repro, ...)

Arguments

Details

This rate is obtained by taking the per capita rateE_r(w)\psi(w) atwhich energy is invested in reproduction, as calculated bygetERepro(),multiplying it by the number of individualsN(w) and integrating overall sizesw and then multiplying by the reproductive efficiency\epsilon and dividing by the egg sizew_min, and by a factor of twoto account for the two sexes:

R_{di} = \frac{\epsilon}{2 w_{min}} \int N(w) E_r(w) \psi(w) \, dw

Used bygetRDD() to calculate the actual, density dependent rate.SeesetReproduction() for more details.

Value

A numeric vector with the rate of egg production for each species.

Your own reproduction function

By defaultgetRDI() callsmizerRDI(). However you canreplace this with your own alternative reproduction function. Ifyour function is called"myRDI" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "RDI", "myRDI")

Your function will then be called instead ofmizerRDI(), with thesame arguments. For an example of an alternative reproduction functionseeconstantEggRDI().

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRates(),mizerResourceMort()

Get all rates needed to project standard mizer model

Description

Calls other rate functions in sequence and collects the results in a list.

Usage

mizerRates(params, n, n_pp, n_other, t = 0, effort, rates_fns, ...)

Arguments

Details

By default this function returns a list with the following components:

• encounter frommizerEncounter()

• feeding_level frommizerFeedingLevel()

• e frommizerEReproAndGrowth()

• e_repro frommizerERepro()

• e_growth frommizerEGrowth()

• pred_rate frommizerPredRate()

• pred_mort frommizerPredMort()

• f_mort frommizerFMort()

• mort frommizerMort()

• rdi frommizerRDI()

• rdd fromBevertonHoltRDD()

• resource_mort frommizerResourceMort()

However you can replace any of these rate functions by your own ratefunction if you wish, seesetRateFunction() for details.

Value

List of rates.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerResourceMort()

Get predation mortality rate for resource needed to project standard mizermodel

Description

Calculates the predation mortality rate\mu_p(w) on the resourcespectrum by resource size (in units 1/year).You would not usually call thisfunction directly but instead usegetResourceMort(), which then calls thisfunction unless an alternative function has been registered, see below.

Usage

mizerResourceMort(params, n, n_pp, n_other, t, pred_rate, ...)

Arguments

Value

A vector of mortality rate by resource size.

Your own resource mortality function

By defaultgetResourceMort() callsmizerResourceMort(). However you canreplace this with your own alternative resource mortality function. Ifyour function is called"myResourceMort" then you register it in a MizerParamsobjectparams with

params <- setRateFunction(params, "ResourceMort", "myResourceMort")

Your function will then be called instead ofmizerResourceMort(), with thesame arguments.

See Also

Other mizer rate functions:mizerEGrowth(),mizerERepro(),mizerEReproAndGrowth(),mizerEncounter(),mizerFMort(),mizerFMortGear(),mizerFeedingLevel(),mizerMort(),mizerPredMort(),mizerPredRate(),mizerRDI(),mizerRates()

Determine whether a MizerParams or MizerSim object needs to be upgraded

Description

Looks at the mizer version that was used to last update the object andreturns TRUE if changes since that version require an upgrade of the object.You would not usually have to call this function. Upgrades are initiatedautomatically byvalidParams andvalidSim when necessary.

Usage

needs_upgrading(object)

Arguments

Value

TRUE or FALSE

Set up parameters for a community-type model

Description

This functions creates aMizerParams object describing acommunity-type model.The function has many arguments, all of which have default values.

Usage

newCommunityParams(  max_w = 1e+06,  min_w = 0.001,  no_w = 100,  min_w_pp = 1e-10,  z0 = 0.1,  alpha = 0.2,  f0 = 0.7,  h = 10,  gamma = NA,  beta = 100,  sigma = 2,  n = 2/3,  kappa = 1000,  lambda = 2.05,  r_pp = 10,  knife_edge_size = 1000,  reproduction)

Arguments

Details

A community model has several features that distinguish it from amulti-species model:

• Species identities of individuals are ignored. All are aggregated into asingle community.

• The resource spectrum only extends to the start of the community spectrum.

• Reproductive rate is constant, independent of the energy invested inreproduction, which is set to 0.

• Standard metabolism is turned off (the parameterks is set to 0).Consequently, the growth rate is now determined solely by the assimilatedfood

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

The resultingMizerParams object can be projected forward usingproject() like any otherMizerParams object. When projectingthe community model it may be necessary to keep a small time step sizedt of around 0.1 to avoid any instabilities with the solver. You cancheck for these numerical instabilities by plotting the biomass or abundancethrough time after the projection.

Value

An object of typeMizerParams

References

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

See Also

Other functions for setting up models:newMultispeciesParams(),newSingleSpeciesParams(),newTraitParams()

Examples

params <- newCommunityParams()sim <- project(params, t_max = 10)plotBiomass(sim)plotSpectra(sim, power = 2)# More satiation. More mortalityparams <- newCommunityParams(f0 = 0.8, z0 = 0.4)sim <- project(params, t_max = 10)plotBiomass(sim)plotSpectra(sim, power = 2)

Set up parameters for a general multispecies model

Description

Sets up a multi-species size spectrum model by filling all slots in theMizerParams object based on user-provided or defaultparameters. There is a long list of arguments, but almostall of them have sensible default values. The only required argument isthespecies_params data frame. All arguments are described in moredetails in the sections below the list.

Usage

newMultispeciesParams(  species_params,  interaction = NULL,  no_w = 100,  min_w = 0.001,  max_w = NA,  min_w_pp = NA,  pred_kernel = NULL,  search_vol = NULL,  intake_max = NULL,  metab = NULL,  p = 0.7,  ext_mort = NULL,  z0pre = 0.6,  z0exp = n - 1,  ext_encounter = NULL,  maturity = NULL,  repro_prop = NULL,  RDD = "BevertonHoltRDD",  kappa = 1e+11,  n = 2/3,  resource_rate = 10,  resource_capacity = kappa,  lambda = 2.05,  w_pp_cutoff = 10,  resource_dynamics = "resource_semichemostat",  gear_params = NULL,  selectivity = NULL,  catchability = NULL,  initial_effort = NULL,  info_level = 3,  z0 = deprecated(),  r_pp = deprecated())

Arguments

Value

An object of typeMizerParams

Species parameters

The only essential argument is a data frame that contains the speciesparameters. The data frame is arranged species by parameter, so each columnof the parameter data frame is a parameter and each row has the values of theparameters for one of the species in the model.

There are two essential columns that must be included in the speciesparameter data.frame and that do not have default values: thespecies column that should hold strings with the names of thespecies and thew_max column with the maximum sizes of the speciesin grams. (You could alternatively specify the maximum length in cm in anl_max column.)

The⁠species_params dataframe⁠ also needs to contain the parameters neededby any predation kernel function (size selectivity function). This willbe mentioned in the appropriate sections below.

For all other species parameters, mizer will calculate default values if theyare not included in the species parameter data frame. They will beautomatically added when theMizerParams object is created. For theseparameters you can also specify values for only some species and leave theother entries as NA and the missing values will be set to the defaults.So thespecies_params data frame saved in the returned MizerParams objectwill differ from the one you supply because it will have the missingspecies parameters filled in with default values.

If you are not happy with any of the species parameter values used you canalways change them later withspecies_params<-().

All the parameters will be mentioned in the following sections.

Setting initial values

The initial values for the species number densities are set using thefunctionget_initial_n(). These are quite arbitrary and not very close tothe steady state abundances. We intend to improve this in the future.

The initial resource number densityN_R(w) is set to a power law withcoefficientkappa (\kappa) and exponent-lambda (-\lambda):

N_R(w) = \kappa\, w^{-\lambda}

for allw less thanw_pp_cutoff and zero for larger sizes.

Size grid

A size grid is created so thatthe log-sizes are equally spaced. The spacing is chosen so that there will beno_w fish size bins, with the smallest starting atmin_w and the largeststarting atmax_w. For the resource spectrum there is a larger set ofbins containing additional bins belowmin_w, with the same log size. The number of extra bins is such thatmin_w_pp comes to lie within the smallest bin.

Units in mizer

Mizer uses grams to measure weight, centimetres to measure lengths, andyears to measure time.

Mizer is agnostic about whether abundances are given as

1. numbers per area,

2. numbers per volume or

3. total numbers for the entire study area.

You should make the choice most convenient for your application and thenstick with it. If you make choice 1 or 2 you will also have to choose a unitfor area or volume. Your choice will then determine the units for some ofthe parameters. This will be mentioned when the parameters are discussed inthe sections below.

Your choice will also affect the units of the quantities you may want tocalculate with the model. For example, the yield will be in grams/year/m^2 incase 1 if you choose m^2 as your measure of area, in grams/year/m^3 in case 2if you choose m^3 as your unit of volume, or simply grams/year in case 3. Thesame comment applies for other measures, like total biomass, which will begrams/area in case 1, grams/volume in case 2 or simply grams in case 3. Whenmizer puts units on axes in plots, it will choose the units appropriate forcase 3. So for example inplotBiomass() it gives the unit as grams.

You can convert between these choices. For example, if you use case 1, youneed to multiply with the area of the ecosystem to get the total quantity.If you work with case 2, you need to multiply by both area and the thicknessof the productive layer. In that respect, case 2 is a bit cumbersome. ThefunctionscaleModel() is useful to change the units you are using.

Setting interaction matrix

You do not need to specify an interaction matrix. If you do not, then thepredator-prey interactions are purely determined by the size of predatorand prey and totally independent of the species of predator and prey.

The interaction matrix\theta_{ij} modifies the interaction of eachpair of species in the model. This can be used for example to allow fordifferent spatial overlap among the species.The values in the interaction matrix are used to scale the encountered foodand predation mortality (see on the websitethe section on predator-prey encounter rateand onpredation mortality).The first index refers to the predator species and the second to the preyspecies.

The interaction matrix is used when calculating the food encounter rate ingetEncounter() and the predation mortality rate ingetPredMort(). Itsentries are dimensionless numbers. If all the values in the interactionmatrix are equal then predator-prey interactions are determined entirely bysize-preference.

This function checks that the supplied interaction matrix is valid and thenstores it in theinteraction slot of theparams object.

The order of the columns and rows of theinteraction argument should bethe same as the order in the species params data frame in theparamsobject. If you supply a named array then the function will check the orderand warn if it is different. One way of creating your own interactionmatrix is to enter the data using a spreadsheet program and saving it as a.csv file. The data can then be read into R using the commandread.csv().

The interaction of the species with the resource are set via a columninteraction_resource in thespecies_params data frame. By default thiscolumn is set to all 1s.

Setting predation kernel

Kernel dependent on predator to prey size ratio

If thepred_kernel argument is not supplied, then this function sets apredation kernel that depends only on the ratio of predator mass to preymass, not on the two masses independently. The shape of that kernel is thendetermined by thepred_kernel_type column in species_params.

The default forpred_kernel_type is "lognormal". This will call the functionlognormal_pred_kernel() to calculate the predation kernel.An alternative pred_kernel type is "box", implemented by the functionbox_pred_kernel(), and "power_law", implemented by the functionpower_law_pred_kernel(). These functions require certain speciesparameters in the species_params data frame. For the lognormal kernel thesearebeta andsigma, for the box kernel they areppmr_minandppmr_max. They are explained in the help pages for the kernelfunctions. Except forbeta andsigma, no defaults are set forthese parameters. If they are missing from the species_params data frame thenmizer will issue an error message.

You can use any other string forpred_kernel_type. If for example youchoose "my" then you need to define a functionmy_pred_kernel that you canmodel on the existing functions likelognormal_pred_kernel().

When using a kernel that depends on the predator/prey size ratio only, mizerdoes not need to store the entire three dimensional array in the MizerParamsobject. Such an array can be very big when there is a large number of sizebins. Instead, mizer only needs to store two two-dimensional arrays that holdFourier transforms of the feeding kernel function that allow the encounterrate and the predation rate to be calculated very efficiently. However, ifyou need the full three-dimensional array you can calculate it with thegetPredKernel() function.

Kernel dependent on both predator and prey size

If you want to work with a feeding kernel that depends on predator mass andprey mass independently, you can specify the full feeding kernel as athree-dimensional array (predator species x predator size x prey size).

You should use this option only if a kernel dependent only on thepredator/prey mass ratio is not appropriate. Using a kernel dependent onpredator/prey mass ratio only allows mizer to use fast Fourier transformmethods to significantly reduce the running time of simulations.

The order of the predator species inpred_kernel should be the sameas the order in the species params dataframe in theparams object. If yousupply a named array then the function will check the order and warn if it isdifferent.

Setting search volume

The search volume\gamma_i(w) of an individual of speciesiand weightw multiplies the predation kernel whencalculating the encounter rate ingetEncounter() and thepredation rate ingetPredRate().

The name "search volume" is a bit misleading, because\gamma_i(w) doesnot have units of volume. It is simply a parameter that determines the rateof predation. Its units depend on your choice, see section "Units in mizer".If you have chosen to work with total abundances, then it is a rate with units1/year. If you have chosen to work with abundances per m^2 then it has unitsof m^2/year. If you have chosen to work with abundances per m^3 then it hasunits of m^3/year.

If thesearch_vol argument is not supplied, then the search volume isset to

\gamma_i(w) = \gamma_i w^q_i.

The values of\gamma_i (the search volume at 1g) andq_i (theallometric exponent of the search volume) are taken from thegamma andq columns in the species parameter dataframe. If thegammacolumn is not supplied in the species parameter dataframe, a default iscalculated by theget_gamma_default() function. Note that onlyfor predators of sizew = 1 gram is the value of the species parameter\gamma_i the same as the value of the search volume\gamma_i(w).

Setting maximum intake rate

The maximum intake rateh_i(w) of an individual of speciesi andweightw determines the feeding level, calculated withgetFeedingLevel(). It is measured in grams/year.

If theintake_max argument is not supplied, then the maximum intakerate is set to

h_i(w) = h_i w^{n_i}.

The values ofh_i (the maximum intake rate of an individual of size 1gram) andn_i (the allometric exponent for the intake rate) are takenfrom theh andn columns in the species parameter dataframe. Iftheh column is not supplied in the species parameter dataframe, it iscalculated by theget_h_default() function.

Ifh_i is set toInf, fish of species i will consume all encounteredfood.

Setting metabolic rate

The metabolic rate is subtracted from the energy income rate to calculatethe rate at which energy is available for growth and reproduction, seegetEReproAndGrowth(). It is measured in grams/year.

If themetab argument is not supplied, then for each species themetabolic ratek(w) for an individual of sizew is set to

k(w) = k_s w^p + k w,

wherek_s w^p represents the rate of standard metabolism andk wis the rate at which energy is expended on activity and movement. The valuesofk_s,p andk are taken from theks,p andk columns in the species parameter dataframe. If any of theseparameters are not supplied, the defaults arek = 0,p = n and

k_s = f_c h \alpha w_{mat}^{n-p},

wheref_c is the critical feeding level taken from thefc columnin the species parameter data frame. If the critical feeding level is notspecified, a default off_c = 0.2 is used.

Setting external mortality rate

The external mortality is all the mortality that is not due to fishing orpredation by predators included in the model. The external mortality could bedue to predation by predators that are not explicitly included in the model(e.g. mammals or seabirds) or due to other causes like illness. It is a ratewith units 1/year.

Theext_mort argument allows you to specify an external mortality ratethat depends on species and body size. You can see an example of this inthe Examples section of the help page forsetExtMort().

If theext_mort argument is not supplied, then the external mortality isassumed to depend only on the species, not on the size of the individual:\mu_{ext.i}(w) = z_{0.i}. The value of the constantz_0 for eachspecies is taken from thez0 column of the species parameter data frame, ifthat column exists. Otherwise it is calculated as

z_{0.i} = {\tt z0pre}_i\, w_{inf}^{\tt z0exp}.

Setting external encounter rate

The external encounter rate is the rate at which a predator encountersfood that is not explicitly modelled. It is a rate with units mass/year.

Theext_encounter argument allows you to specify an external encounter ratethat depends on species and body size. You can see an example of this inthe Examples section of the help page forsetExtEncounter().

Setting reproduction

For each species and at each size, the proportion\psi of theavailable energythat is invested into reproduction is the product of two factors: theproportionmaturity of individuals that are mature and the proportionrepro_prop of the energy available to a mature individual that isinvested into reproduction. There is a sizew_repro_max at which all theenergy is invested into reproduction and therefore all growth stops. Therecan be no fish larger thanw_repro_max. If you have not specified thew_repro_max column in the species parameter data frame, then the maximum sizew_max is used instead.

Maturity ogive

If the the proportion of individuals that are mature is not supplied viathematurity argument, then it is set to a sigmoidalmaturity ogive that changes from 0 to 1 at around the maturity size:

{\tt maturity}(w) = \left[1+\left(\frac{w}{w_{mat}}\right)^{-U}\right]^{-1}.

(To avoid clutter, we are not showing the species index in the equations,although each species has its own maturity ogive.)The maturity weights are taken from thew_mat column of thespecies_params data frame. Any missing maturity weights are set to 1/4 of themaximum weight in thew_max column.

The exponentU determines the steepness of the maturity ogive. Bydefault it is chosen asU = 10, however this can be overridden byincluding a columnw_mat25 in the species parameter dataframe thatspecifies the weight at which 25% of individuals are mature, which setsU = \log(3) / \log(w_{mat} / w_{mat25}).

The sigmoidal function given above would strictly reach 1 onlyasymptotically. Mizer instead sets the function equal to 1 already at a sizetaken from thew_repro_max column in the species parameter data frame, if itexists, or otherwise from thew_max column. Also, for computationalsimplicity, any proportion smaller than1e-8 is set to0.

Investment into reproduction

If the the energy available to a mature individual that isinvested into reproduction is not supplied via therepro_prop argument,it is set to the allometric form

{\tt repro\_prop}(w) = \left(\frac{w}{w_{\tt{repro\_max}}}\right)^{m-n}.

Heren is the scaling exponent of the energy income rate. Hencethe exponentm determines the scaling of the investment intoreproduction for mature individuals. By default it is chosen to bem = 1 so that the rate at which energy is invested into reproductionscales linearly with the size. This default can be overridden by including acolumnm in the species parameter dataframe. The maximum sizes are takenfrom thew_repro_max column in the species parameter data frame, if itexists, or otherwise from thew_max column.

The total proportion of energy invested into reproduction of an individualof sizew is then

\psi(w) = {\tt maturity}(w){\tt repro\_prop}(w)

Reproductive efficiency

The reproductive efficiency\epsilon, i.e., the proportion of energy allocated toreproduction that results in egg biomass, is set through theereprocolumn in the species_params data frame. If that is not provided, the defaultis set to 1 (which you will want to override). The offspring biomass dividedby the egg biomass gives the rate of egg production, returned bygetRDI():

R_{di} = \frac{\epsilon}{2 w_{min}} \int N(w) E_r(w) \psi(w) \, dw

Density dependence

The stock-recruitment relationship is an emergent phenomenon in mizer, withseveral sources of density dependence. Firstly, the amount of energy investedinto reproduction depends on the energy income of the spawners, which isdensity-dependent due to competition for prey. Secondly, the proportion oflarvae that grow up to recruitment size depends on the larval mortality,which depends on the density of predators, and on larval growth rate, whichdepends on density of prey.

Finally, to encode all the density dependence in the stock-recruitmentrelationship that is not already included in the other two sources of densitydependence, mizer puts the the density-independent rate of egg productionthrough a density-dependence function. The result is returned bygetRDD(). The name of the density-dependence function isspecified by theRDD argument. The default is the Beverton-HoltfunctionBevertonHoltRDD(), which requires anR_max columnin the species_params data frame giving the maximum egg production rate. Ifthis column does not exist, it is initialised toInf, leading to nodensity-dependence. Other functions provided by mizer areRickerRDD() andSheperdRDD() and you can easily usethese as models for writing your own functions.

Setting fishing

Gears

Inmizer, fishing mortality is imposed on species by fishing gears. Thetotal per-capita fishing mortality (1/year) is obtained by summing over themortality from all gears,

\mu_{f.i}(w) = \sum_g F_{g,i}(w),

where the fishing mortalityF_{g,i}(w) imposed by gearg onspeciesi at sizew is calculated as:

F_{g,i}(w) = S_{g,i}(w) Q_{g,i} E_{g},

whereS is the selectivity by species, gear and size,Q is thecatchability by species and gear andE is the fishing effort by gear.

Selectivity

The selectivity at size of each gear for each species is saved as a threedimensional array (gear x species x size). Each entry has a range between 0(that gear is not selecting that species at that size) to 1 (that gear isselecting all individuals of that species of that size). This threedimensional array can be specified explicitly via theselectivityargument, but usually mizer calculates it from thegear_params slot ofthe MizerParams object.

To allow the calculation of theselectivity array, thegear_params slotmust be a data frame with one row for each gear-species combination. So iffor example a gear can select three species, then that gear contributes threerows to thegear_params data frame, one for each species it can select. Thedata frame must have columnsgear, holding the name of the gear,species,holding the name of the species, andsel_func, holding the name of thefunction that calculates the selectivity curve. Some selectivity functionsare included in the package:knife_edge(),sigmoid_length(),double_sigmoid_length(), andsigmoid_weight().Users are able to write their own size-based selectivity function. The firstargument to the function must bew and the function must return a vector ofthe selectivity (between 0 and 1) at size.

Each selectivity function may have parameters. Values for theseparameters must be included as columns in the gear parameters data.frame.The names of the columns must exactly match the names of the correspondingarguments of the selectivity function. For example, the default selectivityfunction isknife_edge() that a has sudden change of selectivity from 0 to 1at a certain size. In its help page you can see that theknife_edge()function has argumentsw andknife_edge_size. The first argument,w, issize (the function calculates selectivity at size). All selectivity functionsmust havew as the first argument. The values for the other arguments mustbe found in the gear parameters data.frame. So for theknife_edge()function there should be aknife_edge_size column. Becauseknife_edge()is the default selectivity function, theknife_edge_size argument has adefault value =w_mat.

The most commonly-used selectivity function issigmoid_length(). It has asmooth transition from 0 to 1 at a certain size. Thesigmoid_length()function has the two parametersl50 andl25 that are the lengths in cm atwhich 50% or 25% of the fish are selected by the gear. If you choose thisselectivity function then thel50 andl25 columns must be included in thegear parameters data.frame.

In case each species is only selected by one gear, the columns of thegear_params data frame can alternatively be provided as columns of thespecies_params data frame, if this is more convenient for the user to setup. Mizer will then copy these columns over to create thegear_params dataframe when it creates the MizerParams object. However changing these columnsin the species parameter data frame later will not update thegear_paramsdata frame.

Catchability

Catchability is used as an additional factor to make the link between gearselectivity, fishing effort and fishing mortality. For example, it can be setso that an effort of 1 gives a desired fishing mortality. In this way effortcan then be specified relative to a 'base effort', e.g. the effort in aparticular year.

Catchability is stored as a two dimensional array (gear x species). This caneither be provided explicitly via thecatchability argument, or theinformation can be provided via acatchability column in thegear_paramsdata frame.

In the case where each species is selected by only a single gear, thecatchability column can also be provided in thespecies_params dataframe. Mizer will then copy this over to thegear_params data frame whenthe MizerParams object is created.

Effort

The initial fishing effort is stored in theMizerParams object. If it isnot supplied, it is set to zero. The initial effort can be overruled whenthe simulation is run withproject(), where it is also possible to specifyan effort that varies through time.

Setting resource dynamics

Theresource_dynamics argument allows you to choose the resource dynamicsfunction. By default, mizer uses a semichemostat model to describe theresource dynamics in each size class independently. This semichemostatdynamics is implemented by the functionresource_semichemostat(). You canchange that to use a logistic model implemented byresource_logistic() oryou can useresource_constant() which keeps the resource constant or youcan write your own function.

Both theresource_semichemostat() and theresource_logistic() dynamicsare parametrised in terms of a size-dependent birth rater_R(w) and asize-dependent capacityc_R. The help pages of these functions givethe details.

Theresource_rate argument can be a vector (with the same length asw_full(params)) specifying the intrinsic resource birth rate for each sizeclass. Alternatively it can be a single number that is used as thecoefficient in a power law: then the intrinsic birth rater_R(w) atsizew is set to

r_R(w) = r_R w^{n-1}.

The power-law exponentn is taken from then argument.

Theresource_capacity argument can be a vector specifying the intrinsicresource carrying capacity for each size class. Alternatively it can be asingle number that is used as the coefficient in a truncated powerlaw: then the intrinsic carrying capacityc_R(w) at sizewis set to

c_R(w) = c_R\, w^{-\lambda}

for allw less thanw_pp_cutoff and zero for larger sizes.The power-law exponent\lambda is taken from thelambda argument.

The values forlambda,n andw_pp_cutoff are stored in a listin theresource_params slot of the MizerParams object so that they can bere-used automatically in the future. That list can be accessed withresource_params().

See Also

Other functions for setting up models:newCommunityParams(),newSingleSpeciesParams(),newTraitParams()

Examples

params <- newMultispeciesParams(NS_species_params)

Set up parameters for a single species in a power-law background

Description

This functions creates aMizerParams object with a singlespecies. This species is embedded in a fixed power-law community spectrum

N_c(w) = \kappa w^{-\lambda}

This community provides the food income for the species. Cannibalism isswitched off. The predation mortality arises only from the predators in thepower-law community and it is assumed that the predators in the communityhave the same feeding parameters as the foreground species. The function hasmany arguments, all of which have default values.

Usage

newSingleSpeciesParams(  species_name = "Target species",  w_max = 100,  w_min = 0.001,  eta = 10^(-0.6),  w_mat = w_max * eta,  no_w = log10(w_max/w_min) * 20 + 1,  n = 3/4,  p = n,  lambda = 2.05,  kappa = 0.005,  alpha = 0.4,  h = 30,  beta = 100,  sigma = 1.3,  f0 = 0.6,  fc = 0.25,  ks = NA,  gamma = NA,  ext_mort_prop = 0,  reproduction_level = 0,  R_factor = deprecated(),  w_inf = deprecated(),  k_vb = deprecated())

Arguments